1 00:00:09,049 --> 00:00:11,251 WELCOME TO THE CLINICAL CENTER GRAND ROUNDS, 2 00:00:11,251 --> 00:00:15,055 A WEEKLY SERIES OF EDUCATIONAL LECTURES FOR PHYSICIANS AND 3 00:00:15,055 --> 00:00:17,691 HEALTH CARE PROFESSIONALS BROADCAST FROM THE CLINICAL 4 00:00:17,691 --> 00:00:20,661 CENTER AT THE NATIONAL INSTITUTES OF HEALTH IN 5 00:00:20,661 --> 00:00:22,463 BETHESDA, MD. 6 00:00:22,463 --> 00:00:25,999 THE NIH CLINICAL CENTER IS THE WORLD'S LARGEST HOSPITAL TOTALLY 7 00:00:25,999 --> 00:00:29,703 DEDICATED TO INVESTIGATIONAL RESEARCH AND LEADS THE GLOBAL 8 00:00:29,703 --> 00:00:32,639 EFFORT IN TRAINING TODAY'S INVESTIGATORS AND DISCOVERING 9 00:00:32,639 --> 00:00:34,808 TOMORROW'S CURES. 10 00:00:34,808 --> 00:00:44,008 LEARN MORE BY VISITING US ONLINE AT HTTP://CLINICALCENTER.NIH.GOV 11 00:00:44,008 --> 00:00:47,345 GOOD AFTERNOON. 12 00:00:47,345 --> 00:00:50,281 IT'S MY GREAT PLEASURE TO 13 00:00:50,281 --> 00:00:54,152 WELCOME YOU TO THE GRAND ROUNDS. 14 00:00:54,152 --> 00:00:57,288 THE CODE IS 15 00:00:57,288 --> 00:01:04,128 PLEASE--WE KINDLY INVITE YOU TO 16 00:01:04,128 --> 00:01:05,263 PROVIDE FEEDBACK BY SCANNING 17 00:01:05,263 --> 00:01:07,365 THE QR CODE. 18 00:01:07,365 --> 00:01:09,467 FOR THOSE APPLYING FOR CME, YOU 19 00:01:09,467 --> 00:01:10,468 WILL RECEIVE A SURVEY LINK VIA 20 00:01:10,468 --> 00:01:11,836 EMAIL. 21 00:01:11,836 --> 00:01:14,138 THE SURVEY WILL BE USED TO 22 00:01:14,138 --> 00:01:15,206 PROVIDE US WITH IMPORTANT 23 00:01:15,206 --> 00:01:15,973 FEEDBACK ABOUT THIS 24 00:01:15,973 --> 00:01:18,075 PRESENTATION AND ALLOWS YOU TO 25 00:01:18,075 --> 00:01:20,378 MAKE ANY SUGGESTIONS FOR FUTURE 26 00:01:20,378 --> 00:01:22,513 GRAND RUINSED TOPICS. 27 00:01:22,513 --> 00:01:23,414 FOLLOWING THE PRESENTATION, 28 00:01:23,414 --> 00:01:25,716 QUESTIONS WILL BE TAKEN FROM 29 00:01:25,716 --> 00:01:28,452 THE MICROPHONE FROM THE AISLES. 30 00:01:28,452 --> 00:01:31,656 IN ADDITION, YOU MAY SUBMIT 31 00:01:31,656 --> 00:01:34,292 QUESTIONS BY PSYCHOLOGY DOWN 32 00:01:34,292 --> 00:01:36,060 AND CLICKING THE LIVE FEEDBACK. 33 00:01:36,060 --> 00:01:38,362 QUESTIONS WILL BE ANSWERED AS 34 00:01:38,362 --> 00:01:39,330 TIME PERMITS AT THE CONCLUSION 35 00:01:39,330 --> 00:01:42,366 OF THE PRESENTATION. 36 00:01:42,366 --> 00:01:52,910 OUR SPEAKER TODAY IS DR. NATAN 37 00:01:53,578 --> 00:01:56,380 BASISTY, AT NIA AND NIH 38 00:01:56,380 --> 00:02:00,885 DISTINGUISHED SKOL LAR. 39 00:02:00,885 --> 00:02:02,286 DR. BASISTY, COMPLETED HIS Ph.d 40 00:02:02,286 --> 00:02:04,121 AT THE UNIVERSITY OF WASHINGTON 41 00:02:04,121 --> 00:02:05,756 6789 FOLLOWING HIS GRAD YOU'D 42 00:02:05,756 --> 00:02:09,694 WORK, HE JOINED THE BUCK 43 00:02:09,694 --> 00:02:12,463 INSTITUTE OF AGING AS A POST 44 00:02:12,463 --> 00:02:15,233 DOKE TORE AL LEVEL TO UNLDSING 45 00:02:15,233 --> 00:02:16,567 AGING PROCESSES AND AGE RELATED 46 00:02:16,567 --> 00:02:22,173 DISEASES. 47 00:02:22,173 --> 00:02:24,075 IN 2021, HE JOINED NIA AS A 48 00:02:24,075 --> 00:02:31,749 INVESTIGATOR. 49 00:02:31,749 --> 00:02:36,087 HIS LABORATORY NIA, OR THE NIU, 50 00:02:36,087 --> 00:02:39,790 IN THE LEVELS TO CLINICAL BIO 51 00:02:39,790 --> 00:02:42,026 MARKERS AND THERAPY PICK FOR 52 00:02:42,026 --> 00:02:44,862 PATHOLOGIST OF AGING. 53 00:02:44,862 --> 00:02:47,765 DEVELOPS AND APPLIES ADVANCE 54 00:02:47,765 --> 00:02:58,009 AND LARGE SCALE GEROPROTEOMIC 55 00:02:58,009 --> 00:03:02,280 TO AGING SUCH AS TYPE 2 56 00:03:02,280 --> 00:03:04,682 DIABETES AND MARKERS. 57 00:03:04,682 --> 00:03:08,586 ULTER ITIONATIONS OF PROTEINS. 58 00:03:08,586 --> 00:03:11,856 DR. BASISTY'S WORK HAS HAD OVER 59 00:03:11,856 --> 00:03:14,425 20 PUBLICATION SXZ SHARED WORK 60 00:03:14,425 --> 00:03:15,493 IN NATIONAL AND INTERNATIONAL 61 00:03:15,493 --> 00:03:25,903 PRESENTATIONS ON AGING. 62 00:03:28,873 --> 00:03:30,241 HIS RECOGNIZATIONS AND K99 63 00:03:30,241 --> 00:03:33,411 PATHWAY TO INDEPENDENCE AWARD. 64 00:03:33,411 --> 00:03:38,449 DR. BASISTY IS A PASSION AL 65 00:03:38,449 --> 00:03:42,019 EDUCATOR AND AWARDED MENTOR 66 00:03:42,019 --> 00:03:47,124 AWARD BACCALAUREATE. 67 00:03:47,124 --> 00:03:49,460 RECOGNIZED TRACK INVESTIGATORS 68 00:03:49,460 --> 00:03:50,594 WHO HAVE DEMONSTRATED DIVERSITY 69 00:03:50,594 --> 00:03:52,863 AND INCLUSION. 70 00:03:52,863 --> 00:03:54,498 TODAY, DR. BASISTY WILL BE 71 00:03:54,498 --> 00:04:01,939 PRESENTING ON FORGING A PATH TO 72 00:04:01,939 --> 00:04:05,443 TRANSLATIONAL GEROPROTEOMIC. 73 00:04:05,443 --> 00:04:09,213 NOW JOIN ME IN WELCOMING OUR 74 00:04:09,213 --> 00:04:13,017 SPEAKER NATAN BASISTY. 75 00:04:13,017 --> 00:04:15,986 >> THANK YOU FOR THE 76 00:04:15,986 --> 00:04:20,624 INTRODUCTION AND THANK YOU TO 77 00:04:20,624 --> 00:04:22,226 BATITO FOR GETTING ME SAFELY. 78 00:04:22,226 --> 00:04:25,229 I'M GOING TO BE PRESENTING ALL 79 00:04:25,229 --> 00:04:27,732 UNPUBLISHED WORK FROM THE LAST 80 00:04:27,732 --> 00:04:29,867 TWO YEARS SINCE I STARTED IN 81 00:04:29,867 --> 00:04:31,635 NIA. 82 00:04:31,635 --> 00:04:35,106 IF ANYTHING I HOPE YOU CAN WALK 83 00:04:35,106 --> 00:04:36,974 AWAY FROM THIS TALK, A 84 00:04:36,974 --> 00:04:40,411 PREACHING THAT THE BIOLOGY OF 85 00:04:40,411 --> 00:04:42,012 AGING, TARGETING A BIOLOGY OF 86 00:04:42,012 --> 00:04:44,548 AGING IS A WAY TO TARGET 87 00:04:44,548 --> 00:04:49,587 DISEASES THAT IS WORTH SPENDING 88 00:04:49,587 --> 00:04:53,324 OUR RESOURCES AND SECONDLY THAT 89 00:04:53,324 --> 00:04:56,460 PROTEOMICS HAS TO OFFER. 90 00:04:56,460 --> 00:04:58,729 OKAY, SO I HAVE NO DISCLOSURES 91 00:04:58,729 --> 00:05:00,564 ACCEPT THAT MAYBE I'LL SAY THAT 92 00:05:00,564 --> 00:05:03,768 I'M NOT A CLINICIAN I'M A BASIC 93 00:05:03,768 --> 00:05:05,603 RESEARCH SCIENTIST AND SOMEBODY 94 00:05:05,603 --> 00:05:07,705 THAT DOES MASS NNDS BECK. 95 00:05:07,705 --> 00:05:10,241 BUT A LOT OF THE STUFF THAT I'M 96 00:05:10,241 --> 00:05:13,677 SHOWING IS GOING TO BE 97 00:05:13,677 --> 00:05:16,414 CLINICALLY ORIENTED SO I DO 98 00:05:16,414 --> 00:05:18,916 INVITE FEEDBACK FROM THOSE WHO 99 00:05:18,916 --> 00:05:21,118 DO FOR OF THIS WORK IN WHAT I 100 00:05:21,118 --> 00:05:25,289 PRESENT TODAY. 101 00:05:25,289 --> 00:05:26,657 SO LEARNING OBJECTIVES, TODAY I 102 00:05:26,657 --> 00:05:28,926 HOPE THAT YOU CAN UNDERSTAND 103 00:05:28,926 --> 00:05:33,931 THE POTENTIAL HEALTH IMPACT OF 104 00:05:33,931 --> 00:05:38,602 UTILIZING THE GEROSCIENCE 105 00:05:38,602 --> 00:05:40,171 APPROACH. 106 00:05:40,171 --> 00:05:44,275 AND I THINK THIS APPLIES TO 107 00:05:44,275 --> 00:05:48,179 USING BIOMARKERS FOR MY SORT OF 108 00:05:48,179 --> 00:05:50,047 PATHOLOGY RIGHT AND THE 109 00:05:50,047 --> 00:05:50,681 CHALLENGES TO THESE BIOMARKERS 110 00:05:50,681 --> 00:05:54,852 SPECIFICALLY. 111 00:05:54,852 --> 00:06:00,357 SO YOU KNOW, THE REASON THAT WE 112 00:06:00,357 --> 00:06:02,660 FOCUS ON AGING, IS BECAUSE 113 00:06:02,660 --> 00:06:03,494 AGING IS THE GREATEST RISK 114 00:06:03,494 --> 00:06:05,196 FACTORS. 115 00:06:05,196 --> 00:06:08,933 SO AS WE AGE, THE PREVALENCE 116 00:06:08,933 --> 00:06:10,801 AND DEATH FROM MULTIPLE 117 00:06:10,801 --> 00:06:12,603 DISEASES GOES UP AND ABOUT 118 00:06:12,603 --> 00:06:21,011 MIDDLE AGE ON WARDS IT STARTS 119 00:06:21,011 --> 00:06:24,181 TO GO EXPONENTIALLY ESPECIALLY 120 00:06:24,181 --> 00:06:26,217 ALZHEIMER DISEASE. 121 00:06:26,217 --> 00:06:27,618 NOT ONLY WILL WE DIE FROM ONE 122 00:06:27,618 --> 00:06:29,553 OF THESE BUT WE'RE GOING TO DIE 123 00:06:29,553 --> 00:06:31,422 OF MULTIPLE OF THESE. 124 00:06:31,422 --> 00:06:36,126 SO IF YOU DIE OF HEART DISEASE, 125 00:06:36,126 --> 00:06:37,862 YOU'LL HAVE PSYCHO PAOENIA, 126 00:06:37,862 --> 00:06:40,231 LOSS OF MUSCLE AND THERE IS 127 00:06:40,231 --> 00:06:42,433 ALSO A ISSUE OF MORE BIDITY. 128 00:06:42,433 --> 00:06:47,071 AND WE DON'T THINK THIS IS A 129 00:06:47,071 --> 00:06:50,040 COINCIDENCE. 130 00:06:50,040 --> 00:06:51,008 SOMETHING ABOUT AGING ENABLES, 131 00:06:51,008 --> 00:06:52,610 CREATES AN ENVIRONMENT THAT IS 132 00:06:52,610 --> 00:06:56,080 PER MISSABLE TO THE DEVELOPMENT 133 00:06:56,080 --> 00:06:56,614 AND PROGRESSION OF THESE 134 00:06:56,614 --> 00:07:01,519 DISEASES. 135 00:07:01,519 --> 00:07:03,787 AND THUS FAR WE'VE IDENTIFIED 136 00:07:03,787 --> 00:07:05,389 WHAT WE CALL BASIC AGING 137 00:07:05,389 --> 00:07:07,958 PROCESSES THAT ARE DRIVERS OF 138 00:07:07,958 --> 00:07:09,360 MULTIPLE AGED RELATED DISEASES 139 00:07:09,360 --> 00:07:12,429 AND THESE ARE TWO THAT MY 140 00:07:12,429 --> 00:07:15,866 LABORATORY FOCUSES ON. 141 00:07:15,866 --> 00:07:23,874 OUR NUMBER THAT WE NOW KNOW FOR 142 00:07:23,874 --> 00:07:25,476 EXAMPLE, MIO CARDIO 143 00:07:25,476 --> 00:07:27,278 DYSFUNCTION, DNA DAMAGE, 144 00:07:27,278 --> 00:07:29,847 CHANGES THAT ARE THESE BASIC 145 00:07:29,847 --> 00:07:30,848 UNDERLINED PROCESS THAT'S DRIVE 146 00:07:30,848 --> 00:07:34,618 THE DEVELOPMENT OF DISEASES. 147 00:07:34,618 --> 00:07:37,388 AND THIS IS ACTUALLY GOOD NEWS, 148 00:07:37,388 --> 00:07:38,756 RIGHT, BECAUSE IF IT'S TRUE 149 00:07:38,756 --> 00:07:41,292 WHICH I THINK WE HAVE STRONG 150 00:07:41,292 --> 00:07:43,360 EVIDENCE THAT THIS IS TRUE THEN 151 00:07:43,360 --> 00:07:46,096 WE CAN TARGET THESE AGING 152 00:07:46,096 --> 00:07:52,469 PROCESSES AND MITIGATE MULTIPLE 153 00:07:52,469 --> 00:07:54,972 RELATED DISEASES SIMULTANEOUSLY. 154 00:07:54,972 --> 00:07:58,642 THIS WOULD BE A HUGE BENEFIT, 155 00:07:58,642 --> 00:08:00,277 WE SPEND, CURRENTLY WE SPEND A 156 00:08:00,277 --> 00:08:01,512 LOT OF HEALTHCARE AND RESEARCH 157 00:08:01,512 --> 00:08:04,915 COST TO TRY TO CURE ALL OF 158 00:08:04,915 --> 00:08:06,550 THESE INDIVIDUAL DISEASES, VERY 159 00:08:06,550 --> 00:08:07,585 COMPLEX DISEASES ONE AT A TIME 160 00:08:07,585 --> 00:08:09,119 AND WE SPEND A LOT OF 161 00:08:09,119 --> 00:08:10,120 HEALTHCARE AT THE END OF LIFE, 162 00:08:10,120 --> 00:08:11,689 RIGHT. 163 00:08:11,689 --> 00:08:16,660 BUT, IF WE TAKE USE THE 164 00:08:16,660 --> 00:08:19,430 GEROSCIENCE APPROACH, WE ARE 165 00:08:19,430 --> 00:08:22,533 TARGET ONE PROCESS AND MITIGATE 166 00:08:22,533 --> 00:08:24,668 MULTI AGE RELATED DISEASES. 167 00:08:24,668 --> 00:08:26,303 AND TO ADD TO THIS, WE'VE SHOWN 168 00:08:26,303 --> 00:08:28,772 THAT THIS IS THE CASE, AT LEAST 169 00:08:28,772 --> 00:08:30,941 IN PRECLINICAL MODELS. 170 00:08:30,941 --> 00:08:32,576 THE FIELD HAS IDENTIFIED A 171 00:08:32,576 --> 00:08:35,746 NUMBER OF AGING AND SOME OF THE 172 00:08:35,746 --> 00:08:36,647 MOST ROBUST ON THE BOTTOM RIGHT 173 00:08:36,647 --> 00:08:39,883 OF THIS SLIDE. 174 00:08:39,883 --> 00:08:42,419 FOR EXAMPLE, DIETARY 175 00:08:42,419 --> 00:08:45,889 RESTRICTIONS, TREATMENT WITH 176 00:08:45,889 --> 00:08:47,725 DRUGS THAT TARGET SENESIS CELLS 177 00:08:47,725 --> 00:08:50,928 AND ALL OF THESE HAVE BEEN 178 00:08:50,928 --> 00:08:52,630 SHOWN TO INCREASE LIFE SPAN. 179 00:08:52,630 --> 00:08:54,798 THERE IS AT LEAST SOME EVIDENCE 180 00:08:54,798 --> 00:08:57,434 THAT THIS IS TRUE IN PEOPLE, 181 00:08:57,434 --> 00:09:00,337 BUT GOING FORWARD, IT WOULD BE 182 00:09:00,337 --> 00:09:01,672 REALLY NICE TO APPLY THIS 183 00:09:01,672 --> 00:09:02,906 APPROACH IN PEOPLE. 184 00:09:02,906 --> 00:09:08,145 SO MY LAB IS CALLED THE 185 00:09:08,145 --> 00:09:09,513 TRANSLATIONAL GEROPROTEOMIC, SO 186 00:09:09,513 --> 00:09:11,882 WE'RE LOCATED IN THE NIA WHICH 187 00:09:11,882 --> 00:09:13,283 IS BASED IN BALTIMORE. 188 00:09:13,283 --> 00:09:14,051 FROM THE NAME, YOU CAN GUESS 189 00:09:14,051 --> 00:09:16,887 WHAT WE DO. 190 00:09:16,887 --> 00:09:19,189 WE DO GEROPROTEOMIC, SO WE 191 00:09:19,189 --> 00:09:21,025 APPLY TO THE STUDY OF 192 00:09:21,025 --> 00:09:23,060 GEROSCIENCE AND I THINK IT CAN 193 00:09:23,060 --> 00:09:28,932 OFFER A LOT TO THIS FIELD. 194 00:09:28,932 --> 00:09:32,603 WE CAN IDENTIFY THERAPEUTIC 195 00:09:32,603 --> 00:09:34,438 TARGETS BUT ALSO BIO MARKERS 196 00:09:34,438 --> 00:09:37,441 WHERE WE CAN MEASURE THE 197 00:09:37,441 --> 00:09:38,676 ETHICACY OF INTERVENTIONS IN 198 00:09:38,676 --> 00:09:40,678 THE CONTEXT OF AGING. 199 00:09:40,678 --> 00:09:42,513 MY LAB HAS THREE PRIMARY 200 00:09:42,513 --> 00:09:43,681 PROGRAMS, THE FIRST ONE AND THE 201 00:09:43,681 --> 00:09:49,319 ONE THAT I'M GOING TO FOCUS 202 00:09:49,319 --> 00:09:50,854 TODAY IS SINSES BIOMARKERS. 203 00:09:50,854 --> 00:09:53,057 WE HAVE STUDIES BOTH IN HUMANS 204 00:09:53,057 --> 00:09:57,227 AND IN MICE HERE. 205 00:09:57,227 --> 00:09:59,496 BUT, WE ALSO EMPLOY OTHER 206 00:09:59,496 --> 00:10:03,133 TECHNIQUES TO IDENTIFY THE 207 00:10:03,133 --> 00:10:07,371 THERAPEUTIC TARGETS. 208 00:10:07,371 --> 00:10:11,041 WHAT WE USE PROTEINS THAT ARE 209 00:10:11,041 --> 00:10:15,813 BOUND TO DRUGSMENT SO WE CAN 210 00:10:15,813 --> 00:10:19,650 IDENTIFY MECHANISMS AND NEW 211 00:10:19,650 --> 00:10:22,152 THERAPEUTIC TARGETS AND BIO TO 212 00:10:22,152 --> 00:10:23,287 IDENTIFY SELF TYPE OF INTEREST 213 00:10:23,287 --> 00:10:29,293 SO IF YOU HAVE A CANCER SELL OR 214 00:10:29,293 --> 00:10:32,796 A ZENNE SIS CELL THESE CAN BE 215 00:10:32,796 --> 00:10:34,131 TARGETED. 216 00:10:34,131 --> 00:10:36,433 THE NICE THING IS THAT WE DO A 217 00:10:36,433 --> 00:10:38,368 LOT OF STUDIES TO IDENTIFY 218 00:10:38,368 --> 00:10:40,104 CANDIDATE TARGETS. 219 00:10:40,104 --> 00:10:42,406 IN THE NIA, WE HAVE THESE 220 00:10:42,406 --> 00:10:44,041 FLAGSHIP STUDIES THAT WE CAN 221 00:10:44,041 --> 00:10:45,909 ALSO USE TO THEN CONFIRM AND 222 00:10:45,909 --> 00:10:52,282 VALIDATE THE THINGS THAT WE 223 00:10:52,282 --> 00:10:55,886 FIND IN WITH PROTEOMICS. 224 00:10:55,886 --> 00:11:02,659 WE ALSO LONGITUDINAL AND THE 225 00:11:02,659 --> 00:11:04,762 STUDY OF LONGITUDINAL IN MICE. 226 00:11:04,762 --> 00:11:06,663 WE'LL TALK ABOUT THAT LATER. 227 00:11:06,663 --> 00:11:08,165 I'M NOT GOING TO TALK ABOUT 228 00:11:08,165 --> 00:11:10,901 THIS UNTIL I HAVE TIME AT THE 229 00:11:10,901 --> 00:11:11,401 END, THIS IS ALSO VERY 230 00:11:11,401 --> 00:11:13,537 INTERESTING. 231 00:11:13,537 --> 00:11:16,273 MOST PEOPLE MEASURE PROTEIN AND 232 00:11:16,273 --> 00:11:17,841 HOW THEY CHANGE IN DIFFERENT 233 00:11:17,841 --> 00:11:20,377 CONDITIONS. 234 00:11:20,377 --> 00:11:22,212 AS IT TURNS OUT, PROTEIN 235 00:11:22,212 --> 00:11:25,449 TURNOVER CHANGE A LOT DURING 236 00:11:25,449 --> 00:11:26,049 CHANGING SO HOW WE REGULATE 237 00:11:26,049 --> 00:11:33,690 CHANGES. 238 00:11:33,690 --> 00:11:35,759 AND WE CAN UTILIZE MASSTOMTTRY 239 00:11:35,759 --> 00:11:38,262 TO MEASURE TURNOVER RATES IN 240 00:11:38,262 --> 00:11:39,229 HUMAN AND MOUSE STUDIES. 241 00:11:39,229 --> 00:11:43,600 SXL AS IT TURNS OUT, THIS HAS A 242 00:11:43,600 --> 00:11:45,736 REALLY STRONG, YOU KNOW, 243 00:11:45,736 --> 00:11:47,271 CONNECTION WITH LONGEVITY. 244 00:11:47,271 --> 00:11:49,339 SO IF THERE IS TIME, LATER I'LL 245 00:11:49,339 --> 00:11:51,642 TALK ABOUT IT. 246 00:11:51,642 --> 00:11:52,509 BUT OUR LAB MEASURES TURNOVER 247 00:11:52,509 --> 00:11:56,580 RATES. 248 00:11:56,580 --> 00:12:04,788 SO I THINK IN GENERAL MASS INTO 249 00:12:04,788 --> 00:12:05,923 CLINICAL STUDIES. 250 00:12:05,923 --> 00:12:09,626 AND WHAT I'M SHOWING NOW IS 251 00:12:09,626 --> 00:12:12,396 ESSENTIALLY, THE PIPELINE AT MY 252 00:12:12,396 --> 00:12:19,670 LAB IS USING TO IDENTIFY 253 00:12:19,670 --> 00:12:22,673 SENOSES MARKERS. 254 00:12:22,673 --> 00:12:26,343 WE CAN DEVELOP CANDIDATES, 255 00:12:26,343 --> 00:12:27,277 CANDIDATES BIO MARKERS FROM 256 00:12:27,277 --> 00:12:29,146 CELL CULTURE STUDIES. 257 00:12:29,146 --> 00:12:30,948 WE CAN THEN VALIDATE THESE AND 258 00:12:30,948 --> 00:12:37,020 WE CAN THEN TEST THESE IN REAL 259 00:12:37,020 --> 00:12:38,455 HUMAN OR MOUSE TO VALIDATE IF 260 00:12:38,455 --> 00:12:40,023 THEY'RE IMPORTANT. 261 00:12:40,023 --> 00:12:46,864 FOR EXAMPLE, IF WE'RE LOOKING 262 00:12:46,864 --> 00:12:48,232 AT SENESSES TO VALIDATE THAT 263 00:12:48,232 --> 00:12:51,201 THESE ARE CHANGING. 264 00:12:51,201 --> 00:12:53,871 IN ALL STAGES OF THESE 265 00:12:53,871 --> 00:12:56,607 PIPELINE, IT CAN ACCELERATE THE 266 00:12:56,607 --> 00:12:57,140 ABILITY TO DISCOVER THESE 267 00:12:57,140 --> 00:12:57,774 BIOMARKERS. 268 00:12:57,774 --> 00:13:01,645 AND HERE I WANT TO SHOW 269 00:13:01,645 --> 00:13:02,579 DIFFERENT SAMPLES OF TECHNIQUES 270 00:13:02,579 --> 00:13:03,814 THAT WE CAN USE IN TARGET 271 00:13:03,814 --> 00:13:06,950 DISCOVERY. 272 00:13:06,950 --> 00:13:08,118 ON THE BIOMARKER DISCOVERY, AND 273 00:13:08,118 --> 00:13:10,287 THERE IS A LOT OF WORK FLOW. 274 00:13:10,287 --> 00:13:13,257 I WANT TO GET THE POINT THAT 275 00:13:13,257 --> 00:13:16,426 THAT IT'S A VERSIFY. 276 00:13:16,426 --> 00:13:19,162 SO FOR BIOMARKER DISCOVERY WE 277 00:13:19,162 --> 00:13:20,330 CAN COUPLE THESE PREPARATION 278 00:13:20,330 --> 00:13:22,532 WHERE WE'RE SUPPOSE TO IMPROVE 279 00:13:22,532 --> 00:13:33,043 THE COMPREHENSIVENESS AND THE 280 00:13:33,043 --> 00:13:35,846 DEPTH OF OUR PROTEOMICSES. 281 00:13:35,846 --> 00:13:37,447 MOST DON'T PAY ATTENTION TO THE 282 00:13:37,447 --> 00:13:40,484 FORM OF THE PROTEIN BUT THERE 283 00:13:40,484 --> 00:13:41,451 ARE SOME MASS METHODS, THAT 284 00:13:41,451 --> 00:13:43,420 WILL LET US DO THAT AND THESE 285 00:13:43,420 --> 00:13:48,425 CAN HELP US IDENTIFY MORE 286 00:13:48,425 --> 00:13:48,926 SPECIFIC AND SENSITIVE 287 00:13:48,926 --> 00:13:49,226 BIOMARKERS. 288 00:13:49,226 --> 00:13:52,963 AND THEN WE HAVE A NUMBER OF 289 00:13:52,963 --> 00:13:54,131 APPROACHES THAT WILL HELP US 290 00:13:54,131 --> 00:13:55,299 IDENTIFY THE TARGETS OF DRUGS. 291 00:13:55,299 --> 00:13:58,368 SO WE CAN USE THESE TO SCREEN 292 00:13:58,368 --> 00:14:00,203 FOR POTENTIAL TARGETS AND ALL 293 00:14:00,203 --> 00:14:02,773 OF THIS IS FURTHER BENEFITED BY 294 00:14:02,773 --> 00:14:03,473 IMPROVEMENTS IN THE 295 00:14:03,473 --> 00:14:05,042 TECHNOLOGIES THEMSELVES AND THE 296 00:14:05,042 --> 00:14:06,777 ANALYTICAL APPROACHES. 297 00:14:06,777 --> 00:14:10,747 SO WE REALLY HAVE, THERE IS A 298 00:14:10,747 --> 00:14:14,685 LOT OF THAT PROTEOMIC CAN OFFER 299 00:14:14,685 --> 00:14:18,922 TO THE BIOMARKERS AND TARGETS. 300 00:14:18,922 --> 00:14:21,692 SO ONE OF THE, BASIC AGING 301 00:14:21,692 --> 00:14:27,297 PROCESSES THAT WE'RE REALLY 302 00:14:27,297 --> 00:14:29,900 FOCUS IS CALLED CELLULARSENIS. 303 00:14:29,900 --> 00:14:31,034 IT'S A BASIC AGING PROCESS AND 304 00:14:31,034 --> 00:14:33,136 IT'S A STRESS RESPONSE. 305 00:14:33,136 --> 00:14:35,872 IF YOU IMAGINE HEALTHY CELL AND 306 00:14:35,872 --> 00:14:36,807 IT ENCOUNTERED STRESS, THERE 307 00:14:36,807 --> 00:14:39,810 ARE ONLY A FEW THINGS THAT CAN 308 00:14:39,810 --> 00:14:41,578 DO, IF IT'S NOT STRONG ENOUGH 309 00:14:41,578 --> 00:14:43,680 STRESS, IT WILL CONTINUE TO 310 00:14:43,680 --> 00:14:45,082 DIVIDE, IF IT'S TOO STRONG IT 311 00:14:45,082 --> 00:14:47,150 WILL DIE, BUT IF IT'S VERY 312 00:14:47,150 --> 00:14:48,852 STRONG BUT NOT ENOUGH TO KILL 313 00:14:48,852 --> 00:14:53,423 THE CELL, THE CELL CAN ENTER 314 00:14:53,423 --> 00:14:55,692 THIS ALTERNATE STATE, CALLED 315 00:14:55,692 --> 00:14:59,596 CELLULAR ZENSES. 316 00:14:59,596 --> 00:15:01,665 SO THE WILL STOP STOP DIVIDING 317 00:15:01,665 --> 00:15:06,970 AND THEN SECRETE ALL OF THESE 318 00:15:06,970 --> 00:15:11,408 PROTEINS, OR SASS, JUST 319 00:15:11,408 --> 00:15:16,980 REMEMBER SAS IS THE CREATE. 320 00:15:16,980 --> 00:15:19,916 AND IT IT WILL CREATE IN ALL 321 00:15:19,916 --> 00:15:30,293 THE ISSUE' AS WE AGE. 322 00:15:38,368 --> 00:15:41,271 AND DURING AGING, THE CELLS CAN 323 00:15:41,271 --> 00:15:43,106 GET A LOT OF STRESS. 324 00:15:43,106 --> 00:15:48,078 THERE ARE THINGS FOR THE MOST 325 00:15:48,078 --> 00:15:52,482 PART, GENO TOXIC STRESS THINGS 326 00:15:52,482 --> 00:15:54,084 LIKE RADIATION, BUT ALSO 327 00:15:54,084 --> 00:15:55,018 INDUCKTION AND CERTAIN 328 00:15:55,018 --> 00:15:55,652 THERAPIES THAT PATIENTS ALREADY 329 00:15:55,652 --> 00:16:01,191 TAKE. 330 00:16:01,191 --> 00:16:06,163 SO THE CANCER CHEMOTHERAPY OR 331 00:16:06,163 --> 00:16:09,366 EVEN H.I.V. MEDICINES CAN, SO 332 00:16:09,366 --> 00:16:11,668 UNDERSTANDING THEIR BIOLOGY IS 333 00:16:11,668 --> 00:16:12,202 IMPORTANT BUT CLINICALLY 334 00:16:12,202 --> 00:16:15,572 RELEVANT FOR PATIENTS THAT ARE 335 00:16:15,572 --> 00:16:24,047 ON THESE THERAPIES AND IT'S 336 00:16:24,047 --> 00:16:30,620 SHOWING THAT SENESCENCE CAN BE. 337 00:16:30,620 --> 00:16:35,192 IF YOU SELECTIVELY ELIMINATE 338 00:16:35,192 --> 00:16:40,330 SENESENSE SELLS, YOU CAN 339 00:16:40,330 --> 00:16:48,839 PROMODE LONGEVITY IN MICE. 340 00:16:48,839 --> 00:16:50,841 IN ORDER TO LEVERAGE THIS IN 341 00:16:50,841 --> 00:16:54,077 HUMANS, WE'RE GOING TO NEED 342 00:16:54,077 --> 00:16:55,245 DRUGS, DRUGS THAT EITHER 343 00:16:55,245 --> 00:17:01,918 ELIMINATE THE CELLS OR ALTER 344 00:17:01,918 --> 00:17:12,329 THEIR, ONCE WE HAVE A 345 00:17:25,609 --> 00:17:26,343 SENESCENCE, IT WILL HELP US 346 00:17:26,343 --> 00:17:28,078 IDENTIFY THOSE AS WELL. 347 00:17:28,078 --> 00:17:30,380 MY LAB, WELL ACTUALLY BEFORE I 348 00:17:30,380 --> 00:17:31,781 NAME TO THE NIA ONE OF THE 349 00:17:31,781 --> 00:17:36,353 THINGS THAT I FOCUSED ON IS 350 00:17:36,353 --> 00:17:40,657 IDENTIFYING THE THESE 351 00:17:40,657 --> 00:17:42,526 BIOMARKERS USING PROTEOMIC. 352 00:17:42,526 --> 00:17:47,764 SO THIS WAS IN 2020 AND WE USED 353 00:17:47,764 --> 00:17:51,301 THE PROTEOMICS TO SEE THE CELLS. 354 00:17:51,301 --> 00:17:53,103 IN MULTIPLE CONDITIONS AND WE 355 00:17:53,103 --> 00:17:54,371 ASKED WHETHER THESE WERE 356 00:17:54,371 --> 00:17:55,906 ASSOCIATED WITH AGING HUMANS. 357 00:17:55,906 --> 00:17:58,475 SO WHAT WE SHOWED IS THERE IS A 358 00:17:58,475 --> 00:18:00,977 COURSE THAT ARE IN CIRCULATION 359 00:18:00,977 --> 00:18:01,912 AND THEY'RE STRONGLY ASSOCIATED 360 00:18:01,912 --> 00:18:03,280 WITH AGING. 361 00:18:03,280 --> 00:18:04,681 AND WHILE, IN THE DEVELOPMENT 362 00:18:04,681 --> 00:18:06,816 OF THIS STUDY, WE ALSO PUT 363 00:18:06,816 --> 00:18:10,253 TOGETHER A DATABASE WHICH WE 364 00:18:10,253 --> 00:18:13,456 CALL SASP AND WE HOPE THIS WILL 365 00:18:13,456 --> 00:18:16,626 HELP ENABLE RESEARCHERS IF YOU 366 00:18:16,626 --> 00:18:19,462 HAVE A PROTEIN THAT YOU'RE 367 00:18:19,462 --> 00:18:21,598 INTERESTED, YOU CAN QUAER' THIS 368 00:18:21,598 --> 00:18:24,634 TO SEE IF CELLS ARE SECRETING 369 00:18:24,634 --> 00:18:26,503 THE THING THAT YOU'RE STUDIES. 370 00:18:26,503 --> 00:18:28,138 HOPEFULLY THIS IS A RESOURCE 371 00:18:28,138 --> 00:18:30,340 THAT THE COMMUNITY CAN UTILIZE. 372 00:18:30,340 --> 00:18:35,011 BUT WE ALSO WANT TO KNOW, 373 00:18:35,011 --> 00:18:35,912 PRODICT AGE RELATED OUTCOME 374 00:18:35,912 --> 00:18:36,446 BECAUSE THAT'S WHAT WE'RE 375 00:18:36,446 --> 00:18:41,184 INTERESTED IN. 376 00:18:41,184 --> 00:18:43,486 ALSO PREDICTORS IN MORTALITY 377 00:18:43,486 --> 00:18:45,989 AND MORBIDITY AND THIS STUDY 378 00:18:45,989 --> 00:18:48,024 WAS DONE IN THE STUDY BASED IN 379 00:18:48,024 --> 00:18:53,129 ITALY. 380 00:18:53,129 --> 00:18:55,665 BUT, SINCE COMING TO NIA, A LOT 381 00:18:55,665 --> 00:18:56,800 OF THESE STUDIES WERE REALLY 382 00:18:56,800 --> 00:18:58,935 IMPORTANT AND WHAT WE NEED TO 383 00:18:58,935 --> 00:19:01,938 DO IS FURTHER VALIDATE THESE 384 00:19:01,938 --> 00:19:02,472 SPECIFIC PATHOLOGISTS AND 385 00:19:02,472 --> 00:19:03,740 DIFFERENT COHORTS. 386 00:19:03,740 --> 00:19:06,076 BUT SINCE COMING TO THE NIA, A 387 00:19:06,076 --> 00:19:11,648 LOT OF THESE SIGNATURES WERE 388 00:19:11,648 --> 00:19:13,016 BUILT IN FIBERGLASS AND 389 00:19:13,016 --> 00:19:15,285 EPTHLIAL CELLS, BUT IN RESENT 390 00:19:15,285 --> 00:19:17,787 YEARS IT'S BEEN SHOWN THAT I 391 00:19:17,787 --> 00:19:21,224 NAOUN SENSE IS A IMPORTANT FORM 392 00:19:21,224 --> 00:19:23,026 OF SENESCENCE, THERE IS BEEN 393 00:19:23,026 --> 00:19:24,628 STUDIES IN MICE THAT SCENE 394 00:19:24,628 --> 00:19:29,499 SENSE CAN ACT AS ANY SENESCENCE 395 00:19:29,499 --> 00:19:32,836 CELL THEY SECRETE THE SASP AND 396 00:19:32,836 --> 00:19:35,438 DRIVE IN INFLAMMATION. 397 00:19:35,438 --> 00:19:40,443 BUT IT CAN--SO IT'S SORT OF UP 398 00:19:40,443 --> 00:19:41,011 STREAM DRIVER OF SENESCENCE 399 00:19:41,011 --> 00:19:42,712 SYSTEMATICALLY. 400 00:19:42,712 --> 00:19:44,347 SO SINCE COMING HERE, YOU KNOW, 401 00:19:44,347 --> 00:19:47,150 ONE OF THE THINGS THAT I WAS 402 00:19:47,150 --> 00:19:50,387 REALLY INTERESTED IN DOING IS 403 00:19:50,387 --> 00:19:52,188 IDENTIFYING BIOMARKERS FORESEEN 404 00:19:52,188 --> 00:19:53,790 SENSE IN IMMUNE SYSTEM AND THE 405 00:19:53,790 --> 00:19:57,193 REASON FOR THIS IS BECAUSE 406 00:19:57,193 --> 00:20:05,035 THEY'RE UP STREAM, BECAUSE 407 00:20:05,035 --> 00:20:06,536 BECAUSE MONOSENCE ARE IMPORTANT 408 00:20:06,536 --> 00:20:07,270 TO THE BLOOD. 409 00:20:07,270 --> 00:20:08,972 THEY'LL BE EASILY SORTED IF WE 410 00:20:08,972 --> 00:20:11,541 WANT TO LOOK AT THE SCENE SENSE 411 00:20:11,541 --> 00:20:13,109 CELLS IN ISOLATION. 412 00:20:13,109 --> 00:20:23,653 SXL THERE IS SOME EVIDENCE THAT 413 00:20:29,459 --> 00:20:31,127 SENESCENCE IS IN MONOSITE. 414 00:20:31,127 --> 00:20:41,638 WE HAD TO DEVELOP A MODEL OF 415 00:20:42,305 --> 00:20:45,642 SENESCENCE USING--YOU'RE ABLE 416 00:20:45,642 --> 00:20:50,013 TO PRODUCE SENESCENCE USING, 417 00:20:50,013 --> 00:20:53,249 AND INDUCTION AND CONFIRM THESE 418 00:20:53,249 --> 00:20:58,288 IN KNOWN MARKERS. 419 00:20:58,288 --> 00:21:03,293 WE THEN APPLIED PROTEOMICS TO 420 00:21:03,293 --> 00:21:05,929 ASSOCIATE PROTEINS COMING FROM 421 00:21:05,929 --> 00:21:10,333 MONO CYTES. 422 00:21:10,333 --> 00:21:12,402 AND THIS BEEN DONE, BY 423 00:21:12,402 --> 00:21:12,702 RESEARCHERS. 424 00:21:12,702 --> 00:21:14,504 WHAT THEY WERE ABLE TO SHOW 425 00:21:14,504 --> 00:21:19,809 THAT THESE WHITES IN THE MONO 426 00:21:19,809 --> 00:21:24,147 CYTES AND ABOUT 160 OF THESE 427 00:21:24,147 --> 00:21:24,714 PROTEINS ARE INCREASED WITH 428 00:21:24,714 --> 00:21:25,515 SENESCENCE. 429 00:21:25,515 --> 00:21:28,284 AND ONE THING I WANT TO POINT 430 00:21:28,284 --> 00:21:32,889 OUT, IT'S PRETTY CLEAR JUST 431 00:21:32,889 --> 00:21:33,523 LOOKING, THAT INTERFERON 432 00:21:33,523 --> 00:21:34,524 RESPONSES IS ONE OF THE MAINL 433 00:21:34,524 --> 00:21:35,225 OR RESPONSES. 434 00:21:35,225 --> 00:21:39,896 SO ONE OF THE BIGGEST AND MOST 435 00:21:39,896 --> 00:21:41,398 SIGNIFICANT IS INTERFERON AND 436 00:21:41,398 --> 00:21:43,032 THIS IS IMPORTANT FOR THE 437 00:21:43,032 --> 00:21:44,434 BIOLOGY OF THE CELLS AND IT 438 00:21:44,434 --> 00:21:46,069 COULD BE A BIO MARKER. 439 00:21:46,069 --> 00:21:47,937 UNFORTUNATELY AT THE NIA, WE 440 00:21:47,937 --> 00:21:49,739 ALSO HAVE CLINICAL, WE HAVE 441 00:21:49,739 --> 00:21:52,242 CLINICAL COHORTS THAT WE CAN 442 00:21:52,242 --> 00:21:53,676 KIND OF LOOK FOR THESE 443 00:21:53,676 --> 00:21:55,011 SENESCENCE. 444 00:21:55,011 --> 00:21:57,547 SO ONE OF THE FLAGSHIP THAT'S 445 00:21:57,547 --> 00:21:58,948 BALTIMORE STUDY ON AGING AND 446 00:21:58,948 --> 00:22:03,553 HERE, WE HAPPEN TO HAVE A 447 00:22:03,553 --> 00:22:05,422 PROTEOMICS STUDY THAT HAS BEEN 448 00:22:05,422 --> 00:22:10,126 PERFORMED IN OVER 1100 USING 449 00:22:10,126 --> 00:22:12,562 THE SOMO SKIN PLATFORM. 450 00:22:12,562 --> 00:22:14,464 WE HAVE ANOTHER THAT IS NOT AS 451 00:22:14,464 --> 00:22:17,200 BIG BUT THIS IS ONE OF THOSE 452 00:22:17,200 --> 00:22:19,702 STUDIES WHERE THEY DO IN DEPTH 453 00:22:19,702 --> 00:22:22,205 ANALYSIS WHERE THEY DO A LOT OF 454 00:22:22,205 --> 00:22:25,041 OMICS. 455 00:22:25,041 --> 00:22:31,881 AND WE'RE FORTUNATE THAT ONE OF 456 00:22:31,881 --> 00:22:33,183 THE TISSUES. 457 00:22:33,183 --> 00:22:35,752 SO WE CAN TRY TO EXTRACT IT 458 00:22:35,752 --> 00:22:39,355 FROM THE CIRCULATING AND ALSO 459 00:22:39,355 --> 00:22:40,957 THE MONO CYTES AND CIRCULATION 460 00:22:40,957 --> 00:22:42,559 AND ASK WHETHER THE SIGNATURES 461 00:22:42,559 --> 00:22:45,562 ARE PRODICT I HAVE OF DIFFERENT 462 00:22:45,562 --> 00:22:46,930 AGING RELATED OUTCOMES, RIGHT 463 00:22:46,930 --> 00:22:49,265 AND CAN WE DEVELOP SIGNATURES 464 00:22:49,265 --> 00:22:49,799 THAT ARE PREDICTING THESE 465 00:22:49,799 --> 00:22:51,100 THINGS? 466 00:22:51,100 --> 00:22:53,169 SO I'M GOING TO START BY 467 00:22:53,169 --> 00:22:57,073 SHOWING YOU THE DATA THAT WE A 468 00:22:57,073 --> 00:23:01,377 RIDER USING THE BLSA STUDIES. 469 00:23:01,377 --> 00:23:08,985 AGAIN BLSA IS A FLAGSHIP STUDY, 470 00:23:08,985 --> 00:23:11,054 THE LONGEST LONGITUDE STUDY TO 471 00:23:11,054 --> 00:23:13,456 THIS DAY AND THEY MEASURE A LOT 472 00:23:13,456 --> 00:23:17,560 OF AGING OUTCOMES. 473 00:23:17,560 --> 00:23:20,530 FOR THIS, WE HAVE INDIVIDUALS 474 00:23:20,530 --> 00:23:22,365 FROM 92 TO 96 AND HALF OF THEM 475 00:23:22,365 --> 00:23:25,101 ARE FEMALE. 476 00:23:25,101 --> 00:23:27,437 I WANT TO ACKNOWLEDGE, KIENAN 477 00:23:27,437 --> 00:23:29,305 WALKER WHO IS THE RESEARCHER, 478 00:23:29,305 --> 00:23:31,174 HE'S REALLY TALENTED. 479 00:23:31,174 --> 00:23:32,308 HE WOULD BE A REALLY GREAT 480 00:23:32,308 --> 00:23:35,979 PERSON TO INVITE. 481 00:23:35,979 --> 00:23:40,550 HE SPEAR HEADED THE PROTEOMIC 482 00:23:40,550 --> 00:23:43,586 ANALYSIS IN THE BLSA, AND THE 483 00:23:43,586 --> 00:23:46,089 CODIRECTORS OF THE BLSA. 484 00:23:46,089 --> 00:23:47,590 WHAT WE WERE TO DO, WE WERE 485 00:23:47,590 --> 00:23:54,330 ABLE TO FIND THAT THERE WERE 64 486 00:23:54,330 --> 00:23:55,965 MONO CYTES THAT ARE AGE 487 00:23:55,965 --> 00:23:56,266 ASSOCIATED. 488 00:23:56,266 --> 00:23:59,235 AND JUST TO POINT SOME OF THEM, 489 00:23:59,235 --> 00:24:00,637 I'M HIGHLIGHTING THEM IN THIS 490 00:24:00,637 --> 00:24:02,705 VOLCANO PLOT. 491 00:24:02,705 --> 00:24:05,909 SOME TARGETS, WE CAN SEE 492 00:24:05,909 --> 00:24:06,442 INCLUDING OUR INTER FERON 493 00:24:06,442 --> 00:24:07,944 SIGNATURE. 494 00:24:07,944 --> 00:24:11,214 THIS IS ONE OF THE, IN ONE OF 495 00:24:11,214 --> 00:24:13,483 OUR OTHER STUDIES, WE 496 00:24:13,483 --> 00:24:15,552 IDENTIFIED THIS AS A CELL 497 00:24:15,552 --> 00:24:16,719 SURFACE PROTEIN, WE THOUGHT 498 00:24:16,719 --> 00:24:18,555 THIS WAS INTERESTING, WE CAN 499 00:24:18,555 --> 00:24:22,225 SEE THIS EVEN IN CIRCULATION 500 00:24:22,225 --> 00:24:28,031 AND ALSO CD68 IS A WELL-KNOWN 501 00:24:28,031 --> 00:24:29,999 MONOCYTE MARKER. 502 00:24:29,999 --> 00:24:32,602 THAT EVEN IN CIRCULATION, WE 503 00:24:32,602 --> 00:24:33,736 CAN IDENTIFY THESE INTER 504 00:24:33,736 --> 00:24:36,039 CELLULAR MARKERS NOT JUST THE 505 00:24:36,039 --> 00:24:37,407 SECRETED STUFF. 506 00:24:37,407 --> 00:24:41,377 SO WE WANTED TO ASK, OKAY, WITH 507 00:24:41,377 --> 00:24:43,446 THIS 64-PROTEIN SIGNATURE CAN 508 00:24:43,446 --> 00:24:44,547 WE BEGIN TO PREDICT CLINICAL 509 00:24:44,547 --> 00:24:47,317 OUTCOMES? 510 00:24:47,317 --> 00:24:56,192 SO TO DO THIS I HAVE A TALENTED 511 00:24:56,192 --> 00:24:58,294 GRAD STUDENT WHO SPHERE HEADED. 512 00:24:58,294 --> 00:25:01,297 THIS IS ANALYSIS APPROACH THAT 513 00:25:01,297 --> 00:25:03,199 DOES FEATURE SELECTION AND DOES 514 00:25:03,199 --> 00:25:09,872 NOT OVER FIT LIKE IF YOU DID 515 00:25:09,872 --> 00:25:11,975 LINEAR MODELING AND IT 516 00:25:11,975 --> 00:25:13,576 IDENTIFIES A SIGNATURE PROTEINS 517 00:25:13,576 --> 00:25:14,611 THAT ARE BEST PREDICTORS OF 518 00:25:14,611 --> 00:25:16,779 DIFFERENT OUTCOMES. 519 00:25:16,779 --> 00:25:19,482 SO HE TOOK AND TRAINED THIS 520 00:25:19,482 --> 00:25:21,250 MODEL AND TESTED OUT OF SAMPLE 521 00:25:21,250 --> 00:25:23,586 PREDICT I HAVE POWER ON THE 522 00:25:23,586 --> 00:25:26,489 REMAINING 20 PERCENT OF THE BLS 523 00:25:26,489 --> 00:25:29,359 A PARTICIPANTS. 524 00:25:29,359 --> 00:25:31,227 AND WHAT WE FOUND, 525 00:25:31,227 --> 00:25:32,595 SURPRISINGLY, IT PREDICTS A 526 00:25:32,595 --> 00:25:34,931 PRETTY WIDE VARIETY OF THINGS. 527 00:25:34,931 --> 00:25:37,000 AND ONCE OF THE THINGS THAT WE 528 00:25:37,000 --> 00:25:38,267 INCLUDED IN THE ANALYSIS WAS 529 00:25:38,267 --> 00:25:39,102 INFLAMMATION. 530 00:25:39,102 --> 00:25:40,837 THIS IS SOMETHING THAT WE 531 00:25:40,837 --> 00:25:43,940 EXPECTED TO SEE. 532 00:25:43,940 --> 00:25:45,541 WE'RE KIND OF A POSITIVE 533 00:25:45,541 --> 00:25:47,143 CONTROL BUT THESE WERE NOT EVEN 534 00:25:47,143 --> 00:25:51,180 THE STRONGEST THINGS THAT WE 535 00:25:51,180 --> 00:25:52,248 CAN PREDICT CAN SENESCENCE. 536 00:25:52,248 --> 00:25:53,950 SO WE SEE A LOT OF MOBILITY 537 00:25:53,950 --> 00:25:55,952 METRICS. 538 00:25:55,952 --> 00:25:58,287 SO YOU THINK ABOUT WALK PACE, 539 00:25:58,287 --> 00:25:59,722 CHAIR STANDS, THINGS LIKE THAT. 540 00:25:59,722 --> 00:26:02,992 AND THESE ARE GOOD, YOU KNOW, 541 00:26:02,992 --> 00:26:04,360 MARKERS OF OVERALL HEALTH, THEY 542 00:26:04,360 --> 00:26:06,195 ENGAGE A LOT OF SYSTEMS IN THE 543 00:26:06,195 --> 00:26:07,897 BODY AND THESE ARE KNOWN TO BE 544 00:26:07,897 --> 00:26:09,932 GOOD MARKERS OF AGING. 545 00:26:09,932 --> 00:26:12,068 SO I THINK IT'S INTERESTING 546 00:26:12,068 --> 00:26:15,738 KNOWING, WHAT, IF THERE IS MORE 547 00:26:15,738 --> 00:26:17,006 SENESCENCE PROTEINS IN 548 00:26:17,006 --> 00:26:18,841 CIRCULATION, WE CAN PREDICT 549 00:26:18,841 --> 00:26:24,013 THESE METRICS OF OVERALL HEALTH. 550 00:26:24,013 --> 00:26:28,718 WE ALSO SEE, METABOLIC 551 00:26:28,718 --> 00:26:31,554 PARAMETERS, ADL, GLUCOSE, GLIE 552 00:26:31,554 --> 00:26:35,058 GLIS RIDE. 553 00:26:35,058 --> 00:26:38,594 AND WE CAN ALSO SEE THAT THESE 554 00:26:38,594 --> 00:26:39,228 ASSOCIATIONS WERE GOING IN THE 555 00:26:39,228 --> 00:26:39,862 DIRECTION THAT WE EXPECT THEM 556 00:26:39,862 --> 00:26:40,697 TO SEE. 557 00:26:40,697 --> 00:26:43,733 THIS IS A SUB SET, WE KIND OF 558 00:26:43,733 --> 00:26:45,535 PLOTTED, WE TOOK THE SCORES AND 559 00:26:45,535 --> 00:26:46,769 PLOTTED SO WE CAN LOOK AT THE 560 00:26:46,769 --> 00:26:47,637 DIRECTION OF THE ASSOCIATION. 561 00:26:47,637 --> 00:26:49,305 AND YOU CAN SEE THAT ALL OF 562 00:26:49,305 --> 00:26:53,209 THESE ARE AS EXPECTED. 563 00:26:53,209 --> 00:26:58,247 SO NEGATIVES, MORE 564 00:26:58,247 --> 00:26:59,916 INFLAMMATION, BIGGER WAIST SIZE 565 00:26:59,916 --> 00:27:03,019 IS CONNECTED TO SENESCENCE. 566 00:27:03,019 --> 00:27:04,187 AND POSITIVE HEALTH METRICS, 567 00:27:04,187 --> 00:27:09,025 PHYSICAL HEALTH STRENGTH, THOSE 568 00:27:09,025 --> 00:27:10,626 ARE NEGATIVELY ASSOCIATED WITH 569 00:27:10,626 --> 00:27:11,794 HIGHER SENESCENCE. 570 00:27:11,794 --> 00:27:14,297 BUT ONE OF THE THING THAT STUCK 571 00:27:14,297 --> 00:27:16,165 OUT AT THE BEGINNING BECAUSE 572 00:27:16,165 --> 00:27:17,366 ONE OF OUR INITIAL ANALYSIS, IT 573 00:27:17,366 --> 00:27:20,036 WAS THE THING THAT WE CAN MOST 574 00:27:20,036 --> 00:27:21,804 STRONGLY PREDICT IN THE STUDY 575 00:27:21,804 --> 00:27:23,506 WAS WAIST SIZE. 576 00:27:23,506 --> 00:27:25,141 AND THIS REALLY STUCK OUT TO 577 00:27:25,141 --> 00:27:30,880 US, BOWS THERE IS A CLEAR 578 00:27:30,880 --> 00:27:32,715 LINEAR ASSOCIATION BETWEEN THE 579 00:27:32,715 --> 00:27:33,316 LEVEL OF PROTEINS AND WAIST 580 00:27:33,316 --> 00:27:34,784 SIZE. 581 00:27:34,784 --> 00:27:36,652 WE WERE KIND OF CURIOUS WHAT 582 00:27:36,652 --> 00:27:38,054 THIS MIGHT BE AND WHETHER WE 583 00:27:38,054 --> 00:27:42,625 CAN DISSECT IT FURTHER. 584 00:27:42,625 --> 00:27:44,494 BUT THE THING WE WANT TO DO, 585 00:27:44,494 --> 00:27:46,295 ONE OF THE FACTORS IS AGING 586 00:27:46,295 --> 00:27:48,931 ITSELF, AGE ITSELF. 587 00:27:48,931 --> 00:27:51,868 SO AGE ITSELF IS ENOUGH TO 588 00:27:51,868 --> 00:27:54,003 PREDICT MANY TRAITS. 589 00:27:54,003 --> 00:27:55,471 SO WE WANTED TO SEE HOW MUCH 590 00:27:55,471 --> 00:27:58,908 THE SIGNATURE HELPS US TO 591 00:27:58,908 --> 00:28:00,910 PREDICT WAIST SIZE OR OBESITY. 592 00:28:00,910 --> 00:28:04,180 WHAT BRAD DID, HE TOOK THE 593 00:28:04,180 --> 00:28:07,617 PATIENTS AND PUT THEM INTO BMI 594 00:28:07,617 --> 00:28:10,386 OVER 30 OBESE, OVER 30 NON 595 00:28:10,386 --> 00:28:14,290 OBESE AND ASKED HOW WELL WE CAN 596 00:28:14,290 --> 00:28:16,359 PREDICT IF SOMEBODY IS OBESE OR 597 00:28:16,359 --> 00:28:18,394 NOT, BASED ON OUR AGENT SACKS 598 00:28:18,394 --> 00:28:19,996 RIGHT HERE IN THE LINE. 599 00:28:19,996 --> 00:28:21,531 SO AGE DOES NOT HAVE STRONG 600 00:28:21,531 --> 00:28:23,699 PREDICT I HAVE POWER. 601 00:28:23,699 --> 00:28:28,871 BUT, NOW IF WE INCLUDE THE 602 00:28:28,871 --> 00:28:31,307 SENESCENCE, WE SEE SIGNIFICANT 603 00:28:31,307 --> 00:28:32,809 IMPROVEMENT IN OUR ABILITY 604 00:28:32,809 --> 00:28:36,512 WHETHER SOMEBODY IS OBESE. 605 00:28:36,512 --> 00:28:39,048 IT HAS INDEPENDENT OF AGING, 606 00:28:39,048 --> 00:28:40,683 SENESCENCE HAS SOME IMPORTANT 607 00:28:40,683 --> 00:28:42,251 LINKS TO OBESITY. 608 00:28:42,251 --> 00:28:45,721 SO NOW I WANT TO PIVOT FOR JUST 609 00:28:45,721 --> 00:28:49,025 A FEW SLIDES TO THIS GASTAL 610 00:28:49,025 --> 00:28:51,227 STUDIES. 611 00:28:51,227 --> 00:28:55,131 I SHOWED YOU THE ASSOCIATES IN 612 00:28:55,131 --> 00:29:00,169 CIRCULATING PLASMA AND NOW 613 00:29:00,169 --> 00:29:00,770 ASSOCIATION AT THE MONOCYTES 614 00:29:00,770 --> 00:29:01,804 THEMSELVES. 615 00:29:01,804 --> 00:29:02,505 HERE I WANT TO ACKNOWLEDGE 616 00:29:02,505 --> 00:29:07,343 THERE ARE A NUMBER OF 617 00:29:07,343 --> 00:29:09,378 COLLABORATOR WHO WORKED ON THE 618 00:29:09,378 --> 00:29:10,346 GAS TAL WITHOUT WHO THIS WOULD 619 00:29:10,346 --> 00:29:11,881 NOT BE POSSIBLE. 620 00:29:11,881 --> 00:29:16,219 WE ASKED THE SAME QUESTION, CAN 621 00:29:16,219 --> 00:29:16,752 WE IDENTIFY SIGNATURES IN 622 00:29:16,752 --> 00:29:19,388 GASTALTS. 623 00:29:19,388 --> 00:29:22,859 SO THIS COHORT IS MUCH SMALLER. 624 00:29:22,859 --> 00:29:26,996 WE HAD A SIMILAR AGE RANGE, 22 625 00:29:26,996 --> 00:29:30,433 TO 9 4 YEARS, ABOUT 44% FEMALE. 626 00:29:30,433 --> 00:29:32,969 WE WERE ABLE TO SEE IN THE 627 00:29:32,969 --> 00:29:35,471 SMALLER SET, WE WERE ABLE TO 628 00:29:35,471 --> 00:29:40,076 FIND ABOUT 11 PROTEIN THAT'S 629 00:29:40,076 --> 00:29:42,545 WERE ASSOCIATED WITH MONOCYTE 630 00:29:42,545 --> 00:29:44,480 CHANGING WITH AGE. 631 00:29:44,480 --> 00:29:45,414 BUT INTERESTINGLY DESPITING 632 00:29:45,414 --> 00:29:47,717 HAVING A SMALLER DATA SET AND 633 00:29:47,717 --> 00:29:48,885 SMALLER NUMBER OF INDIVIDUALS, 634 00:29:48,885 --> 00:29:50,953 WHEN BRAD TOOK THIS DATA SET 635 00:29:50,953 --> 00:29:54,891 AND DID THE TRAINING AND AUTO 636 00:29:54,891 --> 00:29:57,193 SAMPLE PREDICTION, OUR ABILITY 637 00:29:57,193 --> 00:29:58,194 TO PREDICT WAIST SIZE WAS JUST 638 00:29:58,194 --> 00:29:59,729 AS STRONG. 639 00:29:59,729 --> 00:30:02,265 SO THIS REALLY STUCK OUT, THIS 640 00:30:02,265 --> 00:30:04,400 IS SUCH A SMALLER COHORT, SO 641 00:30:04,400 --> 00:30:06,435 WE'RE NOW INTERESTED IN FINDING 642 00:30:06,435 --> 00:30:08,504 WHAT IS BEHIND THIS ASSOCIATION. 643 00:30:08,504 --> 00:30:10,306 AND WE CAN ALSO PREDICT A FEW 644 00:30:10,306 --> 00:30:11,474 OF THE OTHER ASSOCIATIONS THAT 645 00:30:11,474 --> 00:30:15,144 WE WERE ABLE TO FIND IN THE 646 00:30:15,144 --> 00:30:17,713 BLSA, SO FOR EXAMPLE, WE HAVE A 647 00:30:17,713 --> 00:30:18,681 GRIP STRENGTH AND BMI HERE AS 648 00:30:18,681 --> 00:30:22,685 WELL. 649 00:30:22,685 --> 00:30:27,423 SO ONE THING THAT WE THOUGHT, 650 00:30:27,423 --> 00:30:31,894 OKAY WE'RE GOING BACK TO THE 651 00:30:31,894 --> 00:30:34,563 BLS A NOW, WHERE WE HAVE 652 00:30:34,563 --> 00:30:35,197 INFORMATION ABOUT PEOPLE'S BODY 653 00:30:35,197 --> 00:30:37,133 FAT. 654 00:30:37,133 --> 00:30:41,304 SO WE WANTED TO ASK IS THIS 655 00:30:41,304 --> 00:30:42,672 WAIST SIZE CORRELATION, EXPLAIN 656 00:30:42,672 --> 00:30:44,941 OR BM PI BY FAT? 657 00:30:44,941 --> 00:30:46,842 IS IT BODY SIZE OR FAT? 658 00:30:46,842 --> 00:30:53,282 WE WENT BACK AND ASKED, CAN YOU 659 00:30:53,282 --> 00:30:54,884 CORRELATE FAT WITH PROTEINS. 660 00:30:54,884 --> 00:30:56,252 AND WE APPLIED THE SAME 661 00:30:56,252 --> 00:30:58,087 APPROACH AND WHAT WE FOUND, 662 00:30:58,087 --> 00:31:03,125 HERE I'M PLOTTING A LOT OF FAT 663 00:31:03,125 --> 00:31:05,428 DEPOTS. 664 00:31:05,428 --> 00:31:06,862 NONE OF THEM REALLY STUCK OUT 665 00:31:06,862 --> 00:31:08,064 TO BE THAT MUCH STRONGER THAN 666 00:31:08,064 --> 00:31:10,299 THE OTHER. 667 00:31:10,299 --> 00:31:13,469 THIGH IS A LITTLE BIT HIGHER 668 00:31:13,469 --> 00:31:15,338 BUT ALL ABOUT 4.5 OR HIGHER. 669 00:31:15,338 --> 00:31:16,939 SEEMS THAT THE BODY FAT SEEMS 670 00:31:16,939 --> 00:31:19,508 TO EXPLAIN MOST OF IT. 671 00:31:19,508 --> 00:31:22,511 BUT, WHAT WAS INTERESTING IS IF 672 00:31:22,511 --> 00:31:23,879 WE CORRECT TO BODY SIZE, 673 00:31:23,879 --> 00:31:28,384 BASICALLY IF WE NOW TAKE THESE 674 00:31:28,384 --> 00:31:29,986 VALUES AND TAKE FAT PERCENT 675 00:31:29,986 --> 00:31:32,321 RATHER THAN JUST THE FAT LEVEL. 676 00:31:32,321 --> 00:31:32,855 THIS CORRELATION BECOMES 677 00:31:32,855 --> 00:31:34,590 STRONGER. 678 00:31:34,590 --> 00:31:37,393 SO IT SEEMS TO SUGGEST THAT 679 00:31:37,393 --> 00:31:38,761 INDEPENDENT OF BODY SIZE THAT 680 00:31:38,761 --> 00:31:40,629 EXPLAINS MOST OF THE 681 00:31:40,629 --> 00:31:43,065 CORRELATION THAT WE SEE WITH, 682 00:31:43,065 --> 00:31:47,703 WITH WAIST SIZE. 683 00:31:47,703 --> 00:31:50,873 SO OUR HYPOTHESES IS THAT THERE 684 00:31:50,873 --> 00:31:52,475 IS OBESITY ASSOCIATED IN 685 00:31:52,475 --> 00:31:53,843 SENESCENCE, THIS IS WELL-KNOWN 686 00:31:53,843 --> 00:31:55,978 IN LITERATURE. 687 00:31:55,978 --> 00:31:59,382 WE THINK THAT WE'RE PICKING UP 688 00:31:59,382 --> 00:32:01,684 AN ABBIESITY SIGNATURE. 689 00:32:01,684 --> 00:32:04,220 WE ALSO WANTED TO SEE IF THESE 690 00:32:04,220 --> 00:32:05,388 CORRELATION WZ FAT WERE 691 00:32:05,388 --> 00:32:06,989 INDEPENDENT OF AGE WITH BODY 692 00:32:06,989 --> 00:32:08,624 FAT PERCENTAGE. 693 00:32:08,624 --> 00:32:12,328 SO AGAIN, WE DID THE ROCK PLOTS 694 00:32:12,328 --> 00:32:15,064 AND SHOWED THAT BEYOND THE SEX 695 00:32:15,064 --> 00:32:17,566 A LOENL, IN THE PINK LINE HERE, 696 00:32:17,566 --> 00:32:21,003 I'M SORRY IN THE RED LINE, IF 697 00:32:21,003 --> 00:32:24,874 WE NOW ADD OUR MONO CYTE 698 00:32:24,874 --> 00:32:26,842 PROTEINS THEN WE HAVE A BETTER 699 00:32:26,842 --> 00:32:30,479 PREDICTION OF BODY FAT. 700 00:32:30,479 --> 00:32:38,287 THIS IS DISTINGUISHING PEOPLE 701 00:32:38,287 --> 00:32:41,123 ON THE TOP QUARTILE VERSE THE 702 00:32:41,123 --> 00:32:43,359 BOTTOM QUARTITLE. 703 00:32:43,359 --> 00:32:50,132 WE WONDERED WHAT ARE THE 704 00:32:50,132 --> 00:32:54,970 SENESCENCE CAN PREDICT. 705 00:32:54,970 --> 00:32:57,239 IN THE GRAY LINE IS THE 706 00:32:57,239 --> 00:32:59,708 PREDICTION WHICH IS AGE AND 707 00:32:59,708 --> 00:33:01,977 CORE VARIETIES. 708 00:33:01,977 --> 00:33:03,679 AND ON THE BLUE BARS, SO SOME 709 00:33:03,679 --> 00:33:06,949 OF THESE THINGS THAT WE HAD 710 00:33:06,949 --> 00:33:08,451 SHOWN YOU BEFORE ARE REALLY 711 00:33:08,451 --> 00:33:11,087 EXPLAINED BY INCLUDING AGE IN 712 00:33:11,087 --> 00:33:13,355 THE MODEL OR SEX, GRIP STRENGTH 713 00:33:13,355 --> 00:33:13,956 THAT'S REALLY ASSOCIATED WITH 714 00:33:13,956 --> 00:33:15,458 SEX. 715 00:33:15,458 --> 00:33:20,062 BUT OTHER THINGS WE CAN PREDICT 716 00:33:20,062 --> 00:33:25,301 WITH SENESCENCE, SO THESE 717 00:33:25,301 --> 00:33:26,669 METABOLIC. 718 00:33:26,669 --> 00:33:28,304 SO ANOTHER QUESTION THAT WE CAN 719 00:33:28,304 --> 00:33:35,911 ASK IN THE STUDY IS IS THERE 720 00:33:35,911 --> 00:33:37,113 SEXUAL DIE MORPHISM. 721 00:33:37,113 --> 00:33:38,380 AND THERE IS EVIDENCE IN MICE 722 00:33:38,380 --> 00:33:43,252 TO SUGGEST SO. 723 00:33:43,252 --> 00:33:53,796 SO WE WANTED DOES SENESCENCE GO 724 00:33:54,697 --> 00:33:56,265 UP IN FEMALE VERSUS MALE. 725 00:33:56,265 --> 00:33:58,100 YOU CAN SEE THAT HALF ARE MALE 726 00:33:58,100 --> 00:33:59,735 AND FEMALE. 727 00:33:59,735 --> 00:34:02,638 WE DO HAVE A HIGHER DENSITY 728 00:34:02,638 --> 00:34:04,607 BETWEEN 50 AND 60. 729 00:34:04,607 --> 00:34:10,746 AND IF WE ASK WHICH SENESCENCE 730 00:34:10,746 --> 00:34:11,347 PROTEINS ARE ASSOCIATED WITH 731 00:34:11,347 --> 00:34:13,949 FEMALE? 732 00:34:13,949 --> 00:34:16,051 THERE ARE SOME WITH MORE WITH 733 00:34:16,051 --> 00:34:17,887 FEMALE AND SOME HAVE BEING MALE. 734 00:34:17,887 --> 00:34:19,288 THE ONES THAT I SHARED BETWEEN 735 00:34:19,288 --> 00:34:21,123 THE TWO, IS THERE A DIFFERENCE 736 00:34:21,123 --> 00:34:22,391 IN THE DYNAMIC OF WHEN THESE 737 00:34:22,391 --> 00:34:23,459 BUILDUP. 738 00:34:23,459 --> 00:34:30,132 SO WE WANT TO SEE THE 739 00:34:30,132 --> 00:34:31,534 SENESCENCE GO UP IN ONE VERSUS 740 00:34:31,534 --> 00:34:35,137 THE OTHER. 741 00:34:35,137 --> 00:34:37,006 SO WE DID THIS DIFFERENTIAL 742 00:34:37,006 --> 00:34:38,407 EXPRESSION AND WHAT THIS DOES, 743 00:34:38,407 --> 00:34:40,943 IT PUTS ALL OF THE AGES INTO 744 00:34:40,943 --> 00:34:44,146 BINS AND THEN IT KIND OF SLIDES 745 00:34:44,146 --> 00:34:46,916 ACROSS THE AGE RANGE AND ASKED 746 00:34:46,916 --> 00:34:51,754 DURING THE ANALYSIS DOES THIS 747 00:34:51,754 --> 00:34:53,155 HAVE HIGHER EXPRESSION OF 748 00:34:53,155 --> 00:34:54,990 SENESCENCE THEN THE PREVIOUS 749 00:34:54,990 --> 00:34:56,592 AND BIN AND SUBSEQUENT BIN. 750 00:34:56,592 --> 00:35:00,062 AND WE USE THIS TO IDENTIFY AT 751 00:35:00,062 --> 00:35:02,364 WHAT AGES DO WE SEE INCREASES 752 00:35:02,364 --> 00:35:04,200 OF SENESCENCE BURDEN? 753 00:35:04,200 --> 00:35:09,972 AND WHAT WE FOUND IN BOTH 754 00:35:09,972 --> 00:35:12,575 SEXES, WE SEE A PEEK IN THE 70s. 755 00:35:12,575 --> 00:35:14,376 BUT INTERESTINGLY IN MALES, WE 756 00:35:14,376 --> 00:35:16,078 SEE IT GOING UP IN EARLY AGE. 757 00:35:16,078 --> 00:35:17,646 THIS IS THE GREEN LINE AND 758 00:35:17,646 --> 00:35:19,715 FEMALES WE HAVE THE PURPLE LINE. 759 00:35:19,715 --> 00:35:23,652 SO IT SEEMS TO GO UP ABOUT A 760 00:35:23,652 --> 00:35:24,253 DECADE EARLIER IN MALES THAN 761 00:35:24,253 --> 00:35:28,924 FEMALES. 762 00:35:28,924 --> 00:35:32,094 SO AND WE PART OF THIS 763 00:35:32,094 --> 00:35:33,929 ASSOCIATION IS EXPLAINED BY THE 764 00:35:33,929 --> 00:35:36,432 FACT THAT WE HAD BETTER 765 00:35:36,432 --> 00:35:37,600 STATISTICS HERE BECAUSE THIS IS 766 00:35:37,600 --> 00:35:38,968 WHERE WE HAD THE MOST 767 00:35:38,968 --> 00:35:42,805 INDIVIDUALS BUT WE CONTROL FOR 768 00:35:42,805 --> 00:35:43,472 THIS. 769 00:35:43,472 --> 00:35:46,175 WE SHUFFLED ALL THE AGES AND WE 770 00:35:46,175 --> 00:35:50,412 WOULD ALL OF THIS AFFECT WOULD 771 00:35:50,412 --> 00:35:52,948 GO AWAY. 772 00:35:52,948 --> 00:35:54,750 AND INTERESTINGLY, IF WE JUST 773 00:35:54,750 --> 00:35:59,588 TAKE AND PLOT THE SENESCENCE 774 00:35:59,588 --> 00:36:03,525 PROTEIN, OVER AGE, YOU CAN SEE 775 00:36:03,525 --> 00:36:05,894 THIS PHENOMENA IN MALES AT 776 00:36:05,894 --> 00:36:08,097 ABOUT 60 YEARS OLD, YOU CAN SEE 777 00:36:08,097 --> 00:36:11,100 IT BEGINS TO BE HIGHER IN MALES. 778 00:36:11,100 --> 00:36:13,135 AND IT TAKES FEMALES ABOUT A 779 00:36:13,135 --> 00:36:13,902 DECADE TO CATCH UP TO THAT 780 00:36:13,902 --> 00:36:16,372 LEVEL. 781 00:36:16,372 --> 00:36:18,774 SO IT SEEMS LIKE IT GOES UP 782 00:36:18,774 --> 00:36:21,110 HIGHER IN MALES IF WE COMPARE 783 00:36:21,110 --> 00:36:22,911 INDIVIDUALS OVER 60 TO 784 00:36:22,911 --> 00:36:24,713 INDIVIDUALS UNDER 60, RIGHT. 785 00:36:24,713 --> 00:36:29,985 SO UNDER 60, WE SEE A 786 00:36:29,985 --> 00:36:32,054 DIFFERENCE IN THE SENESCENCE 787 00:36:32,054 --> 00:36:35,090 BUT ONCE GE GET BEYOND 60, IT 788 00:36:35,090 --> 00:36:37,326 NORMALIZED OUT. 789 00:36:37,326 --> 00:36:40,496 SO IN CONCLUSION, THIS 790 00:36:40,496 --> 00:36:41,096 CONCLUDES THE KIND OF HUMAN 791 00:36:41,096 --> 00:36:42,798 PART OF THE TALK. 792 00:36:42,798 --> 00:36:44,633 WHAT I SHOWED YOU, IS WE'RE 793 00:36:44,633 --> 00:36:50,105 ABLE TO SEE THAT SENESCENCE 794 00:36:50,105 --> 00:36:52,207 THAT THESE PROTEIN CHANGES WE 795 00:36:52,207 --> 00:36:55,210 CAN USE THEM AS POTENTIAL 796 00:36:55,210 --> 00:36:58,247 BIOMARKERS IN PEOPLE AND 797 00:36:58,247 --> 00:36:59,815 THEY'RE CORRELATED OF DIFFERENT 798 00:36:59,815 --> 00:37:02,885 AGING RELATED CLINICAL OUTCOMES. 799 00:37:02,885 --> 00:37:03,419 AND PARTICULARLY FAT AND 800 00:37:03,419 --> 00:37:07,690 OBESITY. 801 00:37:07,690 --> 00:37:08,357 IT'S INTERESTING THAT 802 00:37:08,357 --> 00:37:10,459 SENESCENCE INDEPENDENT OF AGE 803 00:37:10,459 --> 00:37:13,495 CAN PREDICT OBESITY IN PEOPLE. 804 00:37:13,495 --> 00:37:15,331 THIS IS INDICATIVE OF OBESITY 805 00:37:15,331 --> 00:37:17,399 AND UNDERLINES THAT, YOU KNOW, 806 00:37:17,399 --> 00:37:20,169 WHEN WE DO STUDIES OF 807 00:37:20,169 --> 00:37:22,237 SENESCENCE OF PEOPLE, TO DO NOT 808 00:37:22,237 --> 00:37:25,574 ONLY FOLKS OF AGED INDIVIDUALS 809 00:37:25,574 --> 00:37:28,043 BUT ALSO OBESE INDIVIDUALS THEY 810 00:37:28,043 --> 00:37:29,378 MAY BENEFIT MORE FROM THESE 811 00:37:29,378 --> 00:37:30,145 INTERVENTIONS. 812 00:37:30,145 --> 00:37:36,752 AND I SHOWED YOU THAT MALE HAVE 813 00:37:36,752 --> 00:37:37,886 HIGHER SENESCENCE. 814 00:37:37,886 --> 00:37:39,221 SO I WANT TO BACK UP A LITTLE 815 00:37:39,221 --> 00:37:41,056 BIT IN THIS TALK. 816 00:37:41,056 --> 00:37:42,358 ONE QUESTION THAT I'M REALLY 817 00:37:42,358 --> 00:37:45,094 INTERESTED IN MY LAB IS HOW 818 00:37:45,094 --> 00:37:46,495 GOOD CAN A CIRCULATING 819 00:37:46,495 --> 00:37:48,063 BIOMARKER OF AGING EVEN BE? 820 00:37:48,063 --> 00:37:49,965 RIGHT. 821 00:37:49,965 --> 00:37:52,034 SO LOOKING AT PROTEINS AND 822 00:37:52,034 --> 00:37:54,203 CIRCULATION, I MEAN, CAN THAT 823 00:37:54,203 --> 00:38:01,443 BE, EVEN A COULD THAT BE EVEN A 824 00:38:01,443 --> 00:38:05,247 USEFUL SURROGATE MARKER? 825 00:38:05,247 --> 00:38:07,049 AND SO, THIS IS REALLY 826 00:38:07,049 --> 00:38:08,751 INTERESTING QUESTION TO US. 827 00:38:08,751 --> 00:38:10,152 BUT THE ANSWER TO THIS 828 00:38:10,152 --> 00:38:10,886 QUESTION, WE'RE GOING TO NEED A 829 00:38:10,886 --> 00:38:12,254 FEW THINGS. 830 00:38:12,254 --> 00:38:22,164 FIRST OF ALL, WE WOULD NEED A 831 00:38:22,164 --> 00:38:23,332 REALLY RIGOROUS LONGITUDINAL 832 00:38:23,332 --> 00:38:26,335 STUDY THAT MEASURES, AGING AND 833 00:38:26,335 --> 00:38:26,902 ALSO COLLECTS THE SPECIMEN 834 00:38:26,902 --> 00:38:28,337 COUPLED TO THAT. 835 00:38:28,337 --> 00:38:30,572 THEN WE CAN WRIGLEY ASKED THE 836 00:38:30,572 --> 00:38:33,308 QUESTION, HOW WELL DO THESE 837 00:38:33,308 --> 00:38:34,009 CIRCULATING BIOMARKERS PREDICT 838 00:38:34,009 --> 00:38:37,112 ALL THE STUFF THAT WE CARE 839 00:38:37,112 --> 00:38:38,313 ABOUT MEASURING. 840 00:38:38,313 --> 00:38:42,985 AND IT TURNS OUT IT'S VERY 841 00:38:42,985 --> 00:38:43,552 TECHNICALLY CHALLENGING AND 842 00:38:43,552 --> 00:38:44,953 I'LL EXPLAIN THAT A LITTLE BIT 843 00:38:44,953 --> 00:38:48,390 LATER. 844 00:38:48,390 --> 00:38:51,393 WE CANNOT REALLY EASILY DO THIS 845 00:38:51,393 --> 00:38:55,464 IN HUMANS BUT WE CAN ADDRESS 846 00:38:55,464 --> 00:38:56,865 THIS IN MICE. 847 00:38:56,865 --> 00:38:59,568 WE CAN CONDUCT THE WHOLE 848 00:38:59,568 --> 00:39:03,038 LIFETIME STUDY AS WELL AS 849 00:39:03,038 --> 00:39:13,582 COLLECTING THE SERUM, SO WE CAN 850 00:39:15,984 --> 00:39:17,486 PREDICT USING SERUM PROTEINS. 851 00:39:17,486 --> 00:39:19,521 IN MICE, WE CAN AT LEAST BEGIN 852 00:39:19,521 --> 00:39:21,623 TO ANSWER THIS QUESTION AND 853 00:39:21,623 --> 00:39:22,324 FORMULATE STRATEGIES, YOU KNOW, 854 00:39:22,324 --> 00:39:25,093 WHAT IS THE BEST WAY TO MAKE A 855 00:39:25,093 --> 00:39:26,829 PREDICTION, WHAT KIND OF THINGS 856 00:39:26,829 --> 00:39:28,997 CAN WE PREDICT WHICH WE CAN 857 00:39:28,997 --> 00:39:30,866 THEN APPLY INTO HUMAN STUDIES. 858 00:39:30,866 --> 00:39:34,503 AND SO, WE'RE ABLE TO ADDRESS 859 00:39:34,503 --> 00:39:36,305 THIS QUESTION LUCKILY, THANKS 860 00:39:36,305 --> 00:39:41,376 TO ONE OF THE OTHER FLAGSHIP 861 00:39:41,376 --> 00:39:51,887 STUDIES AT NIA, WHICH IS THE 862 00:39:52,354 --> 00:39:54,356 STUDY LONGITUDINAL STUDY ON 863 00:39:54,356 --> 00:39:54,556 MICE. 864 00:39:54,556 --> 00:39:57,392 IT STARTED BACK IN 2015 AND 865 00:39:57,392 --> 00:39:58,727 NEARING 4,000 MICE NOW. 866 00:39:58,727 --> 00:40:03,599 BUT WHAT IS REALLY COOL IS THAT 867 00:40:03,599 --> 00:40:04,199 IT'S EXTREMELY COMPREHENSIVE 868 00:40:04,199 --> 00:40:04,766 AND LONGITUDINAL. 869 00:40:04,766 --> 00:40:06,835 SO WHAT THEY DO IS THEY TAKE 870 00:40:06,835 --> 00:40:10,506 MANY DIFFERENT MICE AND THEY 871 00:40:10,506 --> 00:40:12,341 INCLUDE HERE HERE TWO DIFFERENT 872 00:40:12,341 --> 00:40:14,877 STRINGS, WE HAVE MALE AND 873 00:40:14,877 --> 00:40:16,979 FEMALE, THEY HAVE PLANNED 874 00:40:16,979 --> 00:40:22,484 COLLECTIONS OF MANY DIFFERENT 875 00:40:22,484 --> 00:40:24,887 SPECIMENS AND ONE OF THOSE 876 00:40:24,887 --> 00:40:31,093 FORTUNATELY FOR US IS SERUM 877 00:40:31,093 --> 00:40:32,928 THROUGHOUT THE LIFE SPAN, MANY 878 00:40:32,928 --> 00:40:35,697 A LOT OF THESE AGING RELATED 879 00:40:35,697 --> 00:40:37,599 OUTCOMES THAT WE CARE ABOUT ARE 880 00:40:37,599 --> 00:40:41,003 MEASURED. 881 00:40:41,003 --> 00:40:42,371 WE BEGIN TO ANSWER THESE 882 00:40:42,371 --> 00:40:45,407 QUESTIONS ABOUT WHAT WE CAN 883 00:40:45,407 --> 00:40:45,908 PREDICT ABOUT THE SERUM 884 00:40:45,908 --> 00:40:46,608 BIOMARKER. 885 00:40:46,608 --> 00:40:48,544 SO WHAT I'M GOING TO SHOW YOU 886 00:40:48,544 --> 00:40:53,682 NOW IS TWO PARTS THIS IS AN ON 887 00:40:53,682 --> 00:40:55,117 GOING STUDY. 888 00:40:55,117 --> 00:40:57,686 AND FIRST I WANT TO SHOW YOU 889 00:40:57,686 --> 00:41:00,956 HOW WE'RE ABLE TO ESTABLISH TO 890 00:41:00,956 --> 00:41:03,926 THIS WORK AND THEN, SHOW YOU 891 00:41:03,926 --> 00:41:05,294 HOW WE APPLY THIS METHOD IN THE 892 00:41:05,294 --> 00:41:09,398 SCIENCE STUDY. 893 00:41:09,398 --> 00:41:12,167 SO, ONE OF THE MAJOR CHALLENGES 894 00:41:12,167 --> 00:41:16,471 OF DOING THIS IS THAT THERE ARE 895 00:41:16,471 --> 00:41:20,809 TECHNICAL TO DOING PROTEOMICS 896 00:41:20,809 --> 00:41:22,911 AND SERUM. 897 00:41:22,911 --> 00:41:29,318 ALL OF THESE THOUSANDS OF 898 00:41:29,318 --> 00:41:31,119 POTENTIAL MARKERS ARE WITHIN 899 00:41:31,119 --> 00:41:33,422 THAT 1 PERCENT OF FRACTION. 900 00:41:33,422 --> 00:41:37,793 YOU CAN IMAGINE IF YOU TRY TO 901 00:41:37,793 --> 00:41:39,628 DO PROTEOMICS, ALL IS MEASURED. 902 00:41:39,628 --> 00:41:45,701 AND BY THE WAY, ALBUMIN IS 903 00:41:45,701 --> 00:41:49,371 ABOUT THE 60% OF THE TOTAL MASS. 904 00:41:49,371 --> 00:41:52,574 AND ALL OF THOSE TO TENSION 905 00:41:52,574 --> 00:41:55,110 BIOMARKERS MAY BE AT THE END, 906 00:41:55,110 --> 00:41:59,648 SO WE SEE THINGS THINGS LIKE 907 00:41:59,648 --> 00:42:01,750 AISLE 6 AND BETA AND THESE ARE 908 00:42:01,750 --> 00:42:03,619 TOWARDS THE BOTTOM OF THE RANGE. 909 00:42:03,619 --> 00:42:07,556 SO THE IF WE HAVE TO DO A 910 00:42:07,556 --> 00:42:08,523 COMPREHENSIVE, WE HAVE TO 911 00:42:08,523 --> 00:42:09,858 FIGURE OUT. 912 00:42:09,858 --> 00:42:12,394 AND THE OTHER CHALLENGE THAT WE 913 00:42:12,394 --> 00:42:13,428 HAD IS VERY LOW VOLUME OF 914 00:42:13,428 --> 00:42:16,531 SAMPLE. 915 00:42:16,531 --> 00:42:19,301 SO IN HUMANS YOU CAN COLLECT 916 00:42:19,301 --> 00:42:22,738 MILL BUT IN MICE, ONLY MICRO, 917 00:42:22,738 --> 00:42:24,873 YOU CANNOT TAKE TOO MUCH BLOOD 918 00:42:24,873 --> 00:42:26,642 WITHOUT AFFECTING THE HEALTH OF 919 00:42:26,642 --> 00:42:30,345 THE ANIMALS AND ON TOP THAT, A 920 00:42:30,345 --> 00:42:33,815 STUDY LIKE SLAM, YOU CAN 921 00:42:33,815 --> 00:42:37,719 IMAGINE THE SAMPLES ARE 922 00:42:37,719 --> 00:42:40,255 PRECIOUS AND THEY CANNOT ONLY 923 00:42:40,255 --> 00:42:42,357 GIVE THEM TO PROTEOMICS AND 924 00:42:42,357 --> 00:42:43,892 WHATEVER IS REMAINING GOES TO 925 00:42:43,892 --> 00:42:44,693 PROTEOMICS. 926 00:42:44,693 --> 00:42:46,461 SO WE HAD TO DEAL WITH THE 927 00:42:46,461 --> 00:42:49,698 RANGE AND LOW VOLUME. 928 00:42:49,698 --> 00:42:51,066 UNFORTUNATELY WE HAPPEN TO 929 00:42:51,066 --> 00:42:52,701 COLLABORATE AND PARTNER WITH A 930 00:42:52,701 --> 00:42:55,270 COMPANY WHO HAS THE METHOD TO 931 00:42:55,270 --> 00:42:57,005 ADDRESS THE DYNAMIC RANGE 932 00:42:57,005 --> 00:42:58,940 PROBLEM IN SERUM. 933 00:42:58,940 --> 00:43:00,776 THEY'RE CALLED SEAR AND I DON'T 934 00:43:00,776 --> 00:43:03,078 HAVE TIME TO GO INTO DETAIL 935 00:43:03,078 --> 00:43:06,581 ABOUT THIS BUT I'M HAPPY TO 936 00:43:06,581 --> 00:43:07,683 ANSWER QUESTIONS LATER, 937 00:43:07,683 --> 00:43:13,388 ESPECIALLY WHEN THEY USE THESE 938 00:43:13,388 --> 00:43:15,924 NONO PARTICLES. 939 00:43:15,924 --> 00:43:17,325 SO THEY INCUBATE THE SERUM, 940 00:43:17,325 --> 00:43:20,262 THERE IS A SET OF PROTEINS 941 00:43:20,262 --> 00:43:22,431 OUTSIDE, THEY CALL THIS, THIS 942 00:43:22,431 --> 00:43:26,101 IS CALLED THE PROTEIN CORONA 943 00:43:26,101 --> 00:43:28,637 AND PROTEIN CORONA HAS A MORE 944 00:43:28,637 --> 00:43:33,008 DYNAMIC RANGE COMPARED TO THE 945 00:43:33,008 --> 00:43:35,010 ORIGINAL SAMPLE AND CONTAINS. 946 00:43:35,010 --> 00:43:38,847 SO WE TAKE THE PROTEIN CORONA 947 00:43:38,847 --> 00:43:41,483 AND ANALYZE THAT AND WE AN MIEZ 948 00:43:41,483 --> 00:43:43,652 BY MASS RESPECT. 949 00:43:43,652 --> 00:43:47,289 AND THIS ANALYSIS WAS SPHERE 950 00:43:47,289 --> 00:43:52,160 HEADED BY A, HE'S LIKE THE MASS 951 00:43:52,160 --> 00:43:53,562 MASTER AND DOES THIS ANALYSIS. 952 00:43:53,562 --> 00:43:56,098 SO THE FIRST THING WE WANTED TO 953 00:43:56,098 --> 00:43:58,133 KNOW, WHAT IS THE LOWEST VOLUME 954 00:43:58,133 --> 00:44:01,503 THAT WE CAN USE AND STILL GET 955 00:44:01,503 --> 00:44:02,003 COMPREHENSIVE PROTEOMIC 956 00:44:02,003 --> 00:44:04,539 APPROXIMATES. 957 00:44:04,539 --> 00:44:08,577 SO WE TOOK DIFFERENT VOLUMES 958 00:44:08,577 --> 00:44:10,779 AND COMPARED THAT WITH SERUM. 959 00:44:10,779 --> 00:44:12,147 AND WE WERE REALLY HAPPY TO SEE 960 00:44:12,147 --> 00:44:15,817 IS THAT WE CAN GO AS LOW AS TEN 961 00:44:15,817 --> 00:44:19,454 MICRO LITTERS AND STILL GET 962 00:44:19,454 --> 00:44:21,356 VERY COMPREHENSIVE PROTEOMICS. 963 00:44:21,356 --> 00:44:23,892 AND AT THE LOWEST, EVEN AT THE 964 00:44:23,892 --> 00:44:26,895 LOWEST VOLUME, WE HAD A HUGE 965 00:44:26,895 --> 00:44:29,030 IMPROVEMENT SO IT'S AT LEAST 966 00:44:29,030 --> 00:44:30,832 5-FOLD BETTER THAN WHAT WE CAN 967 00:44:30,832 --> 00:44:32,300 DO WITHOUT DOING THESE SPECIAL 968 00:44:32,300 --> 00:44:34,503 SAMPLE PREPARATIONS. 969 00:44:34,503 --> 00:44:35,704 WE ALSO EVALUATED THE TECHNICAL 970 00:44:35,704 --> 00:44:37,205 VARIANCE IN THIS APPROACH. 971 00:44:37,205 --> 00:44:41,376 SO WE LOOKED AT THE CVs AND SAW 972 00:44:41,376 --> 00:44:45,847 THERE WAS ABOUT 6% SO THIS WAS 973 00:44:45,847 --> 00:44:47,749 A PRETTY REPRODUCABLE METHOD. 974 00:44:47,749 --> 00:44:49,785 WE ALSO LOOKED AT THE PROTEINS 975 00:44:49,785 --> 00:44:51,219 THAT WE IDENTIFIED, RIGHT SO WE 976 00:44:51,219 --> 00:44:55,891 WANT TO SEE ARE THESE 977 00:44:55,891 --> 00:44:57,192 BIOLOGICALLY RELEVANT AND WE'RE 978 00:44:57,192 --> 00:44:59,694 ABLE TO SEE THAT A NUMBER OF 979 00:44:59,694 --> 00:45:01,763 THESE PROTEINS, AND WHAT WE 980 00:45:01,763 --> 00:45:02,631 LABELED ARE KNOWN TO PROTEINS, 981 00:45:02,631 --> 00:45:04,766 RIGHT. 982 00:45:04,766 --> 00:45:06,835 SO WE'RE GLAD THAT IN OUR AGING 983 00:45:06,835 --> 00:45:09,004 STUDY, HE'LL BE ABLE TO SEE 984 00:45:09,004 --> 00:45:10,338 THESE PROTEINS WHICH WILL BE 985 00:45:10,338 --> 00:45:11,873 ONE SET OF THE PROTEINS THAT WE 986 00:45:11,873 --> 00:45:14,543 REALLY CARE ABOUT. 987 00:45:14,543 --> 00:45:17,779 SO WE, GIVEN THIS APPROACH, WE 988 00:45:17,779 --> 00:45:20,048 WORKED NICELY FOR LOW VOLUME, 989 00:45:20,048 --> 00:45:22,818 WE NEXT WANTED TO ASK, CAN WE 990 00:45:22,818 --> 00:45:27,656 APPLY THIS TO LIKE AWE MINI 991 00:45:27,656 --> 00:45:30,192 STUDY OF AGING? 992 00:45:30,192 --> 00:45:32,127 IN A SMALL SUB SET OF THAT 993 00:45:32,127 --> 00:45:33,461 STUDY? 994 00:45:33,461 --> 00:45:36,665 SO WHAT WE DID, WE DECIDED TO 995 00:45:36,665 --> 00:45:43,138 GO WITH 20 MICRO LITRE. 996 00:45:43,138 --> 00:45:45,207 AND WE APPLIED THIS INTO A SUB 997 00:45:45,207 --> 00:45:49,110 SET OF 30 MICE THAT WERE EITHER 998 00:45:49,110 --> 00:45:50,045 YOUNG, 12-MONTH-OLD, 999 00:45:50,045 --> 00:45:53,949 24-MONTH-OLD WHICH IS OLD FOR A 1000 00:45:53,949 --> 00:45:57,786 MOUSE AND 30-MONTH-OLD WHICH WE 1001 00:45:57,786 --> 00:45:59,020 CALL GERRIATRIC. 1002 00:45:59,020 --> 00:46:03,124 SO ONE OF THE THINGS THAT WE 1003 00:46:03,124 --> 00:46:05,093 DID IS, SO HERE'S JUST A LITTLE 1004 00:46:05,093 --> 00:46:06,361 REVIEW OF THE STUDY. 1005 00:46:06,361 --> 00:46:10,065 SO WE WERE HAPPY TO SEE IN THIS 1006 00:46:10,065 --> 00:46:12,367 PILOT STUDY, WE SAW SIMILAR 1007 00:46:12,367 --> 00:46:17,138 PERFORMANCE TO BEFORE, SO ABOUT 1008 00:46:17,138 --> 00:46:18,139 2300 PROTEINS ON AVERAGE FOR 1009 00:46:18,139 --> 00:46:21,910 SAMPLE AND WE WERE ABLE TO 1010 00:46:21,910 --> 00:46:22,444 IDENTIFY LIKE ABOUT 4500 1011 00:46:22,444 --> 00:46:23,979 PROTEINS. 1012 00:46:23,979 --> 00:46:31,286 WE LOOKED FOR LINEAR SO THINGS 1013 00:46:31,286 --> 00:46:31,887 THAT CHANGE ACROSS THE AGE 1014 00:46:31,887 --> 00:46:33,388 GROUPS. 1015 00:46:33,388 --> 00:46:35,290 AND HERE, WE WERE ABLE TO 1016 00:46:35,290 --> 00:46:37,559 IDENTIFY A NUMBER OF PROTEINS 1017 00:46:37,559 --> 00:46:38,260 THAT DECREASE AND INCREASE WITH 1018 00:46:38,260 --> 00:46:39,160 AGE. 1019 00:46:39,160 --> 00:46:40,695 AND YOU CAN SEE HERE ON THE 1020 00:46:40,695 --> 00:46:44,733 RIGHT EXAMPLES OF SOME OF THE 1021 00:46:44,733 --> 00:46:47,435 TOP INCREASING PROTEINS, WHICH 1022 00:46:47,435 --> 00:46:50,872 THE, THE WE THOUGHT THE SCENE 1023 00:46:50,872 --> 00:46:56,511 LOR MODELS WERE REALLY NICE. 1024 00:46:56,511 --> 00:46:58,113 INCREASING SOMETHING THAT IS 1025 00:46:58,113 --> 00:46:59,581 AGING STUDIES THAT YOU SEE, NO 1026 00:46:59,581 --> 00:47:05,820 MATTER WHAT YOU'RE MEASURING, 1027 00:47:05,820 --> 00:47:06,388 THERE IS ALWAYS INCREASE 1028 00:47:06,388 --> 00:47:08,790 HETERNE INCREASE. 1029 00:47:08,790 --> 00:47:11,326 WE ALSO WANTED TO KNOW IF WE 1030 00:47:11,326 --> 00:47:11,893 CAN IDENTIFY SEX ASSOCIATED 1031 00:47:11,893 --> 00:47:13,895 PROTEINS. 1032 00:47:13,895 --> 00:47:15,297 OH GETTING AHEAD OF MYSELF. 1033 00:47:15,297 --> 00:47:18,533 WE CAN LOOK AT WHAT ARE THE 1034 00:47:18,533 --> 00:47:19,434 FUNCTIONAL PATHWAYS THAT THESE 1035 00:47:19,434 --> 00:47:20,635 FALL INTO AND ARE THEY WHAT WE 1036 00:47:20,635 --> 00:47:22,637 EXPECT. 1037 00:47:22,637 --> 00:47:28,076 SO WE WERE HAPPY TO SEE THE 1038 00:47:28,076 --> 00:47:31,813 PATHWAYS OR LIPID TRANSPORT AND 1039 00:47:31,813 --> 00:47:33,181 METABOLISM PROTEINS WHICH ARE 1040 00:47:33,181 --> 00:47:35,784 THINGS THAT WE WOULD EXPECT TO 1041 00:47:35,784 --> 00:47:39,387 CHANGE IN THE GERRY. 1042 00:47:39,387 --> 00:47:42,624 AND WE WANTED TO SEE THE SEX 1043 00:47:42,624 --> 00:47:44,025 ASSOCIATED PROTEINS, THAT WERE 1044 00:47:44,025 --> 00:47:45,260 HIGHER IN MALE AND FEMALES AND 1045 00:47:45,260 --> 00:47:46,895 THINGS THAT ARE KNOWN TO BE 1046 00:47:46,895 --> 00:47:48,430 HIGHER IN MALES. 1047 00:47:48,430 --> 00:47:50,932 WE WERE ABLE TO SEE IN THE MICE. 1048 00:47:50,932 --> 00:47:53,034 SO IN HUMAN IT'S KNOWN THAT THE 1049 00:47:53,034 --> 00:47:53,601 PROTEINS ARE HIGHER IN THE 1050 00:47:53,601 --> 00:47:54,669 MALES. 1051 00:47:54,669 --> 00:47:58,340 AND WE WERE ABLE TO SEE THAT 1052 00:47:58,340 --> 00:47:58,907 WITH COMPLIMENT, WITH THIS 1053 00:47:58,907 --> 00:48:02,210 STUDY. 1054 00:48:02,210 --> 00:48:04,713 SO WE'RE HAPPY TORO PRODUCE 1055 00:48:04,713 --> 00:48:05,613 KNOWN CHANGES AND WE'RE 1056 00:48:05,613 --> 00:48:07,482 INTERESTED IN NOT ONLY WHAT IS 1057 00:48:07,482 --> 00:48:08,550 ASSOCIATED BUT IS THE AGE 1058 00:48:08,550 --> 00:48:10,785 RELATED CHANGE THE SAME? 1059 00:48:10,785 --> 00:48:12,654 IS THE TRAJECTORY WITH THE AGE 1060 00:48:12,654 --> 00:48:14,689 OF THE PROTEINS THE SAME WITH 1061 00:48:14,689 --> 00:48:16,124 MALES AND FEMALES? 1062 00:48:16,124 --> 00:48:19,127 SO FOR EXAMPLE, THIS PROTEIN IS 1063 00:48:19,127 --> 00:48:20,729 ON AVERAGE HIGHER ON MALE BUT 1064 00:48:20,729 --> 00:48:21,629 THIS DIFFERENCE GOES DOWN WITH 1065 00:48:21,629 --> 00:48:25,767 AGE. 1066 00:48:25,767 --> 00:48:28,536 AND FINALLY, ONE THING THAT WE 1067 00:48:28,536 --> 00:48:29,871 WERE ALSO REALLY INTERESTED IN 1068 00:48:29,871 --> 00:48:34,009 THE SLAM SITE, WAS TO LOOK AT 1069 00:48:34,009 --> 00:48:34,542 PROTEINS ASSOCIATED WITH 1070 00:48:34,542 --> 00:48:35,377 GLUCOSE LEVELS. 1071 00:48:35,377 --> 00:48:38,847 SO WHAT IS INTERESTING ABOUT 1072 00:48:38,847 --> 00:48:42,317 MICE, IS GLUCOSE LEVELS HAVE A 1073 00:48:42,317 --> 00:48:43,485 DIFFERENT RELATIONSHIP THAN 1074 00:48:43,485 --> 00:48:45,353 WHAT YOU SEE WITH HUMANS AND 1075 00:48:45,353 --> 00:48:49,491 THIS IS ALL PUBLISHED WORK, 1076 00:48:49,491 --> 00:48:51,126 HAVING HIGHER BLOOD GLUCOSE IS 1077 00:48:51,126 --> 00:48:52,894 BAD AND PREDICTS A SHORTER LIFE 1078 00:48:52,894 --> 00:48:54,129 SPAN. 1079 00:48:54,129 --> 00:48:58,466 IN MICE, IF YOU HAVE A HIGHER 1080 00:48:58,466 --> 00:48:59,934 GLUCOSE, IT PROVIDES A LONGER 1081 00:48:59,934 --> 00:49:02,437 LIFE SPAN. 1082 00:49:02,437 --> 00:49:03,805 THESE ARE KEY DIFFERENCES, BUT 1083 00:49:03,805 --> 00:49:07,008 WE WERE INTERESTED IN THE 1084 00:49:07,008 --> 00:49:08,610 PROTEINS ASSOCIATED IN GLUCOSE 1085 00:49:08,610 --> 00:49:11,813 ON MICE BECAUSE THAT MAY ALSO 1086 00:49:11,813 --> 00:49:13,848 PROTICKET DICT LONGER LIFE 1087 00:49:13,848 --> 00:49:14,049 SPANS. 1088 00:49:14,049 --> 00:49:15,950 AND INTERESTINGLY WE WERE ABLE 1089 00:49:15,950 --> 00:49:18,753 TO FIND ABOUT 100 PROTEINS THAT 1090 00:49:18,753 --> 00:49:23,324 WERE ASSOCIATED WITH GLUCOSE 1091 00:49:23,324 --> 00:49:26,094 LEVELS. 1092 00:49:26,094 --> 00:49:32,333 AND RJ10 WHICH IS A NICE LINEAR 1093 00:49:32,333 --> 00:49:33,201 ASSOCIATION. 1094 00:49:33,201 --> 00:49:36,438 SO THIS IS SOMETHING THAT WE 1095 00:49:36,438 --> 00:49:41,476 CONTINUE TO FOCUS IN PREDICTIVE 1096 00:49:41,476 --> 00:49:43,311 WITH AGE. 1097 00:49:43,311 --> 00:49:47,849 WE'RE ABLE TO NOW APPLY, 1098 00:49:47,849 --> 00:49:50,351 SUCCESSFULLY APPLY THIS 1099 00:49:50,351 --> 00:49:53,521 PROTEOMICS. 1100 00:49:53,521 --> 00:49:55,156 SO THE METHOD WORKS AND WE WERE 1101 00:49:55,156 --> 00:49:56,791 ABLE TO IDENTIFY SOME CHANGES 1102 00:49:56,791 --> 00:49:59,127 THAT WE EXPECTED TO SEE. 1103 00:49:59,127 --> 00:50:02,831 SO NOW THE QUESTION, SINCE 1104 00:50:02,831 --> 00:50:05,133 WE'RE REALLY ENCOURAGED BY 1105 00:50:05,133 --> 00:50:06,768 THESE RESULTS SO WE DECIDED NOW 1106 00:50:06,768 --> 00:50:10,505 THAT WE WANTED TO APPLY THIS 1107 00:50:10,505 --> 00:50:12,040 METHOD AT SCALE. 1108 00:50:12,040 --> 00:50:15,210 AND WE WANT TO GET TO THIS 1109 00:50:15,210 --> 00:50:16,945 QUESTION WHAT CAN WE ACTUALLY 1110 00:50:16,945 --> 00:50:18,413 PREDICT? 1111 00:50:18,413 --> 00:50:20,014 SO TO ANSWER THIS QUESTION, WE 1112 00:50:20,014 --> 00:50:24,419 HAVE TO USE A LOT MORE SAMPLES. 1113 00:50:24,419 --> 00:50:25,787 THIS DATA HAS BEEN RECENTLY 1114 00:50:25,787 --> 00:50:26,588 COLLECTED BUT I'LL JUST SHOW 1115 00:50:26,588 --> 00:50:27,489 YOU A LITTLE BIT OF WHAT WE'VE 1116 00:50:27,489 --> 00:50:28,623 SEEN. 1117 00:50:28,623 --> 00:50:33,394 SO NOW WE HAVE, UP TO 6 1118 00:50:33,394 --> 00:50:35,430 LONGITUDINAL SAMPLES HER MOUSE. 1119 00:50:35,430 --> 00:50:40,001 WE HAVE INTRODUCED BOTH STRAINS 1120 00:50:40,001 --> 00:50:43,104 SO WE HAVE SIX, THREE MALE AND 1121 00:50:43,104 --> 00:50:46,875 FEMALE. 1122 00:50:46,875 --> 00:50:51,880 AND HIGH IN GLUCOSE. 1123 00:50:51,880 --> 00:50:53,515 AND WE HAVE AGE RELATED OUTCOME 1124 00:50:53,515 --> 00:50:57,986 SO WE CAN ASK THE QUESTION WHAT 1125 00:50:57,986 --> 00:51:01,055 CAN THE PROTEINS PREDICT. 1126 00:51:01,055 --> 00:51:06,794 SO I WOULDN'T SHOW YOU MUCH 1127 00:51:06,794 --> 00:51:07,395 HERE, BECAUSE THIS IS PRESH 1128 00:51:07,395 --> 00:51:08,429 DATA. 1129 00:51:08,429 --> 00:51:10,698 BUT ONE OF THE THINGS THAT WE 1130 00:51:10,698 --> 00:51:13,234 ADDED THAT WE HAD NOT SEEN IN 1131 00:51:13,234 --> 00:51:15,570 THE PILOT WAS THE STRAIN 1132 00:51:15,570 --> 00:51:16,938 COMPONENT. 1133 00:51:16,938 --> 00:51:17,872 WE WANTED TO SEE HOW STRONG IS 1134 00:51:17,872 --> 00:51:18,840 IT? 1135 00:51:18,840 --> 00:51:21,109 BEFORE I GET TO THAT, JUST TO 1136 00:51:21,109 --> 00:51:22,277 GIVE YOU A LITTLE OVERVIEW OF 1137 00:51:22,277 --> 00:51:23,945 THE DATA SET. 1138 00:51:23,945 --> 00:51:26,047 SO HERE WE HAVE THE SCALE OF 1139 00:51:26,047 --> 00:51:30,218 THE DATA SET, THIS IS NOW 7 1140 00:51:30,218 --> 00:51:31,953 TERABYTES OF DATA. 1141 00:51:31,953 --> 00:51:37,125 SO I DON'T KNOW IF THERE IS 1142 00:51:37,125 --> 00:51:39,194 ANYBODY THAT DOES MASS SPECT 1143 00:51:39,194 --> 00:51:42,931 BUT THIS IS A LOT OF DATA. 1144 00:51:42,931 --> 00:51:44,532 I'VE NEVER HAD DONE SOMETHING 1145 00:51:44,532 --> 00:51:47,802 ON SUCH A LARGE SCALE. 1146 00:51:47,802 --> 00:51:50,738 THE PERFORMANCE WAS AS 1147 00:51:50,738 --> 00:51:51,306 EXPECTED, CUMULATIVE ABOUT 1148 00:51:51,306 --> 00:51:52,140 4,000 PROTEINS. 1149 00:51:52,140 --> 00:51:54,209 BUT NOW WE CAN ADDRESS THIS 1150 00:51:54,209 --> 00:51:56,311 QUESTION OF, YOU KNOW, HOW 1151 00:51:56,311 --> 00:52:00,415 IMPORTANT IS STRAIN OR GENETIC 1152 00:52:00,415 --> 00:52:01,783 BACKGROUND FOR THE 1153 00:52:01,783 --> 00:52:02,417 CIRCULATING--AND THE ANSWER IS 1154 00:52:02,417 --> 00:52:02,951 IMPORTANT. 1155 00:52:02,951 --> 00:52:10,225 VERY IMPORTANT. 1156 00:52:10,225 --> 00:52:10,959 THE SERUM PERTIUM REALLY 1157 00:52:10,959 --> 00:52:12,727 DEPENDS ON THE MOUSE. 1158 00:52:12,727 --> 00:52:15,129 I'M SHOWING YOU PROTEINS THAT 1159 00:52:15,129 --> 00:52:17,532 ARE HIGHER IN THREE. 1160 00:52:17,532 --> 00:52:23,104 AND IN THE 115 PROTEINS THAT I 1161 00:52:23,104 --> 00:52:25,406 PLOTTED, 370 ARE HIGHER, SO A 1162 00:52:25,406 --> 00:52:29,244 HUGE FRACTION OF THE SERUM IS 1163 00:52:29,244 --> 00:52:30,078 DEPENDENT ON THE GENETIC 1164 00:52:30,078 --> 00:52:36,985 BACKGROUND OF THE MOUSE. 1165 00:52:36,985 --> 00:52:38,786 SO THIS, THIS WILL COMPLICATE 1166 00:52:38,786 --> 00:52:42,056 THINGS. 1167 00:52:42,056 --> 00:52:43,224 SO CONSIDERING GENETIC 1168 00:52:43,224 --> 00:52:44,692 BACKGROUND IS IMPORTANT WHEN 1169 00:52:44,692 --> 00:52:48,296 LOOKING AT SERUM MARKERS. 1170 00:52:48,296 --> 00:52:49,664 THESE AFFECT SIZES ARE EVEN 1171 00:52:49,664 --> 00:52:51,199 LARGER THAN WHAT HI SHOWN YOU 1172 00:52:51,199 --> 00:52:52,900 WITH AGE, RIGHT. 1173 00:52:52,900 --> 00:52:56,804 SO THE AFFECT SIZE IS BOTH THE 1174 00:52:56,804 --> 00:52:58,273 SIGNIFICANCE AND MAGNITUDE OF 1175 00:52:58,273 --> 00:52:59,774 THE AFFECTS. 1176 00:52:59,774 --> 00:53:02,710 SO THESE A SAMPLE OF A PROTEIN 1177 00:53:02,710 --> 00:53:03,911 THAT IS DIFFERENT. 1178 00:53:03,911 --> 00:53:06,881 SO EVEN THOUGH YOU CAN SEE 1179 00:53:06,881 --> 00:53:10,518 THERE IS A DIFFERENCE BETWEEN 1180 00:53:10,518 --> 00:53:16,991 THE BLOCK 6 AND MICE. 1181 00:53:16,991 --> 00:53:18,493 IS THERE SOMETHING THAT THEY 1182 00:53:18,493 --> 00:53:21,562 ENRICH TOO? 1183 00:53:21,562 --> 00:53:24,565 AND INTERESTINGLY, THEY HAD 1184 00:53:24,565 --> 00:53:26,134 VERY STRONG, VERY 1185 00:53:26,134 --> 00:53:26,734 CHARACTERISTICS PATHWAYS THAT 1186 00:53:26,734 --> 00:53:29,637 THEY'RE ASSOCIATED WITH. 1187 00:53:29,637 --> 00:53:35,376 SO IN THE C56 BLOCK MICE, THESE 1188 00:53:35,376 --> 00:53:36,311 CHANGES ARE ENRICHING FOR 1189 00:53:36,311 --> 00:53:38,379 IMMUNITY AND PATHWAY. 1190 00:53:38,379 --> 00:53:41,549 INFLAMMATION SEEMS TO BE MUCH 1191 00:53:41,549 --> 00:53:50,758 HIGHER IN THE BLOCK 6 MICE, WE 1192 00:53:50,758 --> 00:53:56,631 SEE CELLULAR BUT IBQUUTENT. 1193 00:53:56,631 --> 00:53:58,299 AND YOU KNOW, IT'S NOT ONLY 1194 00:53:58,299 --> 00:53:59,834 IMPORTANT TO SEE WHAT IS 1195 00:53:59,834 --> 00:54:01,903 DEPENDENT ON THE STRAIN BUT HOW 1196 00:54:01,903 --> 00:54:03,738 THE CHANGE, IS AGING DIFFERENT 1197 00:54:03,738 --> 00:54:04,305 BETWEEN ONE STRAIN AND THE 1198 00:54:04,305 --> 00:54:06,007 OTHER? 1199 00:54:06,007 --> 00:54:09,177 WE LOOKED AT WHAT ARE 1200 00:54:09,177 --> 00:54:11,579 TRAJECTORIES? 1201 00:54:11,579 --> 00:54:13,314 AND THE ANSWER HERE IS ALSO 1202 00:54:13,314 --> 00:54:14,782 THAT IT'S QUITE IMPORTANT. 1203 00:54:14,782 --> 00:54:17,285 SO WE SEE A LOT OF THINGS THAT 1204 00:54:17,285 --> 00:54:20,922 ARE DIFFERENT IN THE AGE 1205 00:54:20,922 --> 00:54:25,059 GENOTYPE INTERACTION. 1206 00:54:25,059 --> 00:54:27,762 I SHOWS A NOT VERY GOOD SAMPLE 1207 00:54:27,762 --> 00:54:36,537 BUT I WANT TO ILLUSTRATE THAT 1208 00:54:36,537 --> 00:54:37,071 WE CAN DEMONSTRATE HIGHER 1209 00:54:37,071 --> 00:54:38,473 STRAIN. 1210 00:54:38,473 --> 00:54:40,575 IN ONE STRAIN THEY WOULD BE 1211 00:54:40,575 --> 00:54:41,676 GOING UP AND ONE THEY MAY BE 1212 00:54:41,676 --> 00:54:42,877 GOING DOWN. 1213 00:54:42,877 --> 00:54:44,278 THESE ARE THE KIND OF THINGS 1214 00:54:44,278 --> 00:54:46,547 THAT ARE GOING TO BE IMPORTANT 1215 00:54:46,547 --> 00:54:49,584 FOR US IN THE BIOMARKER STUDY. 1216 00:54:49,584 --> 00:54:52,086 ALL RIGHT, SO IN SUMMARY, WHAT 1217 00:54:52,086 --> 00:54:55,523 I'VE SHOWN YOU IS THAT WE WERE 1218 00:54:55,523 --> 00:54:57,325 ABLE TO USE THIS NANO PARTICLE 1219 00:54:57,325 --> 00:55:00,628 TO DO IN A LOW VOLUME MOUSE 1220 00:55:00,628 --> 00:55:02,163 SERUM AND AT SCALE. 1221 00:55:02,163 --> 00:55:04,799 AND YOU KNOW, FROM OUR INITIAL 1222 00:55:04,799 --> 00:55:08,102 DATA SET, WE'RE ALREADY ABLE TO 1223 00:55:08,102 --> 00:55:09,504 SEE THE GENETIC BACKGROUND HAS 1224 00:55:09,504 --> 00:55:12,273 A HUGE AFFECT AND ABOUT A THIRD 1225 00:55:12,273 --> 00:55:13,975 ABOUT SERUM IS ASSOCIATED WITH 1226 00:55:13,975 --> 00:55:17,078 STRAIN AND THAT THESE CHANGES 1227 00:55:17,078 --> 00:55:17,678 ARE CHARACTERISTIC OF CERTAIN 1228 00:55:17,678 --> 00:55:22,917 PATHWAYS. 1229 00:55:22,917 --> 00:55:25,586 SO, WE ARE NEARLY OUT OF TIME 1230 00:55:25,586 --> 00:55:25,753 NOW. 1231 00:55:25,753 --> 00:55:27,054 SO I'M GOING TO MAYBE SKIP THIS 1232 00:55:27,054 --> 00:55:28,890 BUT I THINK, THERE IS VERY 1233 00:55:28,890 --> 00:55:30,491 STRONG EVIDENCE IN THE 1234 00:55:30,491 --> 00:55:31,426 LITERATURE THAT YOU KNOW, ONE 1235 00:55:31,426 --> 00:55:33,528 OF THE THINGS THAT WE CARE 1236 00:55:33,528 --> 00:55:36,731 TURNOVER BUT THERE IS A STRONG 1237 00:55:36,731 --> 00:55:39,233 EVIDENCE THAT TURNOVER RATES 1238 00:55:39,233 --> 00:55:41,969 ARE ALSO ASSOCIATED WITH 1239 00:55:41,969 --> 00:55:43,304 LONGEVITY, SO THIS IS SOMETHING 1240 00:55:43,304 --> 00:55:44,071 WORTH STUDIES IN THE FUTURE. 1241 00:55:44,071 --> 00:55:45,940 AND I'LL GET TO THE FUTURE 1242 00:55:45,940 --> 00:55:50,111 SLIDE HERE, IS THAT I HOPE I 1243 00:55:50,111 --> 00:55:51,512 SHOWED YOU THAT PROTEOMICS CAN 1244 00:55:51,512 --> 00:56:01,889 BE USED TO HELP WITH 1245 00:56:07,395 --> 00:56:09,330 TRANSLATIONAL GEROSCIENCE. 1246 00:56:09,330 --> 00:56:13,668 THAT THEY GET US GET BETTER 1247 00:56:13,668 --> 00:56:14,936 COVERAGE AND FLUIDS, 1248 00:56:14,936 --> 00:56:17,738 IMPORTANTLY INCLUDING THESE, IF 1249 00:56:17,738 --> 00:56:21,175 WE CAN COLLECT THESE SAMPLES IN 1250 00:56:21,175 --> 00:56:21,843 OUR AGE RELATED STUDIES, 1251 00:56:21,843 --> 00:56:23,911 CLINICIANS ARE OUT THERE AND 1252 00:56:23,911 --> 00:56:24,846 BANK THEM AND HOPEFULLY LATER 1253 00:56:24,846 --> 00:56:25,813 WE CAN BEGIN TO DO THESE 1254 00:56:25,813 --> 00:56:28,983 STUDIES. 1255 00:56:28,983 --> 00:56:30,384 THESE WELL DESIGNED 1256 00:56:30,384 --> 00:56:34,055 LONGITUDINAL ARE ALSO VERY 1257 00:56:34,055 --> 00:56:37,458 HELPFUL IN ADDRESS THE AGING. 1258 00:56:37,458 --> 00:56:42,296 AND YOU KNOW, I THINK THAT, 1259 00:56:42,296 --> 00:56:43,831 PROTEOMICS CAN HELP A LOT. 1260 00:56:43,831 --> 00:56:44,999 THIS IS NOT SOMETHING THAT I 1261 00:56:44,999 --> 00:56:49,036 COVERED TODAY BUT WE CAN USE 1262 00:56:49,036 --> 00:56:50,438 NEWER METHODS TO GET MORE 1263 00:56:50,438 --> 00:56:52,173 SENSITIVE. 1264 00:56:52,173 --> 00:56:54,709 SO ALL OF THESE DISCOVERY 1265 00:56:54,709 --> 00:56:56,844 METHODS ARE VERY AGNOSTIC TO 1266 00:56:56,844 --> 00:56:57,345 THE VARIANT THAT YOU'RE 1267 00:56:57,345 --> 00:56:58,613 MEASURING. 1268 00:56:58,613 --> 00:57:01,115 SO A LOT OF NOISE COULD BE THAT 1269 00:57:01,115 --> 00:57:02,316 WE'RE NOT MEASURING THE 1270 00:57:02,316 --> 00:57:07,288 SPECIFICALLY VARIANT BUT IT 1271 00:57:07,288 --> 00:57:10,758 COULD HAVE A PTM, BUT WE'RE 1272 00:57:10,758 --> 00:57:11,392 AGGREGATING THESE MEASUREMENTS 1273 00:57:11,392 --> 00:57:14,328 WITH DISCOVERY. 1274 00:57:14,328 --> 00:57:16,230 SO YOU KNOW, USING DEVELOPING 1275 00:57:16,230 --> 00:57:19,000 METHODS THAT ALLOW US TO DO 1276 00:57:19,000 --> 00:57:19,767 MORE SPECIFIC SCALE WILL BE 1277 00:57:19,767 --> 00:57:21,335 IMPORTANT FOR THE FUTURE. 1278 00:57:21,335 --> 00:57:23,204 AND WITH THAT, WOULD I LIKE TO 1279 00:57:23,204 --> 00:57:25,606 THANK A LOT OF PEOPLE WHO WERE 1280 00:57:25,606 --> 00:57:30,778 INVOLVED IN THIS WORK FROM MY 1281 00:57:30,778 --> 00:57:33,781 LAB, PARTICULARLY AMID AREMA 1282 00:57:33,781 --> 00:57:37,018 AND BRAD WHO ARE COLLABORATORS. 1283 00:57:37,018 --> 00:57:41,322 WE HAVE KIENAN WALKER, WHO DID 1284 00:57:41,322 --> 00:57:48,129 THE PROTEOMICS BUT THIS ENTIRE 1285 00:57:48,129 --> 00:57:50,865 BLSA TEAM, ASIR, THE 1286 00:57:50,865 --> 00:57:51,332 TRANSLATION BRANCH 1287 00:57:51,332 --> 00:57:52,533 PARTICULARLY, LEE RAFA AND ALL 1288 00:57:52,533 --> 00:57:54,302 THE INVESTIGATORS WHO DID THE 1289 00:57:54,302 --> 00:57:54,602 SLAM STUDY. 1290 00:57:54,602 --> 00:57:56,971 AND OF COURSE I WANT TO THANK 1291 00:57:56,971 --> 00:57:58,339 ALL THE PARTICIPANT OF THE 1292 00:57:58,339 --> 00:58:02,743 STUDIES AND OF COURSE THE MICE 1293 00:58:02,743 --> 00:58:04,078 WHO SACRIFICE TO BE ABLE TO 1294 00:58:04,078 --> 00:58:05,880 GIVE US THIS DATA. 1295 00:58:05,880 --> 00:58:06,480 I'M HAPPY TO ANSWER YOUR 1296 00:58:06,480 --> 00:58:06,814 QUESTIONS. 1297 00:58:06,814 --> 00:58:07,615 THANK YOU. 1298 00:58:07,615 --> 00:58:17,792 [APPLAUSE] 1299 00:58:19,860 --> 00:58:21,629 >> THANK YOU, BASISTY. 1300 00:58:21,629 --> 00:58:24,432 >> SO THIS CAME FROM THE 1301 00:58:24,432 --> 00:58:27,835 VIRTUAL AUDIENCE AND I THINK 1302 00:58:27,835 --> 00:58:29,804 YOU ADDRESSED IT PARTIALLY. 1303 00:58:29,804 --> 00:58:32,306 ARE ANY OF THESE TESTS BEING 1304 00:58:32,306 --> 00:58:33,441 MADE AVAILABLE OR IS THIS JUST 1305 00:58:33,441 --> 00:58:35,343 A RESEARCH. 1306 00:58:35,343 --> 00:58:38,579 >> THAT'S A GREAT QUESTION. 1307 00:58:38,579 --> 00:58:40,881 RIGHT NOW, WHAT I SHOWED YOU IS 1308 00:58:40,881 --> 00:58:42,783 IN THE RESEARCH STAGE BUT THERE 1309 00:58:42,783 --> 00:58:44,085 ARE EXISTING METHODS THAT WOULD 1310 00:58:44,085 --> 00:58:45,853 BE NOT TOO HARD TO INCLUDE IN 1311 00:58:45,853 --> 00:58:49,523 THE CLINICAL STUDY. 1312 00:58:49,523 --> 00:58:51,258 FOR EXAMPLE, ACTUALLY I HAVE A 1313 00:58:51,258 --> 00:58:52,727 SLIDE FOR THIS. THIS IS THE 1314 00:58:52,727 --> 00:58:53,527 METHODS THAT ARE OUT THERE 1315 00:58:53,527 --> 00:58:58,933 EXISTING. 1316 00:58:58,933 --> 00:59:00,067 ALSO THESE OTHER PROTEOMICS 1317 00:59:00,067 --> 00:59:04,138 STUDIES THAT CAN ALSO OFFER 1318 00:59:04,138 --> 00:59:10,077 REALLY BIG COVERAGE. 1319 00:59:10,077 --> 00:59:10,511 AND THEY'RE PRETTY 1320 00:59:10,511 --> 00:59:11,445 COMPREHENSIVE. 1321 00:59:11,445 --> 00:59:15,082 WHAT I'M SHOWING IS VERY MUCH 1322 00:59:15,082 --> 00:59:16,384 RESEARCH BUT HOPEFULLY WE CAN 1323 00:59:16,384 --> 00:59:19,253 INTEGRATE IT SOON. 1324 00:59:19,253 --> 00:59:21,122 I WOULD ENCOURAGE ANYONE TO 1325 00:59:21,122 --> 00:59:22,289 JUST COLLECT THE BLOOD BEFORE 1326 00:59:22,289 --> 00:59:23,891 AND AFTER THE TREATMENT AND 1327 00:59:23,891 --> 00:59:25,726 SAVE IT AND HOPEFULLY YOU'LL 1328 00:59:25,726 --> 00:59:27,561 RUN INTO A MASS EXPERT IF YOU 1329 00:59:27,561 --> 00:59:30,064 HAVE MONEY, YOU CAN JUST PAY TO 1330 00:59:30,064 --> 00:59:37,038 HAVE THINGS LIKE SOMA LOGICS TO 1331 00:59:37,038 --> 00:59:38,072 THEN FOLLOW-UP WITH MORE 1332 00:59:38,072 --> 00:59:38,673 TARGETED METHODS. 1333 00:59:38,673 --> 00:59:41,242 >> AND THEN JUST AS A QUESTION 1334 00:59:41,242 --> 00:59:47,848 THAT I HAVE, I'M A PEDIATRIC 1335 00:59:47,848 --> 00:59:48,816 ONCOLOGIST INTERESTED IN 1336 00:59:48,816 --> 00:59:50,217 SURVIVALSHIP, THE QUESTION I 1337 00:59:50,217 --> 00:59:52,720 HAVE IS YOU SAID THERE WAS A 1338 00:59:52,720 --> 00:59:53,688 CORRELATION BETWEEN THE 1339 00:59:53,688 --> 00:59:56,424 TREATMENT AND SIDE AFFECTS. 1340 00:59:56,424 --> 01:00:00,127 HAS THAT BEEN LOOKED AT AND IS 1341 01:00:00,127 --> 01:00:03,764 THAT A REVERSABLE THING WHERE 1342 01:00:03,764 --> 01:00:06,634 YOU PROTEINS LATER THEY CAN 1343 01:00:06,634 --> 01:00:10,438 DWINDLE DOWN OR IS IT RELATED 1344 01:00:10,438 --> 01:00:11,939 TO THE THERAPEUTIC CHALLENGE? 1345 01:00:11,939 --> 01:00:13,941 >> THAT'S A VERY GOOD QUESTION. 1346 01:00:13,941 --> 01:00:17,645 WE DON'T HAVE, THERE IS VERY 1347 01:00:17,645 --> 01:00:21,782 LIMITED DATA IN HUMANS, JUST 1348 01:00:21,782 --> 01:00:23,017 NOW PEOPLE ARE LOOKING AT THE 1349 01:00:23,017 --> 01:00:25,252 HUMAN. 1350 01:00:25,252 --> 01:00:27,321 BUT THE BOTTLE AND THE MICE, 1351 01:00:27,321 --> 01:00:30,491 HAVE SHOWN THAT IT IS RELEVANT. 1352 01:00:30,491 --> 01:00:34,929 SO FOR EXAMPLE, IN MICE, THAT 1353 01:00:34,929 --> 01:00:36,964 ARE GIVEN, YOU KNOW, DOXRUBESON 1354 01:00:36,964 --> 01:00:38,866 IT'S A CHEMOTHERAPY, ONE OF THE 1355 01:00:38,866 --> 01:00:45,740 SIDE AFFECTS IS KAOEM BRAIN, SO 1356 01:00:45,740 --> 01:00:51,378 IN MICE, COGNITIVE DEFICITS, 1357 01:00:51,378 --> 01:00:53,514 GIVEN THE MICE A SANA LETIC 1358 01:00:53,514 --> 01:00:54,582 RELIEVED. 1359 01:00:54,582 --> 01:00:55,950 THERE IS SOME SUGGESTION THAT 1360 01:00:55,950 --> 01:00:58,953 IT CAN HELP IN THE SIDE AFFECTS 1361 01:00:58,953 --> 01:01:00,921 OF THIS THERAPY. 1362 01:01:00,921 --> 01:01:01,989 >> ALL RIGHT, WELL THANK YOU. 1363 01:01:01,989 --> 01:01:08,028 OKAY. 1364 01:01:08,028 --> 01:01:08,929 >> THANK YOU FOR THAT GREAT 1365 01:01:08,929 --> 01:01:10,097 TALK. 1366 01:01:10,097 --> 01:01:14,902 SO I HAVE A QUESTION, AND IN 1367 01:01:14,902 --> 01:01:17,671 OUR LAB, WE STUDY AUTO 1368 01:01:17,671 --> 01:01:19,039 INFLAMMATORY DISEASE AND WE 1369 01:01:19,039 --> 01:01:24,545 STUDY A SPECIFIC DISEASE WHERE 1370 01:01:24,545 --> 01:01:27,348 WE SEE SOME RETINAL 1371 01:01:27,348 --> 01:01:28,949 DEGENERATIVE IN PATIENTS AS 1372 01:01:28,949 --> 01:01:30,518 EARLY AS IN THEIR TEENS, ADULTS 1373 01:01:30,518 --> 01:01:33,954 AGE. 1374 01:01:33,954 --> 01:01:36,891 DO YOU THINK LIKE, SENESCENCE 1375 01:01:36,891 --> 01:01:42,129 IMMUNE CELLS HAVE SUCH A ROLE 1376 01:01:42,129 --> 01:01:45,833 IN THESE TYPES OF DISEASES AND 1377 01:01:45,833 --> 01:01:46,734 CAUSING A RAPID DEGENERATION 1378 01:01:46,734 --> 01:01:47,935 EARLY ON? 1379 01:01:47,935 --> 01:01:49,804 >> YEAH, THAT'S A GOOD QUESTION. 1380 01:01:49,804 --> 01:01:53,007 SO THERE IS I'M NOT SURE ABOUT 1381 01:01:53,007 --> 01:01:55,042 THE SPECIFIC DISEASE, BUT THEY 1382 01:01:55,042 --> 01:01:57,344 CAN BE INVOLVED IN EYE DISEASE. 1383 01:01:57,344 --> 01:02:00,614 THERE IS A COMPANY UNITY 1384 01:02:00,614 --> 01:02:02,817 BIOTECHNOLOGY WHO DEVELOPING 1385 01:02:02,817 --> 01:02:04,485 THESE. 1386 01:02:04,485 --> 01:02:08,122 ONE OF THE FIRST SUCCESSFUL 1387 01:02:08,122 --> 01:02:10,157 TRIALS I'VE SEEN ON DRUGS IS 1388 01:02:10,157 --> 01:02:12,760 TARGETING AMD. 1389 01:02:12,760 --> 01:02:15,062 AGE RELATED, SO THERE IS SOME 1390 01:02:15,062 --> 01:02:18,732 DATA THAT SUGGEST THAT YOU 1391 01:02:18,732 --> 01:02:21,268 KNOW, SENESCENCE IS DRIVING AGE 1392 01:02:21,268 --> 01:02:24,205 RELATED PATHOLOGIST. 1393 01:02:24,205 --> 01:02:26,140 BUT IN YOUR STUDY, IF YOU HAVE 1394 01:02:26,140 --> 01:02:29,243 PROTEINS THAT YOU SEE IN YOUR 1395 01:02:29,243 --> 01:02:32,413 MODEL TO SEE IF THOSE ARE 1396 01:02:32,413 --> 01:02:33,113 ASSOCIATED WITH SENESCENCE. 1397 01:02:33,113 --> 01:02:40,287 YEAH, ALL RIGHT, THANK YOU. 1398 01:02:40,287 --> 01:02:45,559 DR. BASISTY, THANK YOU FOR 1399 01:02:45,559 --> 01:02:47,361 YOUR SHARING YOUR WORK. 1400 01:02:47,361 --> 01:02:49,230 WE WILL NOT HAVE A GRAND ROUND 1401 01:02:49,230 --> 01:02:51,498 NEXT WEEK BUT WE WILL SEE 1402 01:02:51,498 --> 01:03:02,009 EVERYBODY IN ABOUT TWO WEEKS.