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Hello, this is Richard Jacobs with the Finding Genius podcast. My guest today is Dr. Sean Gibbons. He's an associate professor, part of the Institute for Systems Biology. We're going to talk about food-derived DNA and fecal metagenomes. I guess perhaps a translation of that is the food we eat, you know, the DNA that comprises the food, whether it be meat, vegetable, whatever. I
I've always wondered this, where does it go and does it affect us and does it interact with our DNA? So I think this may be a very, very interesting call. So welcome, Sean. Thank you. Well, thanks for having me, Richard. Yeah. I don't know if I was right or not, but let's start with your background. So we'll...
How did you get into this area of research? Yeah, I'm a microbial ecologist, I would say. So I started off in environmental microbiology, looking at kind of oceans and rivers and soils in my PhD. But in my postdoc, which is the time after the PhD, I started working on the human microbiome, which is all the bacteria and archaea and fungi and viruses that live in the human gut. And
continued along that line of research into my faculty position when I moved to Seattle at the Institute for Systems Biology. And so my lab now studies the microbiome and how the human microbiome influences our health, our responses to diet and drugs and so on. Okay. And then was this a recent paper or research project or what, you know, the fate of DNA in our food and
and how it affects our body. How did your research turn to that? Yeah, so this was sort of a fundamental question in the human microbiome field. We sort of know that diet is probably the strongest driver of the composition of the gut microbiome, but it's really difficult to get good dietary information from large cohorts of people where you also have microbiome data. The traditional way is
in which we collect dietary data is through questionnaires or self-reporting, sometimes through clinician-guided interviews. And these are expensive and onerous and take a lot of time and effort on the parts of participants and clinicians. And they're prone to various biases. So people are more likely to report good things, like they ate a salad, and they're less likely to report cookies or candy bars. So we know these are problems and issues with self-reporting. And there
And there are various ways of trying to get more objective measures of diet. So people are trying to take pictures of their meals or use, you know, video to track a food intake. And that often leverages things like machine learning to try to infer what's what's on a picture of someone's plate. Then those are still, you know, they work OK, but they don't work.
perfectly and by no means are they any better than the questionnaires. There's some folks working on metabolic markers of dietary intake. So are there small molecules in the bloodstream that you can measure or in urine that give you some information about dietary intake? So that's another piece. But there hadn't really been like a data driven either DNA or metabolomics way to get a
get high resolution information about the diet until fairly recently. I've done a food diary and you're right. If you don't eat something, you shouldn't be eating it. You're like, oh man, all right, I'll put it down. It was very revealing. I had a few friends do it too and I tend to eat the same thing a lot. Some meals are always the same, some vary. They were
There were some days where I ate five, six times a day, little bits. And then some days I only ate like once or twice. It was just interesting to see it all. And yeah, taking pictures, like what I saw was when you first start logging, it is a lot of work. But if you have the same meal at like Joe's Cafe every day, once you've got a picture of it, once you log the nutrients and what's in it, then you're like, oh, I just ate at Joe's Cafe and I had my normal like fish lunch or something. And then it gets much faster and easier to put in the data. I know it's just anecdotally, I'm just letting you know what I've run into, which may be interesting to you. Yeah.
Oh yeah, that is interesting. I think that makes a lot of sense. I tried it for a while with image tracking. There was an app sort of collaborator in Europe was developing to try to track your diet through images. And I was doing that, snapping pictures of everything I was eating every day. And it ended up being a lot of work. I think I have a little more of a heterogeneous diet than you perhaps understand.
I'm kind of eating different things all the time. So every time I had a handful of peanuts, I'd have to take a picture of them. You know, it's a, it was, this became sort of, you know, annoying and difficult and I only could keep it up for about two weeks. So yeah, it's, it's tough. Yeah. And one more thing I saw, I forget who, who it was, but there was the guy that sampled his oral microbiome every day,
for a year and it was very stable except when he traveled overseas and when he got really sick one time. But other than that, you know, it was remarkably resilient and stable to where it was, you know, for the rest of the year. So I don't know, I'll see if I can find that, but, you know. I know the data set you're talking about because that's my colleague, Lawrence David, who comes into this field. So when he was a
graduate student, he collected that data set and he also had dietary tracking over that same period. And yeah, it was surprising how stable his nutrient intake was over time, tended to eat a similar set of nutrients from day to day. I don't know how that breaks down to the individual food items he was eating, but on the nutrient level, he was he had a pretty similar diet from day to day. And so, OK, so now I'll get maybe into the DNA detection of food and where this idea comes from. This is actually an idea that originated, you
in people who study wild animals. So if you wanted to know what a grizzly bear is eating or a humpback whale, you can't ask them. So you have to find some other data-driven methodology. And scientists have turned to meta-barcoding. It's a method called meta-barcoding where you're amplifying specific marker genes from, you know, dietary sources. And in particular, they're looking at how
mitochondrial and chloroplast marker genes and these are little organelles inside of eukaryotic cells and they they contain dna and you can use that dna to tell you which species of animal or plant is in a sample and so wildlife biologists were using these um meta barcoding methods to track diet of wild animals and lawrence david who we just talked about he pioneered
using these same methods for diet tracking in humans. So his lab has been developing methodologies for extracting DNA and having primers and amplifying out specific portions of genes from mitochondria and chloroplasts to try and extract out
human dietary information from stool. It has a few papers in this area. So we knew all this work in our lab, but there was this lingering idea that maybe you could just randomly sequence all of the DNA coming from poop, right? Not trying to amplify out a specific gene, but taking a shotgun approach, just getting all the DNA and sifting through it and then just being able to extrapolate
extracts dietary DNA from that that kind of needle in a haystack problem it seemed like a maybe impossible task by Christian Diener who was a really talented computational biologist in my lab who's now running his own lab at the Medical University of Graz in Austria he he tackled this problem it took about five years to figure out how to get it to work but we we ended up with a method that we
we can apply to what's called shotgun metagenomic sequencing. So here we haven't done any PCR. We've just sequenced random fragments of DNA from stool. And it just so happens that there are, you know, hundreds of thousands of already sequenced stool metagenomes out there that you can download from public databases. And this suddenly would open up a huge universe of samples to be able to apply our method and extract dietary information that we can pair with the microbiome. Because from our perspective,
perspective, our big interest is to be able to understand, you know, if someone eats an onion or a tomato, which species of bacteria are enriched or depleted when you're eating that specific food item? And that's been a hard question to tackle until we've had these kinds of methods. Okay, so you're able to see what the, you know, what the DNA is. So what is it if you look at the big percentages? How much is bacteria?
bacterial? How much is human? How much is the food source? You know, what's the mix look like just on the surface? Yep. Great question. So if you look at the percentages, it's 99% or more is bacterial. So most of the DNA coming from stool is bacteria. 1% or less tends to be host human DNA sequences. And then what we're seeing from our method is that it
that it's between 0.1 and 0.0001% of all the sequences in a stool metagenome are coming from the diet. So it's the tiny, tiny fraction of the data from these samples. So you need, what length of, you know, if you get long enough lengths of DNA, you may miss, maybe it's chopped into much smaller fragments. I know you can only go so small where you really lose fidelity, but what's the sweet spot of the length of base pairs you could look at that, you know, maybe you're missing, I don't know.
That's a good question. So the technologies that are available to us for the most part tend to sequence on the order of 100 to 300 base pairs. These are short read sequencing methods like Illumina. It's pretty short. So that's the kind of data I'm talking about actually is like about 150 base pair sequence length. There are other methods for getting longer reads like PAC
bio where you can get up to you know 10 000 reads in a single and base pairs in a single read but we're mostly talking about short fragments and you're right that a lot of dietary dna is probably
pretty fragmented and chopped up by the time it comes out in stool. So what do you think is happening to it? Is it just getting ground up to the point where it's useless? Or do you think it still could be picked up and, you know, transcribed by RNA and affect cells? Oh, I don't think that there's much of that happening as far as, like,
transcribing the DNA from the diet. I think you essentially you were consuming biomass from animals and plants and it's essentially dead, right? Although some of the cells in there are probably still alive and doing stuff. They're on their way towards dying. And so this is just sort of decaying biomass. The
The majority, though, of the DNA we're detecting is probably DNA that is still somewhat encased or protected within intact cells. So animal or plant cells that happen to sort of survive passage all the way through. And there's some fraction that do. I mean, what do you think is happening? Is it just getting all destroyed? Or do you think our gut bacteria are selectively taking information, you know, from the food that we eat? Are cells taking any information from it or viscose?
the DNA components now really utilize? The DNA is mostly going to be degraded, chopped up, and used as nutrients. So the individual nucleotides can be used by the bacteria to construct their own nucleic acids. It is possible for bacteria to take up
intact fragments of DNA and incorporate them into their genomes. But that's usually using a process called homologous recombination. And so it requires that the fragments of DNA have to be fairly similar to regions that are already present in the genomes of the microbes. And most of eukaryotic
DNA, like in plants and animals, is very, very far diverged from bacteria. And so it would be almost inconceivable that you'd see too much horizontal gene transfer of plant or animal DNA into bacteria. So I doubt that's ever happening. It's mostly that they're just using it like food. They're breaking it down into tiny little monomers and then they're eating those monomers. Do you think it's completely unlikely or, I mean, is there a possibility? Well,
will be left that survives the stomach acid and maybe gets taken out from the small intestine, let's say. I'm not sure if I understand your... Oh, you know, the food we eat, it's getting mechanically and the stomach acid goes to work on it, then with digestive enzymes, the bacteria, you know, so stuff will survive, I guess, to a certain point. And at a certain point, it just gets all, you know, again, ground up and used. But, you know, depending on the foods we eat, maybe there's
I don't know, like certain plants, maybe some of the cell walls are still surviving until way late in the game, way late in the digestion. And therefore, maybe there is a chance that DNA could be preserved and certain bacteria could be used of it or maybe even our own cells somehow. I don't think that's ever happening. That's
the bacteria or our cells are making use of intact dna from the food that that would be highly rare and unlikely and we would see it if it was happening right we could see those horizontal transfer events in the genomes of these organisms and we don't so we know that over billions of years that kind of thing doesn't happen with any kind of regularity but for sure we do see intact cells surviving like you can you can literally see corn in your poop right there there are definitely people
pieces of biomass that aren't completely degraded. And that's the majority of the sequences we're detecting is coming from these pieces of intact, you know, biomass that are surviving passage. So what are you trying to infer from finding? Diet. So, you know, what we've done is we built a method where
where we can map these shotgun sequences to a large database of sequence genomes from animals, plants, and fungi that are known to be in the human diet. So we have something like 450 genomes from all of these different organisms that we've constructed into a database. In that same database, we've also included the human genome and the genomes of all the bacteria, archaea,
viruses and plasmids that are in the NCBI RefSeq. So it's all the reference genomes for all these different organisms. So we want to try to annotate away all of the things that are non-diet so we can kind of classify all of those things as not
dietarily associated and then every read that is kind of consistently and reliably annotated as belonging to one of these food derived genomes that gets counted as one of these species of plants animals or fun dry that are in the human diet so we can count up all those detections of individual sequences from these different things and the in the number of counts we get for a given species of plant or animal that can kind of serve as an abundance of how much of that was in the diet you know it's not perfect
There are biases, like animal cells tend to be degraded more efficiently than plant
plant cells. So plants have a bias to survive better and they're going to be over-detected compared to animals. But if we just sort of make the dumb assumption that we can just use these counts to quantify the intake, we were able to kind of compare our data to gold standard dietary intake data from controlled feeding studies. In our paper, we had a couple of controlled feeding studies
One that maybe I'll briefly touch on was a study where they had two groups of people and they were eating the same exact diets, except in one arm of the study, they fed them one large Haas avocado with their lunch. So that was the only difference between the groups. We took our method. We asked, you know, what is the only food item or what food items are significantly different between these two arms? And only a single thing popped up and it was avocado. So that was a really good validation. And then, you
And we were able to take these food genomes that we were detecting and we can map them to a database where people have kind of quantified the metabolic constituents of the biomass of these different foods. For beef, for example, how many milligrams of cysteine or lysine or what have you, all these individual components are present in that biomass. We can build a sort of nutritional intake profile from that.
detection of these different organisms and um use and like compare that to no nutritional intake from these um gold standard questionnaire data and there we also see really pretty good quantitative agreement between our methods predictions of say total energy intake or total protein total carbohydrate intake and the the questionnaire data so we think it's like a semi-quantitative not perfect but pretty good
estimation of what someone ate two to three days ago. That's another corroboration. That's great.
I know you kind of answered this in terms of DNA, but what is the composition of stool length? What creates all that mass? What is it? Well, let's see. Stool is about 25 to 45% of it is bacterial biomass. Dead or living? Yes. It's about half is dead or living bacteria. The rest is dead or living human epithelial cells that are sloughed off, residual material from the diet, so food,
essentially. And, you know, a few other things that are coming in from like the bile and so on. That's amazing. So almost half is bacterial map. Yes, about half is bacterial. It's the most dense bacterial substrate in the known universe. I would say it's 10 to 12 bacteria per
per gram oh that's crazy yeah that's a lot of bacteria it's a thriving ecosystem down there huh i mean well i don't know why have you looked at uh urine or sweat has any i guess there are other labs that you know of that are looking at the composition of urine the composition sweat the composition of you know exhale there to see like the bacterial loads other facts yeah i mean if you look at the whole human body we have about 40 trillion bacterial cells in the human body and to give you
A comparison, there's about 30 trillion human cells in the body, although bacteria are much smaller by mass than our cells. And so, you know, it's only a few pounds, maybe a pound or half a pound of material of what our microbes are. And that the vast majority of that is in the gut.
So 99 or 99.9% of the bacteria in our bodies is in the colon. And, you know, maybe 1% is spread out across the rest of the body. There are microbes on our skin. There's a fair number in our mouths, in our upper GI, but in
in our urine not so much there's not that many in our urine unless you have a urinary tract infection and in our sweat you know as it's coming out there's really no microbes in the sweat but when it comes out and mixes in with the skin then of course you you get some microbes in there it's a you can think of the body as a donut you know it's one continuous surface the mouth through the gut to the skin in the outside and microbes are covering that entire surface but as soon as you
penetrate through the epithelium where you go through the skin or through the gut wall, all of that is immune protected. So there are no microbes past that barrier. So, I mean, so when someone is, you know,
when they're forming a stool and all that, I would think there'd be an incredible proliferation of bacteria, you know, to consume that material. And then once it's passed out, like, so right before someone would, you know, would go to the bathroom, how much more bacteria would they have percentage wise than they had? And once they eliminate that, you know, what percentage of all their bacteria is now excreted? Is it a significant percentage? Is it like 20% of all the, you know? It's very significant. Yeah. I mean, to the
our biomass in terms of microbes does fluctuate quite a bit with bowel movement. You know, I think I just that cycling is interesting. Like, you know, I
I never thought about that. Yeah, you become maybe more human than microbe every time you have a bum and they get stuck up. Maybe that's why people feel so much better when everyone feels well, you know? Huh. Yeah, I don't know. Maybe it's, I don't know. It's just weird to think of the dynamics of it, but that's what came to mind. So yeah, it's interesting. Yeah, it's an incredible amount of growth, right? In the small intestine or the stomach, there's pretty much no microbes. There's very few, maybe, you know, a few hundred per person.
per milliliter or a thousand per milliliter and then you go from that to 10 to the 12 10 to the 11 per gram in the lower colon so there's just an enormous amount of growth going on from the top of the colon to the bottom of the colon so i can see why it would be so detrimental if you don't go to the bathroom regularly because that's an incredible amount of extra bacteria to build up in you and i can say i guess it would wreak havoc yeah over time if you didn't eliminate it i
I think it's a complicated story because the microbes in our colon is an evolved structure to specifically cultivate microbes. You know, having microbes there is good. And if we didn't, we'd actually be very sick. But you're right that if you are constipated, that also causes problems. We actually had a paper last year showing that you can see in the bloodstream the impact of constipation. What seems to be occurring is the microbes in the gut
They prefer to degrade dietary fibers and turn those into organic acids like short chain fatty acids. And those acids are very healthful to our colonic epithelium and they're good for us. But if stool sticks around for too long, they
they run out of fiber and so they have to switch to some other kind of food source and a couple things they can eat are proteins they can start to ferment proteins that are from the diet but they can also begin to degrade our mucus layer which also contains a lot of protein and the fermentation of this protein produces molecules like p creosol or endoxyl and these are known to be toxins to the kidneys or the liver or even the brain
And so long-term buildup of these toxins in the blood can potentially trigger chronic diseases later in life. So that's sort of the argument we were making in that paper. So you're right. It's sort of like a hot potato. You want microbes in there. You have to have them for a little bit of time. But if you keep them there for too long, that causes problems. Well, with, you know, 10 to the 12 bacteria per gram, I mean, is there...
I would think there'd be tremendous metabolic signaling from all those bacteria. I'm just guessing, but I would think, again, there'd be a tremendous level of signaling. And then once the stool leaves the colon and it's out, now all that's abruptly gone. Literally within, you know, once it happens, within seconds. It's just, I do wonder how the environment, like, if you think about it, the environment radically changes, you know, once you go to the bathroom and stuff. That's true. I mean, it's a very dynamic process.
place, although there are probably a couple of boluses of material moving through your GI at any given time. So there's probably upstream stool that's tomorrow's bowel movement that's coming. And there's also probably residual. It's not like bowel movement, everything just cleanly plops out of you. There is residual biomass that's stuck to the ankle.
and the mucus layer. Yeah. Have you, in your experimentation, your cohorts, do you use, do any of the people use enemas to like, you know, again, clean out the system and start quote unquote fresh? If you did, does that distort the data a lot? Have you looked at that? Oh, it definitely has a larger effect. And there are specific cohorts of people where you can see this happening regularly. So if you get a colonoscopy, for example, you do a bowel prep, which is to take a lot of Miralax, which is polyethylene glycol. And that's
that just cleans you right out. And there's also people who do colonics, right, where they try to get cleaned out for some kind of health reason. You know, I don't have a ton of opinion on the sort of health consequences of colonics, except it does seem to be the case that when people disrupt the biomass of their gut commensals, these are windows of opportunity for opportunistic pathogens to potentially colonize someone and get a toehold.
So we sometimes see that people who have these bowel preps will be colonized by things like Clostridioides difficile or C. diff or other problem organisms. And so I would you know, I wouldn't say you should casually get bowel preps or colonics because they could by by depleting your commensal microbes, which is kind of like a wing of your immune system, they prevent colonization by pathogens. You're sort of opening yourself up to
to petition. Well, what about people that fast for, let's say, more than three days? You know, someone did like a seven-day fast, you would, I mean, pretty much everything that's in there would be pushed out after a couple days. I don't know if that would be anything valuable for your experimentation, you know, like the resumption of eating after that. I don't know. Maybe there's too much to look at and you don't have time, but... There are...
Groups who have looked at that. Yeah. Fasting and its effects on the microbiota. So I will say that it's not simply the dietary substrates that the microbes can consume. There are certain organisms that degrade our mucus layer. So, you know, acromantia is one of these organisms. It's usually a good guy in the gut. It can break down the mucus layer. And in so doing, it actually produces a lot of compounds that can be cross fed to
to a lot of the other organisms in the ecosystem. And so you can sort of sustain some of the bacterial biomass and activity even in the presence of fasting. But
But what will happen is there's going to be more specialization on degrading your mucus layer, which will thin potentially the mucus layer. And, you know, if the mucus layer gets too thin, that can cause problems like inflammation. So inflammatory bowel disease is a disease that's kind of associated with thinning mucus and higher levels of inflammation. I mean, what we've been talking about, it's just, again, I don't know the mechanisms involved, but now I can see why fasting is...
would be so dramatic and have such an effect on the body because again, you're missing that incredibly large bacterial metabolic signal of it not being there in stool if you do it long enough. So again, just sort of speculation about what came to mind. That's all. Totally. And if you're thinking about it in the context of our diet algorithm, we kind of
did a similar analysis where we looked at babies because we kind of assume that babies are not eating solid food that much for the first you know few several weeks of life or months of life and when we run our method on on infants for those first you know that first year of life we do indeed observe that you don't detect that much that many food sequences you
in babies who are still feeding on milk. But around the time of weaning, when they start to eat more solid food, you see a steady rise in the number of food reads detected in their stool. Okay. So what questions, what research questions are you looking at right now that you're going to get data on with regard to the diet stuff? Yeah, right. So I think the next thing to know, we've developed the method, we've validated it. We want to apply it to
it to a very large cohort. So hundreds of thousands of samples from lots of different people eating lots of different things and get coincident data where we have dietary intake information and microbiome composition information to for the first time really map out, you know, if
if you're eating a banana or this kind of mushroom or this particular type of meat how does that impact the composition of the microbiota so I have a PhD student MD PhD student Crystal Perez who's going to work on this project we were collaborating with a researcher in Italy named Nicola sagata and he has access to hundreds of thousands of these metagenomes that we're going to apply
this Medi method of ours too and kind of map out the diet microbiome interaction. Because if you understand that well, if you really know which dietary factors turn up or turn down which particular species of bacteria, you can begin to engineer the microbiome through diet, which is one of the big goals of our field.
What kind of engineering would you want to do? Like, well, I don't know, I guess put something on a vegan diet versus carnivore diet versus keto, et cetera, and then see what the metagenomic sequencing tells you, like what kind of compounds are being produced differently and what the effects are, or like how would you proceed from this? Yeah, I think, you know, one piece of the puzzle is to be able to translate the ecology of the gut to the functional outputs of the gut. That's another part of the lab that works on metabolic modelization
modeling of the gut microbiome. So one good example would be predicting a really important molecule that the microbes make that's good for our health. So one of those is called butyrate. It's one of these short chain fatty acids I mentioned. Butyrate is very anti-inflammatory. It protects people from systemic inflammation, could protect
against aging because part of aging is this rise in inflammation over time. It also is an endocrine active molecule, so it stimulates our enteroendocrine cells to produce hormones like GLP-1, which you've
probably heard of and pyy and these can turn down our appetite and help improve our glucose homeostasis and prevent things like diabetes and pre-diabetes it has a beneficial effects on heart health and all kinds of things so it would be great to know how you can turn it up but the problem is if i feed everybody a banana different amounts of butyrate come out the other side and so we want to be able to predict you know who should eat what to maximize butyrate production
Part of that is using the metabolic models to predict this butyrate from the composition and a given dietary input. But another piece of the picture is, you know, some people just might not have the metabolic capacity to
to produce butyrate and that might be partially because they they haven't opened up the metabolic niche space in their diets that allows for that kind of organism to colonize them and to thrive and so if you could figure out from this diet analysis you know maybe you should eat a combination of chives and garlic and oranges to improve the colonization probability of butyrate producing taxa um
leveraged to help turn up that particular function. Another quick example is protection from pathogen colonization. So I have a student in the lab, Alex, who has a recent paper that just got accepted where we show that we can predict your risk of being colonized by this opportunistic pathogen. And about 50% of the population is at risk of being colonized. But the other 50%, their indigenous microbiota is able to protect
protect them. It's a shield against this organism being able to grow. So how can we convert people who are not protected to those who are protected? Part of that story will be understanding which organisms need to be added to the system in order to kind of provide that wall or that shield. And that might require
changing the diet to allow for the niche space for those particular taxa to be able to colonize that person. So I think all of this together is going to allow us to eventually get to the point where we can rationally engineer the composition of the microbiota to achieve very specific health outcomes that we want to achieve. Yeah, I think it makes sense. And you'll be able to get at it in an intelligent way. That's great. I mean, I guess it would be years until something's commercialized or what will it look like?
When you get to a commercial product that could be used in clinic, would it be like a stool sample test or, you know, a serialized one? Or what do you think it would be? Good question. There's a few companies that have already done things in this space. So we're
One example is a company out of Israel called Day Two. And they initially had a large study where they had a bunch of folks eating different foods and they were measuring their blood glucose responses to eating those foods. And they sequenced their microbiomes and they had a little bit of additional data, clinical data from the population. And they fed all of that into a machine learning algorithm to predict
personalized responses, blood glucose responses to eating foods. And they were able to show that they could design microbiome directed or precision diets that could do better than, say, a Mediterranean diet at bringing down someone's blood glucose. And that became a commercial company called Day Two, where you would
would provide a stool sample. They would build a personalized model that would make predictions for what types of food would be optimal to you. So this is already starting to happen in the machine learning space. My group is doing mechanistic modeling, so not so much machine learning, but taking mechanistic models of the metabolism of microbes in the gut and using that to
predict and rationally engineer the outputs of the microbiome that's still in the r d phase but we recently started a project called my digital gut which we've been funding through philanthropy we all have some beta testers in the next few weeks that'll get their data back but essentially we're building a dashboard where people can simulate
how their microbiome will respond to tens of thousands of different dietary, prebiotic and probiotic interventions for, for example, turning up butyrate production. And so we're going to kind of kick the tires on this platform and potentially use it as a decision support system for clinical trials. And what we want to do is run trials where we can prove that
microbiome directed precision nutritional interventions can do better than a standard of care for a given disease condition like prediabetes or blood pressure, for example. And once we have that clinical evidence in hand, then you have the basis for perhaps a startup company or being able to spin this out as some kind of product for people. So we're thinking about it.
Well, very good. Where can people follow up? What's like one source where they could keep tabs on you and your lab and what you're working on? Yeah, you could visit our lab website, which is
Gibbons, G-I-B-B-O-N-S dot I-S-B-S-I-E-N-C-E dot O-R-G. Okay, very good. Oh, Sean, thanks so much for coming on the podcast and listening to my armchair speculations and being nice about it. I appreciate you being here. Of course. Thanks for having me. If you like this podcast, please click the link in the description to subscribe and review us on iTunes. You've been listening to the Finding Genius Podcast with Richard Jacobs.
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