>> WELCOME, EVERYBODY.
OUR FIRST SESSION IS SCHEDULED
TO START AT 10:00.
LANCE WELLS WILL CHAIR THAT
SESSION WHICH IS ROUGHLY CALLED
GLYCOPROTEOMICS AND MULTI-OMICS.
BEFORE WE GET STARTED, RAJA AND
I WHO CO-ORGANIZED THE EVENT
WANTED TO EXPRESS OUR
APPRECIATION TO PAMELA MARINO
AND AMANDA, WHO HELPED US
COORDINATE TO GET EVERYBODY
TOGETHER.
WE HAVE OVER 320 REGISTRANTS
PARTICIPATING, SO THERE'S
OBVIOUSLY GREAT INTEREST IN WHAT
WE'RE TALKING ABOUT.
I WANT TO POINT OUT AT THE
BEGINNING TOO WE HAVE FOUR
SESSIONS THAT HAVE RATHER BROAD
TITLES.
MANY SPEAKERS MAY NOT SEEM LIKE
THEY FIT INTO THE TITLES,
REFLECTING DIFFICULTIES IN
SCHEDULED PEOPLE FROM AROUND THE
WORLD BUT ALSO REFLECTS THE FACT
THAT MANY OF THE THINGS WE'RE
TRYING TO TALK ABOUT HERE ARE
CROSS-CUTTING AND CUT ACROSS
DIFFERENT DOMAINS OF
BIOINFORMATICS AND BIOLOGY, AND
WE HOPE THAT FROM THIS TWO-DAY
SYMPOSIUM THEMES WILL EMERGE
THAT MAKE IT CLEAR
BIOINFORMATICS AND GLYCOSCIENCE
ARE MERGING TOGETHER AS A
PRODUCTIVE AREA OF INTERACTION
AND WE CAN LEARN FROM DOMAINS
THAT MAY BE MORE MANURE THAN
GLYCOINFORMATICS.
WE HAVE A SERIES OF 20-MINUTE
TALKS, AT THE END WE'LL HAVE A
20-MINUTE CHUNK OF TIME TO
DISCUSS ANYTHING ABOUT THOSE
20-MINUTE TALKS.
AS YOU HAVE QUESTIONS, PLEASE
FEEL FREE TO PUT THEM INTO THE
FEEDBACK SO FROM YOUR END YOU
SHOULD SEE A SEND LIVE FEEDBACK
BUTTON, CLICK AND INTEREST A
QUESTION THAT WILL SHOW UP IN
THE CHAT FOR THE PRESENTERS AND
FOR ORGANIZERS.
WE'LL RESPOND TO THOSE QUESTIONS
DURING THE 20-MINUTE DISCUSSION.
WE'LL GO BACK THROUGH THEM AND
MAKE SURE THAT EVERYBODY'S
QUESTIONS GET ANSWERED.
IF WE CAN'T ANSWER DURING THE
DISCUSSION THE SPEAKERS WILL
HAVE THE OPPORTUNITY TO RESPOND
DIRECTLY TO YOUR QUESTIONS AS
WELL.
WE'RE LOOKING FORWARD TO LIVELY
CONVERSATION AND HOPEFULLY NEW
CONCEPTS WILL EMERGE.
BEFORE I TURN IT OVER TO LANCE,
RAJA, ANYTHING YOU WANT TO ADD?
>> NO, LET'S GET STARTED.
THANK YOU ATTENDING, EVERYBODY.
THANK YOU, LANCE.
>> I'M GOING TO WAIT TILL
EXACTLY 9:59 TO INTRODUCE THE
FIRST SPEAKER.
FIRST SESSION IS GLYCOPROTEOMICS
AND MULTI-OMICS.
MIKE MENTIONED YOU CAN ASK
QUESTIONS THROUGH SEND LIVE
FEEDBACK WITH A 20-MINUTE PANEL
DISCUSSION AT NOON EASTERN TIME
FOR THIS ENTIRE SESSION.
YOU CAN PUT YOUR QUESTIONS IN
THE SEND LIVE FEEDBACK AND
SPEAKERS CAN ADDRESS THOSE AT
NOON.
THE FIRST SESSION IS
GLYCOPROTEOMICS AND MULTI-OMIC,
EACH INDIVIDUAL GIVES A 20
MINUTE TALK ON THE DOT.
WE'LL START OFF WITH DR.
STEPHANIE OLIVIER VAN STICHELEN
FROM MEDICAL COLLEGE OF
WISCONSIN TALKING ABOUT THE
O-GlcNAc DATABASE.
>> THANK YOU FOR THE
INTRODUCTION.
THANK YOU, MIKE, RAJA, PAM,
LINDA, THE TEAM BEHIND FOR
ORGANIZING THIS MEETING.
WE HAVE A GREAT PROGRAM TODAY,
I'M HAPPY TO START IT OFF.
TODAY I'M GOING TO TALK ABOUT
THE O-GlcNAc DATABASE AND WE
TRIED TO SORT OUT LITERATURE,
PROTEOMIC STUDY AND TRANSFORM
THAT INTO A FRIENDLY DATABASE
PEOPLE CAN USE FOR RESEARCH AND
HOPEFULLY BOOST THE FIELD
FORWARD.
SO IF YOU WANT TO GO INTO THE
DATABASE, THAT'S WHAT YOU NEED
TO DO, IT'S A COUPLE SLIDES
LATER AS WELL.
O-GlcNAc IS THIS ADDITION OF
ONE SUGAR, IT HAS BEEN A FIELD,
MY POSTDOC IS ON IT, MY LAB
STARTED THREE YEARS AGO WORKING
ON THIS AS WELL, BEFORE I
STARTED SCIENCE IN 1984 THE
FIRST PUBLICATION ON
O-GlcNAc WAS DONE, AND SINCE
THAT WE HAD THIS INCREASING
NUMBER OF PUBLICATIONS ON THIS
MODIFICATION AND WE'RE ABOUT
LIKE 200+ A YEAR AND THAT'S
INCLUDING ARTICLES, REVIEWS,
METHODS PAPERS, ET CETERA, AND
OVERALL WE'RE REACHING 2,000
PRIMARY ARTICLES ON
O-GlcNAc, YOU CAN IMAGINE
ANY NEW PURINE WE MAY HAVE
IDENTIFIED, RATIO IN RED,
ARTICLES THAT IDENTIFIED PURINE
THAT WOULD TENSIONALLY WOULD
NEED TO BE CONSOLIDATED INTO A
DATABASE.
THE PROBLEM IS THERE WAS A LOT
OF DIFFERENT WAYS TO IDENTIFY
O-GlcNAcYLATION, ENZYMEATIC
LABELING, THAT IS AN EASY WAY TO
SOMETIMES PULL THIS OUT, GETTING
THE HUGE EXCEL SPREADSHEET.
THE NANOBODY WAS A SPREADSHEET,
THAT'S EASIER.
IN VITRO O-GlcNAcYLATION AND
SITES ARE NOT REALLY FRIENDLY ON
AN EXCEL SPREADSHEET SO WE
NEEDED TO UNCOUPLE THOSE THAT
WERE EASIER.
THERE WAS AN ATTEMPT.
WE HAD AN O-GlcNAc DATABASE,
THE DBOGA, WELL USED AND CLOSED
EVENTUALLY AT SOME POINT.
IN 2020 WITH A REALLY TRAUMATIC
EVENT, CLICKING NOSE STICKING
EVERY DAY, THE COVID PANDEMIC,
MY LAB WAS OPEN FOR A YEAR AND A
HALF.
SO WE ENDED UP, ALL OF US AT
HOME, ALL MY LAB AT HOME.
IT WAS NOT OPEN FOR A LONG OF
TIME, HAD A TON OF PAPERS TO
WRITE.
WE WERE THINKING ABOUT WHAT TO
DO DURING THAT TIME AND THOUGHT
ABOUT WRITING A REVIEW, CATCH UP
ON OUR FAVORITE TV SHOWS, ET
CETERA, EVERYBODY PEOPLE DID AND
WE ADDED TO THIS, DECIDED TO
CREATE A DATABASE.
SO THAT'S WHAT CAME OUT OF IT.
WE PUBLISHED THIS IN JANUARY
2021, AND CALLED IT THE
O-GlcNAcOME DATABASE, TO GET
A SPREADSHEET SHEET AND DIDN'T
HAVE A STRUCTURE.
WE USED THE SPREADSHEET TO
ANALYZE AND HAVE A VIEW OF THE
HUMAN O-GlcNAcOME.
WE WERE AT THE SAME TIME TO
RELEASE A DATABASE FOR
O-GlcNAc, THE ATLAS.
WE BOTH THOUGHT ABOUT THE SAME
THING AND YOU SHOULD CHECK OUT
THIS ONE, A DIFFERENT WAY OF
DOING THINGS.
I'LL FOCUS ON OUR DATABASE
TODAY.
BUT THIS HAS MORE POTENTIAL.
HOW DID WE DO IT?
WE NEEDED TO DO AN INITIAL
CURATION OF ALL THE PAPERS AND
AVAILABLE LITERATURE ON THIS.
WE STARTED BY DOING A PubMed
SEARCH FOR O-GlcNAc WORDS I
WOULD SEARCH FOR, REMOVE THE
REVIEW, ON TOP OF THIS COMBINED
WITH THE OMIC DATABASE TO GET
THE LIST WE HAD A HARD TIME,
SUPPLEMENTARY DATA NOT AS
FRIENDLY, SO OVERALL WE ENDED UP
WITH 1700 ARTICLES.
WE EXCLUDED A BUNCH BECAUSE WE
NEEDED SOMETHING THAT WAS
FEASIBLE IN THE TIME WE HAD AND
SOMETHING WE DISCUSSED RIGHT
AWAY.
SO, WE STARTED WITH REMOVING ALL
OF THE NON-HUMAN MODELS, FOCUSED
ON THE HUMAN O-GlcNAcOME,
AND REMOVED REVIEWS, NON-PRIMARY
RESEARCH, SO ONCE WE REVIEWED
METHODS DEVELOPMENT, WE REMOVED
THOSE AS WELL.
WE ENDED UP WITH 755 ARTICLES
THAT WERE WORKING ON
O-GlcNAcOME IN HUMANS, HUMAN
MODELS, HUMAN TISSUE, 378
ARTICLES IDENTIFYING
O-GlcNAc PROTEIN.
IT'S A LOT OF MANUAL CURATION.
THAT'S THE ONLY WAY WE FOUND TO
START SOMETHING FROM IT.
WE'RE TRYING TO MAKE IT BETTER
FOR EVERYBODY.
THANKS TO THOSE AMAZING PEOPLE
THEY SPENT THEIR TIME WITH ME
LOOKING THROUGH ARTICLES,
TALKING ABOUT O-GlcNAc
TRYING TO FIND WHICH PROTEINS
WERE O-GlcNAcATEED.
AND THEN WE SEPARATED THEM,
THOSE THAT DIDN'T HAVE A SITE,
THEY DID MAPPING, SOMETIMES
POINT MUTATION, SOMETIMES BY
MASS SPECTROMETRY, 7,000
O-GlcNAc SITES AT THE END,
USING THE SEQUENCE AS A
REFERENCE.
ALL THAT WITH A REFERENCE NUMBER
FOR EACH PROTEIN CREATEED THE
DATABASE.
THANKS TO THOSE THAT HELPED US
TO PUT IT INTO A FORMAT
ACCESSIBLE TO EVERYBODY, AND IT
HELPS US DO THE QUALITY CONTROL.
EVENTUALLY FLORIAN CAME INTO THE
LAB AS A POSTDOC AND CREATED AND
DEVELOPED THE WEBSITE THAT IS
NOW THE DATABASE.
AND SO THIS WAS A MAJOR STEP
FORWARD, WE'RE DOING EVERYTHING
ON THAT WEBSITE AND UPDATE AS
WELL.
HOW DO WE EXTEND THE CURATION
AND HOW DO WE IMPROVE CURATION?
YOU CAN TELL THIS IS A HEAVY
MANUAL LABOR TO DO ALL OF THOSE
PAPERS BY HAND.
AND IF WE WERE ON TOP OF IT, I
THINK IT'S EASIER TO GET A PAPER
A WEEK OR SOMETHING DONE.
BUT WE STARTING WITH MORE THAN
2,000 PAPERS TO GO FORWARD,
HUMAN AND NON-HUMAN MODELS, AND
NOW WE HAVE ANOTHER BATCH OF
PAPERS WAITING TO BE CURATED AS
WELL SO WE'VE DONE THE HUMAN,
WHICH IS ABOUT 47% OF THE ENTIRE
PAPER LIST WE HAD.
AND NOW WE HAVE ALL OF THE OTHER
MODELS THAT ALSO IDENTIFY
O-GlcNAcATED PROTEIN, RATS,
MOUSE, MIXED USING DIFFERENT
CELL LINES FROM DIFFERENT
MODELS, ALSO LIKE A LOT OF
SMALLER REPRESENTATION MODELS
BUT ALSO A LOT OF INTERESTING
ONES WE WANT TO INCLUDE.
HOW DO WE TRY TO MAKE IT QUICKER
FOR EVERYBODY AND EASIER ON
EVERYBODY?
SO, WE IMPLEMENTED A BACKGROUND
SYSTEM FOR THE DATABASE.
FIRST WE'RE AUTOMATICALLY WITH
THE NEWLY PUBLISHED ARTICLES.
EVERYBODY MONTH SENDING A
REQUEST, TRYING TO SEARCH THE
NEW ARTICLES THAT WERE PUBLISHED
SO WE DON'T ACTUALLY HAVE TO
REDO THE INITIAL CURATION.
A SMALL STEP SAVED A LITTLE BIT
OF TIME AT A TIME, MAKING OUR
SYSTEM MORE VIABLE.
WE CREATED A FRIENDLY INTERFACE
FOR REVIEW.
THIS IS OUR BACKGROUND.
ACCESSIBLE TO ME AND STUDENTS
THAT ARE PARTICIPATING WITH
CURATION, THE MAIN POINT IS WE
CAN GIVE THE LOG-IN ALSO TO
STUDENTS THAT ARE COMING IN AND
WANT TO PARTICIPATE, AND WE'RE
HAVING EVERYBODY PARTICIPATE IN
THE SYSTEM.
HERE THIS IS THE INTERFACE WE'RE
GETTING, SO WE SEARCH THE NEW
ARTICLE, HAVE THE ABSTRACT, THEN
WE'RE DOWNLOADING THE PDF,
UPLOADING THE PDF ON THIS TO
READ IT QUICKLY.
AND THEN WE ARE -- WE HAVE A
FRIENDLY INPUT BOX, SO WE PUT IN
WHAT WE FOUND IN THAT PAPER TO
THE O-GlcNAc AND SAVE THE
UPDATE AND AT SOME POINT PUSHING
UPTAKE FOR THE WEBSITE.
AND THEN WE'RE USING TEXT MINING
AND MACHINE LEARNING TO
ACCELERATE CURATION, THAT'S WHAT
THOSE BOTTOM BOXES ARE.
WE USE OUR NETWORK AND TRAIN
THAT NEURONETWORK -- NEURAL
NETWORK.
WE'RE TRAINING THIS NEURAL
NETWORK TO RECOGNIZE PAPERS AND
THE TEXT IN THOSE PAPERS THAT
ARE LOOKING THAT THEY HAVE
IDENTIFIED O-GlcNAc SITES OR
PROTEIN.
WE GET BACK PREDICTIONS WHICH
ONE WOULD BE, YES, THERE'S
DEFINITELY AN O-GlcNAcYLATED
PROTEIN, AND TO ZERO, LIKELY
NOT, NO TRANSLATION IDENTIFIED
IN THAT PAPER, MOSTLY JOURNALS
WITH CHANGES.
WE'RE TRYING TO GET OUT SOME
INFO, THE NUMBERS, SO WE DON'T
HAVE TO SEARCH EVERY TIME, ALSO
POTENTIAL SITE, ET CETERA.
WHAT WE HAVE IS SENTENCES FROM
THAT PAPER THAT WE EXTRACT
MAKING THE DECISION FOR THE
NEURAL NETWORK AND HOPEFULLY
THOSE SENTENCES ARE CLEAR
ENOUGH SO WE KNOW THAT THIS
PROTEIN IS O-GlcNAcYLATED ON
THAT SITE.
IT HELPS THE DECISION BUT DOES
NOT REPLACE MANUAL CURATION,
HELPS CONFIRM CURATION SOMETIMES
WHEN THE STUDENTS ARE DOING IT
AND ARE NOT USED TO IT BUT IT
DOES NOT REPLACE IT FULLY.
I WANT TO MENTION WE'VE CREATED
A RESOURCE, EDUCATIONAL RESOURCE
FOR TEACHERS, STUDENTS,
SCIENTISTS THAT WOULD LIKE TO
LEARN NEURAL NETWORK AND USE IT
IN A FRIENDLY WAY.
THIS IS WITH EPYNN.NET, THIS
PAPER IS IN REVIEW RIGHT NOW.
THE PREDICTION FROM THE NEURAL
NETWORK LOOKS LIKE THIS.
THIS PAPER FOR EXAMPLE WE HAVE A
GOOD PREDICTION OF HAVING
O-GlcNAcYLATED PROTEIN, NO
WAY WE CAN DESIGN ON THIS ONE,
IT'S IN THE MIDDLE.
THIS WAS A DISCOVERY BY VIRTUAL
SCREENING, WE'RE NOT GOING TO
IDENTIFY, THAT'S A ZERO
PREDICTION.
SO, BECAUSE OF THAT SYSTEM WE
ARE ABLE TO MAKE THE CURATION
QUICKER AND KEEP ON TOP OF IT SO
WE STARTED OBVIOUSLY IN COVID
ABOUT MARCH 2020 TO DO THE FIRST
CURATION, ENDED IN NOVEMBER WITH
THE FIRST CURATION OF THE HUMAN
O-GlcNAcOME AND RELEASE OF
THE WEBSITE OF THE PAPER IN
JANUARY, AND THEN WE DID IN
MARCH 2021, SO ONLY THREE
MONTHS, WE DID THE ENTIRE OTHER
SET.
SO EVERY SINGLE OTHER SPECIES,
42 SPECIES AT THAT POINT, 14,000
PROTEINS, 11,000 SITES, ABOUT
2,000 ARTICLES THAT WERE DONE.
AND THEN THIS ENTIRE CURATION
AND SYSTEM IN THE BACKGROUND,
ALL THAT IS PUBLISHED IN THAT
OTHER PAPER.
NOW AS MUCH LAST MONTH WE'RE AT
43 EXPERIENCE, 16,000 PROTEINS,
1200 SITES.
AND OBVIOUSLY ALL OF THAT IS
BECAUSE OF THE -- NOT BECAUSE,
THANKS TO THE STUDENTS, THE
INITIAL CURATION AS WELL AS
EVERYBODY THAT CAME TO THE LAB
THAT WORKED ON THIS, THE
O-GlcNAc PROJECT, A
CONTRIBUTION TO TRY TO GET THAT
GOING.
SO, NOW WE'VE CREATED A WEBSITE
INTERFACE FOR THAT AS WELL,
RIGHT?
SO IN ADDITION TO TRYING TO KEEP
IT UP WE WANTED TO MAKE IT
FRIENDLY TO SEARCH AND USEFUL
FOR EVERYBODY IN DIFFERENT WAYS
BECAUSE PEOPLE ARE LOOKING FOR
DIFFERENT THINGS IN A DATABASE.
SOMETIMES YOU WANT TO SEEP THE
ENTIRE O-GLYCOME AT LOOK AT ONE
PROTEIN.
CAN YOU GET INTO SEARCHING FOR
YOUR PROTEIN OR EXPLORE FOR ONE
SPECIES IN PARTICULAR IF YOU
WANTED TO.
BUT, FOR EXAMPLE, FROM THIS WE
NOW HAVE THE PROTEIN WITH
HIGHEST NUMBER OF O-GlcNAc
SITES DEFINED TO DATE, THAT
WOULD BE 14 WITH 185, I WAS
SURPRISED BY THE HIGHER NUMBER
ON ONE PROTEIN, THAT WAS PRETTY
CRAZY.
AND THEN WE ALSO WERE ABLE TO
COME OUT, WE HAD A LOT OF
SEQUENCE, THERE WAS A LOT OF
THOSE PUBLISHED AROUND THE --
WE'RE TRYING TO MAKE ONE THE
MOST UP TO DATE SO WE KNOW
THERE'S NO EXACT SEQUENCE BUT
SOMETHING WE KNOW, ALANINE PLUS
TO, PROLINE, SOMETIMES WE'RE
GETTING MORE FOR MINOR SPECIES,
WE CAN UPDATE FOR THOSE AS WELL
BUT FOR NOW HAVING IT FOR HUMAN,
RATS, MOUSE, I BELIEVE.
AND THEN WE'RE TRYING TO GET
BETTER AT THE OTHER ONE BUT NEED
A GOOD BASE OF NUMBER OF
O-GlcNAc SITES AS WELL.
IF YOU WANT TO KNOW YOUR
EXPERIMENT, YOU CAN GO INTO THIS
AND INPUT ALL OF YOUR PROTEIN
IDENTIFIED FROM YOUR STUDY AS
WELL AS THE SITE, IT WILL COME
BACK AS YES OR NO, IS IT IN THE
DATABASE.
SO IF YOU PUBLISH SOMETHING,
IT'S NOT IN THE DATABASE, YOU'RE
LIKE, WHAT?
SO WE HAVE AN INPUT SYSTEM.
YOU CAN -- WE'RE HUMAN AND WE'RE
DOING EVERYTHING MANUALLY.
SO WE HAVE AN INPUT PAGE PEOPLE
CAN SEND THEIR REQUEST.
WE'VE HAD THAT IN THE PAST AND
HAVE INCLUDED THEM.
IF YOU'RE PROTEIN OF INTEREST IS
O-GlcNAcYLATED, YOU WANT TO
KNOW.
THIS IS WHAT YOU GET.
WE'RE MAPPING THIS ON THE
SEQUENCE AS WELL AS PUBLIC
DATABASES AS WELL.
AND POTENTIALLY IF YOU HAVE A
DUAL SIDE O-GlcNAc, WE HAVE
THAT.
WE CROSS-REFERENCE, , YOU HAVE
ALL THE REFERENCE OF THE
PROTEINS, IF YOU WANT TO GET THE
LIST OF EVERYTHING SHOWN, EVERY
SINGLE PAPER, THIS IS WHERE YOU
WOULD HAVE IT, CLICK ALL
REFERENCE AND GET THE LIST.
WE CROSS-REFERENCE WITH OTHER
DATABASES, GlyGen IF YOU
WANT TO GO BACK, AND COMMENTS SO
PEOPLE CAN PUT COMMENTS IF YOU
THINK THERE'S SOMETHING WRONG.
IF THERE IS ANOTHER SPECIES YOU
WILL SEE IT IF IT HAS THE SAME
NAME.
HOW CONFIDENT ARE WE?
HOW DO WE DEFINE PROTEINS, WHICH
METHODS ARE VALID, VERSUS NONE,
SO WE DID NOT WANT TO MAKE THE
CHOICE SO WE CAME UP WITH
O-GlcNAc SCORING SYSTEM.
THE SCORE WE HAVE FOR EACH
PROTEIN CONSISTS OF THIS.
IT'S A NUMBER OF PROTEINS THAT
PUBLISHED THIS, NUMBER OF
CITATIONS THOSE REFERENCES,
EVERYTHING IS NORMALIZED BETWEEN
0 AND 1.
TIME SPENT BETWEEN PUBLICATION,
SO IF IT'S BEEN IDENTIFIED 20
YEARS AGO AND AGAIN, WITH THE
CHANGES IN INSTRUMENTATION AND
ALL, IT'S PROBABLY A GOOD SIGN
OF STILL BEING IDENTIFIED AGAIN.
IF IT WAS IDENTIFIED 20 YEARS
AGO MIGHT HAVE BEEN A DEFOLD OF
FAULT IN THE MACHINE CREATING
FALSE IDENTIFICATION AT THAT
POINT IF IT'S NOT IDENTIFIED
SINCE THEN, IT'S PROBABLY NOT A
REDEFINE.
AND A NUMBER OF FIRST AND LAST
AUTHORS ON THIS PAPER WE CAME UP
WITH THIS THING LIKE DIFFERENT
INSTRUMENTATION CAN BRING
DIFFERENT ERRORS, MULTIPLE FIRST
AND LAST AUTHORS HAVE COME UP
WITH THE SAME IDENTIFICATION,
IT'S A GOOD SIGN AS WELL.
WE COME UP WITH A BONUS AS WELL,
MOST OF THE ERRORS USUALLY COME
FROM BIG PROTEOMIC STUDY WHERE
WE HAVE A LOT, BIG MASS SPEC
STUDY DOES NOT GET AS MUCH AS A
BONUS AS SOMETHING WE DO, A
POINT MUTATION OF A PROTEIN TO
SHOW O-GlcNAcYLATION.
WE COME UP WITH THAT LINE, THIS
IS REALLY NOT A CONFIDENT
IDENTIFICATION AND EVERYTHING
THAT'S UP AND AFTER BECOMES
SIGNIFICANT TO US AND HAS A
COUPLE REFERENCES, A COUPLE
YEARS APART, SOUNDS LIKE A GOOD
PROTEIN.
OBVIOUSLY WE'RE SAYING TO
EVERYBODY THAT EVERYTHING SHOULD
BE DOUBLE CHECKED.
AND THAT'S WHAT THE O-GlcNAc
SCORE LOOKS LIKE.
THIS PROTEIN, LOW GlcNAc
SCORE, ONLY TWO REFERENCE, ONE
IN PARTICULAR PUBLISHED, THREE
CITATION TOTAL, ONLY ONE YEAR,
BASICALLY NOTHING IN BETWEEN.
IT'S LOST IN THE BIG PROTEOMIC
STUDY, A MORE FOCUSED STUDY WILL
HELP DEFINE THAT ONE.
YOU HAVE SOMETHING IN THE MIDDLE
WITH FOUR YEARS BETWEEN
IDENTIFICATION AND KIND OF LIKE
PROTEOMIC STUDY AND WE'RE
GETTING REALLY CONFIDENT ON THAT
ONE.
SOMETHING LIKE THIS ACTUALLY HAS
A LOT OF CITATIONS, EIGHT YEARS
BETWEEN, FOCUS STUDY IN I.T.
MUTATION, ET CETERA, TWO
REFERENCE BUT TWO TEAMS.
THAT'S AN IDEA WHAT THIS IS,
KNOWING WE'RE ONLY DECIDING THAT
THE REALLY LOW SCORE IS NOT
GOOD, IDENTIFICATION, EVERYTHING
AFTER THAT SHOULD BE CONFIDENT.
I'M GOING TO CONCLUDE, I THINK
I'M ON TIME.
WE'VE DONE THIS EXTENSIVE
CATALOG OF O-GlcNAcYLATED
PROTEIN, TRIED TO USE MINING AND
MACHINE LEARNING TO ACCELERATE
CURATION BUT IT IS OBVIOUSLY
SOMETHING THAT WE'RE TRYING TO
IMPROVE AS WELL BECAUSE TEXT
MINING IN THE PAPERS IS HARD
BECAUSE THEY ARE STRUCTURED
DIFFERENTLY.
BUT THAT ALLOWS BIMONTHLY
UPDATE, MOSTLY BY STUDENTS,
DOUBLE CHECKED BY MORE SENIOR
STUDENT OR MYSELF, TRYING TO BE
IMPARTIAL IN IDENTIFICATION NOT
CHOOSE CHOOSING WHICH ARE GOOD
OR NOT GOOD TECHNIQUE, TRYING TO
SCORE RESULT OBJECTIVELY.
CREATED A FRIENDLY INTERFACE FOR
DATA AND EVERYBODY FIND THEIR
WAY.
WE KNOW THERE'S ROOM FOR
IMPROVEMENT, WE LOVE FOR YOU TO
LEAVE COMMENTS, FEEDBACK,
SUGGESTIONS, AND OUR DEVELOPERS
JUST MOVE ON TO ANOTHER JOB SO
WE'RE GOING TO HAVE TO FILL THAT
SPOT AS WELL.
HOPEFULLY SO EVERYTHING DONE BY
STUDENTS MOSTLY, HEY, WE SHOULD
DO THIS.
SO THIS IS THE TEAM THAT DID ALL
THE WORK AND I'M SO GRATEFUL
DURING COVID, IT WAS A GREAT
THING TO DO AND I'M SO HAPPY
ABOUT IT BUT I HAVE TO
ACKNOWLEDGE SUPER CREATIVE DOING
IT.
WE DIDN'T HAVE A DEVELOPER, RAJA
HELPED A LOT.
THANKS FOR PUTTING THAT OFF THE
GROUND AND THEN THE TEAM THAT IS
DEVELOPING EPYNN, MAKING IT
FRIENDLY TO USE NEURAL NETWORK,
AND EARLY DEVELOPMENT OF THE
WEBSITE AND TESTING.
CHECK OUT YOUR PROTEIN PLEASE.
>> ALL RIGHT.
THANK YOU, STEPHANIE.
REMINDER TO EVERYONE, YOU CAN
PUT YOUR COMMENTS IN THE --
CLICK ON SEND LIVE FEEDBACK.
STEPHANIE WILL ADDRESS
QUESTIONS, APPROXIMATELY AROUND
NOON EASTERN TIME TODAY.
NEXT SPEAKER IS JOSHUA SHULMAN
FROM BAYLOR COLLEGE OF MEDICINE,
TALKING TO US ABOUT FUNCTIONAL
GLYCOMICS OF THE LYSOSOME IN
PARKINSON'S DISEASE.
TAKE IT AWAY, JOSH.
>> OKAY.
YOU SEE MY SCREEN OKAY?
>> YES, SIR.
>> PERFECT.
THANKS TO EVERYONE, ORGANIZERS
FOR INVITING ME TO TALK HERE.
I'M GOING TO SHIFT GEARS AND
TELL YOU ABOUT OUR WORK ON
PARKINSON'S DISEASE WHICH
OVERLAPS WITH GLYCOMICS AND
GLYCOINFORMATICS.
SO, THE LYSOSOME OBVIOUSLY IS A
HUB FOR HOMEOSTASIS OF COMPLEX
CARBOHYDRATES INCLUDING
GLYCOLIPIDS AND GLYCOPROTEINS,
AND IT'S ALSO A HUB FOR RISK AND
PATHOGENESIS OF PARKINSON'S.
WE RECOGNIZE AN IMPORTANT ROLE
FOR LYSOSOME IN THE TURNOVER OF
ALPHA-SYNUCLEIN PROTEIN WHICH
FORMS THE LEWY BODY, TINY
PATHOLOGIC FEATURE OF
PARKINSON'S DISEASE.
ALPHA-SYNUCLEIN ITSELF BASED ON
A LARGE BODY OF WORK DISRUPTS
LYSOSOMAL TRAFFICKING AND
AUTOPHAGY, SO LYSOSOMAL FUNCTION
IS DISRUPTED BY ALPHA-SYNUCLEIN.
AND WHAT'S MOVE WHEN THE
LYSOSOME IS DYSFUNCTIONAL,
SUBSTRATES INCLUDING
GLYCOLIPIDS, PARTICULARLY
SPHINGOLIPIDS AND SERUMIDES
APPEAR TO TRIGGER
ALPHA-SYNUCLEIN.
AS ILLUSTRATED IN THE SCHEMATIC
MANY PARKINSON'S DISEASE GENES
ARE IMPLICATING LYSOSOMAL
BIOLOGY AS IMPORTANT FOR DISEASE
RISK.
I'M GOING TO TELL YOU TWO SHORT
VIGNETTES, FIRST RELATES TO THE
RETRO MER.
IN 2011, AUTOSOMAL VARIANTS
CAUSED LATE ONSET PARKINSON'S
DISEASE, AND VPS35 ENCODES A
CORE COMPONENT OF THIS CELLULAR
COMPLEX, RETROMER, TRIMERIC AND
CONTAINS 29 AND 26.
THE RETROMER HAS AN IMPORTANT
ROLE IN TRACKING IN THE SYSTEM,
IN PARTICULAR A ROLE IN THE
TRAFFICKING OF A NUMBER OF
LYSOSOMAL ENZYMES, AND RETROMER
IS STUDIED IN A VARIETY OF CELL
TIMES BUT ROLE IN NEURONS AND IN
AGING BRAIN IS STILL RATHER
POORLY UNDERSTOOD.
VPS35 INCLUDING THE FRUIT FLY
ARE LETHAL, ESSENTIAL GENES, AND
WE GOT LUCKY BECAUSE IT TURNS
OUT WHEN YOU MUTATE VPS29 WE
CAN -- WE HAVE VIABLE FLIES WE
CAN STUDY AS THEY AGE INCLUDING
STUDIES OF THEIR ADULT BRAIN.
SO I WANT TO SHOW WHAT HAPPENS
IN VPS29 MUTANT FLIES.
FIRST OF ALL, THIS IS THE
DROSOPHILA ADULT BRAIN,
EXPRESSION OF VPS29 AND 35, TWO
COMPONENTS OF THE RETROMER, THEY
ARE VERY STRONGLY OVERLAPPING
AND LARGELY CO-LOCALIZING.
AND HIGHER MAGNIFICATION ASSUME
A PUNCTATE DISTRIBUTION WITHIN
THE CYTOPLASM CONSISTENT WITH
THE PATHWAY.
IN THE VPS29 NULL ANIMALS,
VIABLE, AS WE AGE THEM THEY HAVE
REDUCED LIFESPAN WHICH WE CAN
RESCUE WHEN WE GIVE BACK VPS29
WILDTYPE FUNCTION, AS WE AGE
ANIMALS CLIMBING IS ALSO
IMPAIRED.
INTERESTINGLY, IN THE VPS29
MUTANTS, VPS 35 APPEARS TO BE
MISLOCALIZED.
WE SEE VPS 35 FORMING PUNCTA
THAT ARE PERINUCLEAR.
WHAT HAPPENS AT A GRANULAR
LEVEL?
IT APPEARS SYNAPTIC TRANSMISSION
IS IMPAIRED, I'M SHOWING HERE
HIGH FREQUENCY STIMULATION OF
THE DROSOPHILA RETINA, AND WE
SEE GRADUAL AND PROGRESSESSIVE
REDUCTION OF TRANSIENT
POTENTIALS THAT REFLECT SYNAPTIC
TRANSMISSION.
THIS IS A PHENOTYPE TYPICAL OF
SYNAPTIC VESICLE RECYCLING
MUTANTS, AND IN FURTHER STUDIES
OF THE NEUROMUSCULAR JUNCTION
WHERE WE USE FLUORESCENT DYE TO
LOOK AT UPTAKE AND REPLENISHMENT
WE SEE A LOSS IN VPS29 UNITS.
WE STUDY MATURATION OF LYSOSOMAL
ENZYMES, WE SEE THAT THERE'S A
DISRUPTION THAT IS AGE DEPENDENT
IN THE VPS19 MUTANT IN RED WITH
ACCUMULATION OF IMMATURE PRO
FORMS.
ELECTRON MICROSCOPIC LEVEL
PROFOUND DISRUPTIONS.
WE'RE WITHIN THE DROSOPHILA
RETINA WE SEE VERY ENLARGED
LYSOSOMAL STRUCTURES FILLED WITH
ELECTRON DENSE MATERIAL WHICH
CLEARLY REPRESENT UNDIGESTED
SUBSTRATES.
WE SEE SIMILAR ACCUMULATIONS IN
THE ADULT FLY BRAIN, AND I THINK
THESE LOOK LIKE LITTLE BACLAVA,
APPEARANCE OF LITTLE LYSOSOMES,
LIPID LAYERS ACCUMULATING SO A
SIGNIFICANT LYSOSOMAL FAILURE IN
THESE MUTANT FLIES.
WE'VE DONE UNBIASED MASS SPEC
PROTEOMICS COLLABORATIVELY WITH
NICK SEYFRIED AT EMORY, IN OUR
VPS 29 MUTANT SLIDES COMPARED TO
CONTROLS, LOOKED AT FLY HEADS.
ONE REMARKABLE RESULT OF THE
PROTEOMIC STUDIES WAS A VERY
LARGE NUMBER OF SUBUNITS OF THE
VACUOLE AIR VACUOLER APRs.
I WANTED TO HIGHLIGHT I'M
SHOWING A TABLE OF OTHER
LYSOSOMAL ENZYMES INCLUDING
ENZYMES WITH IMPORTANT ROLES IN
METABOLISM OF COMPLEX
CARBOHYDRATES INCREASED IN THE
VPS29 MUTANT, FLY GENE AND HOME
HOMOLOGS.
MANY CAUSE LYSOSOMAL STORAGE
DISORDERS IN HUMANS, AND I
SPECULATE IT MIGHT REFLECT A
COMPENSATORY CHANGE IN THE
SYSTEM IN THE FACE OF A LYSOSOME
THAT IS DYSFUNCTIONAL.
WE'VE ALSO IN PUBLISHED WORK BY
A STUDENT, NOW A BACH IN MY LAB,
TWO HAS DONE WHAT I'M SHOWING,
IN THE VPS 29 MUTANT GLUCOSAL
CERAMIDE ACCUMULATES, GREEN IN
THE RETINA IN THE ABSENCE OF
RETROMER FUNCTION.
ACCUMULATION IS A DEFINING
FEATURE OF GAUCHER DISEASE, AND
I'M SHOWING HERE THE APPEARANCE
OF A GAUCHER CELL, A LIPID-LADEN
MACROPHAGE THAT IS DIAGNOSTIC OF
GAUCHER DISEASE.
THIS IS A RECESSIVE LYSOSOMAL
DISORDER WITH ACCUMULATION DUE
TO LOSS OF ENZYME GBA, AND YOU
MAY KNOW THAT ABOUT TEN YEARS
AGO, SIDRANSKI'S GROUP BEGAN TO
RECOGNIZE THE FAMILIES OF PRO
BAN PATIENTS WITH GAUCHER'S
DISEASE, FAMILIES APPEAR TO BE
ENRICHED FOR RISK FOR
PARKINSON'S DISEASE, THE
PEDIGREE, WHO GRANDFATHERS HAVE
PARKINSON'S DISEASE, ONE PARENT
HAS A TREMOR THAT MIGHT BE
CONSISTENT OF EARLY PARKINSON'S
DISEASE.
AND SO IN SUBSEQUENT WORK THAT
IS NOW A LANDMARK STUDY OF THE
PARKINSON'S DISEASE GENETICS
FIELD, THOUSANDS OF CASES
CONTROLS WERE ASSEMBLED, AND IT
WAS SHOWN IF YOU CARRY ONE OF
THESE GAUCHER-CAUSING MUTATIONS,
HETEROZYGOUS, AT LEAST A
FIVE-FOLD INCREASED RISK OF
DEVELOPING PARKINSON'S DISEASE.
ABOUT FIVE YEARS AGO MY TEAM
DECIDED IN COLLABORATION WITH
THE INTERNATIONAL PARKINSON'S
DISEASE GENOMICS CONSORTIUM TO
ASK THE QUESTION OF WHETHER
BEYOND GBA, WHICH CAUSES
GAUCHER'S DISEASE, DO OTHER
LYSOSOMAL DISORDERS AFFECT
PARKINSON'S DISEASE DISEASE OR
RISK.
WE DID THIS USING A GENETIC
APPROACH EXAMINING RARE DAMAGING
VARIANTS ACROSS 54 LYSOSOMAL
STORAGE GENES AND AGGREGATES.
AND WE ARE SHOWING THE DISCOVERY
AND REPLICATION COHORT THAT WERE
SIZABILITY, IMPORTANT
DIFFERENCES BETWEEN THE
EPIDEMIOLOGY OF PARKINSON'S
DISEASE IN EACH OF THE COHORTS
BUT SAW CONSISTENT RESULTS
ACROSS THESE TWO INDEPENDENT
DATASETS.
THAT WAS THAT WHEN WE LOOK AT
THE VARIANT BURDEN INNING A ,
THERE WAS ENRICHMENT.
PATHOGENIC OR RARE DAMAGING
VARIANTS IN ASSOCIATION WITH
PARKINSON'S DISEASE RISK AND WE
CAN REMOVE GBA FROM THE ANALYSIS
ENTIRELY AND WE COULD STILL SEE
RISK.
THIS RESULT PUBLISHED IN 2017
SUGGESTS BEYOND THE GBA GENE A
BURDEN IS BROADLY ASSOCIATED
WITH INCREASED RISK OF
PARKINSON'S DISEASE.
AND SO KIND OF A CONCEPTUAL
MODEL FOR THINKING ABOUT THIS IS
THAT WITH NEAR ABSOLUTE LOSS OF
FUNCTION IN THESE GENES, YOU
MANIFEST LYSOSOMAL STORAGE
DISORDER.
WITH MODEST PARTIAL LOSS OF
FUNCTION, COMBINED WITH AGING,
LATE IN LIFE YOU APPEAR TO HAVE
A RISK OF PARKINSON'S DISEASE.
ONE CAN IMAGINE HYPOTHETICALLY
THAT THIS WOULD BE DUE TO
ALTERED LYSOSOMAL FUNCTION AND
HOMEOSTASIS AND PROTEOSTASIS OF
PROTEINS LIKE ALPHA-SYNUCLEIN.
NOW, ONE OF THE CHALLENGES WITH
THIS WORK IS THESE VARIANTS ARE
RARE, WE DID NOT HAVE POWER IN
THE STUDY TO TELL WHICH SPECIFIC
STORAGE DISORDER GENES BEYOND
GBA WERE ASSOCIATED.
THERE ARE SOME CLUES FROM OTHER
STUDIES.
FOR EXAMPLE SMPD 1 HAS BEEN
IMPLICATED INPARKINSON'S DISEASE
RISK INDEPENDENTLY BUT WE'VE
BEEN TRYING TO GETS AT SPECIFIC
GENES AND MECHANISMS.
ONE APPROACH WE'VE TAKEN A
LESSON FROM EARLY WORK BY
SIDRANSKI, BEGAN TO RECRUIT
FAMILIES OF PATIENTS WITH
LYSOSOMAL STORAGE DISORDERS, THE
SONIC PROJECT WITH A COUPLE
DOZEN FAMILIES RECRUITED AND WE
DO EXTENSIVE INTERVIEWS OF THE
FAMILIES TO LEARN IF THERE WAS A
HISTORY OF THOSE WITH
PARKINSON'S DISEASE OR SIGNS OF
PARKINSON'S DISEASE AND WE'RE
BEGINNING TO RECRUIT POTENTIAL
CARRIERS FOR GENOTYPING AND
EXAMINATION.
ONE PEDIGREE SHOWN HERE IN WHICH
THE CHILD, PROBAND, HAS A
DISORDER, MUCOPOLYSACCHARIDE,
TWO GENERATIONS REMOVED THERE
WAS A FAMILY MEMBER WITH
PARKINSON'S DISEASE, AND IN BOTH
ONE AND TWO GENERATIONS REMOVED
THERE ARE INDIVIDUALS WHO HAD
PARKINSONIAN SIGNS USING
VALIDATED QUESTIONNAIRE
INSTRUMENT.
IT'S CHALLENGING WITH HUMAN
STUDIES ALONE TO GET AT THE
LEVEL OF PRECISION WE'RE LOOKING
FOR SO WE'VE TURNED AGAIN TO
FRUIT FRY MODELS.
WE OF -- FRUITS OF FRUIT FLY
MODELS.
WE HAVE MODELS OF PARKINSON'S
DISEASE, WE EXPRESS
ALPHA-SYNUCLEIN WHICH FORMS LEWY
BODY THROUGHOUT THE NERVOUS
SYSTEM, WITH DOPAMINERGIC LOSS,
AS YOU AGE THERE'S PROGRESSIVE
LOCOMOTORROR DYSFUNCTION.
WE DID A GENETIC SCREEN OF THE
HOMOLOGS IN FRUIT FLY.
80% ARE CONSERVED IN FLIES,
FUNDAMENTAL METABOLIC ENZYMES.
THE TEST, EACH OF THE GENES WHEN
MANIPULATED IN THE FRUIT FLY AS
TO WHETHER OR NOT THEY MODIFY
ALPHA-SYNUCLEIN-INDUCED
LOCOMOTOR IMPAIRMENT, THIS WORK
WAS DONE BY GRADUATE STUDENT IN
MY LAB IN COLLABORATION WITH MY
COLLEAGUE AT BAYLOR, YU AND
BOTAS.
YOU CAN SEE ON THE RIGHT SLOWER
CLIMBING THAN CONTROLS.
WE'RE USING A ROBOTIC-ASSISTED
LOCOMOTOR SCREENING ASSAY WITH
VIDEO TRACKING, TESTED 94
HOMOLOGS OF 40 CONSERVED HUMAN
LYSOSOMAL STORAGE DISORDER GENES
WHICH INVOLVED LOOKING AT MORE
THAN 300 RNA INTERFERENCE
KNOCKDOWN STRAINS AND FOLLOWED
LOOKING AT OVEREXPRESSION OF
ENZYMES WHERE POSSIBLE, WHEN THE
REAGENTS WERE AVAILABLE.
SO THESE ARE SOME OF THE
REPRESENTATIVE RESULTS.
AND SO WHAT I'M SHOWING, I WILL
FOCUS ON THE RIGHT, THE FLY GENE
IS THE HOMOLOG OF NPC1, YOU CAN
SEE AGE DEPENDENT CONTROL
ANIMALS, NPC1A OVERLAPPING WITH
CONTROLS.
HERE IS THE ALPHA-SYNUCLEIN.
PROGRESSIVE LOCOMOTOR
DYSFUNCTION.
WHEN YOU REMOVE 50% OF NPC1A
STRONG ENHANCEMENT OF LOCOMOTOR
PHENOTYPE CAUSED BY
ALPHA-SYNUCLEIN.
SO, WE'VE THROUGH THE SCREEN
IDENTIFIED 15 LYSOSOMAL
IDENTIFIEDERS OF TOXICITY,
POTENTIALLY QUITE INTERESTING
THAT THE GENES COLLAPSE INTO
METABOLIC PATHWAYS INCLUDING
THOSE RELEVANT TO GLUCOBIOLOGY,
BESIDES GBA WHICH WE ALSO
RECOVERED FROM THE SCREEN
INVOLVED IN SINGLE LIPID
METABOLISM, CHOLESTEROL
METABOLISM.
SEVERAL GENES HAVE INDEPENDENT
EVIDENCE FOR HUMAN GENETIC RISK
OF PARKINSON'S DISEASE FROM OUR
OWN WORK AND OTHER PUBLISHED
WORK.
AND WHAT WE'RE NOW TRYING TO DO
IS MAP SOME FINDINGS ONTO
METABOLIC PATHWAYS, AND BRINGING
IN METABOLOMICS.
SO USUALLY THIS IS WHERE I STOP
BUT I JUST SAY THIS IS WORK IN
PROGRESS BUT BECAUSE OF THE
THEME OF THE SESSION, MULTIOMIC
INTEGRATION, I THOUGHT I WOULD
STRUGGLE THROUGH ONE ADDITIONAL
SLIDE OF WHAT WE'RE TRYING TO DO
WHICH IS VERY PRELIMINARY BUT A
ROTATION STUDENT HELPED US PULL
TOGETHER THIS DATA TO SHOW YOU
AS A PROOF OF PRINCIPLE.
WE'VE OBTAINED MASS SPEC
LIPIDOMICS FROM ALPHA-SYNUCLEIN
AND CONTROL ANIMALS, THIS IS
FROM FLY HEADS.
WE LOOKED AT TWO TIME POINTS, 5
AND 20 DAYS, DETECTED SENSE
SENSITIVELY LIPIDS.
THERE WAS SIGNIFICANT INCREASE
IN HEX 1 CERAMIDE, FOCUSING ON
THIS TWO ADJACENT METABOLIC
TRANSFORMATIONS BECAUSE THE GOAL
OF THIS TYPE OF INTEGRATION AT
LEAST FROM PERSPECTIVE WE'RE
TAKING IS TRY TO UNDERSTAND
MECHANISM BY WHICH HEXCER WOULD
ACCUMULATE IN PRESENCE OF
ALPHA-SYNUCLEIN IN OUR
PARKINSON'S DISEASE FLY MODEL.
WHAT WE NEXT DO IS OVERLAY
LONGITUDINAL TRANSCRIPTOME
PROFILES FROM THE SAME MODELS.
WE HAVE DATA FROM MANY TIME
POINTS AS YOU CAN SEE HERE.
WE COMPUTE AGE-ADJUSTED
DIFFERENTIAL EXPRESSION ACROSS
THE WHOLE TRANSCRIPTOME, AND
THEN WHAT WE PULLED OUT ARE
CHANGES IN MESSENGER RNAs THAT
ARE SIGNIFICANT FOR ALL ENZYMES
WE MAP TO THE METABOLIC
TRANSFORMATIONS THAT AFFECT HEX
1 CERAMIDE.
SO YOU CAN SEE THAT THERE ARE
SEVERAL MESSENGERS FOR ENZYMES
THAT GO UP SIGNIFICANTLY AND IN
ONE CASE DOWN SIGNIFICANTLY.
AND I'M SHOWING YOU FOR ONE THE
AGE-DEPENDENT INCREASE IN
CONTROL ANIMALS BUT SIGNIFICANT
SHIFT IN THE EXPRESSION OF THIS
GENE FROM THE RNAseq DATA IN
THE ALPHA-SYNUCLEIN FLY.
THE QUESTION IS WHAT EFFECT
WOULD MANIP CHANGES HAVE ON
HEX1CER?
DOES IT AFFECT TRANSFORMATION IN
WHICH DIRECTION IN OF?
WHICH DIRECTIONS, PROMOTING THE
FORMATION OF HEX1CER, BGA IS
HERE, INCREASING EXPRESSION,
THIS SUPPORTS, AND MOST OF THE
CHANGES EXCEPT WHAT WE SEE FOR
THESE ENZYMES SUPPORT AN
INCREASE IN HEX1CER, SUPPORTS
THE MECHANISM OF WHAT WE OBSERVE
WITHIN OUR DATA.
OKAY.
I SEE THAT I'M COMING ON THE END
OF MY TIME.
DO I HAVE A MINUTE LEFT?
ONE MINUTE.
OKAY.
WE'RE VERY INTERESTED IN THE
POSSIBILITY OF OLIGOGENETIC.
A FIFTH HAVE MORE THAN ONE
VARIANT, MULTIPLE GENETIC
LESIONS IN THE PATHWAY MIGHT
INTERACT IN AN OLIGOGENIC MODEL
TO AFFECT PARKINSON'S DISEASE
RISK, DIFFICULT TO ADDRESS USING
HUMAN GENETICS AND STATISTICS SO
WE'RE LOOKING AT SINGLE CASES.
HERE IS A FAMILY THAT WE'VE BEEN
IN CONTACT WITH, AN INDIVIDUAL
WITH PARKINSON'S DISEASE AND
FAMILY HISTORY OF PARKINSON'S
DISEASE HAS MUTATION IN GBA AND
ARSA, AFFECTING CERAMIDE
BIOLOGY.
A LOT OF EVIDENCE FROM
PARKINSON'S DISEASE GENETICS
SUPPORTING LYSOSOMAL BIOLOGY AND
ENZYMES THAT AFFECT THE
GLYCOGENOME.
AND SO I THINK I DON'T WANT TO
OBSCURE THE FACT THERE'S A LOT
OF EVIDENCE ACCUMULATING IN THE
FIELD THAT PARKINSON'S DISEASE
IS A LYSOSOMAL DISORDER AND MY
SUMMARY SLIDE, I DON'T HAVE TO
GO OVER THIS, I WANT TO GET BACK
ON TIME.
I THANK THOSE WHO CONTRIBUTED TO
THE WORK WHICH I TRIED TO
RECOGNIZE ALONG THE WAY.
MY COLLABORATORS, AND OUR
FUNDERS AND THANK YOU FOR YOUR
ATTENTION AND INVITING ME TO
PRESENT.
>> THANK YOU, JOSH.
WE'RE LOOKING FORWARD TO HAVING
DISCUSSION AROUND NOON.
IF YOU HAVE ANY QUESTIONS FOR
JOSH, YOU CAN PUT THEM IN THE
SEND LIVE FEEDBACK.
AND JOSH WILL ADDRESS THOSE IN A
COUPLE HOURS.
NEXT SPEAKER IS ANAND MEHTA FROM
MEDICAL UNIVERSITY OF SOUTH
CAROLINA, HE'S GOING TO TALK
ABOUT GLYCOMICS OF LIVER CANCER.
>> THANK YOU VERY MUCH.
IF YOU CAN'T SEE THE SCREEN JUST
YELL OUT AND I'LL TRY TO DO
SOMETHING.
>> WE'RE GOOD.
>> I'M GOING TO TALK TODAY ABOUT
WHAT WE'VE BEEN DOING ON
GLYCOMICS OF LIVER CANCER.
USUALLY PEOPLE TALK ABOUT
GLYCANS, WE SAY THEY ARE
COMPLICATE AND DIFFICULT.
ANOTHER TITLE COULD BE GLYCANS
ARE EASY, THE CANCER IS
COMPLICATED.
THAT IS THE ROOT OF WHAT WE SEE.
I WOULD MAKE THE ARGUMENT
GLYCOMICS IN LIVER CANCER HAS A
LONG HISTORY, PROBABLY THE MOST
STUDIED CANCER OUT THERE.
CAN BE A MODEL DISEASE FOR OTHER
PEOPLE TO LOOK AT AS WELL.
WHEN I TALK ABOUT LIVER CANCER
I'M GOING TO TALK ABOUT TWO
MAJOR FORMS OF LIVER CANCER THAT
ARISE.
THE TYPE OF CANCER REALLY STEMS
FROM THE ORIGIN CELL FROM WHICH
THAT CANCER ORIGINATES.
WE TALK ABOUT HEPATOCELLULAR
CARCINOMA ON THE LEFT THAT
ARISES, CANCER OF THE
HEPATOCYTES, 80% OF LIVER
CANCERS WE SEE.
THE OTHER FORM OF LIVER CANCER
ARISES FROM BILE DUCTS,
CHOLANGIOCYTES.
PEOPLE GET LIVER CANCER FROM
UNDERLYING INFLAMMATORY PROCESS.
VIRAL HEPATITIS, ALCOHOL, NOW
FATTY LIVER DISEASE, THE LARGER
DRIVER OF LIVER CANCER IN THE
UNITED STATES, THE SAME THING
DRIVING THE RISK OF INCREASED
RISK AND INCREASED DEVELOPMENT
OF CHOLANGIOCYTES CARCINOMA.
THE UNDERLYING ISSUE IS
INFLAMMATION THAT LEADS TO
FIBROSIS CIRRHOSIS, LEADING TO
CANCER THAT EVENTUALLY CAN BE
LIFE-THREATENING.
BECAUSE HGC IS THE PRIMARY FORM,
I'M GOING TO TALK ABOUT THAT,
WE'LL TALK ABOUT CHOLANGEO
CARCINOMA AT THE END.
THE SIMPLE FACT WE HAVE NO GOOD
TREATMENTS, DETECTING EARLY IS
ESSENTIAL, SURGICAL RESECTION,
CUT IT OUT.
IF YOU HAVE A SMALL TUMOR
OVERALL SURVIVAL IS GOOD, FOR
THE MOST PART, 50 TO 60%.
PEOPLE CAN LIVE FOR OVER FIVE
YEARS.
IT'S CONSIDERED IDEAL.
WELL, NOT IDEAL BUT BETTER IN
THAT SITUATION.
IF YOU DON'T CATCH IT EARLY, THE
OUTCOME IS BAD, NO MATTER WHAT.
AND THAT'S BECAUSE CUTTING OUT A
3-CENTIMETER LESION IS DOABLE,
CUTTING OUT A 10-CENTIMETER
LESION IS NOT GOOD AT ALL.
EARLY DETECTION IS ESSENTIAL.
AND PEOPLE HAVE BEEN TRYING TO
FIND BIOMARKERS FOR YEARS.
BUT IT'S SOMETHING THAT'S BEEN I
WOULD SAY FOR GLYCOMA COMMUNITY
A SUCCESS.
FROM JAPAN IN THE EARLY '80s
AND '90s, THEY SHOWED, AND
LOTS OF VERY MANY OF PEOPLE HAVE
SHOWN, GLYCANS CHANGE IN SERUM
OF PATIENTS WITH LIVER CANCER.
IN THIS CASE IDENTIFYING CHANGES
IN FUCOSYLATION, ON AFP, WE'LL
COME BACK TO THAT.
THAT'S BEEN FOLLOWED BY OTHER
GROUPS.
WE SHOWED THAT CHANGES IN
ANOTHER PROTEIN AND MANY GROUPS
LOOKED AT TIME WHERE THEY CAN
LOOK AT EITHER TOTAL SERUM
GLYCOANALYSIS OR GLYCOPROTEOMICS
TO SO THERE ARE GLYCAN CHANGES
LIVER CANCER, IT'S ACCEPTED,
FDA-APPROVED TEST, PART OF THE
ALGORITHM FOR DETECTION OF LIVER
CANCER, BEING DEVELOPED
COMMERCIALLY BY OTHER GROUPS.
MOST OF THAT WORK WAS DONE IN
SERUM.
WHAT IS HAPPENING IN THE TISSUE?
NOT WELL KNOWN.
DO ALTERED GLYCANS AND
GLYCOPROTEIN COME FROM THE
TUMOR, THE STROMA, NOT
ASSOCIATED WITH THE LIVER?
THAT'S NOT BEEN VERY WELL
STUDIED AND VERY WELL ANALYZED.
SO WE DECIDED TO DO THAT AND DID
THAT USING APPROACH REFERRED TO
HERE AS SPATIAL-GLYCOMICS,
ANOTHER TIME OF OMICS, SPATIAL
OMICS, SO SPATIAL GLYCOMICS
USING MALDI-IMS, USING ENZYMES
WE MADE THAT SEEM TO BE SUITED
FOR THIS APPLICATION.
I KNOW THIS HAS BEEN TALKED
ABOUT AT LENGTH BY OTHER GROUPS
BUT THE KEY THING, CAN YOU USE
PARAFFIN-EMBEDDED SLIDES, SPREAD
A THIN LAYER OF ENZYME OVER THE
TISSUE, ACTIVITY IN LOCALIZED
REGIONS, GLYCANS WILL STAY WHERE
THEY ARE RELEASED.
YOU CAN USE A WIDE VARIETY OF
ENZYMES.
WE USE PNGASE, SIALIDASE, WE
MAKE ALL OF THESE IN HOUSE.
CAN YOU DO VARIOUS THINGS,
ANALYZE TISSUE USING MALDI MASS
SPECTROMETRIER.
A PATHOLOGIST WILL SAY, OH,
THERE'S THE CANCER IN THE MIDDLE
OF THE BIG TISSUE AND WE CAN
LOOK AT THE DATA AND YOU CAN SAY
WHAT GLYCAN IS KNOCKED DOWN IN
THE TISSUE AND YOU'LL SEE IT
KNOCKED OUT OF THE TUMOR BUT
FOUND IN SURROUNDING TISSUE,
BAM, YOU GET A MULTI-FUCOSLYATED
GLYCAN FOUND JUST IN THE TUMOR
AND NOWHERE ELSE.
YOU COULD LOOK AND SAY THAT'S AN
EASY ANSWER, RIGHT?
SO THE FUCOSYLATION COMES FROM
THE TUMOR, THAT'S WHAT YOU GET.
WELL, IT'S NOT THAT EASY.
IF YOU START LOOKING AT MORE
PATIENTS, WE'VE LOOKED AT A
THOUSAND PATIENTS NOW, EITHER BY
TISSUE MICROARRAY OR INDIVIDUAL
PATIENTS, WHAT YOU'LL SEE IS,
WELL, IT'S COMPLICATED WITH LOTS
OF VARIATION.
EACH SPOT REPRESENTS A DIFFERENT
TISSUE AND DIFFERENT PATIENT.
ONE TEAM AT THE TOP, ONE AT THE
BOTTOM.
CANCER PATIENTS ARE SEGREGATED
UP HERE, CIRHOTTIC ON THE
BOTTOM, NORMAL HERE.
BOTTOM TMA PLUS CANCER MINUS
ADJACENT TISSUE NEXT TO THE
CANCER.
WHAT YOU SEE IS WE SEE LOTS OF
BRANCH GLYCAN, SOME PATIENTS
HAVE IT, SOME DON'T.
BRANCH FUCOSLYATED GLYCAN, SOME
DO, SOME DON'T.
IT VARIES QUITE DRAMATICALLY BUT
IF DO YOU STATISTICS IT LOOKS
GOOD.
YOU'LL IDENTIFY LOTS OF GLYCAN
INCREASED IN CANCER, FANTASTIC
P-VALUES, TEN TO THE MINUS 16,
TEN TO THE MINUS 8, WHATEVER,
ROCKS LOOK GREAT TOO.
IT LOOKS AGAIN LIKE YOU COULD
SAY THE CANCER IS EASY.
YOU HAVE BRANCH FUCOSLYATED
GLYCAN ASSOCIATED WITH VAST
MAJORITY OF CANCERS, BUT, AGAIN,
IT'S NOT THAT SIMPLE.
THERE ARE LOTS OF GLYCAN
CHANGES.
THE BLUE REPRESENTS CANCER, RED
RECOMMENDS NORMAL ADJACENT
TISSUE.
WHAT YOU'LL FIND IS, YEAH, THERE
ARE LOTS OF GLYCANS HIGHER IN
THE NORMAL TISSUE, LOW IN CANCER
TISSUE, LOTS IT'S A PRODUCT
RELATIONSHIP.
YOU WOULD EXPECT SOMETHING
FUCOSLYATED, NOT FUCOSLYATED.
THERE'S VARIATION, WE HAVEN'T
IDENTIFIED ALL THE VARIATION.
EVERY CANCER WE LOOKED AT HAS
SOME GLYCAN CHANGE BUT NOT
ALWAYS CONSISTENT.
THAT'S THE THING THAT'S
IMPORTANT.
EVERY TUMOR TISSUE WE LOOK AT IN
LIVER CANCER HAS A GLYCAN CHAIN
BUT NOT ALWAYS CONSISTENT.
THAT'S THE KEY POINT.
A GREAT EXAMPLE IS HERE, TEN
PATIENTS FROM TMA, THE SAME
PATIENTS, THESE PATIENTS ARE
THESE PATIENTS.
NOT SURPRISING, THIS IS THE
LEVEL OF 2293, WE LOOKED BY
ORTHOGONAL METHODS, PATIENTS
WITH HIGH LEVELS DON'T TEND TO
HAVE LOTS OF THIS VERSION.
YOU COULD MAKE THE ARGUE, THAT'S
NOT SURPRISING, YOU HAVE TO HAVE
THIS TO GET THIS.
IT'S JUST A RANDOM EVENT OF
WHETHER OR NOT YOU HAVE
FUCOSYLATION IN THE CELL.
WE WANT TO LOOK AT THAT MORE
CAREFULLY AND SAY IS THAT REALLY
TRUE?
IS IT JUST A RANDOM --
FUCOSLYATED OR NOT OR RELATED TO
CANCER ITSELF?
TO LOOK AT THAT WE PARTNERED
WITH A GUY THAT WAS AT MOUNT
SINAI AT FIRST, NOW AT U.T.
SOUTHWEST, PUBLISHED IN THE
EARLY 2000s, 2010, NEW ENGLAND
JOURNAL OF MEDICINE, SUBTYPING
SYSTEM FOR LIVER CANCER.
SO, THESE CANCERS VARY IN THEIR
GENETICS.
THEY VARY IN AGGRESSIVENESS.
VERY IN WHAT BIOMARKERS DETECT
THEM AND OUTCOME.
CONFIRMED BY A PAPER DONE BY A
HEPATOLOGIST OUT OF MAYO, LOUIS
ROBERTS, WHO GOT THE SAME THING
USING A LARGER DATABASE.
SO IT'S KIND OF GETTING SOME
LEVEL OF TRACTION.
MEANINGFUL YET?
WE DON'T KNOW BUT WE'RE LOOKING.
WE HAD A STUDENT, ANDREW, WHO
DECIDED TO LOOK AT THESE
PATIENTS ALREADY SUBTYPED AND
SAY WHAT ARE THE GLYCANS THAT
CHANGE HERE?
IS HETEROGENEITY WHEN WE DIDN'T
KNOW THE SUBTYPE ACTUALLY
RELATED TO SUBTYPE OF CANCER?
TO MAKE A LONG STORY SHORT THE
ANSWER IS YES.
SO THAT IF YOU LOOK, FOR
EXAMPLE, AT THE S1 TYPE TUMORS,
TUMORS THAT ARE STROMAL IN
NATURE BY GENETICS IN SOME WAY,
BY INFLAMMATION, THESE ARE P53
MUTANT TUMORS, TGF-BETA DRIVEN,
BIG HONKING GLYCAN CHANGES,
FUCOSLYATED.
AP POSITIVE, AGGRESSIVE, MOST
GLYCAN CHANGES ARE NOT IN THE
TUMOR AT ALL, THEY ARE IN
STROMA, NOT WHAT WE EXPECTED,
NOT DIRECTLY IN THE TUMOR.
S3 TUMORS TENDED TO HAVE --
THIS WAS CONSISTENTLY SEEN WITH
LOTS OF TUMORS.
S1 TUMORS WHEN YOU RESECT THEM,
IF YOU CATCH THEM EARLY AND
RESECT THEM, THEIR OUTCOME IS
GOOD.
THOSE PEOPLE DO DO THE BEST.
S2 TUMORS HAVE BAD OUTCOMES.
THEY ACTUALLY --
WHEN YOU RESECT THEY ARE THE
ONES THAT HAVE THE BIGGEST
ISSUES.
SO, THAT STROMA AND GLYCAN
CHANGES WITHIN THAT STROMA WE
THINK ARE ACTUALLY CONTRIBUTING
TO THAT POOR OUTCOME.
AND WE'RE STUDYING THAT NOW.
S3 TUMORS UNFORTUNATELY ARE VERY
WELL TREATED IF CAUGHT EARLY BUT
MOST ARE MISSED BECAUSE THERE'S
NO GOOD BIOMARKERS.
AP NEGATIVE, NOT REALLY
SYMPTOMATIC, SO PATIENTS SHOW UP
WITH A 14-CENTIMETER TUMOR AND
NO MATTER WHAT YOU WANT TO DO
EVEN THOUGH IT'S NOT AGGRESSIVE
THEY ARE NOT CANDIDATES FOR
TRANSPLANTS, NOT CANDIDATES FOR
RESECTION, SO THEY UNFORTUNATELY
HAVE BAD OUTCOMES.
WE'RE MISSING THESE PATIENTS
FROM A BIOMARKER PERSPECTIVE
COMPLETELY.
AND THIS GRAPH SHOWS THE
ANALYSIS IN THE PATIENT SET, TWO
PATIENT SETS WE LOOKED AT,
ANALYZED BY TWO INSTRUMENT, THE
TEND IS THE SAME THAT THESE S1
TYPE TUMORS THAT WE HAVE THE
MOST GLYCAN CHANGES IN THE
TUMOR, S3 TUMORS TEND TO HAVE
NONFUCOSLYATED VERSION, AND
OTHER CHANGES OCCUR BUT
CONSISTENT.
WE'VE NOW GOTTEN ANOTHER SAMPLE
SET COMPLETELY -- WELL, BLINDED
TO SUBTYPE TO US, AND WE'RE
LOOKING AT THEM.
THERE ARE ROUGHLY 70 TUMORS, AND
70 ADJACENT TUMOR SECTIONS WE'RE
LOOKING AT, TO PREDICT AND GIVE
BACK TO CLINICIANS, S1, S2, S3.
WE HOPE TO GET SERUM SAMPLES TO
MATCH PATIENTS, WE HAVE 26 SERUM
SAMPLES THAT MATCH WITH THE
TISSUE.
SO WE'RE GOING TO TAKE A LOOK AT
THAT.
I'LL SHOW YOU DATA,
CHERRY-PICKED, DESIGNED TO SEE A
DIFFERENCE, WE'LL GO TO THAT IN
A SECOND.
BECAUSE OF THE SMALL SIZE WE'RE
POWERED TO SEE BIG DIFFERENCES,
SMALL DIFFERENCES WILL NOT BE
SEEN BUT WE'LL GET INTO THAT.
HERE'S WHAT IT LOOKS LIKE.
AGAIN, WE HAVE A PIECE OF
TISSUE, TUMOR IN THE MIDDLE, AND
THIS TUMOR WHEN ANDREW LOOKS HAS
A LOT OF GLYCAN CHANGES JUST
WITHIN THE TUMOR THAT LOOK
PERFECT AND AS YOU WOULD EXPECT.
THERE IS INTRATUMOR
HETEROGENEITY, THAT MAY BE
RELATED TO HOW THEY ARE HANDLED,
TREATED.
BUT A PATIENT LIKE THIS I WOULD
SAY WITH CONFIDENCE TO THE
CLINICIAN, THAT'S AN S1 TUMOR,
WE CAN MOVE ON.
SO IS THAT THE EASY PART?
WELL, NO.
SO YOU WOULD HAVE A TUMOR LIKE
THIS THAT LOOKS SIMILAR TOO,
THAT HAS THE BRANCHED NOT
FUCOSLYATED GLYCAN IN THE TUMOR
AND THEN HAS THE BRANCHED
FUCOSLYATED GLYCAN OUT OF TUMOR,
PROBABLY S2 OR S3.
THAT'S A BIG DIFFERENCE.
AND SO IT'S COMPLICATED.
IT'S NOT AS EASY AS WE THINK.
CANCERS ARE NOT FALLING INTO
WELL-DEFINED GENOMIC CATEGORIES.
ONE THING WE'RE DOING NOW IS
SAYING, LOOK, THE GLYCOMICS IN
SOME WAYS ARE JUST AS IMPORTANT
AS THE GENOMICS.
SO WE'RE MOVING TO REPLACE THE
GENOMIC CLASSIFICATION WITH
GLYCOMIC CLASSIFICATION.
IT'S IMPORTANT TO REALIZE THAT
GENOMICS HAS GREAT VALUE.
I'M NOT JUST MISSING THAT.
GLYCOMICS IN SOME WAYS HAS JUST
AS VITAL INFORMATION AND YOU CAN
DO GLYCOMIC CLASSIFICATION OF
THE TUMORS JUST AS EASILY AS YOU
CAN GENOMIC CLASSIFICATION OF
TUMORS.
WE'RE MOVING IN THAT DIRECTION
NOW.
SO CHOLANGIOCARCINOMA IS
COMPLICATED.
UP TOP THIS IS A BRANCH GLYCAN,
FOUND IN HCC, NOT IN
CHOLANGIOCARS NO, MA'AMA TRUE IN
THE FIRST TMA AND SECOND TMA.
IN CONTRAST YOU'LL SEE A SMALLER
GLYCAN, FUCOSLYATED, WE HAVEN'T
CONFIRMED YET BUT LOOKS LIKE
IT'S A BISECTED GLYCAN WITH
FUCOSE, ONE OR TWO FUCOSES,
HIGHLY EXPECTED IN CHOLANGIO
CARCINOMA.
HCC, LOTS OF DRIVE CAN --
GLYCAN CHANGES, THE FUCOSE
DEFINES WHAT SUBTYPE IT IS AND
GENETICS AND DEFINES THE OUTCOME
OF THAT PATIENT AND HOW
AGGRESSIVE THE CANCER WILL GROW
AND METASTASIZE.
STROMA AND GLYCANS IN THE STROMA
PLAY AN IMPORTANT ROLE IN THE
OUTCOME OF THE PATIENT, LOOKING
NOW.
THERE ARE LOTS OF OTHER CHANGES,
BISECTS FOUND IN
HEPATOCARCINOMA, TO A SMALLER
DEGREE.
WE'RE LOOKING INTO THAT.
GENETICS ARE DRIVING.
WHAT DOES THIS MEAN IN TERMS OF
BIOMARKER?
FOR THE MOST PART, THE BIOMARKER
THAT PEOPLE HAVE BEEN USING,
AFP, DETECTS ONE TYPE.
FUCOSLYATEED VERSIONS AND
PROTEINS WE'VE IDENTIFIED
COMPLEMENT AND CAN DETECT BOTH
OF THESE.
THESE BRANCHED FOUND PRIMARILY
IN S3-TYPE TUMORS ARE MISSED
RIGHT NOW BY MOST BIOMARKERS.
WE'RE NOW HAVING TO ADDRESS
THAT.
WHAT I WILL SAY, WHAT'S
INTERESTING, THIS IS CHERRY
PICKED DATA, THAT THOSE PATIENTS
THAT HAVE HIGH LEVELS OF
FUCOSYLATION IN TISSUE TEND TO
HAVE HIGH LEVELS OF FUCOSYLATION
SERUM.
THOSE PATIENTS WITH LOW LEVELS
OF FUCOSYLATION IN TISSUE HAVE
LOW LEVELS IN SERUM.
IF YOU JUST CHERRY PICK HIGHS
AND LOWS WE'VE LOOKED IT THE
HIGHS AND LOWS TO START WITH,
THAT'S THE PROBLEM.
WE DID IT TO SEE WILL IT HOLD
UP.
THE RELATIONSHIP IS STRONG, FOR
UNFUCOSLYATED, THERE'S A STRONG
RELATIONSHIP BETWEEN TISSUE AND
SERUM.
WE'LL EXPLOIT THAT DOING A
METHOD REFERRED TO AS
GLYCOTYPER, REPLACING THE TISSUE
WITH ANTIBODY ARRAY, YOU REPLACE
AND GET ANTIBODY SPECIFIC
CAPTURE INFORMATION AND GLYCAN
INFORMATION, THE METHOD
DEVELOPED OUT OF THE
GLYCOALLIANCE THAT KRUEGER RUNS
AND A COLLABORATION WITH BRIAN,
KNOWING BRIAN HAAB, WE COULD USE
THAT METHOD TO DO GLYCAN
ANALYSIS, TRUE GLYCAN ANALYSIS,
HIGH-THROUGHPUT WAY, PUSHED BY A
STUDENT ALLISON BLACK IN THE
LAB, JUSTING ON A BE ANTIBODY
ARRAY TO RAPTURE PROTEINS OF
INTEREST, RELEASING GLYCAN,
EXACTED SAME TISSUE WAY, DOES
GLYCAN ANALYSIS, USING THIS
METHOD TO LOOK AT 140 CANCER
PATIENTS, 270 CIRHOTTIC
PATIENTS, JUST STARTED WITH 6
PROTEINS, AND WITH JUST THOSE
LOOKING AT A MIXTURE OF GLYCAN
WE CAN GET UP, NOW THERE'S LOTS
OF DATA HERE BECAUSE YOU HAVE
MULTIPLE PROTEINS, 15, 20, 30
GLYCANS PER PROTEIN, WE'RE
ADDING PROTEINS TO THAT.
THAT'S HOW WE'RE GOING TO
EXPLOIT THE GLYCANS IN TISSUE TO
LOOK IN SERUM TO IDENTIFY
EVERYBODY THAT'S GOT LIVER
CANCER.
USING IMAGING WE SHOW GLYCAN
CHANGES IN HCC TISSUE, CAN USE
THAT INFORMATION TO SUBTYPE
TISSUE, AND MOVING TO DEVELOP
ANTIBODY ARRAYS TO ALLOW US TO
LOOK AT THIS IN BOTH SERUM AND
OF IN A WAY THAT REFLECTS WHAT'S
HAPPENING IN THE TISSUE.
WITH THAT I'LL END.
ACKNOWLEDGE THE WIDE VARIETY OF
COLLABORATORS, APOLOGIZE FOR THE
YELLOW TYPE, IT'S HARD TO SEE ON
MY SCREEN, PROBABLY ON YOURS.
AND I WOULD LIKE TO THANK THE
CLINICAL COLLABORATORS THAT GAVE
US SAMPLES AND THE PATIENTS WHO
HAVE GIVEN THE TISSUE AND SERUM
WE USE FOR ANALYSIS.
WE'RE SAMPLE HOGS.
DO EVERYTHING IN HUMAN SERUM AND
HUMAN TISSUE AND CAN'T DO
ANYTHING OUT THE PATIENTS THAT
GIVE THAT SAMP AM -- SAMPLE TO
US.
I'M INDEBTED TO THAT
INDIVIDUALS.
>> THANK YOU FOR AN EXCITING
TALK.
REMINDER, YOU CAN SEND QUESTIONS
TO HIM OR ANY SPEAKERS AT SEND
LIVE FEEDBACK, WE'LL ADDRESS
THOSE IN AN HOUR.
NEXT SPEAKER IS REBEKAH GUNDRY,
FROM UNIVERSITY OF NEBRASKA
MEDICAL CENTER.
>> GOOD MORNING.
THANKS FOR THE INVITE TO SPEAK.
I'M DELIGHTED TO HAVE THE TIME
TO TELL YOU ABOUT A FEW
DIFFERENT BIOINFORMATIC TOOLS
WE'VE DEVELOPED TO HELP OVERCOME
SOME UNIQUE CHALLENGES WE FACE
WHEN PERFORMING CELL SURFACE
PROTEOMICS, IT'S INVOLVED IN THE
PROGRESSION OF DISEASE.
BY KNOWING WHICH PROTEINS RESIDE
ON THE CELL SURFACE AND HOW THEY
CHANGE AMONG CELL TIMES AND
DISEASE PROGRESSION WE CAN LEARN
HOW CELLS RESPOND TO STRESS AND
DISEASE, FIND TARGETS FOR DRUGS
AND DELIVERING PAYLOADS TO
SPECIFIC CELL TYPES AND REGIONS
AND BECAUSE THEY CAN BE CLEAVED,
THEY CAN BE EXPLOITED.
WE KNOW RELATIVELY LITTLE ABOUT
WHICH PROTEINS RESIDE ON THE
SURFACE OF MOST HUMAN CELL
TYPES DUE TO LIMITATIONS OF
COMMON APPROACHES TO ANALYZE.
ANTIBODY BASED SCREEN, THERE
AREN'T ENOUGH GOOD ANTIBODIES.
TRANSCRIPTOMICS CAN'T DEFINE IT
BECAUSE mRNA DOESN'T ITEM YOU
ABOUT PROTEIN ABUNDANCE OR
LOCATION.
GENERIC PROTEOMICS CAN'T DEFINE
IT, THEY ARE NOT WELL
REPRESENTED.
TRADITIONAL APPROACHES DO NOT
EXPERIMENTAL DEFINE PROTEINS
THAT RESIDE AT THE CELL SURFACE.
TO GET GOOD COVERAGE YOU NEED TO
DO ENRICHMENT FOR LOWER
ABUNDANCE AND HYDROPHOBIC
PROTEINS.
THERE HAVE BEEN STRATEGIES
DEVELOPED AND APPLIED TO MORE
THAN 300 SURFACE PROTEOMIC
STUDIES, LOTS OF CHEMISTRIES
AVAILABLE BUT GENERALLY ONE MAIN
DISTINCTION IN THE STUDY DESIGN
ARE THAT RESULTING MASS
SPECTROMETRY DATA EITHER
CONTAINS SOME TYPE OF STAR OR
TAG ON THE PEPTIDES THAT CAN BE
USED AS EVIDENCE THAT THE
PEPTIDE IDENTIFIED BY THE METHOD
ORIGINATED FROM THE
EXTRACELLULAR DOMAIN OR DATA DO
NOT CONTAIN EVIDENCE SO RESULTS
MUST BE ANNOTATED BASED ON
PREDICTION.
THERE ARE SOME CHALLENGES.
A LOT OF STRATEGIES.
THE CHOICE THAT YOU ARE GOING TO
USE WILL DEPEND ON APPLICATION
AND GOALS, RIGHT NOW CHALLENGING
TO COMPARE RESULTS WITH NO
CONSENSUS WHAT CRITERIA ARE USED
TO DEFINE SURFACE PROTEIN.
THAT MEANS STARTING FROM THE
SAME EXPERIMENTAL DATA DIFFERENT
LABORATORIES COULD REPORT
DIFFERENT INTERPRETATIONS
DEPENDING ON WHICH GENE ONTOLOGY
TRENDS THEY USE OR WHICH
DATABASE ANNOTATIONS ARE USED.
MOST USE AFFINITY ENRICHMENT
STEP, REAGENTED PLAGUED BY
NON-SPECIFIC BINDERS, NO
EXPERIMENTAL EVIDENCE IN
RESULTING DATA, IT CAN BE
CHALLENGING TO HAVE CONFIDENCE
PROTEINS IDENTIFIED WERE
LOCATED AT THE CELL SURFACE AT
THE TIME.
IT CAN BE CHALLENGING TO
PRIORITIZE THE FOCUS OF
FOLLOW-UP STUDIES.
TODAY I'M GOING TO TELL YOU
ABOUT THREE TOOLS TO OVERCOME
THESE CHALLENGES.
FIRST A BRIEF OVERVIEW OF THE
PURPOSE AND FEATURES AND SHOW
REAL WORLD APPLICATION.
I'LL BEGIN WITH CIRFESS,
CHALLENGES INCLUDE HOW CAN YOU
QUICKLY PREDICT WHICH OF THESE
METHODS WOULD COVER YOUR
PROTEOME SUBSET OF INTEREST
DEPENDING WHICH CHEMISTRY IS
USED.
ONCE YOU HAVE DATA HOW CAN YOU
EVALUATE IF THE PROTEINS THAT
YOU IDENTIFY IN YOUR EXPERIMENT
ARE ACTUALLY THEORETICALLY
POSSIBLE BY THAT METHOD GIVING
EVIDENCE YOU SPECIFICALLY
IDENTIFIED THEM OTHER ARE THEY
LIKELY NON-SPECIFIC BINDERS.
SO BEFORE DO YOU AN EXPERIMENT
YOU CAN PREDICT WHICH METHOD
WILL GIVE YOU THE BEST COVERAGE,
AND AFTER THE EXPERIMENT CAN
CHECK WHETHER A PROTEIN YOU
IDENTIFIED IS EXPECTED TO BE
CAPTURED BY THAT METHOD TO GIVE
CONFIDENCE THAT YOU'RE SEEING IT
BECAUSE IT WAS SPECIFICALLY
CAPTURED BY THE CHEMISTRY YOU'RE
USING.
CIRFESS INTEGRATES WHAT IS
NECESSARY FOR MASS SPECTROMETRY,
IF EXTRACELLULAR PEPTIDES ARE
ABLE TO BE ENRICHED AND
IDENTIFIED BY MASS SPECTROMETRY.
AND YOU CAN DO EITHER A SINGLE
PROTEIN OR BATCH LOOKUP AND FOR
EACH ENTRY YOU CAN SEE A LOT OF
DIFFERENT INFORMATION, YOU CAN
SEE THE SURFACE PROTEIN
CONSENSUS SCORE AND OUTPUT FROM
THREE SIGNAL PEPTIDE PREDICTIONS
AND TWO TRANSMEMBRANE
PREDICTION.
FOR EACH THE NUMBER OF PREDICTED
MASS SPECTROMETRY PEPTIDES IS
DISPLAYED.
SO FOR A PROTEIN THAT YOU
IDENTIFY IN EXPERIMENT, YOU CAN
SEE IF YOU WOULD EXPECT TO SEE
WITH YOUR APPROACH OR IS IT
LIKELY NON-SPECIFIC BINDER?
HERE I'M SHOWING EXAMPLE OF A
NON-GLYCOSYLATED PROTEIN.
THERE'S NO EXTRACELLULAR
LOCALIZED.
YOU CAN SEE DISCREPANCY OR
CONSISTENCY.
AND YOU CAN SEE THEORETICAL
COVERAGE OF PROTEOMIC BASED ON
LABELING CHEMISTRIES.
OVERALL IT CAN HELP EVALUATE
RESULTS TO DETERMINE IF A
PROTEIN MAKES SENSE OR IF THEY
ARE LIKELY NON-SPECIFIC BINDERS.
NEXT UP IS VENEER, LACK OF
STANDARDIZATION, GENE ONTOLOGY
AND DATABASE ANNOTATIONS ARE
USED TO ASSERT PROTEINS
IDENTIFIED IN AN EXPERIMENT ARE
CELL SURFACE PROTEINS, STRATEGY
IS LIMITED BECAUSE ONE PROTEIN
CAN HAVE MULTIPLE ASSOCIATED
SUBCELLULAR LOCATIONS THAT
CHANGE AMONG CELL TYPES AND
CONDITIONS, EVIDENCE IN ONE IS
NOT EVIDENCE OF LOCATION IN
ANOTHER CELL TYPE, NO CONSENSUS
TO USE TO DEFINE SURFACE
PROTEIN.
CONSEQUENCES THAT IF WE ASSUME
LOCATIONS, WE COULD WASTE TIME
PURSUING PROTEINS NOT LOCALIZED
TO CELL SURFACE.
WE DEVELOPED A WEB-BASED
APPLICATION, VENEER, PROVIDING A
WAY TO ASSESS DATA.
IT USES EXPERIMENTAL DATA AND
CRITERIA TO CLASSIFY PROTEINS AS
SURFACE LOCALIZED.
BY HAVING DETAILS IN AN EASY
FORMAT WITH OUTPUT FROM MULTIPLE
TOOLS FOR CONTEXT THIS HELPS
USER HAVE CONFIDENCE THE
PROTEINS ARE REALLY COMING FROM
THE CELL SURFACE.
IT WILL FILTER, ANNOTATE AND
SUMMARIZE DATA, APPLICABLE TO
VARIETY OF METHODS DESCRIBED
THAT ENRICH GLYCOPEPTIDES, THIS
IS THE MOST SPECIFIC METHOD FOR
SURFACE PROTEINS.
IT'S AGNOSTIC OF MASS
SPECTROMETRY PLATFORM OR SEARCH
TOOLS.
WHATEVER TOOLS YOU HAVE
AVAILABLE ARE COMPATIBLE.
YOU CAN IMMEDIATELY SEE A
SNAPSHOT VIEW OF THE PROTEINS
AND BINDERS AND DOWNLOAD.
OUTPUT FILE DATA ARE CLASSIFIED
AS NON-SPECIFIC BINDERS, OR
SPECIFIC BINDERS, YOU CAN FIND
EACH CATEGORY IN A SEPARATE TAB,
NON-SPECIFIC BINDERS ARE
PROTEINS THAT WERE IDENTIFIED BY
PEPTIDES THAT DIDN'T CONTAIN
DEAMINATION IN SEQUENCE
MUTATION.
CANDIDATE SURFACE PROTEINS
CONTAIN PEPTIDES WITH
DEAMINATION IN SEQUENCE MOTIF
AND A SUBSET FIT EXTRA CRITERIA
INCLUDING THEY HAVE A SURFACE
SCORE INDICATING THAT THERE
SHOULD BE EXTRACELLULAR
GLYCOPEPTIDES OR THEY HAVE A
SURFACE PROTEIN CONSENSUS SCORE
GREATER THAN 2 INDICATING THEY
HAVE BEEN PREDICTED OR CONTAIN
PREDICTED PEPTIDE.
THE JUSTIFICATION, THERE'S NO
PREDICTION ALGORITHM THAT'S
PERFECT SO IF A PROTEIN SLIPS
THROUGH FOR ONE, LIKELY CAUGHT
BY ANOTHER.
ALL THE DATA CAN DECIDE HOW THEY
WANT TO USE DATA BASED ON CLEAR
EVIDENCE.
SO TO AVOID HAVING TO QUERY
REPOSITORIES ONCE YOU HAVE YOUR
DATABASE, YOUR LIST OF PROTEINS
IDENTIFIED VENEER ANNOTATES
EVERY ENTRY WITH INFORMATION
FROM A DOZEN RESOURCES RELEVANT
FOR CELL SURFACE PROTEINS.
ALL OF THE CURATION, ANNOTATION
AND OUTPUT ARE STANDARDIZED.
A TSV FILE IS FORM ATED.
VENEER PROVIDES STANDARDIZING
AND PROCESSING.
FINALLY SURFACE GENIE.
ONCE YOU GENERATE A LIST OF
PROTEINS IT CAN BE CHALLENGING
TO PRIORITIZE WHICH PROTEINS TO
PURSUE.
SOMETIMES DEPENDING ON THE
SAMPLES YOU'RE INTERESTED IN YOU
MIGHT NOT BE ABLE TO DO A
SURFACE PROTEOMIC EXPERIMENT BUT
WANT TO USE DATA TO PREDICT
USEFUL SURFACE MARKERS.
SO THIS PROVIDES A SOLUTION TO
CHALLENGES BY COMBINING A RAPID
WAY TO PRIORITIZE BASED ON
SIGNAL DISPARITY AND BECAUSE IT
INTEGRATES CONSENSUS BASED
PREDICTION OF LOCATION, CAN ALSO
BE USED TO RANK ORDER CANDIDATES
STARTING WITH DATA THAT DOES NOT
PROVIDE EVIDENCE OF LOCATION.
IT TAKES A LARGE DATASET,
PRIORITIZING MOLECULES AND GIVES
INDEX WHETHER IT'S LIKELY TO BE
A SURFACE PROTEIN.
OTHER PERMUTATIONS.
USER CAN SELECT A SCORING.
YOU COULD USE OMNIGENIE, OR IF
YOU USE mRNA OR GLOBAL
PROTEOMICS, YOU USE THE
GENIESCORE.
SO, THE VISUALS SHOW
DISTRIBUTION OF PROTEINS THAT
HAVE BEEN PREVIOUSLY PREDICTED
TO BE SURFACE LOCALIZED AMONG
FOUR PREDICTION ALGORITHMS OR
PUBLICATIONS, THE GRAPH SHOWS
RANK ORDER PRIORITIZATION OF
PROTEINS IN THE DATASET BASED ON
SCORING OPTIONS SELECTED.
HIGHEST RANK ARE THOSE PROTEINS
THAT ARE MOST DISTINCT BETWEEN
THE DATASETS, THIS COULD BE
BETWEEN CELL TYPES OR
BIOLOGICAL CONDITIONS,
DOWNLOADED FOR FURTHER ANALYSIS.
SURFACEGENIE IS THE TOOL WE USE.
THESE TOOLS ARE FREELY AVAILABLE
AS EASY TO USE WEB APPLICATIONS,
ALL SOURCE CODE IS AVAILABLE ON
GITHUB.
NOW I'M GOING TO SHOW EXAMPLES
OF HOW WE'VE APPLIED TOOLS TO
EVALUATE A NEW SURFACE PROTEIN
CAPTURE METHODOLOGY AND
DEMONSTRATE WHY WE BELIEVE
EXPERIMENTAL EVIDENCE IS
CRITICAL FOR SURFACE PROTEIN
MAPPING.
MAJOR FOCUS INVOLVES STUDYING
MOLECULAR AND CELLULAR EVENTS IN
PROGRESSION OF HEART FAILURE.
WE WORKED WITH HUMAN SAMPLES,
WITH SPECIAL REQUIREMENTS.
SO ADULT HUMAN CARDIAC MYOCYTES
DO NOT PROLIFERATE IN CULTURE
AND WE CAN'T USE METABOLIC
LABELS STRATEGIES, ONLY HAVE A
LIMITED AMOUNT OF SAMPLE
AVAILABLE.
CLASSIC METHODS ARE NOT
APPLICABLE.
TO IDENTIFYs FROM CELLS WE
ISOLATE, REENGINEERED A METHOD
FROM A DECADE AGO, TAKING
ADVANTAGE OF THE FACT MOST ARE
GLYCOSYLATED.
GLYCAN STRUCTURES ARE
BIATENYLATED, RELEASED, AND
IDENTIFIED BY MASS SPECTROMETRY.
WE USE UPDATED CHEMISTRY AND
FULLY AUTOMATED SYSTEM, APPLYING
THE METHOD TO IDENTIFY FROM A
FEW HUNDRED THOUSAND CELLS.
THIS METHOD WORKS WELL FOR A
RANGE OF CELL TIMES.
WE'RE DETECTING 3 TO 700
PROTEINS IN A SINGLE EXPERIMENT
WITH GREATER THAN 80%
SPECIFICITY OF CAPTURE EVERY
TIME.
VENEER IS WHAT WE USE TO
EVALUATE IN A CONSISTENT WAY TO
CALCULATE THE NUMBER OF PROTEINS
AND SPECIFICITY.
THE APPROACH IS WORKING WELL FOR
SAMPLE SIZES THAT WE EXPECT TO
OBTAIN FROM HUMAN HEARTS.
TO PUT THE RESULTS IN CONTEXT OF
PERFORMANCE OF PREVIOUS METHODS,
WE USE VENEER TO REANALYZE 282
FILES FROM STUDIES THAT ALSO USE
GLYCOCAPTURE METHOD.
YOU CAN SEE THAT THERE IS QUITE
A LOT OF VARIABILITY IN THE
SPECIFICITY.
SOME ARE NEAR 80% SPECIFICITY,
MINIMUM WE SEE IN A MICRO SCALE
EXPERIMENT MANY ARE BELOW 50%.
THE SAME CRITERIA ARE APPLIED TO
EACH DATASET.
TO ILLUSTRATE HOW IT'S HELPFUL,
I'LL SHOW A CLOSER LOOK AT TWO
STUDIES, BOTH RELIED ON GENE
ONTOLOGY INSTEAD OF EXPERIMENTAL
EVIDENCE TO ANNOTATE RESULTS.
THE REPORT FOR SURFACE PROTEINS
WAS 67%, WITH REANALYSIS THAT
DROPS.
CLOSER INSPECTION REVEALS SOME
PROTEINS REPORTED TO BE SURFACE
LOCAL ITSED BASED ON AN
VACCINATION INCLUDE ACTINOMYCIN.
IN THE NEXT EXAMPLE, THEY
CAPTURED WITH 78% SPECIFICITY
BUT VENEER CAN FIND EXPERIMENTAL
EVIDENCE FOR SURFACE
LOCALIZATION FOR 29%, USED TO
RAPIDLY EXTRACT EXPERIMENTAL
EVIDENCE OF SURFACE
LOCALIZATION, CONFIDENCE TO
PROTEINS NOT FULLY
CHARACTERIZED.
HOW DO YOU CONFIRM IT IS
WORKING?
WE APPLIED CELLSURFER PLATFORM,
COMBINATION WITH BIOINFORMATICS
TOOL APPLIED TO FOUR PRIMARY
HUMAN CARDIAC CELL TYPES
ISOLATED FROM HUMAN HEART.
WE'VE IDENTIFIED MORE THAN 1100
CELL SURFACE N GLYCOPROTEINS.
THIS ALLOWS TO SEE WHY
PREDICTIONS ARE NOT ENOUGH.
WE IDENTIFIED, HAVE EXPERIMENTAL
DATA FOR 30 THAT CONFLICT WITH
PREDICTED, 66 NOT PREDICTED TO
BE GLYCOSYLATED, 155 NOT
DETECTED IN RNAseq ANALYSES,
AND 429 NOT IN A RECENTLY
DESCRIBED PREDICTED HUMAN
SURFACE OME, WHY EXPERIMENTAL
DATA ARE SO IMPORTANT AND
DESPITE MODERN SEQUENCING
TECHNOLOGIES NOTHING COMPARES TO
AN ACTUAL EXPERIMENT.
WE IDENTIFIED A PROTEIN WE'VE
PREVIOUSLY ONLY OBSERVED IN
PRIMARY CARDIOMYOCYTES, WE HAVE
DATA FOR MORE THAN 90 HUMAN CELL
TYPES.
THIS IS A PROTEIN, NO
PUBLICATIONS, NO KNOWN FUNCTION,
NO PREVIOUS OBSERVATION IN THE
HEART.
ON ASKING MICRO SCALE CSE, WE
IDENTIFIED ALL FOUR REGIONS,
LOOK AT THIS PROTEIN SEQUENCE
USING CIRFESS, THEY PROVIDE
DIFFERENT RESULTS AND MAY
EXPLAIN WHY WHY OUR DATA
CONFLICTS.
WE USE THAT INFORMATION TO
DEVELOP NEW MONOCLONAL ANTIBODY
FOR EXTRACELLULAR REGION OF THE
PROTEIN.
THESE ANTIBODIES ARE USED FOR
IMAGING HEART TISSUE, SEEING
PROTEIN CO-LOCALIZED WITH CELLS
THAT EXPRESS CARDIOMYOCYTE
MARKER TROPONIN AND SHOW THE
PROTEIN IS ALONG THE MEMBRANE,
CO-LOCALIZES WITH KNOWN ION
CHANNELS WHICH I'M SHOWING HERE.
WHEN BROWSING RESOURCES TO SEE
WHAT'S KNOWN WE FOUND THE HUMAN
PROTEIN ATLAS DOES NOT PREDICT
THIS TO BE A PLASMA MEMBRANE
PROTEIN.
THESE ARE ALL CELL LINES THAT
OUR LAB AND THREE OTHERS HAVE
MAPPED USING CELL SURFACE
PROTEOMICS AND NEVER FOUND THE
PROTEIN IN THE CELL TYPES.
WHEN USE OUR MONOCLONAL ANTIBODY
WE DO NOT FIND EVIDENCE OF
PROTEIN IN CELL LINES AND
MOREOVER USING A TARGETED MASS
SPECTROMETRY DO NOT SEE THE
PROTEIN POINTING TO THE NEED FOR
CONTINUING VIGILANCE RELYING ON
PREDICTIONS WITH ANTIBODY-BASED
EVIDENCE FOR SURFACE PROTEIN
LOCALIZATION.
IF WE RELIED ONLY ON GENE
ONTOLOGY AND DATABASE WOULD HAVE
NEVER SELECTED THIS PROTEIN,
HELP US TO NOT OVERLOOK THE
PROTEIN OF INTEREST BECAUSE OUR
MOST RECENT EXPERIMENTS REVEALED
THIS DOES DECREASE IN ABUNDANCE
IN CARDIOMYOCYTES IN THE FAILING
HEART.
WE'RE GLAD WE HAVE ANTIBODIES
FOR NEXT-STEP STUDIES.
WE STARTED WITH A LIST OF 526
PROTEINS, AND COMPARING FROM
FAILING TO NON-FAILING HEARTS
AND PRIORITIZED PROTEINS OF
INTEREST FOR FUTURE STUDIES
SHOWING RANK ORDER BASED ON OMNI
GENIE SCORE FROM SURFACE GENIE
AND CORRESPONDING DO THE PLOTS
OF THOSE DIFFERENT BETWEEN
CARDIOMYOCYTES FROM FAILING AND
NON-FAILING HEARTS.
CHALLENGES INCLUDE LACK OF
STANDARDIZED ANALYSIS METHODS
WHICH MAKE IT CHALLENGING TO
GAIN CONFIDENCE.
WE'VE IDENTIFIED THREE TOOLS.
WHEN WE COMBINE WITH TECHNOLOGY
WE HAD RAPID STRATEGY FOR
AUTOMATED MAPPING APPLICABLE TO
NEARLY ANY HUMAN PRIMARY CELL
TYPE.
ONTOLOGY AND ANNOTATIONS ARE
VALUABLE, THEY ARE NOT ALWAYS
ENOUGH.
HAVING METHODS THAT GIVE ENDENS
OF LOCATION COMBINED WITH TOOLS
CAN HELP UNCOVER NAVIGATION,
APPLICABLE TO ANY HUMAN CELL
TYPE.
FINALLY MICRO SCALE AND VENEER
ARE LIMITED, WE LOOK FORWARD TO
NEW TOOLS TO INCLUDE INTACT
PROTEINS, AND NON-GLYCOSYLATED
PROTEINS.
EACH WERE DEVELOPED IN A
COLLABORATION.
I WANT TO ACKNOWLEDGE AMAZING
LAB MEMBERS AND THANKS TO MIKE
AND THE ORGANIZATION COMMITTEE
FOR THE INVITE TO PRESENT TODAY.
THANK YOU FOR ATTENDING.
>> THANK YOU, REBEKAH.
REBEKAH WILL BE ABLE TO ANSWER
QUESTIONS IN 40 MINUTES, DURING
THE PANEL DISCUSSION.
SEND YOUR QUESTIONS TO SEND LIVE
FEEDBACK.
NEXT IS DR. BRIAN HAAB FROM VAN
ANDEL INSTITUTE TALKING ABOUT A
UNIFIED APPROACH FOR GLYCAN
ARRAY DATA.
>> THANKS FOR THE OPPORTUNITY TO
SPEAK TODAY AND PRESENT THIS
WORK.
I'D LIKE TO TELL YOU ABOUT A
UNIFIED APPROACH AND TOOL SYSTEM
FOR HANDLING GLYCAN ARRAY DATA
FROM ANY PLATFORM IN ORDER TO
ANALYZE, USE, ACCESS THE GLYCAN
ARRAY DATA.
I WANT TO ACKNOWLEDGE -- I CAN
SHARE MY SCREEN, BY THE WAY.
LET ME TURN ON MY VIDEO SO YOU
CAN SEE ME.
JUST A SEC HERE.
I GUESS YOU CAN SEE ME THERE.
SO, I REALLY WANT TO ACKNOWLEDGE
ZACHARY, THE SCIENTIST AND
DEVELOPER, RESPONSIBLE FOR
BUILDING THIS AND DESIGNING THE
SCIENCE SO MUCH OF IT BEHIND IT
ALL.
SO, I'M PRESENTING THE TALK BUT
HE'S ONLINE RIGHT NOW AND WILL
BE AVAILABLE DURING THE PANEL
DISCUSSION AS WELL.
THIS WAS MOTIVATED -- I ADVANCE
USING -- OH, THE POINTER, OKAY.
HUGE INCREASE IN VOLUME AND
DIVERSITY OF GLYCAN ARRAY DATA.
THIS TIMELINE SHOWS THE
INTRODUCTION OF BRAND NEW
SYSTEMS, NOT JUST PUBLICATIONS
BUT BRAND NEW SYSTEMS.
YOU CAN SEE IN THE LAST FIVE OR
SIX YEARS THERE'S BEEN INCREASE
IN RATE OF NEW TYPES OF THINGS
COMING OUT.
FOR A LONG TIME SINCE THE EARLY
INTRODUCTION, EARLY DAYS OF
INTRODUCTION OF THE GLYCAN
ARRAY, EXPERIMENTS WERE
GENERALLY DONE THROUGH THE CFG
ARRAY, A GREAT PLATFORM, GENERAL
PLATFORM FOCUSING ON MAMMALIAN
BIOLOGY PRIMARILY BUT WORKED
VERY WELL.
THE MAIN VALUE, ONE OF THE HUGE
OUTCOMES OF THE CFG ARRAY WAS TO
ESTABLISH IN THE GLYCOBIOLOGY
COMMUNITY THIS WAS A POWERFUL
USEFUL PLATFORM.
IT STIMULATED DEVELOPMENT OF A
LOT OF BIOINFORMATICS TOOLS THAT
MADE USE OF THE HUGE AMOUNT OF
DATA THAT WERE MADE PUBLICLY
AVAILABLE, A VERY WIDELY HEAVILY
USED SUCCESSFUL TOOL.
BUT NO AARRAY WITH MEET THE
NEEDS OF EVERY PROJECT.
OTHER RESEARCHERS STARTED
DESIGNING THEIR OWN SYSTEMS FOR
COMPLEMENTING THE TECHNOLOGY OF
THE CFG ARRAY AND DESIGNING
CONTINUE -- CONTENT TO ASK
SPECIFIC QUESTIONS, SIALIC ACID
BIOLOGY, PLANT BIOLOGY,
STIMULATED BY DEVELOPMENT IN
SYNTHESIS OF GLYCANS THAT MOVED
THINGS ALONG MUCH FASTER.
OTHER TYPES OF TECHNOLOGIES
USING DNA SEQUENCING, MASS
SPECTROMETRY READOUTS, ANOTHER
BASED ON DNA SEQUENCING READOUT.
AND THEN THE GLYCANS COVER A
WIDE RANGE OF CONTENT.
WE HAVE CELL WALL ARRAY, A WIDE
VARIETY OF CONTENT NOW COVERING
GLYCANS, OLIGO MANNOSE, SO
FORTH.
THERE'S A HUGE INCREASE IN ARRAY
TYPES AND CONTENT.
THIS BRINGS TOGETHER A PICTURE
OF GLYCAN-BINDING PROTEINS,
GETTING BETTER PICTURE OF
SPECIFICITY, WIDER AVAILABILITY,
ALSO OF A CHALLENGE TO HANDLE
THIS TYPE OF DIVERSITY AND
ARRAYS.
WE NEED A COMMON MODE OF
ANALYZING THESE DIFFERENT TYPES
OF ARRAYS.
WE ALSO NEED COMMON MODE OF
OUTPUT WHICH THEN CAN INTEGRATE
THE ACCESS AND ANALYSIS.
SO, I WANT TO TELL YOU ABOUT
THESE THREE TOPICS HERE.
ANALYZING VARIOUS GLYCAN ARRAYS,
HOW DO WE DO THAT?
AND THEN ACCESSING THE ANALYZED
RESULTS AND THEN HOW DO WE USE
THAT, WHAT ARE SOME WAYS THAT
THIS RESOURCE CAN BE USED IN
DOWNSTREAM APPLICATIONS, A
COUPLE EXAMPLES OF THAT.
THERE ARE SOME CHALLENGES WE HAD
TO OVERCOME IN THE ANALYSIS OF
DATA FROM ALL PLATFORMS, ONE WAS
READING THE DATA.
IT BEGAN WITH BEING ABLE TO
PARSE THE GLYCAN NAMES WHICH
REALLY THE ONLY REQUIREMENT ARE
NAMES ARE IN TEXT FORMAT, THAT
IT IS BASED ON THE MODIFIED
IOPAC, CAN BE TRANSLATED,
THERE'S A LOT OF SLOPPINESS
BUILT IN, DOESN'T NEED TO BE
PERFECT.
DATA HAS TO BE ALIGNED WITH EACH
GLYCAN NAME, SOME SORT OF
QUANTIFICATION.
THE ALGORITHM FOR ANALYZING USES
ALL AVAILABLE CONCENTRATIONS,
WHEN THERE'S MORE THAN ONE
CONCENTRATION OF THE LECTIN OR
ANTIBODY APPLIED TO GET MORE
ACCURATE ANALYSES OF THE
SPECIFICITIES.
IT PUTS IT IN A COMMON FORMAT
THAT'S INDEPENDENT OF THE SCALE
OF THE QUANTIFICATION BECAUSE
THERE'S A WIDE VARIETY OF TYPES
OF OF QUANTIFICATION, ALLOWING
FOR RELATING BETWEEN DATASETS OR
USING THEM TOGETHER IN COMBINED
ANALYSES.
SO, WE DEVELOPED A COUPLE
SYSTEMS THAT MAKE IT USEFUL, ONE
FOR DEFINING SPECIFICITY OF A
LECTIN.
WE CAN'T EFFECTIVELY DEFINE
SPECIFICITY WITH JUST ONE SIMPLE
DEFINITION, SAY TERMINAL OR
INTERNAL LacNAc, NO MATTER
HOW COMPLEX, THE NATURE OF
LECTINS IS THAT THEY COMBINED A
FEW DISTINCT TYPES OF MOTIFS AND
WITHIN EACH ONE THERE CAN BE
TUNING OF THE AMOUNT OF BINDING
BASED ON SLIGHT ADJUSTMENTS OR
SLIGHT DIFFERENCES IN GLYCAN
ITSELF.
AND SO WE NOW USE A FAMILY OF
MOTIFS TO DEFINE THE
SPECIFICITY, CALL THIS THE
LECTIN MODEL.
SO, THERE'S TWO LAYERS OF THE
MOTIFS WE DEFINE.
ONE IS PRIMARY MOTIFS THAT BROAD
CATEGORIES ARE DIFFERENT TYPES
OF STRUCTURES THAT IT BINDS.
THE SECOND IS FINE SPECIFICITY,
THERE'S MODIFICATIONS OF THE
PRIMARY MOTIF THAT DEFINE A MORE
FINE GRADATION OF BINDING.
WE FIND THE FAMILY SYSTEM OR
THIS MODEL OF FINDENING IS MORE
USEFUL FOR DEFINING THE
SPECIFICITY AND FOR USING THE
OUTPUT AND DOWNSTREAM
APPLICATIONS.
SO THESE MOTIFS ALSO HAVE TO NOT
BE FIXED SUBSTRUCTURES BECAUSE
THERE'S SO MUCH VARIABILITY IN
THE GLYCANS THAT ARE OUT IN
BIOLOGY, AND ALSO BECAUSE THE
BINDING OF THE LECTIN ISN'T
NECESSARILY LINKED TO A FIXED
SUBSTRUCTURE BUT RATHER A
PATTERN THAT HAS VARIABILITY.
SO WE NEED A SYNTAX FOR DEFINING
PATTERNS THAT ARE MORE OR LESS
VARIABLE, SOMETIMES THE PATTERN
COULD BE A FIXED SUBSTRUCTURE
BUT IT MIGHT HAVE VARIABILITY IN
THE TYPE OF MONO SACCHARIDE,
TYPE OF LINKAGE, WHETHER THERE'S
GAPS BETWEEN CONTACT POINTS.
TO HANDLE THAT NEED WE DEVELOPED
A NEW SYNTAX THAT IS USEFUL.
THIS SYNTAX WHERE WE CAN BUILD
IN VARIABILITY INTO THE
MONOSACCHARIDE, USE WILDCARDS IN
EACH OF THOSE LOCATIONS ALLOWS
FOR DESIGNING WITH A SYSTEM
THAT'S STILL FAIRLY COMPACT, CAN
HAVE A TEXT REPRESENTATION,
HUMAN READABLE.
AND IT CAN ALSO BE EASILY
INTEGRATED WITH OTHER SEARCH AND
ANALYSIS SYSTEMS.
THOSE WERE CAPABILITIES THAT
THEN THE MOTIF FINDER ANALYSIS
SYSTEM FOR ANALYZING GLYCAN
ARRAYS WAS BUILT ON, AND THEN
THE OUTPUT IS A STANDARDIZED
OUTPUT WHERE IT'S GOT VARIOUS
FEATURES OF THE OUTPUT WHICH
ALLOW YOU TO VISUALIZE THE DATA,
THE RESULTS, IN A FEW WAYS.
PROVIDED IN A REPORT THAT IS
ONLINE ACCESSIBLE, DATABASE,
I'LL SHOW YOU IN A MINUTE.
AND THE PRIMARY MOTIFS, FIRST
LEVEL OF THE MAIN TYPES, ONE IS
A BOX SCATTER PLOT WHERE THESE
MOTIFS, THIS IS THE MAIN MOTIF,
AND THEN OTHERS, THESE ARE THE
RESIDUAL GLYCANS SO BASICALLY
THE NON-BINDERS, YOU CAN SEE
THERE'S A RANGE OF BINDING, AND
THEN A TABLE GIVES THE GRAPHICAL
STRUCTURE, LATER IN THE REPORT.
THERE'S ALSO A TEXT DEFINITION
OF THESE MOTIFS.
SO YOU CAN SEE FOR ECL THE TOP
MOTIF IS GAL BETA 4 GlcNAc.
ANOTHER VIEW GIVES THE FINE
SPECIFICITY MOTIF, THIS IS THE
PRIMARY MOTIF OF A, GRADATIONS
IN THE FINE SPECIFICITIES,
DEFINITIONS ARE HERE.
A-1, A-STAR, REMAINDER OF THE
OTHER TYPES OF MOTIFS OF THIS
PRIMARY SPECIFICITY.
MOTIF INTENSITY MAP IS USEFUL.
THESE ARE THE FINE SPECIFICITY
MOTIFS.
THESE ARE GLYCANS ON THE ARRAY.
YELLOW MEANS GLYCAN OR MOTIF IS
PRESENT IN THIS GLYCAN, AND THEN
THIS IS THE BINDING INTENSITY OF
THE LECTIN TO THAT GLYCAN.
YOU CAN SEE THAT FOR MOTIF A-1,
IT'S PRESENT IN PRIMARILY
GLYCANS THAT HAVE HIGH BINDING.
YOU CAN SEE AMOUNT OF
REPRESENTATION ON ARRAY, SO THIS
MOTIF HAS THREE GLYCANS THAT
CONTAIN IT, THIS ONE HAS QUITE A
FEW.
YOU CAN SEE THE SECONDARY MOTIF
IS MAINLY PRESENT IN WEAKER
BINDERS.
IT GIVES NICE INDICATION OF HOW
MANY GLYCANS THERE ARE, WHAT
LEVEL OF BINDING IS.
ALSO EXAMPLE GLYCANS IN ARRAY,
FROM HIGH, MODERATE, LOW, WHAT
ARE THE GLYCANS, NOT JUST MOTIF.
FOR A-1, THIS ONE WOULD BE THIS
TOP GLYCAN RIGHT HERE.
SO, THESE REPORTS ARE ACCESSIBLE
IN A DATABASE.
SO THE ABILITY TO DO THIS,
AUTOMATED ANALYSIS OF GLYCAN
ARRAYS FROM ANYPLACE PLATFORM,
THEN PROVIDED THE ABILITY TO
COMPILE GLYCAN ARRAY DATA FROM
MANY PLATFORMS, AND TO PUT THEM
TOGETHER, THE ANALYZED DATASETS,
INTO DATABASE, COMPILE DATA FROM
19 LABORATORIES OR COMPANIES, 35
TYPES OF ARRAYS, 5 METHODS, 13
GLYCAN FAMILIES, 185 UNIQUE
PROTEINS, FOR TOTAL OF 1125
DATASETS IN THE FIRST RELEASE OF
THE DATABASE.
SO, THAT PROVIDES A REALLY NICE
TOOL NOW FOR ACCESSING THE
ANALYZED GLYCAN ARRAY DATA.
SO, WE ASKED WHAT SORT OF
COVERAGE WE HAD IN THE DATABASE.
THIS RIGHT HERE SHOWS YOU THE
SUPPLIERS, THE SOURCES OF THE
GLYCAN ARRAYS.
THESE ARE MOTIFS, 118 MOTIFS
GATHERED FROM GlyGen, FROM
11 FAMILIES.
FOR EACH ARRAY SOURCE WE ASKED,
WE WANTED TO KNOW HOW GOOD THE
COVERAGE IS IN THIS DATABASE OF
VARIOUS MOTIFS, THIS ASKED DO
GLYCANS IN THIS ARRAY CONTAIN
THESE MOTIFS.
RED IS YES, WHITE IS NO.
YOU CAN SEE THAT ALL OF THE
MOTIFS ARE COVERED BY AT LEAST
ONE ARRAY.
SOME MOTIFS ARE COVERED.
THIS IS THE NUMBER OF ARRAYS
BY -- THIS IS THE NUMBER OF
MOTIFS THE ARRAY COVERS, SO THIS
ARRAY COVERS NEARLY ALL OF THE
MOTIFS.
SOME ARE MORE SPECIALIZED, PLANT
CELL WALL JUST HAS ONE HERE, AS
WELL AS HEPARAN SULFATE OF THESE
PRE-DEFINED GlyGen MOTIFS.
OVER THE RESOURCE OF THE
DATABASE BETWEEN THE ARRAYS THEY
COVER MOST MOTIF, ALL THE MOTIFS
THAT WERE PRE-DEFINED FROM THESE
GlyGen MOTIFS.
WE ALSO ASKED WHAT ABOUT THE
BINDERS, THE GLYCAN BINDING
PROTEINS REPRESENTED IN THE
DATABASE, ARE THEY BINDING MOST
OF THESE MOTIFS?
SO WE PREDICTED BINDING TO ALL
OF THE GLYCANS ON THE ARRAYS,
1800 GLYCANS REPRESENTED OVER
ALL OF THE ARRAYS.
AND THEN ASKED WHETHER THESE
MOTIFS ARE PRESENT IN THE
BINDERS.
WE SEE GOOD COVERAGE THERE.
SO THESE ARE THE SAME MOTIFS AND
THIS IS THE NUMBER OF LECTINS
THAT BIND EACH MOTIF.
SO THERE ARE SOME MOTIFS WHERE
THERE AREN'T ANY LECTINS BINDING
THEM BUT PERHAPS AS TIME GOES ON
WE'LL FIND SOME DATASETS WHERE
SOME OF THESE OTHER MOTIFS ARE
ACTUALLY BOUND BY THOSE LECTINS.
WHEN SOMEONE IS LEARNING FOR A
LECTIN THAT BINDS TO A
PARTICULAR MOTIF, FOR THIS MOTIF
THERE WOULD BE MANY LECTINS
AVAILABLE TO MORE OR LESS
DEGREE, AND THEN FOR SOME NOT
ANY.
THIS IS AVAILABLE THROUGH
CARBOGROVE.ORG.
THE SEARCH TOOL IS FLEXIBLE SO
IT DOESN'T NEED TO BE EXACT NAME
MATCHES.
THERE'S A LOT OF SLOPPINESS
BUILT IN.
IT WILL RETURN ALL THE DATASETS
THAT MATCH.
FOR SNA HERE ARE SOME OF THE
MANY SNA DATASETS IN THE
DATABASE.
IT GIVES THE SOURCE OF THE
PROTEIN, DATA SOURCE, SO CFG,
CUPRA, OTHERS.
CLICKING WILL SHOW DIFFERENT
CONCENTRATIONS AND MODEL REPORT,
CLICKING ON VIEW WILL SHOW VIEWS
I SHOWED BEFORE.
THERE'S AN API FUNCTION
AVAILABLE FOR BIOINFORMATICIANS
WHO WOULD LIKE TO ACCESS
DATABASE FOR DEVELOPMENT OF
OTHER APPLICATIONS.
I'D LIKE TO TELL YOU ABOUT USE
EXAMPLES NOW.
THE BASIC USES OF THIS RESOURCE
ARE -- YOU WANT TO USE -- YOU
ANALYZE GLYCAN BINDING PROTEIN
SPECIFICITY, PERHAPS YOU HAVE
ONE IN THE DATABASE AND WANT TO
LOOK AT THE DETAILED ANALYSIS OF
THAT GLYCAN BINDING PROTEIN.
WE HAVE THE STAND-ALONE MOTIF,
NEW GLYCAN ARRAY DATA OR WANT TO
REANALYZE, MOTIF FINDER SOFTWARE
FOR ANALYSIS OF GLYCAN ARRAYS IS
AVAILABLE AS WELL.
BUT THIS HAS THE ALREADY
ANALYZED DATASETS.
OR IF YOU WANT TO FIND THE
GLYCAN BINDING PROTEIN THAT
BINDS A PARTICULAR MOTIF YOU CAN
SEARCH THE MOTIF AND FIND THOSE.
AND THEN LOOK AT THOSE DATASETS
TO SEE HOW WELL IT MATCHES UP
WITH WHAT YOU WANT.
A COUPLE I'D LIKE TO FOCUS ON
FOR A FEW MINUTES HERE IS THESE
TWO HERE.
DESIGNING EXPERIMENTS THAT USE
GLYCAN BINDING PROTEINS OR
INTERPRETING DATA THAT USE
GLYCAN BINDING PROTEINS.
WHAT I MEAN BY THAT IS SO YOU
WANT TO DESIGN AN EXPERIMENT
WHERE YOU'D LIKE TO PROFILE AN
UNKNOWN BIOLOGICAL SAMPLE AND
COMPARE A COUPLE FOR A FEW
DIFFERENT -- YOU WANT TO USE
MAYBE INTEGRATED INFORMATION
FROM SELF LECTINS, HOW DO YOU
THEN CHOOSE THOSE OPTIMALLY?
OR YOU'RE DOING EXPERIMENTS
COLLECTING DATA WITH LECTINS
WITH UNKNOWN BIOLOGICAL SAMPLE
AND WANT TO KNOW WHAT DOES THE
DATA TELL YOU?
LECTINS 2, 3, 4 BIND RELATIVELY
STRONGLY, OTHERS DON'T.
EXPERTS CAN PORE OVER DATA
MANUALLY AND MAKE QUALITATIVE
ESTIMATIONS BUT IS THERE A
QUANTITATIVE WAY TO DO SO WITH
AN ALGORITHM?
KEY FUNCTION IS BEING ABLE TO
PREDICT BINDING TO VIRTUAL
GLYCANS.
BY THAT I MEAN THERE'S A GLYCAN
NOT IN THE DATABASE, HOW DO YOU
KNOW WHETHER THE GLYCAN BINDING
PROTEIN IS GOING TO BIND OR NOT?
WE HAVE BASED ON THE DATASET AN
ALGORITHM THAT CAN PREDICT
BINDING TO VIRTUAL GLYCANS.
SO, FOR EXAMPLE, HERE IS DATA
WHERE YOU CAN PROBE THE LECTIN
BINDING TO -- ANTIBODY ARRAY TO
PROTEIN CAPTURED, THESE ARE CELL
LINES, FOUR LINES, TWO BINDS
LECTIN, TWO UNDERLYING
STRUCTURES, GET MORE INFORMATION
BY TREATING WITH WHO SIALIDASES,
BINDING GOES UP OR DOWN BECAUSE
IT'S EXPOSING THE EPITOPE.
YOU GET INTERESTING BINDING
PATTERNS.
THIS IS FOR DIFFERENT CAPTURE
ANTIBODIES, DIFFERENT CELL
LINES.
YOUR LECTINS HERE, WHETHER THEY
ARE TREATED OR NOT, YOU CAN SEE
WIDE RANGE OF PATTERNS DEPENDING
ON CELL LINE, DEPENDING ON
CAPTURE ANTIBODY.
IT'S A HUGE RANGE OF PATTERNS.
HOW DO YOU MAKE SENSE OF THAT?
EVEN THE BEST EXPERT COULDN'T DO
THAT PROPERLY BECAUSE YOU'VE GOT
JUST TOO MUCH INFORMATION FROM
MULTIPLE LECTINS.
YET THERE'S REAL INFORMATION HERE.
CLEARLY YOU NEED SOFTWARE, WE
CALL THIS GLYCAN SOLVER.
TAKE THE TEST DATA WITH PATTERNS
I SHOWED, ENTER KNOWN
SPECIFICITY OF LECTINS AND
ENZYMES YOU USED.
YOU FEED IT POTENTIAL GLYCANS
THAT IT COULD BE.
HERE'S WHERE IT DOES PREDICTION
OF BINDING TO POTENTIAL GLYCANS
AND BASED ON THAT COMPARED TO
REAL BINDING TELLS YOU
LIKELIHOOD OF VARIOUS GLYCANS
AND MOTIFS IN THE SAMPLE.
OUTPUTS MOTIF ABUNDANCE ESTIMATE
FOR YOU.
THE WAY WE SEE THIS WORKING YOU
HAVE A HYPOTHESIS ABOUT THE
TYPES OF GLYCANS, USE
CARBOGROVE, COLLECT DATA, USE
GLYCAN SOLVER AND EVALUATE HOW
WELL IT DID.
IF THERE'S STILL A LOT OF
QUESTIONS THAT REMAIN YOU GO
BACK AND REDESIGN YOUR
EXPERIMENT AND REPEAT THE
PROCESS UNTIL YOU HAVE SOME
UNDERSTANDING OF GLYCOSYLATION.
IN SUMMARY I'VE TOLD YOU ABOUT
ANALYSIS AND ACCESS OF GLYCAN
ARAPE DATA FROM ANY PLATFORM.
OF USING THE MOTIF FINDER
SOFTWARE, COUPLED WITH
CARBOGROVE DATABASE.
AND USAGE EXAMPLES SEARCH AND AC
SELLS, DESIGN ANALYSIS OF
EXPERIMENTS, OTHER DOWNSTREAM
APPLICATIONS WOULD BE POSSIBLE,
MANY TYPES OF BIOINFORMATICS OR
ANALYSES.
WHAT WE HAVE PLANNED HERE,
FURTHER INTEGRATION OF THE
DATABASE WITH OTHER SYSTEMS.
AND APPLICATIONS, FURTHER
DEVELOPING REPORTS TO BE
INTERACTIVE, PLOTS FOR ALL THE
CONCENTRATIONS, GLYCAN BINDING
CURVES BASED ON CONCENTRATION,
LINKS, SOME THERE ALREADY.
UPDATED ANALYSES, ANALYSIS OF
THE SPACERS, DENSITY,
GLYCOPEPTIDES AS WELL.
SO, WITH THAT, AND THEN I'D LIKE
TO ACKNOWLEDGE ZACHARY KLAMER,
THE DRIVING FORCE.
OTHERS CONTRIBUTED TO DATA AND
IDEAS AND HOW WE COULD USE THESE
THINGS AND WE'VE HAD GOOD CORE
SUPPORT.
I WANT TO THANK THE COMMON FUND
FOR SUPPORT OF THIS WORK AND
ALSO STTR PROGRAM.
WE'VE WORKED WITH THE COMPANY Z
BIOTECH AND ALLIANCE OF
GLYCOBIOLOGYISTS.
SO, WITH THAT WE'LL GO TO THE
NEXT.
WE'RE GOING TO DO PANEL
DISCUSSION LATER.
>> THANK YOU, BRIAN.
IF YOU HAVE QUESTIONS USE THE
LINK FOR SEND LIVE FEEDBACK AND
I'M SURE BRIAN WILL BE HAPPY TO
ADDRESS THOSE QUESTIONS IN ABOUT
20 MINUTES.
MOVING ON TO OUR LAST SPEAKER OF
THE SESSION, DR. DEANNE TAYLOR
FROM CHILDREN'S HOSPITAL OF
PHILADELPHIA, HOW TO INTEGRATE
MULTIOMICS DATA TO IDENTIFY
GENETIC FEATURES AS RELATES TO
PEDIATRIC DISEASES AND
DISORDERS.
>> CAN YOU HEAR ME NOW?
WE
>> WE CAN HEAR YOU NOW.
OH, YOU'RE MUTED.
>> CAN YOU SEE MY SCREEN?
>> YES AND WE CAN HEAR YOU.
>> GREAT.
I'M GOING TO TALK ABOUT
INTEGRATION OF MULTIOMICS DATA
AS PART OF THE COMMON FUND, THE
CENTER AND MY LAB.
I'M WORKING WITH JOHNSON
SILVERSTEIN.
I'M NOT REPRESENTING THE KIDS
FIRST PROGRAM.
I'M REPRESENTING MYSELF AND I'M
ALSO ONE OF THE P.I.s OF THE
KIDS FITTER DATA RESOURCE
CENTER.
A LOT OF WORK HAS BEEN FUNDED BY
NIH COMMON FUND.
ALSO COMMON FUND DATA ECOSYSTEM
PROJECT AND IT'S AN ONGOING
COLLABORATION BETWEEN MULTIPLE
COMMON FUND DATA RESOURCES.
BEFORE I GO TO ANYTHING I'D LIKE
TO POINT OUT THE PEOPLE THAT
HELPED MAKE THIS RESEARCH
POSSIBLE.
BEN STEAR AND TAHA MOHSENI IN MY
LAB DOING MOST OF THE WORK.
ALSO WITH JONATHAN SILVERSTEIN
AND OTHER PEOPLE, I'LL GO BACK
OVER THEM AT THE END.
IT'S BEEN A VILLAGE WHEN IT
COMES TO INTEGRATING LARGE
AMOUNTS OF DATA.
WE STARTED WITH THE UNIFIED
MEDICAL LANGUAGE SYSTEM.
WE MOVED HERE WITH UNIFIED
MEDICAL LANGUAGE SYSTEM TO
INTEGRATE BIOMEDICAL
TERMINOLOGY, 200 ONTOLOGY AND
VOCABULARY, ESSENTIALLY HAVING
THEM LINKED TOGETHER ON CONCEPTS
OR TERMS.
MIGHT BE AN ICD-10 CODE, AND
SEVERAL OTHERS FROM HTO AND
OTHER SOURCES, BUT INSTEAD OF
HAVING EACH AS INDIVIDUAL
THINGS, THEY WILL LINK THEM INTO
ONE CONCEPT, AND THAN WILL NOW
HAVE TERMS FROM ALL OF THESE
MEDICAL LANGUAGE SYSTEMS,
ONTOLOGY LANGUAGE SYSTEMS THAT
INFORM THAT TERM, THAT CONCEPT.
THOSE CONCEPTS DOING THINGS FROM
UNIFIED LEVEL ALLOWS YOU TO LINK
IN TERMINOLOGIES AND A LOT OF
KNOWLEDGE SYSTEMS.
WITHOUT HAVING TO INCREASE THE
SIZE OF YOUR KNOWLEDGE BASTE.
IT ALSO BRINGS IN SNOMED, OMIM,
MeSH, THE HUMAN PHENOTYPE
ONTOLOGY.
YOU CAN USE THIS AS PART, ANYONE
CAN USE IF YOU REQUEST A
LICENSE, USE THIS FOR EXPLORING
RELATIONSHIPS OF KNOWLEDGE
BASES, CERTAIN DISEASES, YOU CAN
MOVE FROM LET'S SAY HUMAN TERM
DOWN TO LET'S SAY
DROSOPHILA-RELATED TERM, WITHIN
ONE DATABASE, ALLOWS FOR A LOT
OF GRAPH-LIKE QUERIES YOU CAN DO
ON YOUR UMLS.
BUT UMLS IS A DATABASE.
I WAS WORKING WITH JONATHAN
SILVERSTEIN, HE ORIGINALLY
STARTED OFF WITH A DATABASE
BASED .
IT BRINGS IN A LOT OF
INFORMATION.
SO JUST WHAT THIS LOOKS LIKE IN
JONATHAN'S GRAPH WE STARTED WITH
IN OUR COLLABORATION, HERE ARE
CONCEPTS I MENTIONED EARLIER,
CODES ARE RELATED TO SOME SORT
OF CLINICAL DRUG, FROM RxNORM,
A QUERY, SHOW ME ALL THE
INFORMATION RELATED TO THE
PATCH, ET CETERA.
THIS BRINGS IN THE DATA.
IT'S A LARGE KNOWLEDGE GRAPH BUT
YOU ONLY RETURN THE THINGS
YOU'RE LOOKING FOR.
IN THAT WAY, HE INTEGRATED ALSO
PHYSIOLOGY ONTOLOGY AND CELL
ONTOLOGY INTO THIS KNOWLEDGE
GRAPH TO SUPPORT WHAT THEY LIKE
TO DO FOR HUB MAP DATA.
THERE ARE ANIMAL BASED DATA IN
HERE BUT ONTOLOGIES IN HERE,
BACKUP NOT COMPLETE.
WE NEED IT TO INCLUDE MOUSE
MODEL.
MOST BRING ONTOLOGY TO THE DATA.
WE WOULD LIKE TO BRING DATA TO
ONTOLOGY, TO INTEGRATE MULTIPLE
DATASETS INTO THE ONTOLOGY SO
THAT NOW YOU HAVE AN INTEGRATED
NETWORK OF PRIMARY DATA,
SECONDARY DATA, AND COMPLETE
ONTOLOGICAL DATA.
THIS STRUCTURE WE'RE USING HERE
THAT JONATHAN AND UMLS DESIGNED
IS THE STRUCTURE WE'D LIKE TO
USE FOR INTEGRATING PRIMARY
EXPERIMENTAL DATA.
WHY ARE WE INTERESTED?
THERE'S AN ONGOING ISSUE WITH
ETIOLOGY DISCOVER OR VARIANT
DISCOVERY IN CHILDHOOD CANCER,
EVERYONE STRUGGLES WITH THIS.
IT'S A LARGE FEATURE SPACE, HOW
CAN WE REDUCE THAT FEATURE SPACE
BY LOOKING AT DATA AND TRYING TO
IDENTIFY THINGS ALREADY KNOWN,
THINGS THAT AREN'T KNOWN OR ARE
NOT NOTICED BUT ARE IN THE DATA.
ONE WAY THIS, FOR EXAMPLE,
VARIANT DISCOVER IS ONE FEATURE
WE'D LIKE TO WORK ON, SUPPORT
SELECTION OF SETS BASED ON
FEATURES THAT WE MAY HAVE DIM
KNOWLEDGE MUCH BECAUSE THERE'S
SO MANY.
COULD WE BRING THEM IN ONE
ENVIRONMENT AND DESILO AND
INTEROPERATE AND USE THE DATA TO
UNDERSTAND THESE CONDITIONS?
A CHALLENGE WAS INTEGRATING AND
QUERY THAT EMPIRICAL DATA AND
UNDERSTAND BIRTH DEFECTS IN
CANCER?
OUR GOAL WAS TO LINK MULTIOMICS
DATA THROUGH KNOWLEDGE APPROACH,
PROPOSING AS YET UNKNOWN CAUSES
FOR STRUCTURAL DEFECTINGS IN
CANCER.
WE HAVE THE EXAMPLE QUERY WITH
HUBMAP DATA, A KIDS FIRST
VARIANT, KIDS FIRST PHENOTYPE,
SHARED GENE, HUBMAP LOCATION,
ONE OF MANY RESULTS FOR ONE
QUERY, LIKE A DATABASE QUERY,
MANY RESULTS IN A TABLE.
INSTEAD OF GETTING -- YOU CAN
GET GRAPHICAL RESULTS FROM OUR
KNOWLEDGE GRAPH, FRP THE
DATABASE, MULTIPLE RETURNS AND
QUERY RETURNS IN THIS MATTER TO,
FOR EXAMPLE, SAY I WANT ALL THE
REFERENCE DATA, CELL TYPE
LOCATIONS IN THOSE TISSUES, ONE
QUERY, FOR EXAMPLE, THAT YOU CAN
DO.
SO WHAT WE DID IS -- WE'RE
MISSING -- HUB MAP CAME IN.
WE HAVE PUT THIS TOGETHER,
INCLUDED RECENTLY SINCE I WAS
INVITED TO THE TALK, PUT IN
GLYCOSYLATION FROM THE DATA
THAT'S ON THE GlyGen
WEBSITE.
IT'S OF IT WILL HELP WITH
PRIMARY EXPERIMENTAL DATA.
WE STARTED WITH KIDS FIRST DATA,
COULD BE ANY DATA
, QUERIED FROM SOURCES TO FIND
EXISTING EXPECTED RELATIONSHIPS
SO WE CAN GET ANY DATA BACK OUT,
SOAP ANY DATA IN THE TOP CAN BE
GOTTEN BACK OUT AGAIN WHEN YOU
DO YOUR QUERY, ON THIS KIND OF
DATA OR ON YOUR OWN DATA, GET
BACK THE DATA AND EXPAND OUT THE
QUERY.
YOU COULD SAY I WANT TO START
WITH GTEx, GLYCOSYLTRANSFERASE
AND OUTWARD, ALLOWS YOU TO
EXPAND AND TELL YOU BASED ON
YOUR KNOWLEDGE OF THE BIOLOGY.
SO, WE INTEGRATED MANY
ONTOLOGIES.
THIS IS SURFACE LEVEL BUT I
WANTED TO SHOW A LOT OF SUPPORT
FOR HUMAN DEVELOPMENTAL STAGES,
DISEASE ONTOLOGY, CELL ONTOLOGY,
WE HAVE HPO, THE HUMAN
PHENOTYPE, ALL THE ONTOLOGIES
INTEGRATED INTO OUR GRAPH,
INTRODUCING A LOT OF DATA, A LOT
OF SUPPORT FOR OUR DATA,
INTRODUCED GENES IN .
THE SIZES WE'VE DONE, INCLUDED
MAMMALIAN PHENOTYPE AND HUMAN
PHENOTYPE.
WE DON'T HAVE TO DO ANYTHING
WITH THAT, IT'S ALREADY THERE.
WE INTRODUCED MAMMALIAN
PHENOTYPE, AND THAT HAS ABOUT
13,000 DATA POINTS, INTERESTED
DATA FROM GTEx, eQTL, GENE
TISSUE EXPRESSION ON THE DATA
POINTS.
WE'VE GOT THE CELL RNAseq
DATA, THIS IS OUR SPECIFIC
DATASET I MIGHT HAVE TIME TO
TALK ABOUT LATER.
GEN CODE DATA, HUBMAP, HUBMAP
10X VERSION 3, 61 DATASETS OUT
OF THAT.
THAT INCLUDES CLUSTER
INFORMATION TISSUE AND GENES.
YOU CAN SEE THE REST OF THE
DATA.
THERE'S OTHER DATA HERE THAT'S
SPECIFICALLY BOTH FROM THE
COMMON FUND DATA ECOSYSTEM OR
SOME OUTSIDE SOURCES TO SUPPORT
IT.
AND THIS IS INTRODUCED AS
INTERESTING DATASETS WE CAN
QUERY AGAINST.
HOW DO WE DO QUERIES?
YOU COULD LOOK FOR HUMAN
PHENOTYPE CONCEPT AND JUMP TO
MAMMALIAN PHENOTYPE CONCEPT.
THIS IS STILL BEING WORKED ON.
MELISSA HANDEL WILL GIVE LOTS OF
GREAT INFORMATION ON THIS KIND
OF STUFF TOMORROW.
BUT WE HAVE OUR OWN PHENOTYPES
WE'VE MAPPED OURSELVES,
INTRODUCED THEM INTO THE
DATABASE.
WE HAVE A MOUSE GENE CONCEPT,
MAMMALIAN CONCEPT, HUMAN GENE
CONCEPT, GTEx, CAN MAP FROM
HUMAN PHENOTYPE THROUGH THE
MOUSE, BACK TO THE HUMAN AND
THROUGH TO GTEx.
FOR EXAMPLE, THERE'S A LOT OF
HEART DEFECTS THAT AREN'T REALLY
TESTED IN HUMANS BECAUSE WE
CAN'T KNOCK OUT GENES IN HUMANS,
NOT EVERYTHING IS RECORDED,
GENES WITH BIRTH DEFECTS ARE
RECORDED BECAUSE OF SURVIVOR
BIAS SO ANY KIND OF BIRTH DEFECT
THAT WOULD HAVE IN FACT AFFECTED
THE EARLY HUMAN HEART WOULDN'T
SHOW UP.
THIS IS OMIM, NOT VIABLE, NOT A
VIABLE PREGNANCY.
SO WE CAN GO THROUGH OUTCOMES
OF ANIMAL MODELS AND COME BACK
WITH HUMANS.
AND THEN EXPLORE HUMAN RESOURCES
THROUGH THE LENS OF THE ANIMAL
MODELS, WHICH WE'RE INTERESTED
IN FOR BIRTH DEFECTS.
WE CAN USE ANALYSES ON PATHWAYS.
I WANT TO SHOW THIS TO SHOW WE
ACTUALLY CAN USE NOT ONLY
REFERENCE DATA FROM DATASETS BUT
CAN INTRODUCE THINGS LIKE NCDB,
CURATED PATHWAYS, JUMPING
BETWEEN LIKE A GENE CONCEPT OR
IN THIS CASE HUMAN GENE, INTO A
PATHWAY AND BACK TO ANOTHER
HUMAN GENE RELATED IN THE SAME
PATHWAY OR LINKED BY A GENE.
WE'RE ABLE TO USE THIS
INFORMATION TO ALSO CONDITION
OUR QUERIES.
AND THE QUERY STRUCTURES ARE NOT
THAT BAD.
IT LOOKS COMPLICATED BUT SOME OF
THE PEOPLE WE WORKED WITH OVER
THE SUMMER, YOU KNOW, BASICALLY
UNDERGRADUATES CAN LEARN
LANGUAGE, HOW TO PUT IT TOGETHER
IN A SUMMER SEMESTER, IT'S NOT
OUTSIDE OF REACH OF MOST
INTERESTED IN PROGRAMMING
LANGUAGES.
REALLY ANY SCIENTIST.
IF YOU'VE DONE THIS TO QUERIES
BEFORE IT'S A LITTLE DIFFERENT
BECAUSE IT THINKS ALONG THE
LINES OF A GRAPH BUT IS
ESSENTIALLY A STRUCTURED QUERY
LANGUAGE OF ITS OWN ON A GRAPH
STRUCTURE.
I JUST GO THROUGH AN EXAMPLE
HERE.
IF YOU'D LIKE TO TALK MORE
LATER, I'M HAPPY TO.
WE HAVE HERE DATA THAT'S
RETURNED.
IF I WANT TO ASK ON SEPTAL
DEFECTS, I WANT TO QUERY, WHAT
YOU GET RETURNED IS AN ACTUAL
LEVEL OF -- NUMBER OF GENES,
CHROMOSOMES WHERE THEY ARE
FOUND, THIS IS WHAT WE ASKED FOR
A RETURN, I'LL GO MORE INTO THIS
IN A MINUTE.
BUT THIS IS ESSENTIALLY A QUERY
THAT'S BASED ON FINDING ALL THE
MOUSE GENES THAT ARE ASSOCIATED
WITH HL SEPTAL DEFECTS, HAVE A
HUGE EXPRESSION IN GTEx.
THIS WOULD STREAM 188 RECORDS,
IN LESS THAN 1 MILLISECOND.
AFTER 9 MILLISECONDS A QUERY IS
DONE ACROSS THE DATABASE ON ALL
THE GTEx TISSUES.
THIS IS A RAPID RETURN ON
INVESTMENT IF YOU'RE ABLE TO USE
EITHER OURS OR SOMEONE ELSE'S
DATABASE YOU POPULATED CAREFULLY
WITH YOUR DATA, YOU CAN GET BACK
A LOT OF QUERIES QUICKLY THAT
WILL ANSWER YOUR QUESTIONS.
SO, I'M GOING TO SKIP THROUGH
THIS.
LET ME GO TO THE DATA.
THIS IS REALLY RECENT STUFF.
THIS WILL SHOW HOW EASY IT IS TO
USE DATA, ALWAYS A CAUTION
BECAUSE I'M NOT A
GLYCOINFORMATICIAN YET,
HOPEFULLY SOMEDAY AS I WORK
WITH COLLEAGUES, BUT INTRODUCING
THIS DATA INTO THE KNOWLEDGE
GRAPH, IF DONE CAREFULLY, CAN
RESULT IN MEANINGFUL RESULTS IN
YOUR QUERIES.
YOU HAVE TO BE CAREFUL.
WE'VE DESIGNED OURS CAREFULLY
BASED ON OUR PARTICULAR SCHEMA
WHICH IS DIFFERENT THAN SOME
OTHERS BECAUSE IT'S BASED ON
THIS IDEA OF CONCEPTS THAT ARE
GUIDING MULTIPLE QUERIES INTO A
SINGLE CONCEPT.
WE CAN CREATE OUR OWN CONCEPTS.
THERE'S A GTEx CONCEPT,
ICD-10, ALL LINKED CONCEPTS, WE
CAN CREATE A CONCEPT AROUND A
PHENOTYPE AND THAT THAT CONCEPT
HAVE MULTIPLE SYSTEMS COME IN ON
THAT PHENOTYPE AND FOR GENES
MOSTLY HTMC CONCEPT, THERE'S
OTHER SOURCES, FOR EXAMPLE THIS
PROTEIN HAS A PROTEIN, CAN COME
BACK AND JOIN UP WITH THAT GENE.
SO, WHY WE'RE INTERESTED IN
STUDYING GLYCOINFORMATICS AS
ALL, ANY OF THESE INFORMATICS,
FOR KIDS FIRST, IT'S GOING BACK
TO WHAT WE DISCUSSED ON
DEVELOPMENT, WHEN DOES
SUSCEPTIBILITY DURING
DEVELOPMENT, I HIGHLIGHTED
HEART, BETWEEN THE AGES OF 3 AND
8 WEEKS, THE HEART IS
SUSCEPTIBLE TO PERTURBATIONS,
FROM A PARTICULAR BLOG.
BUT ESSENTIALLY KOMP 2R G
EFFECTS AND LINCs.
GLYCOSYLATION IS LINKED TO HEART
DEFECTS.
50 INBORN ERRORS OF METABOLISM
DESCRIBED DUE TO CONGENITAL
DEFECTS IN N-LINKED
GLYCOSYLATION.
CARDIAC COMPLICATIONS ARE
ANNOTATED IN 100 CONGENITAL
DISORDERS OF GLYCOSYLATION DUE
TO HYPOGLYCOSYLATION.
INBORN ERRORS IN METABOLISM MAY
HAVE SOMETHING TO DO WITH HEART
DEFECTS, CARDIAC COMPLICATIONS
ANNOTATED IN 100 CONGENITAL
DISORDERS OF GLYCOSYLATION.
VSD AND ASD ARE THE MOST
PREVALENT IN THAT PUBLICATION IN
CONGENITAL DISORDERS.
IF YOU LOOK THROUGH THE LIST,
THEY ARE PROBABLY THE MAJORITY
OF THEM.
THERE MAY BE OTHER TIMES OF
HEART DEFECTS AND SOMATIC
DEFECTS, RELATED.
FOR EXAMPLE GENE GALNT11, HERE
IS A HETEROTAXI.
TYPES ARE THE TYPES WE'RE ABLE
TO GO THROUGH.
WE MIGHT WANT TO START EARLY ON
WITH TRYING TO ASK WHAT TISSUES
HAVE ANYTHING TO DO WITH
GLYCOSYLTRANSFERASES ASSOCIATED
BEFORE WE GO BACK INTO THESE
SPECIFICS.
I WAS GOING TO SHOW FOR THESE
PARTICULAR ANALYSES WE'RE GOING
TO DO, WE'RE GOING TO LOOK AT
GENES ASSOCIATED WITH
GENE-TO-PHENOTYPE INFORMATION
FOR GLYCAN, PROTEIN
GLYCOSYLATION SITES, FOCUS ON
OMIN.
A ONE GENE PER ONE PHENOTYPE
MODEL, THAT'S WHAT THEY OFTEN
PUBLISH.
VARIANT TO PHENOTYPE MODEL.
THERE'S SURVIVOR BIAS.
INDIVIDUALS ARE PROBABLY
SURVIVING UNTIL BIRTH AND ARE
ABLING TO MEASURED FOR PHENOTYPE
VERSUS GENES.
HPO AND HGNC, AND GENE
NOMENCLATURE.
AND WE GOT THIS FROM JACKS.
WE STUCK WITH OMIM AT THIS
POINT.
WE CAN EXPAND THIS IN LATER
ANNUAL SEALS.
IMPC, THIS IS DATA BASED ON
KNOCKOUTS IN MICE.
WE HAVE THE HUMAN VARIANT DATA,
AND THE MOUSE KNOCKOUT DATA,
IDENTIFIED GENE PERTURBATION.
THERE'S ABOUT 8,000 KNOCKOUTS,
GENE PERTURBATIONS, I SHOULDN'T
SAY KNOCKOUTS BUT PERTURBATIONS.
THEY DON'T HAVE TO SURVIVE TILL
BIRTH.
DATA SOURCE WE'RE ABLE TO QUERY.
SYSTEMS ARE MP AND THE HUMAN
ORTHOLOG SET.
HGLT IS THE OTHER SOURCE.
A CHALLENGE IS NOT ALL THE MT
MAPS TO HTL, SO THAT'S A
CHALLENGE WE HAVE, WE HAVE TO
MANUALLY DO A CROSS-WALK ON SOME
OF THESE.
THAT WILL BE FIXED IN THE FUTURE
HOPEFULLY.
IF I WANT TO LOOK AT
GLYCOSYLTRANSFERATIONS, LOOK IN
GTEx, WHAT GENES ARE EXPRESSED
IN CERTAIN TISSUES.
I CAN'T SHOW THIS DATA RIGHT NOW
BUT I CAN SAY THAT WE COULD LOOK
AT GENES ABOVE A CERTAIN LEVEL,
COMPARED FROM TISSUE TO TISSUE
IF PEOPLE ARE INTERESTED, TRYING
TO UNDERSTAND HOW CERTAIN
GLYCOSYLTRANSFERATIONS FUNCTION,
TIMES OF TISSUES, WE CAN PULL
OUT ALL OF THIGHS -- THESE
DATA, GENERATED IN THE LAST
SEVERAL HOURS, JUST PULLED OUT
FOR ME.
AND THEN THERE ARE VERY FEW
RELATED TO THESE GENES AND THIS
IS IN MOUSE PHENOTYPES, BUT
THERE'S SOME.
SOMEONE MAY WANT TO IN RESEARCH
LOOK MORE INTO THESE.
A RAPID QUERY WE JUST DID.
ALL THESE HAVE GENES ASSOCIATED
WITH THEM, THERE ARE TWO
GLYCOSYLTRANSFERASES.
INTERESTINGLY ALL THESE
GLYCOSYLTRANSFERATIONS ARE HIGH
UP, EXPRESSING HIGH IN THE
TISSUES THEY ARE ASSOCIATED
WITH.
I'M GOING TO SKIP THROUGH THIS
WITH MICE.
THERE'S A QUERY.
ESSENTIALLY WE CAN GO FROM
MAMMALIAN TO HUMAN, TO
GLYCOSYLATION, AS WE HEARD THAT
MIGHT NOT BE THE MOST UP TO DATE
BUT WE CAN PUT PRIMARY DATA FROM
LET'S SAY PRIMARY SOURCES TO
INCREASE OUR NUMBERS.
THE NUMBER OF GENES, THESE ARE
ASSOCIATED WITH, OOPS, NUMBER OF
GENES ASSOCIATED WITH
PHENOTYPES, MIGHT BE HARD TO
READ.
THESE ARE NUMBER OF GENES
GLYCOSYLATED PROTEINS.
WE LOOKED AT THE OVERALL RATIO
VERSUS ALL GENES IN THE HUMAN
GENOME THAT COULD BE
GLYCOSYLATED, AND THEN THIS HAS
THE SURVIVOR BIAS, WE SEE
SIMILAR IN MOUSE DATA,
HETEROTAXI IN GENERAL HAS LOWER
RATIO OF GENES WITH GLYCOSYLATED
PROTEINS VERSUS NUMBER OF GENES
IN THOSE ASSOCIATED BETWEEN THE
DISEASE AND GENE LIST THEY HAVE.
OF THE THERE'S A DISTINCT
GLYCOPROTEIN.
THE NUMBER OF GENES.
ASD IS FURTHER DOWN.
THE RATIO BETWEEN ALL GENES AND
GENES WITH GLYCOSYLATED PROTEINS
IS LOWER.
AGAIN, THAT WAS STRANGE.
THEN WE LOOKED AT HEART DEFECTS
PHENOTYPES, LOOKED AT ACTUAL
GLYCANS.
AND WE THOUGHT THERE WERE
CERTAIN -- THESE ARE ON THE
MAJOR ISOFORM OF THE GENE, THIS
IS NORMALIZED FOR NUMBER OF
PROTEIN, ON AVERAGE WE SEE IN
HERE FOUND ON PROTEINS, MAJOR
ISOFORMS, HETEROTAXY HAD A FEW,
WHICH WAS INTERESTING.
>> WE NEED TO WRAP UP QUICKLY.
>> SORRY.
QUICKLY, WE'RE ABLE TO -- WE
NEED MORE REFERENCE AND PRIMARY
DATA AND I'LL STOP THERE.
20 MINUTES IS ALWAYS SO HARD.
SORRY ABOUT THAT.
>> NO WORRIES.
THANK YOU.
THANK YOU TO ALL SIX SPEAKERS.
WE SAW A LOT OF EXCITING DATA.
IF THE SIX SPEAKERS COULD TURN
THEIR CAMERAS ON AND UNMUTE
THEMSELVES.
PARTICIPANTS, YOU COULD SEND ANY
QUESTIONS THROUGH THE SEND LIVE
FEEDBACK BUTTON, I'LL START WITH
A FIRST QUESTION, ACTUALLY TO
STEPHANIE.
HOW COULD BE SERVED ARE OH
O-GlcNAc SITES?
>> WE WORK WITH HUMAN AND MOUSE,
ANYTHING AFTER THAT WE'RE UNDER
ABOUT 500 OR LESS SITES
IDENTIFIED, IT'S HARD TO DO A
SEQUENCE THAT WOULD BE VALID.
THERE'S AN EFFORT WE WERE TRYING
TO DO PREDICTION OBVIOUSLY
BECAUSE WE HAD SO MUCH DATA.
AND WE WERE CONSIDERING USING
NEGATIVE AMINO ACID, PRESENTING
TO USE THAT, IT'S NOT READY FOR
PRIME TIME YET OBVIOUSLY.
>> THANKS.
>> DEPENDING ON TERMS OR YOU'RE
TEACHING SET WHETHER IT HAS
TERMS YOU'RE INTERESTED IN, HAVE
YOU BROADENED OUT TERMS TO
CONSIDER REAGENTS WE KNOW AFFECT
O-GlcNAc, DRUGS AND THINGS
LIKE THAT?
HOW DO YOU KNOW WHEN YOU'VE GOT
EVERYTHING?
>> WE DON'T.
NO, WE TRY BEING INCLUSIVE AND
TAG TERMS, WE HAVE COMMON
TECHNIQUE, COMMON CELL LINES,
COMMON TISSUES, WE'RE TAGGING
TERMS LIKE THIS IN CATEGORIES SO
WE CAN PUT THEM AND GET THE
SENTENCES WHERE WE HAVE IN A
GlcNAc TERM AND TISSUE OUT
AND FIGURE OUT WHICH TISSUE
WE'RE IN, FOR EXAMPLE, WHICH
PROTEIN WE IDENTIFY, WE ORGANS.
WE HAVEN'T INCLUDED A DRUG TERM,
FOR EXAMPLE, AT ALL YET.
THAT COULD BE DONE IN THE FUTURE
BUT THIS TEXT MINING IS A
NIGHTMARE AS YOU KNOW IF YOU
TRIED TO DO THIS INCLUDING THE
FACT USING BETA, ALPHA, WHEN YOU
CONVERT AND TRANSLATE, A LOT OF
TUNING, WE HAVEN'T BROADENED
YET.
AND LIKE I SAID WE DON'T USE
THAT, WE DON'T TRUST THAT
PREDICTION 100%.
WE VERIFY EVERYTHING MANUALLY.
BUT IT'S QUICKER THAT WAY STILL.
>> SO, I WAS GOING TO MOVE ON,
THIS QUESTION FOR JOSH.
IT'S A COMBINATION OF A QUESTION
FOR ANAND AND MYSELF.
DO PEOPLE ARE LYSOSOMAL STORAGE
DISEASE ALSO GET PARKINSON'S
DISEASE OR IS THERE A
RELATIONSHIP WITH THE TAU OF
THESE LIKE WITH ALPHA-SYNUCLEIN
IN P.D.?
>> YEAH, THANKS, GREAT QUESTION.
THERE'S A LOT OF OF OVERLAPPING
MECHANISMS BETWEEN
NEURODEGENERATIVE DISEASE
INCLUDING ALZHEIMER'S AND
PARKINSON'S.
LYSOSOMAL PATHWAYS ARE IMPORTANT
IN BOTH.
IF YOU LOOK AT ALZHEIMER'S
DISEASE GENETIC RISK THERE IS
ENRICHMENT FOR PATHWAYS RELATED
TO ENDOLYSOSOMAL TRAFFICKING.
I THINK A NUMBER OF WORK GROUPS
ARE WORKING ON WHETHER OR NOT
LYSOSOMAL STORAGE DISORDERS,
GENETICS, SHOW SIMILAR
RELATIONSHIP TWO WHAT WE KNOW
ABOUT FOR PARKINSON'S DISEASE.
IN THE CASE OF ALZHEIMER'S
DISEASE, I'VE HEARD ABOUT SOME
SUPPORTIVE UNPUBLISHED WORK BUT
I DON'T KNOW IF TOO MUCH HAS
BEEN PUBLISHED.
ONE GOOD EXAMPLE THAT IS WIDELY
KNOWN, GENE GRN, PROGRANULIN, A
CAUSE OF LYSOSOMAL STORAGE
DISORDERS, AND IT IS MENDELIAN
GENE FOR FRONTOTEMPORAL ALS, BUT
GRN AFFECTS RISK OF PARKINSON'S
DISEASE AND ALZHEIMER'S, A LINK
ACROSS NEURODEGENERATIVE
DISORDERS AND LYSOSOMAL
DISORDERS.
THE LYSOSOME CLEAVES TAU AND
THERE'S A LOT OF INTEREST IN
LYSOSOMAL MODIFICATIONS THAT
MILD AFFECT TAUOPATHY.
>> A SELFISH QUESTION.
ARE THERE DATABASES THAT
ACTUALLY PULL TOGETHER HUMAN
GLYCOOH LIPID BIOSYNTHETIC
DISORDERS OTHER THAN THE SORT OF
STANDARD, YOU KNOW, OMIM AND
OTHER DATABASES THAT PULL REAL
PRIMARY DATA RELATED TO
GLYCOLIPID DISTRIBUTIONS OR
SYNTHESIS OR DEGRADATION?
>> IT'S NOT A QUESTION FOR ME,
IS IT?
>> YEAH, IT IS.
>> OR ANYBODY.
>> I DON'T KNOW.
I DON'T KNOW.
ARE YOU REFERRING TO LIKE
NEURODEGENERATIVE KIND OF
SPECIFIC?
>> WITHIN ANY DISEASE DOMAIN.
GLYCOLIPIDS TEND TO BE IGNORED.
YOU'RE ON THE FOREFRONT MUCH OF
PUTTING FUNCTION TO GLYCOLIPID.
>> I'M NOT AWARE OF ONE.
>> THAT PLAY A HUGE ROLE IN
VIRAL INFECTION, VIRUS
TRAFFICKING, THAT'S SOMETHING
THAT COULD BE USEFUL IN
EVERYTHING FROM CANCER TO VIRAL
INFECTION.
THAT'S ACTUALLY A REALLY GOOD
POINT.
IF THAT DOESN'T EXIST, I'VE
NEVER SEEN ONE, BUT THAT'S
REALLY -- I THINK IT WOULD BE
USEFUL.
>> THERE'S A LOT OF INTERESTS IN
SPECIES AS BIOMARKERS FOR
NEURODEGENERATIVE, DEFECTING IN
BLOOD, I DIDN'T TOUCH ON THAT
ASPECT.
WE NEED CAREFUL STUDIES TO
UNDERSTAND WHETHER THEY ARE
PRIMARY, SECONDARY, TERTIARY
PERTURBATIONS, CAUSAL OR NOT.
>> ANAND, YOU FOCUSED ON
FUCOSYLATION BUT COULD YOU
COMMENT ON SIALYATION AND
IMPORTANCE OF ALSO UNDERSTANDING
LINKAGE.
>> YEAH, WE DEFINITELY SEE
ALTERATIONS IN SIALYATION.
I KIND OF LEFT THAT FOR ANOTHER
DAY.
I EITHER TEND TO LIKE -- I LIKE
TO STABILIZE SO RICK DRAKE
DEVELOPED A NICE METHOD FOR
IMAGE AING APPLICATIONS
SIALIC ACID, FOR TISSUE AND
ANTIBODY ARRAYS.
SO WE CAN TALK ABOUT THAT AT
ANOTHER TALK.
THERE ARE DEFINITELY CHANGES IN
SIALIC ACID.
CHANGES ARE LINKAGE SPECIFIC AND
CERTAIN LINKAGES PLAY A GREATER
CONTROL.
SIALIC ACIDS CHANGE EARLY IN
DISEASE, EVEN IN INITIAL
DEVELOPMENT OF SOMETHING LIKE
HEPATITIS, A VERY IMPORTANT ROLE
IN RECRUITING IN AND KEEPING
IMMUNE CELLS WITHIN ENVIRONMENTS
IN THE LIVER.
WE'RE DOING THAT NOW, COMBINING
HYPERION CyTOF BASED SYSTEM
WITH GLYCOYEN IMAGING, YOU CAN
SEE IT NICELY, A PAPER BEING
PRESENTED IN VIENNA THURSDAY I
THINK.
I'M HERE, NOT IN VIENNA, BUT
HOPEFULLY WE'LL SEE THAT SOON.
FOR FUCOSYLATION LINKAGE IS ALSO
SPECIFIC AND VERY IMPORTANT.
MOSTLY WE SEE CORE FUCOSE
SPECIFIC, BY ANALYSIS, OR BY
OFFLINE ANALYSIS, SO A LOT OF
THESE AGGRESSIVE TUMORS HAVE
LOTS OF FUCOSE, CORE AND OUTER
ARM.
>> GREAT.
THAT TIES TO A QUESTION FOR
BRIAN.
THERE'S ALL THIS AND ALSO
TOUCHES ON REBEKAH'S WORK WHICH
IS THERE'S ALL THE CELL SURFACE
GLYCOPROTEOMIC ANALYSIS OR
GLYCOANALYSIS GOING ON. BRIAN,
YOU'VE DONE AN AMAZING JOB
COMBINES ARRAYS TO PREDICT
LECTIN BINDING.
CAN WE CONNECT THE CELL SURFACE
GLYCOPROTEOMIC WITH YOUR DATA?
JUST .
>> JUST CHATTING WITH ZAK, WITH
THE DATA TO CONNECT, WHAT WOULD
BE A GOOD APPROACH, WHEN IT'S
LESS DEFINED, WHEN THERE'S MANY
ISOMERS THAT ARE POSSIBLE TO USE
THE TOOL, THE MOTIF SEARCH AND
GLYCAN DEFINITION TOOL TO MAP
OUT ALL OF THE POTENTIAL GLYCANS
AND USE THOSE GLYCANS AS
COMPARED TO THE MODELS IN THE
DATABASE TO FIND THE VARIOUS
LECTINS THAT COULD BIND THE
RANGE OF GLYCANS THAT ARE COMING
OUT FROM THE GLYCOPROTEOMICS,
DEFINITELY THE FIRST STEP IS
TAKING THE GLYCOPROTEOMICS DATA
TO FINDING RANGE OF COMPLETE
GLYCANS TO SOME DEGREE, EASY
WITH PREDICTION TOOLS TO FIND
SET OF LECTINS THAT COULD BE
BINDING TO THAT SET OF GLYCANS.
THOSE EFFORTS ARE DEFINITELY
ENVISIONED, THE FIRST PART IS
UNDERWAY TO SOME DEGREE, TAKING
THE LESS DEFINED GLYCANS MAPPING
OUT WHAT THEY COULD BE.
LOOKING FORWARD TO LINKING WITH
GlyGen AND THESE OTHER
TOOLS.
>> WHAT YOU SAID, BRIAN, RAISED
A QUESTION WITH DEANNE DID YOU
FIRST A QUESTION FOR REBEKAH.
SORRY.
66% OF CELL SURFACE N LINK
GLYCANS NOT PREDICTED TO BE
GLYCOSYLATED.
>> NOT 66%.
66 TOTAL OUT OF THE 1144.
>> OKAY.
THOSE 66, IS THERE A SYSTEMATIC
REASON, CONSENSUS --
>> YEAH.
YEP, SO THE DATABASES ARE
WONDERFUL BUT THEY ARE TYPICALLY
LIMITED IN THE NUMBER OF
DIFFERENT PREDICTION ALGORITHMS
THEY USE.
SO, IF YOU'RE ONLY USING --
WELL, UNIPRO WILL HAVE ONE
TRANSMEMBRANE DOMAIN PREDICTION,
WHICH PART OF PROTEIN SITS IN
THE EXTRACELLULAR DOMAIN.
IF THAT'S WRONG EVEN IF THERE'S
A SEQUENCE MOTIF IT WON'T
PREDICT THAT IT'S IN THE CELL
SURFACE, EXTRACELLULAR, WON'T
PREDICT IT'S ACTUALLY
GLYCOPROTEIN.
TRENDS WE SEE ARE THAT.
YOU HAVE TO GO BACK TO
LIMITATION IN THE TRANSMEMBRANE
PREDICTION ALGORITHM TO START
WITH, SOME ARE LIMITED BASED ON
ASSUMPTIONS, SOME REQUIRE A
CANONICAL SIGNAL PEPTIDE TO GET
THAT TRANSMEMBRANE PREDICTION.
IF THE PROTEIN LIKE USUALLY THE
ONES WE'RE INTERESTED IN DO NOT
HAVE CANONICAL SIGNAL PEPTIDES,
IT WILL BE DIFFERENT.
THEN IF THE ONE THAT UNIPRO IS
USING IS THE ONE THAT REQUIRED
US CANONICAL SIGNAL PEPTIDE IT
WON'T GET THAT RIGHT THEREFORE
IT WILL NOT ANNOTATE THAT THAT
SEQUENCE MOTIF IS IN THE
EXTRACELLULAR DOMAIN.
SO THAT'S WHY WE EXACTLY THAT
REASON, WHY WE -- WE STARTED TO
BECOME CONCERNED SOME DATA WERE
GIVING PROTEINS BEING
GLYCOSYLATED.
AND THEN JUST TAKE MORE OF AN
APPROACH THAT INCLUDES MORE
PREDICTIONS AND YOU'LL BUILD
MORE EVIDENCE.
>> REGARDING THE CANONICAL
SIGNAL PEPTIDE ARE YOU SAYING
THAT FOR THE 66, THE
CANONICAL -- TALKING ABOUT THE
SEQUENCE, IF THE SIGNAL PEPTIDE
IS NOT ANNOTATED FOR THE
SEQUENCE, IS THAT WHAT YOU'RE
SAYING?
>> NOT ALL 66, BUT FOR MANY THAT
WE'RE INTERESTED IN TRADITIONAL
SEQUENCE THAT ALGORITHMS WILL
PREDICT THEY DON'T SEE THAT
THERE'S A SEQUENCE THERE THAT
WOULD TAKE IT TO THE CELL
SURFACE.
>> CAN WE GO UNTIL 12:37?
>> YES.
>> YOU'RE ON THE VERGE OF TAKING
DEEPER DIVES INTO UNITED WITH
OTHER DOMAINS THAN PROBABLY
ANYTHING, IT'S REALLY EXCITING
TO SEE HOW THAT'S GOING TO
DEVELOP.
I HAVE A CONCEPTUAL QUESTION.
PROBABLY EVERYBODY ON THEM
SCREEN HAS DONE ANY GLYCOMICS OR
GLYCOPROTEOMICS WOULD NOT
ARTICLE WE'RE ONLY SCRATCHING
THE SURFACE, MAYBE WE HAVE ONLY
10%, I DON'T KNOW, OF A GLOBAL
SENSE OF WHAT GLYCOSYLATION
IS.
IF YOUR DATABASE IS INCOMPLETE
HOW MUCH -- WHAT HANDICAP DOES
THAT PUT YOU AT DRAWING
KNOWLEDGE THAT REALLY HAS SOME
CONFIDENCE TO IT?
>> SO, WE'RE LOOKING FOR THE
KEYS UNDER THE STREET LIGHT.
WE ONLY KNOW WHAT WE KNOW, DON'T
KNOW WHAT WE DON'T KNOW.
IT'S GOOD FOR UNDERSTANDING
RELATIONSHIPS YOU DIDN'T EXPECT
IN THE DATA ALREADY.
BUT ARE IN LITERATURE OR SOME
LITERATURES MINED IN UMLS LIKE
MeSH TERMS, FOR EXAMPLE.
BUT THERE'S SOME THINGS WE DON'T
KNOW.
AND SOME THINGS WE'RE INTERESTED
IN, WE'RE BUILDING ALGORITHMS,
HOPEFULLY, AS PART OF THE COMMON
FUND DATA ECOSYSTEM ON THIS
LARGE KNOWLEDGE DATA GAP, MORE
FORMAL INPUT OF PRIMARY DATA,
PRIMARY DATA IS WHERE THE
INTERESTING STUFF COMES IN.
WE HAVE ALL THIS DATA WHICH
PEOPLE HAVE MINED ON ON OF -- OR
ASSEMBLED BUT PRIMARY DATA IS
WHERE DISCOVERIES COME FROM.
AS LONG AS THE RELATIONSHIPS
RELATE TO A GENE, RELATE TO A
SITE OR PROTEIN, YOU CAN TAKE
TWO DISPARATE POINTS IN THAT
KNOWLEDGE GRAPH AND FIND
LINKAGES WHICH MIGHT BE LONG AND
ALMOST SILLY OR QUITE SHORT AND
LEAD TO THINGS THAT YOU MAY NOT
HAVE EXPECTED.
MORE PRIMARY DATA IS ALWAYS
WELCOME.
I CAN TAKE SMALL DATASETS IN.
AN ANALYSIS ON EMBRYONIC HUMAN
HEART IN SWEDEN, LUNDBERG LAB,
6.5 WEEK HUMAN HEARTS AND
CHARACTERIZE TISSUES IN SINGLE
CELL LEVEL, THE DETAIL, AS WE
GET MORE, IT WILL BE HARDER TO
LINK THAT DATA TOGETHER WITHOUT
TOOLS LIKE GRAPH DATABASE
BECAUSE DATA IS TOO
HETEROGENEOUS.
WE'RE UNDERPOWERED IN ONE SENSE.
CERTAINLY DATA THAT'S RAW, GOING
BACK AND DOING REPREDICTION AND
FIXING THAT, WE COULD DO, I'M
HAPPY WITH TRYING TO REPREDICT
MEMBRANE PROTEINS.
BUT IT'S STUFF WE SHOULD PUT IN
AND WE NEED THE BIOLOGY TO
INFORM OUR CHOICES, THE
IMPORTANT PART.
HOW THE DATA RELATES TO EACH
OTHER IS WHAT WE NEED PEOPLE'S
INPUT ON.
AND HOW IT SHOULD RELATE TO EACH
OTHER AND WHAT REFINEMENTS WE
SHOULD DO ON THE DATA.
SO OTHER BIOLOGISTS HAVE TO
WEIGH IN ON THE CREATIONS.
>> THAT'S A GREAT COMMENT TO
WRAP THIS UP.
WE'VE GOT TO BE ABLE TO GET
BETTER AT DATA INTEGRATION AND
WE NEED TO BE DRIVEN BY BIOLOGY
AND SO WITH THAT I'D LIKE TO
THANK THE SIX SPEAKERS TODAY FOR
BEING WILLING TO SHARE THEIR
TIME.
ALSO FOR THE SPEAKERS YOU CAN
SEE QUESTIONS THAT WE DIDN'T GET
A CHANCE TO GET TO, A LOT OF
PEOPLE WHEN THEY PUT IN THE
QUESTION PUT IN THEIR E-MAIL.
TACK THE TIME TO REPLY, THAT
WOULD BE FANTASTIC.
THANKS TO ATTENDEES.
AND WE WILL START BACK AT 1:30
SHARP.
>> WELCOME TO THE AFTERNOON
SESSION.
THIS IS THE VIRAL GLYCOBIOLOGY
SESSION.
I'M JOHN CIPOLLO FROM CBER FDA.
WE HAVE AN INTERESTING SERIES OF
TALKS, BUT FIRST I'D LIKE TO
REMIND EVERYONE TO SEND TO THE
LIVE FEEDBACK LINK AT THE NIH
WEBSITE FOR YOUR QUESTIONS, AND
FROM THERE THEY WILL ACTUALLY
PROBABLY MAKE THEIR WAY Y MAKE
THEIR WAY INTO THE CHAT.
THIS AFTERNOON WE HAVE AN
INTERESTING TALK WITH LASSA
VIRUS AND ALSO WILL BE
DISCUSSING ARRAYS OF SOME --
VARIOUS ARRAYS TO REVEAL SOME
STRUCTURAL FUNCTIONAL
RELATIONSHIPS THAT VIRUSES HAVE
FOUND IN THEIR NICHE.
LASTLY, AS WE'VE WITNESSED OVER
THE LAST TWO YEARS THE
SARS-COV-2 VIRUS HAS BEEN A BIG
FOCUS, PUTTING EMPHASIS ON
GLYCOSYLATION AS THE SPIKE
PROTEIN HAS BEEN OF INTEREST.
I'D LIKE TO INTRODUCE THE FIRST
SPEAKER, RON DISKIN, FROM THE
WEIZMANN INSTITUTE, THE LASSA
VIRUS.
WELCOME, RON.
>> THANKS, JOHN.
THANK YOU FOR THE ORGANIZERS FOR
INVITING ME.
VIRUSES CAN'T REPLICATE ON THEIR
OWN, THERE'S EVOLUTIONARY
PRESSURE TO FIND INTERESTING
WAYS TO PENETRATE CELLS.
ENVELOPE VIRUSES USE SPECIAL
PROTEINS ON THE SURFACE TO
ATTACH TO THE HOST CELL AND TO
LATER ON FUSE THEIR OWN MEMBRANE
WITH THE TARGET CELL MEMBRANE.
AND THESE ARE THE VIRAL
GLYCOPROTEINS, VIRAL SPIKES.
I WILL REFER TO THESE AS
PROTEINS TODAY.
NOW, OF COURSE, THEY UTILIZE A
SPECIAL MOIETY, COULD BE
CHEMICAL MODIFICATION OR
SPECIFIC PROTEINS, THAT THEY ARE
FOUND IN THE CELL SURFACE AND
WE'LL SEE TODAY A VERY NICE OR
MAYBE UNUSUAL EXAMPLE HOW
VIRUSES UTILIZE GLYCANS TO ENTER
CELLS.
OF COURSE, VIRUSES DON'T DO THAT
IN VACUUM.
THERE IS THE HOST RESPONSE, A
WHOLE COMPLICATED ARENA OF
INTERACTIONS GOING ON HERE.
GLYCOBIOLOGY PLAYS A CENTRAL
ROLE, A LOT OF GLYCANS INVOLVED,
DEFINITELY INFLUENCES THE MANY
PROCESSES IN THE CELL ENTRY OF
VIRUSES.
I MENTION VIRAL GLYCOPROTEINS,
WE NEED TO DEFINE THEM.
AFTER TWO YEARS OR MORE OF THE
SARS PANDEMIC WE HEARD ABOUT THE
SPIKE PROTEIN, GAVE THE SARS THE
NAME, ALL THE CLASS 1 VIRAL
SPIKES ON SARS, INFLUENZA, AND
OTHERS, HAVE A SPECIFIC
ARCHITECTURE, SINGLE PROTEIN
GETTING CLEAVED IN ORDER TO BE
ACTIVATED AND THIS POLY PROTEIN
IS ORGANIZED AS A TRIMER ON THE
CELL MEMBRANE.
NOW, THE CLEAVAGE EVENT IS
ACTIVATING THE PROTEIN AND
EXPOSED NEW FOLDING SPACE FOR
THE PROTEINS SO IT EXISTS IN THE
META STABLE ZONE HIGH ENERGY
STATE, THAT CAN LATER REFOLD AND
DRIVE THE MEMBRANE INFUSION.
THE CLEAVAGE EVENT SEPARATES
BETWEEN THE N-TERMINAL PART AND
IN OUR CASE WE CALL IT GP 1, AND
GP2, THE MEMBRANE PART, GP1 IS
RESPONSIBLE FOR RECEPTOR AND
RECOGNITION, IT IT MAKES
RECEPTOR BINDING DOMAIN, AND GP
2.
IT'S A LOADED MOUSE TRAP WITH A
LOT OF POTENTIAL ENERGY, WAITING
TO BE RELEASED.
THIS ENERGY EVENTUALLY IS
TUNNELED TOWARD THE FUSION OF
THE MEMBRANE, YOU CAN SEE
SCHEMATIC DIAGRAM SHOWING THIS
PROCESS.
A FEATURE IS THE COAT, ENDING
GLYCANS THEY USE IN ORDER TO
CONCEAL THEMSELVES FROM THE
IMMUNE RESPONSE.
AND THIS IS A VERY NICE IMAGE, I
TOOK IT FROM A WRITTEN PAPER
ABOUT SARS, IT SHOWS THE GLYCANS
WITH SOME KIND OF MOLECULAR
DYNAMIC SHOW IT SHOWS REALLY HOW
MOST OF THE SURFACE OF THE VIRAL
PROTEIN IS CONCEALED UNDER THESE
GLYCANS.
THIS IS WELL-KNOWN EXAMPLES OF A
GLYCAN FOR VIRUSES, BUT THERE'S
MORE TO IT.
TODAY I'M GOING TO SHOW HOW
LASSA IS ACTUALLY USING A
SPECIAL KIND OF GLYCAN TO ENTER
CELLS.
SO WHAT IS THE LASSA VIRUS?
THE LASSA VIRUS IS A PATHOGENIC
VIRUS, CAUSES LASSA HEMORRHAGIC
FEVER, THE DISEASE.
IT'S A VIRUS ENDEMIC TO WESTERN
AFRICA.
INFECTS THOUSANDS OF PEOPLE
ANNUALLY.
SO FAR WE DON'T HAVE ANY
APPROVED VACCINE FOR THIS VIRUS.
IT ENTERS USING A GLYCAN,
MATRIGLYCAN.
MATRIGLYCAN IS A UNIQUE
MODIFICATION OF THE UBIQUITOUS
PRO TOWN ALPHA DYSTROGLYCAN
FOUND ON MANY CELL TYPES
MODIFIED BY A SERIES OF ENZYMES.
THIS IS AN OLINK MODIFICATION,
SO THERE'S THE FIRST EVENT
PHOSPHORYLATION, THEN ADDITIONAL
FEW ENZYMES, MODIFIED, ADDING A
FEW SUGAR MONOMERS TO CREATE THE
PRIMER.
THIS PRIMER IS RECOGNIZED BY
LARGE1.
MATRIGLYCAN IS REPEATING LINEAR
CHAIN OF SUGAR MADE OF XYLOSE
AND GLUCARONIC ACID.
NOW SHOULD GIVE CREDIT BECAUSE
THE DISCOVERY AND FIGURING OUT
ENZYMES TO GENERATE MATTERY
GLYCANS WAS DONE IN THE LAB OF
KEVIN CAMPBELL, UNIVERSITY OF
IOWA.
HE'S THE ONE THAT SHOULD GET THE
CREDIT FOR THAT.
AND I JUST WANT TO MENTION
LARGE1 IS A UNIQUE TRANSFERASE,
DUAL ACTIVITIES, CAN FACILITATE
IN COOPERATION WITH XYLOSE AND
GLUCARONIC ACID HAVING TWO
CATALYTIC DOMAINS, WE STUDIED
THIS ENZYME, FUNCTIONS AS A
DIMER.
WE HAVE TWO CATALYTIC DOMAINS IN
EACH MONOMER, IN THE MERIC FORM
HAVE AN INTERESTING ASSEMBLY
WHICH THE TWO ACTIVE SIDES OF
THE XYLOSE TRANSFER AND
GLUCARONIC ACID TRANSFERASE
POINT TO THE SAME DIRECTION FROM
TWO OPPOSITE MONOMERS, AN
INTERESTING FEATURE.
WE THINK IT MAY RELATE TOED
PROCESSIVITY OF THE ENZYME AND
THE MATRIGLYCAN IS LONG, COULD
REACH UP TO 100, SOMETIMES
THOUSANDS OF REPEATS, OF THE
XYLOSE AND GLUCARONIC ACID
SUBUNITS.
LASSA VIRUS IS BINDING TO ENTER
CELLS, FIGURED OUT QUITE SOME
TIME AGO ACTUALLY.
IT'S DOING SO WITH ITS VIRAL
SPIKE COMPLEX, VIRAL
GLYCOPROTEIN.
LET'S LOOK AT THIS IN MORE
DETAIL FOR A SECOND.
I TELL YOU THERE'S A GENETIC
STRUCTURE IN WHICH THEY HAVE TWO
SUBUNITS, GP1 AND GP2, IN THE
CASE OF LASSA IT'S MORE
COMPLICATED WITH SIGNAL
PEPTIDES.
SIGNAL PEPTIDE OF THE LASSA
VIRUS AND OF ALL THE OTHER
ARENAVIRUSES THAT BELONG TO THE
SAME FAMILY IS QUITE LONG.
YOU CAN SEE ALMOST 60 RESIDUES
LONG.
AND UNLIKE MOST CASES, ACTUALLY
THIS IS THE ONLY CASE THAT I'M
FAMILIAR WITH, THE SIGNAL
PEPTIDE IS START OF THE MATURE
COMPLEX, VERY IMPORTANT FOR
FUNCTION OF THE COMPLEX, AND
THIS IS A KNOWN THING IN THE
FIELD FOR MANY YEARS.
SO THE SPIKE OF THE LASSA VIRUS
WILL LOOK LIKE THAT.
A SIGNAL PEPTIDE IS INTEGRAL
PART OF THIS COMPLEX.
NOW, THE CELL ENTRY OF THE LASSA
VIRUS IS QUITE COMPLICATED
PROCESS BECAUSE IT BINDS DISTAL
GLYCAN TO THE MATRIGLYCAN ON THE
SURFACE BUT WHEN ENTERS THE
CELLS IN PH-DRIVEN PASSION
DISASSOCIATES, REACHES ENDOSOME,
FINDS SECONDORY RECEPTOR LAMP 1,
TRIGGER THE SPIKE.
IT COULD BE A COMPLICATED
PROCESS AND WE'VE BEEN STUDYING
THIS PROCESS FOR QUITE SOME
TIME, INTERESTING INSIGHT BUT
MANY OPEN QUESTIONS THAT SOME OF
THEM ARE LISTED HERE.
AND ONE OF THE MAIN QUESTIONS WE
HAD WAS HOW LASSA VIRUS BINDS
AND RECOGNIZES MATTERY GLYCANS.
FOR THE FIELD OF VIROLOGY,
FIGURING OUT HOW THE VIRUS
ACTUALLY ATTACHES TO THE
RECEPTOR IS A CRITICAL THING
BECAUSE IF YOU THINK ABOUT
THERAPEUTICS, IF YOU THINK ABOUT
THINGS THAT WILL EVENTUALLY
BLOCK THIS RECOGNITION, THIS IS
A KEY QUESTION TO ANSWER WHEN
YOU DEAL WITH VIRUSES.
WE WERE REALLY BOTHERED WITH
THIS QUESTION OF HOW LASSA VIRUS
BINDS MATRIGLYCANS.
ONE REASON WE HAD SO MANY OPEN
QUESTIONS WE COULDN'T ANSWER,
THE PARTICULAR QUESTION OF THE
MATRIGLYCAN RECOGNITION, WE AND
OTHERS COULDN'T PRODUCE IN A
STABLE FASHION THE ECTODOMAIN,
THE EXTRACELLULAR PORTION OF THE
SPIKE.
IF WE LOOK AT OTHER CLASS 1
SPIKES, LIKE EBOLA VIRUS, FOR
EXAMPLE, YOU CAN SEE HERE IT WAS
VERY EASY TO PRODUCE THE
ECTODOMAIN PORTION AND THAT WAS
VERY AMENABLE FOR STRUCTURAL
STUDIES SO WE HAVE A LOT OF
INFORMATION ABOUT COMPLEX OF
OTHER VIRUSES.
BUT THAT WAS NOT THE CASE FOR
THE LASSA BECAUSE WE DIDN'T --
COULDN'T DO THAT PROBABLY
BECAUSE OF THE UNIQUE STRUCTURAL
ROLE.
WE DECIDED TO TAKE A HOLISTIC
APPROACH AND EXTRACT THE ENTIRE
SPIKE WITH THE TRANSMEMBRANE
DOMAIN.
A BIT MORE INVOLVED TECHNICALLY.
AND THIS IS DEFINITELY NOT THE
FIRST APPROACH, WHEN WE START TO
THINK ABOUT STRUCTURAL
APPROACHES, STRUCTURAL STUDIES
BUT WERE ABLE TO PUT THIS SPIKE
ON, YOU CAN SEE THE INDIVIDUAL
PARTICLES WHICH GAVE NICELY THE
CLASSES, ABLE TO RECONSTRUCT THE
STRUCTURE OFF THE SPIKE SITTING
IN THE MEMBRANE, WERE ABLE TO
RESOLVE THIS TRANSMEMBRANE
REGION.
THIS IS THE POINT I NEED TO GIVE
CREDIT, THIS WAS THE WORK DONE
BY A VERY TALENTED Ph.D.
STUDENT IN MY LAB, MICHAEL KATZ.
AND MAAYAN HELPED MICHAEL.
THIS STRUCTURE INFORMATION WAS
REALLY UNIQUE AND ANSWERED A LOT
OF QUESTIONS.
TODAY I WILL ONLY SHARE A COUPLE
OF INSIGHTS THAT WE LEARNED FROM
THIS STRUCTURE.
AND THE FIRST ONE RELATED TO THE
FIELD, WE'RE TALKING ABOUT
GLYCOBIOLOGY, IT'S QUITE UNIQUE
AND INTERESTING.
SO IF WE LOOK AT THE MEMBRANE
AND ORGANIZATION OF
TRANSMEMBRANE HELICASES, THEY
BELONG TO THE TRANSMEMBRANE BUT
HELP WITH FUSION, SSP FITS IN
THE OUTER PERIPHERY OF THE
CENTRAL BUNDLE.
IT'S QUITE INTERESTING, IT
STARTS WITH THIS TOPOLOGY, SO
THE HERE IS THE INNER LUMEN,
HERE IS THE CYTOPLASM.
THIS IS SSP WHEN PROTEIN IS
PRODUCED, SIGNAL PEPTIDE IS
RECOGNIZED, BY SIGNAL
PEPTIDEASE, CLEAVES, WE SEE A
SWITCH.
HELICAL SEGMENT WE CANNOT
OBSERVE IN DENSITY MAPS WE DON'T
HAVE A STRUCTURAL DESCRIPTION OF
THE HELIX BUT IT FLIPS FROM THIS
STATE TO THIS STATE AND WE HAVE
A LOT OF BIOCHEMICAL DATA THAT
WAS ACCUMULATED OVER THE YEARS,
THAT SUPPORTS THE FACT THAT THIS
C-TERMINAL REGION OF THE SSP IS
LOCATED IN THE CYTOPLASM.
SO THE PART WE ACTUALLY SEE IN
THE STRUCTURE IS SHOWN HERE, SO
WE SEE THIS REGION OF SIGNAL
PEPTIDE.
AND THIS IS THE SIGNAL PEPTIDE
IN THE CONTEXT OF THE COMPLETE
SITE.
WHAT IS THE ROLE OF THE SIGNAL
PEPTIDE?
WELL, I SHOULD ALSO MENTION THE
TOPOLOGY THAT WE OBSERVED WAS
NOT PREDICTED, ALTHOUGH MANY
PEOPLE TRIED TO ADDRESS THAT, I
THINK ONE OF THE CONFUSING
THINGS THAT THERE IS A TOPOLOGY
SWITCH THAT OCCURS WHICH REALLY
CONFUSED THE PEOPLE THAT TRIED
TO ADDRESS THE TOPOLOGY BEFORE.
WHAT IS THE ROLE OF THIS?
IF WE LOOK AT THE STRUCTURE,
THESE ARE ONLY THE CENTRAL
HELICASES, OF GP2, WE TOOK THE
SINGLE PEPTIDE AWAY, YOU CAN SEE
THEY ARE TWISTED AROUND EACH
OTHER.
THIS IS AN INTERESTING THING.
AND WE THINK THAT THIS TWIST IS
NOT STABLE BY ITSELF.
AND IT'S BEING STUDIED IN THE
CONTEXT OF NATIVE SPIKE BY THE
SSP ITSELF.
YOU CAN SEE HOW THE SSP FITS IN
THE BETWEEN THE HELICES AND
HOLDS IN TWISTED CONFIRMATION,
WE BETWEEN IT'S AN INTEGRAL PART
OF THE SPIKE THAT KEEPS IT IN
NATIVE CONFIRMATION.
WE SEE ANOTHER INTERESTING
THING.
YOU CAN SEE DOMAIN SWITCHING,
TRIPLE DOMAIN SWITCHING BETWEEN
MONOMERS.
THIS MONOMER IS A CONTRIBUTING
THIS PART THAT INTERACTS WITH
THE TWO ADJACENT MONOMERS.
YOU CAN SEE EACH ONE IS DOING
THE SAME THING SO WE HAVE
THREE-FOLD SYMMETRY HERE, AND
YOU CAN THINK ABOUT THAT AS A
COVER OF A CARD BOX, THAT IT WAS
THE FLAPS ARE ON TOP OF EACH
OTHER, AND THEY BASICALLY SEAL
THE SPIKE AND MUTUALLY STABILIZE
THIS PARTICULAR CONFORMATION.
THAT'S AN INTERESTING THING TO
SEE.
NOW, THIS LITTLE PART, VERY
IMPORTANT FOR THE SPIKE BECAUSE
THIS IS THE SKI RECOGNITION
SITE.
SKI IS USED TO ACTIVATE
THEMSELVES.
AND YOU CAN SEE WE HAVE 2
ARGININE, 2 LEUCINE, THIS IS THE
PIECE DONATED BY THE NEIGHBORING
MONOMER.
AND YOU CAN SEE IT HERE, THIS IS
THE CLEAVAGE POINT.
THIS IS THE SKI RECOGNITION PART
OF L.
AND WE SAW A SURPRISE HERE.
THIS IS QUITE COMMON ACTUALLY
WHEN WE SOLVE STRUCTURES.
THERE ARE MANY THINGS WE DIDN'T
EVEN THINK THAT WE WOULD SEE.
WE DISCOVERED THEN IN THE
DENSITY, WE SAW THIS EXTRA
DENSITY SITTING AT THE TOP OF
THE SPIKE.
AND THINGS WE RECONSTRUCTED, THE
SPIKE, USING THREE-FOLD
SYMMETRY, C3, FRAGMENTED DENSITY
BUT NEVERTHELESS WERE ABLE TO
FIT PIECES OF MATRIDENSITY, WHAT
WE SEE, YOU CAN SEE IT HERE WE
HAVE SPECIFIC ANTIBODY THAT
RECOGNIZES MATTERY GLYCANS --
MATRIGLYCANS CARRIED.
IT'S NOT SURPRISING WE SEE A LOT
OF HYDROPHILIC INTERACTIONS,
POLAR INTERACTIONS THAT THOSE
POLAR RESIDUES MAKE WITH THE
MATRIGLYCANS, YOU CAN SEE THE
ARGININE 257 AND 256.
THERE FEATURES THAT INTERACT
WITH MATRIGLYCANS.
WE HAVE A DESCRIPTION FOR THE
INTERACTION BUT THIS ALSO GIVES
US A LOT OF INFORMATION ABOUT
OUR CLUES ABOUT WHY WE SEE
CERTAIN CONSERVATION IN CERTAIN
VIRUSES.
THIS WAS QUITE ENIGMATIC,
OBSERVES A FEW YEARS AGO THAT
VIRUSES THAT BELONG TO THIS
FAMILY OF VIRUSES, ARENA
VIRIDAE, USE SKI TO ACTIVATE BUT
DON'T USE MATRIGLYCAN, YOU CAN
SEE A LIST OF VIRUSES THAT
BELONG TO THIS GROUP.
THEY HAVE A VERY CONSERVE THE
FIRST ARGININE, THIS ARGININE,
AND THEN ANOTHER SEQUENCE THAT
IS SEMI CONSERVED BUT RECOGNIZE
BRIDE SKI.
IF YOU COMPARE TO VIRUSES THAT
DO USE MATRIGLYCAN, THE SECOND
SITE IS HIGHLY CONSERVED.
THIS PARTICULAR ARGININE MAKES
CRITICAL CONTACTS WITH
MATRIGLYCANS, SO IT'S STABILIZED
ON ONE SIDE WITH MULTIPLE
INTERACTIONS WITH THE BACKBONE
OF THE PROTEIN, AND ON THE OTHER
SIDE IT MAKES MULTIPLE CONTACTS
WITH MATRIGLYCAN.
THIS IS A CRITICAL FEATURE FOR
THE RECOGNITION OF MATRIGLYCAN,
THAT EXPLAINS THE CONSERVATION
HERE.
I TOLD YOU WE RECONSTRUCTED
DENSITY MAP USING THE SYMMETRY.
MATRIGLYCAN DOESN'T HAVE
SYMMETRY, WE RECONSTRUCTED
WITHOUT IMPOSING ANY SYMMETRY
CONSTRAINTS.
C1 MAP.
NOW WE GOT AN ELONGATED PIECE OF
DENSITY IN WHICH WE COULD ALSO
FIT SUGAR RESIDUES IN BETWEEN
THE INITIAL PIECES THAT WE
MODELED AND HAVE GLUCARONIC ACID
AND INTERESTINGLY GLUCARONIC
ACID IN THIS REGION MAKES
ADDITIONAL INTERESTING CONTACTS
WITH THE PROTEIN ITSELF, IN
PARTICULAR WE CAN SEE HERE THAT
THE CHARGE GROUP IS SERVING AS
N-TERMINAL CAP FOR HELIX OF THE
SPIKE.
NOW, N-TERMINAL CAPS ARE
STABILIZERS, SO IT'S NOT ONLY
BINDING THE SPIKE, IT'S
STABILIZING THE SPIKE IN THIS
PARTICULAR NATIVE CONFORMATION
OF THE SPIKE.
>> A MINUTE, RON.
>> I WILL TRY TO BE FAST.
SO WE WERE WONDERING WHAT THE
SOURCE OF THIS MATRIGLYCAN IS.
IT IS HIGHLY SPECIALIZED YOUR
MEEK MODIFICATION OF ALPHA
DISTRA GLYCAN.
ONE POSSIBILITY LASSA GRABBED
SOME FROM THE CELLS WHILE WE
WERE PURIFYING BUT WE COULDN'T
FIND ANY BIOCHEMICAL EVIDENCE SO
WE CAN SEE IF WE JUST PUT THEM
ON A MEMBRANE CAN DETECT.
HOWEVER, IF WE TAKE THE PROTEIN
SAMPLE WE USED FOR STRUCTURAL
STUDIES OF THE LASSA VIRUS AND
PUT IT ON A MEMBRANE, INDICATING
THE MATRIGLYCAN WAS WASHED AWAY.
WE CANNOT GET ANY CLUES FOR THE
EXISTENCE IN THE DATASETS THAT
WE HAVE, SO WE THINK WE SEE
SOLUBLE PIECES OF MATRIGLYCAN,
WE DON'T KNOW WHAT IS THE
SOURCE, BUT WE CAN SPECULATE
ABOUT ITS ROLE.
IF WE TAKE PSEUDOVIRUSES OF THE
LASSA VIRUS, THE SPIKE THE LASSA
VIRUS AND SUPPLEMENT ARE
ARTIFICIALLY MADE SOLUBLE
MATRIGLYCAN, WE SEE THAT.
WE SEE STRONG AND SPECIFIC
INHIBITION OF THE CELL ENTRY.
WE DON'T SEE THAT WITH VIRUSES
WITH MATRIGLYCAN, INHIBITING
CELL ENTRY OF LASSA VIRUS.
WE PROPOSED THIS MODEL.
THE SPIKES GENERATED ON CELL
SURFACE ARE LOADED WITH SOLUBLE
PIECE OF MATRIGLYCAN AND WE
THINK IT'S LIKELY TO
DISASSOCIATE OVER TIME OR
DISTANCE AS THEY DIFFUSE FROM
THE CELL THEY BUTTED FROM.
IF THEY DO SO, IF THE
MATRIGLYCAN IS DISASSOCIATED
FROM THE PIKES POTENTIATES
ABILITY TO ENGAGE WITH THE NEW
CELL, AND THAT MAKES TWO
REGIMES, IN ONE THE VIRUS CAN
ESCAPE A DYING CELL FROM WHICH
IT WAS FROM AND ON THE OTHER ONE
CAN ACTUALLY ATTACH TO A NEW
CELL AFTER IT RELEASED THE
SOLUBLE MATRIGLYCAN, A MECHANISM
TO ENSURE PROPER VIRAL EGRESS
AND IS DOING ADDITIONAL THINGS
BUT I DON'T HAVE TIME TO
DESCRIBE THAT.
JUST TO SUMMARIZE, I SHOWED A
STRUCTURE OF THE LASSA VIRUS
SPIKE PROTEIN, WE DISCOVERED AN
INTERESTING TOPOLOGY AND ROLE
FOR THIS STRUCTURED SIGNAL
PEPTIDE.
WE HAVE A STRUCTURAL MECHANISM
FOR BINDING BY THE LASSA VIRUS
AND WE ALSO SUGGEST A ROLE FOR
EGRESS AND STABILIZING, I WILL
THANK THE PEOPLE THAT
CONTRIBUTED TO THIS WORK MAINLY
FROM MY LAB AND WILL END.
>> THANK YOU, RON.
VERY INTERESTING TALK.
TO THE AUDIENCE, ADDRESS THE
QUESTIONS BY PLACING THEM IN THE
DROPBOX AT THE NIH SITE WITH
PLENTY OF TRIM TIME TO ADDRESS
IN THE PANEL DISCUSSION.
NEXT TALK, NEXT SPEAKER IS IEVA
BAGDONAITE.
>> THANK YOU FOR THE
INTRODUCTION.
THANK YOU FOR THE INVITATION TO
PRESENT HERE.
WHAT I'M GOING TO TALK ABOUT IS
TO GET AN INTRODUCTION AND
HOST-PATHOGEN INTERACTIONS AND
PROVIDE A FEW EXAMPLES OF HOW WE
USE GLYCOPROTEOMICS TO UNCOVER
FUNCTIONING OF GLYCANS IN VIRAL
BIOLOGY.
SO ALL CELLS ARE COVERED WITH
DENSE LAYER OF GLYCANS,
COMPRISED OF GLYCOCONJUGATES.
AND THEY PLAY A CENTRAL FUNCTION
IN STRUCTURAL INTEGRITY,
CELL-CELL AND HOST-PATHOGEN
INTERACTIONS, BUILD UP IN A
PATHWAY-SPECIFIC MANNER WHERE
INITIATION STEPS ARE MORE
DISTINCTS.
THE FURTHER STEPS ARE LESS
UNIQUE AND OPEN THE TERMINAL
STRUCTURES ON DIFFERENT
GLYCOCONJUGATES IMPORTANT FOR
INTERACTION WITH VIRUSES.
GLYCANS ARE IMPORTANT FOR MANY
STAGES OF LIFE CYCLES OF
VIRUSES, AND THAT INCLUDES
ATTACHMENT AS ENTRY TO HOST
CELLS, VIRAL PARTICLE FORMATION,
INTERACTION WITH INNATE IMMUNE
SYSTEM, AND ALSO GLYCANS HELP
SHAPE ADAPTIVE IMMUNE RESPONSES.
THE HOST CELL SITE CARBOHYDRATES
ARE OFTEN RECEPTORS FOR PATHOGENS.
FOR EXAMPLE, ANTIGENS ARE
INTERACTED BY H. PYLORI,
ROTAVIRUSES.
SIALIC ACIDS ARE OFTEN USED BY
VIRUSES.
FLAVIVIRUSES INTERACT FOR
INITIAL STEPS.
SUCH INTERACTIONS CAN BE
EXPLOITED THERAPEUTICALLY.
ANALOGS ARE WORKING FOR LENTI
VIRUS BY INHIBITING
NEURAMINIDASE.
ON THE VIRUS SIDES, THERE'S ONE
CLEAR ORIGIN OF THE VIRAL
GLYCANS FROM THE HOST CELL
BECAUSE THE VIRALLY ENCODED
PROTEINS TRAVEL TO THE HOST
PATHWAY, ACQUIRING HOST-FED
GLYCANS, OFTEN IT CAN ALSO BE
ENTRY INTO THE CELL BY
INTERACTING WITH HOST LECTINS.
WHEN WE TALK ABOUT THE VIRAL
PARTICLES, WE USUALLY TALK ABOUT
GLYCANS THAT ARE MOST
PREDOMINANT.
IF WE WERE LOOK AT DIFFERENT
CLASSES OF VIRUSES, GLYCAN
DIVERSITY IS SUBSTANTIAL.
HIV AND FLU HAVE THE HIGH
CONTENT OF N-GLYCANS, VERY FEW
O-GLYCANS.
IN ADDITION, THE TYPES OF GLYCAN
STRUCTURES CAN VARY
SUBSTANTIALLY.
FOR EXAMPLE, HIV SO DENSELY N
GLYCOSYLATED A GOOD PROPORTION
ARE IMMATURE, SO-CALLED
OLIGOMANNOSE.
EBOLA VIRUS HAS A HUGE AMOUNT OF
O-GLYCANS, SPECIFIC SITES NOT
DEFINED, QUITE A BIT OF ANIMAL
GLYCANS MULTIPLE PROCESS.
ONE OF THE IMPORTANT FUNCTIONS
OF GLYCANS AND VIRUS OF COURSE
IS THE IMMUNE SHIELDING BY
COVERING DOMINANT EPITOPES.
AND SOME VIRUSES ARE BETTER THAN
OTHERS IN PROVIDING THIS SORT OF
SHIELDING.
FOR EXAMPLE, HIV AND INFLUENZA,
LIKE SEASONAL INFLUENZA VIRUSES,
ARE VERY GOOD AT IMMUNE ESCAPE
AND ALSO OFTEN CORRELATES ARE
HIGH OLIGO MINUS.
IF WE LOOK AT FLU,
OVERGLYCOSYLATED AS ATTENUATED
TRYING TO COVER MORE EPITOPES,
AT THE SAME TIME LOSES SOME OF
THE VIRULENCE.
ACTUALLY THIS IS ONE OF THE
APPROACHES TO IMPROVE, ALTERING
POSITIONS.
HIV GLYCANS UNDERPROCESSED, THIS
CAN BE TARGETED BY SOME
NATURALLY OCCURRING ANTIBODIESRY
RECOGNIZE IMMATURE STRUCTURES
AND CAN CROSS-LINK SPECIFIC
OLIGOMANNOSE GLYCANS ON THE
SURFACE.
AND USED PLANTS AND MICROBIAL
LECTINS AS ANTI-VIRALS, LECTINS
FROM BANANA NEUTRALIZES HIV.
SO WHAT IS REALLY IMPORTANT FOR
BIOLOGICAL STUDIES TO HAVE A
GOOD UNDERSTANDING OF THE
STRUCTURE OF VIRAL PROTEINS,
MAPPING GLYCOSYLATION SITE IS A
BIG PART OF THAT.
DURING THE SARS-COV-2 PANDEMIC
THERE WAS A HUGE INTEREST IN
MAPPING N AND O-GLYCOSYLATION
SITES.
HERE I'M HIGHLIGHTING ONE OF THE
STUDIES, THERE HAVE BEEN QUITE A
FEW OF THOSE.
PRINCIPALLY SARS-COV-2 IS ONE OF
THOSE HEAVILY N GLYCOSYLATED
VIRUSES, WE AMONG OTHERS MAP O
GLYCOCYTE BUT THIS INFORMATION
CAN BE TAKEN FORWARD AND USED
FOR OTHER KIND OF STUDIES LIKE
STRUCTURAL BIOLOGY OR JUST
BIOCHEMICAL ASSAYS AND HELP
DEFINE SOME VERY SPECIFIC
MOLECULAR FUNCTIONING OF
GLYCANS.
FOR EXAMPLE, IT HAS BEEN
DETERMINED THAT GLYCAN GATE
CONTROLS OPENING OF SARS-COV-2
SPIKE PROTEIN AND ALSO
O-GLYCOSYLATION MODULATES
CLEAVAGE OF THE PROTEIN.
SO WHAT WE'RE INTERESTED AT THE
COPENHAGEN CENTER ARE THE
O-GLYCANS.
WHAT WE WISH TO ACHIEVE IS
GENERATE NOVEL O GLYCOPROTEINS
AND COMBINE WITH KNOCKOUT
LIBRARIES TO UNDERSTAND WHAT
STRUCTURES ARE IMPORTANT BUT
ALSO LOOK AT SINGLE SITES AND
CHOOSE SOME OF THE O-GLYCANS
THAT COULD BE RELATED TO
STRUCTURAL FEATURES IMPORTANT
FOR VIRUS BIOLOGY AND COULD
IMPACT STABILITY FUNCTION AND
RECOGNITION OF PROTEINS.
AND TO MANAGE THE SITES WE HAVE
BEEN USING STRATEGY INVOLVING
ENRICHMENT, DIGEST PROTEINS AND
ENRICH WITH LECTINS THAT CAN
CAPTURE AND TRUNCATED O-GLYCANS.
WE'VE DONE THAT ON QUITE A FEW
VIRUSES, IN COLLABORATION WITH
OTHER LABS, WHAT WE'RE TRYING TO
DO WITH ASSOCIATE PROFESSORS AT
THE CENTER IS TO DEVELOP THE WEB
PAGE WHERE WE COULD DEPOSIT AND
STORE THIS INFORMATION AND ALSO
OVERLAY WITH OTHER SOURCE
INFORMATION OF INFORMATION LIKE
IMMUNE EPITOPES, BANDS OF
CONCERN, EVOLUTION CONSERVATION
FEATURES.
SO ONE OF THE FIRST VIRUSES WE
LOOKED AT WAS HERPES SIMPLEX
TYPE 1 AFFECTING A HUGE PORTION
OF THE POPULATION, AND THERE ARE
NO VACCINES AVAILABLE SO FAR, IT
CAN BE QUITE LIFE-THREATENING
FOR THE IMMUNOCOMPROMISED
INDIVIDUALS, ASSOCIATED ALSO
WITH NEUROLOGICAL DISORDERS SUCH
AS ALZHEIMER'S.
AT THE TIME WE MAPPED MORE THAN
70 O GLYCOSITES, INVESTIGATED
OTHER MEMBERS, CLOSELY RELATED
SUCH AS HERPES ZOSTER AND
DISTANT RELATED BY MEGALOVIRUS,
THERE WERE SOME QUITE CONSISTENT
FEATURES IN WHERE WE FIND THE
O-GLYCANS IN THE HOMOLOGOUS
PROTEINS.
THAT PROMPTED US TO ALSO LOOK
INTO THE FUNCTIONAL RELATIONSHIP
OF THE VIRAL GLYCANS AND HOST
CELL.
WE USED CELLS LACKING TRUNCATED
GLYCANS BY KNOCKOUT OF
CHAPERONE, WHICH RESULTED IN
TREMENDOUS REDUCTION IN THE
VIRAL TITERS AND SAW MORE
MODERATE REDUCTION IN TITERS FOR
A CLOSELY RELATED VIRUS BUT ALSO
RETARDED TISSUE USING THE VIRUS
WITH TRUNCATED O-GLYCANS.
ACTUALLY WE ARE EXPLORING THIS
FURTHER BY INVESTIGATING BOTH
DIFFERENT OTHER TYPES OF GLYCAN
PATHWAYS, ON THE HERPES VIRUS
BIOLOGY INCLUDING STAGES OF THE
VIRAL CYCLE BUT ALSO STARTING TO
CHOOSE SOME OF THE INDIVIDUAL
SITES THAT ARE PERFORMING
PROTEIN FOCUS.
I WOULD LIKE TO TALK ABOUT THE
EBOLA VIRUS GLYCOSYLATION.
EBOLA VIRUS NEEDS LITTLE
INTRODUCTION, ATTENTION VERY
PATHOGENIC WITH HIGH MORTALITY
RATES, SURFACE GLYCOPROTEIN
SPIKE IS HARBORING THIS VERY
HUGE DOMAIN, STRUCTURE IS NOT
WELL DETERMINED, USUALLY
EXCLUDED FROM CRYSTALLOGRAPHIC
STUDIES.
RECONSTRUCTION OF SPIKES DERIVED
FROM VIRUS PARTICLES THEMSELVES,
EVEN THOUGH PRECISE STRUCTURES
ARE NOT DETERMINED YOU CAN SEE
COMPRISES A BIG PART OF THE
PROTEIN.
ACTUALLY IT IS NEEDED, IT NEEDS
TO BE CLEAVED IN ORDER TO
INTERACT WITH NPC RECEPTOR IN
THE LYSOSOME.
THE EARLIER ENTRY INTO THE CELL
GETTING THE MEMBRANES CLOSER,
THE DOMAIN ITSELF CAN ACTUALLY
INTERACT WITH SOME LECTINS, ALSO
ONE NOT A LECTIN, THAT ALSO
PLAYS A ROLE.
THE FUNCTIONS ARE NOT VERY
PRECISELY DEFINED, AND NEITHER
IS THE STRUCTURE.
SO WHAT WE SET OUT TO DO IS MAP
THE O GLYCOSITES AND USE
VIRUS-LIKE PARTICLES, USED WITH
LECTIN ENRICHMENT APPROACH.
INTERESTING TO INVESTIGATE WHAT
DO THE DIFFERENT TYPES OF
STRUCTURES DO FOR THE VIRUS
INFECTIVITY, ALSO HOW COULD
ISOFORM-SPECIFIC INITIATION
AFFECT.
FOR THAT WE USED CELLS WITH
KNOCKOUT, AND WE ALSO USED
KNOCKOUTS OF GALNT 1, 2, 3,
ELIMINATING THE SITE INITIATION
GOVERNED BY THE THREE ISOFORMS.
AND WHEN WE LOOKED AT VIRAL
PROPAGATION WE SAW BOTH
DIMINISHED RNA CONTENT, BOTH ON
THE 1 AND 6 SECTION AND
IMPORTANTLY ALSO SAW TEN-FOLD
DIMINISHED TITERS.
THIS WAS REALLY INTERESTING THAT
NOT ONLY GLYCAN COMMUNICATION
BUT SELECTIVE INITIATION BY
THREE FORMS AFFECTED VIRAL
TITERS.
HERE WE WANTED TO INVESTIGATE SO
WHICH OF THE TYPES WAS INITIATED
BY ISOFORMS.
WE USED MASS SPECTROMETRY
APPROACH EXPRESSED RECOMBINANT
FULL LENGTH SPIKES IN CELL
LINES, IN WILDTYPE KNOCKOUTS WE
THEN PURIFIED PROTEINS, THEN
DIGESTED PROTEINS AND LABELED
THEM WITH DIFFERENTIAL MASS SPEC
WHICH ALLOWED US TO MIX THEM
TOGETHER AND DISCRIMINATE LATER.
AFTER LECTIN ENRICHMENT COULD
QUANTIFY HOW MUCH PEPTIDES
DERIVED FROM CELLS.
AND WE COULD UNDERSTAND WHICH
SITES WERE GOVERNED BY WHICH
ISOFORM.
SO WE SAW THE LARGEST EFFECTS
FROM GALNT1 WITH LARGE NUMBERS
UPREGULATED, ALSO GLYCOPEPTIDES.
YOU CAN SEE THAT ON A WESTERN
BLOT, THERE'S ACTUALLY A VISUAL
MATCH IN THE PROTEIN WHEN YOU
KNOCK OUT.
IF WE OVERLAY THAT WITH THE
OUTCOME OF PROTEIN WE IS SEE T1
SITES WERE QUITE EVENLY
DISTRIBUTED ALONG THE DOMAIN,
THERE WAS ALSO ONE SPECIFIC SITE
BY 2 AND 3, MIX THE SITES WHERE
SEVERAL HAD AN EFFECT.
WHAT CAN YOU USE THIS SORT OF
DATA FOR?
ASIDE FROM THE STRUCTURAL
CHARACTERIZATION.
FOR ONCE WE CAN TRY AND DIG
DEEPER INTO MOLECULAR FUNCTIONS,
RELATED TO LOSS OR DECREASE IN
VIRAL TITERS WE WANTED TO SEE
WHICH IS MOST EFFECTED, USED
SOME PSEUDOVIRUS EXPERIMENTS, WE
SAW THERE WAS THE GLYCOSYLATION
OF HOST CELLS TO WHICH INTEREST
WAS DIMINISHED ENTRY BY THE
WILDTYPE SUED VIRUS.
ANOTHER WAY HOW WE CAN USE THIS
INFORMATION IS FOR GENERATION OF
IMMUNOGENS, IMAGINE FROM YOU
PRODUCE A SPIKE PROTEIN IN THE
CELL, WOULD GENERATE IMMUNE
RESPONSES, SO THAT COULD HELP
UNDERSTAND MECHANISMS.
BY LOOKING AT SOME KNOWN
RECOGNITION SITES FOR ZMapp
AND THERAPEUTIC ANTIBODIES CAN
SEE THAT OVERLAPS WITH SOME
SITES WE'VE IDENTIFIED.
WE WOULD LOOK INTO TAILORED
THERAPEUTICS, OTHER
GLYCOSYLATION INHIBITORS.
THEY LOOKED AT EPITOPES OF
NATURALLY ACQUIRED AND VACCINE
INDUCED ANTIBODIES AND THAT JUST
MAPS IN A SINGLE RESOLUTION
ALONG THE LENGTH OF THE PROTEIN
AND BOTH LOOKED AT IgG AND
IgM SIGNATURES.
AND WHAT BECOMES EVIDENT THAT IF
YOU LOOK AT THE DOMAIN THERE ARE
DIVERGENT SIGNATURES BETWEEN THE
VACCINATED --
>> TWO MINUTES.
>> ALMOST DONE.
BETWEEN THE VACCINATED AND
ACTUAL SURVIVORS.
AND IF WE OVERLAY, QUITE CLEAR
YOU SEE SOME SIGNATURES THERE.
MORE ROBUST DATA SCIENCE WOULD
COME IN HANDY TO CORRELATE THESE
SORTS OF DATA.
THAT'S WHAT WE'RE DOING ON OUR
PAGE, WE HOPE TO IMPROVE.
I HOPE I'VE SHOWN GLYCANS PLAY
MULTIPLE ESSENTIAL ROLES,
PATHOGEN INTERPLAY, THAT EBOLA
VIRUS GLYCOPROTEIN IS
SELECTIVELY GLYCOSYLATED GUY
GALNAC-T 1, T2, T3, IF WE HAVE
COMBINED AND ACQUIRED SUCH
PROTEOMIC YOAMS -- PROTEOMES
CAN LOOK AT SITES AND ROBUST
SOLUTIONS WOULD HELP IN
CORRELATING ALL SORTS OF DATA,
VACCINOLOGY AND OTHER SORTS OF
DATA.
I WOULD LIKE TO THANK YOU FOR
YOUR ATTENTION.
SOMETHING IS STUCK.
AND EVERYBODY INVOLVED.
MY COLLEAGUES FROM COPENHAGEN
AND THOSE FROM PUBLIC HEALTH
AGENCY AND THOSE THAT DID THE
LIVE AND PSEUDOVIRUS
EXPERIMENTS.
>> THANK YOU.
VERY INTERESTING.
AGAIN, FOR THE AUDIENCE DROP
YOUR QUESTIONS IN THE NIH
DROPBOX, THERE WILL BE PLENTY OF
TIME AT THE END IN A PANEL
DISCUSSION FOR YOUR QUESTIONS.
MOVING TO THE NEXT SPEAKER, YAN
LIU, IMPERIAL COLLEGE, GLYCAN
RECEPTORS OF THE POLYOMAVIRIDAE
REVEALED BY MICRO AWAY.
>> THANK YOU.
I'M TRYING TO MINIMIZE MYSELF.
YES, IT'S A GREAT PLEASURE TO
ATTEND THIS MEETING AND TO
REPRESENT THE GLYCOSCIENCE
LABORATORY AT IMPERIAL COLLEGE
AND TELL YOU ABOUT THE WORK OF
THE GROUP MUSING
GLYCOMICROARRAYS.
YOU'LL BE HEARING ABOUT SOFTWARE
USAGE, MANAGING DAY-TO-DAY
MANAGEMENT OF THE MICROARRAY
DATA AND ALSO RESEARCH
HIGHLIGHTS, MICROARRAY ANALYSIS
OF THE FAMILY OF POLYOMA VIRUSES
OF HUMAN AND ANIMAL ORIGIN.
I'D LIKE TO START FROM A
CELEBRATION OF 20 YEARS
ANNIVERSARY OF THE EVENT OF THE
MICROARRAY TECHNOLOGIES.
SO THE FIRST TWO PLATFORMS
INTRODUCED BY PROFESSOR FEIZI
WERE FOLLOWED BY OTHER MAJOR
PLATFORMS BY NIH, AND THE
MICROARRAY SYSTEM.
THERE HAVE BEEN FAST-GROWING
COMMUNITY AS MENTIONED IN A TALK
ALSO.
THERE ARE EMERGING PLATFORMS AND
TECHNOLOGIES, DIFFERENT FROM THE
TRADITIONAL MICROARRAY
TECHNOLOGIES.
SO, THE GLYCAN MICROARRAY
REMAINS AS ONE OF THE MOST
IMPORTANT AND POWERFUL
TECHNOLOGIES IN GLYCOSCIENCES
AND GLYCAN BINDING SPECIFICITY.
WITH THE LAST AMOUNT OF GLYCAN
MICROARRAYS FROM DIFFERENT
PLATFORMS THERE'S A NEED FOR
ENTER NATIONAL GLYCAN REPOSITORY
FOR SHARING AND MAKING
MICROARRAY DATA AVAILABLE FOR
THE BROAD SCIENTIFIC COMMUNITY.
GLYCOSCIENCE COMMUNITY AND ALSO
BEYOND.
SO, THIS IS BEING DEVELOPED
UNDER THE INITIATIVE WHICH WE'RE
PART OF.
THE MAKING THE GLYCAN ARRAY DATA
AVAILABLE IS ALSO CRUCIAL FOR
INTRODUCTION OF DATA TO THE
WIDER RESOURCES.
THE RECORDING TEAM META DATA
INFORMATION IS ESSENTIAL FOR THE
DESIGN OF GLYCAN MICROARRAY
REPOSITORY.
I WOULD LIKE TO MENTION THE
MICROARRAY GUIDELINES TO WHICH I
CONTRIBUTED TO THE DESIGN OF THE
FIRST VERSION AS PART OF THE
INSTITUTE SUPPORTED INITIATIVE.
MIRAGE STANDS FOR MINIMUM
INFORMATION REQUIRED FOR A
GLYCOMIC EXPERIMENT.
AND WITH THE GROUP OF GLYCAN
ARRAY EXPERTS, SO WE HAVE
IDENTIFIED EIGHT AREAS,
HIGHLIGHTED IN THE WORK FLOW,
REQUIRED FOR GENERATING GLYCAN
ARRAY AND OBTAINING BINDING
DATA, GUIDELINES HAVE BEEN MET,
FOR EACH OF THE AREA.
AND THIS AT THE CORE OF THE
EXPERIMENTAL META DATA IN THE
GLYCAN REPOSITORY, WHICH WE ARE
DEVELOPING AT THE MOMENT.
I'D LIKE TO EMPHASIZE HERE META
DATA ARE ALSO CRUCIAL FOR
CURATION AND COMPARISON, THIS IS
THE DIRECTION THAT WE'RE
INTERESTED IN THE FUTURE WORK.
BRIEF INTRODUCTION OF OUR
TECHNOLOGY BASED ON GLYCOLYTICS,
DIFFERENT SOURCES CAN BE
CONJUGATED ON MICROARRAY SLIDES,
SO THESE CLUSTERS, WITH ELEMENT
OF MOBILITY THIS TYPE OF
PRESENTATION FAVORED BY MOST OF
THE RECOGNITION WE HAVE STARTED
SO FAR.
IN PARTICULAR FOR VIRUSES, AND
VIRAL ADHESIONS, INCLUDING THOSE
FROM THE REQUIREMENTS FOR THE
PRESENTATION OF GLYCANS FOR
BINDING SUCH AS THE PENTAMERIC
ONES IN THE LATER PART OF MY
TALK.
WE SEE MICROARRAY SYSTEMS, LARGE
GLYCAN, WE HAVE ESTABLISHED
CARBOHYDRATE MICROARRAY FACILITY
WITH SUPPORT OF WELLCOME TRUST,
EVERY TEN YEARS, THIS IS
BIOMEDICAL RESOURCE FOR THE
BROAD SCIENTIFIC COMMUNITY.
I'M CURRENTLY LEAD OF THE
MICROARRAY FACILITY WITH FUNDING
SUPPORT, WITH MY COLLEAGUES AND
ONE OF OUR MAJOR THEMES IN THE
PERIOD IS TO SHARE OF OUR
PUBLISHED GLYCAN MICROARRAY
DATASET TO THE WIDER COMMUNITY
THROUGH INTERNATIONAL GLYCAN
MICROARRAY DATA REPOSITORY.
FROM THE EARLY STAGE, SOFTWARE
HAS BEEN THE MAINSTAY FOR
DAY-TO-DAY MICROARRAY DATA
MANAGEMENT, WE'RE QUITE LUCKY
BECAUSE WE HAVE RECORDED OVER
8,000 DATASETS SINCE ALMOST DAY
ONE OF OUR MICROARRAY RESEARCH.
INITIALLY USING THIS PROTOTYPE,
DEVELOPED FOR INHOUSE USE, WITH
SUPPORT FROM WELLCOME TRUST AND
ALSO NIH COMMON FUND WE HAVE
DEVELOPED AND RELEASED NEW
SOFTWARE, CARBARRAYART.
THIS SOFTWARE IS BEING DEVELOPED
AS IT IS BEAUTIFUL GLYCAN ARRAY
SOFTWARE LINKING WITH
REPOSITORY.
TO HIGHLIGHT ADVANTAGES OF THE
SOFTWARE COMPARED WITH PREVIOUS
VERSION, USING WINDOWS AND MAC
SUITS ALL ARRAY FORMATS, LAYS
OUT ADVANTAGE COMPARED TO OUR
PROTOTYPES, ALLOWS STORAGE NOT
ONLY THE SCAN WITH OUR DATA AND
ALSO RAW DATA IMAGES, AND
EXPERIMENTAL METADATA
COMPARABLE WITH MIRAGE
GUIDELINES, THE AIM TO USE THE
SOFTWARE FOR DATA DEPOSITION, TO
AND FROM THE GLYCAN ARRAY
REPOSITORY.
THIS IS A COLORFUL WORKFLOW,
DATA MANAGEMENT WORKFLOW, ON THE
RIGHT IS EXPERIMENTAL DATA
WORKFLOW, EXPERIMENTAL WORKFLOW,
USERS CAN CREATE GLYCAN LIBRARY
USING THIS ENTIRE, THESE GLYCAN
SEQUENCING FORMATS AND ARRAY
LAYOUT, AND THE METADATA FROM
THE GLYCAN BINDING SAMPLES AND
FROM EXPERIMENTAL PROTOCOLS CAN
BE RECORDED.
TOGETHER WITH THE RAW DATA FROM
THE SCANNER.
THE DATA CAN BE PROCESSED WITH
THIS INFORMATION AND PRESENTED,
DISPLAYED AS HEAT MAPS WITH
FILTERING FUNCTIONS AS WELL AS
REPORTING.
THESE FUNCTIONS MADE SOFTWARE AS
DAY-TO-DAY GLYCAN MICROARRAY
DATA MANAGEMENT IN OUR FACILITY
AND ALSO CAN BE WIDELY USED IN
GLYCAN ARRAY LABORATORIES.
THE DATA, METADATA COMBINED WITH
MIRAGE DEADLINES GATHER THIS
INFORMATION THROUGH REPOSITORY.
I'M GOING TO GO THROUGH THE DATA
USING OUR GLYCAN ARRAYS ON THE
STUDIES OF HOST GLYCAN BY
POLYOMAVIRUSES.
A BRIEF INTRODUCTION, THEY CAN
INFECT DIVERSE MAMMALS AND
BIRDS, FISH AND INVERTEBRATES.
MORE THAN 100 DISCOVERED SO FAR,
ONLY 14 HAVE BEEN PROPOSED TO BE
HUMAN TROPIC.
INFECTIONS IN HUMANS USUALLY
GIVE NO SYMPTOMS IN HEALTHY
INDIVIDUALS, SOME VIRUSES CAN
CAUSE SEVERE DISEASES IN
IMMUNOCOMPROMISED INDIVIDUALS.
IT IS RESPONSIBLE FOR ATTACHMENT
OF POLYOMAVIRUS, BINDING THROUGH
GLYCAN RECEPTORS.
THE NARROW SPECIFICITY SPECIAL
FEATURE FOR POLYOMA VIRUSES, VP1
PROTEINS, I'M SHOWING A FEW WELL
KNOWN HUMAN POLYOMAVIRUSES TO
HIGHLIGHT.
THE POLYOMAVIRUS CAUSE SKIN
DISEASE, SKIN CANCER, SKIN
DISEASES.
USING THE GLYCAN MICROARRAYS, WE
HAVE DISCOVERED THE NARROW
SPECIFICITY OF MANY OF THE
VIRUSES.
WE ARE FOLLOWING UP THE STUDIES
LOOKING AT MUTANTS OF THE
VIRUSES ISOLATED FROM PATIENTS.
TODAY I WOULD LIKE TO FOCUS ON
THE TWO RECENTLY DISCOVERED
HUMAN POLYOMAVIRUSES.
THESE ARE DETECTED IN HUMAN
LIVER ISSUES, AND THOSE
IDENTIFIED IN PANCREATIC
TRANSPLANT PATIENTS.
USING THE ALIGNMENT OF BINDING
REGIONS, BC AND DE LOOPS.
IN BOTH CASES CLOSE RELATIONSHIP
OF HUMAN VIRUSES WERE FOUND WITH
THE NUMBER OF ANIMAL VIRUSES.
RAISING THE QUESTION ABOUT
ORIGINS AND EVOLUTION OF THE TWO
VIRUSES.
I LIKE THIS IMAGE FROM THE A
PAPER SHOWING OVERVIEW OF VP1
AMINO ACID, TO THE COMPLEX
STRUCTURES OF VP1 WITH LACTOSE.
ON THE LEFT A VIRUS WITH
CHIMPANZEE, THE RIGHT HUMAN
POLYOMAVIRUS WITH BIRD
POLYOMAVIRUS.
THE MESSAGE HERE IS THAT BINDING
SITES, YOU CAN SEE SUGARS IN
GREEN COLOR HERE, HIGHLY
CONSERVED AS HIGHLIGHTED IN RED
COLOR HERE.
THEY ARE HIGHLY CONSERVED ACROSS
DIFFERENT VIRUSES, DIFFERENT
SPECIES.
HOWEVER, THE SURROUNDING
RESIDUES NEAR THESE SIALIC ACID
BINDING SITES, YOU CAN SEE THE
COLORS, THEY DISPLAY MUCH LOWER
LEVEL OF CONSERVATION SUGGESTING
DIFFERENT RECEPTOR SPECIFICITY
MAY BE OBSERVED BY DIFFERENT
VIRUSES.
WE CANNOT CONCLUDE FROM
STRUCTURE STUDIES WITH SIMPLE
SUGARS.
WE CAN LOOK IN MICROARRAYS,
COMPARISON WITH THE CHIMPANZEE
POLYOMAVIRUS, USING ARRAY OF
GLYCANS, WE CAN DISPLAY THE
GLYCANS ACCORDING TO LINKAGE AND
THESE ARE CLOSELY RELATED,
HIGHLY CONSERVED, VP1 PROTEINS
BIND TO TYPE 1 SEQUENCE AND
NON-SIALYATED SEQUENCE.
COMPARISON OF THE HUMAN VIRUS,
CLOSELY RELATED POLYOMAVIRUS
ALSO SHOWS PREFERENCE.
AND THIS IS COMPARISON OF HUMAN
POLYOMAVIRUS WITH SHEEP
POLYOMAVIRUS.
THE BINDING SITES IS HIGHLY
CONSERVED, THE DIFFERENCE IS
PINK, DISTINCTIVE BINDING HAS
BEEN OBSERVED.
WE'RE SEEING BINDING THROUGH THE
NON-SYLATED.
THERE WAS ADDITIONAL BINDING IN
COMPARISON TO HUMAN POLYOMA, SO
WE CAN OBSERVE BINDING.
THIS IS QUITE STRIKING WITH THE
GOOSE POLYOMAVIRUS, ABRUPT
FINDINGS, THIS PROVIDES SOME
EXPLANATION, THIS IS ONE OF THE
FEW KNOWN POLYOMAVIRUSES THAT
CAN INFECT DIFFERENT AVIAN
HOSTS.
I HAVEN'T WHETHER THIS DID CROSS
TO OTHER SPECIES, BUT IT'S A
CROSS-SPECIES INFECTIVITY, A
REAL PATHOGEN CAUSING THE
DISEASE IN THE GEESE.
>> TWO MINUTES.
>> ALMOST DONE.
I DON'T KNOW IF YOU CAN SEE MY
TITLE.
FOOTPRINTS.
THIS IS A FOOTPRINT, HEAT MAP.
THE GROUP OF POLYOMAVIRUSES I
DESCRIBED JUST NOW, GENERATED IN
OUR MICROAWAY SOFTWARE, HEAT MAP
SENSATION.
AND THIS IS A LARGE ARRAY.
900 PROBES.
WE CAN SEE THAT THERE IS THE
CLOSELY RELATED HUMAN AND ANIMAL
VIRUSES SHARING A LOT OF
SIMILARITY IN THE GLYCAN
BINDING, AND SHOWING DIFFERENCE,
THEY SHARE SIMILARITY BUT HAVE
DISTINCT DETAILS.
IT'S SOMETHING I WOULD LIKE TO
HIGHLIGHT HERE, VERY EXCITING,
SO THE GLYCAN BINDING OBSERVED
WITH MANY OF THE VIRUSES, THIS
HAS BEEN FOLLOWED UP BY
STRUCTURAL STUDIES.
I CAN'T SHARE THE DETAILS TODAY,
OF THE PROBES, THAT HOPEFULLY WE
WILL BE ABLE TO PRESENT
STRUCTURAL INSIGHT OF THESE
INTERACTIONS IN THE NEAR FUTURE.
IN SUMMARY MICROARRAY DATA,
CLOSE REVEAL DETAILS OF
BIPEDDING CHARACTERISTICS,
HELPING ADVANCE OUR
UNDERSTANDING OF RECEPTOR-BASED
CELL TROPISM DIFFERENCES,
OCCURRENCE OF CROSS-SPECIES
TRANSMISSION AND ALSO EVOLUTION
OF POLYOMAVIRUSES, AND ONGOING
STUDIES WHERE USING MICROARRAYS
TO MONITOR SPECIFICITY, IN
TRANSPLANTS, CAN KIDNEY
TRANSPLANT PATIENTS, SO MUTANTS,
WE'RE MONITORING MUTANTS OF
CELLS IN THE KIDNEY TRANSPLANT
RECIPIENT.
WE HAVE A LARGE ARMY OF
MICROARRAY DAY, DIFFERENT
VIRUSES, INCLUDING HUMAN -- THE
VIRUS IS DESCRIBED TODAY, ALSO
OTHER VIRUSES INCLUDING NUMBER
OF BK MUTANTS AND ADENOVIRUS,
ROTAVIRUS, ET CETERA.
THESE ARE STORED IN OUR
CarbARRAYART, THEY HAVE THE
GLYCANS, ARRAY INFORMATION
TOGETHER WITH SIMPLE METADATA,
EXPERIMENTAL METADATA, WE CAN
STORE THE PARAMETERS, PROCESS
DATA TOGETHER WITH RAW DATA FROM
THE SCANNERS, ALREADY THIS WILL
BE -- THIS WILL BE READY FOR
SUBMISSION DEPOSITION INTO THE
REPOSITORY WHEN THE REPOSITORY
IS LAUNCHED.
I'D LIKE TO THANK MY COLLEAGUES
IN THE LABORATORY, PAST AND
PRESENT.
ALSO OUR COLLABORATORS OUR
ADVISER AND COLLABORATORS.
THANK YOU FOR YOUR ATTENTION.
THANK YOU.
>> THANK YOU, YAN.
VERY NICE TALK.
AGAIN, WE CAN ADDRESS QUESTIONS
AT THE PANEL DISCUSSION.
>> THANK YOU.
>> I CAN STOP SHARE.
THANK YOU.
>> NEXT SPEAKER IS LARA MAHAL,
FROM UNIVERSITY OF ALBERTA,
TITLE IS SYSTEMATICALLY
UNCOVERING THE ROLES OF
GLYCOSYLATION IN HOST-PATHOGEN
INTERACTIONS.
>> THANK YOU SO MUCH.
CAN EVERYONE SEE MY SCREEN?
EVERYTHING GOOD?
GREAT.
I FIRST WANT TO THANK YOU FOR
THE INVITATION TO GIVE OUR TALK
ON UNPUBLISHED WORK FROM MY LAB.
THIS IS AN INFORMATICS
SYMPOSIUM, MY LAB FOCUSES ON
SYSTEMS BASED UNDERSTANDING,
SYSTEMS-BASED APPROACHES TO
UNDERSTANDING GLYCAN, LARGE
SCALE DATASETS, HOW WE CAN USE
INFORMATICS TO IDENTIFY HOW
SUGARS WORK.
AND SO THE MAIN TOPIC IS I WORK
IN MICROARRAYS AND DATA
INTEGRATION, SO MY LAB CREATED
LECTIN MICROARRAYS, NOW THIS
TECHNOLOGY IS ALMOST 20 YEARS
OLD.
WE-- THE BASIC IDEA OF LECTIN
MICROARRAY WE CAN TAKE ANYTHING,
CELL CULTURE, HUMAN TISSUE, YOU
NAME IT, EXTRACT GLYCOPROTEINS,
THEY HAVE N AND O-GLYCANS AND
YOU CAN SOMETIMES LOOK AT
GLYCOLIPIDS.
WE LABEL THE SAMPLES WITH ONE
FLUOROPHORE, HAVE A BIOLOGICAL
REFERENCE IN MY LAB, A SECOND
FLUOROPHORE, AND ADD EQUAL
AMOUNTS TO LECTIN MICROARRAY,
BINDING OF THESE TWO SAMPLES
GIVE US AN IDEA BECAUSE WE
UNDERSTAND THE LECTIN BINDING OF
VERY SPECIFIC MOLECULAR
STRUCTURES AND HOW THEY ARE
CHANGING OVER BIOLOGICAL
SAMPLES.
THIS, FOR EXAMPLE, IS AN EXAMPLE
FROM OUR PANCREATIC CANCER DATA,
LOOKING AT A MOUSE MODEL, THE
SUGARS ARE TRAINING AS THE MOUSE
DEVELOPS PANCREATIC CANCER.
WHY DOES A LECTIN MICROARRAY
HAVE ANY ADVANTAGES OVER THE
OTHER GLYCOMIC METHODS?
SOME ADVANTAGES WERE DISCUSSED
BY MY COLLEAGUES WHEN TALKING
ABOUT THINKING ABOUT HOW YOU
DISCOVER WHAT LECTIN YOU WOULD
USE TO LOOK AT GLYCOPROTEINS.
AND ONE OF THE ADVANTAGES IS
THAT YOU ARE USING PROBES THAT
CAN THEN BE USED TO DO MORE
DISCRETE DISCOVERY.
YOU CAN DO GLYCOPROTEOMIC
PULLDOWNS, AN EXAMPLE FROM WORK
IN CANCER CELL, WHERE WE DID
GLYCOPROTEOMIC PULLDOWNS TO
DISCOVER WHY IT IS CORE
FUCOSYLATION IS CHANGING IN
MEDICAL MOMENT A METASTASIS, THE
ROLE IN CELL MIGRATION.
SAME PROBES CAN BE USED AS A
PROBE ON, SAY, TISSUE CULTURE OR
IN THIS PARTICULAR CASE HUMAN
TISSUES WHERE WE'RE NOW ABLE TO
LOOK AT LOCALIZATION AND
EXPRESSION OF SPECIFIC SUGARS ON
IN THIS CASE CANCER TISSUE.
AGAIN, THIS IS MELANOMA
METASTASIS, PRIMARY MELANOMA,
SEEING A SWITCH FROM
FUCOSYLATION FROM ONE TWO TO TWO
SIX IN THE CORE.
WHAT ELSE CAN IT GO?
TELLING ABOUT THE BASIC
TECHNOLOGY, ONE OF THE GREAT
THINGS IS THAT IT'S REALLY
HIGH-THROUGHPUT.
WE HAVE 4 SUBARRAYS PER SLIDE,
IN TEN SLIDES CAN DO 240
SAMPLES, STARTING TO APPROACH
OUR AVERAGE ANALYSIS, TYPICALLY
DOING AROUND 200 SAMPLES AND
ANALYSIS.
WE HAVE A DUAL COLOR APPROACH,
WHICH I'VE TALKED ABOUT, THIS
MAKES FOR RAPID AND ROBUST
QUANTITATION, BECAUSE OF THE
FACT DATA IS IN THE SAME FORMATS
IT'S INTEGRATIVE.
ARRAYS INCLUDE ANTIBODIES.
WE'RE CONSTANTLY EXPANDING,
FEEL FREE TO CONTACT US, WE
WOULD LOVE TO ADD MORE.
WE'RE USING MACHINE LEARNING
ANNOTATION.
AGAIN, THE POINT OF USING A
LECTIN ARRAY, IF WE LOOK AT
HYBRID N LINK GLYCAN, IF WE WANT
TO FIGURE OUT WHETHER IT HAS
SIALIC ACID THAT'S NOT SO EASY
USING A TYPICAL MASS
SPECTROMETRY APPROACH IN A
HIGH-THROUGHPUT WAY.
BUT IF YOU WANT TO SEE WHETHER
OVERALL 2, 6 SIALYATION IS
INTEREST, IT IS DISCRETE IN
BINDING FOR THIS PARTICULAR
GLYCAN.
INDEED IT DOESN'T BIND IF THIS
SUGAR IS A SIALIC ACID AT THE 3
POSITION INSTEAD.
NOT ONLY THAT, BUT BECAUSE WE
KNOW THE VERY SPECIFIC LINKAGE
WE KNOW THE BIOSYNTHETIC ENZYMES
THAT CAN MAKE IT.
ST6GAL 2 AND -- 1 AND 2, IN
TERMS OF BIOLOGICAL PATHWAYS TO
UNDERLIE SUGARS TO SEE IF WE'RE
GETTING THROUGH CURRENT DATA.
TO TALK MORE ABOUT HOW WE
UNDERSTAND WHAT THESE ARE
ACTUALLY BINDING, WE HAVE
PUBLISHED WORK IN ACS CHEMICAL
BIOLOGY, TUBING THE 611 GLYCAN
MICROARRAY I A DATASET GENERATED
A TIME AGO, LOOKED AT
COMMERCIALLY AVAILABLE LECTINS.
SO USING THESE DATASETS AS INPUT
WITH HELP OF DANIEL'S GROUP, WE
DID MACHINE BASED LEARNING
ANNOTATION OF THE LECTINS, THIS
IS A COMBINATION OF BOTH
MACHINE-BASED LEARNING AND HAND
ANNOTATION FROM TIME LOOKING AT
DATASETS.
WHAT WE HAVE CREATED ARE SERIES
OF TABLES, THIS IS A GUIDE TO
ALL THE COMMERCIALLY AVAILABLE
LECTINS AVAILABLE, AND I MADE IT
SO IT'S OPEN ACCESS, SO ANYONE
CAN DOWNLOAD AND PLEASE DO USE
IT.
BASICALLY WHAT WE'VE CREATED IS
A GUIDE TO PREDOMINANT BINDING
MOTIF, IDEA OF THE RULES.
WE FOUND SURPRISING THINGS.
MAACKIA BINDS, WE ALREADY KNEW,
BUT WHAT I DIDN'T KNOW AND WHAT
IS NOT OBVIOUS TO THE FIELD IS
THAT FUCOSYLATION ON THIS
GlcNAc INHIBITS BINDING.
THE MOST POPULAR AND FAMOUS 2, 3
SYLATED GLYCAN IS NOT BOUND BY
MAA.
SO THESE SPECIFICITIES ARE
THINGS THAT WERE REVEALED BY THE
ANNOTATION WE DID USING MACHINE
LEARNING.
SO, AGAIN, IF YOU'RE INTERESTED
IN SPECIFICITY YOU WANT TO
FIGURE OUT WHAT MIGHT BIND YOUR
FAVORITE GLYCANS, THIS IS A GOOD
NEW RESOURCE TO USE.
HOW DO WE USE LECTINS TO
ACTUALLY GET TO BIOLOGICAL DATA?
THE REAL POINT OF THE TALK IS
TALKING ABOUT OUR WORK IN
SARS-COV-2.
SO WE'VE DONE WORK IN CANCER, I
WANT TO DO A SHOUT OUT BRIEFLY
TO WORK IN PANCREATIC CANCER
RECENTLY PUBLISHED IN MOLECULAR
CELL PROTEOMICS, WE'VE LOOKED AT
THE MOUSE MODEL AND HUMAN
PANCREATIC CANCER TO SEE WHAT
OVERLAMBS AND WHAT DOES NOT SO
WHAT CAN YOU MODEL FROM THE
HUMAN TUMOR IN A MOUSE?
WE IDENTIFIED IN PARTICULAR 2, 6
SIALIC ACID, VERY IMPORTANT AND
CHANGING IN HUMAN AND MOUSE, IN
THIS PAPER CREATED A GAL 1
FLOCKOUT KNOCKOUT MOUSE SPECIFIC
TO PANCREAS AND SHOWED IN THE
PRESENCE OF THE KNOCKOUT WE
ACTUALLY DID NOT FORM, IN THE KC
MOUSE, DID NOT FORM THE KINDS OF
PANCREATIC LESIONS AND EARLY
STAGES IN THE KC MOUSE.
WE RETAINED THE PHENOTYPE OF THE
PANCREAS THAT ONE WOULD HOPE TO
HAVE A NORMAL ONE FOR MUCH
LONGER IN THE KC MOUSE MODEL
WHEN IT WAS KNOCKED OUT,
IMPLYING 2, 6 SIALIC ACID AS A
ROLE.
THIS HAS BEEN GONE OVER ALREADY,
MOSTLY WHEN WE TALK ABOUT
GLYCANS IN CONTEXT OF
HOST-PATHOGEN INTERACTION, MANY
TALKS ALREADY HAVE FOCUSED ON
BINDING OF GLYCANS BY THE VIRUS
AS HOST ENTRY OR AS LOOKING AT
THE GLYCOSYLATION OF THE VIRUS
AS A WAY OF PREVENTING
RECOGNITION.
THERE ARE MANY OTHER THINGS THAT
GLYCOSYLATION PROVIDES IN TERMS
OF HOST RESPONSE.
ONE AS WE ALL KNOW MUCINS TRAP
PATHOGENS, IN THE RESPIRATORY
TRACT, THE GUNK WE GET WITH
VIRUSES TENDS TO TRAP AND GET
RID OF PATHOGENS.
IN ADDITION ANTIBODY
GLYCOSYLATION CAN CHANGE
EFFECTOR FUNCTIONS, THAT HAS
IMPACT ON HOW WE RESPOND TO
PATHOGENS.
WE HAVE LECTIN MEDIATED IMMUNE
RESPONSES, COMPLEMENT PATHWAY,
COMPLEMENT RECOGNITION OF
PATHOGENS TO REMOVE THEM AND
INFORMATION THAT THIS MAY PLAY A
ROLE IN SORT OF SOME OF OUR
REACTIONS TO PATHOGENS AS WELL
WHEN WE'RE INFECTED.
WE IDENTIFIED ASSOCIATION WITH
SEVERITY IN INFLUENZA INFECTION,
YOU CAN SEE THE STAINING OF AN
INFECTED LUNG IN THE FERRET.
IT'S SIGNALING TO ACTIVATE
COMPLEMENT CASCADE IN
INFLAMMATIONS.
I JUST WANT TO POINT THIS OUT AS
ANOTHER EXAMPLE HOW THESE
APPROACHES ARE STARTING TO GET
TO MOLECULAR MECHANISMS THAT MAY
UNDERSTAND -- HELP US UNDERSTAND
HOW WE RESPOND TO ILLNESS.
IN THIS TALK I'M ACTUALLY GOING
TO FOCUS ON THE THING WE'RE ALL
FOCUSED ON TO OUR DETRIMENT,
COVID.
I'M GOING TO TALK ABOUT SOME
LATEST UNPUBLISHED WORK ON
SARS-COV-2.
I WANT TO POINT OUT STUDENTS WHO
HAVE DONE THIS WORK.
SHE IS GRADUATING, AND RIC AND
EMMA, AND MD/PHD .
WHAT DO WE KNOW ABOUT GLYCANS IN
SARS-COV-2 PATHOGENESIS?
WE FOCUSED ON GLYCOSYLATION OF
THE PROTEINS, WE KNOW THEY ARE
GLYCOSYLATED, THIS IS PERSON IN
TERMS OF BINDING OF ANTIBODIES
IN RECOGNITION TO THE SPIKE.
WE KNOW INNATE IMMUNE LECTINS
CAN RECOGNIZE VIRAL PARTICLES
AND IN RECENT WORK WE KNOW THAT
SARS-COV-2 CAN USE SIALIC ACID
AS A CO-RECEPTOR.
CAN ALSO USE HEPARIN.
IF WE THINK ABOUT WHAT'S WRONG
ABOUT COVID AND WHAT WE'RE
SCARED ABOUT, IT'S DEATH.
LOTS OF PEOPLE GET SARS-COV-2
AND HAVE RELATIVELY LOW LEVEL
RESPONSES.
IT'S LIKE A COLD OR NOT LIKE A
COLD, MORE LIKE A REALLY BAD FLU
BUT WITH POTENTIAL VERY, VERY
HORRIBLE LONG-TERM EFFECTS.
I WON'T DOWNPLAY IT AND SAY IT'S
NEAR THOSE THINGS BUT I DO THINK
THE SEVERITY, THE FACT THAT A
SIGNIFICANT PORTION OF PEOPLE
DIE IS WE'RE THE MOST CONCERNED
ABOUT.
IS INTEREST A WAY TO THINK ABOUT
PEOPLE WHO ARE DYING AND THOSE
THAT AREN'T, WE LOOKED AT THE
PLASMA OF PEOPLE WHO ARE
NEGATIVE, THIS CAME FROM THE UGA
COHORT IN GEORGIA, MILD
SARS-COV-2, CLASSIFIED AS
UNHOSPITALIZED, PEOPLE WHO ARE
MODERATE, HOSPITALIZED WITHOUT
O2 OR SEVERE ON OXYGEN.
AND WE LOOKED AT THEIR GLYCOMICS
USING LECTIN MICROARRAY.
WE SAW CHANGES IN 2, 3 SIALIC
ACID.
WE SAW CHANGES IN GLYCANS AND
SAW CHANGES IN Q 6 SIGH ALEX
ACID.
WE SAW 2, 6 BUT NOT 2, 3 WAS
ACTUALLY RELATED TO THE SEVERITY
OBSERVED.
WE SAW THAT IF YOU LOOK AT SNA
BINDING PEOPLE WHO ARE MILD
VERSUS MODERATE VERSUS SEVERE,
WITH SEVERE HAD HIGHER LEVELS OF
2, 6 SIALIC ACID, HIGHER THAN
CONTROLS.
THEY HAD LOWER LEVELS OF 2, 3
BUT WASN'T SEVERITY DEPENDENT,
NO REAL DIFFERENCE BETWEEN MILD,
MODERATE, SEVERE COHORT, SHOWING
WHY WE HAVE TO CARE ABOUT THE
EPITOPE, SPECIFIC LINKAGE, 2, 6
AND 2, 3 ARE NOT THE SAME.
THIS LECTIN IS SPECIFIC FOR 2, 3
SIALIC ACID ON O-LINKED CORES,
WHERE THE UNDERLYING SUGAR IS
GalNAC, NOT REPRESENTATIVE OF
2P 3, FINE SPECIFICITY IN HOW
GLYCOSYLATION IS WORKING IN THE
BODY.
WE WANTED TO SEE, WELL, WE SEE
THIS IN THE PLASMA, IS THERE
RELATIONSHIP TO WHAT'S GOING ON
IN TISSUE?
FOR THAT WE ACTUALLY LOOKED AT A
COHORT OF PEOPLE FROM NEW YORK
WHO DIED OF COVID AND COMPARED
THEM TO PEOPLE WHO HAD DIED OF
OTHER CAUSES, MANY OF WHICH WERE
STILL RESPIRATORY IN NATURE.
WE LOOKED AT THE HARD, KIDNEY,
UPPER LOBE OF LUNGS OF THESE
PEOPLE.
THIS IS JUST TO SHOW TISSUES
WEREN'T SO HORRIBLY DEGRADED
THAT WE COULDN'T GET ANYTHING
OUT OF THEM.
THIS IS H&E STAINING, YOU CAN
SEE THAT THE PATIENTS WHO DIED
OF COVID-19 LUNGS WERE A MESS
COMPARED TO THOSE, EVEN THOUGH A
LOT OF PATIENTS HAD RESPIRATORY
DISORDERS, NOTHING LIKE COVID.
WE LOOKED AT THE ARRAY.
ONE THING I LOVE, I HATE THAT
PEOPLE HAD TO DIE TO SEE THIS
BUT ONE OF THE THINGS WE OFTEN
GET IN THE COMMUNITY IS, OH, WHY
DO YOU CARE ABOUT SUGARS?
I THINK THIS IS THE PERFECT
ANTIDOTE.
PEOPLE WHO DIED OF WIDE VARIETY
OF CAUSES IN THE NEGATIVE, COVID
IN THE POSITIVE, HEART, KIDNEY,
LIVER AND UPPER AND LOWER LOBES
OF THE LUNGS HAVE DISTINCT
GLYCANS.
YOU CAN SEE WHAT TISSUE IS.
SEPARATE FROM WHETHER OR NOT
THEY HAD COVID.
WHAT THAT TELLS ME IS THAT AS
WE'VE ALWAYS THOUGHT IN THE
GLYCOCOMMUNITY CARBOHYDRATES ARE
AN INDICATOR OF TISSUE TYPE AND
CELL TYPE.
AT A FUNDAMENTAL LEVEL.
THAT'S REFLECTED IN GLYCAN OF
TISSUES.
TO GET MORE SPECIFIC, WE LOOKED
AT CHANGES OCCURRING,
STATISTICALLY DIFFERENT FROM
THOSE WHO DIED IN COVID AND DID
NOT.
I WANT TO POINT OUT WE ACTUALLY
SAW HIGHER LEVELS OF 2, 3 IN THE
TISSUE OF OF LIVER AND UPPER AND
LOWER LOBE OF THE LUNG, AND SAW
THAT CHANGE IN 2, 6 BUT ONLY IN
THE LOWER LOBE OF THE LUNG LIE
LECTIN MICROARRAY.
WHEN WE LOOK WITH HISTOCHEMISTRY
WE SEE BEAUTIFUL STAINING WHICH
A LECTIN THAT WAS CREATED IN MY
LABORATORY FOR 2P 3 SIALIC ACID.
IN THE LIVER AND UPPER AND LOWER
LOBE OF THE LUNG COMPARED TO THE
COVID NEAPING -- NEGATIVE
CONTROLS.
WE DID SEE SOME INCREASED
STAINING IN THE UPPER LOBE OF
THE LUNG, WITH SNA.
WE USED SNA BACK TO OUR PLASMA,
TO LOOK AT MILD COVID VERSUS
SEVERE COVID AND TO SEE WHAT IS
GOING ON IN TERMS OF THE
GLYCOPROTEINS WE'RE OBSERVING.
WE DID GLYCOPROTEOMICS AND FOUND
A LOT OF PROTEINS THAT WERE
ASSOCIATED WITH COMPLEMENT,
BEING PULLED DOWN STRONGLY IN
THE SEVERE COVID COHORT.
INDEED WHEN WE LOOKED AT
ENRICHMENT AND LOOKED AT THE
PATHWAY, WE SAW COMPLEMENT
ACTIVATION CAME UP VERY HEAVILY.
AS BEING SOMETHING THAT WAS
ENRICHED IN 2, 6 SYLATED
PROTEINS IN THE SEVERE COVID
COHORT.
COMPLEMENT HAS BEEN SHOWN TO
PLAY A ROLE.
WHAT WE KNOW IS THAT THE
COMPLEMENT PROTEINS, WE SEE A
LOT OF COMPLEMENT PROTEINS
ASSOCIATED WITH DOWNSTREAM
COMPLEMENT CASCADE AS HIGHLY
SYLATED.
WHEN WE LOOK TO SEE WHETHER THIS
IS BECAUSE COMPLEMENT PROTEINS
WERE HIGHER OR WHETHER IT WAS
BECAUSE THE SIALYATION IS
CHANGING WE SAW THAT ACTUALLY
IT'S BOTH.
HERE IT'S NEGATIVE.
HERE IS THE INPUT VERSUS
PULLDOWN.
IN THE C5 WE GET A DEPENDENT
INCREASE IN 2, 6 SIALYATION,
DON'T SEE HIGHER LEVELS, WHEN WE
LOOK AT C 9 IN THE SEVERE WE DO
SEE SLIGHTLY HIGHER LEVELS OF
THE C 9 BUT SEE A LOT OF
INCREASE IN 2, 6 SYLATED PULLED
DOWN, ARGUING THEY THEY ARE
DIFFERENTIALLY SYLATED.
WE STAINED AND FOUND IN THE SAME
LOCATIONS OBSERVING THE STRONG
STANDING OF 2, 6 LINING
AIRWAYS.
IT'S DRAMATIC IN C 9 AS WELL,
LINING THE AIRWAYS.
WHEN WE LOOK AT 2, 6 SIALIC ACID
VERSUS THE ENZYME THAT CONTROLS
2, 6 SIALYATION IN PATIENTS THEY
HAVE MUCH HIGHER LEVELS OF
STAINING OF 2, 6 OF ST ST6GAL1.
IT GOES UP ACROSS THE BOARD.
LIVER LEVELS ARE SO HIGH
ADDITIONAL INCREASE IS HARD TO
TELL BY HISTOCHEMISTRY.
WE SEE 2, 6 SIALYATION GOING UP
AND SOME EVIDENCE OF RELATIVE
LEVEL IN TRAN SCRIPT OMICS IN
WORKS PUBLISHED, RELATIVE LEVEL
TO C5 TURNS UP IN SARS-COV-2.
>> ANOTHER MINUTE, LARA.
>> I'M ON CONCLUSION, GOOD
TIMING.
>> WHAT WE OBSERVE IS INCREASES
IN 2, 6 SIALYATION ARE OBSERVED
WITH SEVERITY, ENRICHED IN THE
COMPLEMENT PATHWAY AND
COMPLEMENT OVERACTIVATION IS
EMERGING AS A CRITICAL FACTOR.
WE DON'T KNOW WHAT THE ROLE OF
THE 2, 6 SIALYATION IS IN
POTENTIALLY MODULATING THE
COMPLEMENT CASCADE, IT'S
POSSIBLE THAT THIS IS PARTLY A
MECHANISM TO INCREASE THE
LIFETIME IN SERA WHICH COULD
CONTRIBUTE TO OVERACTIVATION,
BUT THERE'S ALSO EMERGING
EVIDENCE THE GLYCOSYLATION OF
THESE COMPLEMENT PROTEINS
THEMSELVES MAY BE CHANGING OUR
ACTIVITIES IN WAYS THAT HAVE NOT
BEEN EXPLORED OR DEVELOPED.
AND SO WITH THAT I WANT TO
ACKNOWLEDGE THE LAB, ESPECIALLY
THOSE WHO DID THE DATA, WHO GOT
ALL OF THIS DATA, AND ARE
WRITING THE PAPER CURRENTLY WITH
ME.
THANK YOU.
>> FANTASTIC TALK.
THE LAST TALK BEFORE PANEL
DISCUSSION, ELISA FADDA FROM
MAYNOOTH UNIVERSITY,
COMPUTATIONAL GLYCOSCIENCE IN
THE COVID ERA.
LESSONS LEARNED AND A FEW
PERSPECTIVES.
>> CAN YOU HEAR ME?
>> YEAH.
>> GOOD MORNING, EVERYBODY, FROM
BRISBANE.
I'M SHARING MY SLIDES NOW.
IT'S VERY EXCITING TO BE HERE.
THANK YOU FOR THE INVITATION.
I'M PRETTY EXCITED MY TALK CAME
AFTER LARA BECAUSE SHE TOUCHED
ON SO MANY POINTS THAT HELPED ME
THROUGH MY OWN TALK.
ESPECIALLY IN HIGHLIGHTING HOW
OUR APPROACHES IS COMMON,
COMPLEMENTARY AND WE NEED EACH
OTHER TO ADVANCE GLYCOSCIENCE.
I'M GOING TO FOCUS ON
COMPUTATIONAL GLYCOSCIENCE, MY
FIELD OF RESEARCH, WHERE YOU USE
HIGH PERFORMANCE COMPUTING TO
RECONSTRUCT GLYCOSYLATION, SO
OFTEN MISSING, AND VERY OFTEN
NOT EASY TO RECONSTRUCT BECAUSE
OF LACK OF DATA.
DATA WE NEED IS MOSTLY LIKE FROM
GLYCOMICS, GLYCOPROTEOMICS TO
TELL US WHAT TYPE OF
GLYCOSYLATION IS THERE, WHERE IT
IS, WHAT IS THE RELATIVE
POPULATION.
WE TAKE THAT INFORMATION AND WE
REBUILD, COMMONLY REPRESENTED
WITHIN A CLASSICAL MECHANICS
FRAMEWORK THROUGH EMPIRICAL AND
STUDY THROUGH HIGH PERFORMANCE
COMPUTING, MOST COMMONLY THROUGH
MOLECULAR DYNAMICS APPROACHES OR
VARIATION ON THAT FORM TO OBTAIN
GLYCAN STRUCTURE INFORMATION.
WHICH THAT IS EXTREMELY
IMPORTANT TO STUDY MOLECULAR
RECOGNITION, TO UNDERSTAND THE
ROLE OF GLYCOSYLATION AND TODAY
I'M GOING TO FOCUS AGAIN AS LARA
MENTIONED ON SOMETHING THAT'S
CONSTANTLY STILL IN OUR MINDS,
SARS-COV-2.
AND TO DESIGN GO-TO THERAPEUTICS
APPROACHES FOR THESE KIND OF
DISEASES.
I CHOSE HEAT MAP INSTEAD OF
CLASSIC 3D STRUCTURE OF GLYCAN
TO REPRESENT THE STRUCTURAL
INFORMATION WE OBTAINED.
THIS IS NOT A CASE.
NOT AN ACCIDENT.
THIS IS WHAT WE GET.
AND THIS IS BECAUSE
CARBOHYDRATES, COMPLEX
CARBOHYDRATES, DO NOT OFTEN HAVE
WHAT WE PERCEIVE AS A STRUCTURE.
THEY ARE NOT -- "THEY" DON'T
HAVE AT LEAST ONE STRUCTURE BUT
OCCUPY A FAMILY OF STRUCTURES
THAT WE COMMONLY CALL IN THE
CONFIRMATION ENSEMBLES, LIKE THE
STRUCTURE OF GLYCANS.
THIS IS DUE TO ACTUALLY THE
CHEMICAL NATURE OF THE GLYCAN
STRUCTURE THAT IS LIKE THE
LINKAGES BEING A SINGLE BOND AND
THE STRUCTURE OF THE RING THAT
OFTEN DEPENDING ON WHAT MONO
SACCHARIDES WE TALK ABOUT, CAN
BE QUITE FLEXIBLE AS YOU CAN SEE
IN THIS CASE THROUGH THIS PATH
CONNECTING THE STABLE CHAIR TO
AN ALTERNATIVE CHAIR LESS
STABLE.
IN THIS PICTURE HERE
REPRESENTING A STRUCTURE, IN
PURPLE, OR DEEP RED, THE FAMILY
OF ASSOCIATED STRUCTURES IN
GRAY.
YOU CAN PROBABLY UNDERSTAND
WHERE THE ORIGIN OF THE
DIFFICULTIES OF CHARACTERIZING
CARBOHYDRATE STRUCTURE BY
EXPERIMENTAL MEANS.
IT JUST DOESN'T HAVE A
STRUCTURE.
SO, THE REASON WHY GLYCANS CAN
BE ASSOCIATED LIKE LOOSELY WITH
INTRINSIC DISORDER LIKE THE TERM
THAT'S MOSTLY USED FOR PROTEINS
HOWEVER I THINK IS VERY
APPROPRIATE IN CASE OF
CARBOHYDRATES THE FACT THEY ARE
LINKED BY A SINGLE BOND.
SINGLE BOND IS CHARACTERIZED BY
TORSIONS, NOT RANDOM, CANNOT
LIKE ADOPT ANY VALUE, I'LL
DISCUSS VERY EXTENSIVELY IN
GREAT REVIEW BY ROB WOODS
RECENTLY IN CHEMICAL REVIEWS
LIKE THESE TORSIONS THEY HAVE
VERY SPECIFIC MINIMUM.
TO MINIMIZE ASSOCIATED LIKE A
GIFT, LET'S SAY LIKE A
FLEXIBILITY, SEVERAL UP TO THREE
DEPENDING WHAT TORSION.
THIS IS A FLEXIBLE LINKAGE, 2, 6
SIALIC ACID.
IN TERMS OF DYNAMICS, I TELL YOU
IT'S PRECISE MINIMUM, IT IS
QUITE FLEXIBLE.
THE LINKAGE BETWEEN GAL AND
GlcNAc DOESN'T MOVE AS MUCH
BUT IT DOES.
THIS GETS MORE COMPLICATED AS
STRUCTURE OF THE GLYCAN ROADS,
FROM A TRISACCHARIDE, WITHIN THE
GLYCAN LET'S SAY CONTEXT, IT'S
NOT A PARTICULARLY DIFFICULT
GLYCAN TO DEAL WITH OR
PARTICULARLY SOPHISTICATED
GLYCAN, JUST VERY COLORFUL IN
TERMS OF AMOUNT OF MONO
SACCHARIDE AND HAS A COMPLEX
DYNAMIC.
THAT CHANGES, WE DISCOVERED
THROUGH M.D. BASED ON SEQUENCE
AND BRANCHING.
GLYCANS DYNAMICS DEPENDS ON
SEQUENCE AND BRANCHING.
REPRESENTED HERE A CLOSE
CONFIRMATION, THEY HAVE TWO
ARMS, 3 AND 6 ARM, 3 ARM IS
ALWAYS EXTENDED BECAUSE OF THE
RIGIDITY OF THE LINKAGE, THE
ALPHA 6 ARM IS IN AN EQUILIBRIUM
BETWEEN OPEN AND CLOSED FORM,
YOU CAN SEE HERE THE CLOSED FORM
BECOMES PREDOMINANT, THE SIZE OF
THE ARM INCREASES, THIS IS THE
ONE REPRESENTED ON THE
RIGHT-HAND SIDE.
HOWEVER, IF I ADD A XYLOSE,
PRESENT IN PLANTS, THE
EQUILIBRIUM OF THE ARM IS
COMPLETELY INVERTED, THE ARM IS
PREDOMINANTLY OPEN AND
ACCESSIBLE, LIKE THE REDUCTION.
SO THIS IS AN IMPORTANT FINDING
BECAUSE IT GOES DOWN TO
UNDERSTAND BIOSYNTHESIS OF THE
GLYCOFORMS, PREDOMINANTLY DUE TO
INTRAMOLECULAR INTERACTION
BETWEEN THE ARM AND REST OF THE
GLYCAN, THE MORE COMPLEX THE
GLYCAN THE HIGHER THE
POSSIBILITY THOSE INTERACTIONS
OCCUR.
WHAT ABOUT INTRAMOLECULAR
INTERACTIONS?
THIS IS WHAT HAPPENS IN
GLYCOPROTEINS.
THIS ENSEMBLE OR FAMILY OF
CONFIRMATION THAT A GLYCAN HAS,
DUE TO ITS OWN STRUCTURE, THAT
ARE OBTAINABLE THROUGH M.D.,
SHIFTED BY INTERACTIONS IN
PROTEINS.
THIS IS THE CASE, FOR EXAMPLE,
OF A CD16A WHERE THERE'S LIKE IN
THIS CASE A MAN 5 BOUND TO
POSITION N 45, WE FIND IN THIS
CASE MAN 5 IS LOCKED IN AN OPEN
CONFIRMATION, BECAUSE OF
INTERACTION OF THIS ARM WITH
PROTEIN SO THE ARM IS TRAPPED IN
THAT CLEFT HERE, STABILIZES OPEN
CONFIRMATION, THE REASON WHY IN
THIS PARTICULAR POSITION THERE'S
A PREDOMINANT OR VERY HIGH
INCIDENCE OF HYBRID N-GLYCANS
WHICH HAVE ONLY THREE ARMS THAT
IS FUNCTIONALIZED BECAUSE
ACTUALLY ONCE THE GLYCAN BECOMES
A MAN 3 IS NOT AVAILABLE,
AFFECTS BIOSYNTHESIS, VERY
IMPORTANT INFORMATION THAT YOU
CAN OBTAIN BY OBSERVES WHAT THIS
CONFIRMATION, SO THE FAMILIES OF
STRUCTURES THAT M.D. CAN PROVIDE
MEAN.
SO THEY LOOK LIKE, WHAT THEY
LOOK LIKE.
SO, WE USE THEM RECENTLY TO
REBUILD GLYCOPROTEINS USING THIS
STRUCTURE OBTAINED IN SOLUTION
AND DID IT TO REGLYCOSYLATE
ALPHA FOLD DATABASE WHICH
DOESN'T HAVEFULLY GLYCOSYLATION,
THIS IS PROOF OF PRINCIPLE
RECENTLY PUBLISHED WITH
UNIVERSITY OF YORK.
WHAT WE FOUND IS ALPHA FOLD IS
ABLE TO REPRODUCE GLYCOSYLATION
SITE WITH HIGH ACCURACY, WITHOUT
FLOWING ANYTHING ABOUT
GLYCOSYLATION BUT LEARNED
THROUGH ARTIFICIAL INTELLIGENCE
AND THIS IS THE AMAZING POWERS
OF ARTIFICIAL INTELLIGENCE THAT
LARA HIGHLIGHTED.
IN PURPLE, YOU SEE ENCLOSED
TRYPTOPHAN, IT IS THERE,
REPRODUCED VERY WELL BASED ON
THE LEARNED PDB STRUCTURE AND
STACKS LIKE THIS VERY HIGHLY
POPULATED MAN 5 CLOSED
CONFIRMATION.
BECAUSE OF THE USEFULNESS OF
THIS STRUCTURE, WHAT WE'RE DOING
AS TOOLS BUILDING A DATABASE
THAT FOR NOW IS STILL IN STAGE
BECAUSE WE JUST GOT FUNDED FOR
IT BUT WE HAVE ALL THE
STRUCTURES THAT WE ACTUALLY GAVE
TO DIFFERENT RESEARCH GROUPS,
ESPECIALLY DURING THE COVID
TIMES TO REGLYCOSYLATE PROTEINS
FROM THE PDB NON-GLYCOSYLATED OR
INCOMPLETELY GLYCOSYLATED, WHAT
WE'RE GOING DO IS IT PUT THEM UP
LIKE FOR PEOPLE TO USE AND WE
HAVE A GLYCOBUILDER IN THE
MAKING.
THE ADVANTAGE, THEY HAVE
REPRESENTATIVE -- THEY ARE
REPRESENTATIVE OF THE ENSEMBLE,
FOR MANY PEOPLE NOT AWARE OR
LET'S SAY EXPERIENCED WITH
GLYCANS THERE WILL BE NO
MISTAKING ISOMERS, LINKAGES,
DIFFERENT SPECIES WILL BE
PRE-BUILT IN APPROPRIATE
CONFIRMATION.
GLYCOBUILDER WILL MAKE IT EASIER
FOR YOU TO REBUILD LOADS OF
GLYCANS, WE'RE AWARE OF THAT SO
TRYING TO TROUBLESHOOT THIS
DENSELY GLYCOSYLATED PROTEIN
SUCH AS THE SPIKE OF SARS-COV-2.
SO, LIKE THE GLYCOBUILDER IS
PRETTY MUCH AN AUTOMATION OF
THIS STRUCTURE ALIGNMENT OF LIKE
LET'S SAY ONE OF THE EQUILIBRIUM
STRUCTURES ONTO THE EXISTING
GlcNAc OR IN THE ABSENCE OF
GlcNAc, AND THIS IS WHAT WE
DID TO REBUILD MANUALLY, AT
FIRST, OVER TWO YEARS AGO, TO
REBUILD THE GLYCOSYLATION OF THE
SARS-COV-2 WHICH AS I'M GOING TO
PRESENT HERE AS A CASE OF CASE
EXAMPLE FOR THIS WORK.
WE RECONSTRUCTED THE GLYCAN
SHIELD, YOU CAN SEE IN THE IMAGE
FROM THE STRUCTURES FROM THE
PAPER IN COLLABORATION PUBLISHED
IN THE ACS CENTRAL SCIENCE.
IT COVERS MOST OF THE STRUCTURE
OF THE SARS-COV-2 SPIKE WITH
SOME EXCEPTIONS, THERE ARE RBD
WHEN OPEN AS YOU CAN SEE IN PART
OF THE NTB, RECOGNIZEDDED BY
NEUTRALIZING ANTIBODIES.
SO HOW CAN COMPUTATIONAL
GLYCOSCIENCE HELP IN THIS CASE?
IT HELPS A LOT.
IN MANY AREAS.
SO, LARA MENTIONED THE GLYCANS
PLAY AN ESSENTIAL ROLE IN VIRAL
INFECTION SO THEY ARE HELPING
IMMUNOGENIC IMMUNE ESCAPE, WE
AND OTHERS HAVE DETERMINED HOW
MUCH OF THAT STRUCTURE OF THE
PROTEIN IS COVERED BY GLYCANS.
WE ALSO DISCOVERED THAT THE
GLYCAN SHIELD IS FUNCTIONAL,
LIKE THIS IS FOR THE FIRST TIME
IN A VIRAL PROTEIN, THERE IS NOT
STANDING BACK AND PROTECTING THE
STRUCTURE FROM IMMUNE CONDITION,
IT'S JUST A BIG WORD.
BUT IT'S ALSO PART OF THE
ARCHITECTURE OF THE SYSTEM,
INTEGRAL PART.
THIS POSITION OR THIS GLYCAN IN
THIS POSITION BEING FUNCTIONAL
SO THEY HELP THE MECHANISTIC
PROCESS OF OPENING AND
RECOGNITION OF THE SPIKE.
WITHOUT THEM THE SPIKE IS
USELESS, IT DOESN'T WORK.
ALSO BECAUSE OF WHAT I SAID,
BECAUSE OF THE PARTICULAR
ARCHITECTURAL GLYCANS, DEPENDS
ON SEQUENCING AND BRANCHING WE
DISCOVERED POSITIONS ARE EQUALLY
FUNCTIONAL SO THE PROGRESS OF
THE PARTICULAR GLYCOSYLATION OF
THOSE POSITION IS ACTUALLY
INSTRUMENTAL FOR PERFECT OR
OPTIMAL FUNCTIONINGOF THE SPIKE.
WHAT WE'RE LOOKING, ALSO WE'VE
LOOKED AT, LOOKING INTO, IS THE
EVOLUTION OF THE GLYCAN SHIELD,
WE TALK ABOUT -- WE ARE PLAGUED
BY THE GREEK LETTERS THAT FOCUS
PARTICULARLY ON PROTEIN
EVOLUTION.
SO HOW THAT REGULATES
INTERACTIONS WITH RECEPTORS.
HOWEVER, WHAT IS OFTEN OR ALWAYS
MISSING IS INFORMATION ABOUT HOW
GLYCAN SHIELD, SUCH A PART OF
THE SPIKE FUNCTION, IS EVOLVING.
YOU CAN SEE IN THE VIDEO MAGENTA
SPOTS ARE ALL THE VARIATION THAT
WE HAVE NOTED ALONG THE
PHYLOGENY, THE SHIELD IS
CHANGING A LOT.
SOMETHING WE'VE SEEN, IF YOU
REMEMBER COV 1 EPIDEMIC IN 2003,
A COUSIN OF THIS VIRUS, HAS LIKE
THE SPIKE OF THAT VIRUS LIKE
MUTATED NOW, LIKE ONE OF THE
GLYCOSYLATION SITES IS LOST.
THIS IS NOW IN THE POSITION 370,
SO THERE'S NOT GLYCOSYLATION IN
COV 2, IT'S ONE OF THE MOST
IMPORTANT SITES FOR THE
STABILITY OF THE OPEN SPIKE AND
WE THINK IT WAS REPLACING THAT
POSITION ORIGINALLY TO BE LOST
WHEN 2, 3, 4 CAME UP.
THE PRESENCE OF N 370 IS 0 IS IN
MOST CURRENTLY CIRCULATING, IN
THE PHYLOGENY, YOU CAN SEE IT IS
CONSERVED.
WHAT WE DISCOVERED IS THAT N370
IS ACTUALLY USEFUL TO KEEP THAT
RBD SHOWN HERE IN ORANGE OPEN
BUT IS NOT VERY USEFUL WHEN THIS
SPIKE IS CLOSED BECAUSE IT JUST
WRAPS AROUND LIKE AS YOU CAN SEE
HERE, THE CLOSED RBD AND
ACTUALLY COMING FROM AN IDEATION
TO RBD BECAUSE IT'S LINKED, IT
JUST LOCKS DOWN.
OCCUPYING A CLEFT ON THE RDB,
YOU CAN SEE VERY STABLE IN THIS
CASE THROUGH THE 1, 6 ARM OR 1,
3 ARM FLIES OFF ABOVE PRETTY
MUCH WHERE THE ACE-2 BINDS.
LIKE THE RECENT WORK ACTUALLY
THAT CAME LIKE OUT OF SAME TIME
AS OURS IN bioRxiv, CAME OUT
IN JANUARY, PROVED THE REMOVAL
OF N370 INCREASES REPLICATION,
SO LIKE IT'S HIGHLY INSTRUMENTAL
BY REMOVING THIS N370 INCREASED
VIRULENCE OF THE VIRUS.
SO GLYCAN SHIELD IS IMPORTANT,
GLYCAN SHIELD REGULATES
INFECTION, AND ONE GLYCAN
MATTERS.
ALSO THIS GLYCAN IS QUITE
INTERESTING BECAUSE THE CLEFT IT
LEAVES IS EVOLUTIONARILY
DESIGNED TO BIND TO GLYCANS.
WE'RE SEEING A PAPER THAT LARA
MENTIONED, SHE WAS INVOLVED IN
IT, THAT THE RBD BINDS A WHOLE
LOT OF DIFFERENT GLYCANS WITH
DIFFERENT AFFINITIES, VERY
SPECIFICALLY.
GLYCANS WITH SPECIFIC FEATURES
ARE BOUND, DIFFERENT FEATURE,
DIFFERENT AFFINITY.
THE WORK HAS SHOWN BINDING TO
RBD WITH THE SAME AFFINITY AS
GAGS, AS YOU CAN SEE HERE, LOWER
AFFINITY, THEY ARE MUTUALLY
EXCLUSIVE.
ALSO WITH LOWER AFFINITY TYPE 1A
AND TYPE 1H, AND ALSO LIKE AN
N-GLYCAN.
WHICH IS NOT SURPRISING
CONSIDERING THAT IT IS MISSING,
LIKELY SOMETHING HAD REPLACED
THAT IN ITS ABSENCE.
THIS GIVERS MASSIVE ADVANTAGE TO
THE VIRUS BY BINDING ALL THE
GLYCANS EXPRESSED ON CELL
SURFACE, LIKE FOR LOCALIZATION,
THAT'S FOR SURE.
HOW DOES THAT -- HOW TO COMBINE,
VERY DIFFICULT QUESTION, WE
CAN'T ANSWER.
YOU SEE CLEFT HERE BOUND TO THE
370 FROM CLOSE BY, TURNS AROUND,
YOU CAN SEE A LOVELY POCKET,
DESIGNED FOR BINDING CORE FUCOSE
AND LEFT IS ACTUALLY VERY
EXTENDED TO BIND THE TWO ARMS.
THIS IS WORK THAT SHOWED POLYMER
CAN BIND THAT CLEFT IN PART,
BECAUSE -- THAT CLEFT CAN BE
BOUND OR CAN BE -- CAN HELP IN
THE BINDING OF SULFATED GAG LIKE
SHOWN BY ROMY'S WORK, AND WE
FOUND IT BINDS TO TM 1, IN THIS
CASE SIALIC ACID IN THIS POCKET
WITH DIFFERENT BINDING SITE OF
NEUTRAL GLYCANS, THAT'S THE
LEFT, ACROSS THIS POCKET OF THE
SIALIC ACID, OCCUPIES THE END OF
THE 1, 6 ARM OF THE N370 GLYCAN
THAT WE CAN DESIALITE.
THEY GINNED -- THEY BIND WITH
THE SAME AFFINITY, THIS IS LIKE
A CORROBORATING EVIDENCE THIS IS
GOOD BINDING SITE FOR IT.
IN COMPETITIVE BINDINGS WE CAN
SEE IN COLLABORATION, WITH
BINDING SITE IN AGREEMENT WITH
DATA FROM ROMY'S GROUP AND OTHER
PEOPLE THAT STUDIED HEPARIN
BINDING.
THEY ARE MUTUALLY EXCLUSIVE AND
THIS IS WHAT YOU CAN GET FROM
DIFFERENT APPROACHES LIKE
THROUGH DOCKING AND EXTENSIVE
MOLECULAR DYNAMICS.
THIS IS NEUTRAL.
THAT OCCUPIES THE TOP OF THE
LEFT, NOT THE SAME POSITION AS
THE GLYCAN, SIALIC ACID, THE
BOTTOM OF THE CLEFT BUT HERE AT
THE TOP.
THE VARIATION OR STRUCTURAL
COMPLEXITY, THE ARCHITECTURE OF
THE CLEFT WAS DESIGNED TO
ACCOMMODATE A DIVERSE IN TERMS
OF TYPE OF MONO SACCHARIDES, TO
BIND DIFFERENT GLYCANS IN
DIFFERENT POSITIONS, THAT'S OF
THAT CAN -- THAT'S THE REASON
WHY WE PROBABLY SEE LOTS OF
GLYCAN BINDING THE SPIKE IN THIS
POSITION OF DIFFERENT AFFINITY.
SORRY IF I'M A BIT LATE.
I'D LIKE TO THANK THE PEOPLE
THAT MADE THIS WORK POSSIBLE SO
MY STUDENTS IN MY GROUP, AMERO'S
GROUP, INSTRUMENTAL FOR INSIGHT
IN THIS FIELD, ONGOING WORK OF
THE EVOLUTION OF GLYCAN SHIELD
AND COLLEAGUES FROM THE BINDING
ON DIFFERENT GLYCANS THROUGH THE
RBD, INSTITUTE FOR GLYCOMICS,
HOSTING ME FOR MY SABBATICAL,
AND THANK YOU FOR YOUR
ATTENTION.
THANK YOU VERY MUCH.
>> THANK YOU.
VERY NICE TALKS.
NOW WE'LL MOVE TO OUR PANEL
DISCUSSION AND SPEAKERS, PLEASE
TURN ON YOUR CAMERAS AND UNMUTE
AND WE CAN MOVE TO SOME
QUESTIONS.
I HAVE THE FIRST QUESTION FROM
LANCE TO RON, DO YOU KNOW WHAT
IS THE REDUCING END OF THE
STRUCTURE WITH REGARDS TO WHERE
IT CAME FROM, WAS IT POSSIBLE TO
GENERATE SHEDDING GIVEN THE
BONDS IN THE UNDERLYING M 3
STRUCTURE, THIS LEADS BACK TO
WHAT THE ACTUAL --
>> YEAH, EXCELLENT QUESTIONS.
I DON'T HAVE A GOOD ANSWER TO
PROVIDE.
WE DON'T HAVE GOOD DENSITY.
IT'S CLEAR THE MATRIGLYCAN PIECE
IS LONGER THAN THE ONE WE
ACTUALLY MODELED BECAUSE THE
DENSITY PROJECTING, SO WE DON'T
SEE THE END.
THE SOURCE, IT COULD BE RELEASED
BY SOME KIND OF
PHOSPHODIESTERASE, BUT ANOTHER
POSSIBILITY ONE OF THE LARGE
ENZYMES, LARGE 1 OR LARGE 2,
SEEM TO SYNTHESIZE UNBOUND OR
UNFREE MATRIGLYCAN BECAUSE WE
CAN DO THAT IN THE TEST TUBE AND
THE PRECURSORS ARE AVAILABLE IN
THE CELLS SO MAYBE A GENERATION
OF MATTERY GLYCANS.
>> THAT'S PART OF THE
HYPOTHESIS, BINDS IN THE POCKET,
FREE GLYCANS, THAT WOULD MAKE
SENSE.
MOVING ON, I HAVE A QUESTION
DIRECTED TO LEVA.
HAVE YOU OBSERVED COMPENSATORY
RESPONSE IN YOUR KNOCKOUTS,
DERIVED FROM THE VIRAL
GLYCOPROTEINS, ALSO HAVE YOU
PERFORMED ANY O GLYCAN RELEASE
TO FORTIFY YOUR LIBRARY IN CELL
SYSTEMS?
>> FIRST QUESTION, WE'RE NOT
ACTUALLY LOOKING AT N-GLYCANS ON
PURIFIED PROTEINS BUT WOULD NOT
EXPECT CHANGES IN GLYCOSYLATED
POSITION, THE ONLY DIFFERENCE
COULD BE?
SITE SPECIFIC STRUCTURES, LEVEL
OF PROCESSING AND MATURITY.
>> THAT WAS MY QUESTION, THE
REASON I ASKED THAT IS BECAUSE
WORKING WITH A NUMBER OF
DIFFERENT PROTEASE AND NEMATODES
WE NOTICED OTHER GLYCOSYLATION
PROCESSES CAN BE UPREGULATED IN
COMPENSATION.
>> OKAY.
>> IT WAS INTERESTING.
I THOUGHT IF YOU HAD NOTICED
ANYTHING IT MIGHT BE
INTERESTING.
SECOND PART, O GLYCAN RELEASE.
>> WE HAVEN'T DONE RELEASED
ANALYSIS ON PROTEINS THEMSELVES
BUT HAVE COMPREHENSIVE KNOWLEDGE
OF CELL LINE WE USED, SO A
COLLEAGUE FROM THE LAB DEVELOPED
THIS VERY ROBUST PROFILING
METHOD, SHE USED FOR THESE
CELLS, PUBLISHED IN ANALYTICAL
CHEMISTRY RECENTLY.
AND WE PRINCIPALLY SEE 60 TO 70%
O-GLYCANS, UP TO 10% FOR TWO.
>> I WAS WONDERING ABOUT THE
INSECT CELL LINES YOU LOOKED AT.
>> YES, THAT WAS FOR THE
SARS-COV-2.
SO THAT'S NS2, WE EXPECT
STRUCTURES FOR SINGLE GALNIC BUT
HAVEN'T DONE THE ANALYSIS.
>> I HAVE A QUESTION.
DO YOU THINK THE VIRUSES ARE
ACTUALLY REGULATING THE
EXPRESSION OF GLYCOSYLATION
MACHINERY IN THE HOST CELLS?
IS THERE ANY TRANSCRIPTOMIC
DATASETS, DATA BASES THAT YOU
COULD USE TO VALIDATE THAT?
>> YEAH, THERE ARE.
WE KNOW SOME ARE.
WE KNOW, FOR EXAMPLE, THAT VIRUS
UPREGULATES T3, ENHANCES
GLYCOSYLATION AND COULD BE THER
ON THE -- OTHER WAY AROUND BUT
OTHER VIRUS REGULATES
TRANSFERASES, YES, IF THERE ARE
SOME PUBLIC LIP AVAILABLE DATA
OF INFECTED VERSUS NON-INFECTED
MATERIALS COULD FISH OUT
SIGNATURES.
>> A STUPIDER QUESTION MEASURE,
DO ANY VIRUSES CARRY THEIR OWN
ENCODED GLYCOCELE TRANSFERASE OR
IS IT HOST?
>> FOR HUMAN VIRAL PROBABLY
ENTIRE HOST.
GIANT VIRUSES THAT INFECT LIKE
THESE ALGAE AND OTHER SMALL
MICROORGANISMS THAT THEY ENCODE
A MASTERY OF THEIR OWN AND BUILD
ALL KIND OF WEIRD N-GLYCANS.
>> COOL.
>> ONE MORE QUESTION FOR RON.
THE SPIKE PROTEIN DOES IT LOOK
LIKE LAMININ DOMAIN, ARE THESE
COMPLETELY DIVERGENT EVOLUTION?
>> YEAH, COMPLETELY DIFFERENT.
WE DON'T SEE ANY SIMILARITY.
THERE ARE COMMON FEATURES LIKE
POLAR INTERACTIONS BUT THIS IS
VERY GENERAL.
>> THANKS.
>> I HAVE A QUESTION THAT MAY BE
DIRECTED TO BOTH LARA AND ALSO
ELISA, THE SIALYATION WORK,
DIFFERENT MODES REALLY, BUT
WONDERING MERS WAS REPORTED TO
BIND AT THE N INTERPRET
N-TERMINUS.
IF YOU LOOKED AT THE N-TERMINUS
AND SOME STUFF IN THE RECEPTOR
BINDING DOMAIN SOME REGIONS ARE
INTERESTING BUT WONDERING IF
THERE WAS ANYTHING ON THE
N-TERMINAL SIDE AND LARA IF
THERE'S ANYTHING YOU MIGHT
COMMENT ON AS FAR AS POSSIBILITY
THAT SARS-COV-2 SPIKE IS BINDING
SOME OF THESE SIALIC ACIDS.
>> I THINK IT IS TRUE, I WASN'T
SURPRISED WHEN IT BOUND BECAUSE
CORONAVIRUS HAVE A HISTORY OF
BINDING.
WHEN IT WAS REVEALED, I WASN'T
SHOCKED BUT I'LL ALISA TALK
ABOUT WHERE.
>> YEAH, THE NTD BINDS IN MERS,
BINDS SIALIC ACID.
I THINK ONE OF THE FIRST THINGS
WE LOOKED AT IN 2020,
IMMEDIATELY, OH, GALECTIN
DOMAIN, IS IT CONSERVED FOR NTB
WHICH IS MOSTLY, HOWEVER THE
LOOPS THAT ARE RESPONSIBLE FOR
THE SIALIC ACID BINDING IN MERS
ARE COMPLETELY CHANGED IN
SARS-COV-2 SPIKE AND ACTUALLY
WHAT IS LIKE INTERESTING IS THE
SARS-COV-2 GROUP 2 GLYCANS, NOT
ONE, TO COVER THAT SIDE.
THE PROTEIN CHANGES WERE NOT
ENOUGH TO DISCOURAGE BINDING
SIALIC ACID, TWO GLYCANS ON TOP,
SO IT'S VERY BUSY, LET'S SAY,
AREA.
SO IT'S VERY UNLIKELY THAT
SARS-COV-2 WOULD BIND SIALIC
ACID IN THE NTV, HOWEVER THERE'S
EVIDENCE THAT IT BINDS SIALIC
ACID.
WHERE IS STILL UP TO THE NTD, UP
TO LET'S SAY THE DETERMINATION,
I HAVEN'T SEEN ANYTHING
PARTICULARLY CONVINCING IN THAT
REGARD.
IT'S VERY DIFFICULT TO DETERMINE
TO BE HONEST BECAUSE ALL THOSE
PARTS THAT MIGHT BE INVOLVED IN
BINDING ARE EXTREMELY
DISORDERED.
SO THEY ARE ACTUALLY NEVER
RESOLVED, RARELY RESOLVED IN
CryoEM STRUCTURES.
WHAT JOHN ACTUALLY SHOWED, THE
RBD THAT BINDS SIALIC ACID IN A
CONTEXT OF A VERY SPECIFIC
SEQUENCE WHICH IS LIKE, FOR
EXAMPLE, GIVEN BY GM 1 AND 2,
DOESN'T BIND POLY SIALIC ACID.
IT BINDS SPECIFICALLY.
WITHIN LIKE THE RBD IN THIS
CASE.
AGAIN, THE STRUCTURE IS
DISORDERED.
A BINDING SITE WE THINK IS
PROMISING BECAUSE OF ALL LET'S
SAY DIFFERENT LIKE DATA FROM
OTHER RESOURCES, BUT LIKE THE
NAIL IN THE COFFIN WOULD BE A
STRUCTURE THAT PROBABLY --
SOMETHING WE'VE BEEN CHASING
DOWN.
WE STILL HAVE CHASING TO DO.
HOPE THAT ANSWERS YOUR QUESTION.
>> MAYBE THIS IS A STRETCH,
GAMMA CORONAVIRUS BIND SIALIC
RECEPTOR RECEPTOR, WE AND OTHERS
HAVE LOOKED AT RBD, THEY ARE NOT
REALLY -- STRUCTURAL RELATED BUT
NOT SO MUCH AT THE AMINO ACID
ACID LEVEL, BUT WE HAVE SEEN
BINDING IN GALECTIN, I WAS
WONDERING IF THERE'S ANY
CORRELATION BETWEEN THAT AND
WHAT YOU MAY BE SEEING AT THE
RBD IN SARS-COV-2.
>> I'M HERE.
YEAH, LIKE THE GAMMA SPIKE IS
QUITE SIMILAR.
I WOULD SAY ONE OF THE MAIN
DIFFERENCES ACTUALLY IS
N-GLYCOSYLATION, ADDITIONAL SITE
WE'RE WORKING ON AT THIS POINT.
SO LIKE THERE IS EVIDENCE THAT
IT BINDS SIALIC ACID, JUST THE
POINT IS LIKE WHERE.
IT'S POTENTIALLY ON THE VERY
DISORDERED FOLD AT THE TOP OF
THE VERY BEGINNING OF THE
N-TERMINUS BUT BECAUSE IT'S SO
DISORDERED, IT'S VERY DIFFICULT
TO DETERMINE HOW IT BINDS, WHERE
IT BINDS.
IT DOESN'T BIND VERY TIGHTLY
BECAUSE OTHERWISE WE WOULD HAVE
A STRUCTURE AT THIS POINT.
SO I THINK IT'S JUST LIKE WE
DON'T HAVE ANY -- WE WEREN'T
SUCCESSFUL AT FINDING A BINDING
SITE ON THE NTD, WE HAVEN'T
REALLY FOCUSED ON THAT, TO BE
HONEST.
BUT YEAH, OTHER PEOPLE DID AND
NOTHING PARTICULARLY CONVINCING
CAME OUT.
>> SORRY, I WANTED TO ADDRESS
BOTH -- THE OTHER PART OF THE
QUESTION, HOW MIGHT THAT BE
RELATED TO THE FACT THAT WE'RE
SEEING SHIFTS.
IT GOES BACK TO SOMETHING THAT
YOU SAID, MIKE.
WE DON'T KNOW HOW -- WHAT THE
RELATIONSHIP IS BETWEEN THE
CHANGING SIALYATION OF PROTEINS
AND WHAT WE'RE SEEING ON THE
LUNG.
BUT I WOULD SAY THAT I THINK
IT'S INTERESTING THAT
CORONAVIRUSES HAVE A TENDENCY TO
BIND SIALIC ACID IN PARTICULAR,
COMING UP AS CHANGING UPON
INFECTION BY CORONAVIRUSES.
BY SARS-COV-2.
AND SO I DON'T KNOW IF THERE'S A
RELATIONSHIP THERE BUT THERE IS
EVIDENCE THAT VIRUSES DO CHANGE
GLYCOSYLATION PATTERNS OF
INFECTION, AND ALSO CAN
STIMULATE RESPONSES THAT ARE
BENEFICIAL TO THEMSELVES.
I WOULDN'T BE SURPRISED IF
THERE'S SOME SORT OF CORRELATION
BETWEEN THE FACT THAT WE'RE
SEEING SIALIC ACID IN PARTICULAR
COME UP AND THE FACT THAT THESE
IN GENERAL ARE BINDERS.
IT COULD ALSO PARTLY BE BECAUSE
IT HAS A HUGE ROLE IN
SUPPRESSION OF IMMUNITY, AND
THAT COULD ALSO BE PART AS WELL.
>> OR THE VIRUSES ARE TAKING
ADVANTAGE OF THE SHIFT.
>> EXACTLY.
>> I WAS INTRIGUED BY THE NEW
JERSEY VARIANT, A LOT OF STRANGE
THINGS COME OUT OF NEW JERSEY, I
WENT TO HIGH SCHOOL IN NEW
JERSEY.
THAT'S OKAY.
BUT THE SHIFT FROM RECOGNIZING
ASYLATED MOTIFS SEEMS LIKE A BIG
SHIFT FROM CHARGE TO UNCHARGED.
ARE THERE EXAMPLES OF THAT
REALLY SIGNIFICANT SHIFT IN
RECOGNITION?
>> THIS IS VERY NOBLE FINDING, I
THINK THE STRUCTURE STUDIES
STILL ONGOING, I'M NOT QUITE
SURE IF I CAN DISCUSS TOO MUCH
DETAIL AT THE MOMENT BUT THIS IS
NOT KNOWN BEFORE FOR OTHER
POLEYOMA VIRUSES, FOR THE ONCE
WE ANALYZED.
THIS IS THE FIRST TIME.
SO WE'RE OBSERVING BY THIS GROUP
OF VIRUS.
>> FARMING ARRAY DATA IN THE
FUTURE IS GOING TO BE OUR WAY TO
UNDERSTAND THAT.
THAT'S WHERE WE'RE GOING TO SEE
IT FIRST.
THERE'S ONE OTHER SIALIC RELATED
QUESTION, THAT MIGHT BE A GOOD
ONE.
>> I BELIEVE THAT WAS TO ME.
SO, NANCY, IT'S AN EXCELLENT
QUESTION, NOBODY KNOWS.
>> REPEAT THE QUESTION.
>> THE QUESTION, IS THERE ANY
CORRELATION BETWEEN DISEASE
SEVER SEVERITY OF
NEUROAMINIDASE.
WE SEE NEURAMINIDASE EXPRESSION
IN THE SERA, WE DON'T KNOW.
HONESTLY AT THE MOMENT IT'S NOT
SOMETHING PEOPLE HAVE LOOKED
FOR.
>> GREAT.
WE'VE REACHED ALMOST 15 MINUTES
PAST OUR ORIGINAL STOP TIME SO
--
>> A QUICK QUESTION.
THE SLIDE THAT YOU SHOWED, I WAS
TRYING TO UNDERSTAND, I HAVEN'T
READ THE PAPER, BUT IT LOOKS
LIKE YOU CAN USE ALPHA 4 DATA TO
PREDICT WHICH SITES ARE
GLYCOSYLATED BASED ON THE
FORMATION OF THE MODEL.
AND IF THAT IS TRUE, IS THAT
TRUE? THAT'S WHAT I'M TRYING TO
UNDERSTAND.
>> PREDICT IS A BIG WORD.
OUR ARGUMENT WAS SITE IS
ACTUALLY -- THE SITE'S
ARCHITECTURE IS PRESERVED, ALPHA
FOLD MODEL REPRODUCES
ARCHITECTURAL FEATURES SUCH AS
STACKING RESIDUES, HYDROPHOBIC
WITHOUT KNOWING A THING ABOUT
GLYCOSYLATION BECAUSE IT DOESN'T
DO MODIFICATION AS WELL AS
REPRODUCES VERY WELL CAVITIES
THAT SHOULD CONTAIN CO-FACTORS,
LIPIDS, THE EXAMPLE WE CHOSE
CONTAINS A MASSIVE LIPID IN THE
PDB BUT NOT THE ALPHA FOLD
MODEL, THE POCKET IS PERFECTLY
REPRODUCED.
IT LEARNED FEATURES THAT FROM
THE PDB BUT IT IS ENRICHED BY
LIKE THE BIOLOGY THAT GOES
AROUND THE STRUCTURE AND DOESN'T
KNOW THAT, IT LEARNS THINGS THAT
ARE DUE TO DIFFERENT REASONS BUT
TO DIFFERENT -- HASN'T REALLY
PROCESSED WHY.
THAT'S MACHINE LEARNING PROBLEM
IN A WAY, IN THE SENSE THAT IT
DERIVES INFORMATION WITHOUT
LET'S SAY NEEDING TO KNOW WHY
THAT INFORMATION.
THE POINT WAS ALPHA 4 MODELS ARE
USEFUL IF YOU WANT TO USE THEM
TO REBUILD COMPLETELY FULL MODEL
OF PROTEIN BECAUSE THEY DO
REPRODUCE THE STRUCTURAL
FEATURES OF STABLE GLYCOSYLATION
SITES IF THAT ANSWERS YOUR
QUESTION.
>> THAT DOES.
VERY GOOD.
THANK YOU.
>> THANK YOU.
A QUICK NOTE ABOUT WHAT MIKE WAS
SAYING, QUESTION, THIS FEATURE
OF IF I UNDERSTAND CORRECTLY OF
THE BINDING EPITOPES, IF YOU
THINK ABOUT THE LOSS OF
GLYCOSYLATION SITE ON RBD, AS
DISCUSSED EARLIER, COMPLEX
N-GLYCAN, THOSE ARMS CAN BE SIGH
AL LATED OR NOT, BOUND BY
INTERACTIONS, THE WHOLE ARM IS
TIGHTLY BOUND, IT DOESN'T HINGE
ON THE SIALIC ACID.
IT HINGES ON THE OLD SEQUENCE
THAT HOLDS THAT SIALIC ACID SO
THE FACT SIGH ALEX -- SIALIC
ACID IS THERE OR NOT, THE CLEFT
IS SO DEEP AND LINED WITH
HYDROPHOBIC RESIDUES THAT FORM
INTERACTION WITH GALACTOSE,
GLUCOSE, MONO SACCHARIDES ARE
BOUND.
IT CAN BE BOUND ANYWAY ANYWAY,
DUE TO EVOLUTION, LOSS OF
GLYCOSYLATION HAS GIVEN THIS
VIRUS THE ABILITY OF BINDENING,
VARIETY OF GLYCANS.
AS LONG AS THEY REPRODUCE BITS
OF THAT N-GLYCAN, THEY CAN BE
BOUND.
THAT'S NOT SUPER SURPRISING.
THAT'S PROBABLY MY READING INTO
IT.
ONE REASON WHY THIS VIRUS DOES
THIS FEATURE.
>> AN INFORMATIC QUESTION,
FRONTIER OF INFORMATICS, WE'RE
IGNORING THE FACT THAT
GLYCOPROTEIN IS A MIX OF THINGS.
ARE THERE TOOLS TO SKIM THROUGH
TO SEE IF SOME ARE BETTER, TO
SPIN THE WHEEL AND SEE THE
GLYCAN STRUCTURES?
>> HOW THE GLYCANS CAN CHANGE
THE STRUCTURE, THAT'S I THINK A
MATTER, VERY EXTENSIVE, A GREAT
QUESTION AND, YEAH, SO IF I CAN
ANTICIPATE WITH THAT TYPE OF
GLYCAN AT 370 MATTERS FOR THE
STRUCTURE OF THE RBD.
WHICH IS GREAT BECAUSE IT'S JUST
WHAT WE ALL EXPECT, THE GLYCAN
MODULATES THE STRUCTURE OF A
PROTEIN.
THAT WOULD BE LET'S SAY LIKE
REALLY GOOD NEWS TO TELL THE
PEOPLE THAT ONLY DO PROTEINS.
YOU NEED TO HAVE THEM TO KEEP
YOUR STRUCTURE PRESERVED.
THAT'S MY TWO CENTS.
>> THANKS.
DO YOU WANT TO CLOSE US UP?
>> YEAH, WELL, IT WAS REALLY
GREAT.
THANK YOU TO ALL THE SPEAKERS.
IT WAS ENLIGHTENING.
AND I THINK WE PROBABLY HAVE
MORE QUESTIONS THAN WHEN WE
STARTED, WHICH IS GREAT BECAUSE
ALL THIS DATA IS REALLY GIVING
US MORE TO DRIVE ON.
SO WITH THAT I WANT TO THANK THE
ORGANIZERS FOR INVOLVING ME WITH
ALL THESE GREAT SPEAKERS, RAJA
AND MIKE, YOU PUT TOGETHER A
GREAT DAY HERE.
WITH THAT, TRYING TO HOLD BACK
MY OWN QUESTIONS, THANK YOU,
EVERYONE.
WE'LL SEE SOME OF YOU TOMORROW
FOR THE NEXT DAY SESSION.
>> THANKS, EVERYBODY.