2022-10-24 - Presentation Dr. David Sinclair - ARDD2022 - Great release of unpublished data from David Sinclair's Lab

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    0:00 [Music]
    0:00 foreign
    0:03 [Music]
    0:06 we are on to our final speaker who is on
    0:09 Zoom
    0:10 David we are ready for your talk
    0:12 whenever you are ready to share your
    0:15 slides all right let's do it hi everyone
    0:17 wish I could be there wish I was there
    0:20 uh next year for sure next oh that's
    0:23 great
    0:24 I'll hold you up to that promise I've
    0:26 got my uh my father visiting from
    0:28 Australia he's 83 and
    0:31 I think that took precedent but uh next
    0:33 year I'll bring him with me that would
    0:35 be amazing that would be amazing okay
    0:37 well thanks for inviting me let me get
    0:39 my
    0:40 presentation up and running
    0:43 perfect great and I'll set a timer so
    0:47 that we're good
    0:49 okay all right so um yeah it's it's uh
    0:52 it's amazing to be here this conference
    0:54 to your credit uh and the other
    0:56 organizers um just it's growing to be
    0:59 one of the Premier conferences in the
    1:02 world in this topic so hats off to you
    1:04 and I've really enjoyed the the talks
    1:07 uh so just because I don't have much
    1:08 time I'll get straight into it
    1:10 uh so I've been doing this for a while
    1:12 started at MIT working in little yeast
    1:14 cells and we discovered that and by we I
    1:16 mean a lot of other people as well uh
    1:18 the team discovered that uh their
    1:21 reaction to genomic instability uh
    1:24 particularly repetitive regions uh leads
    1:26 to the reorganization of Chromatin which
    1:28 leads to changes in gene expression and
    1:30 phenotypes of aging and what we work on
    1:33 in the lab is that that process that we
    1:36 worked on in Lenny grunty's lab in yeast
    1:38 in the mid-1990s uh is also true for
    1:41 mammals um and that there's a backup
    1:44 copy of information to reset the
    1:46 epigenome
    1:48 uh so what I'll do today is I'll share a
    1:50 fair bit of unpublished research it's uh
    1:53 it's really a coming out moment here
    1:55 um so this is how we think in my lab
    1:57 um similar to many others we like to
    1:59 understand why we get old
    2:01 uh and we think that the Hallmarks are
    2:04 part of a whole system and we think that
    2:06 the loss of information in particular is
    2:09 is very important and information comes
    2:12 in two types in in biology mainly it's
    2:14 the DNA and the epigenome control
    2:16 systems and we have a a lot of evidence
    2:20 in the field increasingly so that the
    2:22 epigenetic noise as we call it and the
    2:25 loss of differentiation is a major
    2:28 driver of of the process we call aging
    2:31 and illness
    2:33 um so I'm going to talk about efforts in
    2:36 many organisms including humans to slow
    2:39 down aging by targeting epigenetic
    2:42 modifiers
    2:43 um which include the sirtuins which we
    2:47 worked on and continued to do so
    2:50 I'm going to talk about the ability to
    2:52 reverse aspects of Aging including
    2:55 epigenetic age
    2:57 and I'm going to talk about what we
    2:58 really have talked about a lot at this
    3:00 conference and in my lab we are
    3:02 fascinated with the
    3:04 um the goal of having accurate clocks
    3:07 that represent epigenetic aging if not
    3:10 other aspects of Aging
    3:12 um it's a bit of a complicated slide but
    3:14 it does cover what we work on in my lab
    3:17 this was from a review back in 2008 when
    3:20 we published a paper that showed that
    3:23 not just in yeast but in mammals
    3:24 chromatin modifiers move around during
    3:26 aging and that process can be
    3:28 accelerated by extreme cellular damage
    3:31 and what we used in this case was the
    3:33 double-stranded DNA break or dsb and we
    3:36 use an enzyme called people ppo1 to
    3:39 accelerate what looked like aging and I
    3:42 think you've heard probably me talk
    3:44 about this before so I'll go quickly but
    3:46 we've used this system to understand the
    3:49 process of Aging in mammals and and how
    3:52 to reverse it and just some some aspects
    3:55 I want to point out
    3:56 NAD is a cofactor co-substrate as you
    3:59 know so Twins and we found that up
    4:03 regulating cert one gene expression as
    4:05 well as NAD levels in the cell can slow
    4:08 down this reorganization of the
    4:10 epigenome and prevent loss of gene
    4:13 expression patterns and we did this in a
    4:15 mouse back in 2008 in in neurons in the
    4:18 brain but we've gone a long way since
    4:20 then
    4:21 come a long way oh we also I want to
    4:24 report a little bit about mib626 which
    4:26 is an NAD
    4:27 precursor uh it's a polymorph of
    4:30 nicotinamide mononucleotide that is GMP
    4:35 pure it's been in humans and I'll update
    4:37 you on that and how that seems to work
    4:39 and our goal there is to have a drug
    4:42 that will treat
    4:44 um not just aging but diseases of aging
    4:47 and they're about I think right now
    4:49 we've got five clinical trials up and
    4:52 running and I'll share some results of
    4:54 that
    4:55 um so what about the clocks uh we have a
    4:58 paper that's up online it's not yet
    5:00 published this is work by Patrick
    5:02 Griffin and Jen Lee in my lab two
    5:04 students who came up with the idea that
    5:07 you can greatly reduce the the cost of
    5:10 sequencing by uh transposon tagging DNA
    5:14 samples and we can pull 500 or more
    5:17 people or dogs or mice and we've built
    5:21 some and Patrick and Jenna built some
    5:23 really accurate clocks for Mouse tissues
    5:26 and in humans you can see down below
    5:28 wherever I can I give you directions to
    5:31 go follow up on this news
    5:34 um these are hot off the press human
    5:36 clocks from Patrick they're really great
    5:38 they've got um
    5:40 all the attributes that you'd want this
    5:42 is from human blood we have brought in
    5:44 over a thousand human samples from
    5:47 people a variety of Ages and you can see
    5:49 that the the uh our values are really
    5:52 great and uh the uh midday of 3.5 uses
    5:57 it's not bad for a first start uh and
    5:59 this is again just half the presses uh
    6:01 the mouse ones look really good too I
    6:04 would also phenotypic age clocks which
    6:05 we've published on if you're interested
    6:07 you could check that out
    6:10 uh how about slowing aging well we've
    6:12 worked on activators of sirtuins for a
    6:15 long time Resveratrol was the one back
    6:16 20 years ago we've developed some
    6:19 synthetic ones and some NAD precursors
    6:21 uh mib626 is as I mentioned an oral
    6:25 formulation of a polymorph of mh66 that
    6:28 stabilized crystalline and it's gone
    6:30 through a lot of safety studies over the
    6:32 last few years headed by this lab are
    6:35 shally basin's group at Brigham and
    6:37 Women's Hospital and David Livingston
    6:40 runs the group at Metro biotech which is
    6:42 a massachusetts-based company that's
    6:44 been making NAD precursors both
    6:46 synthetic and natural for the last
    6:48 decade
    6:49 uh and that and full disclosure this was
    6:51 spun out of my lab and others including
    6:54 Sheena Mai and Raj after
    6:57 uh how do you measure uh what the effect
    6:59 might be well one of the ways that
    7:01 Shelley's lab did it was to look at
    7:03 endurance and strength uh within an MRI
    7:07 and so this is uh an example of the
    7:10 machine that they built to insert the
    7:11 patients into the machine and measure
    7:14 their endurance strength and things like
    7:16 uh ATP NAD oxygenation
    7:20 um and some of the data that hasn't
    7:21 probably been published yet but we're
    7:22 starting to talk about it and this is
    7:24 their group's data not mine uh is that
    7:27 uh
    7:27 a number of things happen when you take
    7:29 uh 1 000 milligrams from roughly a month
    7:33 of this uh substance orally
    7:37 and you can see here an example of some
    7:39 of the data uh the repetitions the
    7:41 failure of that leg exercise uh
    7:44 significantly greater after taking
    7:45 mib636 this is similar or at least
    7:48 reminiscent to the mouse data that we
    7:50 published in 2018 on endurance in mice
    7:53 due to increased vascularization of the
    7:57 action on endothelial cells
    8:00 um and others nice bit of data I would
    8:03 admit it was a surprise a nice surprise
    8:05 is that Shelley's group showed that um
    8:08 lipids and cholesterol uh went in the
    8:11 right direction for improved Health uh
    8:13 as you can see here
    8:16 uh HDL was not affected which was also
    8:18 reassuring
    8:21 uh what about acceleration of Aging now
    8:23 we do this for two reasons one is to
    8:25 understand why we age because if we can
    8:27 cause it then we have some idea that
    8:29 we're on the right track but we also
    8:31 want to know if we can have a model of
    8:35 Aging that's much quicker because
    8:36 typical longevity experiment takes way
    8:38 too long and in organoids organoids are
    8:40 way too young when you build an organide
    8:42 from a stem cell an ipsc they're age
    8:45 zero which is not that useful and so
    8:48 we've used this system which you may
    8:50 know about it's close to getting
    8:52 accepted we hope uh last last stages of
    8:56 this uh 12-year Journey
    8:59 um actually I started this project with
    9:00 Philip oberdorf when I was 39 so I've
    9:03 aged a little uh this project is uh the
    9:06 combination of 20 labs around the world
    9:10 to see if we can displace chromatin
    9:13 modifiers in a way that mimics aging and
    9:15 we've done that and that's this work
    9:17 which we call the ice system and we have
    9:19 cell based systems and mouse systems to
    9:21 do that
    9:22 and we'll use it in a minute I'll show
    9:24 you later but we can drive aging
    9:26 forwards as far as we can tell
    9:27 epigenetically and the other Hallmarks
    9:29 of Aging accelerate as well for the most
    9:32 part
    9:33 the reversal this is a very hot topic as
    9:36 you all know we use uh typically we use
    9:40 a combination of three of the yamanaka
    9:42 factors rather than four we leave out
    9:44 cmic and we're using other methods
    9:46 chemicals now as well we published this
    9:50 um in 2020 so I'm not going to repeat
    9:53 that data but we did use it to
    9:55 rejuvenate neurons in mice and restore
    9:57 Vision in mice that had damaged or old
    10:00 retinas by targeting the neurons
    10:03 um and we find that we need all three of
    10:05 those factors O S and K shown down here
    10:08 in this Vector that we can control with
    10:10 Doxycycline Teton and Ted off system if
    10:12 you would like to try it this isn't uh
    10:15 injections in the eye which was in the
    10:17 the nature paper this one's the whole
    10:19 brain and we're using a variety of aavs
    10:21 now to infect different tissues and the
    10:23 whole body in the in efforts to not just
    10:26 accelerate aging but take those mice and
    10:28 wild type and reverse them and see what
    10:30 happens and this is work that I'm
    10:32 showing you from Xiao Tian uh primarily
    10:36 and jeun jeunyang and their goal is to
    10:39 reverse the age of the brain and see
    10:41 what happens
    10:42 and uh well this was the paper I just
    10:44 wanted to remind you and we had a lot of
    10:47 help from people in the field some of
    10:48 who are at this conference uh Morgan's
    10:50 here uh that in was great help Steve
    10:53 Horvath there's a long list you can see
    10:55 here I couldn't have done any of this
    10:57 without their Labs as well what was
    10:59 amazing about this was that we showed
    11:01 that there's a repository of youthful
    11:04 information that can reset a cell's
    11:06 epigenome we measure gene expression on
    11:08 the right you can see it's nicely reset
    11:10 proportionally to how this genes were
    11:12 expressed when they were young this is
    11:13 RNA and on the left I think even
    11:16 just as impressive is the methylation
    11:18 patterns were largely restored as well
    11:21 um and the clock uh which uh Morgan and
    11:24 Steve and that even helped us show uh
    11:26 was as well and one of the
    11:28 interesting things in this paper is we
    11:29 found that damage to neurons accelerates
    11:33 epigenetic aging as well so it's not
    11:35 just DNA cutting
    11:37 um or time it's extreme cell stress like
    11:40 a nerve Crush that can do it as well
    11:43 uh this has been commercialized or is
    11:46 being commercialized by life biosciences
    11:48 a boston-based company also spun out of
    11:50 my lab you can check it out
    11:53 um and you'll probably get the website
    11:54 if you spell The Sciences correctly
    11:57 there's an e in there
    11:59 um Blackberry Sciences was started in
    12:01 2017 uh Bruce Cassandra at Mass Eye and
    12:04 Ear has done a lot of the pre-clinical
    12:06 work and safety work we've now got a
    12:08 fair amount of data and we're now in
    12:10 non-human primates with a goal uh of
    12:12 some time in uh the next whole to 24
    12:15 months of uh starting uh work towards an
    12:18 IND and getting into humans if all goes
    12:20 well and our goal is to reverse
    12:22 blindness in a variety of different
    12:24 disorders in humans
    12:26 but it is remarkable if you reverse the
    12:28 age of neurons in the retina you can
    12:30 restore vision and gene expression this
    12:32 way
    12:33 um of course I'm standing on the back of
    12:34 some very big names uh in this field who
    12:37 uh showed pointed the way uh this is new
    12:41 data unpublished I was allowed to share
    12:43 this from Bruce Cassandra's lab
    12:45 uh he and I are infecting not the
    12:47 neurons here but the retinal pigment and
    12:49 pythelium cells rpe and these give rise
    12:52 to uh the photoreceptors and these
    12:54 degrade over time particularly in
    12:56 macular degeneration and what Bruce has
    12:58 found is that he can actually restore
    13:00 the morphology and the function of these
    13:02 cells and restore eyesight back to a
    13:04 young State similar to what happens when
    13:06 you rejuvenate neurons in the eye as
    13:08 well
    13:09 the system uh in the lab now that we're
    13:11 doing is more human we have inducible
    13:13 human neurons uh where we can
    13:16 differentiate them both into flat
    13:17 cultures and three-dimensional and we do
    13:20 pro-aging with ice and reversal with osk
    13:22 typically okay I'll give you some
    13:25 examples of what we do
    13:27 and this is work mainly by Xiao and Jay
    13:29 in the lab we grow the nerves on these
    13:31 little culture dishes that can sense
    13:33 electrical signals uh when we have a
    13:35 control induction of no osk there's not
    13:40 very good electrical firing but when we
    13:42 rejuvenate them
    13:44 uh with uh the osk treatment uh just for
    13:48 a short amount of time this just takes a
    13:50 little over a week we see that there's a
    13:54 great induction of function
    13:57 um and it continues out the longer we go
    13:59 this is day 50 that you're seeing on the
    14:01 right so that's just neurons that's
    14:03 human neurons we've got Alzheimer's
    14:04 patient neurons as well we can make
    14:07 these into little brain organoids you
    14:09 can see here that we can turn on all of
    14:11 the three yamanaka factors that we like
    14:13 in these organoids which have a similar
    14:15 brain structure to ours great models
    14:19 um for human aging we can age these
    14:21 forward and get them to take on
    14:23 inflammatory and senescent signatures
    14:26 which I think will be a useful model
    14:28 in terms of reversal uh we've done this
    14:30 in mice as well we can now deliver the
    14:33 osk system into various places in the
    14:37 brain whole brain or in this case on the
    14:39 right the excitatory neurons and in both
    14:43 cases we get an improvement in learning
    14:45 in these old mice which is what I think
    14:49 might have we might find out as a field
    14:51 is that diseases of old age particularly
    14:53 in the brain I'm thinking of like
    14:55 Alzheimer's if you make the the brain
    14:57 young then the diseases just go away
    14:59 because most of these diseases of Aging
    15:01 are caused by edging I mean it's obvious
    15:03 to a lot of us but to the rest of the
    15:05 world it's not
    15:07 um so I want to thank the people who
    15:09 made this possible uh there's a large
    15:11 group The the clinical trial group is at
    15:14 Brigham and Women's Hospital there are
    15:16 four other clinical trials that are
    15:18 ongoing there's one actually for
    15:20 Alzheimer's disease that's starting to
    15:21 recruit there's one um well I won't say
    15:25 the whole lot but they're interesting
    15:26 ones
    15:28 um you can see that we've got
    15:29 collaborators both for exercise
    15:31 physiology the hormones and this is um
    15:34 the mib636 group at Metro biotech
    15:38 uh here we have uh the people in my lab
    15:40 that helped or did major contributions I
    15:43 want to point out the work of Jay who
    15:45 did the Ice Mouse
    15:47 um and matoshi I want to thank everyone
    15:49 really but I want to focus on I
    15:52 mentioned Xiao Jian
    15:55 um for the osk work uh Yuan Chang Liu
    15:58 who left the lab after he published
    16:00 rightly so uh he's uh not listed but he
    16:03 was the first author on that nature
    16:05 paper and I also want to point out Chris
    16:07 who's doing reversal of senescence uh
    16:10 Gerald who's working on a lot of the
    16:13 mouse projects I could go on I won't uh
    16:15 do that but I do want to thank Patrick
    16:17 who I probably didn't mention enough he
    16:19 was the one that came up with this idea
    16:21 to do the time seek clock
    16:23 um so many collaborators it's impossible
    16:25 to thank everybody these are the
    16:26 highlights and I am so grateful for
    16:29 their uh advice uh reagents and uh and
    16:32 friendship over the years and the people
    16:34 who uh supported the work financially
    16:36 have been amazing too
    16:38 um so I'll stop there and take any
    16:39 questions thank you for having me
    16:47 thank you so much David we have a
    16:50 question here in the front from one of
    16:52 our Inspire ambassadors
    16:54 they're the high school students that
    16:56 are involved in the erdd organization
    17:00 hi my name is Andrea
    17:02 um I was just wondering so you you said
    17:05 that you used three of the Fourier
    17:06 Monica factors when looking at eyesight
    17:09 Rejuvenation have you tested any other
    17:11 combinations of these yamanaka factors
    17:15 we tested all combinations
    17:18 um and
    17:19 we needed three uh four uh worked as
    17:23 well but we were concerned about the
    17:26 loss of cellular identity so we left c
    17:28 MC out and those three are necessary if
    17:31 we just do one or two combinations uh it
    17:34 did not work and if we gave them
    17:36 individually in different viruses it
    17:39 still didn't work so it seems like we
    17:40 need to package them into the same AV
    17:41 three of them put them in a polycystron
    17:44 and get those in were there slight
    17:46 changes maybe but the the big difference
    17:49 was when when we put in all three now we
    17:52 were trying for years different factors
    17:53 so we tried nanog and that didn't work
    17:55 very well it was pretty toxic
    17:58 um and I think I mean it sounds like we
    18:00 just tried something and it worked but
    18:01 it was actually many years of one
    18:03 Chang's efforts to find this particular
    18:05 combination
    18:08 great thank you so much uh it's quite
    18:11 late here and we have we have a couple
    18:13 questions Paul sorry it's late for me I
    18:15 guess it's a couple of questions in the
    18:17 back there
    18:19 hi I was wondering if you also tested
    18:21 the data that you showed in rpe cells in
    18:24 pathological conditions either genetic
    18:26 or chemical
    18:29 oh uh yeah so it's been done in a uh a
    18:32 mouse model of macular degeneration
    18:37 um with the the iodide model if you're
    18:39 familiar with it and it uh it works well
    18:42 in that situation okay cool cool thanks
    18:46 there's another one from Lawrence
    18:48 hi David uh I don't know if you can see
    18:50 me it's Lauren Zion from vitadel I am
    18:53 wondering about the the time sake if you
    18:55 can talk a bit about the implications
    18:57 there uh how would it look like for the
    19:00 consumer what does this enable what are
    19:03 the uh downsides as well
    19:07 uh yeah I don't know of any true
    19:10 downsides uh
    19:12 the cost is the main thing so what we do
    19:14 is we we barcode each each person's
    19:17 sample and we run them through the same
    19:19 sequencing reaction there's also an
    19:21 enrichment step that I didn't mention uh
    19:23 and so it currently costs us
    19:25 less than a you know basically a few
    19:27 dollars less than five dollars per
    19:29 sample and that's just on the bench here
    19:30 not high throughput not optimized so we
    19:33 think we can get the test down to really
    19:35 low amounts and for the consumer that's
    19:37 of course a benefit
    19:40 um it this technology is licensed to a
    19:42 company that next year will if all goes
    19:45 well provide a product to Consumers
    19:48 um to try this out but I wouldn't
    19:50 release that until we've shown that it
    19:52 works well we've done I think a couple
    19:53 of thousand people so far we have 10 000
    19:56 samples waiting to be tested and about
    19:58 250 000 people on the waitlist so we
    20:01 will use those numbers to aim to get a
    20:03 really good clock and not put it out
    20:04 there at all if it doesn't work I'm
    20:07 assuming the costs doesn't include the
    20:09 the logistics right so it would only
    20:10 make sense in in batches with shipping
    20:13 or something like that
    20:14 yeah you know the shipping will be more
    20:17 expensive than the cost of the test
    20:18 eventually but yeah that that's just the
    20:21 cost of the reagents and the the
    20:23 sequencing yeah
    20:25 great one more uh hello David uh so my
    20:29 question is have you ever tried to uh
    20:31 combine also fasting and color reduction
    20:34 as a synergistic effect in your
    20:36 reprogramming with the harmonica factors
    20:38 uh we have not we have not we
    20:42 have tried a few chemicals in
    20:43 combination and we're still working on
    20:45 that but no I think it's a good idea
    20:48 um
    20:49 it may be that some of the genes that I
    20:52 talked about today could be involved
    20:54 we'll try it
    20:56 thank you
    20:57 all right very cool thank you so much
    20:59 David that was a fantastic and inspiring
    21:02 talk we're looking forward
    21:08 and we're living we're looking forward
    21:10 to seeing you here next year so thank
    21:13 you so much and everybody will
    21:16 reconvene in 13 hours maybe we'll see
    21:19 each other in the bar thank you so much
    21:21 for today and see you later
    21:29 [Applause]
    21:33 [Music]
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