Jacob Kimmel

Yeah, so I think there are a couple different ways one can tackle this.

One is you have to think about what's the selective pressure that would make one live longer and encode for higher health over longer durations.

Do you have that selective pressure present?

There's another, which is are there any anti-selective pressures that are actually pushing against that?

And there's a third piece of this, which is something like the constraints of your optimizer.

If we think about the genome as a set of parameters and the optimizer as natural selection, then you've got some constraints on how that actually works.

You can only do so many mutations at a time.

You have to kind of spend your steps that update your genome in certain ways.

So tackling those from a few different directions, like what would the positive possible selection be?

As you highlighted, it might be something like, well, if I'm able to extend the lifespan of an individual, they can have more children, they can care for those children more effectively, that genome should propagate more readily into the population.

And so one of the challenges then, if you're trying to think back in sort of a thought experiment style of evolutionary simulation here, would be what were the conditions under which a person would actually live long enough for that phenotype to be selected for and how often would that occur?

And so this brings us back to some very hypothetical questions, things like what was the baseline hazard rate during the majority of human and private evolution?

The hazard rate is simply, what is the likelihood you're going to die on any given day?

And that integrates everything.

That's like diseases from aging, that's getting eaten by a tiger, that's falling off a cliff, that's like scraping your foot on a rock and getting an infection and dying from that.

And so from the best evidence we have, the baseline hazard rate was very, very high.

And so even absent aging, you're unlikely to actually reach those outer limits of possible health where aging is one of the main limitations.

And so the number of individuals in the population that are gonna make it later in that lifespan, where using some of your evolutionary updates to try and actually push your lifespan upward is relatively limited.

So the amount of gradient signal flowing back to the genome then is not as high as one might intuitively think.

Yeah, you got to contribute resources back to the group.

You can't just be a freeloader.

You need to get calories, go out in the jungle, get some berries.

I entirely agree with that particular thesis.

You know, I think in biology in general, when you're trying to engineer a given property, be it being healthier longer, be it making something more intelligent, and this is true even at the micro level of trying to engineer a system to manufacture a protein at high efficiency, you always have to start by asking yourself, did evolution spend a lot of time optimizing this?

If yes, my job is going to be insanely hard.

If no, potentially there are some low-hanging fruit.

And so I think this is a good argument for why potentially intelligence wasn't strongly selected for.

And I think actually the lifespan argument plays back into intelligence to a degree.

You start to ask, okay, if I have intelligence that's able to compound over time and I can develop, you know, for instance, in some hypothetical universe, my fluid intelligence lasts much longer in my lifespan.

Right.

If the number of people who are reaching something like 65 is very small in a population, you're not necessarily going to select for alleles that lead to fluid intelligence preservation late into life.

This is actually part of my own pet hypothesis around some of the interesting phenomenology in when discoveries are made throughout lifespans.

So there are some famous results where, for instance, I'm going to get the exact age a little bit wrong, but in mathematics, most great discoveries happen roughly before 30.

Why should that be true?

That doesn't make sense.

You can sort of put down a bunch of societal reasons for it.

Oh, maybe you sort of become staid in your ways.

Your teachers have caused you to restrict your thinking by that point.

But really, that's true across centuries.

That's true across many different unique cultures around the world.

That's true in both cultures from the East and cultures from the West.

That seems unlikely to me.

I think a much simpler explanation is that for whatever reason, our fluid intelligence is roughly maximized at the time where the population size during human evolution was maximal.

If you had to pick an age at which fluid intelligence was selected most strongly for, it's probably around 25 or 30.

That's probably about the age of the adults in the large populations that were being selected for during the rest of evolution.

And so I think there's a lot of reason here to think that actually there's interplay between many features of modern humans and how long we were living and how that dictates some of the features that occur that rise and fall throughout our lives.

Do you know the Alexander von Hummelt story?

No.

So Alexander von Hummelt is one of the most famous scientists in history.

He's kind of forgotten now.

But he had this one expedition to South America where he climbed Mount Chimborazo at a time when very few Europeans had done that.

And so he was able to observe various ecological layers that were repeated across latitudes and across altitudes.

Mm-hmm.

And it caused him to formulate an understanding of how selection was operating on plants at different layers in the ecosystem.

And that one expedition was the basis of his entire career.

And so when you see something named Hummel, just to give you a sense of how famous this guy is, it's usually Alexander von Hummel.

It's not like this is some like massive, prosperous German family name that just happens to be really common.

It's this one guy.

And so really it was like this singular year in which he conceived a lot of our modern understanding of botany and selective pressure.

Yeah, so then the next piece of the evolutionary story is like, is there anything selecting against longevity?

Like, okay, let's just pretend everything I said was wrong.

Can I still make an argument that maybe evolution hasn't maximally optimized our for our longevity?

One argument that comes up

And I'll caveat and say, I don't know how strong some of the mathematical models that people put together here are.

You can find people using the same idea to argue for and against.

But there's this notion of what's called kin selection, that if you sort of take a selfish gene view of the world, that really this is the genome optimizing for the genome's propagation.

It's not trying to optimize for any one individual.

then actually optimizing for longevity is a pretty tricky problem because you have this nasty regularization term, which is that if you're able to make a member of the population live longer, but you don't also counteract the decrease in their fitness over time, meaning you maybe extend maximum lifespan, but you haven't totally eliminated aging, then the number of net calories contributed to the genome as a function of that person's marginal year and their own calorie consumption is less than if you were to allow that individual to die and actually have two 20-year-olds, for instance, that sort of follow behind them.

And so there is a notion by which a population being laden demographically with many aged individuals, even if they did have fecundity persisting out some period later in life, is actually net negative for the genome's proliferation.

And that really a genome should optimize for turnover and population size at max fitness.

So I think this is where another ML analogy is helpful, which is something like, well, actually a two-layer neural network is technically a universal approximator, but we can never actually fit them in such a way.

And why does that occur?

People will wave their hands, but it basically comes down to we don't really know how to optimize them, even if you can prove out in a formal sense that they are universal approximators.

And so I think we have similar optimization challenges with our genome as the parameters and evolution as the optimization algorithm.

And one of those is that your mutation rate basically bounds the step size you can take.

So if you imagine that at each generation, you get some number of inputs, you can select for some number of alleles.

Well, the max number of variations in the genome is set by mutation rate.

If you dial your mutation rate up too high, you probably get a bunch of cancers.

So you're selected against.

If you have it too low, you can't really adapt to anything.

So you end up with this happy medium, but that limits your total step size.

And then the number of variants you can screen in parallel is basically limited by your population size.

And so for most of evolution, there were lots of forces constraining population size as well.

One of the dominant source of selection on the genome is really prevention of infectious disease.

And it seems like when you study the history of early modern man, infectious disease is actually what shaped a lot of our population demographics.

And so there's a lot of pressure pushing for those step sizes, those updates to the genome, really to be optimizing for protection against infectious disease rather than other things.

And so even if you imagine that maybe the arguments on the former and the first and the second of these possible, you know, positive selection being absent for longevity and potentially some negative selection existing.

You could, I think, construct a reasonable argument for why humans don't live forever, why the genome hasn't optimized for that, simply based on these optimization constraints.

You have to imagine not only that the positive selection is there and the negative selection is absent, but that when you think about sort of the weighted loss term of all the things the genome is optimizing for, that the weight on longevity is high enough to matter.

And so even if you imagine it's there, if you simply imagine that the lambdas are dialed toward infectious disease resilience more effectively, then you can construct an argument for yourself.

And so I think really when you start to ask, why don't we live forever?

Why didn't evolution solve this?

You actually have to think about an incredibly contingent scenario where both the positive selection is there, the negative selection is absent.

And you have a lot of our evolutionary pressure going toward longevity to solve this incredibly hard problem in order to construct the counterfactual in which longevity is selected for and does arise in modern man and in which we are optimal.

And so I think that puts human aging and longevity and health really in this category of problem in which evolution has not optimized for it.

Ergo, it should be, relatively speaking, relative to a problem evolution had worked on, easy to try and intervene and provide health.

And I think in many ways, the existence of modern medicines, which are incredibly simplistic, we are targeting a single gene in the genome and turning it off everywhere at the same time.

And yet the fact that these provide massive benefit to individuals is another sort of positive emission or piece of evidence.

Yeah, it's actually an excellent question that I haven't heard posed before.

So we think about where do antibiotics come from?

To your point, we could synthesize them.

They're just metabolites largely of other bacteria and fungi.

You think about the story of penicillin, what happens?

Alexander Fleming finds some fungi growing on a dish, and the fungi secrete this penicillin antibiotic compound, and so there's no bacteria growing near the fungi.

And he says he has this lightbulb moment of, oh my gosh, they're probably making something that kills bacteria.

There's no prima facie reason that you couldn't imagine encoding an antibiotic cassette into a mammalian genome.

I think part of the challenge that you run into is that you're always in evolutionary competition.

There's this notion of what's called the Red Queen hypothesis.

It's an allusion to the story in Lewis Carroll's Through the Looking Glass, where the Red Queen is running really fast just to stay in place.

So when you look at sort of pathogen host interactions or competition between bacteria and fungi that are all trying to compete for the same niche, what you find is they're evolving very rapidly in competition with one another.

It's an arms race.

Every time a bacteria evolves a new evasion mechanism, the fungus that occupies the niche will evolve some new.

And so part of why that there is this competitiveness between the two is they both have very large population sizes in terms of number of genomes per unit resource they're consuming.

There are trillions of bacteria in a drop of water that you might pick up.

So there's trillions of copies of the genome, massive analog parallel computation.

And then at the same time, they can tolerate really high mutation rates because they're prokaryotic.

They don't have multiple cells.

So if one cell manages to mutate too much and it isn't viable or it grows too fast, it doesn't really compromise the population and the whole genome.

Whereas for metazoans like you and I, if even one of our cells has too many mutations, it might turn into a cancer and eventually kill off the organism.

So basically what I'm getting at, and this is a long-winded way of getting there, is that

bacteria and other types of microorganisms are very well adapted to building these complex metabolic cascades that are necessary to make something like antibiotics.

And it's necessary to maintain that same mutation rate and population size in order to maintain the competition.

Even if our human genome stumbled into making an antibiotic, most pathogens probably would have mutated around it pretty quickly.

I'm going a bit beyond my own knowledge here.

So I want to say my strong hypothesis would be yes, I can't point to direct evidence today.

There are some examples of this where, for instance, bacteria that fight off viruses that infect them, bacteriophages, have things like CRISPR systems.

And you can actually go and look at the spacers, the individual guide sequences that tell the CRISPR system which genome do you go, where do you cut.

And you find some of these guides that are very ancient.

It seems like this bacterial genome might not have encountered that particular pathogen for quite a while.

And so you can actually get sort of an evolutionary history of what was the warfare like, what were the various conflicts throughout this genomic history just by looking at those sequences.

In mammals, where I do know a bit better, we do have examples of this, where there is this coevolution of pathogen and host.

Imagine you have some antipathogen gene A fighting off some virus X. Well, you then actually update, so now you have virus X prime and antipathogen gene A prime.

Now virus X prime goes away, but actually virus X still exists and we've lost our ability to fight it.

Those examples really do happen.

And so there's a prominent one in the human genome.

So we have a gene called Trim5-alpha.

And it actually binds a endogenous retrovirus that is no longer present, but was at one point actually resurrected by a bunch of researchers.

And it was demonstrated that it is the case.

We have this endogenous gene, which basically fits around the capsid of the virus like a baseball and a glove and prevents it from infecting.

And it turns out, if you look at the evolutionary history of that gene, and you trace it back through monkeys, you can actually find that a previous iteration inhibited SIV, which is the cousin of HIV in humans.

And so old world monkeys actually can't get SIV, whereas new world monkeys can, and humans can, obviously.

And so it seems like what happened, and you can actually make a few mutations in TRIM5-alpha and find that this is true,

is that Trim5-alpha once protected against an HIV-like pathogen in the primate genomes.

And then there was this challenge from this massive endogenous retrovirus.

And it was so bad that the genome lost the ability to fight off these HIV-like viruses in order to restrict this endogenous retrovirus.

And you can see it because that retrovirus integrates into our genome.

There are like latent copies, like the half bodies of this virus all throughout our DNA code.

And then this particular retrovirus went extinct.

Reasons unknown.

No one knows why.

But we didn't, like, re-update that piece of our host defense machinery to fight off HIV again.

And so we're in a situation where you can go in and take human cells and make just a couple edits in that TRMM5 alpha gene, and it's

currently protecting against a virus which no longer exists.

And you can edit it back to actually restrict HIV dramatically.

So there are plenty of examples.

So you could imagine the same thing for antibiotics.

We're like, hey, this particular, you know, defense mechanism went away because the pathogen evolved its own defense to it.

Well, the pathogen might have lost that defense long ago.

And if you could sort of extract that historical antibiotic, that historical antifungal, potentially it actually has efficacy.

Yeah, I think a great explanation for understanding a lot of evolution and how you're able to actually adapt to new environments, new pathogens, is that gene duplication is possible.

And this explains a whole lot.

If you look at most genes in the genome, they actually arise at least at some point in evolution from a duplication event.

So that means you've got gene A, it's doing, you know, it's performing some job, and then some new environmental concern comes along.

Maybe it's like a lack of particular source of nutrient.

Maybe it's a pathogen challenging you.

And maybe gene A, if it were to dedicate all of its energies, so to speak, you were to mutate it to solve this new problem, could be adapted with a minimal number of mutations.

but then you lose its original function.

So we have this nice feature of the genome, which is it can just copy and paste.

And so occasionally what will happen in evolution is you get a copy paste event.

Now I've got two copies of gene A and I can preserve my original function in the original copy.

And then this new copy can actually mutate pretty freely because it doesn't have a strong selective pressure on it.

So most mutations might be null.

I've got two copies of the gene.

I can have lots of mutations in it accumulate.

Nothing bad really happens because I've got my backup copy, my original.

And so that you can end up with drift.

It's not that the base rate goes up.

It's not like DNA polymerase is more erroneous or that you're just like doubling it.

It's not like, oh, well, I've got two copies.

That is true, but I don't think it's the main mechanism.

One of the main mechanisms that just makes it difficult for evolution to solve a problem is that if a mutation breaks a gene or somewhere along the path of edits, imagine there are three edits that take a host defense gene from restricting SIV to restricting this new nasty PT endogenous retrovirus.

Well, if one edit just breaks the gene, two edits just breaks the gene, three edits fixes it, it's really hard for evolution to find a path whereby you're actually able to make those first two edits because they're net negative and that net negative for fitness.

You need some really weird contingent circumstances.

Through duplication, you can create a scenario where those first two edits are totally tolerated.

They have no effect on fitness, you've got your backup copy, it's doing its job.

And so even though the mutation rate is low, some of these edits actually aren't that large.

I'm going to forget the number of edits, for instance, in TRIM5-alpha for this particular phenomenon we're talking about for memory.

But it's in, like, the tens.

It's not that you need massive kilobase scale rearrangements.

It's actually a fairly small number of edits.

And basically, you can just align the sequence of this gene in new world versus old world monkeys and then for humans.

And you find there's a very high degree of conservation.

You can arrange genes in the human genome by homology to one another.

And what you find is even in our current genome, even without having the full historical record, there are many, many genes which are likely resulting from duplication events.

One like trivial way that you can check this for yourself is like just go look at the names of genes.

And very often you'll see something where it's like gene one, gene two, gene three, or, you know, type one, type two, type three.

And if you then go look at the sequences...

Sometimes those names arise from like they were discovered in a common pathway and they have nothing to do with each other.

A lot of the time it's because the sequences are actually quite darn similar.

And really what probably happened is they evolved through a duplication event and then maybe did some swapping with some other genes.

And you ended up with these quite similar, quite homologous genes that now have specialized functions.

So it's like when evolution has a new problem to solve, it doesn't have to start from scratch.

It starts from, like, what was the last copy of the parameters for encoding a gene that is getting close to solving this?

Okay, let's do a copy-paste on that and then iterate and fine-tune on those parameters as opposed to having to start with, like, ab initio, some random stretch of sequence somewhere in the genome has to become a gene.

Interesting.

Right.

The number of calories they can gather for the population.

Yeah, I think that's correct.

I don't think that there is a single monocausal explanation for aging.

I think there are layers of molecular regulation that explain a lot.

For instance, I have dedicated my career now to working on epigenetics and trying to change which gene cells use because I think that explains a lot of it.

But it's not that there is like some upstream bad gene X and all we have to do is turn that off and suddenly aging is solved.

And so I think the most likely outcome is that when we eventually develop medicines that prolong health in each of us, it's not going to fix everything all at once.

There's not going to be a singular magic pill, but rather you're going to have medicines that add multiple healthy years to your life, years you can't otherwise get back.

But it's not going to fix everything at the same time.

You are still going to experience for the first medicine some amount of decline over time.

And this gives you an example of if you think about evolution as a medicine maker in this sort of anthropomorphic context, why it might not have been selected for immediately.

Yeah.

So we're working on something called epigenetic reprogramming, which very broadly is using genes called transcription factors.

I like to think about these as sort of the orchestra conductors of the genome.

They don't perform many functions directly themselves, but they bind specific pieces of DNA and then they tell which genes to turn on, which genes to turn off.

They eventually put chemical marks on top of DNA, on some proteins that DNA surrounds.

And this is one of the answers, this particular layer of regulation called the epigenome.

It's the answer to this fundamental biological question of how do all my cells have the same genome, but ultimately do very different things.

Your eyeball and your kidney have the same code, and yet they're performing different functions.

And that may sound a little bit simplistic, but ultimately I think it's kind of a profound realization.

And so that epigenetic code is really what's important for cells to define their functions.

That's what's telling them which genes to evoke from your genome.

What has now become relatively apparent is that the epigenome can degrade with age.

It changes.

The particular marks that tell your cells which genes to use can shift as you get older.

This means that cells aren't able to use the right genetic programs at the right times to respond to their environment.

You're then more susceptible to disease.

You have less resilience to many insults that you might experience.

And our hope is that by remodeling the epigenome back toward the state it was in when you were young, right after development, that you'll be able to actually address myriad different diseases whose one of strong contributing factors is that cells are less functional than when you were at an earlier point in your life.

So we're going after this by trying to find combinations of these transcription factors that are able to actually remodel the epigenome so that they can bind to just the right places in the DNA and then shift the chemical marks back toward that state when you were a young individual.

Oh, how I wish it were straightforward.

No, it's very likely.

Each of these transcription factors binds hundreds to thousands of places in the genome.

And one way of thinking about it is if you imagine the genome is sort of the base components of cell function, then these transcription factors are kind of like the basis set in linear algebra.

It's different combinations and different weights of each of the genes.

And so most of them are targeting pretty broad programs.

And there are no guarantees that aging actually involves moving perfectly along any of the vectors in this particular basis set.

And so it's probably going to be a little tricky to figure out a combination that actually takes you backward.

There's, again, no guarantees from evolution that it's just a simple reset.

And so it's actually a critical part of the process that we run through as we try and discover these medicinal combinations of transcription factors we can turn on is to ensure that they not only are making an age cell revert to a younger state.

We measure that a couple different ways.

One is simply measuring which genes those cells are using.

They use different genes as they get older.

You can measure that just by sequencing all of the mRNAs, which are really the expressed form of the genes being utilized in the genome at a given time.

You see that age cells use different genes.

Can I revert them back to a younger state?

Colloquially, we call this a looks-like assay.

Can I make an old cell look like a young one based on the genes it's using?

And maybe more importantly, we go down and drill to the functional level.

And we measure, can I actually make an age cell perform its functions, its object roles within the body, the same way a young cell would?

And these are the really critical things you care about for treating diseases.

Can I make a hepatocyte, a liver cell in Greek, function better in your liver so it's able to process metabolites like the foods you eat, how it's able to process toxins like alcohol and caffeine?

Can I make a T cell respond to pathogens and other antigens that are presented within your body?

So these are the ways in which we measure age, and so we need to ensure that not only does the combination of TFs that we find actually have positive effects along those axes, but we then want to also measure any potential detrimental effects of that image.

So there are canonical examples where you can seemingly reverse the age of a cell, for instance, at the level of a transcriptome, but simultaneously you might be changing that cell's type or identity.

So Shinya Monaka, a scientist who won the Nobel in 2012 for some work he did in about 2007, discovered that you can just take four transcription factors and actually just by turning on these four genes, turn an adult cell all the way back into a young embryonic stem cell.

This is a pretty amazing existence proof that shows that you can reprogram a cell's type and a cell's age simultaneously just by turning on four genes.

Out of the 20,000 genes in the genome, the tens of millions of biomolecular interactions, just four genes is enough.

That's a shocking fact.

And so we actually have known for many years now that you can reprogram the age of a cell.

The challenge is that simultaneously you're doing a bunch of other stuff, as you alluded to.

You're changing its type.

And that might be pathological.

If you did that in the body, it would probably cause a type of tumor called a teratoma.

So we measure not only at the level of the genes a cell is using.

Do you still look like the right type of cell?

Are you still a hepatocyte?

Are you still a T cell?

If not, that's probably pathological.

But you can also use that same information to check for a number of other pathologies that might develop.

Did I make this T cell hyperinflammatory in a way that would be bad?

Did I make this liver cell potentially neoplastic, proliferate too much even when the organism's healthy and undamaged?

And you can check for each of those at the level of gene expression programs and then likewise functionally.

Before you put these molecules in a human, you actually just functionally check in an animal.

You make an itemized list of the possible risks you might run into.

Here are the ways it might be toxic.

Here are the ways it might cause cancer.

Are we able to measure deterministically and empirically that that doesn't actually occur?

You're entirely right.

You know, Shinya Amanaka was able to do this with a relatively small team with relatively few resources and achieve this remarkable feat.

So it's entirely worth asking, why can't a similar procedure work for arbitrary problems in reprogramming cell state, whether it be trying to make an age cell act like a young one, disease cell act like a healthy one?

Why can't you just take 24 transcription factors and randomly sort through them?

So there were two features of Xinyue's problem that I think make it amenable to that sort of interrogation that aren't present for many other types of problems.

And this is why he's such a remarkable scientist.

Most of science is problem selection.

You don't actually get better at pipetting or running experiments after a certain age, but you do get better at picking what to do.

And he's amazing at this.

So the first feature is that measuring your success criterion is trivial in the particular case he was investigating.

He's starting with somatic cells that, in this case, were a type of fibroblast, which literally is defined as cells that stick to glass and grow in a dish when you grind up a tissue.

So it sounds fancy, but it's a very simplistic thing.

So he's starting with fibroblasts.

You can look at them under a microscope, and you can see their fibroblasts just based on how they look.

And then the cells he's reprogramming toward are embryonic stem cells.

So these are tiny cells.

They're mostly nucleus.

They grow really, really fast.

They look different.

They detach from a dish.

They grow up into a 3D structure.

And they express some genes that will just never be turned on in a fibroblast by definition.

So actually how he ran the experiment was he just set up a simple reporter system.

So he took a gene that should never be on in a fibroblast, should only be on in the embryo, and he put a little reporter behind it so that these cells would actually turn blue when you dumped a chemical on them.

And then he ran this experiment in many, many dishes with millions upon millions of cells.

The second really key feature of the problem is this notion that those cells he's converting into amplify.

They divide and grow really quickly.

So in order for you to find a successful combination, you don't actually need it to be efficient almost at all.

The original efficiency Yamanaka published, the number of cells in the dish that convert from somatic to an induced pluripotent state back into a stem cell is something like a basis point or a tenth of a basis point, so like 0.01, 0.001%.

If these cells were not growing and they were not proliferating like mad, you probably would never be able to detect that you had actually found anything successful.

It's only because success is easy to measure once you have it, and even being successful in very rare cases, one in a million,

amplifies, and you can detect it, that this I think was amenable to his particular approach.

So in practice, what he would do is dump these factors or this group of 24 minus some number, eventually whittling it down to four.

He would dump these onto a group of cells.

And over the course of about 30 days, just a few cells in that dish, like a countable number on your fingers, would actually reprogram.

But they would proliferate like mad.

They form these big, what we call colonies, because it's like a single cell that just proliferates and forms a bunch of copies of itself.

Right.

They form these colonies.

You can see with your eyeballs by holding the dish up to the light and looking for opaque little dots on the bottom.

You don't need any fancy instruments.

And then you could stain them with this particular stain and they would turn blue based on the genetic reporter he had.

So now we look at those key features of the problem and we pick any other problem we're interested in.

I'm interested in aging, so that's the one I'm going to pick for explanation.

how difficult is it to measure the likelihood of success or whether you've achieved success for cell age?

Well, it turns out age is much more complicated in terms of discriminating function than actually just comparing two types of cells.

An old liver cell and a young liver cell, prima facie actually look pretty darn similar.

It's actually quite nuanced, the ways in which they're distinct.

And so there isn't a simple trivial system where you just like label your one favorite gene or you can just

Just give the young cells cancer.

Just make the old ones cancer, and then they'll grow.

Yeah, Dorcas, you've solved it for me.

So there's no trivial way that you can tell whether or not you've succeeded.

You actually need a pretty complex molecular measurement.

And so for us, a real key enabling technology, and I don't think our approach would really have been possible until it's emerged, was something called single-cell genomics.

So you now take a cell, rip it open, sequence all the mRNAs it's using.

And so at the level of individual cells, you can actually measure every gene that they're using at a given time and get this really complete picture of a cell state, everything it's doing, lots of mutual information to other features.

And from that profile, you can train something like a model that discriminates young and aged cells with really high performance.

It turns out there's no one gene that actually has that same characteristic.

So unlike in Yamanaka's case where a single gene on or off is like an amazing binary classifier, you don't have that same feature of easy detection of success in aging.

The second feature is as you highlighted, we can't just turn these into cancer cells, success doesn't amplify.

And so in some ways, the bar for a medicine is higher than what Yamanaka achieved in his laboratory discovery.

You can't just have 0.001% success and then wait for the cells to grow a whole bunch in order to treat a patient's disease or, you know, make their liver younger, make their immune system younger, make their endothelium younger.

You need to actually have it be fairly efficient across many cells at a time.

And so because of this, we don't have the same luxury Yamanaka did of taking a relatively small number of factors and finding a success case within there that was pretty low efficiency.

We actually need to search a much broader portion of TF space in order to be successful.

And when you start playing that game and you think, okay, how many TFs are there?

Somewhere between 1,000 and 2,000, depends on exactly where you draw the line and developmental biologists love to argue about this over beer, but let's call it 2,000 for now.

And you wanna choose some combination, let's say you guess it's like somewhere between one and six factors might be required.

The number of possible combinations is about 10 to 16.

So if you do any like math on the back of a napkin, in order to just screen through all of those, you would need to do many orders of magnitude more single cell sequencing than the entire world has done to date cumulatively across all experiments.

And so it's just not tractable to do exhaustively.

And so that's where actually having models that can predict the effect of these interventions comes in.

If I can do a sparse sampling, I can test a large number of these combinations.

And I can start to learn the relationship of what a given transcription factor is going to do to an age cell.

Is it going to make it look younger?

Is it going to preserve the same type?

I can learn that across combinations.

I can start to learn their interaction terms.

Now I can use those models to actually predict in silico for all the combinations I haven't seen, which are most likely to give me the state I want.

And you can actually treat that as a generative problem and start sampling and asking which of these combinations is most likely to take my cell to some target destination in state space.

In our case, I want to take an old cell to a young state.

Yeah.

But you could imagine some arbitrary mappings as well.

And so I think as you get to these more complex problems that don't have the same features that Shinya benefited from, which were the ability, again, to measure success really easily.

You can see it with your bare eyes.

You don't even need a microscope.

And two, amplification.

As you get into these more challenging problems, you're going to need to be able to search a larger fraction of the space to hit that higher bar.

That would be very much my contention.

And one piece of evidence for this is that's the way the development works.

You know, it's kind of a crazy thing to think about, but you and I were both just like a single cell.

And then we were a bag of undifferentiated cells that were all exactly alike.

And then somehow we became humans with hundreds of different cell types all doing very different things.

And when you look at how development specifies those unique fates of cells, it is through groups of these transcription factors that each identify a unique type.

And in many cases, actually, the groups of transcription factors, the sets that specify very different fates, are actually pretty similar to one another.

And so evolution has optimized for being able to just swap one TF in or swap one TF out of a combination.

and get pretty different effects.

And so you have this sort of like local change in sequence or gene set space leading to a pretty large global change in output.

And then likewise, many of these TFs again are duplicated in the genome.

And because mutations are going to be random and they're inherently small changes at the level of sequence at a given time,

Evolution needs a substrate where in order to function effectively, these small changes can give you relatively large changes in phenotype.

Otherwise, it would just take a very long time across evolutionary history for enough mutations to accumulate in some duplicated copy of the gene for you to evolve a new TF that does something interesting.

And so I think we're actually in most cases in biology, due to that evolutionary constraint, small edits need to lead to meaningful phenotypic changes in a relatively favorable regime for generic gradient-like optimizers.

You know, it would be maybe a little bit overstating to say evolution is like using the gradient, but there is a system kind of like if you've heard of evolution strategies, where basically the way you optimize parameters is you can't take a gradient on your loss, so you make a bunch of copies of your parameters, you randomly modify them,

And then you compute a gradient on your parameters against your loss.

And so you can take a gradient in that space.

That's kind of how I imagine evolution is working.

And so you need lots of those little edits to actually lead you to have meaningful step sizes in terms of the ultimate output that you have.

And to think like, you know, why would transcription factors... Maybe this is getting a little bit too gigabrained about it, but like, why does the genome even have transcription factors?

Like, what's the point?

Why not just have every time you want a new cell type, you like engineer some new cassette of genes or some new totally de novo set of promoters or something like this?

I think one possible explanation for their existence rather than just an appreciation for their presence is that

Well, having transcription factors allows a very small number of base pair edits at the substrate of the genome to lead to very large phenotypic differences.

If I break a transcription factor, I can delete a whole cell type in the body.

If I retarget a transcription factor to different genes, I can dramatically change when cells respond and have, you know, hundreds of their downstream effector genes change their behavior in response to the environment.

And so it puts you in this regime where transcription factors are a really nice substrate to manipulate as targets for medicines.

In some ways, they might be like evolution's levers upon the broader architecture of the genome.

And so by pulling on those same levers that evolution has gifted us, there are probably many useful things we can engender upon biology.

I don't know about that, but if I can give you, I'll give you like a real cringe analogy that sometimes I deploy, but it requires a very special audience.

I think you'll probably be the one who fits into it.

I don't know about your audience, but you will.

You can kind of think about it like, you know, if you think about how attention works, like queries, keys, values, TFs are kind of like the queries, the genome sequences they bind to, kind of like the keys.

Genes are kind of like the values.

And it turns out that structure then allows you to very efficiently in terms of

editing space.

You can change just one of those embedding vectors, in this case, one of those sequences, and get dramatically different performances or total outputs.

And so I do think it's kind of interesting how these structures recur throughout biology, you know, in the same way that the attention mechanism seems to exist in some neural structures.

I think it's kind of interesting that you can very easily see how that same sort of querying and information storage might exist in the genome.

Yeah, or Eddie Chang has found like positional encodings probably exist in humans using Neuropixels.

If you haven't read these papers.

Oh, yeah.

So he implants these Neuropixel probes into individuals, and then he's able to talk to them, look at them as they read sentences.

And what he finds is that there seem to be certain representations which function as a positional encoding across sentences.

So they fire at a certain frequency, and it just increases as the sentence goes on and then like resets.

And so it seems exactly like what we do when we train large language models where you've got some function.

Yeah, yeah, yeah.

Why don't we have a million of these pills?

Okay, I'll try and take those in stride, and they're pretty different answers.

First answer is there actually are pathogens that utilize transcription factors as part of their life cycle.

So like a famous example of this is HIV.

HIV encodes a protein called TAT, and TAT actually activates NF-kappa B. And so HIV, sorry to back up a little bit, is a retrovirus.

Starts out as RNA, turns itself into DNA, shoves itself into the genome of your CD4 CT cells.

And so then it needs this ornate machinery to actually control when does it make more HIV and when does it go latent so it can hide and your immune system can't clear it out.

And this is why HIV is so pernicious is you can kill every single cell in the body that's actively making HIV with like a really good drug.

But then a few of them that have like lingered and hunkered down just turn back on.

And so people call this the latent reservoir.

Same with Hep B, right?

Well, hep B, hep C can both do this sort of like latent sort of behavior.

And so HIV is probably the most pernicious of these.

And one way it does it is this gene called TAT actually interacts with NF-kappa B. NF-kappa B is a master transcription factor within immune cells.

Typically, if I'm going to like horribly reduce what it does and some immunologists can crucify me later, it like increases the inflammatory response of most cells.

They become more likely to attack given pathogens around them on the margin.

And so it'll turn on an F-kappa B activity and then uses that to drive its own transcription and its own lifecycle.

And so I can't remember quite all the details now exactly of how it works, but part of this circuitry is what allows it to, in some subset of cells where some of that upstream transcription factor machinery in the host might be deactivated, it goes latent.

And so as long as the population of cells it's infecting always has a few that are like turning off the transcription factors upstream that drive its own transcription, then HIV is able to persist in this latent reservoir within human cells.

So it's just one example offhand.

Then there are a number of other pathogens, and unfortunately I don't have quite as much molecular detail on some of these, but...

they will interface with other parts of the cell that eventually result in transcription factor translocation to the nucleus and then transcription factors being active.

This actually segues a little bit to your second question on why aren't there more medicines targeting TFs?

In a way, I think many of our medicines

ultimately downstream are leading to changes in TF activity, but we haven't been able to directly target them due to their physical location within cells, and so we go several layers upstream.

If you think about how a cell works in sensing its environment, it has many receptors on the surface, it has the ability to sense mechanical tension and things like this, and ultimately, most of what these signaling pathways lead to is to tell the cell, use some different genes than you're using right now.

That's often what's occurring.

And so that ultimately leads to transcription factors being some of the final effectors in these signaling cascades.

So a lot of the drugs we have that, for instance, inhibit a particular cytokine that might bind a receptor, or they block that receptor directly, or maybe they hit a certain signaling pathway,

Ultimately, the way that they're exerting their effect is then downstream of that signaling pathway, some transcription factor is either being turned on or not turned on, and you're using different genes in the cell.

And so we're kind of taking these like crazy bank shots because we can't hit the TFs directly.

So that sort of begs the question, like, why can't you just go after the TF directly?

Yeah.

Traditionally, we use what are called small molecule drugs where they're defined just by their size.

The reason they have to be small is they need to be small enough to wiggle through the membrane of a cell and get inside.

And then you run into a challenge, which is if you want to actually stick a small molecule between two proteins that have a pretty big interface, meaning like they've got big swaths on the side of them that all, you know, sort of line up and form a synapse with one another, then you would need a big molecule in order to inhibit that.

And it turns out that TF's binding DNA is a pretty darn big surface.

And so small molecules aren't great at disrupting that and certainly even worse at activating it.

So small molecules can get all the way into the nucleus, but they can't do much once they're there.

They're just too small.

And then the other classic modalities we have are recombinant proteins.

We make a protein like a hormone in a big vat.

We grow it in some Chinese hamster ovary cells.

We extract it.

We inject it into you.

This is how, for instance, like human insulin works that we make today.

Or you make antibodies, antibodies produced by the immune system.

These run around and find proteins that have a particular sequence.

They buy into it.

And often they just like stop it from working by glomming a big thing onto the side.

So those are too big to get through the cell membrane, so then they can't actually get to a TF or do anything directly.

So we take these bank shots.

So what changes that today and why I think it's pretty exciting is we now have new nucleic acid and genetic medicines where you can, for instance, deliver RNAs to a cell that can get through using tricks like lipid nanoparticles.

You wrap them in a fat bubble.

It looks kind of like a cell membrane.

It can fuse with a cell, put the mRNAs in the cytosol.

You can make a copy of a transcription factor there.

And then it translocates to the nucleus the same way a natural one would and exerts its effect.

And likewise, there are other ways to do this using things like viral vectors.

But I think we've only very recently actually gotten the tools we need to start addressing transcription factors as first-class targets rather than treating them as like maybe some ancillary third-order thing that's going to happen.

How do you get them in there?

So I think there are many ways one could imagine solving it.

I'll sort of like narrow the scope of the problem to saying, I think delivering nucleic acid is a pretty good first order primitive.

Ultimately, the genome's nucleic acids, the RNAs that come out of it are nucleic acids.

So if you can get nucleic acid into a cell, you can drug pretty much anything in the genome effectively.

So you can reduce this problem to asking, how do I get nucleic acids wherever I want them to any cell type very specifically?

So today, there are two main modalities that people use, both of which have some downsides.

The first one that we've touched on already is lipid nanoparticles.

These are basically fat bubbles.

And by default, they get taken up by tissues which take up fat, like the liver, and they can be used sort of like Trojan horses.

They can release some arbitrary nucleic acid, usually RNA, maybe encoding your favorite genes, in our case, transcription factors.

into the cell types of interest.

You can play with the fats, and you can also tie stuff onto the outside of the fat, like you can attach a part of an antibody, for example, to make it go to different cell types in the body.

And I think the field is making a lot of progress on being able to target various different cell types with lipid nanoparticles.

So even if nothing else worked for the next several decades, I think companies like ours would have more than enough problems to solve with the cells that we can actually target.

Another prominent way people go after this is using viral vectors.

The basic idea being viruses had a lot of evolutionary history and very large population sizes.

They've evolved to get into our cells.

Maybe we can learn something from them, even better Trojan horses.

So one type of virus people use a lot is called an AAV.

Those AAVs carry DNA genomes, and so you can get genes, whole genes into cells.

They've got some packaging sizes.

You can think of it kind of like a very small delivery truck, so you can't put everything you want into it.

They can go to certain cell types as well.

And then on top of just where do you actually get the nucleic acid to begin with, you can engineer the sequences a bit.

And that basically allows you to add like a knot gate on it.

You can make it turn off the nucleic acid in certain cell types, but you're never going to use the sequence engineering to get nucleic acid into cells where it didn't get delivered in the first place.

So you can sort of start broad with your delivery vector and then use sequence to narrow down to make it more specific, but not the other way around.

So I think both of those methods are super promising.

Again, if nothing else emerged for decades, we'd still have tons and tons of problems as a therapeutic development community to solve even using just those.

I do think I have one sort of very controversial opinion, which, you know, people can roast me for later.

You have just one?

I have many controversial opinions.

One of them is that I think both of these probably in the limit will not be the way that we're delivering medicines in the year 2100.

If you think about viral vectors, no matter what, they're always going to be some amount of immunogenic.

You're always going to have your immune system trying to fight them off.

You can play tricks.

You can try and cloak them, et cetera, et cetera.

But they're always going to have some toxicity risk.

They also don't go everywhere.

It's not that we have examples of like a single viral species that infects every cell type in the body and we just need to engineer it to make it safe.

We would have to also –

engineer the virus to go to new cell types.

So there's some limitations there.

LNPs likewise have some problems.

They can go to tons of cell types.

That's what largely we're working on.

We're super excited about it.

But there are some physical constraints.

They just have a certain size, and they have to get from your bloodstream, out of your bloodstream, toward a given target cell, and they have to not fuse into any of the other cells along the way.

So there's a whole gamut they have to run.

Ultimately, I think we're probably going to have to solve delivery the way that our own genome solved delivery.

So we have this same problem that arose during evolution, which is how do I patrol the body, find arbitrary signals in the environment, and then deliver some important cargo there when some set of events happens?

How do I find a specific place and only near those cell types release my cargo?

And really, the problem was solved by the immune system.

So we have cell types in our body, T cells and B cells, which are effectively engineered by evolution to run around, invaginate whatever tissues they need to.

They can climb almost anywhere in the bodies.

There's nowhere they can't get access almost.

And then once they sense a particular set of signals, and they've got a very ornate circuitry to do this, they run basically an AND gate logic, they can release a dis-specified payload.

And right now, the way our genome sets them up, the payload they release is largely either enzymes that will kill some cell that they're targeting or kill some pathogen or some signal flares that call in other parts of the immune system to do the same thing.

So that's super cool, but you can think about it as a modular system that evolution's already gifted us.

We've got some signal and environmental recognition systems, so we can find particular areas of the body that we want to find, and then some sort of payload delivery system.

I can deliver some arbitrary set of things.

And I imagine if we were to rip von Winkle ourselves into 2100 and wake up, the way we will be delivering these nucleic acid payloads is actually by engineering cells to do it, to perform this very ornate function.

Those cells might actually live with you.

You probably will get engrafted with them, and they might persist with you for many years.

They deliver the medicine only when the environment within your body actually dictates that you need it.

And so you actually won't be seeing a physician every time this medicine is active.

Rather, you'll have a more ornate, responsive circuit.

The other exciting thing about cells is that they're big and they have big genomes.

And so you actually have a large palette to encode complex infrastructure and complex circuitry.

So you don't need to limit yourself to like the very small RNAs you can get in that might encode a gene or two, or in our case, a few transcription factors.

You don't have to limit yourself to this tiny AAV genome that's only a few kilobases.

You've got billions of base pairs to play with in terms of encoding all your logic.

So I think that's ultimately how delivery will get solved.

We've got many, many stepping stones along the way.

But if I could, like, clone myself and work on an even riskier endeavor, that's probably what I would do.

Not literally every single cell.

I'll asterisk it there.

So example, T cells don't go into your brain.

They can, but it's generally a pathology when they get in there.

So it's not like literally every cell, but almost every cell in your body is surveilled by the immune system.

So there are very, very few what we call immune privilege compartments in your body.

It's things like the joints of your knees and your shoulders, your eyeball, and your brain, basically.

There might be a couple of these.

I think the ear probably falls into that category.

A funny way of thinking about this is all the gene therapy people using viruses, they want to deliver to the immune privilege compartments because their drugs are immunogenic and they're limited to a very, very small set of diseases.

So in a way, it's like the shadow of all the diseases you can't address with viruses is what you can address with cells.

And given the complementarity between them, it's like, okay, you can probably cover the entire body.

And so they can't literally go everywhere.

But I think your analogy to the CAR-T work is very apt as well, where you can think of that two-component system.

I've got some detection mechanism for the environment I want to sense to perform some function.

And then I have some sort of payload that I deliver.

CAR-T's engineer the first of those and leave the second exactly the same as the immune system does.

So

they engineer, go recognize this other antigen that you wouldn't usually target, some protein on the surface of a cell, for instance, and then deliver the payload you would usually deliver if it was infected by a virus or if you saw that it was foreign in some way, whereas cancer cells usually don't actually look that foreign.

Most of their genes are the same genes that

are in your normal genome, and that's why it's hard for the immune system.

Yeah, the Red Queen race is quite sophisticated.

If you want to just throw a new tool into biology, you somehow have to get around one side of that equation.

Yeah.

Just to give the delivery folks credit, they're currently ahead.

There are currently no reprogrammed medicines for aging, and there are medicines that deliver nucleic acids.

So they're still winning the race against us right now.

But to your point, I hope the lines cross.

I hope we out-compete them.

So I do think, actually, even if you were able to only target some subsets of cells, it's not that you would see this strange Frankensteinian benefit in health in some aspects and lack of benefit entirely in others.

I think what we found across the history of medicine is that actually the body is an incredibly interconnected complex system.

And if you're able to rescue a function, even in one cell type and one tissue, you often have knock-on benefits in many places that you didn't initially anticipate.

One way we can get examples of this is through transplant experiments.

So both in bone marrow and in liver, for example, we have fairly common transplant procedures that occur in humans.

And so we can compare old humans who get livers from young people or old people, and in a way ask a pretty controlled question.

What occurs as a function of just having a young liver?

Is it that, for example, you can eat a lot of fatty food and drink a lot and be fine, or is it that actually you see broader benefits?

And the latter seems to be true.

They have reduced risk of several other diseases and overall better survival as a function of having a younger liver than they do for an older one, suggesting that actually because these tissues are so interconnected,

Many of these organs like the liver, like your adipose tissue or endocrine organs, they're also sending out signals to many other places in your body, helping coordinate your health across multiple tissue systems.

Even just one tissue can benefit other tissue systems in your body at the same time.

HSCs are another example where there are many circumstances where one of the – and then I'll summarize.

This is mostly examples taken from a wonderful book by Frederick Applebaum who trained with Don Thomas, the physician who invented human bone marrow transplants.

There are many circumstances where patients got a bone marrow transplant and actually cured another disease they had as a result, maybe unanticipated, where it's even just the replacement of this one special cell type, HSCs, has knock-on effects throughout the body.

You know, there were symptoms of these diseases that presented in myriad ways throughout their system, but ultimately its root cause was even just a single cell.

There are counterexamples as well where you can go into animals

and break even just one gene in one specific subset of T cells.

You can break a gene that encodes for a transcription factor in their mitochondria called TREM, and you actually dramatically shorten the lifespan of mice.

One gene in one special type of C cells can give you that type of pathology.

And so it sort of implies the inverse may also exist.

Yeah, I think it's one example because it is a hormone and your endocrine system coordinates a lot of the complex interplay between your tissues.

I don't think the story is fully written yet on exactly why GLP-1 and GIP-1, you know, broadly incretin mimetic medicines like Ozempic have so many knock-on benefits.

But I think they're a great example of this phenomenon.

If someone told you, I'm going to find a single molecule and I'm going to drug it, and it's not only going to have benefits for weight loss, but also for cardiovascular disease, also possibly for addictive behavior, and maybe even preventing neurodegeneration, you would have told them they were crazy.

And yet, just by acting on the small number of cells in your body which are receiving this signal, the interplay and the communication between those cells and the rest of your body seems to have many of these knock-on benefits.

So it's just one existence proof.

Very small numbers of cells in your body can have health benefits everywhere.

And so even if cellular delivery does not emerge by 2100 as I imagine it will, then I still think that you're going to have the ability to add decades of healthy life to individuals by reprogramming the age of individual cell types and individual tissues.

Interesting.

How many transcription factors?

Yeah.

I think just a countable number.

I think some of those that we found today that have efficacy are somewhere between one and five and that that's a small enough number that you can encapsulate it in current mRNA medicines.

So already in the clinic today, there are medicines that deliver many different genes as RNA.

So there are medicines where, for instance, it's a vaccine as a combination of flu and COVID proteins, and they're delivering 20 different unique transcripts all at the same time.

And so when you think about that already as a medicine that's being injected into people in trials, the idea of delivering just a few transcription factors is seemingly quotidian.

And so thankfully, I don't think we'll be limited by the size of the payloads that one can deliver.

One other really cool thing about transcription factors is that the endogenous biology is very favorable for drug development.

the expression level of transcription factors in your genome relative to other genes is incredibly low.

If you just look at the rank ordered list of what are the most frequently expressed genes in the genome by the count of how many mRNAs are in the cell, transcription factors are near the bottom.

And that means you don't actually need to get that many copies of a transcription factor into a cell in order to have benefits.

And so what we've seen so far and what I imagine will continue to play out is that even fairly low doses of these medicines, which are well within the realm of what folks have been taking for now more than a decade, are able to induce really strong efficacy.

And so we're hopeful that not only will the actual size of the payload in terms of like number of base pairs not be limiting, but the dose shouldn't be limiting either.

In principle, it could be one time.

I think that would be an overstatement for today, but I can sort of talk you through the evidence from like the first principles back to the reality of like what's the hardest thing we have in hand.

So epigenetic reprogramming is basically how the cell types in our bodies right now are able to adopt the identities that they have.

And the existence proof that those epigenetic reprogramming events can last decades is that my tongue doesn't spontaneously turn into a kidney.

So these epigenetic marks can persist for decades throughout a human life or, you know, hundreds of years if you want to take the example of a bowhead whale, which uses the same mechanism.

And we also know that with very targeted edits, other groups have done this, folks like Luke Gilbert now at the Ark Institute, who I think of as like one of the great unsung scientists of our time, have been able to make a targeted edit in a single locus and then show that you can actually make cells divide 400 plus times over multiple years in an incubator in the lab.

So imagine like a hothouse where you're just trying as hard as you can to break this mark down and it can actually persist for many years.

Other companies have actually now dosed some editors similar to the ones that Luke developed in his lab in monkeys and shown they last at least a couple years.

So in principle, the upper bound here is really long.

You could potentially have one dose and it lasts a very long time, potentially decades, as long as it took you to age the first time maybe.

We don't have data like that today, so we don't want to overstate.

We do have data that these positive effects can last several weeks after a dose.

And so you could imagine, even without many leaps of faith up toward this upper bound limit of what's possible, just from the data we have in hand now, that you could get doses every month, every few months, and actually have really dramatic benefits that persist over time, rather than needing, for instance, to get an IV every day, which might not be tractable.

I think it's less likely.

I think you have a prior that evolution has given you a reasonable basis set for navigating the states that human cells might want to occupy.

And in our case, we know that the state we're trying to access exists.

is encoded by some combination of these TFs.

It does arise in development, obviously.

We're trying to make an old cell look young, not look like some Frankenstein cell that's never been seen before.

That said, we don't have any guarantees that the way aging progresses is by following the same basis set of these transcription factor programs in the genome that are encoded during development.

So I don't think it's unreasonable to ask

Would your eventual ideal reprogramming medicine necessarily be a composition of the natural TFs or would it include something like TFs from other organisms as you posit or even entirely synthetic transcription factors as well?

Things like SuperSox.

SuperSox is a particular publication from Sergei – I mispronounced his last name – Vilachenko.

where they mutated the SOX2 gene and they made more efficient iPSC reprogramming.

So they could take somatic cells and turn them into pluripotent stem cells more effectively than you could with just the canonical Yamanaka factors, which are OCT4, SOX2, KLF4, and MYC.

IPSC reprogramming never happens in nature, so there's no reason to necessarily believe that the natural TFs are optimal.

And so even really simple optimizations, like just mutagenizing one of the four Yamanaka factors we already know about or swapping some domains between a few TFs seem to improve things dramatically.

So I think that's a pretty good signal that actually there's a lot of gradient to climb here and that potentially for us, the end state products we're developing in 2100 are more like synthetic genes that have never existed rather than just compositions of the natural set.

The best evidence is that it's probably not cellular.

So the reason your skin sags is there's a protein in your skin called elastin, which does exactly what you'd think it would based on the name.

It kind of keeps your skin elastic-y like a waistband and holds it to your face.