Academic blindspots | B. Kennedy, Buck Institute, J. Deelen, Max Planck Institute, L Cox, Oxford Uni
8:57AM Mar 12, +0000
Speakers:
Allison Duettmann
Lynne
Joe
Tom
Aubrey
Nir
Robert
Karl
Brian
Joris
Keywords:
aging
people
pathway
biomarkers
question
humans
drug
studies
centenarians
brian
intervention
develop
cell
test
yeast
clinic
measure
longevity
target
disease
So today, we are really, really, I think, blessed with with Brian Kennedy yoson and Lynn Cox, who will shine a little bit of a light on what kind of bottlenecks we have in research and academia at the moment, that would be beneficial to resolve after we tackled industry. In the last meeting, we're now moving on to academia. And we'll be discussing a few other areas as well in the next few weeks. So I'm really, really happy to have Brian Kennedy, he was dean and Lynn Cox here. Thank you so much for joining, I won't take any time further away from you guys. And Brian, if you will, perhaps want to start with a few areas of interest that you think are a good opportunity to long term risk in advancing research. And
so I just put together a few slides and
hang on a sec.
And I think that it's just really to illustrate the point that, you know, I think it's great that the private sector has gotten into aging research, you know, five years ago, or 10 years ago, especially, there really weren't companies in the space, there wasn't money to develop. It ideas for widespread use, and that this change has been one of the revolutions has happened to the field over the last decade. But I also think there's a perception that the basic research is is is something that's no longer needed. And I feel like that. That's very unfortunate. And there, there are a lot of questions that that can really be answered yet. I'll just mention a couple of them. And I just wanted to put this up first, I went back to my old slides, I found this from around 2006. You know, and this shows the three of the major pathways that we were studying with regard to aging are three of the major genes at the time tour a six kinase and protein kinase A, which was also the RAS pathway nice. And I didn't mention sirtuins insulin IGF we could have put those up. All of these pathways are almost all of them that are linked to aging that we're studying now came from invertebrate studies of aging, because these organisms age very quickly. At the time with very little money, it was possible to do studies where we could interrogate the whole genome to see which genes affect aging and the non biased way. And we did that for yeast and found that almost 300 genes that regulate aging. And in now, these these organisms are kind of going by the wayside in terms of funding, and not rodents, but but yeasts and worms and flies. And I feel like that's a big mistake. We need to keep doing this basic research, and there's going to be major discoveries that come out of that that are that are not going to be found out very easily if we don't. And to give you an example, you know, the there's, as I said, 300 genes that regulate agent in use, we really don't know how they fit together into genetic epistasis networks, how they coordinate aging, which ones regulate the same pathways or different pathways, and a single celled organism like geese, baby, the only way to easily answer that question. And, you know, around 2013, there were two papers published one that I was involved with on pillars of aging on the right, and one from Carlos Lopez, Odin and colleagues of the left with hallmarks of aging. And they overlap a lot with each other. But both papers really emphasize the point of how all of these pathways are interconnected in a network. And that it was really the preservation of the network that underlies healthy aging. And both of them tried to establish hierarchies. I didn't put the one slide up from the hallmarks were from the pillars, we basically said that everything was quite connected. And if you found an intervention like inhibition of Tor signaling, you could read that out as an improvement of all seven of these pillars. It wasn't as if only one pathway was being targeted. And that was leading to aging. And so we still say that and you know, when I say that a lot of people nod their heads, but I don't think we understand biologically what this network means yet and how its regulated. And if you know, we say like towards it the note of a network. But I don't know that anyone can really explain that at a level that we can understand from from a conceptual point of view. So that's another example where there's really open questions that are not being addressed in the aging field. You know, in the meantime, we've gotten to all these different kinds of interventions, their lifestyle interventions in the middle and red drugs and small molecules that that I'm working on and many other labs are working on and even more exotic things. Gene therapy, stem cell therapy, and a range of other things that are being conceptualized at the moment. And all, that's great. And some of that can be funded by private sector, although some of this more stuff that's out there at the moment, they may have bigger effects on aging.
And in the long term, but it's not really ready for primetime in terms of private sector development, that stuff is hard to find as well, too. So that sort of blue space, how do we really change the aging paradigm, I think is a challenge. I listed all these small molecules we work on. And a lot of these were funded or now funded by companies. And I mentioned some of them here. But a lot of them aren't. And we're still doing discovery phase of small molecules. And so that's something that needs to be done. But the main thing I want to focus on is, how do we get human with these interventions? And that means validating the effects of small molecules? How do we understand aging from a personalized level? And how do we find the interventions that at scale to really impact humanity? Now, private sector, the challenge here is that they pick their molecules are their biomarkers of aging. And that's great, I get I work with a number of these companies. But they're they're sort of more general questions that I think needs to be answered. And what is how do we validate longevity intervention. So for instance, if you, a number of the companies that are being best invested in in the ag space, are really targeting disease. And so that would fall into this top category where you have an intervention that looks good, because it targets a longevity pathway. But then you try to find a disease indication to treat, because that's the way to get something approved by the FDA and to get reimbursed by the drug industry. By the way, I'm not really weighing in one way or another on these strategies, I'm just putting them out. So this is what companies are often doing at the moment. And then there's more, more direct aging studies. So this would be this one in the middle would be a health span study with, for instance, a team study with Metformin, where you're trying to prevent multiple kinds of chronic diseases, and people that are still relatively healthy by preserving their health span. They're expensive long term studies, but it could be highly valuable. And then they're aging biomarker studies, which is what we're focusing on, which are shorter term, six months studies. Using biomarkers they really are, I think, are not fully validated yet, but we tend to believe in them. So like one of the things we're doing in my lab now is trying to recalibrate how we do mouse studies, so that they're aligned with the shorter term biomarker studies in humans, and so that we can work back and forth more easily. They've just to give you an idea how these studies are being done. This is one sponsored by a company for the alpha ketoglutarate that I've talked about a lot. And this is one done by our lab. So focus on this bottom one, because it's academic, there, we're looking at just the sustained release, we're taking 45 to 65 year olds, doing six month interventions are using aging biomarkers is outcomes. Now, it's hard to get companies generally to fund this at the moment. And this is this works for this company, because it's a natural product. But for drugs, it's not easy to do this, because there's no indication that really can be reimbursed by for so a company struggles with this still. And so what we're trying to do is test is up to 10 of these small molecules in the small study so that we can compare and contrast how different interventions affect aging. And I think that's important, because if you're, if everybody's just testing one in their own context, we don't know which ones work best in which people under which context. And so by testing multiple ones at the same site, we can start to compare what's likely to work where and so we're using a number of different biomarkers. I won't spend time on this slide.
That and this is another challenge, though, is that one biomarker is is often used for outcomes and studies. And we don't know how these aging based biomarkers interact with each other yet, this is the one you probably all heard of, which is DNA method, methylation clock. But even there, there's about 10, different methylation clocks that are available. And so from an academic perspective, we can take a more holistic approach, and use multiple biomarkers of aging and multiple interventions and align those. And so those are the kinds of things that I think they need to be done because we have to validate what works in humans. And some of that can be done from a private sector side. But there are other also advantages to doing that on the academic side. So I'll just stop by this is our Healthy webinar series shameless plug, you can go there and hear discussions by a variety of speakers in the aging field, a couple of which I noticed during the call and a couple of others I'm going to reach out to so I'm doing this at three in the morning. So if you're listening to me now you have to say yes, when I asked you to do the webinar series, and so that's all I have. So thanks a lot.
Thank you so much. Thank you so much for this overview. And yeah, I think there was a lot there. And unless, either any questions or comments from anyone in the group, feel free to raise your hand? And I would be curious if you, you know, let's say, what, what kind of concrete fo could a new product take on in the space? Because this seems something that, you know, products that are already up and running, you know, should just be doing more of, but is there a particular, it's a call to action that you want to put out?
Well, again, I think that I just want to re emphasize the need for basic science that, that the the key discoveries that led to this field, none of which could have been funded by the private sector at the time. And there are many more key discoveries that can be made, some of which will be surprising, I think, and change how we think about aging. And it's hard to I think it's hard to point, the advantage of academic researchers that you're you're not pointing in one direction, you know, and that you're trying to take people with good ideas and invest in them. And the discoveries they make, will be surprising. And sometimes they're not even directed at aging, and they lead to key understanding about aging. I think that's one of the reasons we fail. And academically when we say we're going to put $100 million in Alzheimer's disease, because in reality, the discovery that leads to the breakthrough, an Alzheimer's disease may be somebody working in biophysics, asking some question completely that they didn't even know was related to Alzheimer's disease. And so academic science works by funding, you know, smart people with good ideas, and then letting serendipity take it run its course. So we're not really doing as much of that anymore. And things are becoming heavily focused on translation, even from NIH and other great government funding agencies. And I think there's a risk that we're going to lose long term discoveries that could have even more value by doing.
Thank you so much. Okay, we have one question from Carl here. Oh,
I just want to say, Brian, I completely agree with you that the private sector explosion does not at all mean that the fundamental academic work is less important. I think that the right analogy to point to when you're talking about this is the is what happened with computer science, where lots of government funding of fundamental academic work created the infrastructure to enable the commercial explosion that happened in the 90s and created the world wide web and, you know, in the bubble, the.com bubble, and then even though there was a bubble and a crash, obviously, the the tech sector has has changed the world. And that's what we think the longevity sector is going to do. And in fact, the commercial explosion directly led to a massive push of people going into the academic field in that area. And I think that's going to happen here. And that's what we want to have happen. And that will lead to more funding and more bright smart minds working in this area in the academic sector.
Yeah, I hope that's the case, too.
All right. Robert, did you want to ask me a question?
Sure. So just wondering what what is the lowest hanging fruit in your opinion for something like what the foresight longevity for you're
looking to do later this year?
Well, you know, again, I think it's trying to understand what we mean when we say network regulating aging, you know, and how do we how do we directly find out when you modulate a certain pathway, like the Tor pathway, or Sir tuis? or pick, take your pick? How do we directly assign the primary roles of this pathway in modulating aging? How do we if it's really just preserving the network? What does that mean from a biologic perspective? And I have a hard time understanding that and I think that I haven't seen anywhere in the literature really begin to describe that yet. We know lots of interventions. And we have lots of papers about that, like this intervention does a and it does B it affects inflammation and affects stem cells. But what are the primary effects of this intervention? And how does that get translated into a preservation of a network? That would be a question that I struggle to answer.
Thank you.
All right, we have one more by Chris Carlson and then we're moving on to Lynn
O'Brien I do a lot of simulation. So I resonate with what you said about understanding simpler systems. Before you go too complex, I always want to understand y equals mx plus b before I look at a Bessel function, as anyone, I'm a little out of touch with all this, has anyone removed, you have tried removing the extra chromosomal DNA circles that cause death in yeast?
Yeah, that can be done. And there is a modest effect on lifespan. So you get yeast that live longer. But that doesn't like make immortal yeast. In fact,
with killing
it, good question. And in fact, the you know, I think that when you look at replicative, aging, and he said this a number of times a mother cell divides, and that's how I got into this field. And many grantees lab like seven, seven, you know, lifetimes ago, the, the, what you see is that you have the cells divided about 25 times and you knock out all the genes, and then you start combining the Long live knockouts. And what happens is you get to 45 generations, you hit a new barrier. Okay. And presumably, the reason you don't have nothing affects that barrier is because when the cells were only dividing 25 times that 45 barrier was irrelevant, if you could have knocked out that barrier. But it didn't matter, because the cells were dividing for a reason, the first set of reasons. And so we have no idea what that second barrier is in yeast. And I suspect they're going to be barriers like that in humans as well. And if we're lucky enough to get past the first set of limitations, we're going to run into a second set that we may not understand at all at this point. So that's another place I think these could be valuable is you know, they're there. They're going to be waves of limitations. It's not like if we just solve these first problems, we're going to get in trouble. They lead to a mortality. So
can I just want a quick phone? So Cindy Canyon told me the worms choke to death. Has anyone tried to
address that? They choked to death?
Yeah, the, the, you know, get the nerves that go to the bacteria. And they really can't.
Yeah, yeah. I mean,
yeah, I mean, I think it's, it's still, in my opinion, an open question what the cause of death is and number of these organisms, and now it changes when you perturb the system and extend lifespan. Okay.
One of the best things that ever happened to me was about 17 years ago when the University of Cambridge decided not to give me a proper job. And the reason and the reason they didn't was because I said, what I thought would be the answer to exactly what Brian just raised the succession of new problems that might arise during human aging. If we succeed in extending healthspan and lifespan, I said, we're going to be doing far more primate research. Again, the whole trend to limit primate research has got to go into reverse. And they didn't like that it was politically incorrect.
And just getting in on. Alright, well, Brian, we're gonna let you go into the chat. Thank you, really, I think from everyone, for for getting up at this incredible hour. And thank you so much for your presentation. It was really, really like a wonderful overview. And anyone who Brian is asking now in the chat to be featured in his longevity series, you have to say yes, just a reminder. Thank you so much.
Thanks.
I am Alright, next up, we have Lin Lin, welcome. I'm going to share more info about beauty in the chat. Thank you so much for being willing to present.
My pleasure. I'm just going to try and share my screen now. Let's hope that works. Because the stuff I hope. Yeah, cool. Right? Well, I mean, it's, it's a very hard act to follow Brian. So I'm going to go in a slightly different direction. Obviously, our task is to look at underexplored ideas in academic research and where we can go. And of course, we already have quite a lot of well explored ideas. So all these seven deadly things and the hallmarks of aging that Brian was just talking about, but we do need more of this. And it's exactly what he was saying we need the basic research to tie everything together. I'm a basic molecular cell biologist, I have a very strong interest in cell senescence. But what I was thinking of today is instead of going to the stuff that we already think we know quite a lot about we don't know enough about but we know quite a lot about it, is to try and look at things where we simply don't know enough at all, and the opportunities that those things offers. So I was going to look briefly at new approaches to drug discovery for aging and how that interacts. Within silico modeling of aging biology, so, so the synergism, between getting a decent model, Brian talked about epistatic epistatic effects, network interactions and things like that. And that is absolutely crucial to understanding how to develop new drugs. So, again, I mean, Brian said all this already, but aging isn't easy. It's a complex system. It's some an emergent biological system. So things change emerge after the system over time. And you have really, really complicated intertwine networks with some key nodes. But they all talk to each other all of the time. And traditionally, farmers have gone through the model of one target one drug, I don't know, my computer's overtaking me on this sorry, one target one drug, where you know exactly what you're you're aiming to develop a drug against, and you develop that drug to essentially kill the thing. So you try and completely ablate, a pathogen use antibiotics to kill bacteria. Or if you've got a driver mutation, gain of function driver mutation in a cancer, you will try and completely block the function of that particular, my BCR abl kinase, or more subtle approaches now like restoring loss of function, but historically, it's always been one drug, one target. Now in aging, what we know is the interconnectedness of all of this stuff and redundancy within those pathways. If you take out one node of the pathway, you're going to have another pathway kick in. Or if you completely ablate a pathway, then you might mess up the whole of the network. And we can think of these interconnected nodes in a sort of mechanical way. So mimicking them with an old fashioned pocket, watch where each of the cogs and each of those little parts of the cog and everything interacts. And you need every single component of these to work properly together, in order to get a functional system. And so my view of farmer is they've taken the back of the watch, and they're trying to fix one particular cogs that may have gone sort of slightly out of kilter. But what happens is you take a hammer to it, and you completely smash that cog. And of course, if you're trying to do this in a living biological human cell, the impact of that might be to mess up the whole of the rest of the system. So I have concerns about this inhibition, total inhibition approach. And what I think we might need to do is take a much more nuanced approach, fine tuning, retuning, a delicate system and essentially, restoring homeostasis. So aging as the loss of homeostasis, maybe we need to just gently tweak that watch mechanism, and get everything ticking over properly again. So an approach that avoids this farmer issue of one drug, one target, we need to be thinking in terms of damping down different nodes of a complex internet interconnected network, rather than specifically killing one individual node. And that does require systems biology thinking. And it requires the type of stuff that Brian was talking about, all those genetic screens in yeast where we identify, sorry, computer playing, identify individual components that talk to other components and work out how they all work. And one way of doing systems biology and drug discoveries is to think in terms of drug synergy. So developing poly pharmacology where we have one agent that might be able to modulate multiple nodes all at the same time. And in doing this, we don't need to know what the target is, we can go in completely agnostic to target, as long as we know what the function is that we want to correct. And the function is essentially restoring a normal homeostatic phenotype.
So if we go into function, first, phenotypic drug screening. And so at the moment, we know that aging results in multi morbidity and multiple diseases of aging. And currently, they're all treated in different clinics by different clinicians with different drugs. And we end up with polypharmacy, where older people have 1015, even 20 different drugs rattling around in the mall interacting with each other and potentially in harmful ways. And what we need instead is to look at central core shared processes like the aging processing cluster diseases, where we can hit the core of it all. And there's an opportunity for discovery here of poly pharmacology, poly pharmacologically acting agents, things that can hit multiple different components all at once. Now, in order to do that, we need to sort of different way of understanding things. So this is probably a bit out of the box. But I think we can radically re understand the way we look at biology and take lessons from Silicon Valley, where we have two problems. Oops. We've got a hardware issue and we have a software issue. And we don't actually know whether aging is a problem with the hardware. We're pretty sure that at least some of it is we can see problems with the hardware, but we think there's also problems with the size And when we're talking hardware and biology, we can be quite reductionist, and we can say the hardware is essentially a cell. It's the nuts and bolts of the organelles macromolecules. And the software is the way that those bits talk to each other. It's the information flowing through the system in these biochemical pathways. And so if we can think of biochemistry as a computational problem, then maybe as we accumulate more biochemical data, we can plug it into a program. And we can start to run these programs and mimic aging in silico. So for example, we've been working on mtorr. It's something that Brian obviously is also working on a lot. And we already know quite a lot about the signaling pathways and turas hubs and nodes, taking in information from outside the cell integrating that information and coming up with a solution. And we also know that this pathway is normally incredibly well regulated in young cells, but it's constitu tively switched on in old cells. So we can start to model this in a computational way, where we can think in terms of the inputs coming through logic gates, so mtorr responses to nutrients and cytokines, and those will come through and all gate, it will also respond to low energy and stress by not being switched on. So that's a NOR gate. And it integrates those two segments, two sets of signals and decides whether or not to push yourself through the proliferative pathway. And in this case, where there is stress, but nutrients, it will not signal for proliferation, but in an old cell that programming has gone wrong. So instead of mTOR, taking two inputs and deciding it acts as an OR gate, and it'll just it only needs one of those inputs before it decides to activate. And so this essentially is a bug in the software that happens in senescent cells. And so all we need to do is go back in that and debug the software. So as a proof of concept we've been trying to do this and MTL comes in different forms. So there's m talk, one and m talk. So most people will be familiar with a drug rapamycin, which predominantly hits on top one, but we've been working with opponents or inhibitor that hits both them talk one and two, to see if we can actually modulate and reprogram debug that piece of software. And essentially, if we treat old senescent cells with a patent or inhibitor, we can actually restore their proliferative capacity, we can restore all their metabolic functions. But oddly enough, unlike a Silicon Valley solution, resetting the software in this case also resets the hardware. So this is a very simplistic example of how you can just model a couple of components of a biochemical pathway in an in silico way. And that might lend to a new approach to developing poly poly pharmacological drugs, by using phenotypic screening, and then by developing an in silico aging cell. And of course, the complexity of aging means that developing an in silico, aging human is going to be quite tough. But I mean, if people can write programs for Call of Duty, maybe they can write programs for the in silico, aging human. What we know we don't know about the complexity is how all these things interact. So how genes interact with each other how the chemicals and components of the cell interact with tissues, organs, and systems, how our microbiome talks to all of that. And then even more complicated layers on top how exposure to environmental factors impacts epigenetically, both within our own cell generations, but also intergenerationally between people. And so what we need, and again, this is something that Brian has touched on biomarkers, we really need to know what's going on, we need a full biochemical workup, you can't design an in silico aging cell if you don't understand the biochemistry of the pathways that you're modeling. And then we need some really clever people who can do Systems Modeling. But we also need big big data. So not just bio biology omix big data, but we need to incorporate data coming in from the socio economic side of aging. And so Tina Woods ice is on the call and and she's been instrumental in setting up something called the open house data project. So I think if we can finally bring together all the different communities so the academic communities to work in biology, socio economic health,
computer programming, all these sorts of things, and perhaps we can finally come up with a way of doing something really different but but but quite a game changer in this field. So I'll stop sharing at that point. I hope that wasn't too crazy.
Thank you so much. Thanks a lot for that and presentation a lot. Again, a lot was in there. And I had a comment from near in the chat. Would you like to make that publicly?
Oh, no, I didn't really need to make it public. Just a weird you know, I would just say look a we started calling to German. omics genomics sounds maybe like a Western like genomic, you know, but using omics for a gerontology. And we're all facing this challenge. And we are all coming with this approach. And I just gave another paper that kind of says what Lynn said. In other words, and Leanne, I love the software hardware a slide. So viewers. But thank you that was that was terrific. That's all I just wanted to another reference there.
Okay. Thanks.
Awesome. Thank you, Robert. And if anyone else has a question, please say so in the chat or just?
Sure. Just a quick question. So what in your view are the best available in silico models applicable to what you were just talking about right now.
And there are a few online models of the in silico cell. But essentially, I don't think they're based on enough biochemical data to make any sense. And so God, I should have this off the top of my head, and I'm sorry, it's late in the UK as well. That there's one based in Germany, that that's pretty good. But it can't model the complexities that we know exist. And the problem at the moment is that you can put in a single factor, and you can read out maybe two or three factors. And what I'm saying is that what you need to do is be able to change, one tiny component like phosphorylation of one particular factor in the signaling pathway, and read out everything all the way across. And I simply don't think those, that level of complexity exists in any of the programs we've got. So it's not entirely novel, people are trying to do the in silico, modeling, there was a group in Newcastle also trying to do this. But it's just simply that the quality of the material used to set up the programming at the moment, I don't think is there. So if anybody else on the call has a much better idea, I'm more than happy to, to yield the floor on that. So if anyone's come up with a virtual sell, that actually works, I'd be delighted to hear it. So sorry about that. I don't actually have an answer to that.
No, thanks.
Essentially,
what I want to know.
All right, lovely. Are there any questions, comments, I may be missing?
I have a question. Are there areas where you think systems pharmacology is limited by tools, either for characterization or experimental intervention? Because I think that's obviously, one way in which science makes progress that is basic scientists say this is a known unknown. This is something which you would like to be able to measure, or a way that we would like to experimentally intervene, we can't do it. And then ask people in the physical sciences and engineering, can you develop a tool or methodology that would have this set of performance characteristics?
That's really quite cool. And can I give you two answers. One is a tool that isn't the pharmacology side of things, but it's actually a generation of new chemicals face type of tool. So at the moment, a lot of the stuff that we're looking at is natural product libraries, or the general chemical libraries. But what we think you might need in order to have poly pharmacological effect is to join to have common tutorial libraries made of drug like fragments. And we need tools in order to generate those. So I mean, I've got a brilliant postdoc in my lab, who's looking at doing that through a directed evolution program. And so essentially trying to generate new chemical space using a tool that is agnostic in in the way you develop it. But the readout is simply do you have a phenotypic change based on the activity of products that that particular component has managed to generate? So that's one type of tool at the very top of the tree, can we make new chemicals, new drug like molecules that will have impact? And then in terms of the readouts at the other end? Because the readouts are so complex, and we want to be target agnostic, I think the readout ought to be the target should be say a senescence cell and the readout should be Can we make the cell senescence cell not senescent anymore, or as consortium of C. elegans people has been doing can we take an old one and make it not old, again, using drug treatments. So the buck Institute obviously has been doing a lot of that type of screening. So in terms of tools that the community can bring to bear, imaging tools and things like that, we might need to know a target if we're going to specify we Want you to look at the relocalization of a particular protein in the context of drug treatment. But to me, actually, I don't need that level of detail. If I want to find a for drugs effective, I want to have a complete phenotypic change. Does does that make any sense at all? Okay, thanks.
Alright, I think now we have a comment from you.
Can I? Can I? Can I say something? Tom, you've been asking this question every time we've met. And I appreciate that. And I learned a lot. Because if we want to, you know, be out there, we should, we should move the technology, right. And the reality for us, is that we actually believe in think that aging is much easier to crack than cancer, or the brain. In a end, the day has paved the road for a lot of the technology where we're using, look, when you have if you have cancer, you have a whole new genome on your body that is different than yours. It's only not only one genome, it's probably several genomes. And there are different even from every cancer in the world. cancer, cancer is really a mess to tackle. And we think that aging is much simpler than that, at least for now. So I think that's the reality. And we really have to think hard of what others are not paving that will pay for us. But the technologies that everybody else is using is probably good for us to largely.
Not only I do see another you there from Lynn, too. Yeah. Thank you for the general comment. Yeah, I think and Tom keep keep on asking their questions. And always lovely. Thank you, everyone. And now we have our final contribution to the question, what kind of research opportunities maybe we missing right now, you always thank you so much for joining again, you joined, I think, quite early on last year, and our meetings, and I'm really, really happy that you found time to join again, you also completed the health extension challenge on the Google Doc that I shared. Thank you very much for joining us today. I'm super excited for what you're going to share with the group. And I'll share your bio in the chat.
So I didn't prepare a presentation. But I just want to tell a bit more about the direct question that I'm going into. And that I think is important. And it's actually two things that I want to talk about. The first is about biomarkers and how to better get them into the clinic. And the second is how we can use the genetics of loneliest people to actually find out better targets that we should pharmacologically candidate. So starting with a biomarker, so what I realized when I was working still in Leiden, in the group of aliens, Rathbone, so really in the clinic, that not many things that are actually coming from the model organisms are used in the clinic. So people are trying to do all these kinds of intervention studies, and they develop compounds and want to want to use them. But actually, there's another issue that's coming before that that's, that's, for example, that we identified already very nice biomarkers in many different studies, even in humans, but they are not used by the clinicians themselves when they treat the patient. So one of the things that I definitely think deserves attention, and that I'm working on also in Cologne, is trying to use all the biomarkers that are already out there, test them in really clinical populations to see if they are really good biomarkers in these people combine them with currently existing things that are used in the clinic, for example, people are still measuring your cholesterol and your triglycerides, but already much better markers available that say something about your health. So my main one of my main goals is to bring some of these things also that I identified myself, like this metabolomic biomarkers into the clinical setting and see if we can actually replace the things that are currently used there. And also bring the things that are found in animal models to the clinic, because that's an even step that even bigger, because I work also with the people in the Max Planck and they identified very nice biomarkers, but they are kind of stopped. So they identified in say, worm or mouse and then it stops. And I actually want to improve the the process by which they actually show more evidence in the animal model so that it can be brought to the human. And one of the things that I think is important, for example, is if you take a human and you look at it in the clinic, what are you normally doing, you're taking blocks, and you do some physical tests. But what I do people doing with animal models, they all take all kinds of tissues and test all kinds of things, but they don't take the blood or the blood is the best translational thing between the two species. So I think for example, we should focus more on testing things in the blood of mice or actually say Okay, if we have an intervention that works on the mice, what does it do with the blood? Can we then also see the same similar things in humans that undergo similar kind of intervention. So we need to harmonize this better. And also, like Brian said, and I think it was, was very good that he also tries to create mouse studies that mimic the human situation, I think that is also very important that the most studies become more like human studies. So that hopefully, findings are more translatable. So for me, that's one of the main challenges that I'm working on to really get things that are already discovered. It's not novel discovery all the time, but that are already out there. For example, the epigenetic clocks and the biomarkers that we identify them, bring them actually in the clinic, to the clinic to a population where they could be relevant.
And then the second part that I'm working on a lot is on the genetic. So what I see is that a lot of research that is basic research is coming from the animal models, which works very well, we identified very nice, some common pathways like incident signaling that seemed to be very relevant for the humans. However, we sometimes do not see the same effects in the humans. And actually, in the humans. When we look, for example, at the genetics, it's really hard to find the shared genetic mechanisms between all these people that explain why they are becoming so old. So I think we should also work on this. So I recently wrote a review about this, where I explained how I think we could better use this data from this long lift individuals to test this in the animal model. So instead of coming from the animal, from the animal models to humans, we should go from the humans and the loneliest people, and try to mimic these effects in the animal models. And everyday know what his mechanisms are, we should try to develop pharmacological interface that mimics effect. So because then we know that these are effects that are actually observed in humans. Take for example, randomizing, if you just give full rapamycin to an individual, it blocks completely mTOR one. However, in only humans, we normally do not see that we don't see the complete blockage, we see much milder effects. So it might be good to use this data that we have in humans, and see kind of how we should should target these different mechanisms in a way that is more natural, so that hopefully, also the drugs will translate better to the humans. So these were actually the two main points I want to bring up and that that's what I'm working on in my group. And I'm really interested to hear how you feel about this. If you have, if you agree, disagree and an open discussion, I'm more for an open discussion than me presenting something.
Thank you.
Lovely, thank
you so much. Are there any immediate questions or comments? Otherwise? I'll just start with one i think that you know, Robin, started with, with Brian Well, what would be kind of like a project that could tackle this kind of this endeavor? Because it seems like you know, maybe like a database, which different people would share their experiences so they can get retranslated? between the different experiments? I don't know, what do you think? Well,
one of the things, for example, is that I would be interested to kind of collect all kinds of biomarkers together and test them in one study, I think if we if we can get depth down, I would already be very happy. I mean, this is one, if you take the epigenetic clock, anything that we have generate, they have never been tested in the same study and not in the right study. So yeah, what. So that would be a kind of if you have a really good database, where everybody agrees on Okay, these are the biomarkers that we are now trusting and that people should be testing, and then use these and that people then start using all these kind of markers as much as possible in their studies. And I noticed this is a big challenge, because some of these markers are very costly. So one of the things I didn't mention yet, and I think we should also work on is trying to develop markers that are less costly, and more and much easier to, to measure in humans. Like for example, if you take the epigenetic age, it costs you a couple of 100 euros per sample, if you don't have a big study, it's going to be expensive. And I think we want to come up with markers that are a bit cheaper, like the triglyceride measure in the clinic is quite cheap that we can then use and then also it would be more interesting for for, for example, doctors to actually use these kind of markers. So coming up with a good database of markers, where there is agreement between researchers that we think they are good markers would be also a very good starting point. And I don't think such a database is actually there at the moment.
Well, we will discuss particularly biomarker standardization as well and leading meetings but if people here already know of a few that of their current favorite efforts, then we'll be useful to know,
raise a very quick thing in terms of biomarkers in response to what you were just saying there. And there's a new paper out in Nature Communications about diagnosing Parkinson's disease on the basis of lipids in Seaborn. So different competitions or ceremonies and things like that. And of course, that is an incredibly cheap, easy way of doing it. And it was based on one nurses ability to smell Parkinson's patients. And so they did some mass spec of the secreted components in CBM. So I think when we've been looking at biomarkers, we've been looking in a very limited way at what we think of biomarkers. And we've just got to be a bit more adventurous about the types of things that we consider as a biomarker.
Yeah, I completely agree. I think we are limiting myself mostly to measuring things that are in the body, but ignore these kinds of things that you're just mentioned. Yeah, I completely agree with that. Yeah.
Lovely. Are there any other questions or comments? All right. So let's say if you had a specific topic, if you had a specific project that was particularly focusing on work, we have a job job EDS, do you want to make your comment? He asked about a skin biopsy.
Yeah, so the question is,
I'm just in a non ideal environment. But um, yeah, so, you know, collecting, collecting samples from humans is hard, unless it's blood. But you know, that's a very narrow window on what's going on in the body, because blood cells are kind of weird. And so just kind of reaching for is there some other sort of non blood anchor, but it's not too hard to get from people. And I know that's like skim punch biopsies are unpleasant, but not that bad. If not like taking a tissue sample from the brain or something. Is that prevalent? I think it's practical, is there some other thought of how you could expand the envelope?
It's definitely I mean, we also doing skin biopsies can be taken quite quite easily, I would say, the challenge there is to make it into something where you can actually test something on. So normally would bring it, for example, in South Georgia and start testing there. And do you run into the limitation, that you can now test a lot of samples at the same time, which is the advantage of law to just take it and you, you can do high throughput measurements quite easily. And there are not many I mean, you can also take
saliva, for example, which which has similar properties. But there are not many other tissues that are easily taken from humans, where you can do high throughput measurements on to that you can actually do it in 1000s of individuals at the same time, which would also be needed. I mean, every want to bring, what some people sometimes forget is when we want to bring something really into the clinic, it needs to be high throughput, and it needs to be easily measurable. And a lot of focus now is on discovering the biomarkers, but there's not so much work yet, on further developing them into something that can be measured high throughput. And, and, and, and cheap. And I think that is also a challenge that that we should be working on. I mean, we have used, for example, this is metabolomics platform, where we were really happy with because for 30 euros per sample, we could measure and they could measure 1000s of samples at once they also measuring the whole UK Biobank, and it was really standardized and everything. So we could directly compare between different studies. And these are the things that we are looking for, because that's what we would need in the clinic.
Or is good for us whether you've looked at urine because we were contemplating it in a bunch of patients. And obviously there's a volume issue. So you have to spin it all down. But there's a lot of cells come out in urine. And people tend to think of it as a sterile fluid, a cellular, but it's not there's a lot of biological material there with components that can be measured. And of course, that's something that people aren't particularly worried about giving away. So is it something that might be useful to start looking for particular biomarkers and
yeah, that's actually a good point I forgot to mention urine is indeed also good, good fluid. And that's actually what what we are also doing so for example, with with the metabolomics platform, it can also be done on urine, you get less markers, because there's less present there, but it can also be used to to identify biomarkers. So also one of my next plan is to use urine and blood from the same individuals, and then test indeed, how well they correlate with each other when we, when we measure specific biomarkers, but yes, it can definitely be used also to, to measure something, it's less, you can measure less than in blood. But still, it's a it's a nice fluid to, to use, and it's easy to obtain. And I mean, the same, it's to we it's also possible to obtain that in a less in a non invasive way, but people are not so confident normally to provide it. But yeah, I mean, there, we can also say something about the microbiome when we eat when we have to, and things like this. So there are ways that we can also use other tissues. But blood is still at the moment, the thing that people always kind of take, but I think maybe we should also work in this clinical studies on collecting this other kind of tissue so that we can see if this provides additional information that's not provided in blood.
Thank you. I don't know. So cow putting his foot on? I don't know if that means he was back. Come on. I know.
I have a question. That's more for near but and then, you know, in response to your does anybody want to throw out any, any other crazy things, um, so near, let's take the, you know, pessimistic view and assume that field is going to be unsuccessful for the next couple decades. But that the study of supercentenarians is super important. You know, in two decades or more, there are going to be a lot more supercentenarians. What should sort of government funded academic research be doing now with the people who are in their, like 90s, or 80s, to start tracking in terms of that will give us better, you know, what do you wish you could have done 2030 years ago, with the batch that you have now that, you know, if we start now we can get governments to help ensure that, you know, whoever replaces you and in a generation where maybe you have you're still going because Metformin works so well, you know, what do you want to have happen? So that 20 years from now, the the larger batch of supercentenarians? Have, we have better data on them?
That's a terrific question. Do you know that I'm 100 years old, actually, I don't know if it shows. But it's interesting that you say that because, you know, the pharmaceutical regeneron. And, and that's not the only one, but the pharmaceutical regeneron. They say, hey, if we had a genotype, or if we had a whole genome sequencing of all, whatever, 6 billion people in the world, in their electronic medical record, we would cure every disease in the world, okay? That's their view. And that's what they're they're doing. Because what when we come from nematode to mice and develop drug to humans, we fail very often, the experiments have all been done. And that's why regeneron NFR and Mian temples are starting a project where we're recruiting the first extra 10,000 Super centenarians around the world, because we know that their aging has been slowed significantly, and they have a mechanism that we could work on. We have done it before. So in fact, what you're saying is exactly what I wanted 20 years ago, and I recruited 750 centenarians and their findings, they're about to increase it by locks by log squared, because they're holding the the really the secret a for longevity. As far as a and I'm doing everything that was mentioned with T lymphocytes and B lymphocytes. We already have it, it's a service, you can you can look at the Natan shock center of my Institute, and you can get help on biomarkers on metabolomics and proteomics, we're collecting all that. I want to make just one point that is really important. If you're going to come and say I want plasma of centenarians or something like that, remember that centenarians, on one hand, they achieved things, thanks to their genome, probably, whatever, cause they're slow aging. On the other hand, they're done now, you know, the end of their life, they like their their chances of dying, the next year is about 30%. And whatever you're measuring, can either indicate what brought them here, or predictive of of, of their death that's coming in the next year. And that's why in this study, in my study in regeneron study, we're going to recruit their offspring, because at least their offspring are Far from death, and they can actually have the best phenotypes. Our best phenotypes is when we compare offspring of centenarians. And we have 1500 of those to 1500 people without longevity in their family. And then all of a sudden, you see things like their IGF level, the HDL level, every every an add on proteomic. You see on proteomics that, and I showed that to before that, that you get like 700 proteins between 65 and 95. But when you look at the offspring, they have only 20% of that, because they're still, they're still younger, and they have special proteins that only they have, and others don't have.
So do the math alarm clocks on the offspring of centenarians look better than the age match controls,
we are doing it now we did the whole a methylation essay that is less, less informative as a clock. And they fall actually halfway between the centenarians and their control. Well, but centenarians are older. And the question is which methylation patterns are inherited, it is possible that the methylation pattern are inherited. So there's a lot of work to do on the biology of methylation and not only the clocks, and we're doing the clocks. Now. I hope that's helpful. But that's a great question we want just like everybody for diabetes, or for cancer stuff, they're trying to find the genes. Yeah, this is very important. And the paper that I I attached in the box might makes the point of how we go from genetic of centenarians to developing drugs, which is kind of what Leanne talked about.
Any other comments or questions? All right, well,
I have a question for you. I want to know his opinion on it. So
do you believe near that there
is kind of a shared mechanism in all these super centenarians? But do you also believe more that it's, they all have their own way of of getting older? And that is also kind of what's limiting us? Why we do not identify these shared mechanisms yet in the in the genetic studies that we did?
So So the answer is that what we're doing now, with the help of all those people that claim talked about, you know, the people who know, to deal with computers and know how to, you know, you know, this, this thing of increasing the data is increasing the hell in a haystack to find a needle, right? So how can you do it and one of the things we're doing we were doing is we were, look, we're putting all the genetic difference into pathways, okay? And it's the pathways that are really, really very telling, okay, the insulin IGF signaling pathway mTOR signaling pipe pathway, MP kinase, are really the major pathways the distinct between those with longevity and those without, so I think it's all there, we just know have to know what to ask and realize that, you know, a just one, one snip at a time means nothing we are, we are made of many, very many snips on the tie up at a time, and some will constantly change others. So we have, we have to do this extra work.
I raised one point about what you were saying here, I think it's absolutely critical that we talk about the pathways, when we've been looking at some of our proteomics data. So many of the factors do not change in what would be deemed statistically significant manner. But when you've got 50 proteins that are all involved in exactly the same biochemical pathway, and they all shift in the same direction, to just under that two fold cut off, then the pathway becomes significant to like peers, 10 to the minus 57, or something. And you just you have to think in terms of the complex biology, rather than just thinking a biomarker is a protein, we will measure a level of a protein. It's all how these things talk to each other and interact that I think is really crucial. But so pathway analysis, I would say, is key to all of this. So I'm glad you brought that up. Thank you.
All right, lovely. Thank you so so much, every now 30 minutes over time, but I think it was worth it. And that little excursion at the end. Hey, yours. And Lynn, thank you so much for a already contributing the challenges to the dog. I think you knew for your comments, and thank you all for joining was really fantastic. We have full house today. And for those who continue who want to continue talking, I'm going to open up our gathering space, our gather lounge, and in that you can just sit tables and decentralized chat to each other, if you'd like. And I will be seeing many of you again very soon functional about presentation. I think john may have already left but that one is the next one up, super excited for it and will be the status, specifically regeneration in the brain. So thank you very, very much for everyone for joining and thank you and I'll see you very soon, hopefully.