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DR. ASHLEY VARGAS: …[welcome] you to this…to
this workshop. My name’s Ashley Vargas.

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I’m a program director at the National Institute
for Child Health and Human Development, and

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I’m truly honored to serve as one of the
co-chairs, along with my colleague, Dr. Krista

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Zanetti, for this workshop on Multigenerational Nutritional
Influences on Health and Disease.

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Nutrition is arguably the central thread in
the tapestry of life. Without nutrition, life doesn’t exist.

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So, while so much research has focused on
how nutrition affects the individual, today

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we’re going to really focus on the crucial
role nutrition plays in knitting together

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families and generations of health. Next slide, please.

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I’ll keep going while we’re working on
the next slide.

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So, this is really a worthy but complex endeavor,
and today we have experts from across the globe gathered here in the room with us and
virtually to tackle this difficult concept.

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As you can see by this diagram here, it’s
not very simple, and so you’ll hear different parts of this story today throughout all the
presentations on what are…what we’re considering individual-level influences and then again
tomorrow on the larger family and societal

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influences. So, during these conversations, what we need
from you all in the room and online is to ask us the difficult, critical questions.

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So, for those of you online, please click on
the Zoom Q&A box to ask those critical questions. And for those in the room, we just got an
update on how to use the microphones, so please

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use them during the discussion. Again, everyone, your role is to ask these
hard questions that move us towards a better understanding of research needs and opportunities.

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Now, it is my pleasure to introduce leaders
in the field. First, we’ll hear from Dr. Diana Bianchi,
the Director of NICHD, followed by Dr. Conrad

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Choiniere, who is the Director of the Office
of Analytics and Outreach for the Center of Food Safety and Nutrition…Applied Nutrition
at FDA, and we will also hear from Dr. Christopher

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Lynch, who is the Acting Director of the NIH
Office of Nutrition Research, and they are going to provide our opening remarks. Dr. Bianchi?

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DR. DIANA BIANCHI: Thank you so much, Ashley,
and thanks to all of you for attending this workshop.

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To echo Ashley’s comments, I’d like to
welcome everyone to this very important 2-day workshop focused on the Multigenerational
Nutrition Influences on Health and Disease.

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The NIH is co-hosting with our sister agency,
the FDA. It’s great to be here in person, those of
us who are in person this morning to enjoy

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each other’s company and to listen and learn
from each other. And to those of you who are unable to join
us in person, thank you for tuning in via the Webex.

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I don’t know about all of you, but during
the pandemic, I learned all these amazing

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IT skills from home: I can fix my laptop,
I can record myself, I now know what a ring

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light is, etc.

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So for those of you who are online, who didn’t
hear my first few welcoming comments, I’m

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welcoming you on behalf of our cohost, the
FDA.

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Multigenerational influences are…are rooted
in the developmental origins of health and

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disease theory, also known as DOHaD.

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And this impacts nearly all the science that
NICHD supports.

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Moreover, the DOHaD paradigm aligns with NICHD’s
mission of “leading research and training

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to understand human development, improve reproductive
health, enhance the lives of children and

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adolescents, and optimize abilities for all.”

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And it aligns with our vision of healthy pregnancies,
healthy children, healthy and optimal lives.

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And it took a long time to come up with that
new mission and vision statement.

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Importantly, nutrition is a crosscutting theme
of the 2020 strategic plan, and we strategically

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partner with many institutes, centers, and
offices at the NIH, as well as many of our

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sister and brother agencies across the government
and in the private sector, to advance the

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science that supports the nutritional health
of families.

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We recognize that nutrition is crucial for
overall health, growth, and development, as

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well as disease prevention. However, nutrition,
as you all know, is a complex topic to study,

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especially since nutrition can be an exposure,
including dietary intake and food access.

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It affects health and developmental outcomes,
as well as...it’s a health outcome itself.

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It’s often assessed with biomarkers of nutritional
status.

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Historically and currently, the NICHD is very
actively engaged in research that addresses

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the impact and importance of nutrition, particularly
for healthy pregnancies, the postpartum lactation

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period for the mother, fetal growth and development,
and the well-being of infants throughout childhood

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and adolescence.

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For example, NICHD has invested in the NICHD
Fetal Growth Study to better understand the

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dynamics of human fetal growth and to develop
standards for fetal anthropometric parameters

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for all races, all of which are impacted by
maternal nutrition during gestation.

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NICHD was also very involved in the overall
strategic plan for nutrition research at NIH,

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which is a plan for the years 2020–2030.

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And I am a co-chair of the NIH Nutrition Research
Task Force.

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NICHD further serves as a key institute in
the ongoing Nutrition for Precision Health,

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which is powered by the All of Us Research
Program, the goal of which is to develop algorithms

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that predict individual responses to food
and dietary patterns.

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Moreover, we recently engaged the scientific
community with the topic-relevant workshops,

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including, number one, Breast Milk Ecology, Genesis
of Infant Nutrition, with the algorithm—with

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the acronym, sorry—BEGIN—it’s the BEGIN
Workshop Series—and number two, Biomarkers

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of Nutrition for Development Knowledge, Including
Dietary Sufficiency, with the acronym BOND-KIDS.

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Nothing to do with James Bond.

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And the Bridging the Biological and Communication
Sciences on Nutrients and Environment…Environmental

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Contaminants in Foods to Support Child Development
Workshop, which we cohosted again with our

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partner at the FDA.

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NICHD also coordinates with other funders
of nutrition research, such as the Bill & Melinda

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Gates Foundation and the Office of Nutrition
Research at the NIH, to maximize the support

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of maternal and early-life nutrition research.

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Over the next two days, you’ll hear from experts
across the globe addressing the intersection

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of nutrition with the DOHaD paradigm and the
impact of nutrition across multiple generations

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of families, with an ultimate goal of identifying
gaps and research opportunities in this important

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area of science.

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I’m looking forward to hearing some of the
presentations, either live or online, and

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the discussions, and seeing the ideas and research
opportunities generated from this workshop

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put into action.

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So, in closing, I’d like to thank the Office
of Nutrition Research for sponsoring and co-chairing

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this workshop, along with the many staff across
the NIH and the FDA who contributed to the

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development of this very robust agenda.

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So, thanks for your attention, and enjoy the
next two days.

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And I will hand the podium over to my next
presenter.

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DR. CONRAD CHOINIERE: Thank you, Dr. Bianchi,
and thank you all for…for joining us today

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and participating in this workshop.

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You know, this is a very important topic for FDA, not
just from a nutritional standpoint, but also

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from a food safety standpoint.

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And I know you might be thinking about food-borne
illness, but that’s not the food safety

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issue that I’m talking about.

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Dr. Bianchi did allude to it in some of the
work that we have been doing recently with

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respect to environmental contaminants in the
food supply and how that can affect childhood

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development.

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In particular, there are certain subsets of
contaminants that are in…they’re naturally

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found in the Earth’s crust and/or they may
be there from pollution, and so we have…we,

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we launched our Closer To Zero plan back in
2021 to focus specifically on reducing the

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presence of lead and arsenic and cadmium and
mercury in foods that are commonly consumed

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by babies and young children.

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Now this…what first started out as an action
plan has slowly grown into a whole-of-government

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effort, not just thinking about FDA’s role
in reducing the levels in the foods themselves,

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but thinking about: How do we reduce the levels
in the commodities that are used to make these

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foods?

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These are the foods that we want our children
to eat, the fruits and vegetables and whole

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grains that are important for healthy development.

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Thinking about how we can lessen not only
the levels in those commodities and foods, but

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also how do we lessen exposure to those foods
by providing advice to consumers—perhaps

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dietary advice.

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But this workshop in particular, I think,
might get at even one step further, which

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is thinking about: How do we reduce the impacts
of those exposures?

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Because we know that nutrition plays a role
in mitigating and even blunting the effects

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of exposure to these contaminants.

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For example, we know that children with nutrient
deficiencies or inadequacies are more likely

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to take up lead into their bones.

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We know that later…in later stages of life,
perhaps during pregnancy and lactation, the

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same lead that is in that bone…in those
bones can be mobilized and, therefore, potentially

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transferred to the next generation.

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And if you think about that, those biological
processes in the context of issues related

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to environmental justice or nutrition security,
you can see that we have a role as government

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agencies to implement policies to either…to…to
break that vicious cycle.

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And at least not reinforce that vicious cycle.

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So, we’re really looking forward to the
scientific discussion today.

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Much of what we do—all of what we do at
FDA—needs to have strong scientific foundation

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to help inform our policies moving forward
in this space.

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And that’s why we really value our opportunity
to…to be here as cohost with NIH...this workshop.

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We see this as a great step for us to better
understand the biological mechanisms that

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are governing the nutrient and contaminant
roles in childhood development and…and understanding

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how that can affect our lifelong health…health.

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So, thank you, again, for having us here today,
and I’m really looking forward to hearing

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the discussion today.

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DR. CHRISTOPHER LYNCH: Hi. Good morning. My name is Christopher Lynch, and I’m the
Acting Director of the NIH Office of Nutrition Research.

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I want to welcome you to this workshop on
Multigenerational Nutritional Influences of

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Health.

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I thank the scientific leads, Dr. Vargas and
Zanetti, for…and the NIH Organizing Committee

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for bringing us together.

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Around the time I arrived at NIH, the New
England Journal of Medicine published a simulation

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model that sought to estimate the risk of
adult obesity for the United…for the U.S.

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population of children.

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They projected that 30…57% of those children
that were studied in 2016, 57%, would be obese

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by age 35, consistent with the rising trends
in obesity and other diet-related diseases.

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It needs to be a priority that we address
these rising trends through…through research.

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Nutrition is both a biological variable and
as like the air we breathe, a ubiquitous environmental

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exposure.

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It adds or synergizes with other environmental
exposures and social influences of health

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to impact our wellness and disease susceptibility.

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However, it’s been long unknown, including
as outcomes of previous NIH workshops, that

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nutrition and other exposures not only affect
the adults, but…but act early in development

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of their offspring, especially during fetal
life, and have profound effects on susceptibility

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to…to these later.

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And there is strong evidence, as we’ll hear
about in this workshop, that these susceptibilities

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are being transmitted transgenerationally.

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Several studies, both in humans and experimental
animals, suggest that altered risk of adult

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diseases may be linked to the maternal environment
and nutritional status around conception and

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implantation.

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And other evidence suggests that future risks
of disease may be linked to what your father

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or grandfather ate, as well.

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The specific diseases implicated span the
range of interest across many NIH institutes

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and call not only for NIH-wide collaboration
and coordination, but a trans-federal approach

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to address this problem.

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Hence, the strategic plan for NIH Nutrition
Research prioritizes examining the role of

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periconceptional and prenatal nutrition on
with other fetal and postnatal exposures in

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the development and disease outcomes.

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As another important exposure, it also calls
upon us to enhance our knowledge of human

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milk composition and the translational role
of its components.

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We are hoping that, through this workshop,
we will identify what is already known about

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the factors involved in the developmental
origins and transgenerational transmittance

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of disease risks, but also to identify the
gaps and opportunities with key measures and

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potential study designs for NIH to reflect
upon using preclinical and clinical models.

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Thanks again for coming, and welcome.

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DR. ASHLEY VARGAS: Well, I want to thank our exceptional
leadership for the support of this workshop

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and their vision going forward.

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And I also want to really thank our keynote
speaker, who’s going to speak next.

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Dr. Usha Ramakrishnan is going to give us
our first introduction into this topic, and

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she’s from Emory University.

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She’s going to talk about the established
evidence of nutrition and diet on multigenerational

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effects on health.

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And we gave her a very difficult topic, so
I want us all to appreciate the coverage that

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she’s going to provide over the next 30-40
minutes, and then we’ll have an opportunity

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for Q&A and discussion. Dr. Ramakrishnan.

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DR. USHA RAMAKRISHNAN: Hello, everyone. And thank you very much and Ashley, it was…I…I
felt I was back in school—but I still am,

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I guess, when I tell people I’m at a university..."you're still studying?". 

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But it’s…it’s really a privilege and
an honor to have been asked to do this keynote presentation.

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And let me open up my presentation.

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[Inaudible] Mm-hm. Okay. Yeah. So, we’re all set.

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And this is really the work of many generations…many
scientists.

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And I want to first to start with a caveat.

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If I’ve left anyone out, please forgive
me for errors of commission and omission.

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And I’m really looking forward to the more
detailed presentation.

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So, it’s going to be a little bit more as
an overview.

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So, you know, what are, you know, intergenerational
effects?

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So, the…the first thing, of course, is,
you know, what are intergenerational effects?

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It’s not a new concept.

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If we talk to our grandmothers or grandparents,
like, we’ve known this for a long time.

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But when we put this into the context of nutrition
as way back as almost 100 years ago, this…I

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found this useful quote where Mussey said
that, “It must be borne that the diet of

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a given generation may affect several generations
hence.”

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So, it’s not a new idea.

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It’s been there, but clearly it’s not
an easy area to study when we’re sitting

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at places like NIH for policymakers or for
people right on the ground.

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Now, what do you tell a mother?

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It’s always the mother’s fault.

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I’m talking as a mom, right?

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But anyway, the…those are sort of the difficult
challenges.

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And Emanuel was someone who worked on intergenerational
effects.

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And I just put a few definitions and these
all fit right there.

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I’m not going to read the quote with what
Dr. Bianchi said.

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What’s been the mission of NICHD?

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What’s been the career of many people here
in this room and people who went before them

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that, you know, we’re looking for ways in
which we can improve the…develop…human

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development for the next generation.

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What is it we tell people when you have?

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And just broadly, intergenerational effects,
it’s…it’s a composite, right?

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It reflects the shared…that it’s genetic.

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We’ve all heard about nature versus nurture,
etc., but it…it…it’s a shared environment,

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which now we know gets encoded, and to a certain
extent, it’s modifiable.

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And I really have had the privilege of being
mentored, and I continue to get his advice

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of Reynaldo Martorell—many of you know of
his work—who just recently retired.

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He’s still very active.

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He commented on my presentation, as the Woodruff
Professor of International Nutrition at Emory

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and Chair.

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And almost a decade ago, he did this excellent
review on intergenerational effects in linear

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growth, and this was a quote from that, again,
addressing how complex it is when we’re

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thinking about, you know, intergenerational
effects when looking at the interactions,

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looking at genetic predisposition, when looking
at the environment, and then obviously, there's some

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mechanisms that we’re still trying to unravel
when we are looking for potential ways we

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can improve the next generation.

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And in that construct, I think it’s really
important to recognize there are critical

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periods, and some of you may have heard about
the first 1,000 days.

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Folks like me, I’m not a DOHaD person.

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I say I’m maternal child nutrition, which
is a mouthful, but I think I don’t have

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to convince the audience here and the group
that, you know, gestation is clearly a very

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critical window.

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And as you look at this figure on the right,
which has really got, you know, all the way

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from preconception to the 70s, we can
see that, you know, there are different windows

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when something which is important may be not
so important later on, etc.

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But it’s also…it’s not to scale to highlight
that gestation and early childhood are very,

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very important windows when we…we are concerned
about long-term effects.

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And I really enjoyed reading the review article.

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It’s nice to see Dr. Breton here…you know,
definitions, what do we mean?

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And this is just, you know, conceptualization.

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We talk about intergenerational, transgenerational,
keeping our generation straight sometimes

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is difficult.

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So obviously, with the animal model, which
you’re going to hear more of…so, you’re

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able to go through several generations.

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But if you think of the parents, that’s
sort of the F0, and the mother is expecting.

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Then that baby is F1, but we know that the
baby already is carrying the next generation’s

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germ cells, or the F2.

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And then, you know, you can go all the way
to F3 when we’re studying.

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So always, you know, when we think about:
Does it work

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in maternal and child health? We’re looking
at exposures.

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When…when do you look at the exposure?

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Is it during early childhood of the parent,
to the early childhood of the child, and the

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next generation?

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I thought this was useful just to remind us
which…which is intergenerational.

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It’s all intergenerational, but we have
now seen the literature transgenerational,

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and the main point I wanted to make is sometimes
these effects skip.

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You may not see it in sort of that immediate
generation, and then they may manifest later.

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And we’re not going to live for 200 years,
but I think we have some really exciting developments

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that may allow us to examine these effects.

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And this is just another conceptual framework
to capture the complexity that Rey put together

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when they were trying to understand intergenerational
effects, you know...I put it as nutrition.

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It was really focusing on linear growth, and
I’m not going to read through all of it,

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but it was just…just to highlight that even
when we think about something as simple as,

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sort of, child growth and the early birth
outcomes, we know maternal nutrition during

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pregnancy is very important.

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A lot of research has been done.

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We know what…you know, weight gain during
pregnancy, diet during pregnancy impacts the

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child’s immediate birth outcomes and growth
and development.

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But we also know that there are many factors
that are determined prior to conception, whether

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it is, you know, the attained size of the
mother—that is a function of what her exposures

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were much earlier—her preconceptual nutritional
status; all of those also influence and may

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interact with what she’s consuming during
pregnancy, from a nutrition perspective.

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And then, of course, there’s the whole environmental
factors.

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So, there…there are so many layers, and
trying to sort this out is complicated.

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But we need to be aware that it is a complex
issue, and having conceptual frameworks, at

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least for me, has been very useful when we
try to say, “Okay, I’m trying to address

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this piece of it.”

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And it truly also emphasizes the importance
of team science.

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We need people from different areas.

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We need different people working.

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It’s not, you know, just the pediatricians
or the obstetricians, even in that field of

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new things.

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But it’s…it’s across the lifespan.

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You need people who are working in genetics.

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You need people who are working in environmental
health.

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We talk about community.

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There’s that bottom line of intergenerational
transmission of poverty.

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You know, that is not…we don’t know if
it’s encoded, but we know environment may

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or may not change across generations, and
what do we do about it, and what are the implications

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of that?

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And so, I’m looking forward to the sessions
tomorrow on sort of the underlying factors.

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So, we have a whole presentation on animal
studies.

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We’ve learned a lot.

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I mean, it’s easier to work with animals.

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They’re not inexpensive, so I want to really
thank all the funding that’s happened over

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the years.

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And I’m just going to do a quick summary
slide, and you’ll get more details from

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my next presenter.

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Is…we know that, especially, studies that
have been done in rodents, some in mice, that

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especially undernutrition food deprivation
has long-term effects where, you know, there

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have been studies over several generations.

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00:26:21,240 --> 00:26:25,900
And so they like to see Dr. Galler here,
who has done some of the leading work in this.

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00:26:25,900 --> 00:26:33,460
And so, we know that in the…in the animal
model that these effects persist across generations,

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and some can be reversed.

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But some of the behavioral things don’t
get reversed, even when you, you know, come…improve

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nutrition, say, several generations later.

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In the field of diabetes, there’s been a
lot of work showing about, you know, the fetal

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00:26:50,429 --> 00:26:53,929
programming that happens in animal studies.

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00:26:53,929 --> 00:27:00,140
There’s quite a bit of interest in showing
how, you know, the function of the placenta—not

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00:27:00,140 --> 00:27:05,460
the placenta…well, the placenta too—but
the pancreatic function, the size, insulin,

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00:27:05,460 --> 00:27:09,380
insufficiency that may start much, much earlier.

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00:27:09,380 --> 00:27:14,450
And last, but not least, we, you know…I
started with, sort of, undernutrition, but

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we have… most of the world has experienced
the nutrition transition, where we have both

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under- and overnutrition.

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We have poor diet quality, so the whole thing
of what’s happening in overweight and obesity,

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where, especially in this country, more than
a third of women are entering pregnancy with

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many of these metabolic problems, and what
are the implications for the next generation?

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So, those are some of the areas where we’ve
got…we’ve learned a lot, and I’m looking

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forward to the next presentation.

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00:27:44,350 --> 00:27:49,080
So, I’m going to move to human studies—watching
my time.

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00:27:49,080 --> 00:27:52,970
I first want to acknowledge Dr. David Barker.

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I mean, he really pioneered in this area,
and this quote, which brought back to the

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fore that when we talk about what’s happening
during pregnancy, it’s not just for that

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00:28:05,559 --> 00:28:10,260
immediate birth outcome, but it has implications
way beyond.

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And this is even within that, you know, one
F0, F1.

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And now we’re wanting to look, and he…he
really pioneered and led the way.

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00:28:18,450 --> 00:28:22,769
Inspired many scientists to start working
in this space.

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00:28:22,769 --> 00:28:29,210
And his early work that show…elegantly showed
the increased risk of, you know, chronic disease

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00:28:29,210 --> 00:28:34,460
later in life as a function of what happens,
you know, in gestation has been, you know,

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00:28:34,460 --> 00:28:35,460
replicated.

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00:28:35,460 --> 00:28:41,490
The best examples come from the Famine Studies.

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My colleague Aryeh Stein at Emory has been
part of the scene with the Dutch famine that

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00:28:47,400 --> 00:28:49,289
happened in…during World War II.

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00:28:49,289 --> 00:28:53,330
It was short, acute, but has been very carefully
followed up.

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00:28:53,330 --> 00:29:00,399
And we have evidence of, you know, this intergenerational
effect that some of these are in the same

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00:29:00,399 --> 00:29:03,409
cohort, but a lot of these are following the
survivors, right?

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The babies who were conceived and born.

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00:29:06,149 --> 00:29:11,830
They’ve been…you know, they were born
in the late 40s. And there’s also evidence

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that for poor nutrition and low birth weight
is associated with increased risk of obesity

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00:29:17,980 --> 00:29:19,350
and cardiovascular disease.

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00:29:19,350 --> 00:29:21,830
Now, these are not easy studies to do.

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There are challenges.

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00:29:23,370 --> 00:29:25,190
You have survivor bias.

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00:29:25,190 --> 00:29:31,669
Epidemiologists can…can rave and rant forever,
and it’s an actual experiment, but we’ve

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00:29:31,669 --> 00:29:32,929
learned a lot.

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00:29:32,929 --> 00:29:37,590
And I think that’s what’s important to
understand what’s the next steps.

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00:29:37,590 --> 00:29:43,440
This is work by Caroline Fall, who was one
of Dr. Barker’s mentees at the University

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00:29:43,440 --> 00:29:44,440
of Southampton.

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00:29:44,440 --> 00:29:49,630
Has worked with amazing cohorts in India,
which is my country of origin.

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And the purpose of sharing this slide is to
show how elegantly it’s a combination, right?

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00:29:56,350 --> 00:30:01,890
I mean, this…if you look at all these graphs,
the…the tallest bar is the black one in

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00:30:01,890 --> 00:30:03,429
the left corner.

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00:30:03,429 --> 00:30:09,580
And those are children who were born small,
so experienced some fetal growth restriction.

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00:30:09,580 --> 00:30:15,371
But by mid-, you know, by mid-childhood or
even as adults, they had…they were in the

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upper third of BMI.

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00:30:16,530 --> 00:30:22,620
So, you see this increased risk where there’s
a mismatch, one might say, and that doesn’t

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00:30:22,620 --> 00:30:24,630
mean we want the small people to remain small.

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00:30:24,630 --> 00:30:26,210
But how do we adapt?

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00:30:26,210 --> 00:30:32,830
And it has really important implications for,
you know, the advice and the nutritional interventions

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that are needed not just during gestation,
but during early childhood, during the school-age

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00:30:37,460 --> 00:30:43,690
years to think in terms of outcomes, like
in this case diabetes, which is very, very

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00:30:43,690 --> 00:30:46,640
rampant, I guess is the word.

357
00:30:46,640 --> 00:30:53,100
But the global burden is very large, and this
is showing the developmental origins.

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00:30:53,100 --> 00:30:58,880
Early work that I did when I started as a
postdoc was to look at intergenerational effects

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00:30:58,880 --> 00:30:59,880
on birth size.

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00:30:59,880 --> 00:31:04,409
This was a review and, you know, we found
about 14 studies.

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00:31:04,409 --> 00:31:10,880
Most of it was from developed countries and,
you know, the effect size was about 10 to

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20 grams.

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00:31:11,880 --> 00:31:16,820
But when we used data from the INCAP study,
which I’ll talk a little bit about, which

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00:31:16,820 --> 00:31:20,550
is an intervention trial, but this was using
an observational design.

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00:31:20,550 --> 00:31:24,350
We found that the intergenerational effect
was twofold.

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00:31:24,350 --> 00:31:29,220
So, there was, you know, for every 100 grams
change in maternal birth…birth weight, the

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00:31:29,220 --> 00:31:35,720
offspring, there was…it…it explained per
the gram obviously why it’s complicated,

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00:31:35,720 --> 00:31:39,880
and this was done at a time when we didn’t
have the entire cohort.

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00:31:39,880 --> 00:31:46,010
So, in the next few slides, I’m going to
share some really interesting findings that

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00:31:46,010 --> 00:31:47,179
Dr. Stein shared.

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00:31:47,179 --> 00:31:49,309
This is the cohort study.

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00:31:49,309 --> 00:31:51,299
Some of you may have heard about it.

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00:31:51,299 --> 00:31:57,889
It really was pulling together cohorts from
low/middle-income countries that were…have

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00:31:57,889 --> 00:32:03,929
been followed up from, you know, early…pretty
much from gestation through adulthood.

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00:32:03,929 --> 00:32:07,860
And it’s from South Africa,

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00:32:07,860 --> 00:32:14,470
New…it’s the New Delhi Birth Cohort, the
Guatemala study, and I’m leaving one more

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00:32:14,470 --> 00:32:15,470
country out.

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00:32:15,470 --> 00:32:16,470
Cebu, the Philippines.

379
00:32:16,470 --> 00:32:18,570
So, some of you may have heard.

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00:32:18,570 --> 00:32:23,130
These studies have individually contributed
to our knowledge about the importance of maternal

381
00:32:23,130 --> 00:32:24,929
and child nutrition.

382
00:32:24,929 --> 00:32:30,990
But what I’m focusing on is, you know, what’s
the evidence that parental growth during childhood

383
00:32:30,990 --> 00:32:33,440
influences offspring birth weight?

384
00:32:33,440 --> 00:32:36,580
So, this is that generational effect.

385
00:32:36,580 --> 00:32:38,580
And this has been published.

386
00:32:38,580 --> 00:32:46,290
Yaw Addo who worked at Emory and is now at
CDC was able to demonstrate that if the mother

387
00:32:46,290 --> 00:32:50,149
was stunted…so these were…these cohorts
were done in settings where there was a lot

388
00:32:50,149 --> 00:32:51,250
of undernutrition.

389
00:32:51,250 --> 00:32:55,730
Varying rates of linear growth retardation.

390
00:32:55,730 --> 00:33:04,269
But maternal stunting at age 2 was associated
with a 100-gram difference, so they were lighter

391
00:33:04,269 --> 00:33:06,740
at birth.

392
00:33:06,740 --> 00:33:10,070
And there was…this was nonsignificant, but
they also looked at paternal because there

393
00:33:10,070 --> 00:33:11,070
was this thing.

394
00:33:11,070 --> 00:33:15,039
So, clearly showing importance of maternal
nutritional status.

395
00:33:15,039 --> 00:33:20,460
When they then looked also at conditional
weight, so this is not just stopping at birth.

396
00:33:20,460 --> 00:33:22,260
You know, stunting is a composite.

397
00:33:22,260 --> 00:33:25,390
If a child is stunted at age 2, how does that
happen?

398
00:33:25,390 --> 00:33:27,200
It could be because the child was born small.

399
00:33:27,200 --> 00:33:32,990
It could be due to results that are experienced
during early childhood, first 1,000 days.

400
00:33:32,990 --> 00:33:39,820
And what was great is that these cohorts had
enough data that one could partition out,

401
00:33:39,820 --> 00:33:41,299
you know, is it the birth weight?

402
00:33:41,299 --> 00:33:43,799
Does it go up during the first 1,000 days?

403
00:33:43,799 --> 00:33:47,269
And then subsequent, you know, relative weight
gain.

404
00:33:47,269 --> 00:33:53,450
So, this is showing you, you know, for parental
birth weight and growth…linear growth.

405
00:33:53,450 --> 00:34:01,820
And what you see here is a significant effect
of maternal, you know, birth weight on subsequent

406
00:34:01,820 --> 00:34:07,429
generations, but also what happens in linear
growth during the first 1,000 days.

407
00:34:07,429 --> 00:34:11,880
Now, interestingly, this analysis did show
some effects of fathers.

408
00:34:11,880 --> 00:34:14,310
So, fathers matter.

409
00:34:14,310 --> 00:34:20,860
It’s not only the mothers, and I do want
to emphasize it’s smaller, but that also

410
00:34:20,860 --> 00:34:22,700
then begs the question: What are the mechanism?

411
00:34:22,700 --> 00:34:25,339
It’s not just what the mother consumed or
breast milk.

412
00:34:25,339 --> 00:34:30,859
It could be other mechanisms involved and
that it’s transmitted across generations.

413
00:34:30,859 --> 00:34:36,679
And with all the concerns of, you know, overweight,
obesity, we also…they also looked at relative

414
00:34:36,679 --> 00:34:37,800
weight gain.

415
00:34:37,800 --> 00:34:44,619
And what you found here is, you know, mother’s
weight gain was positively associated.

416
00:34:44,619 --> 00:34:47,050
Now, one can say, “Is this good or bad?”

417
00:34:47,050 --> 00:34:48,139
It depends on the context.

418
00:34:48,139 --> 00:34:51,010
If you’re working in settings where there’s
a lot of people with growth restrictions,

419
00:34:51,010 --> 00:34:52,010
this is good.

420
00:34:52,010 --> 00:34:57,410
So, then the emphasis of, you know, addressing
adolescent health, school-age health in settings

421
00:34:57,410 --> 00:35:01,980
where there’s a lot of underweight becomes
important.

422
00:35:01,980 --> 00:35:10,290
This next few slides are not intergenerational,
but just help you understand: What are the

423
00:35:10,290 --> 00:35:11,290
mechanisms?

424
00:35:11,290 --> 00:35:16,430
Why should maternal or paternal growth influence
the next generation?

425
00:35:16,430 --> 00:35:21,349
And these cohort data had very useful information
on the…that same generation.

426
00:35:21,349 --> 00:35:24,730
In other words, they have the birth weight of
the parents, but they knew what the

427
00:35:24,730 --> 00:35:26,950
attained height of the parents were.

428
00:35:26,950 --> 00:35:28,770
They knew other factors.

429
00:35:28,770 --> 00:35:32,700
And what is shown here is conditional growth
and adult height.

430
00:35:32,700 --> 00:35:38,530
So, it’s the parent…I’m still in the
same generation here, but it could be mediated

431
00:35:38,530 --> 00:35:40,180
that the mothers were taller.

432
00:35:40,180 --> 00:35:44,849
And we know maternal height’s an important
determinant, but the mothers grew better when

433
00:35:44,849 --> 00:35:45,849
they were children.

434
00:35:45,849 --> 00:35:50,040
They resulted in being taller adults and therefore
had better birth outcomes.

435
00:35:50,040 --> 00:35:52,130
So, that’s really what’s shown here.

436
00:35:52,130 --> 00:35:54,440
And you…you see that.

437
00:35:54,440 --> 00:35:57,869
And notably, relative weight was not associated.

438
00:35:57,869 --> 00:36:04,320
So, that’s good news in some ways, but the
key here is that early part, right?

439
00:36:04,320 --> 00:36:10,950
You see where birth weight is the most important
determinant part, which is why it takes generations

440
00:36:10,950 --> 00:36:15,240
to make differences, to, you know, to improve
adult height.

441
00:36:15,240 --> 00:36:22,431
Since the interest has been in body mass index,
they looked at that, as well, and found that

442
00:36:22,431 --> 00:36:29,990
linear growth had very small effects, but
definitely relative weight gain was associated

443
00:36:29,990 --> 00:36:31,660
with higher BMI.

444
00:36:31,660 --> 00:36:34,260
Now, one might say, “Well, is that good
or bad?”

445
00:36:34,260 --> 00:36:37,140
It depends on body composition.

446
00:36:37,140 --> 00:36:40,850
The data…there’s not enough information,
so this is a gap, but the data—at least

447
00:36:40,850 --> 00:36:46,660
from the cohort—was based on anthropometry,
suggests that this was increasing lean body

448
00:36:46,660 --> 00:36:47,810
mass and not fat mass.

449
00:36:47,810 --> 00:36:53,290
But clearly, this is something we need to
work more on…on understanding, you know, the limitations

450
00:36:53,290 --> 00:36:54,400
of BMI.

451
00:36:54,400 --> 00:36:59,420
And I’m getting the look.

452
00:36:59,420 --> 00:37:04,440
I don’t want to leave without talking about
attained schooling, which is a really important

453
00:37:04,440 --> 00:37:05,440
factor.

454
00:37:05,440 --> 00:37:10,710
You know, mothers are better educated, are
able to take better care of themselves, etc.

455
00:37:10,710 --> 00:37:16,760
So, my final sets of slides is really…I
was looking for transgenerational effects,

456
00:37:16,760 --> 00:37:17,770
and there are very few studies.

457
00:37:17,770 --> 00:37:19,770
And these are difficult to do.

458
00:37:19,770 --> 00:37:23,700
I mean, just getting two generations is challenging.

459
00:37:23,700 --> 00:37:28,190
Going more than that…but again, there’s
some interesting research from the Dutch famine

460
00:37:28,190 --> 00:37:29,710
study.

461
00:37:29,710 --> 00:37:33,500
And finally, I do want to talk about what
do we do.

462
00:37:33,500 --> 00:37:34,950
I’m fine?

463
00:37:34,950 --> 00:37:35,950
Okay.

464
00:37:35,950 --> 00:37:38,170
Is what do we know from intervention studies?

465
00:37:38,170 --> 00:37:40,609
Because most of this is observational.

466
00:37:40,609 --> 00:37:45,950
And so the Barbados Study was one…it was,
you know, not a randomized controlled trial,

467
00:37:45,950 --> 00:37:49,640
but severely malnourished children received
rehabilitation.

468
00:37:49,640 --> 00:37:56,560
They’ve been followed up, and the…the
key part here was that there were long-term

469
00:37:56,560 --> 00:37:58,750
detrimental effects.

470
00:37:58,750 --> 00:38:01,260
So, the importance is…okay, it’s fine.

471
00:38:01,260 --> 00:38:05,920
We will just, you know, if we have a severely
malnourished child, we need, ethically, to

472
00:38:05,920 --> 00:38:06,990
treat them.

473
00:38:06,990 --> 00:38:08,839
But it’s better to prevent wasting.

474
00:38:08,839 --> 00:38:13,950
It’s better to prevent severe malnutrition
because there are these long-lasting effects

475
00:38:13,950 --> 00:38:16,020
on human development.

476
00:38:16,020 --> 00:38:22,750
And the INCAP study is different, in that
it is a randomized controlled trial that was

477
00:38:22,750 --> 00:38:24,420
funded in the ʼ70s.

478
00:38:24,420 --> 00:38:30,670
So, this is thanks to NICHD’s investment
in maternal child nutrition, and it’s…we’ve

479
00:38:30,670 --> 00:38:36,970
been very fortunate, thanks to Ray’s leadership
and several others, and support from NIH,

480
00:38:36,970 --> 00:38:40,950
especially NICHD that this cohort is still
being followed up.

481
00:38:40,950 --> 00:38:44,930
We actually are looking at aging outcomes
as we speak.

482
00:38:44,930 --> 00:38:48,329
And I say “we”; it’s really…Aryeh
is the PI.

483
00:38:48,329 --> 00:38:54,280
But you all may have seen this, but it was,
you know, four villages where they got a nutritional

484
00:38:54,280 --> 00:38:55,280
supplement.

485
00:38:55,280 --> 00:38:59,360
This was done in a setting where there was
a lot of undernutrition, poor diet quality.

486
00:38:59,360 --> 00:39:01,160
So, this was fortified supplement.

487
00:39:01,160 --> 00:39:05,480
This is something to keep in mind; it had
all the micronutrients.

488
00:39:05,480 --> 00:39:10,090
That was…Atole and Fresco was kind of the
Kool-Aid of those days, which they didn’t

489
00:39:10,090 --> 00:39:11,090
have saccharin.

490
00:39:11,090 --> 00:39:15,930
They didn’t want to do it for safety reasons,
so had some sugar.

491
00:39:15,930 --> 00:39:17,500
And it was actually ad libitum.

492
00:39:17,500 --> 00:39:20,829
You know, pregnant women came in and consumed
it lactating, so the exposure was there.

493
00:39:20,829 --> 00:39:25,020
It was in the field for 7 years, but very,
very detailed information.

494
00:39:25,020 --> 00:39:27,890
And we’ve had a lot of follow-up studies.

495
00:39:27,890 --> 00:39:31,079
This was where I sort of started when I came
to Emory.

496
00:39:31,079 --> 00:39:35,130
I was working on the Thrasher Birth Weight
Study, which was looking at birth weight of

497
00:39:35,130 --> 00:39:39,080
these children who had benefited from the
INCAP Study.

498
00:39:39,080 --> 00:39:46,290
And some key findings were that across the
generations, you can see that certainly there

499
00:39:46,290 --> 00:39:47,470
were improvements.

500
00:39:47,470 --> 00:39:54,150
The linear growth retardation that was seen
in the parental generation improved.

501
00:39:54,150 --> 00:39:59,740
But the differences by the intervention group
still persisted, if you look at that.

502
00:39:59,740 --> 00:40:04,640
So that’s kind of, you know, comparing the
linear growth of generation one to generation

503
00:40:04,640 --> 00:40:08,000
two, and it’s in a longitudinal one…fashion.

504
00:40:08,000 --> 00:40:11,380
And if we look specifically at offspring birth
weight and anthropometry.

505
00:40:11,380 --> 00:40:14,079
So, this is…the mother is…the parents
received the intervention.

506
00:40:14,079 --> 00:40:15,930
This was their babies.

507
00:40:15,930 --> 00:40:16,930
That’s intergenerational.

508
00:40:16,930 --> 00:40:18,720
There was significant effect.

509
00:40:18,720 --> 00:40:26,740
So, it’s the effect of an intervention is
no long…we were able to attribute it to

510
00:40:26,740 --> 00:40:31,680
receiving the atole versus the fresco…to
improved birth weight, improved height.

511
00:40:31,680 --> 00:40:36,700
These…they were still children when the
follow-ups were done of varying ages, and

512
00:40:36,700 --> 00:40:38,810
also head circumference.

513
00:40:38,810 --> 00:40:45,540
But not many differences in the body composition
measures, so that’s something to keep in

514
00:40:45,540 --> 00:40:46,540
mind.

515
00:40:46,540 --> 00:40:52,270
But most importantly, there was also evidence
of increased returns to schooling, which is

516
00:40:52,270 --> 00:40:53,270
really important.

517
00:40:53,270 --> 00:40:57,200
So, you know, there are a part…not everything
is explained by being heavier and taller,

518
00:40:57,200 --> 00:40:58,200
right?

519
00:40:58,200 --> 00:41:01,330
And there…some of it’s mediated, but there
were those investments.

520
00:41:01,330 --> 00:41:08,849
And there have been, of course, the concern
about increased chronic disease, and this

521
00:41:08,849 --> 00:41:12,710
is data to suggest that there, you know, there
was.

522
00:41:12,710 --> 00:41:14,640
These children were taller.

523
00:41:14,640 --> 00:41:20,210
Adiposity measures were higher, but surprisingly,
the prevalence of diabetes was lower.

524
00:41:20,210 --> 00:41:25,710
So again, there’s always this trade-off
that we’re concerned…do no harm, but this

525
00:41:25,710 --> 00:41:29,810
is what the data shows in the follow-up of
the adults.

526
00:41:29,810 --> 00:41:37,810
And finally, I just want to look like coming
back…so, this is an overview on…on mechanisms.

527
00:41:37,810 --> 00:41:44,150
I think work that’s been done by Dr. Yajnik
in India, the Barker group.

528
00:41:44,150 --> 00:41:49,859
Their group clearly, especially from animal
studies, show that the whole DOHaD paradigm

529
00:41:49,859 --> 00:41:55,900
that undernutrition in the first 1,000 days
it’s not just…birth weight’s a crude

530
00:41:55,900 --> 00:41:56,900
indicator.

531
00:41:56,900 --> 00:42:02,619
But really, what’s happening is there are
effects across various organs.

532
00:42:02,619 --> 00:42:06,700
And the one which is really exciting as we
speak, we’ve been working…is looking at

533
00:42:06,700 --> 00:42:08,430
the brain, you know, the brain…the HPA axis.

534
00:42:08,430 --> 00:42:11,859
The brain for the pituitary to the pancreas,
etc.

535
00:42:11,859 --> 00:42:18,741
So, that’s…I think we…we learned a lot
from it, and this is not easy to read, but

536
00:42:18,741 --> 00:42:25,309
Caroline—I know her, I’m using her first
name—received a very prestigious award in

537
00:42:25,309 --> 00:42:27,599
India and is a part of that publication.

538
00:42:27,599 --> 00:42:31,770
She’s showing how when you’re adapted
to something like diabetes, the fetal programming,

539
00:42:31,770 --> 00:42:33,040
and the various mechanisms.

540
00:42:33,040 --> 00:42:37,940
So, it’s very complex, but it again says
why looking at intergenerational effects,

541
00:42:37,940 --> 00:42:41,809
even when we’re dealing with chronic disease
prevention, is very important.

542
00:42:41,809 --> 00:42:43,309
How does this happen?

543
00:42:43,309 --> 00:42:46,200
As I said, I’m not a genetic expert.

544
00:42:46,200 --> 00:42:48,680
But there’s been a lot of things.

545
00:42:48,680 --> 00:42:52,250
These are the genetic markers, and do they
stay?

546
00:42:52,250 --> 00:42:54,080
Do they go away?

547
00:42:54,080 --> 00:42:59,800
But there is quite a bit of emerging evidence
in the few cohorts that they have biomarkers

548
00:42:59,800 --> 00:43:08,030
available where they’re able to link that
early exposure to poor nutrition to biomarkers

549
00:43:08,030 --> 00:43:09,390
in the next generation.

550
00:43:09,390 --> 00:43:11,980
And some of that’s famine study.

551
00:43:11,980 --> 00:43:16,890
They found some evidence of certain genes
that maybe have influenced…explained the

552
00:43:16,890 --> 00:43:17,890
chronic disease risk.

553
00:43:17,890 --> 00:43:25,930
Andrew Prentice from MRC has done work…really
elegant work in…in the Gambia, showing how

554
00:43:25,930 --> 00:43:31,030
folate and B-12, so micronutrients…it’s
not all about just, you know, how much you

555
00:43:31,030 --> 00:43:33,599
eat, but diet quality is important.

556
00:43:33,599 --> 00:43:37,200
And there’s been…I’m just giving a few
examples.

557
00:43:37,200 --> 00:43:42,079
I haven’t shared it, but been very happy
to have done a preconception intervention

558
00:43:42,079 --> 00:43:43,079
in Vietnam.

559
00:43:43,079 --> 00:43:45,890
We’re following up the cohort, and we have
banked samples.

560
00:43:45,890 --> 00:43:51,740
So, there’s a lot of opportunity to look
at some of these mechanisms that may explain

561
00:43:51,740 --> 00:43:52,810
intergenerational effects.

562
00:43:52,810 --> 00:43:57,130
And this is almost my last slide.

563
00:43:57,130 --> 00:44:02,870
I think we always…most of what I shared
has come from, you know, cohorts that were

564
00:44:02,870 --> 00:44:03,870
in the ’70s, ’80s.

565
00:44:03,870 --> 00:44:06,770
We’ve dealt a lot with undernutrition.

566
00:44:06,770 --> 00:44:13,700
But we know that we are in the nutrition transition—or
sometimes past it—that in pregnancy, you

567
00:44:13,700 --> 00:44:20,140
can have too little of something, too much
of something, and diet quality that one really

568
00:44:20,140 --> 00:44:24,540
needs to think of this, you know, very broadly.

569
00:44:24,540 --> 00:44:26,470
But that they coexist.

570
00:44:26,470 --> 00:44:31,150
I think that’s the challenge when you’re
talking about obesity, or overweight, which

571
00:44:31,150 --> 00:44:33,460
is affecting so many women in the [inaudible].

572
00:44:33,460 --> 00:44:38,150
You know, they get pregnant.

573
00:44:38,150 --> 00:44:42,010
Diet may be inadequate from other micronutrient
perspectives.

574
00:44:42,010 --> 00:44:46,200
It’s not just, “Okay, you’re going to
tell the woman to start,” right?

575
00:44:46,200 --> 00:44:51,980
And we’ve had…lot of work in advancing
even recommendations for gestational weight

576
00:44:51,980 --> 00:44:52,980
gain.

577
00:44:52,980 --> 00:44:53,980
What is optimal weight gain?

578
00:44:53,980 --> 00:44:56,440
What…what are you going to tell women to
eat when they’re pregnant?

579
00:44:56,440 --> 00:45:00,230
It has to be tailored to their individual
circumstance.

580
00:45:00,230 --> 00:45:03,930
Yes, it can become burdensome, but people
just don’t bother, right?

581
00:45:03,930 --> 00:45:09,180
That’s really what’s important, and that
gets me to, you know, thinking, really, you

582
00:45:09,180 --> 00:45:13,050
have to think in terms of a life course.

583
00:45:13,050 --> 00:45:14,490
Pregnancy is very important.

584
00:45:14,490 --> 00:45:20,430
I work on it, but having worked on it for
30 years, I like to say it’s too late, sometimes.

585
00:45:20,430 --> 00:45:24,030
We have to start and work at various points.

586
00:45:24,030 --> 00:45:28,550
And this is what, you know, Dr. Cho had put
in as a life-course approach.

587
00:45:28,550 --> 00:45:31,780
And then that really requires everybody to
work together.

588
00:45:31,780 --> 00:45:36,240
You don’t have people competing for the
same limited resources.

589
00:45:36,240 --> 00:45:44,750
And this is my…just to show that, you know,
the burden of micronutrient deficiencies,

590
00:45:44,750 --> 00:45:49,920
of overweight, of underweight persists globally,
and they vary by the setting.

591
00:45:49,920 --> 00:45:56,150
This is just using geographic regions, but
those of us who work in global health know

592
00:45:56,150 --> 00:45:58,890
that these vary even locally.

593
00:45:58,890 --> 00:46:01,700
Some…right here in the U.S., we know there
are disparities.

594
00:46:01,700 --> 00:46:07,510
Similarly, if you go to a country like India
or other parts, it’s very diverse.

595
00:46:07,510 --> 00:46:12,569
So, we really need to think about the context
that we’re working in as we think about

596
00:46:12,569 --> 00:46:18,200
what to advise to improve the health and development
of generations to come.

597
00:46:18,200 --> 00:46:23,540
And I will leave you with some challenges,
which I think we will address.

598
00:46:23,540 --> 00:46:28,910
Clearly, you can see what we’ve learned
is because of the investment in longitudinal

599
00:46:28,910 --> 00:46:32,079
research, which takes time to follow.

600
00:46:32,079 --> 00:46:37,099
So, thinking creatively how we can harness
what comes.

601
00:46:37,099 --> 00:46:42,800
What…you know, we don’t want to wait another
50 years to say, “Oh, this is what you should

602
00:46:42,800 --> 00:46:43,800
do.”

603
00:46:43,800 --> 00:46:51,140
But it still is really important to keep that
long-term vision and find ways of…of advancing

604
00:46:51,140 --> 00:46:53,519
our knowledge.

605
00:46:53,519 --> 00:47:00,680
Understanding the mechanisms is clearly something…an
area where I think with our…more biomarkers

606
00:47:00,680 --> 00:47:03,849
but interpreting them becomes challenging.

607
00:47:03,849 --> 00:47:10,710
And I think with the dual burden, those are
things that I think are going to be the next

608
00:47:10,710 --> 00:47:14,800
frontier as we think about, you know, metabolic
abnormalities.

609
00:47:14,800 --> 00:47:19,630
We tend just…to kind of just say “underweight,”
“overweight,” “macronutrients,” but

610
00:47:19,630 --> 00:47:21,470
we know it’s much, much more complicated.

611
00:47:21,470 --> 00:47:26,309
But thank you very much, and I look forward
to the rest of the presentations in the workshop.

612
00:47:26,309 --> 00:47:34,460
DR. ASHLEY VARGAS: Thank you so much, Dr. Ramakrishnan.

613
00:47:34,460 --> 00:47:38,930
What a wonderful talk and a broad overview
of…as we can see, many people’s work,

614
00:47:38,930 --> 00:47:40,510
and many decades of your own work.

615
00:47:40,510 --> 00:47:41,589
So, thank you for that.

616
00:47:41,589 --> 00:47:46,890
I’m joined by my fantastic colleague, Dr.
Kellie Casavale from FDA, and we’re going

617
00:47:46,890 --> 00:47:51,369
to move into a moderated discussion for the
next 9 or so minutes.

618
00:47:51,369 --> 00:47:56,589
So, we’ll start with a general question,
and then for those of you in the room, just

619
00:47:56,589 --> 00:47:58,190
feel free to raise your hand.

620
00:47:58,190 --> 00:48:03,410
Can you…so, your first slide sort of talked
about intergenerational, transgenerational…and

621
00:48:03,410 --> 00:48:07,220
we called this workshop very intentionally
“multigenerational,” but tried to be inclusive.

622
00:48:07,220 --> 00:48:11,710
But can you talk a little bit about this definition
and your interpretation of them?

623
00:48:11,710 --> 00:48:22,220
DR. USHA RAMAKRISHNAN: Right. So, I think definitions are…are important,
and multigenerational health…the main point

624
00:48:22,220 --> 00:48:28,160
is when people tend to think of intergenerational,
we may stop with just two or a couple because

625
00:48:28,160 --> 00:48:31,119
that transmission is a little more clearer,
right?

626
00:48:31,119 --> 00:48:36,220
It’s what…“You are what your mother
eats,” kind of language is provided.

627
00:48:36,220 --> 00:48:41,450
But when you get to transgenerational, it
is that understanding the mechanisms, you

628
00:48:41,450 --> 00:48:47,800
know, the father’s role, which may not be
apparent in that immediate generation.

629
00:48:47,800 --> 00:48:53,760
But it may get…become apparent two generations
later, so that’s my understanding.

630
00:48:53,760 --> 00:49:00,020
And again, I…I really would recommend Dr.
Breton et al., the ECHOS paper, where you had

631
00:49:00,020 --> 00:49:02,200
a whole box of definitions.

632
00:49:02,200 --> 00:49:08,100
But it’s important to keep that…so, my
thing is we just need to keep our generations

633
00:49:08,100 --> 00:49:09,100
straight.

634
00:49:09,100 --> 00:49:11,100
DR. ASHLEY VARGAS: Be consistent.

635
00:49:11,100 --> 00:49:16,010
DR. USHA RAMAKRISHNAN: You know, I like the F0,
F1, F2, F3.

636
00:49:16,010 --> 00:49:18,480
I hope that helps.

637
00:49:18,480 --> 00:49:23,360
DR. CHRISTOPHER LYNCH: Yeah, so…yeah. This is Chris Lynch.

638
00:49:23,360 --> 00:49:29,210
So, what I’m wondering about is, you know,
given that we have these transgenerational

639
00:49:29,210 --> 00:49:33,470
effects, and since today is Prime Day, how
do we get back to prime?

640
00:49:33,470 --> 00:49:39,950
Like, it…it seems to me that if we are even
using animal models, the animal models were

641
00:49:39,950 --> 00:49:46,619
affected by the, you know, the previous stresses
and things like that that their parents had

642
00:49:46,619 --> 00:49:48,300
in the colony or…or whatever—

643
00:49:48,300 --> 00:49:54,240
DR. CHRISTOPHER LYNCH: —so, how do we get to
baseline to identify mechanisms?

644
00:49:54,240 --> 00:49:56,329
Or is that necessary…necessary to do?

645
00:49:56,329 --> 00:50:03,460
DR. USHA RAMAKRISHNAN: I would argue it’s not…it’s
a bit like being on a moving train, if…if

646
00:50:03,460 --> 00:50:06,290
that’s an analogy.

647
00:50:06,290 --> 00:50:10,040
It’s…I think go…baseline…you can go
back then.

648
00:50:10,040 --> 00:50:15,869
You know, the…you know, the work that I
said one of the studies was fascinating from…from

649
00:50:15,869 --> 00:50:21,920
Sweden, where they looked at famine exposure
in the 19th century, and then followed the

650
00:50:21,920 --> 00:50:29,630
cohorts in the 20th century and have found…so,
it’s good to find data sources, whether

651
00:50:29,630 --> 00:50:37,070
it’s an animal or, you know, human studies
that allow you to sort of do those linkages.

652
00:50:37,070 --> 00:50:40,819
But it’s, you know, it…it’s a challenge.

653
00:50:40,819 --> 00:50:44,210
And I tend to be…“What’s the goal?”

654
00:50:44,210 --> 00:50:47,110
What…what do we, you know, what do we want
to come up with?

655
00:50:47,110 --> 00:50:52,420
What…and I think that’s going to be important
if we understand…if we want to understand

656
00:50:52,420 --> 00:50:57,200
the mechanisms, how is that going to help
in…I am a public health person, so…how

657
00:50:57,200 --> 00:51:04,160
is that going to affect what I’m advising
a young girl who’s planning to get pregnant

658
00:51:04,160 --> 00:51:08,069
or a woman who’s pregnant or her baby is
overweight?

659
00:51:08,069 --> 00:51:14,920
I would argue that you…you can’t control
for everything, but characterizing well and

660
00:51:14,920 --> 00:51:16,850
doing good history is important.

661
00:51:16,850 --> 00:51:18,819
I think that would also help.

662
00:51:18,819 --> 00:51:21,339
DR. ASHLEY VARGAS: Excellent.

663
00:51:21,339 --> 00:51:26,079
So, we have questions in the room, but I just
want to remind those of you online, please

664
00:51:26,079 --> 00:51:28,240
click the Q&A button on Zoom to ask questions.

665
00:51:28,240 --> 00:51:29,910
We want to make sure we’re including everyone.

666
00:51:29,910 --> 00:51:31,560
So, who’s next in the room?

667
00:51:31,560 --> 00:51:35,600
DR. RICHARD PILSNER: Richard Pilsner, Wayne State
University.

668
00:51:35,600 --> 00:51:39,930
Just to clarify on the intergenerational versus
the trans.

669
00:51:39,930 --> 00:51:45,280
In the field of environmental health sciences
that we…we distinguish the two by the cell

670
00:51:45,280 --> 00:51:46,930
being actually exposed.

671
00:51:46,930 --> 00:51:54,130
So, if F0 mother is exposed to something,
then F1 and F2 are considered intergenerational.

672
00:51:54,130 --> 00:51:56,370
F3 would then be the transgenerational.

673
00:51:56,370 --> 00:51:59,080
That animal has not been exposed.

674
00:51:59,080 --> 00:52:05,430
Through the male, the transgenerational will
be the F2 offspring.

675
00:52:05,430 --> 00:52:07,430
DR. USHA RAMAKRISHNAN: Thank you.

676
00:52:07,430 --> 00:52:12,200
DR. ASHLEY VARGAS: These are the hard discussions
we want to have, so thank you.

677
00:52:12,200 --> 00:52:13,520
AUDIENCE MEMBER: Oh, yeah.

678
00:52:13,520 --> 00:52:15,799
So, thanks very much for that nice overview.

679
00:52:15,799 --> 00:52:20,931
I…I have many questions, but I’m going
to just ask you two questions right now, including

680
00:52:20,931 --> 00:52:22,930
lots of methodological questions, as well.

681
00:52:22,930 --> 00:52:28,080
So, my…and I’m going to stick to only
the human stuff that you talked about.

682
00:52:28,080 --> 00:52:32,859
One, you mentioned the body mass index and
adiposity.

683
00:52:32,859 --> 00:52:39,030
So, you know, if you do simple correlation
analyses, these things are highly correlated,

684
00:52:39,030 --> 00:52:41,869
extremely difficult to…to tease them out.

685
00:52:41,869 --> 00:52:48,400
And if you’re going to be doing multigenerational
studies using cohorts, it’s probably most

686
00:52:48,400 --> 00:52:51,260
practical to use body mass index.

687
00:52:51,260 --> 00:52:55,750
We have done these things in randomized controlled
trials, and it’s adequate when you…when

688
00:52:55,750 --> 00:52:59,680
you try to tease them out, even to predict
biological markers.

689
00:52:59,680 --> 00:53:02,069
So that…that’s one thing I’d like to
talk about.

690
00:53:02,069 --> 00:53:07,290
The other thing you can comment about: When there
is your regional differences in obesity rates,

691
00:53:07,290 --> 00:53:13,809
I wonder whether that is really malnutrition
or overnutrition or, really, increasing toxic

692
00:53:13,809 --> 00:53:15,410
and environmental exposures.

693
00:53:15,410 --> 00:53:21,970
And then the final thing is, in terms of randomized
controlled trials you talked about, you didn’t

694
00:53:21,970 --> 00:53:24,380
show what the baseline measures were.

695
00:53:24,380 --> 00:53:29,000
People don’t really put on weight over very
short period of time…and wonder how you

696
00:53:29,000 --> 00:53:33,710
control for those type of things at baseline
or else the…the, you know, but in a short

697
00:53:33,710 --> 00:53:36,260
period of time, you would not expect to see
much differences.

698
00:53:36,260 --> 00:53:39,000
I hope I didn’t ask too much questions.

699
00:53:39,000 --> 00:53:43,180
DR. USHA RAMAKRISHNAN: Well, I’ll…I’ll try
to answer a couple of the questions.

700
00:53:43,180 --> 00:53:48,990
The methodological one is really important,
and I kind of went a bit quickly on it.

701
00:53:48,990 --> 00:53:55,890
One of the ways we got around in the…especially
in the cohort analysis the sort of interdependence,

702
00:53:55,890 --> 00:54:02,230
right?—I mean, when a child grows, we know
that so much of it is highly correlated—was

703
00:54:02,230 --> 00:54:07,530
to use the statistical approach of conditionals,
which allows you—so, I won’t get into

704
00:54:07,530 --> 00:54:12,520
all the details—but it allows you to look
at the effect after having accounted for the

705
00:54:12,520 --> 00:54:13,520
correlation.

706
00:54:13,520 --> 00:54:19,890
So that’s as best as we can, and that…that…and
that’s been used in various fields as…as

707
00:54:19,890 --> 00:54:20,890
a marker.

708
00:54:20,890 --> 00:54:23,650
So, one way we’ll sort of tease apart the
growth.

709
00:54:23,650 --> 00:54:30,500
So, in other words, the weight gain, just
say from 2 to 4 years, that is not dependent

710
00:54:30,500 --> 00:54:34,589
on where the child was at 2 years or birth.

711
00:54:34,589 --> 00:54:37,130
It…it…it’s the residue of…[inaudible].

712
00:54:37,130 --> 00:54:40,030
So that’s one of the ways.

713
00:54:40,030 --> 00:54:43,609
Now, your comment about the background is
really important.

714
00:54:43,609 --> 00:54:50,730
But these are the…many of the studies that
I saw from the humans were done in a background

715
00:54:50,730 --> 00:54:53,380
with…in settings where there was a lot of
undernutrition.

716
00:54:53,380 --> 00:54:57,450
Whether it was the Dutch Famine, which is
short-term, acute malnutrition, if you want

717
00:54:57,450 --> 00:55:04,280
to think of a very brief period, and then
things went back to, you know, food…food

718
00:55:04,280 --> 00:55:10,000
availability…with the INCAP Study in many
of these settings where the…the baseline,

719
00:55:10,000 --> 00:55:14,710
there was a lot of undernutrition, poor quality
of diet, etc.

720
00:55:14,710 --> 00:55:20,339
But the key here is the trial was done during
a period when maternal child nutrition…it

721
00:55:20,339 --> 00:55:22,480
was a maternal and child nutrition study.

722
00:55:22,480 --> 00:55:24,760
They improved…they provided food supplements.

723
00:55:24,760 --> 00:55:29,450
The original study showed in the randomized
controlled trial that you could improve birth

724
00:55:29,450 --> 00:55:31,720
weight, something reduced in that short-term.

725
00:55:31,720 --> 00:55:36,450
But these are the follow-ups, so the design
remains.

726
00:55:36,450 --> 00:55:40,690
There are challenges with attrition, who remains
in the cohorts.

727
00:55:40,690 --> 00:55:43,020
It’s very challenging for those to stay there.

728
00:55:43,020 --> 00:55:47,790
But I hope that helps you, and…which is
why that’s the gap that the applicability

729
00:55:47,790 --> 00:55:52,339
of some of those relationships to the baseline
today remain.

730
00:55:52,339 --> 00:55:55,539
We still have to keep doing it.

731
00:55:55,539 --> 00:56:00,039
But I think that shouldn’t stop us from
understanding mechanisms.

732
00:56:00,039 --> 00:56:04,420
I like to sort of say, pregnancy is still
9 months.

733
00:56:04,420 --> 00:56:09,390
There’s some things about human biology
which have remained, but how do we understand

734
00:56:09,390 --> 00:56:10,390
the mechanisms?

735
00:56:10,390 --> 00:56:13,380
I hope that helps address some of the concerns.

736
00:56:13,380 --> 00:56:15,380
MS. KELLIE CASAVALE: All right, this is Kellie
Casavale.

737
00:56:15,380 --> 00:56:19,960
We’re going to now take a couple of questions
from our virtual audience.

738
00:56:19,960 --> 00:56:24,140
We have two questions in the chat, and they’re…seem
to be somewhat related, so I’m going to

739
00:56:24,140 --> 00:56:25,280
ask them both together.

740
00:56:25,280 --> 00:56:31,600
And they’re all…they’re both about really
capturing changes over time, you know, in…in

741
00:56:31,600 --> 00:56:32,600
studies.

742
00:56:32,600 --> 00:56:39,290
And so, the audience is interested to hear
about how studies are able to tease out what

743
00:56:39,290 --> 00:56:45,130
is nature versus nurture impacts when following
participants over their life course, including

744
00:56:45,130 --> 00:56:50,680
things such as the food environment and how that
can change across generations pretty drastically.

745
00:56:50,680 --> 00:56:55,109
And also, how to account for those changes
in…in things such as changes in socioeconomic

746
00:56:55,109 --> 00:56:56,109
status?

747
00:56:56,109 --> 00:56:58,109
DR. USHA RAMAKRISHNAN: Yeah.

748
00:56:58,109 --> 00:57:02,470
So, the…the good news, especially in studies
like the INCAP Cohort or even in the other

749
00:57:02,470 --> 00:57:07,330
cohort study, they just don’t measure, you
know, body size.

750
00:57:07,330 --> 00:57:10,430
A lot of information has been collected.

751
00:57:10,430 --> 00:57:14,289
There actually have been a lot of papers to
show how change in wealth influences some

752
00:57:14,289 --> 00:57:15,319
of these outcomes.

753
00:57:15,319 --> 00:57:24,069
So, it really comes to having the ability
to get that comprehensive picture at various

754
00:57:24,069 --> 00:57:28,080
time points, and then it’s a string of beads,
right?

755
00:57:28,080 --> 00:57:29,080
So, absolutely.

756
00:57:29,080 --> 00:57:30,080
And it…it’s expensive to do.

757
00:57:30,080 --> 00:57:31,530
I…I’ve struggled with my own.

758
00:57:31,530 --> 00:57:36,859
I have cohort studies, which have been done
more contemporarily and usually the first

759
00:57:36,859 --> 00:57:38,319
thing is, “Well, you didn’t find anything.”

760
00:57:38,319 --> 00:57:42,599
But I’m like, “But I might find something
if I continue following these cohorts up.”

761
00:57:42,599 --> 00:57:43,890
And which we are.

762
00:57:43,890 --> 00:57:49,290
I've done a study on omega-3 fatty acids, and
we’re finding genetic polymorphisms predict

763
00:57:49,290 --> 00:57:55,420
response to maternal intervention when the
children are now adolescents.

764
00:57:55,420 --> 00:58:00,900
So, we have to keep at it and measure all
these important developments.

765
00:58:00,900 --> 00:58:03,819
DR. ASHLEY VARGAS: Thank you, Dr. Ramakrishnan.

766
00:58:03,819 --> 00:58:07,290
Everyone join me in thanking her as we move
to our next session.

767
00:58:07,290 --> 00:58:09,290
DR. USHA RAMAKRISHNAN: Thank you.

768
00:58:09,290 --> 00:58:13,890
DR. ASHLEY VARGAS: For those of you online, we’re
going to take a brief minute break as we transition

769
00:58:13,890 --> 00:58:14,920
people at the podium.

770
00:58:14,920 --> 00:58:16,920
DR. ANDREW BREMER: Are we on?

771
00:58:16,920 --> 00:58:17,920
We’re live?

772
00:58:17,920 --> 00:58:18,920
This is fantastic.

773
00:58:18,920 --> 00:58:20,890
Gosh, it’s great seeing people!

774
00:58:20,890 --> 00:58:25,369
I…I’m…you know, Ashley and Christopher
were super nice, and they gave…they gave me a script.

775
00:58:25,369 --> 00:58:26,710
I’m going to still—going to go rogue.

776
00:58:26,710 --> 00:58:30,119
I, you know, it…it is great to see everyone
here, and I…and…and for those on campus,

777
00:58:30,119 --> 00:58:35,260
we really appreciate you taking time out of
your day to…to be here and join us.

778
00:58:35,260 --> 00:58:37,420
It’s…there’s…there’s…there’s…there’s
power and connection, and I…I…I don’t

779
00:58:37,420 --> 00:58:42,260
want to obviate them and minimize the…the
beauty of having people online.

780
00:58:42,260 --> 00:58:44,070
But it’s great to have people here in the
room.

781
00:58:44,070 --> 00:58:45,480
So…so, we’re…we’re going to continue.

782
00:58:45,480 --> 00:58:48,990
My…my job…Chris is just reminding me I
have to stay on time, so I’m not going to

783
00:58:48,990 --> 00:58:50,280
go too rogue.

784
00:58:50,280 --> 00:58:52,119
But, yeah, welcome to Session 1.

785
00:58:52,119 --> 00:58:55,230
It’s…yeah, as you do have in your…that
you have in your handout and the…the agenda.

786
00:58:55,230 --> 00:59:01,320
And the session is entitled The Foundational
Context for Multigenerational Studies on Nutrition.

787
00:59:01,320 --> 00:59:03,300
As Ashley mentioned, I’m Andrew Bremer.

788
00:59:03,300 --> 00:59:07,211
I had the…the pleasure of working closely
with Ashley and Kimberlea and…and many other

789
00:59:07,211 --> 00:59:08,790
of my NIH colleagues.

790
00:59:08,790 --> 00:59:11,480
And so, again, it’s great…it’s great
to be here.

791
00:59:11,480 --> 00:59:13,740
And I have the pleasure of moderating the
session.

792
00:59:13,740 --> 00:59:14,740
And I…yeah.

793
00:59:14,740 --> 00:59:15,740
I may go rogue.

794
00:59:15,740 --> 00:59:16,930
I call audibles, but I will keep us on time.

795
00:59:16,930 --> 00:59:19,280
And we have three great panelists today.

796
00:59:19,280 --> 00:59:22,701
The…the…the first this morning is Dr.
Janne Boone-Heinonen from Oregon Health & Science

797
00:59:22,701 --> 00:59:23,701
University.

798
00:59:23,701 --> 00:59:28,470
And she will begin the session with a presentation
on the Historical Data and International Observational

799
00:59:28,470 --> 00:59:32,990
Studies on Multigenerational Effects of and
on Nutrition and Diet.

800
00:59:32,990 --> 00:59:37,250
And then Dr. Oliver Rando from UMass Chan
Medical School will follow with a presentation

801
00:59:37,250 --> 00:59:42,200
on Historical Animal Studies on Multigenerational
Effects of and on Nutrition and Diet.

802
00:59:42,200 --> 00:59:47,030
And then last, my NIH colleague who I have
lots of stories about, Sonia Arteaga from

803
00:59:47,030 --> 00:59:51,610
the NIH Environmental influences of Child
Health Outcomes Program, otherwise known as

804
00:59:51,610 --> 00:59:57,910
ECHO, will follow and share Relevant Considerations
from the ECHO Preconceptional Origins of Child

805
00:59:57,910 --> 00:59:59,180
Health Outcomes Workshop.

806
00:59:59,180 --> 01:00:00,980
I think it was held 2 years ago, Sonia?

807
01:00:00,980 --> 01:00:04,770
And I…I had the privilege of…of being
a part of that workshop, as well.

808
01:00:04,770 --> 01:00:10,160
I do…as Ashley mentioned, I would like the
virtual participants to…to be engaged, and

809
01:00:10,160 --> 01:00:14,780
certainly feel free to…to…to send your
questions to presenters throughout the presentations

810
01:00:14,780 --> 01:00:16,960
to the question and answer box.

811
01:00:16,960 --> 01:00:20,900
And then certainly my…my…my colleague…I
want to thank, Lieutenant Commander Kimberlea

812
01:00:20,900 --> 01:00:23,799
Gibbs for keeping me in line and for helping
me with the questions.

813
01:00:23,799 --> 01:00:27,510
I think it’s also classic that we’re back
in person, and my computer will not connect

814
01:00:27,510 --> 01:00:29,210
even to the NIH Guest Internet.

815
01:00:29,210 --> 01:00:34,689
So, I’m totally relying on Kimberlea because
I…even I can’t access anything.

816
01:00:34,689 --> 01:00:36,720
And so, with that, again, welcome, everyone.

817
01:00:36,720 --> 01:00:38,190
It’s…it’s wonderful to see people in
person.

818
01:00:38,190 --> 01:00:41,260
And with that, I will turn the floor over
to Janne.

819
01:00:41,260 --> 01:00:42,680
Take it away, Janne.

820
01:00:42,680 --> 01:00:44,820
DR. JANNE BOONE-HEINONEN: Well, good morning.

821
01:00:44,820 --> 01:00:46,910
I’m really excited to be here.

822
01:00:46,910 --> 01:00:51,740
This is a incredible workshop, and I’m,
I’m really happy to be a part of it.

823
01:00:51,740 --> 01:01:01,280
So, my task today in the next 20 minutes is
to provide an overview of the epi research

824
01:01:01,280 --> 01:01:03,560
regarding multigenerational effects of nutrition.

825
01:01:03,560 --> 01:01:07,480
You're ahead of these clips.

826
01:01:07,480 --> 01:01:08,480
Okay.

827
01:01:08,480 --> 01:01:18,750
So, what I’ll do is I will give a summary
of kind of how the field has evolved in three

828
01:01:18,750 --> 01:01:20,520
or four main dimensions.

829
01:01:20,520 --> 01:01:25,339
We’ll talk about nutrition-related exposure
measures, the types of outcomes that have

830
01:01:25,339 --> 01:01:32,299
been examined, study design, and the types
of research questions, and we’ll wrap up

831
01:01:32,299 --> 01:01:33,839
with challenges and opportunities.

832
01:01:33,839 --> 01:01:40,940
My focus here will be on intrauterine effects
of maternal nutrition.

833
01:01:40,940 --> 01:01:46,090
Acknowledging that there are other very important
topics, including, you know, other exposures,

834
01:01:46,090 --> 01:01:49,560
as well as other multigenerational processes.

835
01:01:49,560 --> 01:01:52,270
All right.

836
01:01:52,270 --> 01:01:57,529
So, we’ll start with nutrition-related exposure
measures.

837
01:01:57,529 --> 01:02:03,119
You know, this is, of course, a graph we’ve
all seen over and over again.

838
01:02:03,119 --> 01:02:08,520
This is showing higher risk in this case for
coronary heart disease with lower birth weight.

839
01:02:08,520 --> 01:02:15,700
This has of course, been replicated for many
study populations, countless outcomes.

840
01:02:15,700 --> 01:02:20,670
The key here is that this is interpreted as
maternal undernutrition.

841
01:02:20,670 --> 01:02:29,960
So, there is another sort of body of work
that has…sort of more directly assesses

842
01:02:29,960 --> 01:02:35,830
maternal undernutrition, examining maternal
anthropometry measures, things like short

843
01:02:35,830 --> 01:02:41,410
stature, pelvic dimensions, and these are
indicators of undernutrition, especially in

844
01:02:41,410 --> 01:02:48,410
low-resource populations.

845
01:02:48,410 --> 01:02:54,020
On the sort of a lagging, but parallel body
of work is the maternal overnutrition.

846
01:02:54,020 --> 01:03:03,390
It’s...sort of, body of work, starting with
gestational diabetes, was…was a sort of a

847
01:03:03,390 --> 01:03:05,000
starting point for this.

848
01:03:05,000 --> 01:03:11,400
So…so now, we’ve delved into things like
maternal BMI, gestational weight gain, and

849
01:03:11,400 --> 01:03:12,779
body composition.

850
01:03:12,779 --> 01:03:19,260
So…so far, you might have noticed that we
still haven’t actually examined maternal

851
01:03:19,260 --> 01:03:20,260
nutrition.

852
01:03:20,260 --> 01:03:27,280
So, I’ll go over a few of those measures
of the types of exposures that we have examined.

853
01:03:27,280 --> 01:03:34,220
So, on the maternal undernutrition side, there’s…there’s
a really rich body of work, which our previous

854
01:03:34,220 --> 01:03:36,549
presenter talked about.

855
01:03:36,549 --> 01:03:43,081
And this…these are examining, sort of, temporal
changes in contextual drivers of maternal

856
01:03:43,081 --> 01:03:44,081
undernutrition.

857
01:03:44,081 --> 01:03:45,880
These are the famine studies.

858
01:03:45,880 --> 01:03:48,240
Also seasonal effects.

859
01:03:48,240 --> 01:03:56,560
This is…shown here is the Dutch Hunger Cohort,
showing the very pronounced decrease in food

860
01:03:56,560 --> 01:04:02,060
rations and then the subsequent pronounced,
sort of recovery of food rations.

861
01:04:02,060 --> 01:04:07,690
And so, what we have is…is a discrete period
of undernutrition.

862
01:04:07,690 --> 01:04:13,819
The colors at the top…those are pregnancy
trimesters for multiple pregnancies.

863
01:04:13,819 --> 01:04:20,869
And so, one major contribution of these studies
is kind of the recognition that the effects

864
01:04:20,869 --> 01:04:34,200
of undernutrition experienced at different
periods of pregnancies have, sort of, distinct

865
01:04:34,200 --> 01:04:35,200
effects.

866
01:04:35,200 --> 01:04:42,240
Many studies also examine actual maternal
dietary intake, energy intake, things like

867
01:04:42,240 --> 01:04:49,900
low-dietary protein, micronutrient deficiency,
and then on the flip side, supplementation

868
01:04:49,900 --> 01:04:50,980
intervention.

869
01:04:50,980 --> 01:04:55,440
Okay, and we also have biomarker—nutritional
biomarkers—the…for circulating blood,

870
01:04:55,440 --> 01:05:00,109
as well as cord blood nutrient levels.

871
01:05:00,109 --> 01:05:11,619
Okay, so this is the type of work that’s
been done in human epi populations.

872
01:05:11,619 --> 01:05:17,600
On the overnutrition side, we also sometimes
call this high calorie malnutrition, and really

873
01:05:17,600 --> 01:05:21,310
what this is getting at is the…sort of the
Western diet that promotes obesity.

874
01:05:21,310 --> 01:05:30,119
The types of dietary-intake measures that
kind of comprise the bulk of the field examine

875
01:05:30,119 --> 01:05:36,380
things like macronutrient intake, especially
fat intake, different types of food groups;

876
01:05:36,380 --> 01:05:38,410
so, these are…and…and types.

877
01:05:38,410 --> 01:05:45,339
So, things like fruit and vegetable intake,
sugar-sweetened beverage intake, dietary patterns.

878
01:05:45,339 --> 01:05:55,060
We see these...sort of Western diet, kind of,
overall patterns examined in many of these

879
01:05:55,060 --> 01:05:58,980
cohorts, as well as dietary interventions.

880
01:05:58,980 --> 01:06:03,400
And then, we have the same types of biomarkers
that we see in the undernutrition side.

881
01:06:03,400 --> 01:06:06,849
So, this is really important.

882
01:06:06,849 --> 01:06:14,740
The types…the dietary assessment in these studies,
you know, these epi studies…and so, typically

883
01:06:14,740 --> 01:06:18,260
they’re measured with food frequency questionnaires.

884
01:06:18,260 --> 01:06:22,859
Sometimes dietary recalls, usually one or
two.

885
01:06:22,859 --> 01:06:24,740
Occasionally, we see food diaries.

886
01:06:24,740 --> 01:06:29,609
Those are much more, sort of, resource and
time intensive.

887
01:06:29,609 --> 01:06:32,480
And then the exposure period in which diet
was—is—assessed.

888
01:06:32,480 --> 01:06:41,290
Typically, we’re seeing one or two assessments
in pregnancies, kind of in varying trimesters.

889
01:06:41,290 --> 01:06:50,010
Very few studies examined a preconception
diet, and few studies examined postnatal diet.

890
01:06:50,010 --> 01:06:51,010
Oops.

891
01:06:51,010 --> 01:06:52,400
Wrong way.

892
01:06:52,400 --> 01:06:53,400
Okay.

893
01:06:53,400 --> 01:07:05,160
So now, we’ll shift to the types of outcomes
that, sort of…that we have seen to date.

894
01:07:05,160 --> 01:07:13,810
So, until reasonably recently, these types
of outcomes comprised really the bulk of the

895
01:07:13,810 --> 01:07:15,140
field.

896
01:07:15,140 --> 01:07:24,960
So, clinical is over examining various types
of maternal nutrition status, as I just described,

897
01:07:24,960 --> 01:07:26,430
related to clinical risk factors.

898
01:07:26,430 --> 01:07:29,720
These are things like BMI, blood pressure,
lipids.

899
01:07:29,720 --> 01:07:35,819
It really…it’s all phases of life starting
from childhood all the way through the end

900
01:07:35,819 --> 01:07:37,150
later in adulthood.

901
01:07:37,150 --> 01:07:46,400
In the lower corner on the—just an example—of…of
one of these studies, that’s showing higher

902
01:07:46,400 --> 01:08:00,150
child fat mass index with high levels of sugar-sweetened
beverage intake for the mother in the second trimester.

903
01:08:00,150 --> 01:08:02,920
So, that’s the red bars compared to the
blue bars.

904
01:08:02,920 --> 01:08:12,119
Okay, and then we have a whole host of clinical
disease and mortality measures that have been examined

905
01:08:12,119 --> 01:08:17,859
in a sort of cardiometabolic realm, as well
as mental health, cognitive function, cancer,

906
01:08:17,859 --> 01:08:22,500
and the list goes on.

907
01:08:22,500 --> 01:08:23,819
Okay.

908
01:08:23,819 --> 01:08:32,020
So, coming in later to the field are…including an
examination of these intermediate outcomes.

909
01:08:32,020 --> 01:08:38,040
So, at the top are postnatal factors, lactation
and growth.

910
01:08:38,040 --> 01:08:42,279
The…the…it doesn’t quite fit here, I
just wanted to note that we could talk about

911
01:08:42,279 --> 01:08:43,740
that for a very long time.

912
01:08:43,740 --> 01:08:46,040
But I won’t.

913
01:08:46,040 --> 01:08:48,540
But those are…those are important, as well.

914
01:08:48,540 --> 01:08:54,759
I’ll focus on the biologic and behavioral
mechanisms, and so this is a really interesting,

915
01:08:54,759 --> 01:08:58,609
sort of, crosstalk between the animal and
the human studies.

916
01:08:58,609 --> 01:09:02,170
We’re learning more and more about how these
processes work.

917
01:09:02,170 --> 01:09:08,179
So, when I talk about biological mechanisms,
we’re talking about, you know, epigenetics

918
01:09:08,179 --> 01:09:12,420
metabolomic changes, placental changes, structural
changes.

919
01:09:12,420 --> 01:09:15,859
These are things like alterations in kidney
volume.

920
01:09:15,859 --> 01:09:21,199
That’s the kind of thing that we can measure
in humans.

921
01:09:21,199 --> 01:09:26,380
Functional changes, things like altered stress
response.

922
01:09:26,380 --> 01:09:31,150
And behavioral mechanisms, especially in the
overnutrition world.

923
01:09:31,150 --> 01:09:36,290
We’re thinking about things like alterations
and appetite regulation and reward system.

924
01:09:36,290 --> 01:09:41,651
So, these are the, kind of, the steps, you
know, through which we get from maternal nutrition

925
01:09:41,651 --> 01:09:44,859
status to some of these outcomes.

926
01:09:44,859 --> 01:09:47,830
So, just a couple of examples.

927
01:09:47,830 --> 01:09:54,600
So, on the biological mechanisms, this is
a example of the study out of the Dutch Hunger

928
01:09:54,600 --> 01:09:56,960
Winter Cohort.

929
01:09:56,960 --> 01:10:06,200
They were examining effects of exposure to
that famine during gestation on DNA methylation.

930
01:10:06,200 --> 01:10:14,179
And they’re specifically interested in both
methylation patterns as mediators of the effects

931
01:10:14,179 --> 01:10:20,050
of metabolic disease in adulthood.

932
01:10:20,050 --> 01:10:22,830
And then just an example of behavioral mechanisms.

933
01:10:22,830 --> 01:10:30,860
This graph is showing…essentially, we’re
seeing worse appetite regulation in girls

934
01:10:30,860 --> 01:10:33,600
born either lower or higher birth weight.

935
01:10:33,600 --> 01:10:42,960
Okay, so, of course, the biological and behavioral
mechanisms sort of lead into each other.

936
01:10:42,960 --> 01:10:48,030
But this is kind of one way to separate them
out.

937
01:10:48,030 --> 01:10:49,030
Okay.

938
01:10:49,030 --> 01:10:56,429
So, third thing we’ll talk about is study
design.

939
01:10:56,429 --> 01:11:04,969
So, there’s a really wide range of, sort
of, types of studies that have been examined.

940
01:11:04,969 --> 01:11:14,060
You know, these very long-term studies that
really sort of started the field, where we

941
01:11:14,060 --> 01:11:20,650
see the things like birth weight, exposure
to famine associated with disease and mortality

942
01:11:20,650 --> 01:11:21,850
many, many decades later.

943
01:11:21,850 --> 01:11:27,080
And so, these all relied on analysis of historical
data.

944
01:11:27,080 --> 01:11:31,440
And mostly in countries that have better historical
data than the U.S.

945
01:11:31,440 --> 01:11:37,420
I’m coming from the U.S. perspective.

946
01:11:37,420 --> 01:11:45,750
There are a large body of work using linked
clinical records for the…the mother and

947
01:11:45,750 --> 01:11:46,750
the child.

948
01:11:46,750 --> 01:11:51,600
So, this…in this way, we can examine...really
the you know, any type of measure that’s

949
01:11:51,600 --> 01:11:53,090
recorded in the clinical records.

950
01:11:53,090 --> 01:11:58,980
So, we have, you know, maternal BMI, maternal
conditions, birth outcomes, and we can relate

951
01:11:58,980 --> 01:12:03,870
them to growth, BMI, blood pressure, etc.,
in the offspring.

952
01:12:03,870 --> 01:12:08,190
However, we’re limited in this case in the
length of follow-up.

953
01:12:08,190 --> 01:12:12,520
We can really…you know, we’re limited
by how long the children stay in the system.

954
01:12:12,520 --> 01:12:20,140
And so, really, it’s…typically, we see
these out sort of into early to mid-childhood.

955
01:12:20,140 --> 01:12:23,060
And then, of course, we have prospective studies.

956
01:12:23,060 --> 01:12:33,270
This is a very, very short, sample of the
studies that are…are ongoing.

957
01:12:33,270 --> 01:12:37,900
In this case, we can get really detailed measures.

958
01:12:37,900 --> 01:12:42,340
We can measure maternal…actual maternal
nutrition.

959
01:12:42,340 --> 01:12:46,190
We can collect cord blood and placenta.

960
01:12:46,190 --> 01:12:51,860
And then follow up on children and collect
some of these, sort of, biobehavioral, sort

961
01:12:51,860 --> 01:12:55,300
of, intermediate outcomes that we might be
interested in.

962
01:12:55,300 --> 01:12:56,300
Okay?

963
01:12:56,300 --> 01:13:01,429
But this is…you know, we’re even more
limited in how far out in the life span we

964
01:13:01,429 --> 01:13:04,850
can follow the offspring.

965
01:13:04,850 --> 01:13:06,300
Okay.

966
01:13:06,300 --> 01:13:13,760
And then there have been a handful of studies
that have achieved this three-generation follow-up.

967
01:13:13,760 --> 01:13:20,300
Usually these are kind of combinations of
studies, usually sort of anchored in one prospective

968
01:13:20,300 --> 01:13:21,409
cohort.

969
01:13:21,409 --> 01:13:28,100
We can then follow up the…the children of
that cohort as they age.

970
01:13:28,100 --> 01:13:34,110
We can link back, for example, to the maternal
birth record and learn information about the

971
01:13:34,110 --> 01:13:35,880
grandmaternal characteristic.

972
01:13:35,880 --> 01:13:45,180
But again, we’re limited both in the follow-up
time and the types of data that we can obtain

973
01:13:45,180 --> 01:13:50,160
going back in time.

974
01:13:50,160 --> 01:13:53,810
Okay.

975
01:13:53,810 --> 01:14:00,380
And so, in the context of trying to understand
intrauterine effects, we also need to recognize

976
01:14:00,380 --> 01:14:03,990
that there are other multigenerational processes
at play.

977
01:14:03,990 --> 01:14:08,100
There are ongoing environmental influences.

978
01:14:08,100 --> 01:14:09,270
We have genetic effects.

979
01:14:09,270 --> 01:14:12,300
We have behavioral processes, as well.

980
01:14:12,300 --> 01:14:20,010
So, ways to try to address and kind of isolate
these intrauterine effects just kind of…I’m…I’m

981
01:14:20,010 --> 01:14:22,139
putting them into sort of two boxes.

982
01:14:22,139 --> 01:14:27,909
The first is the...sort of the type of research
questions that we’re asking.

983
01:14:27,909 --> 01:14:33,710
And so, this is really getting at, you know,
it was a couple of slides ago where we’re

984
01:14:33,710 --> 01:14:39,409
kind of probing and testing kind of steps
of these theorized processes and…and to

985
01:14:39,409 --> 01:14:42,780
see if they hold up in human populations.

986
01:14:42,780 --> 01:14:49,210
So, examining these…these intermediate outcomes
in our studies.

987
01:14:49,210 --> 01:14:54,650
And then, kind of, these analytic causal estimation
approaches.

988
01:14:54,650 --> 01:15:01,969
Things like sibling studies, paternal negative
exposure controls, Mendelian randomization,

989
01:15:01,969 --> 01:15:10,980
randomized trials that are really trying to
address and control for these quote-unquote

990
01:15:10,980 --> 01:15:14,130
“other multigenerational processes.”

991
01:15:14,130 --> 01:15:15,480
Okay.

992
01:15:15,480 --> 01:15:18,180
All right.

993
01:15:18,180 --> 01:15:25,170
So, the last major section here will be just to
talk about the types of research questions

994
01:15:25,170 --> 01:15:27,690
we’re asking.

995
01:15:27,690 --> 01:15:28,949
Okay.

996
01:15:28,949 --> 01:15:40,800
So, really, the bulk of the field can be characterized
as asking these sort of two main types of

997
01:15:40,800 --> 01:15:47,030
questions: So, does X maternal nutrition status
affect Y offspring outcome?

998
01:15:47,030 --> 01:15:54,800
Or, if we’re interested in these mediators,
you know, do…does…does an intermediate

999
01:15:54,800 --> 01:15:57,139
mediate the effect of X on Y?

1000
01:15:57,139 --> 01:16:01,770
This is really where most of the…the focus
is.

1001
01:16:01,770 --> 01:16:11,071
So, I’m also coming from a public health
perspective, and so the corresponding intervention

1002
01:16:11,071 --> 01:16:17,440
for…for these types of studies is, you know,
for the exposures where we see higher risk.

1003
01:16:17,440 --> 01:16:23,139
The intervention is: We should, of course,
be reducing that exposure.

1004
01:16:23,139 --> 01:16:31,730
So, if we’re recognizing kind of these ongoing
environmental influences, implicitly what

1005
01:16:31,730 --> 01:16:37,150
we’re really saying is we need to reduce
the exposure in, kind of…in settings with

1006
01:16:37,150 --> 01:16:39,130
persistent barriers to healthy nutrition.

1007
01:16:39,130 --> 01:16:46,130
You know, we know that the contextual influences
on nutrition tend to be constant from one

1008
01:16:46,130 --> 01:16:48,480
generation to the next.

1009
01:16:48,480 --> 01:16:49,920
Okay.

1010
01:16:49,920 --> 01:16:57,710
And so, this next sort of types of research
questions, we’re really focusing on the

1011
01:16:57,710 --> 01:17:03,560
offspring that have already experienced those
exposures.

1012
01:17:03,560 --> 01:17:11,659
And really, trying to get them set up to be
sort of the next generation of parents.

1013
01:17:11,659 --> 01:17:17,780
And so, if we’re kind of stemming from our
knowledge of these biological and behavioral

1014
01:17:17,780 --> 01:17:25,070
mechanisms, they imply many things, but I’ll
talk about two.

1015
01:17:25,070 --> 01:17:30,480
About the offspring that experience the…that
are…sort of going through these processes.

1016
01:17:30,480 --> 01:17:36,370
So, the first is that they may experience
differential response to postnatal conditions.

1017
01:17:36,370 --> 01:17:42,090
You know, again, things like stress response,
exercise response, and there’s a body of

1018
01:17:42,090 --> 01:17:47,750
work—early work—that’s…that supports
that.

1019
01:17:47,750 --> 01:17:52,150
And then the second is…it’s kind of recognizing
postnatal contextual effects.

1020
01:17:52,150 --> 01:18:00,179
So, if we’re thinking about nutrition and
diet, you know, there is strong evidence that

1021
01:18:00,179 --> 01:18:05,090
maternal nutrition status affects the ability
to regulate appetite.

1022
01:18:05,090 --> 01:18:10,460
What does this mean in our current food environment,
all the food cues, the food marketing that

1023
01:18:10,460 --> 01:18:17,159
we’re exposed to every day, all day it seems…it
seems sometimes?

1024
01:18:17,159 --> 01:18:18,159
Okay.

1025
01:18:18,159 --> 01:18:23,320
And so, the kind of the corresponding interventions
for this work is things like precision prevention

1026
01:18:23,320 --> 01:18:24,760
and public health mitigation.

1027
01:18:24,760 --> 01:18:31,120
So, how can we kind of interrupt these processes,
mitigate the effects of these prenatal exposures?

1028
01:18:31,120 --> 01:18:32,120
Okay.

1029
01:18:32,120 --> 01:18:41,130
And I’ll wrap up with challenges and opportunities.

1030
01:18:41,130 --> 01:18:48,570
So, there are many, and I picked one, which
is our measures.

1031
01:18:48,570 --> 01:18:55,820
We have really very blunt measures, both for
nutrition, as well as maternal nutrition status.

1032
01:18:55,820 --> 01:19:02,110
It’s difficult to measure nutrition at any
time point, much less understanding changes

1033
01:19:02,110 --> 01:19:04,030
over time.

1034
01:19:04,030 --> 01:19:06,280
We have bio...biomarkers.

1035
01:19:06,280 --> 01:19:11,050
Many of them can result from multiple exposures.

1036
01:19:11,050 --> 01:19:20,980
And so, kind of the improved measures, are…is
my…my ask for the field.

1037
01:19:20,980 --> 01:19:28,300
And then, in terms of opportunities in the
short term, you know, I think that we’re

1038
01:19:28,300 --> 01:19:36,500
seeing increased use of methods that can better
combine data and…and evidence from, maybe,

1039
01:19:36,500 --> 01:19:39,440
different types of study designs.

1040
01:19:39,440 --> 01:19:45,380
The things like systems science methods and
also the data sharing and collaboration; things

1041
01:19:45,380 --> 01:19:48,570
like ECHO, which we’ll hear about next.

1042
01:19:48,570 --> 01:19:54,860
And in the long term, I actually have a question
mark here, and hopefully the…the…the biology

1043
01:19:54,860 --> 01:19:59,130
people in the room can help answer it today
and tomorrow.

1044
01:19:59,130 --> 01:20:06,790
My impression is that we’re getting closer
to identifying biologic signatures that could

1045
01:20:06,790 --> 01:20:16,080
indicate either maternal nutrition exposures
or some of those intermediate outcomes.

1046
01:20:16,080 --> 01:20:23,659
So, things like epigenetics, metabolomics, microbiome,
placental measures.

1047
01:20:23,659 --> 01:20:25,000
Okay.

1048
01:20:25,000 --> 01:20:30,880
And just very quickly, I’d like to acknowledge
NIH, my collaborators, staff, and students.

1049
01:20:30,880 --> 01:20:39,300
In particular, I would like to thank...or acknowledge
the importance of these career development

1050
01:20:39,300 --> 01:20:47,770
awards in helping me and others kind of delve
into this aspect of the field.

1051
01:20:47,770 --> 01:20:51,520
Thank you. Yeah.

1052
01:20:51,520 --> 01:20:59,489
DR. ANDREW BREMER: Fantastic. The next one’s going to be Oliver.

1053
01:20:59,489 --> 01:21:04,960
DR. OLIVER RANDO: Well, thank you all for having
me. I’m excited to be here.

1054
01:21:04,960 --> 01:21:10,100
I sort of realize that I pitched my talk a
little bit wrong for this audience, but my

1055
01:21:10,100 --> 01:21:16,190
goal is to some extent to give you guys a
sense for the lack of clarity in the mechanistic

1056
01:21:16,190 --> 01:21:20,900
studies of how intergenerational effects work,
where we're in rodents, and we have a lot

1057
01:21:20,900 --> 01:21:23,190
more access to mechanisms.

1058
01:21:23,190 --> 01:21:29,360
So, I sort of come into this field from the
field of epigenetics.

1059
01:21:29,360 --> 01:21:35,110
Just because that term means many things to
many people, I’ll use the 1970s definition

1060
01:21:35,110 --> 01:21:39,480
of epigenetics, where something has to be
heritable, either mitotically or meiotically,

1061
01:21:39,480 --> 01:21:41,179
without a change in DNA sequence.

1062
01:21:41,179 --> 01:21:47,810
So, if two organisms have different phenotypes
that they pass on to their kids, they differ

1063
01:21:47,810 --> 01:21:50,929
in some phenotypes, and they can pass those
on even though they’re genetically identical;

1064
01:21:50,929 --> 01:21:55,330
that would be an epigenetic…epigenetically
heritable trait.

1065
01:21:55,330 --> 01:21:59,489
And so, the cocktail party version of this,
all your cells…all the cells in your body

1066
01:21:59,489 --> 01:22:01,670
come from the fertilized egg.

1067
01:22:01,670 --> 01:22:06,210
So, first approximation, they all have the same
genome, but when a liver cell divides, it

1068
01:22:06,210 --> 01:22:09,790
never makes a skin cell or a kidney cell,
even though they share the same genome.

1069
01:22:09,790 --> 01:22:12,900
So, state…liverness is an epigenetic state.

1070
01:22:12,900 --> 01:22:21,679
Now, decades of genetic analysis, in Drosophila
in particular, have identified molecular pathways

1071
01:22:21,679 --> 01:22:26,610
that are involved in cell state inheritance,
like chromatins, the polycomb trithorax genes

1072
01:22:26,610 --> 01:22:27,690
from…from Drosophila screens.

1073
01:22:27,690 --> 01:22:33,841
There’s also, of course, DNA modification
in small RNAs.

1074
01:22:33,841 --> 01:22:38,150
So, these are the three big epigenetic inheritance…pathways.

1075
01:22:38,150 --> 01:22:44,380
Now, in terms of thinking about inheritance
from one generation to the next, inter- or

1076
01:22:44,380 --> 01:22:49,409
transgenerational inheritance, depending on
how long it goes on…this is not how we divide,

1077
01:22:49,409 --> 01:22:52,500
of course, unlike cell and state inheritance.

1078
01:22:52,500 --> 01:22:58,110
So, in order for any information to make it
from me to my children, it has to survive

1079
01:22:58,110 --> 01:23:05,840
the tremendously disruptive processes of gametogenesis,
and then all the information required to sort

1080
01:23:05,840 --> 01:23:11,470
of generate a coherent physiological phenotype
in the next generation has to be present in

1081
01:23:11,470 --> 01:23:14,489
the fertilized zygote, the spark of life.

1082
01:23:14,489 --> 01:23:18,840
And all of that information then has to be
sufficient through the course of development

1083
01:23:18,840 --> 01:23:21,500
to turn into a physiologically meaningful
phenotype.

1084
01:23:21,500 --> 01:23:25,050
Now, that’s not to say that this doesn’t
happen.

1085
01:23:25,050 --> 01:23:29,710
There are inter- and transgenerational genetic
inheritance paradigms.

1086
01:23:29,710 --> 01:23:34,750
The first was discovered…so, this…I’m
actually doing sort of history of epigenetics

1087
01:23:34,750 --> 01:23:36,550
more than of animal studies, sorry.

1088
01:23:36,550 --> 01:23:43,260
So…the first discovery of the transgenerational
epigenetic effect was by Bateson and Punnett

1089
01:23:43,260 --> 01:23:47,300
in the 1920s in their studies of the rogue
garden pea.

1090
01:23:47,300 --> 01:23:54,090
The same phenomenon was later called paramutation
in the 1950s, in corn, by Brink and Coe.

1091
01:23:54,090 --> 01:24:02,070
And so, the idea here is that you have purple
corn and green corn in a paramutable maize…system.

1092
01:24:02,070 --> 01:24:05,610
They differ in the expression of a transcription
factor of B1.

1093
01:24:05,610 --> 01:24:08,800
B1 drives the expression of the anthocyanin
biosynthesis genes.

1094
01:24:08,800 --> 01:24:09,800
Okay?

1095
01:24:09,800 --> 01:24:11,880
So, that makes you purple.

1096
01:24:11,880 --> 01:24:18,110
So, they differ in whether they have repressed
B1 or they have an active copy of B1.

1097
01:24:18,110 --> 01:24:22,449
Now, the interesting thing about this genetically
is that if you cross purple and green corn,

1098
01:24:22,449 --> 01:24:24,929
you’ll get all green babies.

1099
01:24:24,929 --> 01:24:25,929
Okay?

1100
01:24:25,929 --> 01:24:31,159
Now, if this were a Mendelian Trait, it would
mean that green was big B, big B. Purple was

1101
01:24:31,159 --> 01:24:34,680
little B, little B. And you have a bunch of
heterozygous green kids.

1102
01:24:34,680 --> 01:24:41,510
But now, if you cross those green kids, two
purple corn, you would expect 50% purple and

1103
01:24:41,510 --> 01:24:44,060
50% green in Mendelian genetics.

1104
01:24:44,060 --> 01:24:46,820
And what you get is all green offspring.

1105
01:24:46,820 --> 01:24:49,639
And this will persist for hundreds of generations.

1106
01:24:49,639 --> 01:24:55,869
So, they call this paramutation, as though
the inactive copy of the allele was infecting

1107
01:24:55,869 --> 01:24:56,869
the active copy of the allele.

1108
01:24:56,869 --> 01:24:57,869
Okay?

1109
01:24:57,869 --> 01:25:02,530
So, this is a…of course, it’s going through
the germ cells, so this is transgenerational,

1110
01:25:02,530 --> 01:25:04,409
and it’s more than one generation.

1111
01:25:04,409 --> 01:25:08,110
So, this is transgenerational epigenetic inheritance.

1112
01:25:08,110 --> 01:25:13,760
And so…decades of genetic analysis of this,
starting with Brink and Coe, really pioneered

1113
01:25:13,760 --> 01:25:20,480
by Vicki Chandler later in the molecular era
have defined a bunch of genetic requirements

1114
01:25:20,480 --> 01:25:25,830
for paramutation, and this includes the small
RNA pathways, DNA modification and chromatin.

1115
01:25:25,830 --> 01:25:31,510
So, all three of the epigenetic pathways,
which all talk to each other, are all required

1116
01:25:31,510 --> 01:25:34,160
for…for paramutation.

1117
01:25:34,160 --> 01:25:39,080
Now, one of the really interesting implications
of epigenetic inheritance through the germ

1118
01:25:39,080 --> 01:25:45,909
line is that it resurrects an ancient idea,
because, unlike DNA sequence, epigenetic marks

1119
01:25:45,909 --> 01:25:47,850
are very environmentally responsive.

1120
01:25:47,850 --> 01:25:48,850
Right?

1121
01:25:48,850 --> 01:25:52,320
So, if you [inaudible]…the entire chromatin
landscape changes dramatically.

1122
01:25:52,320 --> 01:25:56,810
Small RNAs are induced by various stimuli
in the rest of them.

1123
01:25:56,810 --> 01:25:57,810
Right?

1124
01:25:57,810 --> 01:26:00,160
So, this is in contrast to DNA sequence.

1125
01:26:00,160 --> 01:26:05,179
And what this means is that the environment
experienced in one generation has the potential

1126
01:26:05,179 --> 01:26:10,520
to modify phenotypes in future generations,
which is the point of this meeting.

1127
01:26:10,520 --> 01:26:15,040
Now, this was tested…historically by August
Weismann.

1128
01:26:15,040 --> 01:26:18,760
This is the German embryologist who defines
the cell line/germ line distinction.

1129
01:26:18,760 --> 01:26:25,489
And so, what he did is he cut the tails off
of mice and mated them to each other, and…saw

1130
01:26:25,489 --> 01:26:27,540
no change in tail length in the offspring.

1131
01:26:27,540 --> 01:26:28,540
Okay?

1132
01:26:28,540 --> 01:26:35,290
So, this was a very influential refutation
of Lamarckian inheritance, or the inheritance

1133
01:26:35,290 --> 01:26:36,830
of acquiring characters.

1134
01:26:36,830 --> 01:26:41,880
Now, today we would see a number of problems
with this experiment.

1135
01:26:41,880 --> 01:26:46,179
One is, maybe if your parents tell you that
someone’s going around cutting your

1136
01:26:46,179 --> 01:26:49,960
tails off, the useful thing to do is not to
have a shorter tail, but to run away from

1137
01:26:49,960 --> 01:26:52,110
people in lab coats.

1138
01:26:52,110 --> 01:26:56,910
The other is that, even at the time, it’s
a little bit rigged because…you know, Lamarck

1139
01:26:56,910 --> 01:27:01,219
and anyone who thinks about it for 30 seconds
recognizes that, you know, soldiers from wars

1140
01:27:01,219 --> 01:27:05,230
throughout history who’ve lost their limbs
don’t have children with stunted limbs.

1141
01:27:05,230 --> 01:27:11,909
So, Lamarck had already invoked this idea
of desire or utility in order to gate information

1142
01:27:11,909 --> 01:27:13,400
to pass onto kids.

1143
01:27:13,400 --> 01:27:17,980
So, mutilation is maybe not the best environmental
condition to be looking at.

1144
01:27:17,980 --> 01:27:18,980
Okay.

1145
01:27:18,980 --> 01:27:24,130
So…so, that’s the end of sort of…epigenetic
inheritance history.

1146
01:27:24,130 --> 01:27:29,800
We basically and now…thousands of groups
have basically revisited this type of paradigm,

1147
01:27:29,800 --> 01:27:35,199
where you poke an animal and then you look
at its kids to look for inter- or transgenerational

1148
01:27:35,199 --> 01:27:37,860
epigenetic effects.

1149
01:27:37,860 --> 01:27:44,040
So, we do this in inbred mice, so compared
to what all of the human people…all the

1150
01:27:44,040 --> 01:27:49,170
human people here…all of the epidemiologists
here have to deal with, we at least don’t

1151
01:27:49,170 --> 01:27:54,070
have to worry about things like selection
on particular alleles, right?

1152
01:27:54,070 --> 01:27:58,860
So, if I starve and it kills all my sperm
with G in position 1,000 instead of A, this

1153
01:27:58,860 --> 01:28:02,920
could be, you know, genetic selection, as
opposed to epigenetic inheritance.

1154
01:28:02,920 --> 01:28:05,510
We’re using inbred mice.

1155
01:28:05,510 --> 01:28:11,270
We studied paternal effects because in mammals,
mom is baby’s first environment.

1156
01:28:11,270 --> 01:28:15,369
Fetal alcohol syndrome is not magic in epigenetic,
you’re just poisoning a baby.

1157
01:28:15,369 --> 01:28:21,080
That’s not to say that these things aren’t
important for public health and the rest of it.

1158
01:28:21,080 --> 01:28:25,360
But as an epigenetic mechanist, it’s much
easier to think about paternal effects because

1159
01:28:25,360 --> 01:28:30,449
you can set up a cross so that dad just leaves
sperm and a memory.

1160
01:28:30,449 --> 01:28:31,449
Okay?

1161
01:28:31,449 --> 01:28:36,090
And we basically do the Weismann experiment,
except we make two big changes.

1162
01:28:36,090 --> 01:28:41,830
One is that we…we now study a lot of different
paradigms, but the first one we studied was

1163
01:28:41,830 --> 01:28:42,830
low protein.

1164
01:28:42,830 --> 01:28:47,531
And this fits, and I can get into this, it’s
a little bit long-winded to talk about, but

1165
01:28:47,531 --> 01:28:53,480
there’s…modeling studies sort of find
that under…only under certain environmental

1166
01:28:53,480 --> 01:28:58,820
conditions will it be useful to tell your
kids about the future, and mutilation is not

1167
01:28:58,820 --> 01:28:59,910
one of them.

1168
01:28:59,910 --> 01:29:04,290
Whereas…sort of suboptimal diets do make
sense.

1169
01:29:04,290 --> 01:29:11,110
So, we raise brothers from weaning to sexual
maturity on low-protein or controlled diets.

1170
01:29:11,110 --> 01:29:14,320
We mate them with females for 1–2 days and
take them out.

1171
01:29:14,320 --> 01:29:19,030
So, the males are not directly interacting
with the kids, and ideally, they’re not

1172
01:29:19,030 --> 01:29:22,530
modifying, say the kids’ microbiome through
defecation in the cage.

1173
01:29:22,530 --> 01:29:28,630
We’re really trying to minimize male involvement
to an ejaculate.

1174
01:29:28,630 --> 01:29:31,190
We let the females be pregnant, have kids.

1175
01:29:31,190 --> 01:29:35,900
At 3 weeks, we grind up the kids, and we…at
the time it was micro-raised, we would look

1176
01:29:35,900 --> 01:29:39,840
at instead of one quantitative trait, tail
length, we’re measuring 30,000.

1177
01:29:39,840 --> 01:29:40,840
Okay?

1178
01:29:40,840 --> 01:29:43,250
Specifically, we’ll get the list.

1179
01:29:43,250 --> 01:29:50,710
And so, what we found…we just signed out
of the Zoom.

1180
01:29:50,710 --> 01:29:53,540
I don’t know what to do about this.

1181
01:29:53,540 --> 01:30:01,800
Yes, it’s going to work here…I don’t
know if it’s…is anyone on Zoom and knows

1182
01:30:01,800 --> 01:30:03,690
whether it’s…it’s going?

1183
01:30:03,690 --> 01:30:04,690
Good?

1184
01:30:04,690 --> 01:30:05,690
Okay.

1185
01:30:05,690 --> 01:30:06,690
Sorry.

1186
01:30:06,690 --> 01:30:12,290
I want those 15 seconds back.

1187
01:30:12,290 --> 01:30:17,860
Okay…so, when we look at the kids…so,
this is a usual microwave heat map, basically

1188
01:30:17,860 --> 01:30:22,210
every column we’re comparing a low-protein
offspring…and the offspring, of course,

1189
01:30:22,210 --> 01:30:23,340
are…are treated identically.

1190
01:30:23,340 --> 01:30:26,389
They’re under controlled conditions, controlled
moms, controlled milk.

1191
01:30:26,389 --> 01:30:32,489
Every column is a comparison between the liver
of one low-protein and one controlled offspring.

1192
01:30:32,489 --> 01:30:34,400
Low protein is labeled red.

1193
01:30:34,400 --> 01:30:35,400
Controlled is green.

1194
01:30:35,400 --> 01:30:39,900
So, all of the green stuff are genes downregulated,
depending on bad eating and diet…all of

1195
01:30:39,900 --> 01:30:40,900
your red genes are upregulated.

1196
01:30:40,900 --> 01:30:44,160
And as you can see, we have not entirely penetrance.

1197
01:30:44,160 --> 01:30:50,960
So, if you look across the red, you see still
some green on the left.

1198
01:30:50,960 --> 01:30:53,800
So, it’s a 90% penetrance…gene expression
changes.

1199
01:30:53,800 --> 01:30:55,710
Very significant.

1200
01:30:55,710 --> 01:31:01,380
The downregulated genes are nothing in particular,
the upregulated genes are very highly enriched

1201
01:31:01,380 --> 01:31:08,360
for lipid and cholesterol metabolism, specifically
cholesterol biosynthesis the most.

1202
01:31:08,360 --> 01:31:13,460
And this is not just a gene expression phenotype,
when we do metabolomics in these offspring,

1203
01:31:13,460 --> 01:31:18,680
we find very significant decreases…significant
but small decrease in free cholesterol and

1204
01:31:18,680 --> 01:31:21,470
a very significant decrease in cholesteryl
ester.

1205
01:31:21,470 --> 01:31:26,880
This is the last I’m going to talk about
the physiology of the offspring phenotype.

1206
01:31:26,880 --> 01:31:33,380
My understanding from metabolism people, which
I am not, is that a cholesteryl ester over

1207
01:31:33,380 --> 01:31:37,840
free cholesterol effect suggests a bile acid
phenotype.

1208
01:31:37,840 --> 01:31:40,880
And so, perhaps what’s going on with the
kids—and again, we haven’t done any of

1209
01:31:40,880 --> 01:31:44,739
this kind of physiology—is that they’re
dumping more bile acids to try to extract

1210
01:31:44,739 --> 01:31:49,310
more nutrients out of food, and they’re
homeostatically upregulating the cholesterol

1211
01:31:49,310 --> 01:31:53,280
biosynthesis pathway to keep up with the loss
of cholesterol.

1212
01:31:53,280 --> 01:31:54,280
Okay?

1213
01:31:54,280 --> 01:31:59,660
So, I don’t know…yeah…I don’t need
that slide.

1214
01:31:59,660 --> 01:32:02,530
This is to show…sorry, this is actually
useful.

1215
01:32:02,530 --> 01:32:07,940
So, those are just all the genes in the cholesterol biosynthesis
pathway, colored by their expression change.

1216
01:32:07,940 --> 01:32:12,389
And you can see they’re all coherently upregulated.

1217
01:32:12,389 --> 01:32:13,520
Okay.

1218
01:32:13,520 --> 01:32:18,550
This is the one slide that sort of hits what
you guys kind of wanted me to do a lot of,

1219
01:32:18,550 --> 01:32:23,880
which is…so, ours is one of the earlier
studies, although not…not the earliest study

1220
01:32:23,880 --> 01:32:25,950
in paternal effects in rodents.

1221
01:32:25,950 --> 01:32:31,980
There’s been this Anderson study in 2006
before, as well as a lot of work from Michael

1222
01:32:31,980 --> 01:32:37,830
Skinner in…endocrine disruptors, but for
us, the paper that pushes us out the door

1223
01:32:37,830 --> 01:32:39,150
is the top one, there.

1224
01:32:39,150 --> 01:32:46,280
So, Margaret Morris found that in rats, males
on a high-fat diet sired daughters with glucose

1225
01:32:46,280 --> 01:32:48,800
control phenotypes.

1226
01:32:48,800 --> 01:32:54,650
The next one down is a low-protein that looks a lot like what ours...you'll see
later.

1227
01:32:54,650 --> 01:33:00,739
They find that not only do you get lipid metabolism,
but also cardiovascular parameters, like vessel

1228
01:33:00,739 --> 01:33:05,180
compliance and stuff, change, as well as glucose
control.

1229
01:33:05,180 --> 01:33:09,460
And glucose control…poor glucose control
is probably the most common phenotype people

1230
01:33:09,460 --> 01:33:16,340
see in paternal dietary and other kids of
paradigms.

1231
01:33:16,340 --> 01:33:20,980
The next set down, I highlight because here,
what’s going on is that people are either

1232
01:33:20,980 --> 01:33:27,090
over- or undernourishing pregnant moms and
taking their sons as the paternal generation.

1233
01:33:27,090 --> 01:33:28,090
Okay?

1234
01:33:28,090 --> 01:33:30,510
And what’s interesting here, these guys
were much smarter than us.

1235
01:33:30,510 --> 01:33:34,360
Primordial germ cell development occurs during
fetal growth.

1236
01:33:34,360 --> 01:33:38,960
And so, what they’re trying to do is intervene
during cytosine methylation establishment.

1237
01:33:38,960 --> 01:33:47,360
So, parental imprint, if you know about imprinting
disorders, are established in PGC.

1238
01:33:47,360 --> 01:33:52,400
So, these guys are trying to…to interfere
with primordial germ cell development.

1239
01:33:52,400 --> 01:33:57,420
Whereas the…the top two papers and us are
doing sort of postnatal, so during the life

1240
01:33:57,420 --> 01:33:58,590
span of things.

1241
01:33:58,590 --> 01:34:02,860
And it’ll be very interesting at some point—I
don’t have this in my, you know, future

1242
01:34:02,860 --> 01:34:09,230
directions, but I’d really like to understand
the distinction between effects that are mediated

1243
01:34:09,230 --> 01:34:15,550
with PGC perturbations or mature germ cells.

1244
01:34:15,550 --> 01:34:22,950
These papers just point out that the dietary
effects are not, sort of, permanent.

1245
01:34:22,950 --> 01:34:26,350
So, let’s see one from…yeah, Laurie Goodyear.

1246
01:34:26,350 --> 01:34:31,280
So, males on high-fat diets, their kids have
all sorts of metabolic changes.

1247
01:34:31,280 --> 01:34:35,720
But if you exercise males on high-fat diets,
you can reverse those paternal effects.

1248
01:34:35,720 --> 01:34:41,000
So, you’re not, sort of, doomed by what
you did 3 years ago to…to your kids having

1249
01:34:41,000 --> 01:34:45,130
a lifetime of…at least in terms of the dietary
paradigms.

1250
01:34:45,130 --> 01:34:49,540
So, dietary paradigms are one great arm in
the paternal effects literature.

1251
01:34:49,540 --> 01:34:56,350
A second—and I’m only showing two of…hundreds
and hundreds of papers here—are paternal

1252
01:34:56,350 --> 01:34:57,350
stressors.

1253
01:34:57,350 --> 01:35:05,370
So, things like early maternal…separation,
which is what Mansuy studies a lot of.

1254
01:35:05,370 --> 01:35:10,820
Or sort of chronic variable stressors or social
instability, like [inaudible].

1255
01:35:10,820 --> 01:35:15,330
So, all of those things affect anxiety-related
traits in the kids.

1256
01:35:15,330 --> 01:35:22,430
A third big arm of things, and I…I really
ought to have…some of Skinner’s work here,

1257
01:35:22,430 --> 01:35:29,360
are environmental, sort of, xenobiotics, whether
it be NIEHS-type compounds like, you know,

1258
01:35:29,360 --> 01:35:37,280
[inaudible] or BPA, or…or jet fuel, or NIDA-type
compounds, like toxicants we take ourselves—so

1259
01:35:37,280 --> 01:35:41,440
we said nicotine, cocaine, and ethanol—both
have intergenerational effects.

1260
01:35:41,440 --> 01:35:46,610
And then the last type, the last big type
of thing that affects offspring is the dad’s

1261
01:35:46,610 --> 01:35:48,460
paternal age.

1262
01:35:48,460 --> 01:35:52,840
And one thing you guys will notice here is
that the four kinds of paternal affects out

1263
01:35:52,840 --> 01:35:55,400
there are all different NIH institutes.

1264
01:35:55,400 --> 01:35:59,130
And this actually makes it very difficult
for those of us who want to understand them

1265
01:35:59,130 --> 01:36:00,130
broadly, right?

1266
01:36:00,130 --> 01:36:04,210
There’s NIDDK for the diets, although they
won’t fund anything in this field because

1267
01:36:04,210 --> 01:36:08,090
if it touches sperm, they’re not interested.

1268
01:36:08,090 --> 01:36:15,050
NIEHS for the toxicants; NIMH for the stress;
and NIA for aging.

1269
01:36:15,050 --> 01:36:19,840
And so, this is where…Dr. Bianchi isn’t
here anymore, but I was sort of heartened

1270
01:36:19,840 --> 01:36:26,190
to hear some effort of the NIH to try to get
some cross-institute interest in this field.

1271
01:36:26,190 --> 01:36:27,190
Okay.

1272
01:36:27,190 --> 01:36:33,170
So, these are maybe, like…I showed about
30 of something, like 1,000 papers now, linking

1273
01:36:33,170 --> 01:36:36,420
paternal environmental conditions to off…offspring
traits.

1274
01:36:36,420 --> 01:36:41,310
Okay, we’ve already heard about the Dutch
Hunger Winter, so, of course, there is some

1275
01:36:41,310 --> 01:36:46,000
evidence for this kind of thing in humans,
starting with Hales and Barker.

1276
01:36:46,000 --> 01:36:50,460
The point I want to make here, since you guys
have already heard about this, is that all

1277
01:36:50,460 --> 01:36:56,159
the traits people see in paternal effect studies
in rodents also show up in maternal effect

1278
01:36:56,159 --> 01:37:02,360
studies in rodents from people like Rebecca
Simmons and [inaudible], and also in humans,

1279
01:37:02,360 --> 01:37:07,110
despite the fact that the oocyte and sperm
could not carry more different information

1280
01:37:07,110 --> 01:37:09,380
to the kids.

1281
01:37:09,380 --> 01:37:13,940
And so, I think this may give us a mechanistic
clue in the sense that I wonder whether or

1282
01:37:13,940 --> 01:37:17,219
not paternal effects are acting through altered
classifications.

1283
01:37:17,219 --> 01:37:18,219
Okay.

1284
01:37:18,219 --> 01:37:22,400
So, here’s just a cartoon of how all these
things work.

1285
01:37:22,400 --> 01:37:25,119
You poke dads, you affect kids.

1286
01:37:25,119 --> 01:37:30,159
Since I’m running long per slide, I’ll
just very briefly note that while it’s…the

1287
01:37:30,159 --> 01:37:35,160
obvious hypothesis is that all these things
are mediated by sperm epigenetic marks, it’s

1288
01:37:35,160 --> 01:37:40,330
worth pointing out there are many other ways
that dads can talk to kids, from seminal fluids—there’s

1289
01:37:40,330 --> 01:37:45,650
some great literature on Drosophila from Mariana
Wolfner, and a bit less with…in emerging

1290
01:37:45,650 --> 01:37:52,719
literature from people like Liz Ra…I’m
blanking on her name, in Australia.

1291
01:37:52,719 --> 01:37:58,389
Anyway, on seminal fluid effects on offspring
development and traits, cryptic maternal effects

1292
01:37:58,389 --> 01:38:05,270
where moms judge mate quality and change how
they allocate resources to kids, and microbiome

1293
01:38:05,270 --> 01:38:06,270
transfer.

1294
01:38:06,270 --> 01:38:10,250
And so, in order to deal with these things,
now we and a lot of other people…a lot of

1295
01:38:10,250 --> 01:38:14,290
the reproduction in the lab goes through IVF,
where you take the sperm away from seminal

1296
01:38:14,290 --> 01:38:18,630
fluids, the mom never sees the dad, there’s
no microbiome transfer.

1297
01:38:18,630 --> 01:38:20,330
And we can recapitulate our…our phenotypes
through IVF.

1298
01:38:20,330 --> 01:38:24,929
So, at least in our system, the information
is in sperm or co-purifies with sperm.

1299
01:38:24,929 --> 01:38:31,389
The other nice thing about IVF is that it…it
allows us to take maternal effects seriously,

1300
01:38:31,389 --> 01:38:35,960
which we’re now…as in epigenetically seriously,
because you can now separate moms into oocyte

1301
01:38:35,960 --> 01:38:37,840
donors and uterus donors.

1302
01:38:37,840 --> 01:38:42,030
So, we can separate fetal provisioning from
oocyte epigenome.

1303
01:38:42,030 --> 01:38:46,860
So, what I wanted to do, and I…I’ll just
try to blow through this really quickly, is

1304
01:38:46,860 --> 01:38:53,290
point out the state of…everything I’ve
told you about so far is correct and true,

1305
01:38:53,290 --> 01:38:55,880
and I think as…as much as we really know.

1306
01:38:55,880 --> 01:39:03,780
So, there are hundreds of proposed mechanisms
for paternal effects on kids, and I just want

1307
01:39:03,780 --> 01:39:08,739
to sort of briefly go through the problems
with all of them, to point out the state of

1308
01:39:08,739 --> 01:39:10,110
where we are in this field.

1309
01:39:10,110 --> 01:39:15,230
So, for cytosine methylation, the major problem
that people don’t think about is what I

1310
01:39:15,230 --> 01:39:17,410
call the “digital sperm” problem.

1311
01:39:17,410 --> 01:39:23,790
So, when you see in report an 80% to 90% change
in methylation in the liver, that could double

1312
01:39:23,790 --> 01:39:28,699
the number of cells that make growth hormone
or something, and it could give you a physiological

1313
01:39:28,699 --> 01:39:29,699
change.

1314
01:39:29,699 --> 01:39:33,360
But that’s a multicellular tissue;every
baby is made from one sperm.

1315
01:39:33,360 --> 01:39:40,400
And so, when you change from 80% to 90% methylated,
what that means is you’ve gone from 8 out

1316
01:39:40,400 --> 01:39:45,300
of 10 to 9 out of 10 sperm carrying a methyl
group there. And that should only change

1317
01:39:45,300 --> 01:39:46,659
the penetrance within the litter.

1318
01:39:46,659 --> 01:39:49,050
So, problem one.

1319
01:39:49,050 --> 01:39:55,369
Problem two is that dad’s methylation is
almost completely erased by mom at fertilization.

1320
01:39:55,369 --> 01:40:01,660
The only places that escape this are actually
hardwired by having a binding site for protein

1321
01:40:01,660 --> 01:40:02,930
called ZFP57.

1322
01:40:02,930 --> 01:40:08,969
So, unless you’ve changed methylation near
a ZFP57 binding site, the methylation is going

1323
01:40:08,969 --> 01:40:13,349
to have to do something very quickly at fertilization
before it’s lost to the wind.

1324
01:40:13,349 --> 01:40:15,960
So, that’s methylation.

1325
01:40:15,960 --> 01:40:19,020
Chromatin I’ll skip in the interests of
time.

1326
01:40:19,020 --> 01:40:22,170
Basically, nobody has every correctly measured
sperm chromatin.

1327
01:40:22,170 --> 01:40:27,639
It turns out in mouse, what everyone’s measuring
is cell-free DNA that sticks to the sperm.

1328
01:40:27,639 --> 01:40:33,260
But again, the issue with even the people
who’ve measured histones in various places,

1329
01:40:33,260 --> 01:40:38,670
there’s a penetrance problem where the…the
enriched locations for nucleosomes seem to

1330
01:40:38,670 --> 01:40:40,910
be in only a subset of sperm.

1331
01:40:40,910 --> 01:40:45,190
So again, being able to program penetrant
changes in offspring through chromatin is

1332
01:40:45,190 --> 01:40:46,530
an issue.

1333
01:40:46,530 --> 01:40:52,080
We favor small RNAs, but those also favor…face
quite a few problems.

1334
01:40:52,080 --> 01:40:57,179
One is that sperm, of course, are 1,000 times
smaller than the oocyte and carry much less

1335
01:40:57,179 --> 01:40:59,489
RNA per femtoliter.

1336
01:40:59,489 --> 01:41:04,560
So, do sperm deliver anything meaningful to
change the biochemistry of the oocyte?

1337
01:41:04,560 --> 01:41:09,239
This is just…we started to get good at…at
measuring small RNAs and oocytes from the plantation

1338
01:41:09,239 --> 01:41:11,270
embryos, which are very limiting amounts of
stuff.

1339
01:41:11,270 --> 01:41:17,380
And, you know, if you compare an oocyte and a
zygote, which differ by sperm-delivered stuff,

1340
01:41:17,380 --> 01:41:18,990
you can barely tell the difference.

1341
01:41:18,990 --> 01:41:21,810
So, very little is delivered by sperm.

1342
01:41:21,810 --> 01:41:28,030
The other problem is that we now know that
we and everyone else have been cloning sperm

1343
01:41:28,030 --> 01:41:29,030
RNAs poorly.

1344
01:41:29,030 --> 01:41:31,330
So, it turns out sperm have very unusual small
RNAs.

1345
01:41:31,330 --> 01:41:36,690
In addition to microNAs they carry halves
of tRNAs.

1346
01:41:36,690 --> 01:41:41,940
We thought only 5 prime halves; we now understand
that the 3 primes halves are there, as well.

1347
01:41:41,940 --> 01:41:46,861
And so, all of the models that people have
been proposing are now complicated by: Are

1348
01:41:46,861 --> 01:41:52,460
you just delivering a 5 prime half or a nicked
tRNA or two separate halves, and how do they

1349
01:41:52,460 --> 01:41:53,800
function molecularly?

1350
01:41:53,800 --> 01:42:02,030
So, this is our working model for how…how
our low-protein system works.

1351
01:42:02,030 --> 01:42:07,630
So as sperm leave the testes, they actually
spend 2 weeks in this understudied tube called

1352
01:42:07,630 --> 01:42:09,110
the epididymis.

1353
01:42:09,110 --> 01:42:13,130
During this time period, the small RNAs they
carry change completely.

1354
01:42:13,130 --> 01:42:18,790
We think the epididymis ships RNAs to sperm,
but that’s…I’m not 100% sure of that.

1355
01:42:18,790 --> 01:42:20,500
But certainly, the RNAs change.

1356
01:42:20,500 --> 01:42:25,920
So, the epididymal maturation is a really
good opportunity for the world to affect your

1357
01:42:25,920 --> 01:42:28,010
germ cells.

1358
01:42:28,010 --> 01:42:32,489
The levels of specific tRNA fragments change
in response to a low-protein diet.

1359
01:42:32,489 --> 01:42:38,340
We think what that does is either accelerates
or decelerates zygotic gene inactivation.

1360
01:42:38,340 --> 01:42:44,760
And in doing so, we speculate that this effects
the investment of the embryo in the trophectoderm

1361
01:42:44,760 --> 01:42:52,929
versus the inner cell mass and therefore alters
investment in embryo both per se versus [inaudible].

1362
01:42:52,929 --> 01:42:56,730
So, in other words, a secondary placental
effect.

1363
01:42:56,730 --> 01:43:00,670
So, with that, I want to raise the questions.

1364
01:43:00,670 --> 01:43:04,880
So the first…actually, the first big thing
I’d like to emphasize for this audience

1365
01:43:04,880 --> 01:43:10,630
is that the paternal effect field is a mess
because even people who study the exact same

1366
01:43:10,630 --> 01:43:14,770
thing, like high-fat diet, everybody uses
a different paradigm, right?

1367
01:43:14,770 --> 01:43:19,239
So, some people have high-fat diet for 8 weeks,
some for 12 weeks.

1368
01:43:19,239 --> 01:43:26,070
Some people are using ZFP57N, and some are
using J. I’m…a couple of us in the field

1369
01:43:26,070 --> 01:43:31,211
are really trying to get a…a coalition together
where we all do the exact same experiment

1370
01:43:31,211 --> 01:43:37,570
and sort of control for investigator and mouse
facility endemic microbiome, all that kind

1371
01:43:37,570 --> 01:43:38,570
of stuff.

1372
01:43:38,570 --> 01:43:42,530
It’s been very hard to drum up interest,
but I think the field absolutely needs it

1373
01:43:42,530 --> 01:43:46,150
because it’s arrogant to think that the
diet we feed the mice is the only thing they

1374
01:43:46,150 --> 01:43:47,880
listen to.

1375
01:43:47,880 --> 01:43:52,590
And so, I’d really like to sort of get just
a bunch of people going backwards in time

1376
01:43:52,590 --> 01:43:57,750
and doing…but doing the exact same experiments,
in order to sort of identify the robust and

1377
01:43:57,750 --> 01:44:01,960
less robust aspects of paternal effects.

1378
01:44:01,960 --> 01:44:06,960
We don’t know the mechanistic basis, I think
I’ve made that clear, so that’s an unanswered

1379
01:44:06,960 --> 01:44:07,960
question.

1380
01:44:07,960 --> 01:44:12,929
And then, for me, the deepest question about
paternal effects is what we call the bandwidth

1381
01:44:12,929 --> 01:44:14,360
question.

1382
01:44:14,360 --> 01:44:18,980
How much…if you accept that sperm can pass
information to kids, then the question is,

1383
01:44:18,980 --> 01:44:20,260
how much, right?

1384
01:44:20,260 --> 01:44:24,239
So, for cytosine methylation, every CPG can
be a bit.

1385
01:44:24,239 --> 01:44:25,650
There are 25 million CPGs.

1386
01:44:25,650 --> 01:44:30,660
You could tell your kids infinitely complex
information about the world.

1387
01:44:30,660 --> 01:44:36,820
Alternatively, what we often find, the dietary
people see metabolic traits change, the stress

1388
01:44:36,820 --> 01:44:39,040
people see anxiety-related traits change.

1389
01:44:39,040 --> 01:44:43,260
When you look under other people’s lamp
poles, you see their traits.

1390
01:44:43,260 --> 01:44:47,990
So, in other words, it could be that you just
tell your kids about overall quality of life,

1391
01:44:47,990 --> 01:44:50,430
and this is an intergenerational stress response.

1392
01:44:50,430 --> 01:44:53,900
And we sort of favor the lower bandwidth side
of this.

1393
01:44:53,900 --> 01:44:57,150
But I think this is a really deep and important
question to understand.

1394
01:44:57,150 --> 01:45:03,480
How…if you accept that information is passed
on, how much is passed on?

1395
01:45:03,480 --> 01:45:07,150
And I’ve already gone a couple minutes over
time, so I’ll just skip this.

1396
01:45:07,150 --> 01:45:13,170
These are the people in my lab who did all
this stuff, and this is sort of a model view

1397
01:45:13,170 --> 01:45:16,860
of our lab, and thanks for your time.

1398
01:45:16,860 --> 01:45:20,790
DR. ANDREW BREMER: And Oliver, thank you so much.

1399
01:45:20,790 --> 01:45:23,330
And actually, we…by…we’re going to bring
Sonia up.

1400
01:45:23,330 --> 01:45:26,810
But I would…but you…you teed me up beautifully
for two things.

1401
01:45:26,810 --> 01:45:30,620
You…you know, your…your talks and…and
Dan’s, and the…the talks we heard today

1402
01:45:30,620 --> 01:45:33,730
really span a breadth of time, and that’s
intentional.

1403
01:45:33,730 --> 01:45:38,010
And that’s…so, we, you know, it…it underscores
the importance of cross-disciplinary science

1404
01:45:38,010 --> 01:45:39,900
when we talk about these…these…super important
issues.

1405
01:45:39,900 --> 01:45:42,270
So…so, you said it’s not what we wanted.

1406
01:45:42,270 --> 01:45:43,489
That…that’s exactly what we wanted.

1407
01:45:43,489 --> 01:45:44,489
So…so, thank you.

1408
01:45:44,489 --> 01:45:49,000
And you also teed me up for the important…I
like how you codified institution-specific

1409
01:45:49,000 --> 01:45:53,210
science, but I…I want to do a shout out
to...to Chris Lynch and…and…and

1410
01:45:53,210 --> 01:45:56,840
the Office of Nutrition Research, really,
is that body that…that’s trying to coordinate

1411
01:45:56,840 --> 01:45:58,310
that…that IC-specific science.

1412
01:45:58,310 --> 01:46:01,190
So…so, Chris, thanks to you and your efforts.

1413
01:46:01,190 --> 01:46:04,250
And with that, we’ll…we are…we’re
running a little bit late, but we will make…make

1414
01:46:04,250 --> 01:46:05,570
up for it in…in the Q&A, and Sonia, the
floor is yours.

1415
01:46:05,570 --> 01:46:07,570
DR. SONIA ARTEAGA: Great.

1416
01:46:07,570 --> 01:46:08,570
Thank you so much.

1417
01:46:08,570 --> 01:46:14,820
I’m so excited to talk to you today about
ECHO and our recent workshop.

1418
01:46:14,820 --> 01:46:16,119
You heard two great talks.

1419
01:46:16,119 --> 01:46:21,890
One is an overview of observational studies,
and also a great talk on epigenetics.

1420
01:46:21,890 --> 01:46:23,949
And we’re going to switch gears a little
bit now.

1421
01:46:23,949 --> 01:46:28,530
I’m going to talk about the ECHO program
and our recent workshop.

1422
01:46:28,530 --> 01:46:34,850
So, I want to start off by having all of you
think about this question as I talk: So, what

1423
01:46:34,850 --> 01:46:39,760
are some of the lessons that we learned from
the workshop, and how can that inform future

1424
01:46:39,760 --> 01:46:40,760
research?

1425
01:46:40,760 --> 01:46:47,989
So, I’m going to talk about ECHO, the workshop,
how that influenced the next cycle of ECHO.

1426
01:46:47,989 --> 01:46:50,750
And if I have time, a little bit on our nutrition
research.

1427
01:46:50,750 --> 01:46:55,440
So, let me start off by giving you a brief
overview of ECHO.

1428
01:46:55,440 --> 01:46:58,920
You can go to echochildren.org to find out
more in-depth information.

1429
01:46:58,920 --> 01:47:06,500
So ECHO started…the first cycle started
in 2016, and it ends…the first cycle, in

1430
01:47:06,500 --> 01:47:08,550
20…in August 20[23]…so, next month.

1431
01:47:08,550 --> 01:47:12,300
And the mission is to enhance the health of
children for generations to come.

1432
01:47:12,300 --> 01:47:17,260
And the goal is to understand effects of…of
a broad range of early environmental exposures

1433
01:47:17,260 --> 01:47:19,730
on child health and development.

1434
01:47:19,730 --> 01:47:26,630
And really, there are five key ECHO outcomes,
and so they are pre-, peri- and postnatal;

1435
01:47:26,630 --> 01:47:32,260
upper and lower airways; obesity; neural development;
and positive health.

1436
01:47:32,260 --> 01:47:36,570
And the idea is to inform solutions to…these
common pediatric outcomes.

1437
01:47:36,570 --> 01:47:40,150
There are two components to ECHO.

1438
01:47:40,150 --> 01:47:44,560
There’s the observational and intervention
side.

1439
01:47:44,560 --> 01:47:51,340
The intervention side is in IDeA states, and
IDeA states are those states that traditionally

1440
01:47:51,340 --> 01:47:53,460
receive less NIH funding than other states.

1441
01:47:53,460 --> 01:47:56,530
I’m going to focus on the cohort side.

1442
01:47:56,530 --> 01:48:04,070
So in ECHO, we really think about the broad
range of early environmental exposures from

1443
01:48:04,070 --> 01:48:08,390
society to biology and from conception to
5 years.

1444
01:48:08,390 --> 01:48:10,310
Those are our exposure periods.

1445
01:48:10,310 --> 01:48:20,820
Children in ECHO are enrolled from in utero
to age 20, 11 months, 30 days.

1446
01:48:20,820 --> 01:48:26,690
The overall scientific goal is to answer solution-oriented
questions about the effects of a broad range

1447
01:48:26,690 --> 01:48:29,770
of early environmental exposures on child
health and development.

1448
01:48:29,770 --> 01:48:39,080
And really, in this first cycle, what we tried
to do…so we tried to weave together 69 different

1449
01:48:39,080 --> 01:48:44,020
maternal–child…dyads that came in…or
cohorts that came in across the country.

1450
01:48:44,020 --> 01:48:45,960
So, we’re weaving together this cohort.

1451
01:48:45,960 --> 01:48:49,580
That was the first cycle, and then I’ll
talk about the second cycle.

1452
01:48:49,580 --> 01:48:58,470
The first cycle…we have a really diverse
cohort, 26% Hispanic, 43% White, 12% Black.

1453
01:48:58,470 --> 01:49:01,370
We’re across the country.

1454
01:49:01,370 --> 01:49:09,040
What we’ve done so far is we have data on
over 100,000…107,000 participants; 65,000

1455
01:49:09,040 --> 01:49:11,040
of those are from kids.

1456
01:49:11,040 --> 01:49:14,719
About 34,000 are in active follow-up as we
speak.

1457
01:49:14,719 --> 01:49:18,260
We have extant essay…assay data.

1458
01:49:18,260 --> 01:49:25,869
We have HHEAR assay data, and we have biospecimens
on…or 83,000 biospecimens.

1459
01:49:25,869 --> 01:49:33,430
So, we have a lot we’ve done so far, and
this data’s also available to the scientific

1460
01:49:33,430 --> 01:49:34,820
community through DASH.

1461
01:49:34,820 --> 01:49:40,030
This is a data and specimen hub through NICHD.

1462
01:49:40,030 --> 01:49:47,071
So, you can access that data, and currently
we have about 1,300 publications, and about

1463
01:49:47,071 --> 01:49:52,230
100 of those are related to the ECHO-wide
cohort.

1464
01:49:52,230 --> 01:49:56,480
Many of them are cohort specific, but every
day there are more and more ECHO-wide studies.

1465
01:49:56,480 --> 01:50:04,280
And just quickly want to mention, for those
predocs and postdocs, we currently have a funding

1466
01:50:04,280 --> 01:50:08,890
announcement available to look at the ECHO
data through DASH.

1467
01:50:08,890 --> 01:50:10,340
So, okay.

1468
01:50:10,340 --> 01:50:16,530
I gave you a broad overview of ECHO during
the first cycle.

1469
01:50:16,530 --> 01:50:18,719
And so, we’re about to start cycle two in
September.

1470
01:50:18,719 --> 01:50:23,410
But a couple years ago we thought about: Where
are we with the science, and where do we want

1471
01:50:23,410 --> 01:50:24,530
to go forward?

1472
01:50:24,530 --> 01:50:31,610
So we had a workshop on the Preconceptional
Origins of Child Health Outcomes, and it was

1473
01:50:31,610 --> 01:50:33,780
a trans-NIH workshop.

1474
01:50:33,780 --> 01:50:42,560
We had many partners, including Drew and Somdat
and many others from different NIH institutes.

1475
01:50:42,560 --> 01:50:49,250
And the idea was…the goal really was to
assess the state of the science, research

1476
01:50:49,250 --> 01:50:53,170
gaps and opportunities, to think about: What
are the multiple exposures we should include

1477
01:50:53,170 --> 01:50:54,659
in a future study?

1478
01:50:54,659 --> 01:50:56,310
How do we…and how do we do that?

1479
01:50:56,310 --> 01:51:00,980
So, what is the science, and how do we do
it?

1480
01:51:00,980 --> 01:51:08,650
So, we had six themes: lifestyles…lifestyle
factors, including obesity.

1481
01:51:08,650 --> 01:51:13,900
Recruitment and study design—how do we recruit
a diverse sample?

1482
01:51:13,900 --> 01:51:14,900
What about fathers?

1483
01:51:14,900 --> 01:51:17,030
And we’ve talked a little bit about that.

1484
01:51:17,030 --> 01:51:19,250
So, we…that was one of our themes.

1485
01:51:19,250 --> 01:51:23,340
We were interested in physical and chemical
exposures, and psychosocial and societal exposures.

1486
01:51:23,340 --> 01:51:27,920
And this was open to the public.

1487
01:51:27,920 --> 01:51:33,670
You can go to the website and find our booklet
that has all the speaker information.

1488
01:51:33,670 --> 01:51:41,420
Okay, so, I’m going to focus on the lifestyle…what
we learned from the lifestyle/obesity section.

1489
01:51:41,420 --> 01:51:45,409
So, we asked all of our speakers questions.

1490
01:51:45,409 --> 01:51:54,260
So, from the evidence of yours and other studies,
to what extent do modifiable lifestyle factors

1491
01:51:54,260 --> 01:51:59,889
or obesity in women or men before pregnancy
cause adverse health outcomes—or lead to

1492
01:51:59,889 --> 01:52:01,940
positive health—in their offspring?

1493
01:52:01,940 --> 01:52:04,510
How do you recruit moms and dads during the
preconception period?

1494
01:52:04,510 --> 01:52:07,070
And what are some of the key research gaps?

1495
01:52:07,070 --> 01:52:09,260
And here’s a list of our speakers.

1496
01:52:09,260 --> 01:52:11,840
So, what are some of the key themes?

1497
01:52:11,840 --> 01:52:13,329
So, lifestyle factors.

1498
01:52:13,329 --> 01:52:19,159
So, sleep, physical activity, diet, they’re
all intertwined with each other…so…and

1499
01:52:19,159 --> 01:52:20,679
with obesity.

1500
01:52:20,679 --> 01:52:28,190
And so, we…to inform multicomponent interventions…researchers
should examine these in combination, not just

1501
01:52:28,190 --> 01:52:29,300
in isolation.

1502
01:52:29,300 --> 01:52:35,790
We…the need exists to examine the…the
benefits and risks of weight loss before pregnancy,

1503
01:52:35,790 --> 01:52:38,050
so during the preconception period.

1504
01:52:38,050 --> 01:52:44,500
Also, the embryonic period appears to be a
critical period for long-term child health,

1505
01:52:44,500 --> 01:52:48,750
emphasizing the need to recruit men and women
before conception.

1506
01:52:48,750 --> 01:52:56,050
So, some of the main themes were about the
challenges in recruiting and retaining diverse

1507
01:52:56,050 --> 01:53:03,330
populations into a preconception study and
following them through pregnancy and into

1508
01:53:03,330 --> 01:53:04,330
childhood.

1509
01:53:04,330 --> 01:53:08,840
There were a lot of research gaps related
to health disparities and equity.

1510
01:53:08,840 --> 01:53:11,260
And once again, the role of fathers is really
important.

1511
01:53:11,260 --> 01:53:15,480
So, these are some of the high-level themes
that emerged from this workshop.

1512
01:53:15,480 --> 01:53:17,900
So, we had this workshop 2 years ago.

1513
01:53:17,900 --> 01:53:22,540
What did we do with the knowledge that we
gained from the workshop?

1514
01:53:22,540 --> 01:53:27,630
So, with what we learned from the workshop,
it informed the next cycle of ECHO.

1515
01:53:27,630 --> 01:53:31,460
So, what is the future of ECHO?

1516
01:53:31,460 --> 01:53:37,150
So for the next…ECHO will have another, second
7-year cycle.

1517
01:53:37,150 --> 01:53:41,660
And so, we’re going to extend and expand
the ECHO cohort to further investigate the

1518
01:53:41,660 --> 01:53:47,770
roles of a broad range of exposures from society
or biology, and we’re including the preconception

1519
01:53:47,770 --> 01:53:53,480
period, and we’re going to look at this
on ECHO’s five health outcomes among diverse

1520
01:53:53,480 --> 01:53:54,480
populations.

1521
01:53:54,480 --> 01:53:56,670
So, how are we going to do this?

1522
01:53:56,670 --> 01:54:00,420
So it begins in September [2023] through May
2030.

1523
01:54:00,420 --> 01:54:09,400
We’re going to extend by following the existing
kids in ECHO, and we’re going to expand

1524
01:54:09,400 --> 01:54:14,130
by recruiting an additional 20,000 women and
their partners, recruiting…recruited during

1525
01:54:14,130 --> 01:54:17,690
pregnancy and following their kids.

1526
01:54:17,690 --> 01:54:19,760
And this will include a preconception pilot.

1527
01:54:19,760 --> 01:54:26,349
So, right now, the preconception pilot, we’re
thinking potentially about 10,000 individuals

1528
01:54:26,349 --> 01:54:28,210
will become part of this pilot.

1529
01:54:28,210 --> 01:54:33,770
And so, these are individuals who are at moderate
to high probability of a subsequent period…pregnancy.

1530
01:54:33,770 --> 01:54:39,130
So, these are individuals that are going to
be recruited during the interval pregnancy

1531
01:54:39,130 --> 01:54:44,260
period, from the first ECHO pregnancy to the
second ECHO pregnancy.

1532
01:54:44,260 --> 01:54:56,410
So, this will result in a large 60,000…participant
cohort from preconception through adolescence.

1533
01:54:56,410 --> 01:55:01,090
In the next cycle, we have a number of expanded
scientific opportunities, and I’m not going

1534
01:55:01,090 --> 01:55:02,890
to go through all of them.

1535
01:55:02,890 --> 01:55:07,350
But just to point out, their preconception
exposures is one big area that we’re going

1536
01:55:07,350 --> 01:55:09,800
to look at.

1537
01:55:09,800 --> 01:55:16,010
We released seven funding announcements a
year ago, and this just shows that two of

1538
01:55:16,010 --> 01:55:21,840
the funding announcements will directly feed
into the preconception pilot.

1539
01:55:21,840 --> 01:55:27,420
And as I mentioned, there’s going to be
10,000…at least 10,000 potential pregnant

1540
01:55:27,420 --> 01:55:32,050
participants in this pilot and their conceiving
partners.

1541
01:55:32,050 --> 01:55:35,349
We think it’ll result in about 3,000 live
births.

1542
01:55:35,349 --> 01:55:38,110
It…it may be higher or lower.

1543
01:55:38,110 --> 01:55:44,090
We asked all of the applicants to these funding
announcements to provide exploratory aims to

1544
01:55:44,090 --> 01:55:52,500
look at these preconception…exposures, and
we also in the…in the RFAs and funding announcements,

1545
01:55:52,500 --> 01:55:58,020
we asked for them to look at…at the participants
during the preconception period for three

1546
01:55:58,020 --> 01:55:59,020
visits.

1547
01:55:59,020 --> 01:56:03,060
So, there’s a little bit of a timeframe
there.

1548
01:56:03,060 --> 01:56:09,680
So now, to remind you about the first cycle,
we have these 69 different cohorts.

1549
01:56:09,680 --> 01:56:14,840
They were coming together, they were…we
were weaving together the ECHO-wide cohort.

1550
01:56:14,840 --> 01:56:18,480
In the next cycle, we will have…we have
the quilt, right?

1551
01:56:18,480 --> 01:56:21,940
We’re no longer developing the quilt…quilt,
we have the quilt.

1552
01:56:21,940 --> 01:56:26,429
There will be one ECHO cohort with many cohort
study sites.

1553
01:56:26,429 --> 01:56:30,540
All of the ECHO cohort study sites will follow
the same protocol.

1554
01:56:30,540 --> 01:56:36,719
It is posted, so you can go online right now
and look at our current protocols.

1555
01:56:36,719 --> 01:56:42,940
It is our core protocol; there will be specialized
measures that will be…the preconception

1556
01:56:42,940 --> 01:56:44,730
protocol’s not developed yet.

1557
01:56:44,730 --> 01:56:46,219
It’s being developed.

1558
01:56:46,219 --> 01:56:48,360
And there are specialized measures that will
be developed.

1559
01:56:48,360 --> 01:56:53,290
And these specialized measures will be either
more frequent or more intense measures in

1560
01:56:53,290 --> 01:57:00,030
three main areas with…in terms of exposures,
so…lifestyle, psychosocial, and physical

1561
01:57:00,030 --> 01:57:01,650
and chemical.

1562
01:57:01,650 --> 01:57:07,610
So, in the next cycle of ECHO, the investigators
will develop the specialized protocol where

1563
01:57:07,610 --> 01:57:15,340
they will select measures to go deeper, do
a deeper dive in these areas.

1564
01:57:15,340 --> 01:57:16,340
Okay.

1565
01:57:16,340 --> 01:57:21,880
So, I went pretty fast there, but I gave you
a high-level overview of ECHO.

1566
01:57:21,880 --> 01:57:26,070
I told you about…we have this preconception
workshop.

1567
01:57:26,070 --> 01:57:32,170
And then I told you a little bit about how
that workshop informs the next cycle of ECHO,

1568
01:57:32,170 --> 01:57:35,900
and what we’re doing in…in the next cycle
of ECHO, which is about to start in September.

1569
01:57:35,900 --> 01:57:42,050
So, now I’m going to give you just some
examples of ECHO nutrition research.

1570
01:57:42,050 --> 01:57:47,710
As I mentioned, we have over 1,300 publications,
so I’m just picking out a couple more…a

1571
01:57:47,710 --> 01:57:49,140
couple of examples.

1572
01:57:49,140 --> 01:57:56,400
You’ll hear examples of more ECHO research
from speakers later in the day and tomorrow,

1573
01:57:56,400 --> 01:58:00,480
but just going to mention a couple.

1574
01:58:00,480 --> 01:58:05,040
So, this study was about maternal diet.

1575
01:58:05,040 --> 01:58:11,090
So, this is from one of our cohorts, the PETALS
cohort, about…almost 2,300 participants;

1576
01:58:11,090 --> 01:58:21,060
78% ethnic minorities, so very diverse; 57%
were overweight and…or obese pre-pregnancy.

1577
01:58:21,060 --> 01:58:26,900
And in essence, what they found was that poor
maternal diet quality in early pregnancy was

1578
01:58:26,900 --> 01:58:31,790
associated with a greater altering birth weight,
Z-score, and increased risk of the baby being

1579
01:58:31,790 --> 01:58:33,790
large for gestational age at birth.

1580
01:58:33,790 --> 01:58:40,329
So, this…this finding, we see this finding
over and over in various studies.

1581
01:58:40,329 --> 01:58:47,780
Another study, the Healthy Start study, which
was mentioned earlier, had about…a little

1582
01:58:47,780 --> 01:58:53,920
over 1,200 participants, 13% African American,
26% Hispanic.

1583
01:58:53,920 --> 01:59:00,400
And what they did is they developed a maternal
diet index, and they looked at allergic diseases.

1584
01:59:00,400 --> 01:59:06,679
So, the idea is that maternal diets relate
not just to obesity and growth, but also to

1585
01:59:06,679 --> 01:59:07,850
other outcomes.

1586
01:59:07,850 --> 01:59:15,670
So, in this index that they developed, what
they found was that certain foods…so,

1587
01:59:15,670 --> 01:59:22,800
vegetables and yogurts were protective, whereas
other foods, such as french fries, potatoes,

1588
01:59:22,800 --> 01:59:27,199
rice, grains, red meat…were related to negative
outcomes.

1589
01:59:27,199 --> 01:59:33,450
So, this index during pregnancy was associated
with a reduced odds of a combined offspring

1590
01:59:33,450 --> 01:59:36,260
diagnosis of any allergy, excluding wheeze.

1591
01:59:36,260 --> 01:59:46,210
So, they looked at medical records from…for
kids for…from 0 to 4 to assess outcomes.

1592
01:59:46,210 --> 01:59:53,050
This next study is from the PRISM Cohort on
the Northeast.

1593
01:59:53,050 --> 01:59:59,550
And it looked at joint effects—so not just
one exposure, but joint effects—of metals

1594
01:59:59,550 --> 02:00:04,030
and nutrition on birth weight for gestational
age, for Z score.

1595
02:00:04,030 --> 02:00:10,420
So, they had about 500 mother–infant dyads:
40% African-American, 36% Hispanic.

1596
02:00:10,420 --> 02:00:18,469
And in essence, they found maternal nutrition
and metal expose…exposures jointly effected

1597
02:00:18,469 --> 02:00:20,150
newborn…newborns’ birth weight.

1598
02:00:20,150 --> 02:00:26,500
And interestingly, they found that a better
diet can help a male fetus maintain normal

1599
02:00:26,500 --> 02:00:34,880
birth weight, and that there were beneficial
effects for different dietary vitamins.

1600
02:00:34,880 --> 02:00:39,300
And so, what this really means is looking
at…this begs the question of looking at

1601
02:00:39,300 --> 02:00:42,140
multiple exposures and then the mechanisms.

1602
02:00:42,140 --> 02:00:44,690
So, they really talk about, well, what are
the mechanisms?

1603
02:00:44,690 --> 02:00:47,530
Why are certain vitamins protective?

1604
02:00:47,530 --> 02:00:50,280
But we need deeper research.

1605
02:00:50,280 --> 02:00:57,980
So, these are examples of cohort-specific
studies in ECHO in the first cycle, and these

1606
02:00:57,980 --> 02:01:02,920
studies can be replicated in the larger ECHO
cohort.

1607
02:01:02,920 --> 02:01:12,900
So, an example of an ECHO-wide study is this
study by Kate Sauder.

1608
02:01:12,900 --> 02:01:17,270
She looked at 15 cohorts, so about 10,000
participants.

1609
02:01:17,270 --> 02:01:20,780
She looked at maternal diets in pregnancy.

1610
02:01:20,780 --> 02:01:28,610
And she was interested in disparities in risks
of inadequate and excessive intake of micronutrients.

1611
02:01:28,610 --> 02:01:34,180
And she estimated…she looked at the intake
of 19 nutrients from foods alone and foods

1612
02:01:34,180 --> 02:01:35,180
and supplements.

1613
02:01:35,180 --> 02:01:47,630
And that’s just a map of the different ECHO
cohorts where her participants came from.

1614
02:01:47,630 --> 02:01:51,389
And so, I’m not going to go through this
entire list.

1615
02:01:51,389 --> 02:01:54,119
But the headline is: There are many disparities.

1616
02:01:54,119 --> 02:02:02,340
So, there’s disparities in…in terms of
higher risk of inadequate intake and higher

1617
02:02:02,340 --> 02:02:06,960
risk of excessive intake.

1618
02:02:06,960 --> 02:02:14,051
And the interesting thing about this particular
study is…so, she’s gone on to do more

1619
02:02:14,051 --> 02:02:19,989
studies and trying to find the optimal supplement.

1620
02:02:19,989 --> 02:02:24,190
And there really are not optimal supplements
out there.

1621
02:02:24,190 --> 02:02:28,890
And so she’s working with different entities
to try and develop optimal supplements.

1622
02:02:28,890 --> 02:02:33,860
But this really gets to precision nutrition
research.

1623
02:02:33,860 --> 02:02:41,730
The next step for this type of research is…so,
she saw that there are disparities in excessive

1624
02:02:41,730 --> 02:02:43,570
and inadequate intake.

1625
02:02:43,570 --> 02:02:46,290
Now, what are the outcomes in kids?

1626
02:02:46,290 --> 02:02:54,180
So, that’s the next step to access what…how
does this relate to outcomes in…in the children?

1627
02:02:54,180 --> 02:03:01,440
So, some of the key takeaways from just…this
is just a couple studies that I showed you…is

1628
02:03:01,440 --> 02:03:08,920
poor maternal diet in early pregnancy is associated
with increased risk for offspring, birth weight;

1629
02:03:08,920 --> 02:03:15,880
it’s also associated with…it can be protective
in terms of this prenatal diet that…vegetables

1630
02:03:15,880 --> 02:03:23,930
and yogurt intake could be associated with
a reduce odds of…of allergic outcomes.

1631
02:03:23,930 --> 02:03:28,020
Maternal nutrition and metal exposure jointly
effect newborns’ birth weight.

1632
02:03:28,020 --> 02:03:33,300
So, the importance of looking at more than
one exposure and that there are many disparities

1633
02:03:33,300 --> 02:03:41,260
that exist in terms…excuse me…in terms
of prenatal diet.

1634
02:03:41,260 --> 02:03:44,969
So, thinking about considerations.

1635
02:03:44,969 --> 02:03:48,210
So, these are really challenges and opportunities.

1636
02:03:48,210 --> 02:03:51,110
And the way I think about them is, there’s
the science.

1637
02:03:51,110 --> 02:03:53,659
What are our research questions?

1638
02:03:53,659 --> 02:03:55,800
And then, how do we do it?

1639
02:03:55,800 --> 02:04:01,930
So with the science, there are many, many,
many research questions we still need to address.

1640
02:04:01,930 --> 02:04:06,139
So, to what extent does a mother’s diet
and weight trajectory during preconception…how

1641
02:04:06,139 --> 02:04:09,750
does it associate with childhood obesity?

1642
02:04:09,750 --> 02:04:13,389
How much do epigenetics mediate these associations?

1643
02:04:13,389 --> 02:04:15,830
What is the interplay between multiple exposures?

1644
02:04:15,830 --> 02:04:19,369
We talked about that and stress during the
preconception period.

1645
02:04:19,369 --> 02:04:22,040
What is the role of paternal diet?

1646
02:04:22,040 --> 02:04:29,610
There’s very little that we know about the
role of…of dads in terms of child outcomes.

1647
02:04:29,610 --> 02:04:37,389
There hasn’t been much research in this
area, and how to do this research is challenging.

1648
02:04:37,389 --> 02:04:43,800
And then there’s a lot more that we need
to do in terms of addressing health disparities.

1649
02:04:43,800 --> 02:04:51,849
In terms of how do we do it, a big question
that we face within ECHO is: How and when

1650
02:04:51,849 --> 02:04:54,110
do you recruit preconception participants?

1651
02:04:54,110 --> 02:05:00,270
So, we’re recruiting them during that interval
period, but they’ve already had one baby.

1652
02:05:00,270 --> 02:05:02,639
Does that make a difference?

1653
02:05:02,639 --> 02:05:07,349
The reason we’re recruiting them during
that interval period is because they’re

1654
02:05:07,349 --> 02:05:10,239
already a participant, so they’re easier
to follow.

1655
02:05:10,239 --> 02:05:11,250
But is that ideal?

1656
02:05:11,250 --> 02:05:12,969
I…I don’t know.

1657
02:05:12,969 --> 02:05:13,969
Right?

1658
02:05:13,969 --> 02:05:17,070
That’s…but that’s what was feasible.

1659
02:05:17,070 --> 02:05:18,610
What measures do we assess?

1660
02:05:18,610 --> 02:05:22,820
So the preconception protocol is currently
being developed.

1661
02:05:22,820 --> 02:05:27,579
There’s only three visits, and these have
to be short visits, so what are the top measures

1662
02:05:27,579 --> 02:05:28,579
to assess?

1663
02:05:28,579 --> 02:05:31,840
And also, with the dads, how do we assess
them?

1664
02:05:31,840 --> 02:05:34,270
What do we assess within the dads?

1665
02:05:34,270 --> 02:05:35,850
How do we recruit them?

1666
02:05:35,850 --> 02:05:39,590
Actually, we don’t…we call them conceiving
partners.

1667
02:05:39,590 --> 02:05:42,400
How do we recruit the conceiving partners?

1668
02:05:42,400 --> 02:05:44,780
There’s a lot of challenges with that.

1669
02:05:44,780 --> 02:05:50,969
And then, a big question is: How best do we
recruit and retain diverse participants?

1670
02:05:50,969 --> 02:05:57,800
We’ve been told that, in many of…with
our existing cohorts, that diverse participants

1671
02:05:57,800 --> 02:06:03,770
don’t always…or many families don’t
always plan pregnancies, so you don’t just

1672
02:06:03,770 --> 02:06:08,889
want to enroll the planners, because then
you might have a…a biased…sample.

1673
02:06:08,889 --> 02:06:14,990
So, how best do we enroll and retain diverse
participants is another challenge.

1674
02:06:14,990 --> 02:06:20,159
And so, with that, I go back to our discussion
question.

1675
02:06:20,159 --> 02:06:22,790
Thank you very much.

1676
02:06:22,790 --> 02:06:27,720
DR. ANDREW BREMER: Sonia, thank you very much.

1677
02:06:27,720 --> 02:06:30,500
And Krista, I totally botched it when it came
to time management.

1678
02:06:30,500 --> 02:06:32,130
But what I did succeed in is a great…a great
discussion.

1679
02:06:32,130 --> 02:06:34,280
Thank you for all the speakers.

1680
02:06:34,280 --> 02:06:41,079
You know, this in the past…past hour we’ve
gone from the single gamete to the population

1681
02:06:41,079 --> 02:06:42,079
and back.

1682
02:06:42,079 --> 02:06:43,140
But it’s pretty cool.

1683
02:06:43,140 --> 02:06:47,050
I do…for those online, please use some…we…we
do have a little bit of time.

1684
02:06:47,050 --> 02:06:50,320
I…not a lot, but we…we…we do want to
capture your questions.

1685
02:06:50,320 --> 02:06:55,340
So, for those online, please do submit them
in Q&A, and then those in the room…and thank

1686
02:06:55,340 --> 02:06:57,190
you, Kimberlea, for keeping me on track.

1687
02:06:57,190 --> 02:06:59,420
And those in the room, feel free to raise
your…raise your hand.

1688
02:06:59,420 --> 02:07:01,900
I have a question, but I didn’t want to
do…I’ve already spoken a lot today.

1689
02:07:01,900 --> 02:07:06,370
Are there any questions in the room or online?

1690
02:07:06,370 --> 02:07:07,420
A few online.

1691
02:07:07,420 --> 02:07:09,599
Let’s start with someone online.

1692
02:07:09,599 --> 02:07:11,599
DR. KIMBERLEA GIBBS: Okay.

1693
02:07:11,599 --> 02:07:16,830
So, this is for Dr. Rando: For the sperm studies,
relative to humans, has the data shown that

1694
02:07:16,830 --> 02:07:23,610
gastric bypass in male obese fathers improved
the outcomes in offspring sperm and child

1695
02:07:23,610 --> 02:07:24,610
health?

1696
02:07:24,610 --> 02:07:29,190
DR. OLIVER RANDO: So in…are they saying, in
humans, gastric bypass—

1697
02:07:29,190 --> 02:07:31,620
DR. KIMBERLEA GIBBS: Yes, sir.

1698
02:07:31,620 --> 02:07:36,510
DR. OLIVER RANDO: Yeah. So, in…I don’t know that study.

1699
02:07:36,510 --> 02:07:42,380
But in rodent models, several groups, Hannon’s
group and Laurie Goodyear, have both shown

1700
02:07:42,380 --> 02:07:47,360
that high fat effects on offspring can be
reversed by exercise in the men.

1701
02:07:47,360 --> 02:07:54,260
So again, it is…exposure history is reversible
in the parental generation.

1702
02:07:54,260 --> 02:08:00,420
It sort of came up, I think in…in the first
talk, the question of the extent to which

1703
02:08:00,420 --> 02:08:07,830
your program’s predispositions in the offspring
can be…can be ameliorated.

1704
02:08:07,830 --> 02:08:10,750
That I’m less…I know less about.

1705
02:08:10,750 --> 02:08:16,250
But certainly there’s some evidence in rodents
that high fat effects can be reversed prior

1706
02:08:16,250 --> 02:08:17,250
to conception.

1707
02:08:17,250 --> 02:08:19,250
DR. KIMBERLEA GIBBS: Ashley?

1708
02:08:19,250 --> 02:08:21,250
DR. ASHLEY VARGAS: Ashley Vargas.

1709
02:08:21,250 --> 02:08:24,710
Thank you all for such a fabulous presentation.

1710
02:08:24,710 --> 02:08:28,739
I think we started with a high understanding
of what’s going on in humans, and we jumped

1711
02:08:28,739 --> 02:08:33,151
into what’s going on epigenetically in mice,
but…but also some in humans.

1712
02:08:33,151 --> 02:08:35,860
And then we ended in lessons learned from
ECHO, which sounds disparate.

1713
02:08:35,860 --> 02:08:42,670
But the whole point is to think about how
we can attack this really difficult question

1714
02:08:42,670 --> 02:08:47,940
about nutrition across multigenerations in
a new way, because we know it’s very expensive.

1715
02:08:47,940 --> 02:08:49,280
It’s multidisciplinary.

1716
02:08:49,280 --> 02:08:50,400
It’s really hard.

1717
02:08:50,400 --> 02:08:57,650
But I wondered if any of the speakers had
any examples of how other studies have intentionally

1718
02:08:57,650 --> 02:09:03,960
incorporated both human cohort data and maybe
animal model data to help answer these difficult

1719
02:09:03,960 --> 02:09:04,960
questions.

1720
02:09:04,960 --> 02:09:07,860
And so, the one example I can think of is
MoTrPAC.

1721
02:09:07,860 --> 02:09:13,190
But I don’t know if the…or if you have
suggestions on…on how we might do that intentionally.

1722
02:09:13,190 --> 02:09:17,040
We know we need data from both animals and
humans.

1723
02:09:17,040 --> 02:09:21,409
DR. ANDREW BREMER: And that was my question too.

1724
02:09:21,409 --> 02:09:28,770
DR. OLIVER RANDO: Well, I…maybe I’ll start.

1725
02:09:28,770 --> 02:09:34,420
It’s certainly much easier in the rodent
models to look at multiple generations and

1726
02:09:34,420 --> 02:09:35,800
to look at offspring, right?

1727
02:09:35,800 --> 02:09:42,340
And it’s also easier to do…and, yeah…to
make them eat, basically, a diet that’s

1728
02:09:42,340 --> 02:09:44,260
the equivalent of eating butter sticks.

1729
02:09:44,260 --> 02:09:48,300
So, you have a lot more power there.

1730
02:09:48,300 --> 02:09:52,599
There are a couple people who’ve tried to
translate where you can sort of do similar

1731
02:09:52,599 --> 02:09:58,510
rodents and human studies at the level of
the germ cell molecular contents, right?

1732
02:09:58,510 --> 02:10:04,050
So, Larry Feig at Tufts has done a lot of
social instability work in mice and he sees

1733
02:10:04,050 --> 02:10:10,349
a couple microRNAs change, and then men who
have…I think it’s the ACE questionnaire,

1734
02:10:10,349 --> 02:10:16,309
Adverse Childhood Experiences, so early life
stressors show similar changes to the same

1735
02:10:16,309 --> 02:10:17,309
microRNA.

1736
02:10:17,309 --> 02:10:23,110
So, I’ve always, for my…my own lab, I’ve
always hoped to one day move into humans,

1737
02:10:23,110 --> 02:10:27,760
but it would mostly be at the level of looking
at molecular stuff and sperm because, you

1738
02:10:27,760 --> 02:10:33,239
know, if I hope to look at anything about
traits in the kids before I die, I would have

1739
02:10:33,239 --> 02:10:35,250
to study those, you know, start those experiments
already.

1740
02:10:35,250 --> 02:10:40,239
So, these kinds of things, like, our trainees
are going to be the ones who start seeing…because,

1741
02:10:40,239 --> 02:10:45,670
you know, most of the traits that you see
in paternal effects are not, like, things

1742
02:10:45,670 --> 02:10:47,260
that show up at 5 years old in humans.

1743
02:10:47,260 --> 02:10:49,560
They’re the complex diseases of adulthood.

1744
02:10:49,560 --> 02:10:52,010
So, it’s like diabetes, anxiety.

1745
02:10:52,010 --> 02:10:56,889
So, a lot of these outcomes in humans are
going to show up in the four…you know, people’s

1746
02:10:56,889 --> 02:10:57,889
40s and 50s.

1747
02:10:57,889 --> 02:11:01,940
So, it’s really starting really well-characterized
trials now.

1748
02:11:01,940 --> 02:11:09,660
It’s going to be our trainees who end up
finding out the answers to these things.

1749
02:11:17,976 --> 02:11:25,409
DR. JANNE BOONE-HEINONEN: Oh, sure. No, that sounds good. I’ll just add a couple of things.

1750
02:11:25,409 --> 02:11:33,050
I mean, I think that the…just the…the
crosstalk across disciplines driving the types

1751
02:11:33,050 --> 02:11:37,530
of questions and the measures that we’re
examining and…and, you know, collecting

1752
02:11:37,530 --> 02:11:38,530
in humans.

1753
02:11:38,530 --> 02:11:40,280
I mean, that’s how we’ve been doing it.

1754
02:11:40,280 --> 02:11:46,329
But I also think that, you know, as I mentioned,
you know, systems and science approaches are…I

1755
02:11:46,329 --> 02:11:52,070
mean, they are…they’re really designed
to integrate pieces of evidence from multiple

1756
02:11:52,070 --> 02:11:56,750
studies and, you know, with the ability to…to
conduct simulations.

1757
02:11:56,750 --> 02:12:03,809
DR. SONIA ARTEAGA: And I’ll just add, in ECHO,
we focus on humans, but I think it would be

1758
02:12:03,809 --> 02:12:06,860
great to be able to collaborate with those
groups.

1759
02:12:06,860 --> 02:12:11,830
DR. ANDREW BREMER: So, I’m going to…I’m
going to…I’m going to call an audible

1760
02:12:11,830 --> 02:12:13,460
here for the sake of time and our…and our…our
participants.

1761
02:12:13,460 --> 02:12:16,920
But yeah, I want to keep this kind of conversation
going throughout the next 2 days.

1762
02:12:16,920 --> 02:12:20,191
Because then, again, it…it’s the power
of bringing together lots of disciplines to

1763
02:12:20,191 --> 02:12:21,970
this to…to think about these…these…these
questions.

1764
02:12:21,970 --> 02:12:22,970
Yeah, Usha.

1765
02:12:22,970 --> 02:12:26,440
DR. USHA RAMAKRISHNAN: Thank you for a very nice
panel.

1766
02:12:26,440 --> 02:12:32,760
So, I guess I would love to hear from the
group, you know, talking about, you know,

1767
02:12:32,760 --> 02:12:37,890
what new things do we have in our toolkit
and [inaudible] so we don’t have to wait

1768
02:12:37,890 --> 02:12:40,040
another 50 years to figure out something.

1769
02:12:40,040 --> 02:12:46,980
Is…I’d love to hear on the role of metabolomics
and why there’s been different opinions

1770
02:12:46,980 --> 02:12:52,909
on its utility…I’d like to hear…that
could be a potential bridge—animal and human

1771
02:12:52,909 --> 02:12:53,909
studies.

1772
02:12:53,909 --> 02:12:56,219
DR. ANDREW BREMER: Usha, thanks for that question.

1773
02:12:56,219 --> 02:13:00,989
We’re going to chew on that because I…I
need to get to the next speaker, who has to…who

1774
02:13:00,989 --> 02:13:02,360
has to…has to leave.

1775
02:13:02,360 --> 02:13:04,570
So with that, I want to thank all of you.

1776
02:13:04,570 --> 02:13:08,699
This is the…the start of a discussion, so…so
this is…the next 2 days, let’s…let’s

1777
02:13:08,699 --> 02:13:11,670
chew on these points because…because that’s
super important moving forward.

1778
02:13:11,670 --> 02:13:14,770
And with that, I do want to turn…I’m going
to introduce Dr. Thad Schug, who—this is

1779
02:13:14,770 --> 02:13:18,969
great, I’m seeing him in person and not
on screen—from NIEHS, who will moderate

1780
02:13:18,969 --> 02:13:19,969
our next session.

1781
02:13:19,969 --> 02:13:24,480
Again, sorry to cut everyone off; we need
to be respectful of travel time.

1782
02:13:24,480 --> 02:13:25,929
Thanks, everyone.

1783
02:13:25,929 --> 02:13:28,820
DR. THADDEUS SCHUG: Okay.

1784
02:13:28,820 --> 02:13:36,460
So, we’re moving along to session number
two now, Individual[-Level] Aspects of Multigenerational

1785
02:13:36,460 --> 02:13:37,640
Influences of Nutrition.

1786
02:13:37,640 --> 02:13:39,619
I’m Thad Schug of NIEHS.

1787
02:13:39,619 --> 02:13:47,440
And we’re excited to be involved with this
program and meeting because we’ve been struggling

1788
02:13:47,440 --> 02:13:53,170
to better understand the effects of multigenerational
exposures for many years now.

1789
02:13:53,170 --> 02:14:00,389
So, this session, we'll start off with diet
and other exposures to the individual, and

1790
02:14:00,389 --> 02:14:04,429
we’ll wrap up the session with parental
contributions.

1791
02:14:04,429 --> 02:14:12,780
Dr. Kari Nadeau from Harvard University will
begin the session with a presentation on allergy.

1792
02:14:12,780 --> 02:14:16,980
After Dr. Nadeau’s presentation, we’ll
take a break for lunch before moving on to

1793
02:14:16,980 --> 02:14:18,170
the rest of the session.

1794
02:14:18,170 --> 02:14:22,130
DR. KARI NADEAU Thanks, Dr. Schug.

1795
02:14:22,130 --> 02:14:25,880
It’s really a pleasure to be here, and thank
you for inviting me.

1796
02:14:25,880 --> 02:14:30,520
It sounds like a fantastic symposium, and
I’m so glad that people are organizing this.

1797
02:14:30,520 --> 02:14:34,949
This is what we really need, and I’m really
excited that the ECHO program is listening

1798
02:14:34,949 --> 02:14:37,500
and thinking about what to do for the future.

1799
02:14:37,500 --> 02:14:42,070
So, I’ve been asked to look at the intersection
of allergy, asthma, nutrition, and -omics.

1800
02:14:42,070 --> 02:14:43,410
It’s a big topic.

1801
02:14:43,410 --> 02:14:50,860
I’ll be focusing mostly on human studies
today and looking within, across, and between

1802
02:14:50,860 --> 02:14:52,091
families and generations.

1803
02:14:52,091 --> 02:14:58,659
I tried to choose sort of the top-level summaries
of some really good studies that have been

1804
02:14:58,659 --> 02:15:03,650
out there over the next…over the past 2
years to show you today, and then really focusing

1805
02:15:03,650 --> 02:15:07,630
on impact on health and disease as it relates
to allergy and asthma.

1806
02:15:07,630 --> 02:15:10,920
And then finally, I’ll talk about research
and opportunities and challenges.

1807
02:15:10,920 --> 02:15:16,710
So, the genetics of asthma and allergy have
been studied at length.

1808
02:15:16,710 --> 02:15:22,929
In humans, there’s about 128 independent
SNPs that are associated with asthma and allergies,

1809
02:15:22,929 --> 02:15:26,350
and then just 9,700 have been reported in
the last 3 years.

1810
02:15:26,350 --> 02:15:33,489
Now, gratefully, a lot of these genetic loci
actually serve as targets for therapies now.

1811
02:15:33,489 --> 02:15:37,880
And it’s a great way to validate whether
or not these genes and their influences actually

1812
02:15:37,880 --> 02:15:42,059
affect a disease because a lot of these have
been targeted in clinical trials and have

1813
02:15:42,059 --> 02:15:44,000
been shown to work or not work.

1814
02:15:44,000 --> 02:15:50,010
So, I think as we get better at biologics
and inhibiting specific pathways with rationally

1815
02:15:50,010 --> 02:15:54,540
designed drugs, we’re going to be able to
see to what extent these genetic inheritance

1816
02:15:54,540 --> 02:15:57,429
patterns meant in terms of disease.

1817
02:15:57,429 --> 02:16:02,920
I always like to think about this “nature
versus nurture” equation, as was mentioned

1818
02:16:02,920 --> 02:16:08,369
by Usha earlier today, when we look at twins,
and the concordance interestingly enough with

1819
02:16:08,369 --> 02:16:11,489
monozygotic twins is only about 50–60%.

1820
02:16:11,489 --> 02:16:17,020
So, if one twin has allergies, the other twin
does, as well.

1821
02:16:17,020 --> 02:16:20,320
So, there’s a lot of environmental influences
than just allergies.

1822
02:16:20,320 --> 02:16:22,400
For asthma, it’s even less.

1823
02:16:22,400 --> 02:16:23,830
It’s only about 26%.

1824
02:16:23,830 --> 02:16:28,150
This was published back by Kathy Barnes, my
colleague, back in 1998.

1825
02:16:28,150 --> 02:16:35,050
So, again, more influence of environment in
asthma and pretty much an equal influence

1826
02:16:35,050 --> 02:16:37,640
of allergies with environment.

1827
02:16:37,640 --> 02:16:40,819
So, the NIAID actually put a conference together.

1828
02:16:40,819 --> 02:16:47,030
A couple of years ago, Wheatley published
this—she’s a program officer at NIAID—on

1829
02:16:47,030 --> 02:16:50,080
the multigenerational transition of asthma.

1830
02:16:50,080 --> 02:16:55,340
And what I want to point out here is that
their symposium and their conference concluded

1831
02:16:55,340 --> 02:17:01,290
that animal models have shown that there’s
up to three generations of progeny that are

1832
02:17:01,290 --> 02:17:05,460
involved in asthma inheritance patterns, most
of that due to epigenetics and that interplay

1833
02:17:05,460 --> 02:17:07,460
of environment and genetics.

1834
02:17:07,460 --> 02:17:11,359
And then it’s really important as we think
about multigenerational studies to not just

1835
02:17:11,359 --> 02:17:17,439
look at parental influences but also grandparental
inheritance patterns.

1836
02:17:17,439 --> 02:17:26,399
And what I love as an epigenetics researcher
to look at is the loci involved in allergy

1837
02:17:26,399 --> 02:17:33,420
and asthma pathology are exquisitely sensitive
to epigenetic changes.

1838
02:17:33,420 --> 02:17:35,170
And why is that?

1839
02:17:35,170 --> 02:17:42,260
Well, interestingly enough, the loci sit in
key immune cells that direct the pathology

1840
02:17:42,260 --> 02:17:50,750
of allergies and asthma—Th2 cells, Th1,
Tregs, Th0—and many of these different factors,

1841
02:17:50,750 --> 02:17:54,800
these cytokines, are also modulated by epigenetic
effects.

1842
02:17:54,800 --> 02:18:00,090
So, we think this is why there’s such an
interplay between environment and genetics

1843
02:18:00,090 --> 02:18:01,450
and allergies and asthma.

1844
02:18:01,450 --> 02:18:04,910
These could be modified by vitamins, foods,
and other exposures.

1845
02:18:04,910 --> 02:18:07,139
And I’ll give you examples of that later
on.

1846
02:18:07,139 --> 02:18:10,550
And again, I’m going to focus on human studies
today.

1847
02:18:10,550 --> 02:18:13,399
So, let’s talk at the top.

1848
02:18:13,399 --> 02:18:17,590
What are the effects of the grandparents in
allergy and asthma disease outcomes?

1849
02:18:17,590 --> 02:18:22,210
This is Carrie’s paper actually, in which
she…you did a great job summarizing some

1850
02:18:22,210 --> 02:18:28,250
of the risks that were published in the literature
that children with a parent and grandparent

1851
02:18:28,250 --> 02:18:33,170
with asthma over four times the odds of reported
asthma compared to those without a parental

1852
02:18:33,170 --> 02:18:34,439
or grandparent history.

1853
02:18:34,439 --> 02:18:38,729
There’s a lot of environmental explanations
for non-genetic risk factors, which I’ll

1854
02:18:38,729 --> 02:18:40,080
talk about a little today.

1855
02:18:40,080 --> 02:18:44,630
And then importantly, as we think about multigenerational
families and those that are underserved in

1856
02:18:44,630 --> 02:18:48,990
low socioeconomic neighborhoods and environmental
justice issues and red zoning, we really need

1857
02:18:48,990 --> 02:18:57,179
to think about those factors as we understand
and explain asthma risk factors in multigenerations.

1858
02:18:57,179 --> 02:18:58,179
What about the father?

1859
02:18:58,179 --> 02:19:03,230
Well, this is a study that was done in our…through
our colleagues in Northern Europe, who have

1860
02:19:03,230 --> 02:19:08,250
many sites looking at the fathers’ environmental
exposures before conception and asthma risk.

1861
02:19:08,250 --> 02:19:12,360
And I’m really glad that ECHO is now looking
at preconception factors because you can see

1862
02:19:12,360 --> 02:19:19,540
here that smoking in the father versus the
mother…that smoking only prior to conception,

1863
02:19:19,540 --> 02:19:25,639
for example, was more attributed in the father
than the mother to early-onset asthma in the

1864
02:19:25,639 --> 02:19:27,030
progeny.

1865
02:19:27,030 --> 02:19:30,891
And again, that kind of showed itself to be
true in the nonallergic asthmatic children,

1866
02:19:30,891 --> 02:19:31,891
as well.

1867
02:19:31,891 --> 02:19:39,840
So, I’m glad that we’re looking at fathers
as well as mothers, because there might be

1868
02:19:39,840 --> 02:19:42,530
something attributed here in the germline.

1869
02:19:42,530 --> 02:19:49,890
This was also an interesting table that was
published in the same article looking at fathers

1870
02:19:49,890 --> 02:19:56,490
smoking and the…and the attributions to
the smoking exposure of the father but in

1871
02:19:56,490 --> 02:19:59,590
relationship to stratification for paternal
grandmother smoking.

1872
02:19:59,590 --> 02:20:05,380
So, you can see here that there’s an interaction
term going on where the father smoked before

1873
02:20:05,380 --> 02:20:07,740
the age of 15 and 13.

1874
02:20:07,740 --> 02:20:16,479
And so, they put together a model here showing
that, in utero, the grandmother smoking alone

1875
02:20:16,479 --> 02:20:20,319
increased the risk of asthma and allergies,
whereas the father smoking, as well, before

1876
02:20:20,319 --> 02:20:25,960
completion of puberty and then post-puberty
complications with the father either having

1877
02:20:25,960 --> 02:20:27,970
a job of welding or smoking.

1878
02:20:27,970 --> 02:20:32,340
And that exposure increased the risk of allergy
and asthma in the progeny.

1879
02:20:32,340 --> 02:20:34,800
And this is all, again, in humans.

1880
02:20:34,800 --> 02:20:36,460
So, what about the role of the mom?

1881
02:20:36,460 --> 02:20:42,330
Well, a lot has been done, as was spoken by
Sonia, as well, with the maternal dietary

1882
02:20:42,330 --> 02:20:45,180
index that Carina Venter published out of
ECHO.

1883
02:20:45,180 --> 02:20:49,540
But the maternal diet really does play an
important role before and during pregnancy

1884
02:20:49,540 --> 02:20:51,649
in the risk of allergies and asthma.

1885
02:20:51,649 --> 02:20:55,620
This study was done through EDEN cohort by
our colleagues in Europe.

1886
02:20:55,620 --> 02:21:03,840
They studied at least 1,000…you can see
there are 1,140 individuals out of two…2,002

1887
02:21:03,840 --> 02:21:04,859
women.

1888
02:21:04,859 --> 02:21:11,140
And they did huge amounts of questionnaires—28
pages—to be able to understand the diet

1889
02:21:11,140 --> 02:21:16,870
before and around pregnancy and the contributions
of the diet to decreased risk.

1890
02:21:16,870 --> 02:21:18,430
And this is what’s really interesting.

1891
02:21:18,430 --> 02:21:25,410
When you look at how much of a decrease there
was for wheezing or for allergic rhinitis—i.e.,

1892
02:21:25,410 --> 02:21:31,510
hay fever—as it relates to what the mother
ate and the risk of the child at age 3…now,

1893
02:21:31,510 --> 02:21:34,350
wheezing at age 3 is a little bit controversial
because it doesn’t necessarily mean that

1894
02:21:34,350 --> 02:21:38,160
the child’s going to have asthma, because
wheezing at age 3 can also mean they have

1895
02:21:38,160 --> 02:21:42,450
increase infections, so we need to be a little
bit careful as to how to interpret that.

1896
02:21:42,450 --> 02:21:46,729
But what is interesting is you can see the
raw vegetables and how pertinent they were

1897
02:21:46,729 --> 02:21:52,069
to reducing the risk of allergic rhinitis
alone compared to cooked vegetables, for example,

1898
02:21:52,069 --> 02:21:53,270
and again, compared to meat.

1899
02:21:53,270 --> 02:21:58,330
So, this is really something that outside
of ECHO has been confirmed in other birth

1900
02:21:58,330 --> 02:22:00,130
cohorts, as well.

1901
02:22:00,130 --> 02:22:03,910
And what about other maternal dietary risks?

1902
02:22:03,910 --> 02:22:11,101
And this is a child cohort study that was
done—557 mother–child dyads from the Polish

1903
02:22:11,101 --> 02:22:17,510
Mother and Child Cohort looking at the diets
of the moms and how that was related to children’s

1904
02:22:17,510 --> 02:22:19,840
health at age 1, 2, and 7–9 years.

1905
02:22:19,840 --> 02:22:23,240
What I’m going to show you here is the 7–9-year
data.

1906
02:22:23,240 --> 02:22:27,100
And what they showed was that children of
the mothers who were not achieving adequate

1907
02:22:27,100 --> 02:22:33,810
intake of vitamin C during pregnancy had a
higher risk of wheezing and infections.

1908
02:22:33,810 --> 02:22:39,690
And inadequate intake of vitamin C also during
pregnancy was related to higher risk of atopic

1909
02:22:39,690 --> 02:22:40,690
dermatitis.

1910
02:22:40,690 --> 02:22:45,030
Atopic dermatitis is otherwise known as eczema,
and that can be a conduit by which episode

1911
02:22:45,030 --> 02:22:49,580
barrier defects can increase the atopic allergic
phosphorylase, so it’s really important

1912
02:22:49,580 --> 02:22:53,590
to try to decrease allergic skin disease.

1913
02:22:53,590 --> 02:22:58,800
And then finally, inadequate intake of magnesium
was also associated with increased risk of

1914
02:22:58,800 --> 02:23:00,680
wheezing.

1915
02:23:00,680 --> 02:23:07,200
So, I found this study also very interesting
to look at diet-related metabolites because

1916
02:23:07,200 --> 02:23:12,770
I was also asked to talk about -omics and
how we can examine more detailed, granular

1917
02:23:12,770 --> 02:23:13,770
omic-type studies.

1918
02:23:13,770 --> 02:23:20,380
And this was one of the first ones that I
saw in which this cohort in Denmark looked

1919
02:23:20,380 --> 02:23:25,920
at dry blood spots after birth, so it’s
the same thing that we do.

1920
02:23:25,920 --> 02:23:27,840
We look at the Guthrie card.

1921
02:23:27,840 --> 02:23:32,420
And so, this is only after 2 to 3 days of
age, and then they followed these children

1922
02:23:32,420 --> 02:23:38,550
out, and then they examine the risk of allergies
and eczema and asthma at 6 years of age.

1923
02:23:38,550 --> 02:23:39,970
So, what did they find?

1924
02:23:39,970 --> 02:23:45,550
So, what’s really interesting here is in
a large study—and again, in Denmark, so

1925
02:23:45,550 --> 02:23:48,930
we have to look at to what degree that it
will be validated in different ethnicities

1926
02:23:48,930 --> 02:23:51,160
and in different parts of the world.

1927
02:23:51,160 --> 02:23:56,460
But they looked at different metabolites that
they were able to collect, and you can see

1928
02:23:56,460 --> 02:24:05,460
there that if the mother drank a lot of caffeine
and…and that was found in the dry blood

1929
02:24:05,460 --> 02:24:10,810
spot at 2 to 3 days of age, that it reduced
the risk of asthma at 6 years of age.

1930
02:24:10,810 --> 02:24:17,530
So, again, these were found to be relatively
significant in terms of the nutrition of the

1931
02:24:17,530 --> 02:24:21,930
mom as it relates to outcomes later on in
life of the child.

1932
02:24:21,930 --> 02:24:29,800
Interestingly, they also showed that increasing
fruit, green vegetables, and fish decrease

1933
02:24:29,800 --> 02:24:33,560
your risk of not only asthma and allergies
but also infections.

1934
02:24:33,560 --> 02:24:39,200
And, again, this was around pregnancy, 6 years
later.

1935
02:24:39,200 --> 02:24:41,780
So, what about the child’s side?

1936
02:24:41,780 --> 02:24:46,960
So, I want to move forward in the…a field
that I worked on very frequently and what

1937
02:24:46,960 --> 02:24:53,140
Roduit et al. found—and this was through
Europe called the PASTURE study—was that

1938
02:24:53,140 --> 02:24:59,410
increased diet diversity, so that before the
age of about a year, if the child has eaten

1939
02:24:59,410 --> 02:25:05,479
different proteins and been exposed to different
types of diverse diets, and this is actually

1940
02:25:05,479 --> 02:25:11,510
an index now that Carina Venter has worked
on, it actually has been shown to be associated

1941
02:25:11,510 --> 02:25:18,380
with a reduced asthma risk, reduced food allergy
risk, and reduced atopic dermatitis or eczema

1942
02:25:18,380 --> 02:25:19,380
risk.

1943
02:25:19,380 --> 02:25:26,189
So, it seems that that imprinting window that
Usha spoke about earlier today in which you

1944
02:25:26,189 --> 02:25:30,850
have this…one of the main periods, but not
the only period of life by which you can imprint

1945
02:25:30,850 --> 02:25:33,300
your immune system and create tolerance.

1946
02:25:33,300 --> 02:25:39,100
That having that diverse diet, having changes
in protein through the gut, that the gut maintains

1947
02:25:39,100 --> 02:25:45,040
tolerance and that there might be a reduction
in atopic disease in general if we can make

1948
02:25:45,040 --> 02:25:47,529
sure that the diet is diverse in children.

1949
02:25:47,529 --> 02:25:49,850
And that means well in nutrition, as well.

1950
02:25:49,850 --> 02:25:54,530
So, this is a prospective study, instead of
the study that I just showed you, which is

1951
02:25:54,530 --> 02:25:55,550
the PASTURE cohort.

1952
02:25:55,550 --> 02:26:01,660
This is actually a prospective, randomized
clinical trial where proactive, preventative

1953
02:26:01,660 --> 02:26:07,700
food protein diet was shown to decrease the
risk of food allergy in children.

1954
02:26:07,700 --> 02:26:10,790
This was published this past year—well,
2022—in Lancet.

1955
02:26:10,790 --> 02:26:16,450
And what was really interesting is that just
by giving a small dip of peanut butter onto

1956
02:26:16,450 --> 02:26:22,810
the pinky of the mother about 2 days out of
the week, they were actually able to show

1957
02:26:22,810 --> 02:26:26,710
this reduction in food allergy.

1958
02:26:26,710 --> 02:26:31,029
And this was peanuts shown here, but they
also show that with egg and for milk.

1959
02:26:31,029 --> 02:26:35,700
So, you have to give eggs for egg allergy,
milk to reduce milk allergy—you have to

1960
02:26:35,700 --> 02:26:37,160
be specific of the protein.

1961
02:26:37,160 --> 02:26:43,279
But we think this is really interesting in
terms of proactive intervention in the diet

1962
02:26:43,279 --> 02:26:47,710
to make sure that we can prevent disease.

1963
02:26:47,710 --> 02:26:53,760
Finally, many of my patients, as well as my
colleagues, have studied pregnancy and prevention

1964
02:26:53,760 --> 02:26:54,760
in the maternal diet.

1965
02:26:54,760 --> 02:26:59,760
And there was a lot of controversy in the
2000s as to whether or not women should be

1966
02:26:59,760 --> 02:27:04,630
eating nuts, should be eating the very same
things that a lot of society was worried about

1967
02:27:04,630 --> 02:27:07,770
potentially inducing allergies in children
to.

1968
02:27:07,770 --> 02:27:13,240
But what’s been shown definitively now in
larger studies is that there’s a decrease

1969
02:27:13,240 --> 02:27:19,240
in the risk of developing nut allergies in
children with women who actually take nut

1970
02:27:19,240 --> 02:27:23,760
during pregnancy and during breastfeeding.

1971
02:27:23,760 --> 02:27:29,550
So, this is really important because, as we
think about what proteins need to be shown

1972
02:27:29,550 --> 02:27:34,260
to the immune system of not only the pregnant
mom, but also the baby, it might be really

1973
02:27:34,260 --> 02:27:39,800
important to be able to make sure that the
immune system understands and is exposed to

1974
02:27:39,800 --> 02:27:46,229
these different proteins in a diverse, healthy
way with breastfeeding to be able to help

1975
02:27:46,229 --> 02:27:51,590
ensure tolerance later on in life.

1976
02:27:51,590 --> 02:27:55,010
And so, I’d like to now sort of move into
the -omics part of my talk.

1977
02:27:55,010 --> 02:28:01,610
So, I’ve shown some things about multigenerational
aspects of how to potentially prevent allergies

1978
02:28:01,610 --> 02:28:03,550
and asthma, what studies we’ve had.

1979
02:28:03,550 --> 02:28:06,880
I thank our large cohorts, but more needs
to be done.

1980
02:28:06,880 --> 02:28:11,601
But how are we going to study this at a granular,
high-dimensional level?

1981
02:28:11,601 --> 02:28:17,300
So, Usha and Sonia talked about some of the
tools today to look to epigenetics.

1982
02:28:17,300 --> 02:28:19,390
And I think we’re getting better and better.

1983
02:28:19,390 --> 02:28:21,090
Obviously, there’s Methyl-Seq.

1984
02:28:21,090 --> 02:28:24,770
There’s CpG and pyrosequencing that we’ve
done for a while.

1985
02:28:24,770 --> 02:28:29,740
But what about getting to epigenetic associations
at the single-cell level?

1986
02:28:29,740 --> 02:28:34,420
And so, what we’ve done at…at Stanford,
and now I’m at Harvard and my laboratory,

1987
02:28:34,420 --> 02:28:40,640
thanks to an R01 that we were awarded for
the NIEHS that we’d like to understand:

1988
02:28:40,640 --> 02:28:47,950
To what extent women’s health is vulnerable
during periods of exposure to PM2.5 or air

1989
02:28:47,950 --> 02:28:48,950
pollution?

1990
02:28:48,950 --> 02:28:57,760
And what we did was we set up four cohorts—pregnancy
cohorts—that was exposed to low PM2.5 exposure

1991
02:28:57,760 --> 02:28:59,080
versus high PM2.5 exposure.

1992
02:28:59,080 --> 02:29:03,420
We’ve been following these women for almost
20 years, so we really know their exposures

1993
02:29:03,420 --> 02:29:06,930
in Central Valley in California.

1994
02:29:06,930 --> 02:29:12,740
And then we also looked at age-matched women
controls that were not pregnant and also low

1995
02:29:12,740 --> 02:29:16,399
versus high PM2.5 exposures, same definitions.

1996
02:29:16,399 --> 02:29:20,040
And many were Hispanic because we were looking
in the Central Valley.

1997
02:29:20,040 --> 02:29:21,970
We worked with the communities that are vulnerable
there.

1998
02:29:21,970 --> 02:29:24,680
And we’ve been following them for almost
20 years.

1999
02:29:24,680 --> 02:29:30,140
And what we did was get their blood, and we
can do this time-of-flight mass spectrometry

2000
02:29:30,140 --> 02:29:34,730
where we can collect and do about 45 different
parameters of different cell types and at

2001
02:29:34,730 --> 02:29:39,180
the same time look at acetylation patterns
in the DNA.

2002
02:29:39,180 --> 02:29:40,990
And so, why is that important?

2003
02:29:40,990 --> 02:29:43,560
Well, epigenetics is different depending upon
different cell types.

2004
02:29:43,560 --> 02:29:47,440
We heard that from germ line cells versus
somatic cells today.

2005
02:29:47,440 --> 02:29:51,240
But we really wanted to get into exquisite
detail in terms of these epigenetic changes

2006
02:29:51,240 --> 02:29:55,060
and why they’re lasting long for some cells
but not for others.

2007
02:29:55,060 --> 02:29:58,180
And so, we’re trying to get a glimpse of
the immune system in particular, because I

2008
02:29:58,180 --> 02:30:01,819
mentioned that the immune system is such an
important parameter that relates to allergies

2009
02:30:01,819 --> 02:30:02,819
and asthma.

2010
02:30:02,819 --> 02:30:06,149
So, we looked at those cells that were most
important for allergies and asthma.

2011
02:30:06,149 --> 02:30:12,439
And this is, for example, a UMAP study looking
at CD8-positive T-cells and how we can look

2012
02:30:12,439 --> 02:30:14,910
at acetylation patterns within those cells.

2013
02:30:14,910 --> 02:30:23,090
This was, again, part of our R01, and we’re
really excited to show you some [of] the unpublished

2014
02:30:23,090 --> 02:30:24,090
data here.

2015
02:30:24,090 --> 02:30:28,500
This particular method was developed and published
in Cell in 2018 by my colleagues.

2016
02:30:28,500 --> 02:30:33,340
And here you can see we’ve done the same
analysis that they had done in the Cell article,

2017
02:30:33,340 --> 02:30:38,130
and this is a heat map using mixed-effect
modeling in EpiTOF single-cell level.

2018
02:30:38,130 --> 02:30:43,350
And you can see the contributions of air pollution—for
example, in pregnancy versus contributions

2019
02:30:43,350 --> 02:30:49,990
of air pollution alone—and then importantly,
the impact of pregnancy alone on these epigenetic

2020
02:30:49,990 --> 02:30:52,080
profiles in the cell types.

2021
02:30:52,080 --> 02:30:57,020
So, we’re really getting a lot more granular
in terms of how these epigenetic changes might

2022
02:30:57,020 --> 02:30:58,350
affect these cell types.

2023
02:30:58,350 --> 02:31:01,820
And we’re going to be following them now
longitudinally over time, which is what we’re

2024
02:31:01,820 --> 02:31:04,290
doing as part of the R01, which we’re really
grateful for.

2025
02:31:04,290 --> 02:31:10,110
So, again, this is just unpublished data,
a spattering of what the possibilities are,

2026
02:31:10,110 --> 02:31:15,450
and I think this will help us understand more
as to the contributions of epigenetic changes,

2027
02:31:15,450 --> 02:31:20,930
environmental exposures, nutrition, and disease
outcomes later to follow these cells over

2028
02:31:20,930 --> 02:31:21,930
time.

2029
02:31:21,930 --> 02:31:28,710
So, again, moving forward from epigenetic
to more omic-type research to metabolomics,

2030
02:31:28,710 --> 02:31:33,470
we’ve been doing a lot of work in allergies
and asthma in metabolomics.

2031
02:31:33,470 --> 02:31:36,960
This is thanks to a P01 that we have with
NHLBI.

2032
02:31:36,960 --> 02:31:41,729
We’ve looked at sample collections across
two countries: the U.S. and France.

2033
02:31:41,729 --> 02:31:47,130
We looked at preschool-age as well as school-age
children with allergies, allergic asthma,

2034
02:31:47,130 --> 02:31:49,560
or asthma, or healthy controls.

2035
02:31:49,560 --> 02:31:57,380
And then we basically used agnostic clustering
to examine their plasma with full blown metabolomics.

2036
02:31:57,380 --> 02:32:00,720
And we had 470 children involved in this cohort.

2037
02:32:00,720 --> 02:32:06,939
And what we did CCA analysis, you can see
the structures which differentiated themselves

2038
02:32:06,939 --> 02:32:08,630
in terms of metabolomics.

2039
02:32:08,630 --> 02:32:13,510
And I just want to make sure that we clarify
that metabolomics is a great tool if it’s

2040
02:32:13,510 --> 02:32:18,710
used wisely and carefully and thoughtfully
and making sure that all of the samples were

2041
02:32:18,710 --> 02:32:21,550
collected the same, making sure that there
weren’t any technical issues, and making

2042
02:32:21,550 --> 02:32:26,670
sure that there are appropriate controls to
compare to and validation sets to be able

2043
02:32:26,670 --> 02:32:27,779
to test with.

2044
02:32:27,779 --> 02:32:33,500
So, what we found, interesting enough—and
this is somewhat based on nutrition and also

2045
02:32:33,500 --> 02:32:39,430
xenobiotics—is that there were differences
in peptide metabolism, lipid metabolism, and

2046
02:32:39,430 --> 02:32:42,500
xenobiotics metabolism between those that
had allergy, asthma, allergic asthma, and

2047
02:32:42,500 --> 02:32:43,500
healthy.

2048
02:32:43,500 --> 02:32:46,630
And this is, again, between 0 and school age.

2049
02:32:46,630 --> 02:32:52,760
So, we’re trying to understand now more…now
looking retrospectively at their nutrition

2050
02:32:52,760 --> 02:32:57,760
and how this could perhaps discern why or
why not they have that disease.

2051
02:32:57,760 --> 02:33:02,390
We can’t really say in a cause-and-effect
relationship because some of these children

2052
02:33:02,390 --> 02:33:07,660
were already on medication, but we can at
least try to use metabolomics to understand

2053
02:33:07,660 --> 02:33:10,979
the differences in disease in these children.

2054
02:33:10,979 --> 02:33:13,290
What about metabolomics in fecal samples?

2055
02:33:13,290 --> 02:33:20,870
Well, we actually took identical twins raised
in the same household where you have a discordance

2056
02:33:20,870 --> 02:33:22,030
for allergies.

2057
02:33:22,030 --> 02:33:26,420
So, one was completely healthy, and the other
had allergies.

2058
02:33:26,420 --> 02:33:28,510
Those are hard to find.

2059
02:33:28,510 --> 02:33:32,609
And…and they had to have grown up in the
same household.

2060
02:33:32,609 --> 02:33:38,411
And so, what we found using metabolomics with…again,
understanding nutritional differences that

2061
02:33:38,411 --> 02:33:46,439
were very, very minor, we actually found diacylglycerol
to be important for associations with healthy

2062
02:33:46,439 --> 02:33:53,430
twins versus other food components which are
actually found to be more in the allergic

2063
02:33:53,430 --> 02:33:54,430
twin.

2064
02:33:54,430 --> 02:33:56,470
So, again, these are associations, but we
need to understand more about the contributions

2065
02:33:56,470 --> 02:33:58,250
of nutrition to the diet.

2066
02:33:58,250 --> 02:34:02,180
Finally, I’ll end with research challenges
and opportunities.

2067
02:34:02,180 --> 02:34:08,300
I’m really grateful for this group for giving
us, as scientists, the ability to talk about

2068
02:34:08,300 --> 02:34:15,080
what we think might be part of the future
as you move forward at the NIH and with the [ONR].

2069
02:34:15,080 --> 02:34:20,110
I believe that multiexposure research can
be a challenge, but much can be tracked now

2070
02:34:20,110 --> 02:34:25,830
and measured in urine, blood, hair, nails,
teeth, cells, skin samples, test samples over time.

2071
02:34:25,830 --> 02:34:30,370
And this can be an opportunity for multigenerational
exposomics.

2072
02:34:30,370 --> 02:34:34,729
Long-term cohorts are needed across generations
to perform exposomics and epigenetics studies.

2073
02:34:34,729 --> 02:34:35,729
There’s a lot of tools now.

2074
02:34:35,729 --> 02:34:38,980
There’s also a lot of personal exposure
monitoring, and I’m glad ECHO is involved

2075
02:34:38,980 --> 02:34:39,980
in that, as well.

2076
02:34:39,980 --> 02:34:44,500
We need to really separate out nutrition on
the father and the mother of peri- and preconception.

2077
02:34:44,500 --> 02:34:49,189
I’m so glad that ECHO is looking at that
now, Sonia.

2078
02:34:49,189 --> 02:34:53,240
Metabolomic studies are appropriate, but we
have to make sure they’re controlled correctly

2079
02:34:53,240 --> 02:34:57,370
and controlled for the time of day when the
blood samples were collected.

2080
02:34:57,370 --> 02:35:01,430
And good nutrition improves quality of life,
and studying the effects of environmental

2081
02:35:01,430 --> 02:35:06,170
exposure in the diet at any point in time
over the life course and through multigenerations

2082
02:35:06,170 --> 02:35:07,170
is very important.

2083
02:35:07,170 --> 02:35:09,990
So, thank you so much for your time.

2084
02:35:09,990 --> 02:35:11,350
Appreciate it.

2085
02:35:11,350 --> 02:35:17,590
DR. THADDEUS SCHUG: All right. Thanks for the excellent presentation, Kari.

2086
02:35:17,590 --> 02:35:24,580
So, we’re going to take a break for lunch,
and we’ll reconvene for our next presentation

2087
02:35:24,580 --> 02:35:26,010
at 1:40.

2088
02:35:26,010 --> 02:35:27,450
Thank you.

2089
02:35:27,450 --> 02:35:32,720
DR. THADDEUS SCHUG: During the session version…during
this session, virtual parti participants are

2090
02:35:32,720 --> 02:35:37,670
invited to ask questions through the Q&A box.

2091
02:35:37,670 --> 02:35:41,840
And I want to thank Dr. Crystal Barksdale,
who’s here up front with me, from NIMHD,

2092
02:35:41,840 --> 02:35:45,630
who assists in the moderation.

2093
02:35:45,630 --> 02:35:56,810
And so let’s start things off here with
Dr. Friedman, who’s coming in virtually.

2094
02:35:59,691 --> 02:36:01,912
DR. JACOB FRIEDMAN Okay. Thanks everybody.

2095
02:36:01,912 --> 02:36:04,211
And I apologize for not being able to be in
Bethesda.

2096
02:36:04,211 --> 02:36:08,810
It’s an important conference, well organized. I thank Kimberlea for the opportunity to present
virtually.

2097
02:36:08,810 --> 02:36:11,979
I’m…I’m actually coming to you from
Big Sky, Montana, so I didn’t mean to interrupt

2098
02:36:11,979 --> 02:36:15,300
my vacation, but I wanted to participate,
so thanks for the invite.

2099
02:36:15,300 --> 02:36:22,979
So we’re going to move on, I think, in this
program now to a more reductionist view.

2100
02:36:22,979 --> 02:36:25,970
I’m not an epidemiologist, but I work with
epidemiologists.

2101
02:36:25,970 --> 02:36:32,609
I’ve been working for around 25, 30 years
on this problem of transgenerational nutrition

2102
02:36:32,609 --> 02:36:37,140
from the standpoint of gestational diabetes
and maternal obesity.

2103
02:36:37,140 --> 02:36:43,240
So, this is my disclosures, really, nothing
too dangerous here.

2104
02:36:43,240 --> 02:36:51,800
As you can see, most of my funding is out
of DK, right, with a little bit of the ECHO

2105
02:36:51,800 --> 02:36:52,800
projects.

2106
02:36:52,800 --> 02:36:59,510
And then we just got this U54, which is really
exciting; it's prevention of maternal mortality

2107
02:36:59,510 --> 02:37:00,510
and morbidity.

2108
02:37:00,510 --> 02:37:06,359
So where I’m going to go with this talk
is the studies that we’ve done, intense

2109
02:37:06,359 --> 02:37:12,210
phenotyping studies in humans, mothers with
nutrition, but I’ve also worked for the

2110
02:37:12,210 --> 02:37:17,609
last 20 years or so on nonhuman primates—
nobody’s talked about them—as a model for

2111
02:37:17,609 --> 02:37:21,010
developmental programming, particularly understanding
fetal development.

2112
02:37:21,010 --> 02:37:27,500
And you’ll see one of the key outcomes of
these studies will be the fatty liver.

2113
02:37:27,500 --> 02:37:31,410
And if you haven’t heard about fatty liver
disease in…in kids, it’s just as rampant

2114
02:37:31,410 --> 02:37:37,880
as obesity, and it’s kind of where we’re…we’re
really focused with a lot…a lot of interest

2115
02:37:37,880 --> 02:37:39,040
in prevention.

2116
02:37:39,040 --> 02:37:44,010
We also work in stem cells—umbilical cord
stem cells—and epigenetics.

2117
02:37:44,010 --> 02:37:49,610
I’ll show you one final paper that just
got accepted from the ECHO Project that you

2118
02:37:49,610 --> 02:37:51,431
might be interested near the end.

2119
02:37:51,431 --> 02:37:57,960
My disclosures are, I’m not an obstetrician,
and I’ve never been pregnant, so you can

2120
02:37:57,960 --> 02:38:02,970
ask me lots of questions about…about pregnancy,
because I work with obstetricians all the

2121
02:38:02,970 --> 02:38:04,790
time, but not one.

2122
02:38:04,790 --> 02:38:10,600
So the way I present this to the Harold Hamm
Diabetes Center, where I operate now, is prevention

2123
02:38:10,600 --> 02:38:11,600
in that first 1,000 days is so critical.

2124
02:38:11,600 --> 02:38:18,140
We know that there’s, you know, a genetic
risk, probably 40, 50%. There’s probably

2125
02:38:18,140 --> 02:38:23,880
700 alleles for diabetes, and they all contribute
a little bit.

2126
02:38:23,880 --> 02:38:28,090
So, there is a genetic history of how you
get diabetes and obesity.

2127
02:38:28,090 --> 02:38:32,880
We know that body weight runs in families,
but mostly what we’ve been concerned with

2128
02:38:32,880 --> 02:38:34,300
is that genes haven’t changed.

2129
02:38:34,300 --> 02:38:39,510
So, the gestational risk, in our opinion,
in our estimation, is what’s really driving

2130
02:38:39,510 --> 02:38:40,510
this epidemic.

2131
02:38:40,510 --> 02:38:45,939
And if you think about it, probably 30% of
women are obese.

2132
02:38:45,939 --> 02:38:49,300
Probably 50% are obese or overweight.

2133
02:38:49,300 --> 02:38:56,010
And so, there’s one in six women who are
exposing approximately 2 million kids a year

2134
02:38:56,010 --> 02:39:00,260
to this gestational risk, which is just about
body weight; it’s not about nutrition.

2135
02:39:00,260 --> 02:39:02,180
We’ll talk about that.

2136
02:39:02,180 --> 02:39:06,000
The lifestyle risk that kids encounter, you
know, we’ve all talked about, once you’re

2137
02:39:06,000 --> 02:39:12,010
born, you know, whether it’s environmental,
for the food or for the chemical exposures,

2138
02:39:12,010 --> 02:39:19,470
the problem that we have is that all of the…the…the…the
chicken has come home to roost.

2139
02:39:19,470 --> 02:39:26,140
So, all those adult diseases, including youth
type 2 diabetes, is up by 300%, as well as

2140
02:39:26,140 --> 02:39:27,310
type 1 diabetes.

2141
02:39:27,310 --> 02:39:31,060
So both of those things are going up dramatically.

2142
02:39:31,060 --> 02:39:38,790
Here’s an example of the youngest child
to be diagnosed with type 2 diabetes is a

2143
02:39:38,790 --> 02:39:39,790
3-year-old.

2144
02:39:39,790 --> 02:39:40,790
This was not genetic.

2145
02:39:40,790 --> 02:39:43,540
This was as soon as the parents changed the
diet of the kid, diabetes went away.

2146
02:39:43,540 --> 02:39:48,760
But it’s this kind of a thing that it’s
really perpetuating our obesity epidemic,

2147
02:39:48,760 --> 02:39:51,820
which drives all the other things that we’ve
been talking about.

2148
02:39:51,820 --> 02:39:56,630
And it’s estimated today that one in three
children born in 2020 will develop diabetes

2149
02:39:56,630 --> 02:39:57,630
as an adult.

2150
02:39:57,630 --> 02:40:03,399
So, it’s not hard for me as a director of
a diabetes center to talk about prevention

2151
02:40:03,399 --> 02:40:04,399
for the cure.

2152
02:40:04,399 --> 02:40:10,200
We know that if we don’t do anything, these
babies are not built to face today’s challenges.

2153
02:40:10,200 --> 02:40:12,460
They’re not going to be as resilient.

2154
02:40:12,460 --> 02:40:16,660
They won’t do as well intellectually, physically,
and socially, as you’ve all heard about.

2155
02:40:16,660 --> 02:40:19,180
And as adults, they’ll die too soon.

2156
02:40:19,180 --> 02:40:21,630
So, how do I think about diabetes?

2157
02:40:21,630 --> 02:40:29,170
I came into this field actually working on
type 2 diabetes, and now it’s really focused

2158
02:40:29,170 --> 02:40:31,460
on gestational diabetes and the risk for kids.

2159
02:40:31,460 --> 02:40:33,730
So, it’s really gene function and protein.

2160
02:40:33,730 --> 02:40:39,310
And as we’ve been talking about epigenetics,
there’s a small genetic component, which

2161
02:40:39,310 --> 02:40:45,260
we get into later, but it’s the environments
and the obesity, and probably something to

2162
02:40:45,260 --> 02:40:51,990
do with the gut microbiome, that’s driving
the insulin resistance that is part and parcel

2163
02:40:51,990 --> 02:40:53,149
for diabetes.

2164
02:40:53,149 --> 02:40:59,490
And that’s caused by too much lipid, too
much inflammation, which drives insulin resistance,

2165
02:40:59,490 --> 02:41:04,890
along with changes in insulin…or glucose
sensing and a reduction in insulin secretion.

2166
02:41:04,890 --> 02:41:10,300
So, it all comes down to here, but it really
plays out through the pancreas and a lot of

2167
02:41:10,300 --> 02:41:11,689
other tissues.

2168
02:41:11,689 --> 02:41:19,240
Now, we talk about maternal obesity as a strong
risk factor, and it’s actually more prevalent,

2169
02:41:19,240 --> 02:41:22,090
of course, than gestational diabetes.

2170
02:41:22,090 --> 02:41:27,120
And when you look longitudinally, we know
that maternal obesity predicts childhood obesity,

2171
02:41:27,120 --> 02:41:31,149
but what about GDM versus just garden-variety
obesity?

2172
02:41:31,149 --> 02:41:39,910
So, this is a study by Pat Catalano, where
actually measured body composition in 9-year-olds

2173
02:41:39,910 --> 02:41:46,170
who were offsprings of 89 women who are either
GDM or normal glucose tolerance.

2174
02:41:46,170 --> 02:41:50,990
And really, the strongest predictor for the
percent fat at 9 years old was the maternal

2175
02:41:50,990 --> 02:41:54,300
BMI, which odds ratio was over 5.

2176
02:41:54,300 --> 02:41:57,240
But we know that mothers who have type 2 diabetes,
it’s not the same as GDM.

2177
02:41:57,240 --> 02:42:03,979
These are women who have diabetes coming into
pregnancy…actually have the highest risk

2178
02:42:03,979 --> 02:42:04,979
for diabetes.

2179
02:42:04,979 --> 02:42:08,540
I…I wanted to make the point about maternal
triglycerides.

2180
02:42:08,540 --> 02:42:14,069
So, if you don’t have gestational diabetes,
but you have obesity, you generally have metabolic

2181
02:42:14,069 --> 02:42:21,620
syndrome, which involves both maternal triglycerides
and maybe a little bit of glucose, which independently

2182
02:42:21,620 --> 02:42:23,600
predict adiposity at birth.

2183
02:42:23,600 --> 02:42:28,251
So, we always talk about targeting gestational
diabetes, but I think we’re going to have

2184
02:42:28,251 --> 02:42:37,470
to start talking about a way to get at the
fuel source, which is the triglycerides.

2185
02:42:37,470 --> 02:42:41,109
We actually have a pilot study, which we’re
doing randomized control trial intervention

2186
02:42:41,109 --> 02:42:46,430
on women who come in the first trimester with
high postprandial TGs because we know fat

2187
02:42:46,430 --> 02:42:47,430
makes fat.

2188
02:42:47,430 --> 02:42:51,470
And we’re using a point-of-care meter, just
like you use a glucose meter to stick your

2189
02:42:51,470 --> 02:42:58,130
finger, measure your triglycerides, and then
we’re using something called Vascepa, which

2190
02:42:58,130 --> 02:43:03,640
is high-dose omega-3 fatty acids that will
reduce triglycerides outside of pregnancy,

2191
02:43:03,640 --> 02:43:10,150
to pilot this study, both here and in…both
in Oklahoma and in Colorado with my…my collaborators.

2192
02:43:10,150 --> 02:43:17,720
So, we’re trying to take a stab at…just
see if we can lower those triglycerides, what

2193
02:43:17,720 --> 02:43:19,040
will happen.

2194
02:43:19,040 --> 02:43:22,550
Now, to…to the point at hand, we…we know
that there’s overnutrition hypothesis.

2195
02:43:22,550 --> 02:43:30,020
So, this Western diet, high in sugar additives,
low in fiber, high in fat, and it’s…it’s

2196
02:43:30,020 --> 02:43:33,640
part of our…our part and parcel of what
we eat.

2197
02:43:33,640 --> 02:43:40,790
And so, mothers who enter pregnancy with obesity
or diagnosed with GDM, they frequently have

2198
02:43:40,790 --> 02:43:45,950
a metabolic syndrome, which is higher TGs,
higher glucose, more inflammation.

2199
02:43:45,950 --> 02:43:51,320
And even if we fix their diet for a few days,
give them a meal, this is what they have.

2200
02:43:51,320 --> 02:43:56,069
And this only advances with gestation, so
it only gets worse.

2201
02:43:56,069 --> 02:44:01,250
Now, if you talk to neonatologists, which
I worked in neonatology for quite a while,

2202
02:44:01,250 --> 02:44:05,130
the fetus is extremely vulnerable to this
overnutrition.

2203
02:44:05,130 --> 02:44:12,230
So, it has fewer mitochondria, has low antioxidants,
and low pO2; it’s almost like living at

2204
02:44:12,230 --> 02:44:13,230
Mount Everest.

2205
02:44:13,230 --> 02:44:19,561
And so, when you expose the fetus to these
fuels, you’re going to alter the TCA cycle

2206
02:44:19,561 --> 02:44:25,399
activity, de novo lipogenesis, the epigenotypes,
and mitochondrial health.

2207
02:44:25,399 --> 02:44:30,670
We just don’t know quite where that happens
and exactly what the actors are, particularly

2208
02:44:30,670 --> 02:44:32,000
in humans.

2209
02:44:32,000 --> 02:44:34,330
There’s also this inflammatory component,
which may come from the microbiome, which

2210
02:44:34,330 --> 02:44:39,720
I’ll have something to say about further
on.

2211
02:44:39,720 --> 02:44:45,000
So, first of all, the biggest scientific challenge
is what has been said already, and that is:

2212
02:44:45,000 --> 02:44:48,070
Diet is really difficult to measure accurately.

2213
02:44:48,070 --> 02:44:55,520
And this window of when the exposures are
may not be known or may vary by the outcomes

2214
02:44:55,520 --> 02:44:56,520
that you’re studying.

2215
02:44:56,520 --> 02:45:02,170
In other words, some nutrition exposures affect
one organ; others affect another.

2216
02:45:02,170 --> 02:45:08,850
So, even for these well-described prospective
cohort studies, these nutritional measures

2217
02:45:08,850 --> 02:45:12,819
probably begin after some of the windows of
exposure.

2218
02:45:12,819 --> 02:45:17,960
And if you’re just doing it at a single
point in time, it’s really confounded, particularly

2219
02:45:17,960 --> 02:45:20,770
by the subjective measures of dietary recall.

2220
02:45:20,770 --> 02:45:25,510
So, this is an area that’s our scientific
challenge, and I think everybody recognizes

2221
02:45:25,510 --> 02:45:27,149
this at this point.

2222
02:45:27,149 --> 02:45:36,030
So, how I come at this is the following: So,
we know diets, obesity, the microbiome, placenta,

2223
02:45:36,030 --> 02:45:41,660
inflammation, and lipids, they all drive fetal
growth.

2224
02:45:41,660 --> 02:45:48,660
And we know that this changes fetal gene expression,
alters growth, and increase adiposity, and

2225
02:45:48,660 --> 02:45:53,189
result in metabolic syndrome, and plenty of
animal and epidemiology evidence as we’ve

2226
02:45:53,189 --> 02:45:54,189
heard about.

2227
02:45:54,189 --> 02:45:59,510
But what we’re missing is this human evidence—like,
where is it and how does it actually occur?

2228
02:45:59,510 --> 02:46:07,510
So I’ve taken this…this approach the last
10 or 15 years, which is to use a lot of invasive

2229
02:46:07,510 --> 02:46:10,290
tools, both in mother and baby.

2230
02:46:10,290 --> 02:46:16,279
And this is a project that we published on
and continue to publish on where we’re actually

2231
02:46:16,279 --> 02:46:22,010
fixing the…the diets at the time of gestational
diabetes and giving all the meals that we…we

2232
02:46:22,010 --> 02:46:24,930
know would be important for fetal health.

2233
02:46:24,930 --> 02:46:29,890
And we’re measuring the microbiome. We’re
measuring insulin secretion. We’re doing

2234
02:46:29,890 --> 02:46:33,040
fat biopsies and then harvesting the placenta.

2235
02:46:33,040 --> 02:46:38,550
But on the fetal side, we’re developing
these tools where we can take the stem cells

2236
02:46:38,550 --> 02:46:46,250
out of the umbilical cord, we can measure
infant fat, and we can put babies in an MRI,

2237
02:46:46,250 --> 02:46:51,710
which I’ll show you here in a second.

2238
02:46:51,710 --> 02:46:54,840
This is a…a newborn who is going to get
wrapped up in a papoose and stuck in an infant

2239
02:46:54,840 --> 02:46:57,060
MRI in order to measure its liver fat.

2240
02:46:57,060 --> 02:47:01,840
This is a Pea Pod, which you’re probably
familiar with, measures total body fat.

2241
02:47:01,840 --> 02:47:08,479
And this is Kristen Boyle and Kathleen Shapiro,
who worked in the lab isolating the stem cells

2242
02:47:08,479 --> 02:47:11,189
from these babies after these diets.

2243
02:47:11,189 --> 02:47:17,830
What we published back in 2016, probably the
first studies to show that the Wharton’s

2244
02:47:17,830 --> 02:47:24,840
jelly in that umbilical cord gives you an
MSC that epigenetically and metabolically

2245
02:47:24,840 --> 02:47:28,229
is more prone to be a fat cell than a muscle
cell.

2246
02:47:28,229 --> 02:47:34,350
And if it’s a muscle cell, it has low mitochondria
and low respiration if the mother was obese.

2247
02:47:34,350 --> 02:47:42,550
So there is this biological he, or I call
it a canary in the coal mine, that actually

2248
02:47:42,550 --> 02:47:47,620
can help us understand, if we tried to prevent
something, what’s the outcome that has this

2249
02:47:47,620 --> 02:47:48,620
predictive power?

2250
02:47:48,620 --> 02:47:54,670
Here was the data that we published in 2013
on the…on the liver fat of these babies.

2251
02:47:54,670 --> 02:47:58,439
This is a papoose with intralipid as a standard.

2252
02:47:58,439 --> 02:48:03,620
And what we showed in this study was that
the higher the maternal pre-pregnancy BMI,

2253
02:48:03,620 --> 02:48:06,439
the higher the liver fat in these babies.

2254
02:48:06,439 --> 02:48:11,100
It wasn’t how fat the babies were, it was
how fat the mothers were, which kind of gives

2255
02:48:11,100 --> 02:48:13,160
you a clue why they’re developing liver
fat.

2256
02:48:13,160 --> 02:48:18,340
There’s nowhere else for it to go for the
most part except to the sub-Q compartment.

2257
02:48:18,340 --> 02:48:20,319
But why is this important

2258
02:48:20,319 --> 02:48:25,180
is because this might be part of the genesis
of this NAFLD disease.

2259
02:48:25,180 --> 02:48:33,680
And if you look at kids…teenagers with biopsy-confirmed
NAFLD, even if you adjust for their BMI, if

2260
02:48:33,680 --> 02:48:39,680
they’re born with a low birth weight or
a high birth weight, they begin to develop

2261
02:48:39,680 --> 02:48:45,290
NASH, or nonalcoholic steatohepatitis, and
advanced fibrosis based only on birth weight.

2262
02:48:45,290 --> 02:48:51,750
So, there’s a key indicator of where we
start for a disease that’s rampant.

2263
02:48:51,750 --> 02:48:59,050
So, this occurs in 10% of all children, probably
30% of all obese youth or even more, and they’re

2264
02:48:59,050 --> 02:49:01,840
more likely to have this advanced disease
and its symptoms.

2265
02:49:01,840 --> 02:49:07,160
So, I talk a lot of times to gastroenterologists,
pediatric gastroenterologists, that this poor

2266
02:49:07,160 --> 02:49:14,420
child needs a liver biopsy because he has
NASH and there is no…there’s no drugs

2267
02:49:14,420 --> 02:49:18,590
for this—no pharmaceutical therapy—and it
accelerates type 2 diabetes.

2268
02:49:18,590 --> 02:49:23,400
So, when I think about programming, yeah,
I think about obesity, but I think about this

2269
02:49:23,400 --> 02:49:27,150
disease a lot more because it’s more severe
and has more higher consequences.

2270
02:49:27,150 --> 02:49:29,590
And there’s a lot to learn.

2271
02:49:29,590 --> 02:49:36,250
I won’t bore you with all of it, but we
don’t know where the epigenetics are, the

2272
02:49:36,250 --> 02:49:42,189
inflammation, the cell death, fatty acids
that are released that activate the liver

2273
02:49:42,189 --> 02:49:43,320
and cause this injury.

2274
02:49:43,320 --> 02:49:48,610
And we don’t know anything about the…the
repair of this liver.

2275
02:49:48,610 --> 02:49:54,840
So, anytime the liver gets overloaded with
fat, if it proceeds, all of these things,

2276
02:49:54,840 --> 02:50:01,240
whether it’s hypoxia, gut microbiome, lipotoxic
liver, lipids, contribute to this inflammation

2277
02:50:01,240 --> 02:50:03,380
and advancing towards fibrosis.

2278
02:50:03,380 --> 02:50:08,023
So, we’ve got to figure out how to stop
this because this is what we’re faced with.

2279
02:50:08,023 --> 02:50:19,120
We wrote this editorial—or a review article—about
epigenetics in the micro…in the mitochondria.

2280
02:50:19,120 --> 02:50:25,130
So, when we did the mitochondrial metabolomics
and the epigenetics, we found that these neonates

2281
02:50:25,130 --> 02:50:31,189
had…not only were obesity prone if mother
was exposed to the high-fat diet epigenetically,

2282
02:50:31,189 --> 02:50:37,479
but they had all these signs and symptoms
of NAFLD going forward.

2283
02:50:37,479 --> 02:50:45,069
So, it leads me to the next part of this talk,
which is primates.

2284
02:50:45,069 --> 02:50:50,430
So, we can’t really, with good control,
study diet in humans.

2285
02:50:50,430 --> 02:50:56,979
And so, we’ve been down this road the last
15 years to study this primate model, which

2286
02:50:56,979 --> 02:51:02,100
we overfeed the…the mothers in their juvenile
years before they become pregnant,

2287
02:51:02,100 --> 02:51:05,930
and then we ask the question: What happens
in utero?

2288
02:51:05,930 --> 02:51:09,189
What happens to these 3-year-olds as they
grow up?

2289
02:51:09,189 --> 02:51:10,960
So, this is the model.

2290
02:51:10,960 --> 02:51:16,229
Japanese macaques at the Oregon National Primate
Research Center are either fed a control diet

2291
02:51:16,229 --> 02:51:17,360
or a high-fat diet.

2292
02:51:17,360 --> 02:51:19,130
We call it Western.

2293
02:51:19,130 --> 02:51:26,189
It’s not that high, actually, but it does
have components of fructose and cholesterol

2294
02:51:26,189 --> 02:51:28,560
and saturated fat.

2295
02:51:28,560 --> 02:51:33,140
Some of the animals get very obese, but actually
some of the animals are like humans, resistant

2296
02:51:33,140 --> 02:51:38,260
to this diet, but they’re still on the diet,
which allows us to study if it’s diet or

2297
02:51:38,260 --> 02:51:39,260
obesity.

2298
02:51:39,260 --> 02:51:44,650
And then we could also take some of the animals
off the high-fat diet the next pregnancy to

2299
02:51:44,650 --> 02:51:48,370
find out: Is it the obesity or the diet?

2300
02:51:48,370 --> 02:51:53,640
So, I’ll just summarize about 10 or 15 years
of work here.

2301
02:51:53,640 --> 02:52:00,680
So, we’ve identified that high fatty acids
delivers more triglycerides to the placenta,

2302
02:52:00,680 --> 02:52:06,300
which causes inflammation and mitochondrial
changes, and fatty acid transporters that

2303
02:52:06,300 --> 02:52:13,210
then lead to fat accumulation primarily in
the liver, but in many tissues, as well:

2304
02:52:13,210 --> 02:52:17,210
oxidative stress, inflammation, and premature
gluconeogenesis.

2305
02:52:17,210 --> 02:52:23,450
And essentially, these fetal livers, these
animals are born with this lifelong risk for

2306
02:52:23,450 --> 02:52:26,700
the pro-inflammatory response to overnutrition.

2307
02:52:26,700 --> 02:52:34,080
And so, as you know, obese kids, they’re
more likely to get this NAFLD NASH, and it

2308
02:52:34,080 --> 02:52:39,200
may actually be coming from the get-go, from
the actual mother’s diet.

2309
02:52:39,200 --> 02:52:42,600
We’ve also looked—and I’ll show you in
a minute—at the bone marrow.

2310
02:52:42,600 --> 02:52:48,290
So, in the fetus, the stem cells that come
out of your bone marrow make macrophages, they

2311
02:52:48,290 --> 02:52:50,771
migrate into the liver and causes inflammation.

2312
02:52:50,771 --> 02:52:56,029
And a paper we just got done publishing will
show you the epigenetics, actually, in the

2313
02:52:56,029 --> 02:53:01,460
bone marrow.

2314
02:53:01,460 --> 02:53:05,510
And this is [inaudbile] reports that just came out
in Cell.

2315
02:53:05,510 --> 02:53:11,840
And so, this Western diets is skewing the
bone marrow and the liver towards pro-inflammatory

2316
02:53:11,840 --> 02:53:12,850
gene expression.

2317
02:53:12,850 --> 02:53:20,260
And then, when we took the offspring off the
high-fat diets at weaning and at 3 years old,

2318
02:53:20,260 --> 02:53:26,990
2 1/2 years after being on a chow diet, you
still go into the bone marrow and you find

2319
02:53:26,990 --> 02:53:32,450
epigenetic programming and open chromatin
or macrophages that were derived from the

2320
02:53:32,450 --> 02:53:34,149
stem cell that are now inflamed.

2321
02:53:34,149 --> 02:53:40,189
So, it kind of gives us a hint at any inflammatory
disease might actually be hiding in the bone

2322
02:53:40,189 --> 02:53:44,950
marrow because of the mother’s diet in utero.

2323
02:53:44,950 --> 02:53:49,229
People always ask then, “Well, where are
the abnormalities?”

2324
02:53:49,229 --> 02:53:55,609
And just like in humans from our epi studies,
we’ve looked in detail in the fetus in the

2325
02:53:55,609 --> 02:54:00,970
brain, and we can see changes in the brainstem
circuits that program behavior.

2326
02:54:00,970 --> 02:54:05,110
So, the females are more depressed, the males
are more aggressive.

2327
02:54:05,110 --> 02:54:09,260
We’ve measured the placental function or
dysfunction.

2328
02:54:09,260 --> 02:54:12,420
We’ve measured the steatosis and inflammation.

2329
02:54:12,420 --> 02:54:18,660
We’ve actually shown an early collagen deposition,
which proceeds towards NASH.

2330
02:54:18,660 --> 02:54:21,120
The muscle is less sensitive. There’s reduced
mitochondria.

2331
02:54:21,120 --> 02:54:29,640
We haven’t done much on the fat cell at
this point, but the beta cell is also less

2332
02:54:29,640 --> 02:54:35,460
apt to be proliferative, and it’s lost the
alpha cell component of this mass.

2333
02:54:35,460 --> 02:54:39,740
We’ve published all of these papers, you
know, in the last decade or so about this

2334
02:54:39,740 --> 02:54:40,740
model.

2335
02:54:40,740 --> 02:54:41,801
And the microbiome is persistent.

2336
02:54:41,801 --> 02:54:48,080
So, we switched the offspring off the diets
that the mother is eating, and their microbiome

2337
02:54:48,080 --> 02:54:52,850
is persistent, and it actually hypertrophs
the organoids in the gut.

2338
02:54:52,850 --> 02:54:59,300
And I just got done telling you about bone
marrow.

2339
02:54:59,300 --> 02:55:02,420
I just want to…I don’t want to take away
the next speaker’s thunder about the microbiome.

2340
02:55:02,420 --> 02:55:08,319
We talk about this all the time, about how
the maternal diet is modifying maternal microbiome

2341
02:55:08,319 --> 02:55:11,220
and then trying to get these babies what is
a healthy start.

2342
02:55:11,220 --> 02:55:17,149
And so, I took a look at the baby’s stool
from moms who are obese, and I transplanted

2343
02:55:17,149 --> 02:55:18,330
it into mice.

2344
02:55:18,330 --> 02:55:22,649
And this was the paper that we published in
Nature Communications.

2345
02:55:22,649 --> 02:55:29,530
So, I know that these stool samples from a
2-week-old infant will increase inflammation

2346
02:55:29,530 --> 02:55:32,310
susceptibility to NAFLD if you put it in a
germ-free mouse.

2347
02:55:32,310 --> 02:55:39,920
So, we are now doing studies with bacteriologists
to try and identify which species—the early

2348
02:55:39,920 --> 02:55:42,899
Pioneers—are really the drivers of this response.

2349
02:55:42,899 --> 02:55:45,649
I’ll…I’ll…I don’t have enough time
to talk about this.

2350
02:55:45,649 --> 02:55:50,990
I’ll leave you with a couple other studies
that we’ve published recently.

2351
02:55:50,990 --> 02:55:55,600
This is the one that talks about that diet
that we fixed for gestational diabetes.

2352
02:55:55,600 --> 02:56:02,050
So, we compared mothers where we fed every
meal, at the time of diagnosis, a high-complex

2353
02:56:02,050 --> 02:56:08,120
carb diet, which allowed them to pass their
glucose tolerance test, but actually, it was

2354
02:56:08,120 --> 02:56:14,069
an isocaloric diet, and it increased the health
Bifidobacteria and the early life acquisition

2355
02:56:14,069 --> 02:56:17,930
of the infant microbiome in these babies born
to gestational diabetes.

2356
02:56:17,930 --> 02:56:19,940
So, we can do something about this.

2357
02:56:19,940 --> 02:56:24,780
This is a really hard study to do, by the
way, but that’s what it showed.

2358
02:56:24,780 --> 02:56:29,080
And this paper is about to be released probably
in the next month or so.

2359
02:56:29,080 --> 02:56:34,071
And the last I’ll show you, it’s about
cord blood DNA methylation in the Healthy

2360
02:56:34,071 --> 02:56:36,260
Start cohort from the ECHO study.

2361
02:56:36,260 --> 02:56:42,710
So, over 400 infants in which we measure the
cord blood DNA methylation and found changes

2362
02:56:42,710 --> 02:56:45,840
in the pattern of the immune cells and lipid
metabolism.

2363
02:56:45,840 --> 02:56:52,020
And further on, it was associated in mediation
studies with maternal triglycerides.

2364
02:56:52,020 --> 02:57:01,460
And then adiposity at these babies measured
mostly at 4 to 5 months and at 4 to 5 years.

2365
02:57:01,460 --> 02:57:07,840
So to me, this is still…it’s impugning
maternal triglycerides, gene methylation,

2366
02:57:07,840 --> 02:57:10,210
and childhood adiposity.

2367
02:57:10,210 --> 02:57:17,529
So I…I wrote this editorial some years back
about why this is happening.

2368
02:57:17,529 --> 02:57:19,340
I think it’s subcutaneous fat.

2369
02:57:19,340 --> 02:57:23,120
I think it’s hepatic fat, as I’ve shown
you.

2370
02:57:23,120 --> 02:57:28,561
This microbiota gut–liver axis is probably
contributing to inflammatory programming of

2371
02:57:28,561 --> 02:57:33,779
our innate immune system, along with mitochondrial
dysfunction.

2372
02:57:33,779 --> 02:57:39,920
When you add the postnatal Western diets,
you run through all these metabolic disorders

2373
02:57:39,920 --> 02:57:45,830
that wind up as liver disease and other things,
as well, any inflammatory disease that you

2374
02:57:45,830 --> 02:57:47,430
can think of.

2375
02:57:47,430 --> 02:57:51,950
So, what is the most promising scientific
or technological opportunity?

2376
02:57:51,950 --> 02:57:54,620
We were asked to talk about that.

2377
02:57:54,620 --> 02:58:01,390
And I…I think at this moment, we’ve seen
some data on metabolomics, some on epigenetics,

2378
02:58:01,390 --> 02:58:07,180
but I think it’s this diets impacting the
composition and function of these fuels and

2379
02:58:07,180 --> 02:58:11,100
food additives, by the way, to the mother
and fetus.

2380
02:58:11,100 --> 02:58:15,990
I think we’re at the point now where we
can use machine learning; if we have careful

2381
02:58:15,990 --> 02:58:21,510
infant phenotyping, we can probably reveal
specific mechanisms for nutrients and fetal

2382
02:58:21,510 --> 02:58:22,510
health.

2383
02:58:22,510 --> 02:58:24,220
At least, that’s what I hope to see in the
future.

2384
02:58:24,220 --> 02:58:30,550
In the long term, I think our problem is this
nutrient delivery across the placenta.

2385
02:58:30,550 --> 02:58:34,880
There’s been a lot of talk about the release
of these exosomes and the cargo that they

2386
02:58:34,880 --> 02:58:38,439
carry affecting both maternal health and fetal
health.

2387
02:58:38,439 --> 02:58:44,960
So, we’re now working with bioengineering
to target these exosomes to try and deliver

2388
02:58:44,960 --> 02:58:50,859
changes in tissues and cells during pregnancy,
hopefully in the future to deliver either

2389
02:58:50,859 --> 02:58:57,160
healthy mitochondria or reduce the fetal overload
and maybe placental inflammation–involved

2390
02:58:57,160 --> 02:59:02,620
fatty acid transport and all the things that
we know are probably part and parcel of that

2391
02:59:02,620 --> 02:59:03,819
fetus exposure.

2392
02:59:03,819 --> 02:59:08,010
And just…here’s a couple of examples,
you know, in the literature.

2393
02:59:08,010 --> 02:59:10,180
Exosomes carrying microRNAs.

2394
02:59:10,180 --> 02:59:16,420
Exosomes in healthy pregnancies are protecting
against hypoxia and so on and so forth.

2395
02:59:16,420 --> 02:59:19,240
And that’s where I think the future might
be.

2396
02:59:19,240 --> 02:59:27,479
So I’ll just stop there and acknowledge
my collaborators, both in Colorado and in…now

2397
02:59:27,479 --> 02:59:31,830
in University of Oklahoma, and then at Oregon
National Primate Research Center.

2398
02:59:31,830 --> 02:59:40,710
And thank you for the invitation, and I’ll…I’ll
take any questions if there’s time later.

2399
02:59:40,710 --> 02:59:52,020
DR. THADDEUS SCHUG: Okay. We’ll do questions at the end of the session. Next up is Dr. Orjuela-Grimm.

2400
02:59:52,020 --> 02:59:59,180
DR. MANUELA ORJUELA-GRIMM: Thank you very much,
everyone, and particularly the organizers,

2401
02:59:59,180 --> 03:00:08,630
Drs. Zanetti and Vargas, and I’m thrilled to
be able to be here and be back at NIH.

2402
03:00:08,630 --> 03:00:15,050
I am going to give you a…a particular view
of cancer—so, not cancer in general, but

2403
03:00:15,050 --> 03:00:22,030
just using one example from the tumors that
I’ve worked on for a while—and…and talk

2404
03:00:22,030 --> 03:00:26,500
about diet- and…and nutrient-related exposures.

2405
03:00:26,500 --> 03:00:31,090
Also, just from…particularly from one pathway.

2406
03:00:31,090 --> 03:00:38,729
So, I’m going to talk a bit about from the
perspective of using your framework—or the

2407
03:00:38,729 --> 03:00:43,950
conference framework—thinking of multigenerational
influences, maternal characteristics, maternal

2408
03:00:43,950 --> 03:00:50,620
diet, and…and individual influences, from
the perspective of thinking about methyl donors

2409
03:00:50,620 --> 03:00:56,080
and then how that also…we need to account
in…within those at global food policies

2410
03:00:56,080 --> 03:01:01,640
and thinking about diet and feeding practices
as environmental exposures and geographic

2411
03:01:01,640 --> 03:01:02,989
variation.

2412
03:01:02,989 --> 03:01:08,380
So, the image on the left-hand side is a child
with retinoblastoma.

2413
03:01:08,380 --> 03:01:12,050
This is…for those not familiar, that’s
a…something called leukocoria—

2414
03:01:12,050 --> 03:01:14,380
basically, a cat’s eye reflex.

2415
03:01:14,380 --> 03:01:18,479
You’ll see a few images of that.

2416
03:01:18,479 --> 03:01:24,229
It’s…the image on the bottom is where
I take it to measurements of food.

2417
03:01:24,229 --> 03:01:27,630
So, retinoblastoma is a primitive neuro-ectodermal
tumor.

2418
03:01:27,630 --> 03:01:35,510
So, my perspective on carcinogenesis is thinking
from cancer like a neurodevelopmental instance,

2419
03:01:35,510 --> 03:01:38,979
but it…it certainly still is carcinogenesis.

2420
03:01:38,979 --> 03:01:42,260
It arises…just a quick summary of retinoblastoma.

2421
03:01:42,260 --> 03:01:48,590
It arises in the retina in cone cells.

2422
03:01:48,590 --> 03:01:54,450
During eye development, one or both eyes can
be affected, and it exists in three forms.

2423
03:01:54,450 --> 03:02:00,670
A familial form in which it’s inherited,
as you can see there on the left-hand side.

2424
03:02:00,670 --> 03:02:06,069
The first generation has it, and then subsequent
generations, as well.

2425
03:02:06,069 --> 03:02:09,970
That’s a very rare form, and not the form
that I focus on.

2426
03:02:09,970 --> 03:02:16,410
The ones I focus on are the two remaining
ones, where the first generation isn’t affected,

2427
03:02:16,410 --> 03:02:22,190
and then the second generation will either
have both eyes affected or one and basically

2428
03:02:22,190 --> 03:02:28,920
a germline variant, or both hits happening
in somatic cells.

2429
03:02:28,920 --> 03:02:33,750
And I mention that because retinoblastoma
is actually the paradigm for the two-hit model

2430
03:02:33,750 --> 03:02:35,130
of carcinogenesis.

2431
03:02:35,130 --> 03:02:40,640
And the really, the two-hit model of carcinogenesis
came from Al Knudson, who was really looking

2432
03:02:40,640 --> 03:02:45,970
at this mathematically from looking at the
incidents in families affected by retinoblastoma.

2433
03:02:45,970 --> 03:02:49,160
And that has really been the paradigm for
our understanding of cancer.

2434
03:02:49,160 --> 03:02:51,530
However, it is a rare disease.

2435
03:02:51,530 --> 03:02:56,010
It’s one in 15,000 live births.

2436
03:02:56,010 --> 03:03:00,229
The median age at diagnosis is 22 months,
which means when we think about when could

2437
03:03:00,229 --> 03:03:04,460
relevant exposures be happening, we have a
pretty narrow window.

2438
03:03:04,460 --> 03:03:11,620
It has a higher incidence in the Global South,
and incidents within countries where it’s

2439
03:03:11,620 --> 03:03:15,149
been examined is higher in four regions.

2440
03:03:15,149 --> 03:03:18,390
The biology is fairly well understood.

2441
03:03:18,390 --> 03:03:21,149
There’s a retinoblastoma gene.

2442
03:03:21,149 --> 03:03:28,170
It was the first tumor suppressor gene to
be described.

2443
03:03:28,170 --> 03:03:29,170
It sits on chromosome 13.

2444
03:03:29,170 --> 03:03:34,830
The disease manifestations are determined
by changes in the RB1—the retinoblastoma gene.

2445
03:03:34,830 --> 03:03:45,109
It encodes protein pRb, which is a nuclear
phosphoprotein, and really, the product function

2446
03:03:45,109 --> 03:03:51,340
of the retinoblastoma gene and carcinogenesis
is well understood and mutations in it tend

2447
03:03:51,340 --> 03:03:52,680
to be nonsense mutations,

2448
03:03:52,680 --> 03:03:56,450
so, leading the stop codons, truncated protein.

2449
03:03:56,450 --> 03:04:01,860
And because of the function of the retinoblastoma
gene, it kind of tells us very clearly where

2450
03:04:01,860 --> 03:04:03,670
cancer is coming from.

2451
03:04:03,670 --> 03:04:10,080
The mutations happen preferentially on methylated
cytosine, and sometimes the gene can be silenced

2452
03:04:10,080 --> 03:04:11,649
through a methylated promoter.

2453
03:04:11,649 --> 03:04:19,689
So, it makes one think about the possible
role of methylation and methyl donors.

2454
03:04:19,689 --> 03:04:22,319
It is a key regulator in the cell cycle.

2455
03:04:22,319 --> 03:04:27,420
So, really thinking about retinoblastoma protein
is…is really relevant for carcinogenesis

2456
03:04:27,420 --> 03:04:29,090
in general.

2457
03:04:29,090 --> 03:04:35,440
But…and…and thinking, as well, in…from
the perspective of methylated cytosines.

2458
03:04:35,440 --> 03:04:37,700
So, is there a role for methylation?

2459
03:04:37,700 --> 03:04:46,200
The image on the top is a RetCam image of
what the tumor actually looks like when it’s

2460
03:04:46,200 --> 03:04:47,950
causing the retina to be elevated.

2461
03:04:47,950 --> 03:04:53,640
So, the issue is we understand the biology
of retinoblastoma really well; we don’t

2462
03:04:53,640 --> 03:04:55,170
understand the origin of the disease.

2463
03:04:55,170 --> 03:05:00,370
And so, what are the risk factors for development
of…of mutations in RB1 that then we know

2464
03:05:00,370 --> 03:05:02,170
lead to carcinogenesis?

2465
03:05:02,170 --> 03:05:09,100
There’s some evidence from epidemiologic
studies and some evidence on the mutation

2466
03:05:09,100 --> 03:05:12,840
distribution that suggests maybe methylation,
methyl donors could play a role.

2467
03:05:12,840 --> 03:05:18,320
So, from an epidemiologic perspective, what
are the relevant questions?

2468
03:05:18,320 --> 03:05:20,580
Well, when does the tumor actually develop?

2469
03:05:20,580 --> 03:05:27,510
So, as we’re thinking of this from an intergenerational…this
is key from…from my perspective, and is

2470
03:05:27,510 --> 03:05:33,070
the key period of interest tumor initiation,
or is it progression that we’re interested

2471
03:05:33,070 --> 03:05:34,070
in?

2472
03:05:34,070 --> 03:05:36,720
And then, when does the retina actually start
to form?

2473
03:05:36,720 --> 03:05:42,899
It’s just thinking about how we formulate
what we’re interested in, and when does

2474
03:05:42,899 --> 03:05:43,899
the cell of origin replicate?

2475
03:05:43,899 --> 03:05:47,340
And for a long time, cell of origin wasn’t
actually known.

2476
03:05:47,340 --> 03:05:54,720
So, from my perspective, this…this is really
thinking about pregnancy, but really, period…time

2477
03:05:54,720 --> 03:05:59,569
period within pregnancy, and then the first
2 years of life, because the median age of

2478
03:05:59,569 --> 03:06:01,380
diagnosis is 22 months.

2479
03:06:01,380 --> 03:06:09,100
So…so from a nutritional perspective, really
thinking about those periods.

2480
03:06:09,100 --> 03:06:15,250
And our first work was looking at a case-control
study.

2481
03:06:15,250 --> 03:06:18,830
All of my work on retinoblastoma—or almost
all of it—has been based in Mexico and Central

2482
03:06:18,830 --> 03:06:19,830
Mexico.

2483
03:06:19,830 --> 03:06:26,680
This was our first study where we really looked
very crudely at diet and saw that there seemed

2484
03:06:26,680 --> 03:06:31,830
to be an increased risk of having a child
with retinoblastoma if you had low vegetable

2485
03:06:31,830 --> 03:06:41,910
intake during…during your…during pregnancy,
and particularly in the second trimester of

2486
03:06:41,910 --> 03:06:42,910
pregnancy.

2487
03:06:42,910 --> 03:06:48,510
And…and really, when we looked at what were
the vegetables, then it actually seemed that

2488
03:06:48,510 --> 03:06:55,060
it was low folate intake and maybe also carotenoids
that…specifically lutein, zeaxanthin.

2489
03:06:55,060 --> 03:06:58,350
But it was also important to account for other
factors.

2490
03:06:58,350 --> 03:07:04,750
So, we…we had found that low maternal education,
and because we were looking at…at a setting

2491
03:07:04,750 --> 03:07:11,600
in central Mexico being in an urban area versus
being outside of the sort of food delivery

2492
03:07:11,600 --> 03:07:16,390
patterns of…of being in a major urban area
were contributors.

2493
03:07:16,390 --> 03:07:20,109
And so, we went on to follow this.

2494
03:07:20,109 --> 03:07:25,279
And really, here we were looking at intake
from food, not from supplements.

2495
03:07:25,279 --> 03:07:32,720
And so, we became interested in really understanding
the folate pathway, folate metabolism, and

2496
03:07:32,720 --> 03:07:38,630
thinking initially about, really, neural tube
defects, and was this a role of…a really

2497
03:07:38,630 --> 03:07:42,899
folate deficiency in methylenetetrahydrofolate
reductase?

2498
03:07:42,899 --> 03:07:48,590
And so, we obtained our first NCI funding
and had a case-control study, and it was cases

2499
03:07:48,590 --> 03:07:57,340
and friend controls in central and southern
Mexico, recruiting from two hospitals that

2500
03:07:57,340 --> 03:08:00,950
are major referral centers from very different
systems: one the public system of, essentially,

2501
03:08:00,950 --> 03:08:09,810
the uninsured, and one…a very large national
system that includes…or that…that requires

2502
03:08:09,810 --> 03:08:14,340
having at least one parent employed within
the formal sector.

2503
03:08:14,340 --> 03:08:21,390
And so, we had trio blood sample collections,
and this was thinking about nutrient levels

2504
03:08:21,390 --> 03:08:28,090
and genotyping, and also for the cases being
able to collect their tumors.

2505
03:08:28,090 --> 03:08:34,340
And we had information on the perinatal environment
and then diet and nutrient intake in pregnancy

2506
03:08:34,340 --> 03:08:35,680
in the first 2 years of life.

2507
03:08:35,680 --> 03:08:40,790
And here’s some images from data collection,
really throughout central and southern Mexico.

2508
03:08:40,790 --> 03:08:47,330
And what we found was that, actually, there
had been a natural change—or not a natural

2509
03:08:47,330 --> 03:08:52,160
change—but there was a change within Mexico
with the onset of fortification.

2510
03:08:52,160 --> 03:08:58,830
And so, the…we actually needed to change
our focus to not just think about methylenetetrahydrofolate

2511
03:08:58,830 --> 03:09:05,790
reductase, but actually think about what was
the important enzyme within the rate-limiting

2512
03:09:05,790 --> 03:09:08,640
step for the metabolism of folate

2513
03:09:08,640 --> 03:09:10,680
because now we were actually thinking of a
different source.

2514
03:09:10,680 --> 03:09:15,550
It was no longer just coming from natural
sources, but coming from multivitamins and

2515
03:09:15,550 --> 03:09:17,430
from fortified foods.

2516
03:09:17,430 --> 03:09:21,930
So, that dihydrofolate reductase reduce…reduces
folic acid.

2517
03:09:21,930 --> 03:09:27,170
And…and it needs to go through that step
of being…before being metabolized in the

2518
03:09:27,170 --> 03:09:28,170
body.

2519
03:09:28,170 --> 03:09:33,270
And this meant we needed to think about different
polymorphisms that were the rate-limiting

2520
03:09:33,270 --> 03:09:36,290
steps and that were prevalent within the population.

2521
03:09:36,290 --> 03:09:42,150
And so, essentially, this changed our…it…it
turned our entire model upside-down.

2522
03:09:42,150 --> 03:09:49,020
But we found that, actually, we were looking
at a…a really much greater risk coming from

2523
03:09:49,020 --> 03:09:58,439
women who were being exposed, who…who had
less functional metabolism of folic acid,

2524
03:09:58,439 --> 03:10:01,200
and specifically, then, these women who were
taking supplements.

2525
03:10:01,200 --> 03:10:03,410
So, we were no longer looking at deficiency.

2526
03:10:03,410 --> 03:10:08,560
We were actually looking at…at having an
excess intake of a different form that is

2527
03:10:08,560 --> 03:10:15,170
not the natural substrate of…of the metabolizing
enzyme, and we needed to account, as well,

2528
03:10:15,170 --> 03:10:16,170
for the child’s genotype.

2529
03:10:16,170 --> 03:10:20,720
So, when we accounted for both the maternal
and the child’s, then, is where we started

2530
03:10:20,720 --> 03:10:22,760
to see the most relevant findings.

2531
03:10:22,760 --> 03:10:28,359
So, just a little bit about the polymorphism
we were looking at, which had also been looked

2532
03:10:28,359 --> 03:10:32,590
at initially through neurodevelopment and
neural tube defects.

2533
03:10:32,590 --> 03:10:40,560
This is a deletion and a non-coding part of
the gene within an intron, but it is the critical

2534
03:10:40,560 --> 03:10:46,830
rate-limiting step for folic acid to enter
the methyl donor pool.

2535
03:10:46,830 --> 03:10:52,040
And so, then we really wanted to understand:
Where was the folic acid coming from in our

2536
03:10:52,040 --> 03:10:53,040
population?

2537
03:10:53,040 --> 03:10:57,410
And supplements was a part of it, but actually,
supplements were not so commonly used.

2538
03:10:57,410 --> 03:11:00,960
And so, we needed to think about food…food
sources.

2539
03:11:00,960 --> 03:11:06,710
And this really brought us into thinking about
the fact that we rely on…when we think about

2540
03:11:06,710 --> 03:11:12,560
diet, we assume that when we take it to nutrients,
that we actually know what the food contains.

2541
03:11:12,560 --> 03:11:16,590
And…and sometimes that’s questionable.

2542
03:11:16,590 --> 03:11:21,830
And so, we found, really, that we needed to
think about fortification.

2543
03:11:21,830 --> 03:11:28,880
And so, Mexico fortifies wheat and corn flour
with about 50% higher rate than the U.S. does.

2544
03:11:28,880 --> 03:11:32,620
They started a few years after we did.

2545
03:11:32,620 --> 03:11:37,410
It was legislated in 1999, implemented in
2000, was made mandatory, but not enforced.

2546
03:11:37,410 --> 03:11:44,140
And then you can see there, in the U.S., it’s
1.4 milligrams per kilogram fortification

2547
03:11:44,140 --> 03:11:45,930
of flour; in Canada, it’s 1.5.

2548
03:11:45,930 --> 03:11:49,989
And basically, as you go south, it increases
within the Western hemisphere.

2549
03:11:49,989 --> 03:11:53,689
So, the southern cones says, “Let’s do
it even more.

2550
03:11:53,689 --> 03:11:55,030
More is better.”

2551
03:11:55,030 --> 03:11:58,860
But in the EU, there’s actually no fortification.

2552
03:11:58,860 --> 03:12:02,840
And then, as I mentioned, supplement intake
in Mexico is not universal among women of

2553
03:12:02,840 --> 03:12:03,840
childbearing age.

2554
03:12:03,840 --> 03:12:09,970
So, this is just a…a map of fortification
globally—countries with industrially milled

2555
03:12:09,970 --> 03:12:12,380
flour and a rice fortification legislation.

2556
03:12:12,380 --> 03:12:19,750
And so, you can see, there…countries choose
which product…which staple they fortify,

2557
03:12:19,750 --> 03:12:23,410
and not everybody fortifies at the same rate.

2558
03:12:23,410 --> 03:12:30,800
And so, we actually found that we needed to
learn how…the whole industry of bread.

2559
03:12:30,800 --> 03:12:37,590
So, thank you—my program officer is sitting
here—for…for the…for these studies with…NCI

2560
03:12:37,590 --> 03:12:45,189
was generous enough to allow us to really
understand where the…in order to understand

2561
03:12:45,189 --> 03:12:51,260
the measures of the exposure, we needed to
understand where the folate was coming from.

2562
03:12:51,260 --> 03:12:56,590
And we realized that measuring intake…measuring
biomarkers was only part of the story.

2563
03:12:56,590 --> 03:12:58,671
We also needed to measure the food.

2564
03:12:58,671 --> 03:13:05,689
So, we became experts at how food was being
fortified in Mexico.

2565
03:13:05,689 --> 03:13:11,420
And we used our retinoblastoma study really
to get a lens at understanding staples—dietary

2566
03:13:11,420 --> 03:13:20,760
staples—in Mexico, quantified concentrations
of…of folic acid precursors, and link these

2567
03:13:20,760 --> 03:13:21,760
geographically.

2568
03:13:21,760 --> 03:13:25,729
And then, we’re able to work with our wonderful
colleagues at the Public Health Institute

2569
03:13:25,729 --> 03:13:32,319
in Mexico with the NHANES equivalent in Mexico
to really see what happens to folate measures

2570
03:13:32,319 --> 03:13:36,819
in the population when you account for actual
food measurements.

2571
03:13:36,819 --> 03:13:42,170
And so, this graph is basically showing that
in the red line is when you don’t account

2572
03:13:42,170 --> 03:13:49,271
for bread and tortilla contents, and then
when you bring in how much bread contains,

2573
03:13:49,271 --> 03:13:54,630
when you bring in how much tortillas and…and
corn flour, corn masa-based foods contain,

2574
03:13:54,630 --> 03:14:02,300
then…then you start to see that you’re
really underestimating your food content.

2575
03:14:02,300 --> 03:14:04,562
And we also found that place of residence
made a difference.

2576
03:14:04,562 --> 03:14:09,979
And so, we were able to see that, not surprisingly,
in more urban areas, we were more likely to

2577
03:14:09,979 --> 03:14:16,960
rely on fortified flour to make your dietary
staples rather than grinding your own corn.

2578
03:14:16,960 --> 03:14:19,560
So…so, this really introduced us.

2579
03:14:19,560 --> 03:14:26,450
One of the things that we found was that your
folic acid intake…this measure for the population

2580
03:14:26,450 --> 03:14:32,570
was…it turned out to be not reaching the
target population, which are women of childbearing

2581
03:14:32,570 --> 03:14:34,830
age, where they are much more likely to be
under-consuming.

2582
03:14:34,830 --> 03:14:45,840
And instead, we were potentially over-fortifying
children of small surface areas.

2583
03:14:45,840 --> 03:14:48,890
So, potentially unintended consequences.

2584
03:14:48,890 --> 03:14:54,550
So, from starting from trying to understand
carcinogenesis in a very rare tumor—that’s

2585
03:14:54,550 --> 03:15:03,710
a model we found ourselves really looking
at—maybe trying to look more deeply at some

2586
03:15:03,710 --> 03:15:04,710
global food policies.

2587
03:15:04,710 --> 03:15:12,489
This is just mapping out sources of folic
acid within our population—gray is tortillas—and

2588
03:15:12,489 --> 03:15:16,190
the bottom too, you’ll see actually our…our
breads.

2589
03:15:16,190 --> 03:15:22,080
So, we see that these are making up…these
dietary staples are making up a large amount

2590
03:15:22,080 --> 03:15:26,430
of folic acid, particularly in the younger
ages, but really throughout the lifespan.

2591
03:15:26,430 --> 03:15:31,080
And this was…the Public Health Institute
in Mexico made an infogram to really educate

2592
03:15:31,080 --> 03:15:35,100
people about thinking about their sources
of folate and folic acid.

2593
03:15:35,100 --> 03:15:42,060
So, what we found was that the amount you
ingest depends not just on what you eat, but

2594
03:15:42,060 --> 03:15:43,410
also on governance.

2595
03:15:43,410 --> 03:15:49,200
But, you know, folate and folic acid for carcinogenesis
is really, potentially, a double-edged sword.

2596
03:15:49,200 --> 03:15:51,750
So, one size may not fit all.

2597
03:15:51,750 --> 03:15:55,550
It’s important to think of really what’s
the right amount.

2598
03:15:55,550 --> 03:16:00,210
There’s literature—which I’m not going
to discuss because of time—but, really,

2599
03:16:00,210 --> 03:16:05,020
ample evidence on colon, breast, and pancreatic
cancer suggesting an increased risk with both

2600
03:16:05,020 --> 03:16:07,689
insufficient and excess levels of intake.

2601
03:16:07,689 --> 03:16:12,360
So, sort of both extremes, but it’s very
much about thinking who’s too little or

2602
03:16:12,360 --> 03:16:19,170
too much, and just the polymorphism that we
looked at had really been defined within the

2603
03:16:19,170 --> 03:16:23,500
Framingham cohort in terms of how it…it
is actually functional.

2604
03:16:23,500 --> 03:16:28,819
So, really looking at it, not necessarily
from a carcinogenesis perspective, but the

2605
03:16:28,819 --> 03:16:34,720
polymorphism matters only when your intake
is moderately high or very low and not in

2606
03:16:34,720 --> 03:16:35,720
between.

2607
03:16:35,720 --> 03:16:42,090
This polymorphism has been relevant also for
neurocognitive work in…for memory loss.

2608
03:16:42,090 --> 03:16:46,920
And so, really coming back to our thinking
of carcinogenesis from a neurodevelopmental

2609
03:16:46,920 --> 03:16:55,120
perspective because we…because of the tissue
of origin, but we also think that our platform

2610
03:16:55,120 --> 03:16:58,580
may be relevant for also thinking about cognition.

2611
03:16:58,580 --> 03:17:07,180
And in fact, we’re using the same approach
with the collaborators in Mexico City with

2612
03:17:07,180 --> 03:17:13,371
a birth cohort who are looking at effects
of lead on…on cognition and just thinking

2613
03:17:13,371 --> 03:17:18,750
about accounting for ideal amounts of…of
folate.

2614
03:17:18,750 --> 03:17:21,450
And just to show that this isn’t relevant
just for Mexico.

2615
03:17:21,450 --> 03:17:32,390
In fact, in the U.S. in 2016, fortification
went beyond wheat to also specifically including

2616
03:17:32,390 --> 03:17:39,040
corn masa, really targeting the Latino populations
that use corn flour as part of their staples.

2617
03:17:39,040 --> 03:17:42,080
So, this is relevant in the U.S.

2618
03:17:42,080 --> 03:17:46,580
And then just a little bit…a tiny bit of
our other work that is nutrient related that

2619
03:17:46,580 --> 03:17:51,000
comes from retinoblastoma was actually thinking
about sunlight exposure.

2620
03:17:51,000 --> 03:17:55,320
And then that model, we actually had to think
that when this eventually brought us to thinking

2621
03:17:55,320 --> 03:18:01,130
about vitamin D, but we needed to think about
practices of covering children.

2622
03:18:01,130 --> 03:18:07,160
How much of a child’s skin is exposed to
sun, turned out to…actually, sunlight was

2623
03:18:07,160 --> 03:18:08,470
protective for retinoblastoma.

2624
03:18:08,470 --> 03:18:14,310
We had anticipated actually UV exposure would
increase carcinogenesis, but we actually found

2625
03:18:14,310 --> 03:18:16,520
ourselves in…in a different model.

2626
03:18:16,520 --> 03:18:20,180
And where we also needed to think about geography
because we needed to think about altitude

2627
03:18:20,180 --> 03:18:23,260
in terms of thinking about vitamin D exposure.

2628
03:18:23,260 --> 03:18:26,939
And then this eventually then led to thinking
about…to…to finding that, in fact, vitamin

2629
03:18:26,939 --> 03:18:28,930
D was protective,

2630
03:18:28,930 --> 03:18:32,989
and this has been replicated in an unrelated
study in the U.S.

2631
03:18:32,989 --> 03:18:38,810
So, multigenerational impact of individual-level
diet and feeding practices.

2632
03:18:38,810 --> 03:18:43,490
And from our perspective, retinoblastoma has
served as a model for us to understand the

2633
03:18:43,490 --> 03:18:47,840
effects of early prenatal exposure and one-carbon
donors.

2634
03:18:47,840 --> 03:18:53,240
That’s led to us thinking about the examination
of an impact of a food additive on diet and

2635
03:18:53,240 --> 03:18:56,750
early childhood exposure and neural development.

2636
03:18:56,750 --> 03:19:01,550
And then it’s actually led in our work that
I haven’t talked about here, examination

2637
03:19:01,550 --> 03:19:06,810
of these exposures in the context of migration,
resettlement, living conditions, and food

2638
03:19:06,810 --> 03:19:11,550
and nutrition security, which turned out to
be quite related to these exposures and the

2639
03:19:11,550 --> 03:19:12,550
children’s diet.

2640
03:19:12,550 --> 03:19:18,470
So, I’d like to just acknowledge my many
collaborators in Mexico and…and in some

2641
03:19:18,470 --> 03:19:26,470
institutions in the U.S., especially at Tufts,
and some images from our studies and the scientific

2642
03:19:26,470 --> 03:19:27,470
challenges.

2643
03:19:27,470 --> 03:19:33,430
For me, the biggest scientific challenge is…is
measurements, but measurements that can approximate

2644
03:19:33,430 --> 03:19:38,080
the relevant time of exposure and the relevant
tissue.

2645
03:19:38,080 --> 03:19:43,420
And then, most promising scientific or technological
opportunities for me, and the short term,

2646
03:19:43,420 --> 03:19:47,970
it’s the widespread availability of mobile
technology and thinking about how to be creative

2647
03:19:47,970 --> 03:19:50,189
with its use.

2648
03:19:50,189 --> 03:19:54,090
And then long term, the ability to examine
indirect and functional markers of exposure

2649
03:19:54,090 --> 03:19:55,930
through -omics, including examination of impact
on the microbiota.

2650
03:19:55,930 --> 03:19:59,720
Thank you very much.

2651
03:19:59,720 --> 03:20:08,260
DR. ANDREW BREMER: Okay, next up is Dr. Cowardin.

2652
03:20:08,260 --> 03:20:16,040
DR. CARRIE COWARDIN: Good afternoon, everyone, and
thank you to the organizers for inviting me

2653
03:20:16,040 --> 03:20:17,069
to speak today.

2654
03:20:17,069 --> 03:20:20,760
I’ve learned so much already, and I’m
really looking forward to the rest of the

2655
03:20:20,760 --> 03:20:21,760
program, as well.

2656
03:20:21,760 --> 03:20:25,710
So, today I’ll be talking about the gut
microbiome, particularly the maternal and

2657
03:20:25,710 --> 03:20:28,699
early-life microbiome and how that influences
child growth and immunity.

2658
03:20:28,699 --> 03:20:33,720
So, I’d like to begin at the beginning and
start talking about the maternal microbiome

2659
03:20:33,720 --> 03:20:36,369
and…and birth outcomes and what’s known.

2660
03:20:36,369 --> 03:20:41,739
And then a little bit later in the talk, I’ll
get into what we’ve done to…to study this.

2661
03:20:41,739 --> 03:20:47,050
And so I think the maternal microbiome can
influence pregnancy and…and fetal development

2662
03:20:47,050 --> 03:20:48,550
in a variety of ways.

2663
03:20:48,550 --> 03:20:53,340
I think one of the primary is…is through
influencing metabolism and nutrition.

2664
03:20:53,340 --> 03:20:59,620
And so, the microbiome has sort of been increasingly
recognized as an endocrine organ.

2665
03:20:59,620 --> 03:21:03,399
And it influences adipose tissue composition,
nutrient processing and absorption, and lipid

2666
03:21:03,399 --> 03:21:07,899
and glucose metabolism and, through those
functions, can influence obesity and weight

2667
03:21:07,899 --> 03:21:10,470
gain, as well as undernutrition,

2668
03:21:10,470 --> 03:21:13,029
and…and, of course, gestational diabetes.

2669
03:21:13,029 --> 03:21:18,280
I think the other really important function
of the microbiota is influencing immunity.

2670
03:21:18,280 --> 03:21:23,370
And so, we know that the gut microbiome can
regulate maternal immunity and inflammation.

2671
03:21:23,370 --> 03:21:26,739
And this is driven by sort of microbial structural
components.

2672
03:21:26,739 --> 03:21:30,810
So, those can include, like, pathogen-associated
molecular patterns, as well as metabolites,

2673
03:21:30,810 --> 03:21:36,830
and then, actual microbial function, such
as adherence to the intestinal epithelium.

2674
03:21:36,830 --> 03:21:41,050
And through this role can influence preterm
birth, as well as cognitive development.

2675
03:21:41,050 --> 03:21:46,170
And then finally, I think there’s some really
exciting emerging evidence about placental

2676
03:21:46,170 --> 03:21:52,130
development and how microbial metabolites
that originate from the gut actually can regulate

2677
03:21:52,130 --> 03:21:55,560
placental development in using murine models.

2678
03:21:55,560 --> 03:22:01,520
So, particularly, vascularization and nutrient
transport seem to be impacted by the presence

2679
03:22:01,520 --> 03:22:02,729
of the microbiome.

2680
03:22:02,729 --> 03:22:04,939
And, of course, this can have major impacts
on fetal growth.

2681
03:22:04,939 --> 03:22:09,890
So, I think one of the challenges—and I’ll
talk more about other challenges later—but

2682
03:22:09,890 --> 03:22:13,989
one of the challenges is defining what normal
is because there’s so much variation in

2683
03:22:13,989 --> 03:22:16,460
individuals with…with microbiota composition.

2684
03:22:16,460 --> 03:22:22,190
And so, there’s been some studies looking
at the composition of the microbiome throughout

2685
03:22:22,190 --> 03:22:23,190
pregnancy.

2686
03:22:23,190 --> 03:22:28,420
And a few studies have noted increased diversity
in the third trimester, and interestingly,

2687
03:22:28,420 --> 03:22:34,760
if you, if you take microbiota from the third
trimester and actually transfer it to gnotobiotic

2688
03:22:34,760 --> 03:22:41,490
mice, this third trimester microbiota can
confer…increase inflammatory cytokines,

2689
03:22:41,490 --> 03:22:46,370
adiposity, and insulin insensitivity in mice
when you compare it to microbiota from someone

2690
03:22:46,370 --> 03:22:48,470
in the first trimester.

2691
03:22:48,470 --> 03:22:55,609
And so, this may be a…a process of normal
microbiota changing during pregnancy, but,

2692
03:22:55,609 --> 03:22:59,580
of course, this can also be associated with
disease states, as well.

2693
03:22:59,580 --> 03:23:03,000
And I think there’s a couple of different
likely mechanisms underlying this.

2694
03:23:03,000 --> 03:23:04,990
And so, one is hormonal changes.

2695
03:23:04,990 --> 03:23:10,500
So, you know, it’s been shown that progesterone
and estrogen can alter microbiota composition,

2696
03:23:10,500 --> 03:23:13,399
as well as microbiota activity.

2697
03:23:13,399 --> 03:23:16,060
And then, maternal diet is, of course, a huge
factor.

2698
03:23:16,060 --> 03:23:21,920
So, high-fat diet, fiber deprivation, and
undernutrition can all have adverse effects

2699
03:23:21,920 --> 03:23:25,760
on the maternal microbiome.

2700
03:23:25,760 --> 03:23:30,910
And there’s certainly been demonstrated
links between the maternal microbiome and

2701
03:23:30,910 --> 03:23:31,910
birth outcomes.

2702
03:23:31,910 --> 03:23:33,760
And so, this has been shown in high-income
countries.

2703
03:23:33,760 --> 03:23:41,770
So, links between microbiome alpha diversity
and newborn head circumference and preterm

2704
03:23:41,770 --> 03:23:42,770
birth.

2705
03:23:42,770 --> 03:23:44,710
But it’s also been shown in low- and middle-income
countries.

2706
03:23:44,710 --> 03:23:50,110
And I think we’re really starting to develop
an appreciation for the potential role of

2707
03:23:50,110 --> 03:23:51,729
the…the maternal microbiome in undernutrition.

2708
03:23:51,729 --> 03:23:58,830
So, there was a recent study out of Zimbabwe
that actually implicated specific taxa in

2709
03:23:58,830 --> 03:24:03,740
regulating birth weight or…or showed associations
between these taxa and birth weight.

2710
03:24:03,740 --> 03:24:07,949
And then one of the nice things they did was
they really got into microbial function.

2711
03:24:07,949 --> 03:24:13,670
And so, they were looking at starch metabolism
and vitamin B metabolism being associated

2712
03:24:13,670 --> 03:24:18,130
with weight for age G scores and length for
G scores at 1 month.

2713
03:24:18,130 --> 03:24:22,529
And then they found, interestingly, that markers
of biofilm formation in the microbiota were

2714
03:24:22,529 --> 03:24:24,790
associated with reduced birth weight.

2715
03:24:24,790 --> 03:24:30,310
So, I think this is one of the opportunities
to getting past microbial taxonomy and really

2716
03:24:30,310 --> 03:24:32,391
looking at functions.

2717
03:24:32,391 --> 03:24:38,380
And, of course, the maternal microbiome is
also critically linked to immune outcomes,

2718
03:24:38,380 --> 03:24:41,649
as many of the speakers have already discussed
today.

2719
03:24:41,649 --> 03:24:47,030
But there’s links between maternal antibiotic
exposure, dietary exposures to things like

2720
03:24:47,030 --> 03:24:54,840
asthma, atopic dermatitis, food allergy, inflammatory
bowel disease, and…and childhood obesity.

2721
03:24:54,840 --> 03:24:58,310
And then, we’ve seen from…from preclinical
models that maternal…maternal antibiotic

2722
03:24:58,310 --> 03:25:05,439
exposure can also alter offspring microbiota
even in animals that haven’t been themselves

2723
03:25:05,439 --> 03:25:07,910
exposed to those…those antibiotics.

2724
03:25:07,910 --> 03:25:14,560
And so, just a few of the…the changes that
have been shown are increased splenic T and

2725
03:25:14,560 --> 03:25:19,970
B cells linked with asthma susceptibility
after maternal treatment with vancomycin;

2726
03:25:19,970 --> 03:25:24,640
cefoperazone led to decreased regulatory T
cells and increased colitis susceptibility

2727
03:25:24,640 --> 03:25:25,640
in offspring.

2728
03:25:25,640 --> 03:25:32,140
And then, of course, maternal antibiotic exposure
is known to predispose to diet-induced obesity.

2729
03:25:32,140 --> 03:25:38,210
So, beyond direct maternal microbiome effects
in utero, I think it’s also important to

2730
03:25:38,210 --> 03:25:40,550
talk about intergenerational microbial transmission.

2731
03:25:40,550 --> 03:25:46,410
And one of the reasons is because infants
really inherit a lot of their microbiome from

2732
03:25:46,410 --> 03:25:47,449
their mothers.

2733
03:25:47,449 --> 03:25:55,489
So, we know that vaginal- and skin-derived
strains transiently colonized neonates.

2734
03:25:55,489 --> 03:26:01,550
And then, over time, actually, maternal gut–derived
strains really tend to dominate the infant

2735
03:26:01,550 --> 03:26:02,550
gut.

2736
03:26:02,550 --> 03:26:06,770
And so, these strains actually are thought
to be more persistent in the infant gut when

2737
03:26:06,770 --> 03:26:10,050
they’re maternally derived than if they’re
picked up from environmental sources.

2738
03:26:10,050 --> 03:26:15,149
So, you can see a large percentage of that
infant microbiome is actually derived from

2739
03:26:15,149 --> 03:26:16,149
mom.

2740
03:26:16,149 --> 03:26:21,500
And I think there’s also some really interesting
data about microbes within breast milk.

2741
03:26:21,500 --> 03:26:27,020
And so, I think this is an active area of
investigation that we don’t know a ton about

2742
03:26:27,020 --> 03:26:32,399
right now: how maternal microbes get into
breast milk, and then how much those actually

2743
03:26:32,399 --> 03:26:37,030
shape the infant microbiome, but there’s
a lot of excitement in this area.

2744
03:26:37,030 --> 03:26:42,569
We also know that beyond sort of direct colonization
with breast milk microbes, human milk oligosaccharides

2745
03:26:42,569 --> 03:26:46,569
can act as sort of prebiotics for the microbiota.

2746
03:26:46,569 --> 03:26:51,000
And so, they’re actually—I’ll talk more
about them in a minute—but they’re really

2747
03:26:51,000 --> 03:26:54,780
not absorbed by the infant directly, so they
actually act on the microbiota itself.

2748
03:26:54,780 --> 03:27:02,680
There’s some interesting and…and consequential
studies on cesarean section.

2749
03:27:02,680 --> 03:27:06,149
And so, infants delivered by C-section have
altered microbial communities relative to

2750
03:27:06,149 --> 03:27:09,340
vaginally delivered infants.

2751
03:27:09,340 --> 03:27:15,319
And so, one of the effects of…of the C-section
delivery is sort of reduced similarity to

2752
03:27:15,319 --> 03:27:16,319
the maternal microbiome.

2753
03:27:16,319 --> 03:27:22,390
And those differences gradually decrease over
the first few years of life, but they…they…they

2754
03:27:22,390 --> 03:27:27,850
do have sort of long-term consequences for
some immune-linked pathologies.

2755
03:27:27,850 --> 03:27:33,399
So, there have been multiple attempts to seed
infants with maternal microbiota using vaginal

2756
03:27:33,399 --> 03:27:34,399
microbes.

2757
03:27:34,399 --> 03:27:39,880
And so, there’s mixed results in this, but
I think there’s some recent studies showing

2758
03:27:39,880 --> 03:27:46,100
that—or a…a very recent study showing—that
they actually had some success with restoring

2759
03:27:46,100 --> 03:27:52,710
sort of a…a… a vaginally-derived infant
microbiota in a c-section infant using this

2760
03:27:52,710 --> 03:27:54,530
vaginal microbiota transplantation.

2761
03:27:54,530 --> 03:27:57,300
And it actually was associated with improved
cognitive outcomes.

2762
03:27:57,300 --> 03:28:02,210
So, I think there’s some pretty interesting
work going on in this area.

2763
03:28:02,210 --> 03:28:08,899
We know that at least one study has shown
that you can actually transplant fecal microbiota

2764
03:28:08,899 --> 03:28:16,170
in infants, which is…it’s a little bit
scary, but it’s quite interesting showing

2765
03:28:16,170 --> 03:28:21,350
that the fecal microbiota transplant, there’s
extensive screening that they did on the samples,

2766
03:28:21,350 --> 03:28:25,449
but they were actually able to restore a more
normal microbiota.

2767
03:28:25,449 --> 03:28:29,800
Although it’s not clear whether…how this
is linked with…with outcomes.

2768
03:28:29,800 --> 03:28:36,240
So, I think there’s definitely some active
investigation going on in this area.

2769
03:28:36,240 --> 03:28:40,550
And then, just to talk a little bit about
the sort of microbiota development in the

2770
03:28:40,550 --> 03:28:46,970
first few years of life, we know that neonates
are born with minimal or no microbiota.

2771
03:28:46,970 --> 03:28:54,090
This is also an area of debate, but regardless
of which…which side you fall on, I think

2772
03:28:54,090 --> 03:28:58,760
we can appreciate that the biomass at birth
is very low in terms of microbiota.

2773
03:28:58,760 --> 03:29:08,670
So, most microbes are acquired from mom, from
family, from the environment during the first

2774
03:29:08,670 --> 03:29:12,430
days of life, and then this maturation proceeds
over the first 3 years.

2775
03:29:12,430 --> 03:29:19,700
So, if we look at just the number of OTUs
or…or number of different taxonomic units,

2776
03:29:19,700 --> 03:29:23,670
we can see within the microbiota over the
first 3 years, there’s a sort of progressive

2777
03:29:23,670 --> 03:29:25,729
increase over time.

2778
03:29:25,729 --> 03:29:30,630
And beyond this, or I guess more specifically
than this sort of first 3-year window, we

2779
03:29:30,630 --> 03:29:33,399
also know that there’s a major shift during
the weaning period.

2780
03:29:33,399 --> 03:29:41,699
So, the transition from breastfeeding to introduction
of solid foods results in reduction in taxa

2781
03:29:41,699 --> 03:29:44,449
that are primary consumers of milk sugars.

2782
03:29:44,449 --> 03:29:50,410
Those are things like Bifidobacteria, and
increases in those that are consuming plant

2783
03:29:50,410 --> 03:29:51,410
polysaccharides.

2784
03:29:51,410 --> 03:29:55,520
So things like Bacteroides are sort of the
classic example.

2785
03:29:55,520 --> 03:30:00,590
And so, you can see that over time in this…in
this heat map that the first 6–12 months

2786
03:30:00,590 --> 03:30:04,140
of life, there’s a lot of Bifidobacteria
that are present.

2787
03:30:04,140 --> 03:30:10,210
And then in this transition when breastfeeding
stops and complimentary foods are introduced,

2788
03:30:10,210 --> 03:30:16,000
you get an expansion of…of a more diverse
microbiota that more closely resembles an

2789
03:30:16,000 --> 03:30:17,330
adult community.

2790
03:30:17,330 --> 03:30:22,300
So, what are some of the postnatal factors
that influence microbiota assembly?

2791
03:30:22,300 --> 03:30:27,670
I think breastfeeding is a huge one, obviously,
as…as you can see, some…from that transition

2792
03:30:27,670 --> 03:30:29,949
from milk consumers to…to polysaccharide
consumers.

2793
03:30:29,949 --> 03:30:33,830
There are tons of benefits to breastfeeding.

2794
03:30:33,830 --> 03:30:36,670
We know there’s lots of bioactive components
of breast milk.

2795
03:30:36,670 --> 03:30:43,610
So, in addition to those HMOs, they’re antimicrobial
peptides, hormones, immunoglobulin cytokine

2796
03:30:43,610 --> 03:30:47,470
cells themselves, microbes themselves, cells
with the mediators.

2797
03:30:47,470 --> 03:30:53,550
And so, all of those things can potentially
have an effect on the intestinal microbiota.

2798
03:30:53,550 --> 03:30:58,939
And as I said, these…these human milk oligosaccharides,
they’re really interesting example of nature’s

2799
03:30:58,939 --> 03:30:59,949
prebiotic.

2800
03:30:59,949 --> 03:31:05,640
So, they’re not digested in the infant gut,
and they’re minimally absorbed by the host.

2801
03:31:05,640 --> 03:31:10,239
They actually make it to a large intestine,
where they’re fermented by the…the microbiota.

2802
03:31:10,239 --> 03:31:15,370
And there’s all kinds of…of benefits to
growth and immunity that have been demonstrated

2803
03:31:15,370 --> 03:31:17,180
for HMOs.

2804
03:31:17,180 --> 03:31:21,979
I think one area that is less well understood
is what really dictates the composition…the

2805
03:31:21,979 --> 03:31:24,330
HMO composition in breast milk.

2806
03:31:24,330 --> 03:31:29,430
So, we know there’s certain things that
are…play a…a really big role, like the

2807
03:31:29,430 --> 03:31:35,330
time of…the stage of lactation, there’s
very…a lot of variations between individuals,

2808
03:31:35,330 --> 03:31:38,590
but I think there’s some, like, interesting
data coming out showing that there could be

2809
03:31:38,590 --> 03:31:41,760
links with nutrition, other environmental
exposures.

2810
03:31:41,760 --> 03:31:44,960
And I think this is an area that we don’t
understand very well yet, but there’s a

2811
03:31:44,960 --> 03:31:46,810
lot of opportunity here.

2812
03:31:46,810 --> 03:31:52,600
And so, one of the things that got me into…into…interested
in this is, this is a study from mothers in

2813
03:31:52,600 --> 03:31:59,070
Malawi actually showing that mothers of children
with healthy growth had higher levels of sialylated

2814
03:31:59,070 --> 03:32:02,420
oligosaccharides in their breast milk than
mothers of children with…with growth stunting.

2815
03:32:02,420 --> 03:32:03,601
So, we don’t know why that is.

2816
03:32:03,601 --> 03:32:08,550
It could be many different things, but the
fact that there was this difference identified,

2817
03:32:08,550 --> 03:32:10,580
I think suggests that there’s more going
on here.

2818
03:32:10,580 --> 03:32:17,130
So, in addition to breast milk, additional
factors that influenced microbiota assembly

2819
03:32:17,130 --> 03:32:19,939
in early life include diet after weaning.

2820
03:32:19,939 --> 03:32:21,890
So, that’s an obvious one.

2821
03:32:21,890 --> 03:32:27,420
So, we know that children who have under…experience
undernutrition in early life actually have

2822
03:32:27,420 --> 03:32:31,660
microbiota that look immature for their…for
their age.

2823
03:32:31,660 --> 03:32:37,310
So, if you take a healthy child look at their
microbiota, let’s say 6 months of age, and

2824
03:32:37,310 --> 03:32:41,080
you take a child with undernutrition, a lot
of times their microbiota will look more similar

2825
03:32:41,080 --> 03:32:43,729
to that of a younger child.

2826
03:32:43,729 --> 03:32:49,780
And then we can see from a couple of different
studies that treatment with therapeutic foods

2827
03:32:49,780 --> 03:32:54,069
does improve this microbiota composition,
but it’s not always very durable.

2828
03:32:54,069 --> 03:33:00,729
And so, if you look in the figure here, you
can see the microbiota shifting along with…with

2829
03:33:00,729 --> 03:33:06,130
therapeutic food treatment, and it’s becoming
closer to that healthy configuration, but

2830
03:33:06,130 --> 03:33:08,780
it doesn’t quite make it there.

2831
03:33:08,780 --> 03:33:16,630
So, of course, early-life diet has an effect…it
influences obesity, as well.

2832
03:33:16,630 --> 03:33:21,359
We know there’s differences in microbiota
composition in…during obesity.

2833
03:33:21,359 --> 03:33:25,710
And studies have shown that if you take these
microbiota and you transplant it to gnotobiotic

2834
03:33:25,710 --> 03:33:27,580
mice, you can actually see them gain more
weight.

2835
03:33:27,580 --> 03:33:32,250
We do think there’s a sort of causal role
for the microbiome in this…in this area,

2836
03:33:32,250 --> 03:33:33,250
as well.

2837
03:33:33,250 --> 03:33:39,510
And then, as I mentioned, antibiotics either
a maternal antibiotic treatment or antibiotics

2838
03:33:39,510 --> 03:33:43,810
in early life can also predispose to diet-induced
obesity.

2839
03:33:43,810 --> 03:33:49,310
So, I think I…I just want to emphasize what
are the consequences of these differences

2840
03:33:49,310 --> 03:33:55,140
in microbial colonization and why does it
matter that you get a…appropriate microbiota

2841
03:33:55,140 --> 03:33:57,479
maturation and early life.

2842
03:33:57,479 --> 03:34:00,861
And I think, you know, for me, it always comes
back to the immune system and development

2843
03:34:00,861 --> 03:34:02,590
of the immune system.

2844
03:34:02,590 --> 03:34:07,280
And so, from preclinical models, we can see
that exposure to the microbiota during this

2845
03:34:07,280 --> 03:34:10,290
weaning phase is really critical for immune
development.

2846
03:34:10,290 --> 03:34:15,550
So, if you don’t have a microbiota during
this time period, you really miss out on a

2847
03:34:15,550 --> 03:34:17,430
lot of immune regulatory cell development.

2848
03:34:17,430 --> 03:34:21,939
And so, you’re actually much more susceptible
to pathological inflammation later in life.

2849
03:34:21,939 --> 03:34:24,860
So, what are things that are linked to pathological
inflammation?

2850
03:34:24,860 --> 03:34:27,750
Everything, most things that we care about.

2851
03:34:27,750 --> 03:34:31,979
So, infectious disease, metabolic disease,
cognitive development, even obesity, cardiovascular

2852
03:34:31,979 --> 03:34:35,750
disease, all kinds of different…different
diseases have this sort of immune component.

2853
03:34:35,750 --> 03:34:42,930
So, I think this is a really important aspect
of…of the functions of the microbiota in

2854
03:34:42,930 --> 03:34:44,470
early life.

2855
03:34:44,470 --> 03:34:48,660
And there’s a lot of interest in understanding
how this sort of weaning reaction that occurs

2856
03:34:48,660 --> 03:34:51,399
in mice is…is translated to…to humans.

2857
03:34:51,399 --> 03:34:56,470
So, I want to talk very briefly about some
of the work that we’re doing investigating

2858
03:34:56,470 --> 03:34:58,540
microbial mechanisms in childhood undernutrition.

2859
03:34:58,540 --> 03:35:05,960
So, to…to do that, I’ll give you a brief
overview of what we know about undernutrition.

2860
03:35:05,960 --> 03:35:10,380
And I think the biggest thing is just, it’s
not about insufficient diet alone; that’s

2861
03:35:10,380 --> 03:35:17,830
a component of it, but it’s also…reflects
these diverse environmental challenges.

2862
03:35:17,830 --> 03:35:22,850
And so, that’s pathogen exposure, that’s
intestinal inflammation, reduced absorption,

2863
03:35:22,850 --> 03:35:28,561
and intestinal inflammation that ties all
of these things together.

2864
03:35:28,561 --> 03:35:34,819
So, we know that environmental enteric dysfunction,
or EED, is really strongly linked to undernutrition,

2865
03:35:34,819 --> 03:35:37,021
and particularly linear growth stunting.

2866
03:35:37,021 --> 03:35:41,400
So, EED is a…is a subclinical syndrome of…of
its enteropathy.

2867
03:35:41,400 --> 03:35:51,090
And that’s…basically, we think that EED
is causing this failure of nutrient absorption,

2868
03:35:51,090 --> 03:35:53,149
and it’s driven by intestinal inflammation.

2869
03:35:53,149 --> 03:35:58,100
So, it’s been sort of challenging to diagnose
because the gold standard is endoscopy and…and

2870
03:35:58,100 --> 03:35:59,100
biopsy.

2871
03:35:59,100 --> 03:36:03,479
So, it’s a little bit hard to study, but
we know it’s really prevalent in areas where

2872
03:36:03,479 --> 03:36:04,720
undernutrition is common.

2873
03:36:04,720 --> 03:36:09,479
And it may be one reason why therapeutic foods
are not as effective as they should be, is

2874
03:36:09,479 --> 03:36:13,460
because these kids just can’t absorb the
nutrients that we’re giving them.

2875
03:36:13,460 --> 03:36:18,340
And I think there’s also a really important
component of maternal undernutrition, particularly

2876
03:36:18,340 --> 03:36:23,370
in linear growth stunting in children, which
is really strongly linked to later life outcomes

2877
03:36:23,370 --> 03:36:24,620
that we care about.

2878
03:36:24,620 --> 03:36:32,050
So, things like overall mortality, but also
cognitive development, susceptibility to infectious

2879
03:36:32,050 --> 03:36:39,760
diseases, all kinds of negative outcomes of
being stunted in early life can be tied back

2880
03:36:39,760 --> 03:36:40,820
to maternal undernutrition.

2881
03:36:40,820 --> 03:36:45,460
So, we know that mothers who are…experienced
growth stunting in early life are more likely

2882
03:36:45,460 --> 03:36:48,050
to give birth to stunted children themselves.

2883
03:36:48,050 --> 03:36:52,609
And so, we are really focused on understanding
how intergenerational transmission of the

2884
03:36:52,609 --> 03:36:59,050
gut microbiota can tie into this and contribute
to this intergenerational transmission of

2885
03:36:59,050 --> 03:37:00,050
growth stunting.

2886
03:37:00,050 --> 03:37:06,700
And I think this has partly been inspired
by observations in other enteropathies in

2887
03:37:06,700 --> 03:37:07,790
high-income countries.

2888
03:37:07,790 --> 03:37:10,899
So, things like celiac disease and inflammatory
bowel disease.

2889
03:37:10,899 --> 03:37:15,550
We know that in…gut inflammation in those
disorders is linked with negative outcomes,

2890
03:37:15,550 --> 03:37:16,550
as well.

2891
03:37:16,550 --> 03:37:24,199
So, things like preterm birth and pregnancy
loss, as well as, size and fetal development.

2892
03:37:24,199 --> 03:37:29,170
So, how do we go about modeling intergenerational
growth stunting?

2893
03:37:29,170 --> 03:37:33,069
We know that all of these different factors
are contributing and that they’re all sort

2894
03:37:33,069 --> 03:37:38,300
of interrelated, so it’s not just a unidirectional
interaction; all of these things are in this

2895
03:37:38,300 --> 03:37:39,560
complicated network.

2896
03:37:39,560 --> 03:37:43,390
So, the way that we’re approaching this
is using gnotobiotic mice.

2897
03:37:43,390 --> 03:37:48,750
And we’re actually taking microbiota samples
from children with linear growth stunting

2898
03:37:48,750 --> 03:37:52,220
and undernutrition, and we’re using them
to colonize gnotobiotic mice.

2899
03:37:52,220 --> 03:37:55,170
We then breed these mice, and we look at their
offspring.

2900
03:37:55,170 --> 03:37:59,470
And so, we’re actually studying both, sort
of, the paternal components, as well as the

2901
03:37:59,470 --> 03:38:01,010
offspring components of undernutrition.

2902
03:38:01,010 --> 03:38:07,870
And just to give you an example for some of
the data I’m going to show, this is a…a

2903
03:38:07,870 --> 03:38:12,439
length-for-age Z scores from the four donors
that we’ve been using in our gnotobiotic

2904
03:38:12,439 --> 03:38:13,439
mouse models.

2905
03:38:13,439 --> 03:38:16,880
So, we’re using samples that are collected
from these children when they were 6 months

2906
03:38:16,880 --> 03:38:17,880
of age.

2907
03:38:17,880 --> 03:38:22,390
These are children living in Malawi, and we
have two donors that have healthy growth trajectories

2908
03:38:22,390 --> 03:38:26,319
and two donors that have moderate to severe
stunting.

2909
03:38:26,319 --> 03:38:33,210
So, the way that we do this is we actually…we
take germ-free mice, and we colonize them

2910
03:38:33,210 --> 03:38:36,979
when they’re 4 weeks old, and we maintain
them on a malnourished diet for an additional

2911
03:38:36,979 --> 03:38:37,979
4 weeks.

2912
03:38:37,979 --> 03:38:40,270
So, they’re getting this sort of period
of malnutrition in early life.

2913
03:38:40,270 --> 03:38:47,040
At…at…when they’re 8 weeks old, we transfer,
transfer them back to a nutrient-sufficient

2914
03:38:47,040 --> 03:38:51,260
diet, and then we actually breed them, look
at their offspring, and their offspring are

2915
03:38:51,260 --> 03:38:52,910
weaned onto a malnourished diet, as well.

2916
03:38:52,910 --> 03:38:55,420
So, we’re capturing both windows.

2917
03:38:55,420 --> 03:39:02,150
We compare these to mice that are just malnourished
and colonized in early life alone, so they

2918
03:39:02,150 --> 03:39:04,460
don’t have that parental exposure component.

2919
03:39:04,460 --> 03:39:11,000
So, generally, what we find when we do this
is that, in terms of…of weight, we don’t

2920
03:39:11,000 --> 03:39:16,840
see huge differences between…so, here we’re
comparing between the healthy microbiota donors

2921
03:39:16,840 --> 03:39:19,690
and the stunted microbiota donors in both
conditions.

2922
03:39:19,690 --> 03:39:25,210
We don’t see massive differences in weight
between the sort of donor health status, but

2923
03:39:25,210 --> 03:39:27,330
we do see big differences in linear growth.

2924
03:39:27,330 --> 03:39:32,650
So, when we look at tail length, which is
a surrogate for linear growth in mice, we

2925
03:39:32,650 --> 03:39:38,510
see a significant reduction, but it only occurs
when the mice are born to colonized parents.

2926
03:39:38,510 --> 03:39:40,520
It doesn’t occur when you colonize the mice
directly.

2927
03:39:40,520 --> 03:39:46,130
So, what are some of the other things that
we care about with undernutrition?

2928
03:39:46,130 --> 03:39:49,340
We’re really interested in intestinal physiology.

2929
03:39:49,340 --> 03:39:54,800
And so, in addition to linear growth reductions
that are seen, we use this intergenerational

2930
03:39:54,800 --> 03:39:55,960
model.

2931
03:39:55,960 --> 03:40:02,529
We also see reductions in intestinal…small
intestinal villus lengths, which is one of

2932
03:40:02,529 --> 03:40:04,870
those EED-associated phenotypes.

2933
03:40:04,870 --> 03:40:08,970
And then we see a reduction in the thickness
of the…of the muscularis in the intestine,

2934
03:40:08,970 --> 03:40:09,970
as well.

2935
03:40:09,970 --> 03:40:15,880
And I think one of the most interesting changes
to me is that we see a lot of immune signatures

2936
03:40:15,880 --> 03:40:19,640
in the intestine that look a lot like what
we see in humans with EED.

2937
03:40:19,640 --> 03:40:25,250
So, there’ve been some recent single-cell
studies showing what immune changes are happening

2938
03:40:25,250 --> 03:40:28,200
in the gut during EED.

2939
03:40:28,200 --> 03:40:29,540
And we see similar things here.

2940
03:40:29,540 --> 03:40:35,020
So, we see an…a big increase in intraepithelial
lymphocytes, as well as IgA+ plasma cells.

2941
03:40:35,020 --> 03:40:38,819
So, those have both been shown in humans with
EED.

2942
03:40:38,819 --> 03:40:43,380
And I’m not going to go into this a lot,
other than to say all four of our microbiota

2943
03:40:43,380 --> 03:40:45,130
donors are distinct communities.

2944
03:40:45,130 --> 03:40:48,680
So, we don’t think this is just a question
of having the same microbes.

2945
03:40:48,680 --> 03:40:54,300
We think there’s conserved microbial functions
that are being performed in these different

2946
03:40:54,300 --> 03:41:00,620
communities, in addition to probably conserved
host responses to those functions that are

2947
03:41:00,620 --> 03:41:02,500
then having these effects.

2948
03:41:02,500 --> 03:41:05,520
So, I will move on to challenges and opportunities.

2949
03:41:05,520 --> 03:41:10,489
So, I think one of the biggest scientific
challenges for this area of research is just

2950
03:41:10,489 --> 03:41:15,189
the scale and complexity of the microbiome
and…and how many different taxa are there

2951
03:41:15,189 --> 03:41:17,210
and how…and all of their different functions.

2952
03:41:17,210 --> 03:41:22,800
So, I…I think this makes moving beyond description
of the microbiota…so, like, this group is

2953
03:41:22,800 --> 03:41:27,449
different from this group, to actually understanding
what those microbes are doing really important

2954
03:41:27,449 --> 03:41:30,450
but also really challenging.

2955
03:41:30,450 --> 03:41:36,580
And to get at that, I think microbial function
is becoming much more of a focus than just

2956
03:41:36,580 --> 03:41:38,140
describing which microbes are there.

2957
03:41:38,140 --> 03:41:39,909
We really want to understand what they’re
doing.

2958
03:41:39,909 --> 03:41:43,620
There are a lot of different options for therapeutic
interventions.

2959
03:41:43,620 --> 03:41:49,550
So, there’s prebiotics that can nurture
a different microbiome community; probiotics

2960
03:41:49,550 --> 03:41:54,200
that give the microbes themselves; symbiotics,
which are combinations of the two; and then

2961
03:41:54,200 --> 03:41:57,090
postbiotics, which are things like microbial
metabolites, giving those directly.

2962
03:41:57,090 --> 03:42:01,330
There’s lots of different options here,
but we don’t really know what the best approach

2963
03:42:01,330 --> 03:42:02,610
is.

2964
03:42:02,610 --> 03:42:07,550
And then promising scientific and technological
opportunities.

2965
03:42:07,550 --> 03:42:10,810
In the short term, I think there’s been
a lot of progress in developing these models

2966
03:42:10,810 --> 03:42:14,311
that actually harness the complexity of the
microbiota—so, using gnotobiotic mice.

2967
03:42:14,311 --> 03:42:18,180
It’s really important that we can manipulate
those systems so we can actually find functional

2968
03:42:18,180 --> 03:42:19,180
differences.

2969
03:42:19,180 --> 03:42:23,050
So, having a model where we see these immune
changes gives us the opportunity to understand

2970
03:42:23,050 --> 03:42:25,340
what those immune cells are doing.

2971
03:42:25,340 --> 03:42:29,140
And then there’s, of course, the opportunity
to shape the microbiota through diet and beneficial

2972
03:42:29,140 --> 03:42:30,140
microbes.

2973
03:42:30,140 --> 03:42:36,060
Long term, I think this concept of precision
medicine is really appealing in this…in

2974
03:42:36,060 --> 03:42:37,090
this area.

2975
03:42:37,090 --> 03:42:40,380
Of course, when we’re talking about undernutrition
in low- and middle-income countries, we’re

2976
03:42:40,380 --> 03:42:41,380
really concerned about cost.

2977
03:42:41,380 --> 03:42:44,820
So, I think there’s a…there’s a sort
of trade off there that we have to be aware

2978
03:42:44,820 --> 03:42:45,899
of.

2979
03:42:45,899 --> 03:42:52,330
And so, whether it’s something like a…a
geographically specific therapy, even if it’s

2980
03:42:52,330 --> 03:42:59,520
not an individual level, if we can tailor
it to a…a broad dietary context, that could

2981
03:42:59,520 --> 03:43:00,569
be helpful.

2982
03:43:00,569 --> 03:43:04,680
And then, I want to just end by talking about
what I think is a really cool story.

2983
03:43:04,680 --> 03:43:08,270
So, this is actually the lab where I did my
postdoc.

2984
03:43:08,270 --> 03:43:11,600
They designed a microbiota-directed food intervention.

2985
03:43:11,600 --> 03:43:14,970
And I think the…the most interesting thing
about this story, to me, is that it’s based

2986
03:43:14,970 --> 03:43:20,470
on really extensive preclinical investigation
about the microbiota and undernutrition.

2987
03:43:20,470 --> 03:43:25,750
And so, they didn’t just jump in and give
different foods; they identified taxa that

2988
03:43:25,750 --> 03:43:26,949
were associated with growth.

2989
03:43:26,949 --> 03:43:31,510
They screened ingredients in gnotobiotic mice
to find ingredients that boosted those taxa.

2990
03:43:31,510 --> 03:43:35,460
They assembled those ingredients in a couple
different formulations and tested them out

2991
03:43:35,460 --> 03:43:39,470
extensively in gnotobiotic mice and pigs before
they took them into humans.

2992
03:43:39,470 --> 03:43:43,699
And they actually recently published this
paper showing that this might provide a directed

2993
03:43:43,699 --> 03:43:46,910
food that actually increases weight gain significantly
in undernourished kids.

2994
03:43:46,910 --> 03:43:49,550
I think there’s a lot of questions that
come out from this.

2995
03:43:49,550 --> 03:43:52,899
So, could these therapies be more effective
if they’re given earlier?

2996
03:43:52,899 --> 03:43:56,330
Rather than in early life, what if we started
with mothers?

2997
03:43:56,330 --> 03:44:01,060
Could they be given for longer and could they
be tailored to individual populations?

2998
03:44:01,060 --> 03:44:05,640
So, I will stop with that and just acknowledge
my lab members who’ve worked really hard

2999
03:44:05,640 --> 03:44:16,792
on this project and our collaborators and
funding from NICHD, of course.

3000
03:44:16,792 --> 03:44:23,430
DR. THADDEUS SCHUG: Okay, our next speaker is
Dr. Shelly Buffington.

3001
03:44:27,328 --> 03:44:32,420
DR. SHELLY BUFFINGTON: Hello. And thank you so much for the invitation. It’s really wonderful to be here.

3002
03:44:32,420 --> 03:44:35,470
I’ve very much enjoyed the content so far,
and I’m…I’m excited about all of the

3003
03:44:35,470 --> 03:44:36,510
talks tomorrow, as well.

3004
03:44:38,391 --> 03:44:44,260
Okay. So as we’ve already covered today, in the
U.S., roughly three in 10 children are born

3005
03:44:44,260 --> 03:44:47,710
to women with pregestational obesity.

3006
03:44:47,710 --> 03:44:51,720
And as you can see here, it’s not only a
problem in the U.S., but also in…throughout

3007
03:44:51,720 --> 03:44:55,380
the world and in certain areas of the world,
there’s even a greater increase over the

3008
03:44:55,380 --> 03:44:58,149
last, you know, 30 years percentage wise of
the population.

3009
03:44:58,149 --> 03:45:00,850
So this is really a, a global issue.

3010
03:45:00,850 --> 03:45:05,609
And you might ask yourself, why is a neuroscientist
in my interest in this problem?

3011
03:45:05,609 --> 03:45:09,949
Well, of course, the growing prevalence of
maternal diet associated obesity does present

3012
03:45:09,949 --> 03:45:10,960
a mental health challenge.

3013
03:45:10,960 --> 03:45:16,270
And that’s because maternal pregestational
obesity predisposes children to neurodevelopmental

3014
03:45:16,270 --> 03:45:19,270
disorders, including autism spectrum disorder.

3015
03:45:19,270 --> 03:45:24,710
And there’s a growing epidemiological literature
that reflects this.

3016
03:45:24,710 --> 03:45:30,420
One study here, published in Pediatrics in
2018, shows a significant increase in the

3017
03:45:30,420 --> 03:45:35,409
odds ratio for autism spectrum disorder when
you’ve got pregestational diabetes mellitus.

3018
03:45:35,409 --> 03:45:38,960
And there’s also this link, of course, to
obesity.

3019
03:45:38,960 --> 03:45:43,600
And what one…one study I really liked in
2019 was published in…in Autism Research,

3020
03:45:43,600 --> 03:45:47,010
and this is on the Boston Birth Cohort.

3021
03:45:47,010 --> 03:45:51,880
And what this study shows is, it gets even
more into…into the weeds looking at how

3022
03:45:51,880 --> 03:45:52,920
offspring sex.

3023
03:45:52,920 --> 03:45:58,479
So, what they found is that if it was a male
child that was born to a woman that was obese

3024
03:45:58,479 --> 03:46:05,380
prior to pregnancy, and mom had elevated branched
chain amino acids—serum branched chain amino

3025
03:46:05,380 --> 03:46:08,820
acids—postpartum, this led to a tenfold
increase in odds ratio.

3026
03:46:08,820 --> 03:46:13,460
So that means, like, if we’re talking about
a one in 59 children in the U.S. having autism,

3027
03:46:13,460 --> 03:46:18,300
that’s a risk of one in six kiddos born
in that particular parameter.

3028
03:46:18,300 --> 03:46:23,649
I think, importantly, what we can see is that
this increased risk for adverse neurodevelopmental

3029
03:46:23,649 --> 03:46:29,520
outcomes due to maternal overnutrition is
recapitulated in preclinical animal models.

3030
03:46:29,520 --> 03:46:34,900
And so, Elinor Sullivan up at the OHSU has done
a lot of great work in nonhuman primates,

3031
03:46:34,900 --> 03:46:38,050
which is shown in one of her recent studies using
rhesus macaque.

3032
03:46:38,050 --> 03:46:44,771
And what she showed is that when mom is fed
a Western-style diet—that’s going to be

3033
03:46:44,771 --> 03:46:51,399
about 40% kcals from fat—that does decrease
the amount of social interaction that two

3034
03:46:51,399 --> 03:46:53,700
stranger interacting juveniles will have.

3035
03:46:53,700 --> 03:46:59,510
So, they…they spend a lot less time in proximity
to each other—let me see if I can get this…there

3036
03:46:59,510 --> 03:47:04,040
we go—less time in proximity to each other.

3037
03:47:04,040 --> 03:47:05,510
They’re initiating less contact.

3038
03:47:05,510 --> 03:47:11,340
And these animals that were born to moms on
a Western-style diet, have more idiosyncratic—so,

3039
03:47:11,340 --> 03:47:15,090
abnormal—behaviors in this social interaction
paradigm.

3040
03:47:15,090 --> 03:47:18,760
So…and this is true not only in nonhuman
primates, but also in rodents.

3041
03:47:18,760 --> 03:47:22,770
So, this is some of my postdoctoral work,
where we looked at the effects of maternal

3042
03:47:22,770 --> 03:47:29,149
high-fat diet on offspring outcomes, and we
were specifically interested in these neurodevelopmental

3043
03:47:29,149 --> 03:47:30,149
autism-like phenotypes in mice.

3044
03:47:30,149 --> 03:47:36,439
And so, what we showed was that in…especially
in…in male offspring born to a high-fat

3045
03:47:36,439 --> 03:47:42,050
diet–fed mother, we’ve got a decreasing
amount of time spent in interaction, with

3046
03:47:42,050 --> 03:47:46,880
a novel interacting partner in the reciprocal
social paradigm, as well as the Crowley three-chamber

3047
03:47:46,880 --> 03:47:47,880
test.

3048
03:47:47,880 --> 03:47:51,470
And so, what you can see here is when we do
a…a familiar interaction, you know, the

3049
03:47:51,470 --> 03:47:55,670
animals are much more interested in exploring
the box than they are in interacting with

3050
03:47:55,670 --> 03:47:56,670
each other.

3051
03:47:56,670 --> 03:48:00,920
However, when there’s a stranger, a new
interaction partner, we’ve got about 2 out

3052
03:48:00,920 --> 03:48:05,730
of 10 minutes spent interacting in this paradigm
via a normal…a mouse born to a…a mom on

3053
03:48:05,730 --> 03:48:07,080
a regular diet.

3054
03:48:07,080 --> 03:48:12,340
But we lose that social novelty preference
and interest and interaction, and it really

3055
03:48:12,340 --> 03:48:16,980
reduces the overall quality of these interactions,
as you can see by a significant reduction

3056
03:48:16,980 --> 03:48:19,069
in contact duration.

3057
03:48:19,069 --> 03:48:23,029
And so we wanted to go in…and again, we’re
interested, really, in the brain and what’s

3058
03:48:23,029 --> 03:48:24,029
underlying these phenotypes.

3059
03:48:24,029 --> 03:48:28,689
What is the physiological correlate of what
we’re seeing here?

3060
03:48:28,689 --> 03:48:32,159
And so, there’s…there’s a…basically
a social reward circuit in the brain.

3061
03:48:32,159 --> 03:48:37,340
This is a mesocorticolimbic dopaminergic reward
circuit that, of course, is targeted by addictive

3062
03:48:37,340 --> 03:48:42,100
drugs, but really, it’s normally responding
to natural stimulating, including social behavior,

3063
03:48:42,100 --> 03:48:43,479
even affiliative interactions.

3064
03:48:43,479 --> 03:48:49,600
And so we’ve got—coming out of the hypothalamus
shown there, light blue with this rodent brain—we’ve

3065
03:48:49,600 --> 03:48:56,909
got oxytocinergic output on to VTA dopaminergic
neurons that then released dopamine, a salient

3066
03:48:56,909 --> 03:48:59,890
signal on the nucleus accumbens that drives
action.

3067
03:48:59,890 --> 03:49:04,689
And what we found in our maternal high-fat
diet offering shown here in the red is that

3068
03:49:04,689 --> 03:49:08,550
whereas normally after a stranger action,
you’ve got this potentiation of the dopaminergic

3069
03:49:08,550 --> 03:49:13,170
response, which you can see in the blue curve,
has the larger area under the curve.

3070
03:49:13,170 --> 03:49:15,260
You lose that in our maternal high-fat diet
mice.

3071
03:49:15,260 --> 03:49:19,280
So, these animals cannot interpret social
interaction as rewarding.

3072
03:49:19,280 --> 03:49:24,399
And so…and so, not only have we found this,
others have been looking into the underlying

3073
03:49:24,399 --> 03:49:28,699
mechanisms by which maternal high-fat diet
can change brain function and behavior.

3074
03:49:28,699 --> 03:49:31,370
There’s a lot of great data showing that
gut increased circulating cytokines and chemokines

3075
03:49:31,370 --> 03:49:37,380
leading to immune activation that drives activation
of microglia and all of the synaptic changes

3076
03:49:37,380 --> 03:49:43,490
and pruning that can happen there, as well
as reduced dopamine signaling similar to what

3077
03:49:43,490 --> 03:49:47,470
we found with the, the physiological correlative
to reduced social interaction.

3078
03:49:47,470 --> 03:49:50,010
And all of these, none of them are mutually
exclusive, right?

3079
03:49:50,010 --> 03:49:54,159
But so many of them tie into this idea of
the dysbiosis, the gut microbiome could be

3080
03:49:54,159 --> 03:49:58,430
really driving these deficits in brain function
and behavior.

3081
03:49:58,430 --> 03:50:03,620
And so, we see this not only in animal models,
but really parallel data coming out in the

3082
03:50:03,620 --> 03:50:04,620
human literature.

3083
03:50:04,620 --> 03:50:09,359
So, when we’ve got a maternal high-fat diet
drives the altered firmicutes, the Bacteroidetes,

3084
03:50:09,359 --> 03:50:12,800
these are the two most common phyla of gut
microbes to each other.

3085
03:50:12,800 --> 03:50:17,109
And you see that both in mice and in humans.

3086
03:50:17,109 --> 03:50:22,109
We see decrease in short-chain fatty acids,
which are metabolites that are exclusively

3087
03:50:22,109 --> 03:50:31,149
derived from microbes, microbial...
creation of, or short chain fatty acids—excuse

3088
03:50:31,149 --> 03:50:32,149
me.

3089
03:50:32,149 --> 03:50:37,430
And so, then we’ve also got altered gut
microbiome, not only in mom, but also in…in

3090
03:50:37,430 --> 03:50:38,430
offspring.

3091
03:50:38,430 --> 03:50:43,130
So, in the lab, we primarily focus on neurodevelopment,
neurodevelopmental disorders, and one of these

3092
03:50:43,130 --> 03:50:44,130
is autism.

3093
03:50:44,130 --> 03:50:45,739
We know that it’s highly heritable.

3094
03:50:45,739 --> 03:50:52,380
However, even as the genes that we know, including
these synaptic proteins, chromatin remodelers

3095
03:50:52,380 --> 03:50:54,609
that do predispose individuals to autism.

3096
03:50:54,609 --> 03:50:57,890
Those that we know have account for less than
7% of autism cases.

3097
03:50:57,890 --> 03:51:00,700
There’s this huge unaccounted for liability.

3098
03:51:00,700 --> 03:51:05,510
And this is where our two high…hit hypothesis
is coming in, that these environmental factors

3099
03:51:05,510 --> 03:51:11,220
could be a tipping point towards phenotype
manifestation in genetically predisposed individuals

3100
03:51:11,220 --> 03:51:12,220
and autism.

3101
03:51:12,220 --> 03:51:16,359
And so, what we see in the lab is maternal
nutrition, as well as maternal infection with

3102
03:51:16,359 --> 03:51:17,409
live infectious agents.

3103
03:51:17,409 --> 03:51:19,790
And we’re also getting into maternal toxin
exposure.

3104
03:51:19,790 --> 03:51:24,189
But what I’ll tell you about today, and
obviously the purpose of this meeting is for

3105
03:51:24,189 --> 03:51:25,360
maternal nutrition effects.

3106
03:51:25,360 --> 03:51:30,270
And so, previously in the study that we published
in 2016, we found…and I…I briefly went

3107
03:51:30,270 --> 03:51:31,270
into it.

3108
03:51:31,270 --> 03:51:32,300
We’ve got maternal high-fat diet.

3109
03:51:32,300 --> 03:51:36,029
It leads the dysbiosis of the gut microbiome
of the offspring.

3110
03:51:36,029 --> 03:51:42,250
As Carrie was covering, we’ve got this vertical
transmission from mom to baby of a dysbiotic

3111
03:51:42,250 --> 03:51:46,029
gut microbiome driven by high-fat diet maternal
intake.

3112
03:51:46,029 --> 03:51:51,640
And now we’ve got a…oh, so…and we have
brain…brain and behavioral dysfunction.

3113
03:51:51,640 --> 03:51:55,720
However, what we found was that there’s
this ninefold loss of a, a probiotic species

3114
03:51:55,720 --> 03:52:00,761
called L. reuteri in the gut of these maternal
high-fat diet males that, once restored, we

3115
03:52:00,761 --> 03:52:03,130
could restore normal brain function and behavior.

3116
03:52:03,130 --> 03:52:07,699
So, we had…you know, whereas we had a…a
loss of normal social preference, now we can

3117
03:52:07,699 --> 03:52:11,080
restore that with lack of Limosilactobacillus
reuteri, but it’s not just any Lactobacillus

3118
03:52:11,080 --> 03:52:14,350
because L. johnsonii didn’t do the trick,
right?

3119
03:52:14,350 --> 03:52:20,210
And so, we figured out mechanistically that
L. reuteri is working by driving the oxytocinergic

3120
03:52:20,210 --> 03:52:24,890
system, and actually, we can not only rescue
the behavior, but the underlying physiological

3121
03:52:24,890 --> 03:52:25,890
correlates.

3122
03:52:25,890 --> 03:52:32,239
You see this elevated VTA dopaminergic plasticity
following social interaction with a stranger.

3123
03:52:32,239 --> 03:52:34,070
And this is, you know, 24 hours later.

3124
03:52:34,070 --> 03:52:36,060
So, it’s…this is a really strong hit.

3125
03:52:36,060 --> 03:52:42,960
It’s on the level of what you see with addictive
dose of cocaine in these VTA dopaminergic

3126
03:52:42,960 --> 03:52:44,120
neurons.

3127
03:52:44,120 --> 03:52:47,239
In subsequent studies, we showed that L. reuteri
restoration, it’s not…it’s not a one

3128
03:52:47,239 --> 03:52:52,210
trick pony; it doesn’t only work in the…in
the context of the maternal high-fat diet.

3129
03:52:52,210 --> 03:52:56,750
It can actually rescue social deficits in
the Shank3B genetic mouse model for autism,

3130
03:52:56,750 --> 03:53:00,729
as well as the idiopathic model called the
BTBR mice.

3131
03:53:00,729 --> 03:53:04,710
And subsequently, we also found that this
is true in the Cntnap2 mouse model for, for

3132
03:53:04,710 --> 03:53:10,109
autism, where we found that there’s a combination
of microbial and host genetics that drives

3133
03:53:10,109 --> 03:53:14,390
the overall phenotypic profile of the Cntnap2
knockout.

3134
03:53:14,390 --> 03:53:20,420
So, in a recent study out of my lab, we were
really…came back to the question of whether

3135
03:53:20,420 --> 03:53:25,659
it was the…the state of the juvenile gut
microbiome that was driving these behavioral

3136
03:53:25,659 --> 03:53:30,899
deficits, or really because these neurodevelopmental
disorders, they’re really founded in utero.

3137
03:53:30,899 --> 03:53:32,050
Was it moms?

3138
03:53:32,050 --> 03:53:36,040
Was it dysbiosis of mom’s gut microbiome
that was creating an adverse entry uterine

3139
03:53:36,040 --> 03:53:41,140
environment that basically set these F1 offspring
off on the adverse neurodevelopmental trajectory?

3140
03:53:41,140 --> 03:53:45,710
So, this work that was performed by Claudia
Di Gesù, a graduate student in lab who…who

3141
03:53:45,710 --> 03:53:50,689
defended last summer, and a current postdoc,
Lisa Matz, in the lab.

3142
03:53:50,689 --> 03:53:55,040
So again, we wanted to really focus on: How
is this diet changing the maternal gut microbiome

3143
03:53:55,040 --> 03:53:59,820
and how is that altering fetal neuro…fetal
neurodevelopment?

3144
03:53:59,820 --> 03:54:03,880
And the idea for the study came from some
really strong work out of the Elaine Hsaio’s

3145
03:54:03,880 --> 03:54:08,779
lab, showing that there’s differential gene
expression in the embryonic brain of animals

3146
03:54:08,779 --> 03:54:13,710
that are…whose moms are exposed to antibiotics
or in gnotobiotic mice.

3147
03:54:13,710 --> 03:54:17,560
And what she pointed out here is this loss
of expression of netrin-G1.

3148
03:54:17,560 --> 03:54:22,149
This is an…an important gene for axon pathfinding
in the brain.

3149
03:54:22,149 --> 03:54:25,680
And what…the way you see this manifested
here is this is an embryonic brain immunostain 

3150
03:54:25,680 --> 03:54:30,200
for the internal capsule, which is a
bundle of axons that’s important for…it’s

3151
03:54:30,200 --> 03:54:33,880
[inaudible] cortical projection important
for normal sensory processing.

3152
03:54:33,880 --> 03:54:38,770
It’s really thinned out in these animals
that were born to an antibiotic-treated dam.

3153
03:54:38,770 --> 03:54:44,180
Now, this has repercussions in juvenile behaviors,
gut-altered sensory behaviors.

3154
03:54:44,180 --> 03:54:50,420
Now, when Elaine and her group reintroduced
Clostridium [inaudible] spore forming gut

3155
03:54:50,420 --> 03:54:54,569
microbiota that could rescue the thickness
of this internal capsule, and it also led

3156
03:54:54,569 --> 03:54:56,750
to a rescue of the behavior.

3157
03:54:56,750 --> 03:55:02,250
And not only was it these microbiota themselves,
but just four microbially derived metabolites

3158
03:55:02,250 --> 03:55:04,330
was sufficient to do the same thing.

3159
03:55:04,330 --> 03:55:09,311
So, these…these maternal microbes are really
critical to early-life neurodevelopment, fetal

3160
03:55:09,311 --> 03:55:12,500
neurodevelopment of offspring.

3161
03:55:12,500 --> 03:55:16,500
We also were familiar with work from Justin
Sonnenburg’s lab at…at Stanford, showing

3162
03:55:16,500 --> 03:55:21,180
that normally in…in your typical mouse vivaria,
from one generation to the next on a regular,

3163
03:55:21,180 --> 03:55:26,220
you know, high-fiber diet, there’s really
no change in gut microbiome composition across

3164
03:55:26,220 --> 03:55:27,220
generations.

3165
03:55:27,220 --> 03:55:31,420
However, when these mice are on a low-fiber
diet, you’ve got this progressive loss of

3166
03:55:31,420 --> 03:55:35,760
taxa, leading even to extinction of certain
species across generations.

3167
03:55:35,760 --> 03:55:43,010
And so, we asked, so if it’s really the
maternal high-fat diet that’s altering neuro…early-life

3168
03:55:43,010 --> 03:55:48,350
neurodevelopment leading this brain and behavioral
dysfunction offspring then—and creating

3169
03:55:48,350 --> 03:55:53,330
this adverse in utero environment—the best
way we could test this was to use the sisters

3170
03:55:53,330 --> 03:55:55,030
of these animals, these F1 males that we found.

3171
03:55:55,030 --> 03:55:58,649
These were found social deficits in, because
we hypothesized that they would have a similar

3172
03:55:58,649 --> 03:55:59,830
dysbiotic gut microbiome.

3173
03:55:59,830 --> 03:56:01,670
So, follow me here.

3174
03:56:01,670 --> 03:56:07,420
This is a, you know, these are 16S ribosomal
RNA sequencing data from animals that were

3175
03:56:07,420 --> 03:56:08,420
6 months old.

3176
03:56:08,420 --> 03:56:10,229
So that’s about 5 months after weaning.

3177
03:56:10,229 --> 03:56:13,680
And they’ve got this persistent long-term
dysbiosis of the gut microbiomes, similar

3178
03:56:13,680 --> 03:56:17,520
to what you’ve been hearing earlier, that
these dysbiotic gut microbiomes, they are…that

3179
03:56:17,520 --> 03:56:18,569
they endure.

3180
03:56:18,569 --> 03:56:23,810
And so, now we took the sisters of these animals,
and so they’ve got the F1 females, and at

3181
03:56:23,810 --> 03:56:26,390
weaning, we…we weaned everybody onto a regular
diet.

3182
03:56:26,390 --> 03:56:30,630
So, the only exposure to a high-fat diet was
during gestation and lactation.

3183
03:56:30,630 --> 03:56:35,560
However, they had this remnant dysbiotic gut
microbiota from their mom having had the high-fat

3184
03:56:35,560 --> 03:56:36,560
diet.

3185
03:56:36,560 --> 03:56:40,030
So, we had the dysbiosis in the maternal gut
microbiome, even though mom was on a regular

3186
03:56:40,030 --> 03:56:41,649
diet in this case.

3187
03:56:41,649 --> 03:56:45,730
We had an altered serum metabolome and immune
dysregulation, which basically recapitulated

3188
03:56:45,730 --> 03:56:47,630
this adverse in utero environment.

3189
03:56:47,630 --> 03:56:52,100
And we hypothesized that that would lead to
brain and behavioral dysfunction in F2.

3190
03:56:52,100 --> 03:56:53,949
And that’s exactly what we thought.

3191
03:56:53,949 --> 03:56:57,830
So, with just grandma on a high-fat diet,
we’ve got profound social dysfunction.

3192
03:56:57,830 --> 03:57:01,890
These animals…they don’t prefer a mouse
interaction with a mouse over an empty cup.

3193
03:57:01,890 --> 03:57:04,199
They don’t appreciate social novelty.

3194
03:57:04,199 --> 03:57:09,319
There’s no preference for interacting with
the new interaction partner versus a…a familiar

3195
03:57:09,319 --> 03:57:10,319
one.

3196
03:57:10,319 --> 03:57:14,930
We saw that also mildly in the females, but
a stronger phenotype in the males, as you

3197
03:57:14,930 --> 03:57:16,680
can see shown here, reciprocal social interaction.

3198
03:57:16,680 --> 03:57:19,450
We got significant deficit specifically in
the males.

3199
03:57:19,450 --> 03:57:24,920
And this does reflect the stronger phenotypes
in the male human population in the…in the

3200
03:57:24,920 --> 03:57:27,460
autism spectrum disorder community.

3201
03:57:27,460 --> 03:57:33,340
When we looked at the gut microbiome composition
using 16S ribosomal RNA gene sequencing, we

3202
03:57:33,340 --> 03:57:38,260
indeed saw the…the same change in dysbiosis
of the maternal gut microbiome in these F1

3203
03:57:38,260 --> 03:57:40,340
females, just like we saw in the males.

3204
03:57:40,340 --> 03:57:45,030
Interestingly, in F2, we began to see a convergence
of the gut microbiota.

3205
03:57:45,030 --> 03:57:50,940
When we broke this down to the parameters
that were driving the…the disparate clusters

3206
03:57:50,940 --> 03:57:55,550
on that principal coordinate analysis, what
we saw, just like in the males, we saw a significant

3207
03:57:55,550 --> 03:57:56,890
decrease in alpha diversity.

3208
03:57:56,890 --> 03:58:03,230
So, a loss of species in the maternal gut
microbiome and the F1 gut microbiome that

3209
03:58:03,230 --> 03:58:04,930
was reflected here in beta diversity.

3210
03:58:04,930 --> 03:58:09,510
So, we’ve got changes not only in the total
number of species, but what is present.

3211
03:58:09,510 --> 03:58:14,760
And many of these are known short-chain fatty
acid producers that were lost in our F1 maternal

3212
03:58:14,760 --> 03:58:16,810
high-fat diet females.

3213
03:58:16,810 --> 03:58:21,210
Interestingly, when we looked at the F2 gut
microbiome, there was no change in alpha diversity.

3214
03:58:21,210 --> 03:58:25,640
However, we still had altered beta diversity
levels.

3215
03:58:25,640 --> 03:58:30,740
But again, those short-chain fatty acid producers
were…were normalized.

3216
03:58:30,740 --> 03:58:32,410
So, we hypothesized.

3217
03:58:32,410 --> 03:58:36,930
If now…if we go transgenerational and we
breed our F2 animals, right?

3218
03:58:36,930 --> 03:58:41,300
These have regular diet, but they’ve got
this partially restored maternal gut microbiome,

3219
03:58:41,300 --> 03:58:45,899
would that create a more normal and uterine
environment leading to neurotypical fetal

3220
03:58:45,899 --> 03:58:48,970
brain development that we’d see neurotypical
social behavior in F3?

3221
03:58:48,970 --> 03:58:52,080
And that’s…that’s precisely what we
saw.

3222
03:58:52,080 --> 03:58:57,449
We saw normal reciprocal social interaction,
normal performance in the three chamber tests

3223
03:58:57,449 --> 03:59:02,170
for sociability and preference for social
novelty and…and even increased sociability

3224
03:59:02,170 --> 03:59:05,330
or preference for social novelty in our females.

3225
03:59:05,330 --> 03:59:11,090
Now, this didn’t preclude us from trying
to do a rescue of this F2 phenotype with our

3226
03:59:11,090 --> 03:59:13,020
L. reuteri, which worked in F1.

3227
03:59:13,020 --> 03:59:17,710
And so, what we did was we treated these animals
with L. reuteri, right at that weaning period,

3228
03:59:17,710 --> 03:59:20,920
where this…there’s this transition of
the gut microbiota that Carrie was telling

3229
03:59:20,920 --> 03:59:23,130
us about in the last talk.

3230
03:59:23,130 --> 03:59:27,010
L. reuteri was likewise efficient to rescue
the social deficits that we’ve observed

3231
03:59:27,010 --> 03:59:28,500
in our F2 animals.

3232
03:59:28,500 --> 03:59:32,380
And really interesting, now we’ve got this
increased sociability and preference for social

3233
03:59:32,380 --> 03:59:37,250
novelty in our females that was really specific
and…and is still puzzling us mechanistically,

3234
03:59:37,250 --> 03:59:43,520
but we’re super excited about this response
of the female gut microbiome driving female

3235
03:59:43,520 --> 03:59:45,580
social behavior.

3236
03:59:45,580 --> 03:59:50,800
When we did perform 16S ribosomal RNA gene
sequencing, we identified the microbes that

3237
03:59:50,800 --> 03:59:54,170
were present in these L. reuteri–treated
animals.

3238
03:59:54,170 --> 04:00:01,229
We saw now…now we see this significant change
in alpha diversity in our downregulation or

3239
04:00:01,229 --> 04:00:03,310
L. reuteri–treated maternal high-fat diet
animals.

3240
04:00:03,310 --> 04:00:07,380
So, what we think this is doing is getting
rid of some of these opportunistic pathogens.

3241
04:00:07,380 --> 04:00:10,859
So, L. reuteri, I’d like to think of it
as kind of like the sheriff in town that comes

3242
04:00:10,859 --> 04:00:12,460
and clears out the bad guys.

3243
04:00:12,460 --> 04:00:16,770
And what’s interesting to me, you remember
initially we had no change in alpha diversity,

3244
04:00:16,770 --> 04:00:20,530
and now we’ve got this negative collection;
that’s not too surprising, given that L.

3245
04:00:20,530 --> 04:00:23,730
reuteri produces reuterin, which is an antimicrobial
peptide.

3246
04:00:23,730 --> 04:00:28,881
Now, when we break this down by sex, we saw
some moderate changes in the…in the male

3247
04:00:28,881 --> 04:00:33,880
gut microbiome, but when we looked at literally
their sisters—so, these are, you know, male

3248
04:00:33,880 --> 04:00:37,710
versus females, they’re coming from the
same litters, multiple litters, clearly based

3249
04:00:37,710 --> 04:00:41,440
on the numbers that we were showing here—there
was very significant response of the female

3250
04:00:41,440 --> 04:00:43,920
gut microbiome to L. reuteri.

3251
04:00:43,920 --> 04:00:49,779
So, very strong difference in alpha diversity,
and about 39 differentially expressed species.

3252
04:00:49,779 --> 04:00:55,770
And so, we’re really excited about this
idea that you could think of it as a female

3253
04:00:55,770 --> 04:01:00,480
vulnerability to modification with a probiotic
or an…a therapeutic opportunity.

3254
04:01:00,480 --> 04:01:04,600
And so, it…it does make me think that, you
know, we’ve talked about methyl donors,

3255
04:01:04,600 --> 04:01:05,920
folate metabolism, etc.

3256
04:01:05,920 --> 04:01:11,279
So, what prenatal vitamins did in the 1960s
to reduce fetal malformations, maybe we’re

3257
04:01:11,279 --> 04:01:16,770
on a precipice of realizing prenatal probiotics
modifying the maternal gut microbiome to reduce

3258
04:01:16,770 --> 04:01:21,870
risk for neurodevelopmental disorders or other
inflammation associated disorders that originate

3259
04:01:21,870 --> 04:01:23,159
in the field of neurodevelopment.

3260
04:01:23,159 --> 04:01:27,140
And I think this can be extended beyond diet,
although that’s what we’re clearly focused

3261
04:01:27,140 --> 04:01:32,210
on today to maybe reduce risk for other…other
causes of these disorders, as well.

3262
04:01:32,210 --> 04:01:33,210
So, opportunities.

3263
04:01:33,210 --> 04:01:41,820
Thanks in no small part to the advances made
possible by the NIH Human Microbiome Project,

3264
04:01:41,820 --> 04:01:45,949
I think the cost of metagenomic sequencing
is substantially reduced, making this technique

3265
04:01:45,949 --> 04:01:50,570
very accessible to many labs that…throughout
the globe that previously it wasn’t.

3266
04:01:50,570 --> 04:01:54,930
So, now we can—and must, just like Carrie
was saying—we must move from a compositional

3267
04:01:54,930 --> 04:02:01,140
to a truly functional understanding of how
microbial communities contribute to host health.

3268
04:02:01,140 --> 04:02:05,880
And we are beginning to harvest the power
of microbial genetics as we engineer microbes

3269
04:02:05,880 --> 04:02:08,529
for the benefit of host health.

3270
04:02:08,529 --> 04:02:09,529
Challenges.

3271
04:02:09,529 --> 04:02:14,870
Integrated multi-omics will be key to providing
clinical advances toward a risk diagnosis

3272
04:02:14,870 --> 04:02:17,750
and therapeutic dietary guidelines and interventions.

3273
04:02:17,750 --> 04:02:23,060
So, like…like Carrie was saying earlier,
we’ve got to have microbiomes, we’ve got

3274
04:02:23,060 --> 04:02:26,890
to have microbiome sequencing, metabolomics,
we’ve got to have epigenomics.

3275
04:02:26,890 --> 04:02:31,430
And all of this will really inform mechanism,
as well as therapeutic pathways.

3276
04:02:31,430 --> 04:02:36,010
A good goal would be to harness these integrated
multi-omics to yield early life biomarkers

3277
04:02:36,010 --> 04:02:40,870
for neurodevelopmental disorders, for minimally
invasive specimens, and also to establish

3278
04:02:40,870 --> 04:02:44,120
multi-omics as a clinical standard for care
of mothers and infants.

3279
04:02:44,120 --> 04:02:48,300
So, thank you very much, especially the NICHD
for supporting our work and…as well as our

3280
04:02:48,300 --> 04:02:50,475
other funders.

3281
04:02:55,998 --> 04:03:00,830
DR. THADDEUS T. SCHUG: Okay, thanks to the speakers
for keeping mostly on time.

3282
04:03:00,830 --> 04:03:05,620
We’re a little bit behind time, mainly because
my fumbling through the intro.

3283
04:03:05,620 --> 04:03:10,820
So, we’re going to give you a little extra
time and reconvened at 3:30.

3284
04:03:10,820 --> 04:03:20,620
And I was reminded to inform folks that there’s
a store to the left of the cafeteria for coffee

3285
04:03:20,620 --> 04:03:25,978
and snacks if you want to refill before the
final presentation in the conclusion  of the day.

3286
04:03:25,978 --> 04:03:31,280
So 3:30, we’ll begin the last presentation
of today.

3287
04:03:31,280 --> 04:03:56,794
[Room noise]

3288
04:03:56,794 --> 04:04:06,909
DR. THADDEUS SCHUG: Okay, we’re going to continue with the final
presentation of the day, and for that, we

3289
04:04:06,909 --> 04:04:10,899
have Dr. Rick Pilsner from Wayne State University.

3290
04:04:13,498 --> 04:04:17,950
DR. RICHARD PILSNER: All right, I’d first like
to thank all the organizers for inviting me

3291
04:04:17,950 --> 04:04:18,960
here.

3292
04:04:18,960 --> 04:04:22,600
Just a…a note that I’m not a nutritionist.

3293
04:04:22,600 --> 04:04:27,260
I’m a toxicologist, so hopefully the framework
that I’m going to present today can certainly

3294
04:04:27,260 --> 04:04:34,050
be applied to nutritional studies and add
some value in…in this workshop here.

3295
04:04:34,050 --> 04:04:38,939
So, the outline of my talk: We have background,
and we’re going to go over windows of susceptibility

3296
04:04:38,939 --> 04:04:45,630
in a few slides, epigenetic reprogramming
of the male germ cells, and phthalates.

3297
04:04:45,630 --> 04:04:49,850
And then, for results, I’m going to present
preconception phthalate exposure in mice and

3298
04:04:49,850 --> 04:04:55,920
men and something new we’ve been working
on…on sperm epigenetic aging or creating

3299
04:04:55,920 --> 04:05:00,350
an epigenetic clock specific to sperm.

3300
04:05:00,350 --> 04:05:03,990
And then, of course, conclusions and relevance
to nutrition.

3301
04:05:03,990 --> 04:05:10,670
So, modern toxicology, and when we talk about
determinants of the health, we know that it’s…it’s

3302
04:05:10,670 --> 04:05:17,420
a combination of genetics (inherited susceptibility)
and the environment (the modifiable susceptibility),

3303
04:05:17,420 --> 04:05:21,120
and nutrition certainly falls in this category.

3304
04:05:21,120 --> 04:05:24,440
The third thing people think less about is
time.

3305
04:05:24,440 --> 04:05:25,939
So, what is really time?

3306
04:05:25,939 --> 04:05:29,930
As a toxicologist, we can think of time as
duration of exposure.

3307
04:05:29,930 --> 04:05:36,899
This again could sit in a realm of nutrition,
of acute versus chronic, but time is also

3308
04:05:36,899 --> 04:05:40,380
age and the windows of susceptibility.

3309
04:05:40,380 --> 04:05:46,620
What brings all these three factors, the three-way
interaction together, at least in my view,

3310
04:05:46,620 --> 04:05:47,699
is epigenetics.

3311
04:05:47,699 --> 04:05:53,840
All right, so we’ve had an introduction
of DOHaD in the morning, so won’t really

3312
04:05:53,840 --> 04:06:00,750
go into that, but it really, in the context
of DOHaD, how do we define development, right?

3313
04:06:00,750 --> 04:06:06,720
And most of the people have been looking in
utero effects, but there’s also different

3314
04:06:06,720 --> 04:06:12,211
windows of susceptibility across the life
course, obviously, during pregnancy, early

3315
04:06:12,211 --> 04:06:14,290
childhood, puberty, and menopause.

3316
04:06:14,290 --> 04:06:20,399
But one thing people have really not considered
is the preconception period, and that’s

3317
04:06:20,399 --> 04:06:25,550
when germ cells mature, and in males, that
is spermatogenesis.

3318
04:06:25,550 --> 04:06:32,770
So, just want to go through some of the reprogramming
steps in male germ cells.

3319
04:06:32,770 --> 04:06:36,880
The first on the very left there is in
utero reprogramming.

3320
04:06:36,880 --> 04:06:44,729
This is the primordial germ cell specification,
where you see the really large wave of epigenetic

3321
04:06:44,729 --> 04:06:46,770
reprogramming.

3322
04:06:46,770 --> 04:06:51,319
And this is where a lot of the nutritional
and especially a lot of the tox studies kind

3323
04:06:51,319 --> 04:06:53,210
of focus on this window.

3324
04:06:53,210 --> 04:07:00,300
What’s less appreciated is the preconception
period, or during spermatogenesis, sperm takes

3325
04:07:00,300 --> 04:07:06,120
about, in humans, about 74 days to fully mature;
in mice, about 35 days.

3326
04:07:06,120 --> 04:07:12,040
And they have, really, three distinct stages
of windows of susceptibility.

3327
04:07:12,040 --> 04:07:17,859
The first one is the DNA methylation patterns
are…are finally set.

3328
04:07:17,859 --> 04:07:21,989
They’re…the final methylation patterns
are acquired.

3329
04:07:21,989 --> 04:07:24,710
The next is a histone protamine exchange.

3330
04:07:24,710 --> 04:07:33,710
So, the sperm head has to be condensed, so
80–90% of all the histones are replaced

3331
04:07:33,710 --> 04:07:38,250
by very basic protamines that cause a condensation
of the nuclear head.

3332
04:07:38,250 --> 04:07:43,520
So, it’s been thought that the remaining
nucleosomes are associated with genes that

3333
04:07:43,520 --> 04:07:46,850
are important for early life development,
as well.

3334
04:07:46,850 --> 04:07:53,570
Once sperm leaves the testes, they’re not
really fully capable of fertilization.

3335
04:07:53,570 --> 04:07:58,159
They go through a 2-week migration through
the epididymis.

3336
04:07:58,159 --> 04:08:04,850
And here, there’s a lot of exchange, or
interaction with extracellular vesicles, or

3337
04:08:04,850 --> 04:08:05,850
EVs.

3338
04:08:05,850 --> 04:08:10,989
And there’s an exchange of cargo…small
non-coding RNA…protein and everything.

3339
04:08:10,989 --> 04:08:19,779
So, there’s a way that, during this last
2 weeks, the sperm still can respond to the

3340
04:08:19,779 --> 04:08:21,600
current environment.

3341
04:08:21,600 --> 04:08:27,770
So, when I started this preconception research
line, you know, writing grants, I got killed

3342
04:08:27,770 --> 04:08:29,390
in the beginning, right?

3343
04:08:29,390 --> 04:08:34,109
Because they’re like, “Why do you care
about methylation in sperm?"

3344
04:08:34,109 --> 04:08:38,319
"Because we know right after fertilization,
everything’s reprogrammed, as well.”

3345
04:08:38,319 --> 04:08:39,319
Right?

3346
04:08:39,319 --> 04:08:47,590
So we know that imprinted genes…escape the
reprogramming event, as well as some repetitive

3347
04:08:47,590 --> 04:08:53,350
sequences, but we kind of have to revisit
this dogma, and what we’re looking for is

3348
04:08:53,350 --> 04:09:00,680
looking at these escapees, these DNA methylation
or epigenetic, program, escapees whereby the

3349
04:09:00,680 --> 04:09:06,710
environmental cues that are in sperm can be
directly transferred into the offspring.

3350
04:09:06,710 --> 04:09:11,030
So, some examples of what we’ve been doing.

3351
04:09:11,030 --> 04:09:17,020
Our hypothesis was that sperm DNA methylation
mediates the effects of male preconception

3352
04:09:17,020 --> 04:09:19,649
phthalate exposure on early-life development.

3353
04:09:19,649 --> 04:09:26,979
So, we have the exposure on early-life development,
and that’s potentially mediated by sperm

3354
04:09:26,979 --> 04:09:27,979
methylation.

3355
04:09:27,979 --> 04:09:38,239
So, phthalates is my toxic of choice, largely
due to Thad here, as well, telling me that

3356
04:09:38,239 --> 04:09:42,080
it would be a good exposure to look at Phthalates
are everywhere.

3357
04:09:42,080 --> 04:09:47,800
I’m sure most people are familiar with phthalates
who love the new car smell.

3358
04:09:47,800 --> 04:09:48,800
Most people do.

3359
04:09:48,800 --> 04:09:52,580
They don’t realize that’s actually off-gassing
of a lot of the phthalates.

3360
04:09:52,580 --> 04:09:59,460
So, open up your windows when you have a new
car for a couple of weeks is always a good

3361
04:09:59,460 --> 04:10:00,460
thing.

3362
04:10:00,460 --> 04:10:05,109
But a lot of people get exposed to high molecular
weight phthalates through more processed foods,

3363
04:10:05,109 --> 04:10:09,610
so nutrition kind of ties in with phthalates,
as well.

3364
04:10:09,610 --> 04:10:15,760
What you see on the right is a list of the
phthalates that we actually measure, and what’s

3365
04:10:15,760 --> 04:10:19,130
in red are the anti-androgenic phthalates.

3366
04:10:19,130 --> 04:10:26,010
So, most of these are anti-androgenic, and
that could have consequences in…when we

3367
04:10:26,010 --> 04:10:28,750
talk about male reproductive health.

3368
04:10:28,750 --> 04:10:31,909
So, the next slide is a really nice review.

3369
04:10:31,909 --> 04:10:38,790
Dr. Joe Braun was one of the co-authors on
this, looking at the evidence of phthalate

3370
04:10:38,790 --> 04:10:41,180
exposure on male reproductive effects.

3371
04:10:41,180 --> 04:10:48,640
And if we look at preconception or the adult,
again, these anti-androgenic phthalates are

3372
04:10:48,640 --> 04:10:55,670
really associated with decline in semen parameters,
affects the time to pregnancy, as well as

3373
04:10:55,670 --> 04:10:58,689
effects in testosterone, as well.

3374
04:10:58,689 --> 04:11:10,270
So, for our clinical study, this is out of
Baystate Medical study—excuse me, Medical

3375
04:11:10,270 --> 04:11:13,189
Center—in Springfield, Massachusetts.

3376
04:11:13,189 --> 04:11:17,540
We called it the Sperm Environmental Epigenes
and Development Study, or SEEDS.

3377
04:11:17,540 --> 04:11:21,820
Every good cohort has to have a good acronym,
and it took me a while to come up with this

3378
04:11:21,820 --> 04:11:23,080
one.

3379
04:11:23,080 --> 04:11:30,479
But what we ended up doing is measuring phthalates
in both males and females and looking at embryo

3380
04:11:30,479 --> 04:11:40,091
development, and what we found is with blastocyst
quality, we found that DEHP, DBP, BBzP, and

3381
04:11:40,091 --> 04:11:46,949
DINCH metabolites, which is a replacement
for DEHP. They were all associated with diminished

3382
04:11:46,949 --> 04:11:48,960
blastocyst quality.

3383
04:11:48,960 --> 04:11:56,600
We next looked at these specific metabolites
on sperm DNA methylation, and we found 128

3384
04:11:56,600 --> 04:12:07,229
sperm DMRs are differentially methylated regions;
100 of these came from DEHP metabolites itself.

3385
04:12:07,229 --> 04:12:14,810
And finally, we then looked at those sperm
methylation sites associated with diminished

3386
04:12:14,810 --> 04:12:24,210
blastocyst quality, and we found 17 DMRs out
of those 128 that were also associated with

3387
04:12:24,210 --> 04:12:25,960
blastocyst quality.

3388
04:12:25,960 --> 04:12:33,340
And when we ran oncology on these, we actually
came up with hits for in utero…embryonic

3389
04:12:33,340 --> 04:12:34,420
development.

3390
04:12:34,420 --> 04:12:45,640
So, given that we were…that most of our
hits were from DEHP, we were motivated to

3391
04:12:45,640 --> 04:12:49,330
replicate these results in more animal models.

3392
04:12:49,330 --> 04:12:55,140
Epidemiologists have a hard time sometimes
with a lot of cofounding, and a lot of the

3393
04:12:55,140 --> 04:13:01,540
basic scientists want to really have a more
of a defined exposure and controlled experiment,

3394
04:13:01,540 --> 04:13:05,420
so we did that, with male mice.

3395
04:13:05,420 --> 04:13:11,560
We exposed male mice to 2.5 or 25 milligrams
per kilogram per day of DEHP.

3396
04:13:11,560 --> 04:13:25,100
Just a note that the NOAEL for DEHP is…is
4.8, so our low dose is actually below the…the

3397
04:13:25,100 --> 04:13:26,630
allowable, exposure regimen.

3398
04:13:26,630 --> 04:13:36,340
We collected sperm from the F0, as well as
the embryonic and extra-embryonic tissue from

3399
04:13:36,340 --> 04:13:40,140
F1 mice.

3400
04:13:40,140 --> 04:13:45,570
Looking at the effect of male preconception
DEHP exposure on the sperm methylome, this

3401
04:13:45,570 --> 04:13:51,130
is via RRBS, or reduced restriction bisulfite
sequencing.

3402
04:13:51,130 --> 04:13:59,790
We saw a whole host of changes, about 400
to 500 DMRs.

3403
04:13:59,790 --> 04:14:07,900
There was only 20% overlap with the high and
low exposure, and we had a whole host of different

3404
04:14:07,900 --> 04:14:08,900
pathways.

3405
04:14:08,900 --> 04:14:15,630
Nothing terribly specific that we were hoping
to see, but what was really fascinating is

3406
04:14:15,630 --> 04:14:20,970
what we…what we observed in the F1 tissue.

3407
04:14:20,970 --> 04:14:28,550
And so, in the embryonic tissue, we actually
saw a doubling of the amount of DMRs that

3408
04:14:28,550 --> 04:14:29,550
we found.

3409
04:14:29,550 --> 04:14:38,130
So instead of 400, we saw 800, 900, and what’s
even more interesting in the extra-embryonic

3410
04:14:38,130 --> 04:14:45,899
tissue, which will eventually make up the
placenta, we saw three- to fourfold increase

3411
04:14:45,899 --> 04:14:50,069
in the amount of methylation changes.

3412
04:14:50,069 --> 04:14:56,580
And when we look at oncology in these, what
we see is most everything pointing to developmental

3413
04:14:56,580 --> 04:14:57,580
terms.

3414
04:14:57,580 --> 04:15:01,550
So, this is pretty interesting.

3415
04:15:01,550 --> 04:15:09,210
We also ran RNAseq on the same tissues, and
we found a lot of differential expressions.

3416
04:15:09,210 --> 04:15:15,319
Again, the extra-embryonic seemed like it
was pushing a little bit higher, but there

3417
04:15:15,319 --> 04:15:18,939
was a lot of changes in key developmental
genes.

3418
04:15:18,939 --> 04:15:23,149
Hox genes, GATA genes, other things.

3419
04:15:23,149 --> 04:15:28,760
So, not surprisingly, embryonic development
and pattern specification, all these things

3420
04:15:28,760 --> 04:15:29,780
popped up.

3421
04:15:29,780 --> 04:15:33,250
So, I know that was a lot of different slides.

3422
04:15:33,250 --> 04:15:41,189
This is kind of a summary of our animal model,
and what we found is around 400 DMRs, depending

3423
04:15:41,189 --> 04:15:47,820
on the dose of the F0 sperm, and then this
amplified effect of DMRs.

3424
04:15:47,820 --> 04:15:54,320
So, we had a two- to fourfold increase in the
conceptus epigenome changes.

3425
04:15:54,320 --> 04:16:02,120
These were also associated with changes in
gene expression and gene…developmental genes.

3426
04:16:02,120 --> 04:16:12,050
Again, the Hox family, Sox family, GATA family,
CDX1, and this all resulted in pattern specification,

3427
04:16:12,050 --> 04:16:13,899
embryo development, key terms.

3428
04:16:13,899 --> 04:16:21,520
So again, the animal model kind of corroborated
our findings in the human studies, which is

3429
04:16:21,520 --> 04:16:26,670
nice for an epidemiologist to kind of say,
“Hey, this is…this is…these two are

3430
04:16:26,670 --> 04:16:30,350
kind of working in sync.”

3431
04:16:30,350 --> 04:16:36,869
What I want to do next is introduce something
that we’ve published on—and we’re re-inventing

3432
04:16:36,869 --> 04:16:43,120
this, where we have a version 2 coming out—is
looking at sperm epigenetic aging.

3433
04:16:43,120 --> 04:16:51,770
A few people have commented on advanced paternal
age, but the question is: How do…how should

3434
04:16:51,770 --> 04:16:55,920
we define aging or age, right?

3435
04:16:55,920 --> 04:16:58,090
Most people use chronological age.

3436
04:16:58,090 --> 04:17:04,020
You know, is that really a good measure of
age or the aging process?

3437
04:17:04,020 --> 04:17:10,590
Not really, because it does not capture the
intrinsic or extrinsic factors that impact

3438
04:17:10,590 --> 04:17:12,300
the rate of aging.

3439
04:17:12,300 --> 04:17:19,020
So, a great quote from my childhood hero,
Indiana Jones—I don’t know if anyone’s

3440
04:17:19,020 --> 04:17:26,220
seen the recent movie, but I just saw it with
my kids, and it was…it was a good time.

3441
04:17:26,220 --> 04:17:28,770
“It’s not the years, it’s the mileage,”
right?

3442
04:17:28,770 --> 04:17:32,800
So, all…all the people you went in high
school with, you’re the same age, right?

3443
04:17:32,800 --> 04:17:38,860
But everyone’s aging at a different rate,
depending on what they’re exposed to, their

3444
04:17:38,860 --> 04:17:41,280
nutrition, exercise, all of these things.

3445
04:17:41,280 --> 04:17:49,119
So, if we could capture, an…an estimate
of the biological age of…of sperm, that

3446
04:17:49,119 --> 04:17:50,689
would be really cool.

3447
04:17:50,689 --> 04:17:52,600
And that’s what we did.

3448
04:17:52,600 --> 04:17:59,619
We know that there’s…there’s two different
kind of ideas of…of sperm methylation changes.

3449
04:17:59,619 --> 04:18:04,710
We went over epigenetic reprogramming, but
there’s also something called epigenetic

3450
04:18:04,710 --> 04:18:12,900
drift, that as you age, you get this accumulation
of epigenetic errors that you could kind of

3451
04:18:12,900 --> 04:18:16,170
leverage to build biological clocks.

3452
04:18:16,170 --> 04:18:19,130
So, biological clocks are not new.

3453
04:18:19,130 --> 04:18:24,210
Horvath and Hannum have developed really great
clocks for somatic tissue.

3454
04:18:24,210 --> 04:18:32,510
They…they predict cancer, chronic diseases,
mortality, but they are…they have no predictive

3455
04:18:32,510 --> 04:18:39,130
value of sperm, so we went ahead and built
our own sperm epigenetic clock.

3456
04:18:39,130 --> 04:18:43,790
And this is, kind of, just a schematic of
kind of a life course approach, that you could

3457
04:18:43,790 --> 04:18:50,270
have this normal epigenetic drift, but there’s
this epigenetic drift acceleration, depending

3458
04:18:50,270 --> 04:18:53,240
on your lifetime exposures.

3459
04:18:53,240 --> 04:18:57,640
And we consider that sperm epigenetic age.

3460
04:18:57,640 --> 04:19:03,020
So, constructing the clock, we used the LIFE
study.

3461
04:19:03,020 --> 04:19:07,930
This was an NICHD, study done 10–20 years
ago.

3462
04:19:07,930 --> 04:19:17,070
It was a prospective cohort of couples, 501,
from the general population.

3463
04:19:17,070 --> 04:19:23,649
The group stopped contraception and were trying
to actively become pregnant.

3464
04:19:23,649 --> 04:19:26,950
We had 379 semen samples from that.

3465
04:19:26,950 --> 04:19:30,630
We used the EPIC array, which is 850,000.

3466
04:19:30,630 --> 04:19:37,180
Male age was, between 19 and 50 years old,
and we used machine learning…we won’t

3467
04:19:37,180 --> 04:19:43,479
get into that…to, kind of, gather the methylation
sites that best predict age.

3468
04:19:43,479 --> 04:19:53,020
So again, after pre-processing, we were left
with 800,000 CpG sites.

3469
04:19:53,020 --> 04:20:00,870
When we ran associations with chronological
age, we had 85,000 that were, FDR significant.

3470
04:20:00,870 --> 04:20:10,029
If we use a more stringent cutoff, we had
22,000 CpGs that were associated with chronological

3471
04:20:10,029 --> 04:20:11,029
age.

3472
04:20:11,029 --> 04:20:20,550
Using machine learning, it came down to 120
CpGs out of the 800,000 that predicted male

3473
04:20:20,550 --> 04:20:27,170
age, and what we did is we used the regressions,
the residuals from linear regression between

3474
04:20:27,170 --> 04:20:33,210
chronological age and predicted age to kind
of get an understanding of the rate of aging

3475
04:20:33,210 --> 04:20:34,210
of sperm.

3476
04:20:34,210 --> 04:20:44,149
So, if your dot was above this line, that means
you have accelerated aging of sperm.

3477
04:20:44,149 --> 04:20:46,630
There’s a mismatch of that.

3478
04:20:46,630 --> 04:20:53,320
All right, so, looking at kind of the metrics
on this, we had very low error.

3479
04:20:53,320 --> 04:20:58,250
We had a correlation of 0.91 with an MAE of
1.6 years.

3480
04:20:58,250 --> 04:21:00,460
This is within the LIFE cohort.

3481
04:21:00,460 --> 04:21:05,120
We validated this in a clinical IVF cohort,
SEEDS.

3482
04:21:05,120 --> 04:21:07,670
It still performs really well.

3483
04:21:07,670 --> 04:21:15,860
So, our first thing is we looked at sperm
epigenetic age and time of pregnancy using

3484
04:21:15,860 --> 04:21:22,100
Cox proportional hazard model, we found a
highly significant effect of sperm epigenetic

3485
04:21:22,100 --> 04:21:31,399
age on couples’ time-to-pregnancy, so lower
FOR, or fecundability odds ratios, equals

3486
04:21:31,399 --> 04:21:33,330
longer time to pregnancy.

3487
04:21:33,330 --> 04:21:41,340
This is seen in this graph where we have tertiles
with different sperm epigenetic age of being

3488
04:21:41,340 --> 04:21:47,720
young, equivalent, or older, and you can see
a clear dose response at 3 months, 6 months,

3489
04:21:47,720 --> 04:21:52,189
and 12 months of trying to achieve pregnancy.

3490
04:21:52,189 --> 04:21:58,580
So, looking at pregnancy outcomes, we didn’t
see anything with birth weight, length, head

3491
04:21:58,580 --> 04:22:04,670
circumference, but we did find a significant
association with gestational age, such that

3492
04:22:04,670 --> 04:22:11,710
there was a 2-day shorter gestational age
for every year increase in sperm epigenetic

3493
04:22:11,710 --> 04:22:12,720
age, as well.

3494
04:22:12,720 --> 04:22:19,460
So, this is kind of interesting, that can
sperm really impact, kind of, the timing of

3495
04:22:19,460 --> 04:22:20,460
delivery.

3496
04:22:20,460 --> 04:22:25,340
We’re still trying to sort that one out.

3497
04:22:25,340 --> 04:22:30,380
So, the final thing I’m…I’m leaving
you with is, so now that we know that sperm

3498
04:22:30,380 --> 04:22:35,490
epigenetic age is associated with time to
pregnancy, what could be some of the environmental

3499
04:22:35,490 --> 04:22:38,409
factors that are associated with that?

3500
04:22:38,409 --> 04:22:43,369
We found that current smoking—those men
who have…are current smokers—have a higher

3501
04:22:43,369 --> 04:22:45,260
sperm epigenetic age.

3502
04:22:45,260 --> 04:22:47,080
Not surprising.

3503
04:22:47,080 --> 04:22:55,409
We also found a host of phthalates that showed
higher…those men exposed to higher levels

3504
04:22:55,409 --> 04:23:00,449
of phthalates had an increased sperm epigenetic
age, as well.

3505
04:23:00,449 --> 04:23:02,210
Some more of the modern modeling.

3506
04:23:02,210 --> 04:23:09,120
We looked at mixture, so instead of analyzing
one phthalate at a time, we did a mixture

3507
04:23:09,120 --> 04:23:16,670
analysis, which kind of combines all the phthalates,
and we do see this clear dose response curve,

3508
04:23:16,670 --> 04:23:22,090
and this is validated with another method,
quantile G-comp analysis.

3509
04:23:22,090 --> 04:23:23,760
So overall conclusions.

3510
04:23:23,760 --> 04:23:29,760
Sperm…male preconception exposures to anti-androgenic
phthalates influence sperm epigenetics, early-life

3511
04:23:29,760 --> 04:23:37,050
development, and sperm epigenetic age.

3512
04:23:37,050 --> 04:23:39,590
Timing is really important.

3513
04:23:39,590 --> 04:23:44,850
The preconception period is a susceptible
window of germ cell development.

3514
04:23:44,850 --> 04:23:50,680
Men have an environmental responsibility,
too, and that starts at least 3 months prior

3515
04:23:50,680 --> 04:23:51,840
to conception, okay?

3516
04:23:51,840 --> 04:23:59,630
So, we know sperm epigenesis is 74 days, so
at least 3 days—excuse me, 3 months—before

3517
04:23:59,630 --> 04:24:00,630
conception.

3518
04:24:00,630 --> 04:24:07,050
And irrespective of your chronological age,
the biological age of your sperm can impact

3519
04:24:07,050 --> 04:24:08,279
pregnancy success.

3520
04:24:08,279 --> 04:24:11,260
So just two…two slides left.

3521
04:24:11,260 --> 04:24:15,529
These are some of the papers that I kind of
captured that I thought were interesting,

3522
04:24:15,529 --> 04:24:19,380
in terms of diet.

3523
04:24:19,380 --> 04:24:27,390
There some studies looking at sugar intake
here, showing methylation changes, as well

3524
04:24:27,390 --> 04:24:32,409
as small, non-coding RNA changes.

3525
04:24:32,409 --> 04:24:40,070
I think we had a question from the audience
or online about bariatric surgery, and that

3526
04:24:40,070 --> 04:24:45,069
actually changes sperm epigenetic profiles,
both in methylome and small, non-coding RNA,

3527
04:24:45,069 --> 04:24:46,730
so that’s really interesting.

3528
04:24:46,730 --> 04:24:52,840
There was another NICHD study, the FAZST trial.

3529
04:24:52,840 --> 04:24:57,810
Here, they looked at methylation, but as a
group.

3530
04:24:57,810 --> 04:25:06,100
Over 6 months, there was no significant group
changes in sperm methylation from folic acid

3531
04:25:06,100 --> 04:25:07,709
or zinc.

3532
04:25:07,709 --> 04:25:13,380
And then there’s…Jacquetta Trasler’s
group found here that…they actually did

3533
04:25:13,380 --> 04:25:18,580
find some changes in in sperm DNA methylation
with folate exposure.

3534
04:25:18,580 --> 04:25:21,790
All right, so future needs.

3535
04:25:21,790 --> 04:25:24,800
What is the biggest challenge?

3536
04:25:24,800 --> 04:25:28,350
Well, we know it takes two to tango.

3537
04:25:28,350 --> 04:25:34,580
So, most birth cohort studies ignore the paternal
contribution; we need to change that.

3538
04:25:34,580 --> 04:25:38,930
I’m very glad that ECHO 2 is doing the preconception
cohort.

3539
04:25:38,930 --> 04:25:45,960
I’m now going to be part of the ECHO 2 through
Michigan State University, looking at…focusing

3540
04:25:45,960 --> 04:25:53,840
on the preconception period, but also, not
even just the data, but we need to collect

3541
04:25:53,840 --> 04:25:56,989
the biologicals, really, to get this going.

3542
04:25:56,989 --> 04:25:58,550
Promising scientific opportunities.

3543
04:25:58,550 --> 04:26:04,170
The short term is, I think we could leverage
these biological clocks—not just mine, but

3544
04:26:04,170 --> 04:26:10,229
there’s a lot, the Hannum and Horvath clocks
to look at biological aging.

3545
04:26:10,229 --> 04:26:15,290
And then the long term, I think, is…is—we’ve
heard this a few times now in the past hour

3546
04:26:15,290 --> 04:26:19,890
or two—is the integration of the -omic data
through machine learning.

3547
04:26:19,890 --> 04:26:25,180
We kind of have to put all this together,
and hopefully…and at least what we’re

3548
04:26:25,180 --> 04:26:31,300
trying to do is kind of derive risk scores,
so we’re working on something of a fecundity

3549
04:26:31,300 --> 04:26:38,040
risk score, using all of the -omics we have
with sperm at this point.

3550
04:26:38,040 --> 04:26:47,420
So with that, I just want to thank our lab
folks, and I won’t take questions, but we’ll

3551
04:26:47,420 --> 04:26:50,119
do group questions, right?

3552
04:26:50,119 --> 04:26:52,810
All right, thank you.

3553
04:26:52,810 --> 04:26:53,810
DR.

3554
04:26:53,810 --> 04:27:00,380
THADDEUS SCHUG: Hey, can we have the speakers
from the session back up front here?

3555
04:27:00,380 --> 04:27:08,010
And Dr. Nadeau had to go to a prior commitment,
so we’ll be missing her for the question-and-answer

3556
04:27:08,010 --> 04:27:09,010
session.

3557
04:27:09,010 --> 04:27:15,529
So, please, for folks who are attending the
work…the workshop online, ask your questions

3558
04:27:15,529 --> 04:27:23,149
in the Q&A box, and we’ll take questions
from participants in the room, as well.

3559
04:27:23,149 --> 04:27:32,620
UNIDENTIFIED SPEAKER: So, while we wait, it
looks like we do have a question already in

3560
04:27:32,620 --> 04:27:42,743
the queue from the Q&A, and this is actually,
I believe, for Dr. Friedman.

3561
04:27:42,743 --> 04:27:54,106
Dr. Friedman, have you done analyses of fatty
livers in childhood NASH, specifically linoleic acid? Thanks.

3562
04:27:54,106 --> 04:28:05,188
DR. JACOB FRIEDMAN: So, the lipidomic changes
that have been done in most of the nutritional

3563
04:28:05,188 --> 04:28:15,000
studies have proven that cholesterol pathway
and the ceramide pathway are the major drivers

3564
04:28:15,000 --> 04:28:18,560
of the fatty liver disease, at least correlatively.

3565
04:28:18,560 --> 04:28:26,119
Knocking out some of these genes have actually
been helpful in established causes of…established

3566
04:28:26,119 --> 04:28:27,819
models of disease.

3567
04:28:27,819 --> 04:28:36,340
And so, the toxic lipids that I discussed
really revolve around ceramides, [inaudible]

3568
04:28:36,340 --> 04:28:42,840
ceramides, and the cholesterol pathway that
seems to be driving the inflammation.

3569
04:28:42,840 --> 04:29:00,000
That is one of the key steps to moving from
simple steatosis to steatohepatitis, if that’s helpful.

3570
04:29:00,000 --> 04:29:03,560
UNIDENTIFIED SPEAKER: Okay. Great, thank you.

3571
04:29:03,560 --> 04:29:04,970
Any questions in the audience?

3572
04:29:04,970 --> 04:29:07,984
Yes.

3573
04:29:07,984 --> 04:29:13,898
DR. JOE BRAUN: This…okay. You guys can hear me. Joe Braun, Brown University.

3574
04:29:13,898 --> 04:29:18,480
So, these were great talks, and I heard a
lot about, you know, using innovative technologies

3575
04:29:18,480 --> 04:29:19,480
and tools.

3576
04:29:19,480 --> 04:29:23,739
A lot of calls for, you know, more -omics,
more -omes, right?

3577
04:29:23,739 --> 04:29:27,359
And I think, you know, that’s warranted,
but one thing I think that we do is we get

3578
04:29:27,359 --> 04:29:32,989
really excited about these things and tend
to forget about some of the basic, describing

3579
04:29:32,989 --> 04:29:36,120
characteristics of them—the validity, reliability,
reproducibility.

3580
04:29:36,120 --> 04:29:37,520
I think about this with microbiome, right?

3581
04:29:37,520 --> 04:29:42,540
Like, you know, the question of whether the
fetus is born with a microbiome, you know,

3582
04:29:42,540 --> 04:29:45,960
and doing really well-controlled studies to
see, is it contamination?

3583
04:29:45,960 --> 04:29:50,830
Or, if we’re talking about metabolomes,
does it vary with within a day…day to day, etc.?

3584
04:29:50,830 --> 04:29:53,600
And just sort of what your thoughts are and
all of you having used these technologies,

3585
04:29:53,600 --> 04:29:57,910
you know, and particularly, you know, from
the scale of, you know, rodents to humans,

3586
04:29:57,910 --> 04:30:03,510
and even single cells, like sperm, you know,
what…what do you think we need to do for

3587
04:30:03,510 --> 04:30:10,200
that, particularly when we’re talking about,
you know, going to a multi-omics-type platform?

3588
04:30:10,200 --> 04:30:16,850
DR. SHELLY BUFFINGTON: Hi, this is Shelly.

3589
04:30:16,850 --> 04:30:20,489
Yeah, so, I was one of the ones that was saying,
yeah, multi-omics for sure.

3590
04:30:20,489 --> 04:30:24,550
And so, I think we’re really driving home
the idea that, you know, composition can be

3591
04:30:24,550 --> 04:30:27,380
variable, even between you and I, but, you
know, say we’re both healthy.

3592
04:30:27,380 --> 04:30:31,300
[inaudible] the functional output of these
microbes and how they affect host physiology

3593
04:30:31,300 --> 04:30:32,300
is what’s important.

3594
04:30:32,300 --> 04:30:37,590
So even if there is, like, diurnal flux or
etc., what is the actual function?

3595
04:30:37,590 --> 04:30:42,750
So, that’s where I think, you know, with
the microbial genetics combined with metabolomics

3596
04:30:42,750 --> 04:30:45,990
is actually really informative as far as,
like, therapeutics.

3597
04:30:45,990 --> 04:30:50,631
And so…so, that’s why I think it’s really
important to keep doing it, you know, at least

3598
04:30:50,631 --> 04:30:55,760
at the level of, like, genetic investigation,
you know, whole genome, you know, shotgun

3599
04:30:55,760 --> 04:30:59,661
sequencing, as opposed to just, you know,
16S, which will give you true, you know, metabolomic

3600
04:30:59,661 --> 04:31:02,159
information, rather than just predictive metabolomics.

3601
04:31:02,159 --> 04:31:05,590
So, I think that really would be quite valuable.

3602
04:31:05,590 --> 04:31:09,180
And, you know, day-to-day variation, yes,
it’s there.

3603
04:31:09,180 --> 04:31:13,199
I think that, you know, longitudinal studies
are going to be really important, you know,

3604
04:31:13,199 --> 04:31:15,070
especially in humans, but certainly in mouse
models.

3605
04:31:15,070 --> 04:31:18,470
And so, often, we only pick one or two, you
know, time points, and that isn’t really

3606
04:31:18,470 --> 04:31:19,470
the full picture.

3607
04:31:19,470 --> 04:31:23,399
So, I think that you’re making an excellent
point.

3608
04:31:23,399 --> 04:31:33,770
DR. JACOB FRIEDMAN: I’ll just jump in and say
from my point of view—this is Dr. Friedman. The

3609
04:31:33,770 --> 04:31:41,029
multi-omics pathways are going to lead, I
think, ultimately to algorithms and machine

3610
04:31:41,029 --> 04:31:42,029
learning.

3611
04:31:42,029 --> 04:31:45,760
And just like when you do a diagnosis and
you take a medical record, you know, if you

3612
04:31:45,760 --> 04:31:51,790
can plug in enough data points, you can…begin
to identify what we…really are biomarkers.

3613
04:31:51,790 --> 04:31:58,360
So, we’re not talking about causative effects
here; we’re mostly talking about correlative

3614
04:31:58,360 --> 04:31:59,360
effects.

3615
04:31:59,360 --> 04:32:01,400
But it’s going to require large sample sizes.

3616
04:32:01,400 --> 04:32:10,810
And I think the last speaker who gave the
caveats of when a sample is taken, what the

3617
04:32:10,810 --> 04:32:16,229
diet was of the person who gave the sample,
and what their genetic background is all kind

3618
04:32:16,229 --> 04:32:19,960
of figure into whether these things can actually
be made better as predictors.

3619
04:32:19,960 --> 04:32:26,930
And I think that’s going to happen eventually
because that’s the way that the field is going.

3620
04:32:26,930 --> 04:32:30,710
UNIDENTIFIED SPEAKER: I wanted to comment,
as well.

3621
04:32:30,710 --> 04:32:34,300
I really liked your question, and I really
liked your answer, Shelly.

3622
04:32:34,300 --> 04:32:39,840
And I think it’s…the way that I think
about it, personally, is that these are tools,

3623
04:32:39,840 --> 04:32:43,729
and they’re really powerful tools, and they
can point us in a direction, but we still

3624
04:32:43,729 --> 04:32:45,880
have to do the experiments to test them.

3625
04:32:45,880 --> 04:32:47,380
So, we need both approaches, right?

3626
04:32:47,380 --> 04:32:50,000
And we can’t just do one or the other.

3627
04:32:50,000 --> 04:32:55,630
And that takes all kinds of scientists and
all kinds of models, and so…but yeah, I

3628
04:32:55,630 --> 04:33:03,840
think you can get sort of caught up in the
excitement of -omics and maybe not do sort

3629
04:33:03,840 --> 04:33:09,230
of all the due diligence that you need to
really show…those effects experimentally,

3630
04:33:09,230 --> 04:33:10,689
and I think that part is really important.

3631
04:33:10,689 --> 04:33:14,080
And that’s…when we talk about, like, getting
to mechanisms, like, that’s kind of we’re…what

3632
04:33:14,080 --> 04:33:15,330
we’re talking about.

3633
04:33:15,330 --> 04:33:22,920
AUDIENCE MEMBER: And since the microphone
is on, I’ll just…just to add, just…despite

3634
04:33:22,920 --> 04:33:30,109
all the wonders of -omics, I think in addition
to really being aware, as Rick was pointing

3635
04:33:30,109 --> 04:33:36,250
out, about really thinking the periods that
we’re…to be certain that we’re capturing

3636
04:33:36,250 --> 04:33:41,490
the right timing in terms of what’s the
vulnerable period, but the other thing is

3637
04:33:41,490 --> 04:33:46,551
to make certain…all of these -omics require
the samples, and certainly for the humans,

3638
04:33:46,551 --> 04:33:52,770
and the relative invasiveness of being able
to collect the samples at the right times.

3639
04:33:52,770 --> 04:33:59,000
And so, we also have to be mindful that we’re
really capturing the populations that are

3640
04:33:59,000 --> 04:34:00,000
at greatest risk.

3641
04:34:00,000 --> 04:34:05,449
And so, I just want to put out that, in addition,
there’s the complexities of…through the

3642
04:34:05,449 --> 04:34:10,920
different development stages, the challenges
of capturing the samples and…and of being

3643
04:34:10,920 --> 04:34:14,590
inclusive in…in terms of the populations
that we sample.

3644
04:34:14,590 --> 04:34:26,410
UNIDENTIFIED SPEAKER: Okay. We have one question coming in virtually,
and then we’ll go back to the…sorry, and

3645
04:34:26,410 --> 04:34:29,379
then we’ll go back to the live.

3646
04:34:29,379 --> 04:34:34,209
What is the strength of evidence that these
methylation changes are positive?

3647
04:34:34,209 --> 04:34:41,090
So many sites and the genes themselves can
be silenced or turned on based on methylation.

3648
04:34:41,090 --> 04:34:47,549
Is it possible that RNA gene expression is
more meaningful?

3649
04:34:47,549 --> 04:34:55,051
DR. RICHARD PILSNER: Hi, there. Richard Pilsner here.

3650
04:34:55,051 --> 04:35:03,250
Yes, there’s…you know, in the study I
presented in mouse, there was only about 10%

3651
04:35:03,250 --> 04:35:10,260
of the sperm DMRs that were kind of consistent
with…from F0 sperm to F1.

3652
04:35:10,260 --> 04:35:16,789
We did see this amplified effect, but in only
10% of those DMRs and sperm overlaps with

3653
04:35:16,789 --> 04:35:19,400
the embryonic tissue.

3654
04:35:19,400 --> 04:35:25,150
So, certainly the methylation, I think, is
one piece of the puzzle, but it’s not explaining

3655
04:35:25,150 --> 04:35:26,150
everything.

3656
04:35:26,150 --> 04:35:34,891
We have a series of publications we just…just
published looking at small non-coding RNA

3657
04:35:34,891 --> 04:35:40,170
in seminal plasma—extracellular vesicles,
too—and how that’s related to live birth,

3658
04:35:40,170 --> 04:35:43,529
phthalate exposure, as well as semen parameters.

3659
04:35:43,529 --> 04:35:48,449
So, you know, methylation is just one aspect
of kind of this epigenetic transfer of information.

3660
04:35:48,449 --> 04:35:53,131
DR. JACOB FRIEDMAN: I asked the question.

3661
04:35:53,131 --> 04:35:57,170
I guess I want to follow up.

3662
04:35:57,170 --> 04:36:01,449
And that is, if people ask you, “Prove that
methylation matters,” what do you tell them?

3663
04:36:01,449 --> 04:36:03,879
How do you…how do you actually…you got
a mouse.

3664
04:36:03,879 --> 04:36:12,740
You got these sites, and they’re all over
the genome, and far away from the genes, often.

3665
04:36:12,740 --> 04:36:14,830
And I…that’s my problem.

3666
04:36:14,830 --> 04:36:21,570
I’m trying to figure out, like, what would
you do to actually prove that 10 or 12 of

3667
04:36:21,570 --> 04:36:23,180
these sites are causing disease?

3668
04:36:23,180 --> 04:36:26,779
That’s what I want to know.

3669
04:36:26,779 --> 04:36:32,949
DR. RICHARD PILSNER: A hard question from one
of the speakers, I tell you.

3670
04:36:32,949 --> 04:36:35,209
You’re supposed to be kind to people like
me here.

3671
04:36:35,209 --> 04:36:37,209
DR. JACOB FRIEDMAN: No softballs here.

3672
04:36:37,209 --> 04:36:40,160
I’m…I don’t even…I’m not there,
so I can ask a tough one. Sorry.

3673
04:36:40,160 --> 04:36:46,150
DR. RICHARD PILSNER: Yeah. Right, right, yeah. No, that…it’s a…it’s a very good point.

3674
04:36:46,150 --> 04:36:50,740
I mean, what we have is…you know, we could
validate, you know, the findings from the

3675
04:36:50,740 --> 04:36:57,320
-omic approaches using targeted, you know,
pyrosequencing, techniques like that, but,

3676
04:36:57,320 --> 04:37:03,889
you know, if you’re thinking actual causative,
you know, I guess a CRISPR approach, removing

3677
04:37:03,889 --> 04:37:06,650
those methylation marks may be a good way
to go.

3678
04:37:06,650 --> 04:37:13,770
But, you know, I would ask that same question
back to people that do small, non-coding RNA

3679
04:37:13,770 --> 04:37:18,670
and, you know…you know, there’s been a
big push to get away from sperm methylation

3680
04:37:18,670 --> 04:37:23,369
and saying that sperm RNA is more important
or have a big effect.

3681
04:37:23,369 --> 04:37:29,031
I think Dr. Rando commented that there’s
so little sperm in—or RNA in sperm—that

3682
04:37:29,031 --> 04:37:32,000
does it really have an effect on the oocyte?

3683
04:37:32,000 --> 04:37:35,230
So, that’s what I have.

3684
04:37:35,230 --> 04:37:45,119
AUDIENCE MEMBER: If I can just sort of stand
up for small RNAs, because…I mean, I don’t

3685
04:37:45,119 --> 04:37:49,150
think anyone knows how any paternal effect
works, so I don’t mean to say this like

3686
04:37:49,150 --> 04:37:55,299
small RNAs aren’t the answer, but what…part
of the reason they’re quite popular in the

3687
04:37:55,299 --> 04:37:59,850
paternal effects field is that they’re much
easier to test sufficiency than for methylation

3688
04:37:59,850 --> 04:38:05,350
or chromatin changes because you can microinject
RNAs into a zygote, whereas in order to do

3689
04:38:05,350 --> 04:38:10,350
methylation stuff, you’d have to do a dCas9,
you know, like, basically make a transgenic

3690
04:38:10,350 --> 04:38:17,281
to install methylation or a chromatin change
in sperm, so testing…and many people have

3691
04:38:17,281 --> 04:38:22,820
injected small RNAs and seen outcomes in the
offspring, so that’s…whether or not people

3692
04:38:22,820 --> 04:38:28,080
are injecting a physiologically relevant amount—these
are the kinds of questions that one could

3693
04:38:28,080 --> 04:38:33,250
raise against small RNAs, but at least they
sort of structurally have built in an advantage

3694
04:38:33,250 --> 04:38:35,740
that it is very easy to test sufficiency.

3695
04:38:35,740 --> 04:38:39,039
And in many cases, they have been shown to
induce phenotypes.

3696
04:38:39,039 --> 04:38:45,670
Now, reconciling that with the amount of RNA
sperm carry and so on and so forth is a whole

3697
04:38:45,670 --> 04:38:48,199
other question, but anyway, those…but with
your kind of stuff, it’s like…it can be

3698
04:38:48,199 --> 04:38:54,250
very hard to induce just one methylation change
in sperm, much less, like, 50 if you think

3699
04:38:54,250 --> 04:38:56,859
it’s not, like, a mono-epigenetic effect.

3700
04:38:56,859 --> 04:38:58,859
DR. RICHARD PILSNER: Right.

3701
04:38:58,859 --> 04:39:03,420
And just to add on that…it’s “one takes
all” for sperm.

3702
04:39:03,420 --> 04:39:08,930
Dr. Rando mentioned it’s a digital thing,
so for methylation, it’s just…it’s either

3703
04:39:08,930 --> 04:39:14,150
on or off, but one sperm, too—they have
very little.

3704
04:39:14,150 --> 04:39:21,664
The concentrations of the small, non-coding
RNA is super small. So, yes, future research, for sure.

3705
04:39:22,378 --> 04:39:27,490
DR. JACOB FRIEDMAN: Love the concept of epigenetic
aging. I just want to know, is that my true age?

3706
04:39:27,490 --> 04:39:28,490
You know.

3707
04:39:28,490 --> 04:39:31,975
How do you use those information, you know?

3708
04:39:31,975 --> 04:39:37,778
It’s…those clocks are really fascinating,
but not sure how to use it yet.

3709
04:39:37,778 --> 04:39:39,778
DR. THADDEUS SCHUG: Dr. Barker?

3710
04:39:40,102 --> 04:39:47,000
DR. MARY BARKER: Thank you. I have absolutely nothing to contribute to
that conversation at all—I’m a psychologist.

3711
04:39:47,000 --> 04:39:53,250
I don’t know about this stuff—except to
say that the science that you guys have spoken

3712
04:39:53,250 --> 04:39:55,050
about just completely blows my mind.

3713
04:39:55,050 --> 04:39:59,798
It is amazing what you’re doing and fantastic
even just to sit and listen and understand

3714
04:39:59,798 --> 04:40:01,520
the edges of it.

3715
04:40:01,520 --> 04:40:05,468
But all the time I’m listening, I’m thinking
from, obviously, from my perspective, and

3716
04:40:05,468 --> 04:40:09,798
I guess from the wider perspective of maybe
[inaudible] perspective, too, which is about:

3717
04:40:09,798 --> 04:40:10,840
So, where’s the benefit?

3718
04:40:10,840 --> 04:40:12,760
Where’s the human benefit from this?

3719
04:40:12,760 --> 04:40:14,980
What are we going to do?

3720
04:40:14,980 --> 04:40:19,570
Everything you’re doing is predicated on
the basis that nutrition needs sorting out,

3721
04:40:19,570 --> 04:40:23,070
but undernutrition, overnutrition are having
terrible consequences for poor populations.

3722
04:40:23,070 --> 04:40:28,820
Where do…how much time do you guys spend
thinking about where this goes, this work,

3723
04:40:28,820 --> 04:40:30,860
or what we can do with it?

3724
04:40:30,860 --> 04:40:35,330
That’s a genuine question. It’s not a challenge. It’s a question.

3725
04:40:35,330 --> 04:40:40,420
DR. JACOB FRIEDMAN: I will jump in, just because
I also work on the social determinants of

3726
04:40:40,420 --> 04:40:42,620
health in Native Nations in Oklahoma.

3727
04:40:42,620 --> 04:40:47,878
And as I tell people, you know, I can do the
biology, but I don’t solve the sociology

3728
04:40:47,878 --> 04:40:54,350
of what prevents prenatal care; what prevents
proper nutrition in a food swamp, as they

3729
04:40:54,350 --> 04:40:59,100
call it; and what affects families, the social
dynamics of eating.

3730
04:40:59,100 --> 04:41:07,240
There are many things on the psychological
side that I think if we…we spend all our

3731
04:41:07,240 --> 04:41:11,060
time doing biology, we’re going to…we’re
still going to be, at the end of the day,

3732
04:41:11,060 --> 04:41:13,760
trying to figure out, like you say, the low-resource
persons.

3733
04:41:13,760 --> 04:41:17,830
The people who really need our help, they
have other problems that go along with their

3734
04:41:17,830 --> 04:41:24,958
biology, so in answer to your question, that’s
probably another symposium, but there are

3735
04:41:24,958 --> 04:41:34,121
both sides to this phenomenon that we’re
doing. Just my opinion.

3736
04:41:34,121 --> 04:41:40,040
DR. THADDEUS SCHUG: [inaudible]

3737
04:41:40,040 --> 04:41:49,840
AUDIENCE MEMBER: Yes. Great set of speakers. I have some…well, one question, really.

3738
04:41:49,840 --> 04:41:56,840
You know, in addition to the issue of reproducibility,
validity, timing of exposure, and so forth,

3739
04:41:56,840 --> 04:42:05,670
as I listen to all the speakers, you know,
I feel that there is a kind of a crosstalk

3740
04:42:05,670 --> 04:42:07,490
among the different topics.

3741
04:42:07,490 --> 04:42:18,150
So, my question relates to, you know, considerations
regarding obesity; adiposity; folic acid;

3742
04:42:18,150 --> 04:42:26,110
folates, which are different; and the microbiome—because
if you think about it…and I’m asking this

3743
04:42:26,110 --> 04:42:30,350
because I think different individuals and
different researchers have a different perspective.

3744
04:42:30,350 --> 04:42:37,370
So, if you think about obesity—and I didn’t
hear this mentioned at all—and the folate

3745
04:42:37,370 --> 04:42:44,170
metabolic pathway, for example, obesity has
an independent effect on folates, and we have

3746
04:42:44,170 --> 04:42:45,840
done this in controlled feeding studies.

3747
04:42:45,840 --> 04:42:52,430
And this has been established, it is reproducible,
it is found in the literature.

3748
04:42:52,430 --> 04:43:00,610
The microbiome has a connection with folate
because the bacteria needed to create nucleotides

3749
04:43:00,610 --> 04:43:04,990
for DNA synthesis and to produce short-chain
fatty acids.

3750
04:43:04,990 --> 04:43:15,110
Similarly, you can think about the, you know…you
want to go to the genetic aspect and things

3751
04:43:15,110 --> 04:43:22,990
like that, so I’m just thinking in terms
of the studies, like, obesity is very important.

3752
04:43:22,990 --> 04:43:23,990
We are seeing that.

3753
04:43:23,990 --> 04:43:29,840
Should we be thinking about…when we are
looking about the generational effects of

3754
04:43:29,840 --> 04:43:36,950
nutrition, should we be saying, “Okay, we
need ideas or account for differences in body

3755
04:43:36,950 --> 04:43:37,950
size?”

3756
04:43:37,950 --> 04:43:38,950
That’s one.

3757
04:43:38,950 --> 04:43:45,080
I’ll leave it at that, but I have more.

3758
04:43:45,080 --> 04:43:53,532
DR. SHELLY BUFFINGTON: So, in our mouse work,
you know, the first study that we published

3759
04:43:53,532 --> 04:43:56,440
in 2016, we did, like, an 8-week exposure
for a high-fat diet.

3760
04:43:56,440 --> 04:44:02,240
We saw a significant fat mass added to the
dams, but we actually cut that down for the…to

3761
04:44:02,240 --> 04:44:07,430
4 weeks’ exposure, and we saw the same phenotypes,
the F1 after 4 weeks of diet, but that did

3762
04:44:07,430 --> 04:44:12,491
not make the dams obese, and so we’re really
trying to start to separate this dietary exposure

3763
04:44:12,491 --> 04:44:16,310
driving the dysbiosis of the gut microbiome
from the obesity effects in our animals.

3764
04:44:16,310 --> 04:44:21,138
And I haven’t done the study yet, but I
think probably just a week of a high-fat diet

3765
04:44:21,138 --> 04:44:24,340
exposure would be sufficient to drive the
phenotypes that we see, and that’s, you

3766
04:44:24,340 --> 04:44:28,160
know, because we start to…that’s when
the microbiome starts to really shift.

3767
04:44:28,160 --> 04:44:33,330
We don’t see phylum-level changes until
about 4 weeks on diet, but I’m really interested

3768
04:44:33,330 --> 04:44:35,540
in that question: Can we separate the two?

3769
04:44:35,540 --> 04:44:39,150
And kind of like what you’re getting into,
and so that also has therapeutic implications,

3770
04:44:39,150 --> 04:44:40,150
right?

3771
04:44:40,150 --> 04:44:43,700
Because basically, that means, like, even
if a woman’s not obese, there could be dietary

3772
04:44:43,700 --> 04:44:48,090
changes that we’re all interested in here
that could be still targeted, and so we can’t

3773
04:44:48,090 --> 04:44:57,590
exclusively select patients to be within a,
you know, a therapeutic group just based on BMI, right?

3774
04:44:57,590 --> 04:45:03,250
AUDIENCE MEMBER: Just to follow up on that,
I’ll say we see something sort of similar

3775
04:45:03,250 --> 04:45:08,840
in our undernutrition studies where, you know,
4 weeks of an undernourished diet is not enough

3776
04:45:08,840 --> 04:45:15,878
to really change, like, parental body size,
and so we really don’t actually see the

3777
04:45:15,878 --> 04:45:17,590
effects until the F1 generation.

3778
04:45:17,590 --> 04:45:21,090
And even in conventional mice, when we give
the malnourished diet, we actually don’t

3779
04:45:21,090 --> 04:45:26,350
see a significant difference in body size
over about a 3-week treatment.

3780
04:45:26,350 --> 04:45:30,690
But interestingly, if we disrupt the microbiota
with antibiotics, then we do.

3781
04:45:30,690 --> 04:45:34,610
So, I think, you know, it’s complicated
teasing all of those things apart.

3782
04:45:34,610 --> 04:45:42,130
So, there does seem to be something that is
really dependent on having that maternal environment

3783
04:45:42,130 --> 04:45:48,999
that is, I think, separable from just sort
of body size, at least in these sort of controlled experiments that we’re doing.

3784
04:45:48,999 --> 04:45:51,879
DR. THADDEUS SCHUG: Thank you.

3785
04:45:51,879 --> 04:45:58,090
DR. JACOB FRIEDMAN: Yeah, you can…you can also
say that the AMA has just released a statement

3786
04:45:58,090 --> 04:46:02,800
about BMI, just so y’all know, that when
you’re considering a history of physical…of

3787
04:46:02,800 --> 04:46:10,170
a patient, the BMI is now no longer considered
to be the accurate description of their obesity

3788
04:46:10,170 --> 04:46:11,170
status.

3789
04:46:11,170 --> 04:46:15,850
You know, there’s the waist circumference,
there’s the comorbidities, but BMI really,

3790
04:46:15,850 --> 04:46:17,718
in my estimation, is just gravity.

3791
04:46:17,718 --> 04:46:23,560
It’s just…that’s all it is because we’re…we
all respond different to diet, and your BMI

3792
04:46:23,560 --> 04:46:27,090
could be a lot healthier than mine at the
same number.

3793
04:46:27,090 --> 04:46:31,128
So, I think the BMI problem is…is one of
just convenience, but I think it, you know,

3794
04:46:31,128 --> 04:46:38,379
it’s being recognized, as I said, by, you
know, medical societies as not the driver

3795
04:46:38,379 --> 04:46:41,580
of the problem.

3796
04:46:41,580 --> 04:46:45,570
These big obesity drugs, that’s a good example
of which…you know, you’re not going to

3797
04:46:45,570 --> 04:46:51,660
give it to somebody who has a healthy metabolic
state, who maybe has a BMI of 30.

3798
04:46:51,660 --> 04:46:55,270
You’re going to look to see: Do they have
hypertension?

3799
04:46:55,270 --> 04:46:57,990
Are they hyperlipidemic, and what is their
diabetes status?

3800
04:46:57,990 --> 04:46:58,990
And then you’re going to challenge the weight.

3801
04:46:58,990 --> 04:47:03,850
And we may need to do the same thing in pregnancy
at the end of the day.

3802
04:47:03,850 --> 04:47:07,760
A person comes to the…the clinic the first
time, they didn’t plan to be pregnant, but

3803
04:47:07,760 --> 04:47:13,170
they are pregnant, and you look at them, and
you say, “You know, what are your risk factors

3804
04:47:13,170 --> 04:47:14,880
for an unhealthy pregnancy?”

3805
04:47:14,880 --> 04:47:20,120
And your BMI is just one, but then when you
have to think about what comes along with

3806
04:47:20,120 --> 04:47:24,750
that BMI, that’s the more important thing
I think we need to start focusing on is, you

3807
04:47:24,750 --> 04:47:29,280
know, it’s not the BMI, per se.

3808
04:47:29,280 --> 04:47:33,312
It’s…you know, doing clinical research,
that’s what we bump into all the time.

3809
04:47:33,312 --> 04:47:44,071
You do a fat biopsy of these women at the
same BMI, the gene expression pattern could be totally different, for example.

3810
04:47:44,071 --> 04:47:51,520
AUDIENCE MEMBER: So, thank you. I learned a lot, and I have one very specific
question probably for Richard.

3811
04:47:51,520 --> 04:47:58,670
Interesting, you know, in a reproductive biology
perspective, you think it’s just one sperm

3812
04:47:58,670 --> 04:48:02,452
that gets the job done, but we know that’s
not true.

3813
04:48:02,452 --> 04:48:09,160
So, I was just curious: How do you factor
in that, you know, the old-fashioned thing

3814
04:48:09,160 --> 04:48:12,580
for people who can’t conceive is looking
at sperm count, but does that matter?

3815
04:48:12,580 --> 04:48:19,708
In other words, the milieu in which fertilization
occurs, would that be another factor?

3816
04:48:19,708 --> 04:48:21,400
Am I making sense?

3817
04:48:21,400 --> 04:48:25,940
So you know, it’s…the genetic package
is in that one sperm that makes it through,

3818
04:48:25,940 --> 04:48:28,690
but then there’s all the other stuff around.

3819
04:48:28,690 --> 04:48:29,760
Has that been looked at?

3820
04:48:29,760 --> 04:48:34,340
DR. RICHARD PILSNER: Right, and that’s where
we’re doing a lot of…we just finished

3821
04:48:34,340 --> 04:48:40,510
a metabolomic study in seminal plasma, too,
so we’re looking at a lot of different factors

3822
04:48:40,510 --> 04:48:41,950
outside of the sperm.

3823
04:48:41,950 --> 04:48:48,760
So, it may not, as you said, just be the genetic
or epigenetic material that’s in the sperm,

3824
04:48:48,760 --> 04:48:55,810
but it could be kind of its nourishment after
ejaculation or during the migration through

3825
04:48:55,810 --> 04:49:02,021
the epididymis, so there could be a lot of
factors that are kind of pushing the sperm

3826
04:49:02,021 --> 04:49:05,160
in one direction or in the other in terms
of sperm fitness.

3827
04:49:05,160 --> 04:49:13,030
AUDIENCE MEMBER: And the other…I think Dr.
Friedman commented, and I did want to add to that concern.

3828
04:49:13,030 --> 04:49:14,458
Like, well…how…what can we do better?

3829
04:49:14,458 --> 04:49:15,458
And then we know enough.

3830
04:49:15,458 --> 04:49:17,480
BMI has been useful.

3831
04:49:17,480 --> 04:49:22,650
It’s a good public health tool, but we know
it’s not the best measure of body composition

3832
04:49:22,650 --> 04:49:29,100
or metabolic health, and so we have terms
of metabolically healthy, obese, unhealthy,

3833
04:49:29,100 --> 04:49:36,160
so I think…I didn’t hear that, but from
the thinking of biomarkers of metabolic health

3834
04:49:36,160 --> 04:49:41,850
or nutrition, etc., that’s…I didn’t
see that, and how do we integrate that?

3835
04:49:41,850 --> 04:49:47,500
And a question for those of you from the sort
of environmental health or even the micro…you

3836
04:49:47,500 --> 04:49:55,490
know, the microbiome, etc.: Would love to
get your thoughts, but we talk about nutrition—and

3837
04:49:55,490 --> 04:50:02,250
as a nutritionist, myself included—as this
micronutrient and as this micronutrient, etc.,

3838
04:50:02,250 --> 04:50:04,520
but people eat food; they don’t eat nutrients.

3839
04:50:04,520 --> 04:50:11,120
And food is a vehicle for so many other things,
and how does…how could we do that integral,

3840
04:50:11,120 --> 04:50:13,320
so to speak, that it’s also delivering?

3841
04:50:13,320 --> 04:50:19,378
I mean, the additives of the high-fructose
corn syrup and the list goes on, right?

3842
04:50:19,378 --> 04:50:24,780
So, just wanted to hear your thoughts and
your evaluating these other factors.

3843
04:50:24,780 --> 04:50:27,590
How does one capture that?

3844
04:50:30,000 --> 04:50:34,250
UNIDIENTIFIED SPEAKER: I’m not going to
answer your question, but just to add to it

3845
04:50:34,250 --> 04:50:44,580
to say…just to say that…that I think it’s…just
one additional element that I think we need

3846
04:50:44,580 --> 04:50:51,320
to grapple with is not just the inadequacy
of BMI, but also thinking of all the different

3847
04:50:51,320 --> 04:50:56,540
developmental stages that we’re thinking
about as we’re thinking about children or

3848
04:50:56,540 --> 04:50:58,362
life course, in general.

3849
04:50:58,362 --> 04:51:05,090
Just, you know, Z scores doesn’t actually
either really get around the issue of just

3850
04:51:05,090 --> 04:51:13,230
what should we be considering as we’re…if
we’re thinking about anthropometry or an

3851
04:51:13,230 --> 04:51:18,510
alternative to it for children beyond head
circumference and waist circumference.

3852
04:51:18,510 --> 04:51:25,208
But, you know…but we need to somehow consider
variability in shape and…and we need better

3853
04:51:25,208 --> 04:51:30,980
tools for that, so I can’t answer it, but
just wanted to ask.

3854
04:51:30,980 --> 04:51:37,958
DR. THADDEUS SCHUG: Okay, we’ll do one final
question here and then wrap things up so you

3855
04:51:37,958 --> 04:51:40,330
can catch the bus back to the hotel.

3856
04:51:40,330 --> 04:51:46,000
AUDIENCE MEMBER: Hi, so, thank you all for
the wonderful presentations and very…I enjoyed it.

3857
04:51:46,000 --> 04:51:48,430
My question is for Dr. Pilsner.

3858
04:51:48,430 --> 04:51:55,920
I’m thinking about the…your findings where
you said sperm can impact the timing of delivery,

3859
04:51:55,920 --> 04:52:04,590
and from a public health implications perspective,
I was also wondering if your clock…if you

3860
04:52:04,590 --> 04:52:11,600
see your work moving towards loose guidelines
for conception because right now, as we know,

3861
04:52:11,600 --> 04:52:15,730
a lot of the recommendations are very…have
a very heavy emphasis on maternal age.

3862
04:52:15,730 --> 04:52:22,190
And so, you know, I see your clock kind of
playing a role in guiding fathers, as well.

3863
04:52:22,190 --> 04:52:26,878
DR. RICHARD PILSNER: Yeah. Thank you for that question.

3864
04:52:26,878 --> 04:52:33,718
Yeah, we’re really hoping that our clock
will inform clinical decisions in terms of

3865
04:52:33,718 --> 04:52:40,770
if we could potentially get people…couples
to submit a semen sample, and we could develop

3866
04:52:40,770 --> 04:52:46,880
a fecundity risk score to kind of estimate
the probability of them getting pregnant,

3867
04:52:46,880 --> 04:52:51,138
say, within 12 months, which is, you know,
the cutoff for clinical infertility.

3868
04:52:51,138 --> 04:52:58,280
You know, so that could kind of move people
into infertility treatment, IVF, sooner than

3869
04:52:58,280 --> 04:53:05,840
waiting, you know, a year or year and a half
of trying to naturally become pregnant.

3870
04:53:05,840 --> 04:53:09,378
So, that’s kind of our hope.

3871
04:53:09,378 --> 04:53:16,570
DR. THADDEUS SCHUG: Okay, so thank you to the…let’s
give a round of thanks to the speakers here,

3872
04:53:16,570 --> 04:53:21,860
and hand it back over to Krista and Ashley
to close things out.

3873
04:53:21,860 --> 04:53:26,310
DR. ASHLEY VARGAS: Great, so I will be brief.

3874
04:53:26,310 --> 04:53:31,610
I think today we have seen the complexity
of an area of research where we haven’t

3875
04:53:31,610 --> 04:53:35,160
really seen so many disciplines and people
brought together in one room to talk about,

3876
04:53:35,160 --> 04:53:39,790
so I think today was maybe a little jarring,
seeing the different topics that we’ve covered

3877
04:53:39,790 --> 04:53:40,790
today.

3878
04:53:40,790 --> 04:53:44,090
And that was intentional because we wanted
to bring you guys in this room to get all

3879
04:53:44,090 --> 04:53:47,620
you great minds together—and virtually,
I know some of you aren’t in the physical

3880
04:53:47,620 --> 04:53:52,638
room—to really think about how we can move
this field forward as a group, and maybe in

3881
04:53:52,638 --> 04:53:57,700
different directions, but as a group to think
together about the complexity of this problem.

3882
04:53:57,700 --> 04:54:00,130
And so, I don’t know if I can get up our
figure slide.

3883
04:54:00,130 --> 04:54:01,130
Oh, it’s coming.

3884
04:54:01,130 --> 04:54:02,130
Okay.

3885
04:54:02,130 --> 04:54:05,020
Hopefully it’ll come next…that I showed
you guys at the beginning.

3886
04:54:05,020 --> 04:54:09,460
I just want to reorient ourselves to that
figure slide.

3887
04:54:09,460 --> 04:54:10,708
Thank you.

3888
04:54:10,708 --> 04:54:15,780
And so today, we learned just about individual-level
factors, if you can believe it or not.

3889
04:54:15,780 --> 04:54:18,638
Tomorrow, we are going to learn about all
the things down here that are different colors

3890
04:54:18,638 --> 04:54:25,250
besides red, and we’re going to learn about
not only the individual-level factors, but

3891
04:54:25,250 --> 04:54:29,058
also the family- and societal-level factors
that contribute to nutrition’s effects across

3892
04:54:29,058 --> 04:54:30,058
generations.

3893
04:54:30,058 --> 04:54:34,450
And so, just to go back to that idea of nutrition
being a thread, and we’re building…I think

3894
04:54:34,450 --> 04:54:37,420
someone showed a quilt today, which I thought
was beautiful.

3895
04:54:37,420 --> 04:54:39,628
Sonia, I think, showed a quilt today.

3896
04:54:39,628 --> 04:54:44,590
So, we’re building the quilt of families
and, really, generations of families.

3897
04:54:44,590 --> 04:54:46,128
And so, we know it’s complex.

3898
04:54:46,128 --> 04:54:49,430
We thank you for hanging in there, and we
thank you for asking the tough questions.

3899
04:54:49,430 --> 04:54:54,480
And tomorrow, I really want you guys to think
about how we can move this field forward,

3900
04:54:54,480 --> 04:54:56,410
now knowing at least a big piece of the complexity.

3901
04:54:56,410 --> 04:55:02,458
Tomorrow we’re going to add more, but we’ve
really dabbled across so many different disciplines.

3902
04:55:02,458 --> 04:55:08,610
We’ve used so many different model systems
today: humans, primates, computerized models,

3903
04:55:08,610 --> 04:55:09,610
mice.

3904
04:55:09,610 --> 04:55:13,160
We’ve talked about so many different disciplines,
and that’s what it’s going to take to

3905
04:55:13,160 --> 04:55:14,160
move this field forward.

3906
04:55:14,160 --> 04:55:15,940
So, I want to thank all of our speakers today.

3907
04:55:15,940 --> 04:55:20,260
I’m going to end with some…just Cliffs
Notes on what’s going to happen next.

3908
04:55:20,260 --> 04:55:24,208
So, for those of you that are going back to
the hotel for the small get-together, the

3909
04:55:24,208 --> 04:55:27,708
shuttle will be at the Gateway Center at 5
p.m.

3910
04:55:27,708 --> 04:55:30,490
You have a little bit of time to walk there,
but not a huge amount.

3911
04:55:30,490 --> 04:55:35,542
And we will start tomorrow morning at 10 a.m.
as planned.

3912
04:55:35,542 --> 04:55:43,430
So, I believe the shuttle will be picking
you guys up in the hotel at 9 or 9:30.

3913
04:55:43,430 --> 04:55:50,290
We will get clarity on that…9; 9:00 tomorrow
morning to make sure you guys are here in

3914
04:55:50,290 --> 04:55:53,730
enough time to get some coffee and join us
for tomorrow’s session.

3915
04:55:53,730 --> 04:55:59,190
All right, thank you so much. We’ll close for today.