David Wishard
Favorite metabolite
tryptophan
Papers we discussed in this episode
- Emerging applications of metabolomics in drug discovery and precision medicine
- Towards a Rosetta stone for metabolomics: recommendations to overcome inconsistent metabolite nomenclature
Other resources
David Wishart’s talk at the event on Pan-cohort studies in October 2022
Why targeted metabolomics is essential for population health
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Episode Transcript
Alice: Today, I’m joined by David Wishard. You are a professor at the University of Alberta, and you are a very important member of the metabolomics community. You’re involved in many different parts of the life of metabolomics. I let you explain a bit your background and how you got to work with metabolomics and what are your favorite activities that you have at the moment in the field.
David: Sure. It’s, a long story. I got involved in metabolomics because I was trying to come up with a lecture topic about NMR that would be relevant to students in the pharmacy school. In pharmacy, the focus is on small molecules. And for most of my life I’ve been working in large molecules like proteins, and I used NMR to study proteins. So I was forced to do some quick research about small molecule NMR and medical applications in general.
In the course of doing the lecture and the course of building up the material for that, I realized that you could actually use some computers to simply solve a very challenging problem at the time, which is how do you deconvolute mixtures in an NMR spectrum. So at the time that I was doing this back in 1997, there wasn’t even a name for metabolomics: It wasn’t a term what we were doing. We just said, “that’s NMR”. At the time the term “metabonomics” started appearing, maybe in 1998. In 1999, I think the first paper on metabolomics, was called, we were calling it, Chenomics – So Chemistry and Omics. And that actually led to a little spinout company that’s still going (Chenomx). It does NMR based metabolomics. That’s how it got started. That’s how I got started in the area trying to find a topic to talk to students about with small molecules. But it combined my interest in programming and computing along with spectroscopy. Over the years I had to learn how to do GCMS and LCMS and I learned a lot of analytical chemistry that I never really took in school. I guess I’m a backdoor chemist. I came in the wrong way.
Alice: Your group is involved in many different sides of the work in metabolomics. You talked about the measurement, but you’re also very active in cataloguing metabolites. We will get to this later in the podcast. So with databases but also bioinformatic tools you’ve contributed creating and sharing a lot of tools with the community. So are there specific activities that you have or that your group has that you maybe want to mention now? – the main places where your group is visible in the community at the moment.
David: Sure. We have a mixed lab. The dry lab is the computational part, and the wet lab is people running the instruments but also doing sample prep, cell culture, or bacterial work or sample collection. You have molecular biologists, analytical chemists, we’ve got computer programmers, we even have an engineering team that helps with fabricating and designing things. It’s a real mixed group – And of them are working to solve each other’s problems because we all have problems to solve. Our dry lab does a lot of database development and a lot of software development to make untargeted metabolomics a little easier, but also to inform the community about what are involved in metabolites. Our wet lab group works primarily on targeted metabolomics and that’s something I’ve always believed is important because it’s the best way to quantify and I come from a background in NMR where we always quantify. It struck me as very odd that a lot of people in the metabolomics community weren’t too concerned about measuring concentrations.
I think, again, that is coming through viewing metabolomics from a different perspective. Something that we recognized early on was that there wasn’t a good collection of information about metabolites. And so that led us to establish these databases. The Human Metabolome Database (HMDB) was one, and then to develop software to help with analysis, and that was MetaboAnalyst.
And we wanted to do this on the web to make it really accessible to people. So both of those have done very well. We’ve created other resources I think that have also helped standardize things in the community, help inform people in the community. And I think that’s been a real theme for our work to try and democratize metabolomics, make it accessible, make it more amenable to people.
From the Instrumental and wet bench side we run a service facility now. It’s become a national facility in Canada – The so called the Metabolomics Innovation Center (TMIC) and that, too, is intended to democratize metabolomics making people and their research accessible to metabolomics resources because it’s an expensive business to get into.
Alice: This is also something we mentioned in the first season of the podcast – Metabolomics is really a multi-expertise kind of technique. You need to be good at completely different topics to be able to run a whole metabolomic study from beginning to end. And so I guess for someone who is interested in metabolomics but doesn’t have the resources or is beginning, it’s good to have this kind of facilities like TMIC, for example, where all this expertise is already there for you and you don’t have to be an expert at everything to begin with.
David: That’s right. It takes the village, as you say, it is very multidisciplinary. No one person I think knows all of the analytical methods. Chromatography, GCMS, LCMS, ICPMS, NMR. It’s a lot to learn and most people never get that experience in a single PhD or two or three PhDs. It helps to have these core facilities.
Alice: Do you also help with the interpretations of the biological interpretation? Do you have people working exclusively on this as well?
David: We do. And I think this is one of the biggest challenges in metabolomics. It’s getting easier to collect the data but to interpret or process the data is more challenging. The role in developing MetaboAnalyst was to help make the statistical analysis more easier but that’s not what you get really in terms of the biological interpretation.
It requires reading it requires a better understanding. We’ve been trying to develop pathway resources that would help with that. Trying to develop biomarker resources that would also help with the interpretation. And I think that the tendency in a lot of metabolomic studies is to sort of just stop at the statistical end and say, “I am done.”, and not really explore the biological side. And that’s where the really interesting things happen.
Alice: I agree. I don’t know if you heard about the story principle. It’s a book that I’ve been working on. In that book, I talk about like five steps to get people to the interpretation and exactly for what you said that – It doesn’t stop at the statistics, it doesn’t stop at the list of what goes up and what goes down and to really understand what’s going on or to make use of the metabolomics, you have to get to that e extra step to understanding the biology.
I’m also very interested in by informatic tools that help us get closer. It never gets exactly completely to the end story, but it can bring larger bricks of the house that you try to build when you build the story.
David: That’s right. I think that’s a really good point. Science is storytelling. Writing a journal article is a story. I think the other part to which is even beyond the interpretation and this is something that we’ve seen particularly in the clinic or applications to consumer-based systems. You tell them the story and then they say, “Well – now what? What do I do?”. And so it’s not just trying to explain what’s going on, it’s also trying to say how do you fix it? And that’s another challenge I think, which is perhaps even more compelling.
Alice: About your experience with metabolomics. I was wondering, is there something that you wish you had known before you started working with metabolomics that maybe you learned over 20 more years working with it? That’s, if you’d only known that before. Not necessarily something that we’ve discovered with new technologies, but something that as a beginner you don’t know, and then you figure it out later. And that maybe people could benefit from if they are not starting their work with metabolomics.
David: Yeah, when we originally started it was in 2006 it was called the Human Metabolism Project, which led to the development of the human metabolism database. At the time we started the project, we thought there were around 600 metabolites in the human body, and that was what was listed on all the encyclopedias and books. So we thought, this is going be pretty simple. At most we might get a thousand – in the first year we already were up to 2000. Now, some 17 years later, we’re about 250,000 – and that apparently only covers 5% of the true or known metabolism. Multiply that by another 20 fold so we’re maybe about 5 million compounds. I wish I’d known that it was going to be so big. The metabolome universe is so much larger than say the protein universe or the genome universe.
Alice: That is an interesting point. Then what would you say to people who are beginning with this and they’re thinking, okay, how many metabolites can I measure in one study, whether it’s targeted or un targeted, like it’s going to be maximum a few thousand, if I’m lucky. How is that going be relevant to the gigantic-ness of the metabolome? Where should I do this?
David: I think there two extremes that we’re looking at. I mean, one is to try and identify everything and assuming everything is important, but that´s like looking at a lawn: Is every blade of grass important or is the fact that the lawn is green or brown important. And I think as scientists, we’re often curious about the detail, but you still want to be able to see the broader picture. I think one of the things that’s emerged over the last 10 years is that there’s a common set of metabolites that are changed in disease. There are obviously some compounds, particularly compounds that we call the exposome that are causative for disease. And some of these are remarkably low levels. And so there’s a compelling case to be made to say, yes, we need to measure these obscure things that we didn’t know were there because maybe in fact they’re causing large numbers of conditions.
So I can see it from both sides, but from the perspective, how is it impacting on the body or physiology? Maybe there’s only about 400-500 key actors that we need to really look at and measure. I would emphasize that we need to measure those accurately and quantitatively. But it might even be that there’s a smaller set that we can work with to see what has changed and how it’s changed. If we want to understand what is causing the change, then in fact maybe this desire to measure everything will be important. Time will tell.
Alice: What you mentioned about using quantitative measurements is also really important in the context we are interested in today – The application in the clinics. You had a talk last year that is also on the biocrates YouTube channel where you make a strong point that these measurements should be quantitative if we want to have a chance to have them be useful in the clinics. We will come back to this at the end of the episode. For multiple applications this is something that makes a huge difference.
Let us discuss a bit more the HMDB and the different databases that you’ve been building over the years: HMDB comes from the Human Metabolome Project where you had a number of groups that were working together on first identified metabolites and then cataloguing them and then you ended up having something much bigger than you expected. This is always interesting because also when we use omics, we want to look at everything.
This was the same for all omics. And then, I guess, there was a shock at some point when you figured out. What you set out to do? Do you have any regrets?
David: Well, it keeps you busy. At least you’re still employed, when you have a bigger project than you expected. But it, yeah, it’s sort of, you start off with a meal thinking it’s only a single course dinner and find out it’s a 12 course dinner. When we started the human metabolome project, we thought it was more contained, more constrained, and that it was solvable and reachable. But I remember going to a meeting, I think it was in North Carolina, where someone highlighted the fact that metabolomics is probably more complicated than we expected, in part because we eat other metabolomes and and a light bulb kind of went off in my head and said oh – That’s very true. Because in fact we eat plants and plants have a very different metabolome than humans. And we eat a variety of other prepared foods. And so these things have chemicals added to them. Just a matter of doing a quick Google search and realizing that there are 5,000 compounds added to foods and the list of plant phytochemicals was over 300,000 listed in the natural products databases. And I just had a sinking feeling that we are going to be very busy for a very long time.
The intent of the Human Metabolome Project and the database itself is to try and capture that information, make it more accessible make it usable from the perspective of the metabolomics community. Make it searchable and give a standard hub the same way that genebank has helped the genetics and molecular biology; in the same way that the protein data bank has helped with structural biology.
Alice: Does it have to do with the nomenclature; with having a standardized way of naming things and describing things is – or what is for you the added value of having databases that collect everything in one place.
David: I think part of it is standardization. We spend a lot of time identifying the different ways of naming chemicals. Most of them have a dozen different synonyms. I think we also wanted to consolidate information that was scattered. The centralization is a way of at least getting that all in one place so you can look at it. Moving it to the web meant that you didn’t have to have a big book published. If we had it as a book it would be 50,000 pages if that – making it web accessible makes it more practical. It also makes it more searchable. We can search not only by names but you can also search by structures and that’s very easy, especially if you’re a chemist. You think in terms of structure. It also allowed us to put in diagrams like pathways it allowed us to put in spectra so that people could compare things. The visual data has become increasingly important in the database. I think there are other evolutions, revolutions that might be happening. The development of natural language processing and chatbots. It might be a way of allowing more specific queries to the database and giving you textual answers rather than read and read and read.
Alice: That would be great.
David: There’s certainly a way of evolving databases so that they are topic specific more interactive. And I think that’s one of the things that we’ll be trying with our databases over the next year or so.
Alice: Are there uses that have been made of those databases that surprised you? Things you didn’t expect?
David: Yeah, I think there’s always been a lot of surprises. Originally we started making a list not only of metabolites but we also wanted to track the drugs and then we also wanted to start tracking the foods. And so one database was called drug bank and the other one was Food db, and then another one is, human metabolism database. The drug bank, which was a small database became incredibly popular. And we didn’t know why. And then we found out that what people were most interested in was the drug target information that we put in because that hadn’t been put in in any data resource and people were using it to identify new drug targets and to repurpose drugs.
And so that was, “oh, I didn’t know you could do that”. But that’s grown into a very big resource for the drug industry. HMDB – a lot of people have used it for applications and compound classification; reinterpreting mass spectra in ways that we never expected; defining what is biologically or drug relevant or a natural product resource, or what is a natural product. We’re surprised at the ideas that people come up with with the databases. And I think that just sort of reinforces the need for putting it out there and letting people discover new ways of interpreting the data.
Alice: Recently you published with other co-authors a comment in nature metabolism. Saying you are going towards a Rosetta Stone for metabolomics with the recommendations to overcome inconsistent metabolite nomenclatures. Do you have a few key points that maybe you could tell us about, because this is also something we discussed on the podcast a couple of times. It can be difficult to make sure that you exploit your metabolomics to the fullest when you’re not sure that you’re actually naming your metabolites in a way that is understood by all the tools that you want to use. So what were the main points that you wanted to discuss in that paper?
David: I think, one of the more striking things that submerged is that there are several cases where people have rediscovered the same metabolite – over multiple years …and existed under multiple names and so people didn’t realize these things had been around or were discovered for certain applications or people were making claims that they were the first to discover it only to find out years afterwards that someone else had rediscovered it many years before so this is a problem and it’s a problem with nomenclature. It’s a problem how people use traditional names in the literature. There are solutions to using names and some of these are using things like inchy keys or standard unique identifiers. You could also use identifiers from databases, you can do structure searches to see if your molecule resembles something else that has already been known. Making those things available like a Rosetta Stone to help with even the translation of a compound name to what it really is or a chemical translator would help. I still think it’s critical for the journals to adopt this idea of using standard identifiers when compounds are listed or named. And that way we’re all on the same page and speaking the same language. I guess what some level the Rosetta Stone did when we were trying to convert hieroglyphs into the language we could understand.
Alice: To conclude on the topic of the databases: How could someone contribute to, for example, to HMDB? Do you take inputs from the outside and someone who’s interested, who maybe is doing a lot of identification of peaks or also maybe writing their PhD is on this metabolite. Then they have so many interesting literature references about it on functions and stuff. Do you take input from the outside and how does that work?
David: We do, we’d like to have more or we’d like to hear more from people about either corrections, they’d suggest or additions that they’d like to see and improvements that could be done. We have more by accident than by design started taking submissions on metabolites. So we’ve been bringing in mass spectra depositions, NMR spectral depositions, and more recently infrared spectroscopy depositions.
Alice: People write directly to the database, or how does that work?
David: Essentially they will write to us and say, can we deposit it? And then we’ll kind of busily work away. We have been developing deposition tools for a different project, but I think we’ve realized that those deposition tools would work just fine for the HMDB. So we’re thinking that it should or could be possible for people to deposit new compounds or new compound ideas and spectra associated with those compounds. That’s something that may be coming in the next year or so and then that may open the opportunity for people to build on the database or contribute to the database as scientists.
Alice: Great. Another topic I’m really interested in and that we’ve also talked a lot on the podcast is different bioinformatic tools to analyze and understand metabolomics. Of course we can talk about MetaboAnalyst. It’s always the first tool that I recommend to people who begin because it has a lot in one place. So it’s a great place to start. So do you want say a few words about MetaboAnalyst and maybe you want to tell us if you favorite bioinformatic tools. What are your favorite ways to take data and bring it to the biological understanding?
David: MetaboAnalyst is my go-to tool as well. It’s really easy to use and it’s great and it’s continued to be developed by Jeff Shaw over at McGill now. It is always nice to have students start out in your lab and if they really enjoy their work, then they can kind of move on with that and take that ownership.
I think the intention of MetaboAnalyst is to help you with the statistical analysis. And there’s a bit of biological interpretation it can provide you with, but not, quite to the degree that I think people need. Over the last few years we have been working on something called Path Bank or Small Molecule Pathway Database. And what we’re trying to do is capture more information, relating to the physiological effects, the association with metabolites in pathways to their proteins and enzymes, cells and organs and organelles that they’re located in and to help extend the biological interpretation beyond just simply – “this one’s up and this one’s down”.
There’s real utility in MetaboAnalyst for biomarker identification. But again, that’s an end in itself. If you want to understand the biology behind those biomarkers, again, it still requires moving towards pathways. Now pathways can only take you so far. As a rule, when I’m noticing metabolite changes and we’ll look at maybe some pathways I usually (still rather) use the literature. The challenge these days is that the literature is so vast that it’s hard to find the things you need. We’ve been working on an ontology for metabolomics, it’s called ChemFont and we’re using both the data in our databases as well as manual annotation as well as natural language processing to expand what’s in ChemFont. Ontologies are used by people and machines and they’re machine readable and the gene ontology in molecular biology has made a huge difference. So we wanted to have an ontology for metabolites and chemistry and this would allow people to do more meaningful interpretation of their data. The idea is to have an ontology with what we call triples, an object, a verb, and a subject that gives you information, “X does this to Y” or “A does this to C or comes from C”. By generating these facts or statements with references, it would hopefully allow people to save a lot of time that they’re reading the literature, but then they can also start synthesizing ideas or using the computer to help synthesize ideas. And so the intent of having this ontology, combining it with smart language tools or large language models like chatGPT hopefully would give people the ability to extract more useful data in the biology about their metabolite sets.
Alice: And so getting every time a little bit closer to what the human can do. Yes. So that the human has less work to do that could be automated. It’s always the dream. Especially when there’s so much literature now that you can have a tool that helps you to cut through the weeds and then extract the beautiful things that pre-read it for you, and then you can do the final step yourself. But you always have to do the final step yourself, though.
David: I think ultimately you still have to weight: I’m seeing three references that say this and two references that say the opposite. What do I need to do? Exactly What does it mean?
Alice: You probably answered that question just now. What are there tools that you wish existed? Maybe this is one of them? …and it is about to exist.
David: I guess my role in life is to look at problems and try and find solutions to them. And metabolisms has lots of problems; there’s lots of solutions that are still needing to be developed and part of it has come from the scale. I didn’t think it was going to be be such a large, unwieldy set of compounds that we had to work with. I didn’t realize it was going to be so complex. – And that metabolism isn’t just simply catabolism and anabolism. Metabolites have a role in signaling, metabolites have a role in health. They have a role in disease. They have triggers at so many different levels. Before I started we only knew about oncogenes. Now there are onco metabolites. So there are a variety of endogenous toxins, uremic toxins that seem to lie at the root of most chronic diseases. Things that we just didn’t know 20 years ago when we started in this journey. It’s always surprising; that’s what keeps it interesting for me exactly.
Alice: And this takes us to our last topic. I wanted to discuss with you the applications of metabolomics in the clinic or to clinical research. You are involved in projects that target the application to the clinics. Do you want to tell us first about the different projects you’re working on at the moment or things you can talk about? Are there diseases or specific types of applications where you really see promise for metabolomics or where you’re working to use it in the clinical setup?
David: We often underestimate the role of metabolomics already in the clinical setting. – We call it clinical chemistry and clinical chemists don’t want to call it metabolomics. My first encounter after discovering that they could use NMR or for mixtures was dealing with some inborn areas and metabolism and that’s when we first applied these and looked at urine samples of kids who had serious metabolic disorders. It was quite striking just to see the differences and how these compounds can be picked up. Of course, these days we don’t use NMR. It’s mostly LC-MS, but this is one of the great wins from the perspective of metabolomics. We have, I’m not sure, about a hundred thousand LC-MS tests done a week around the world for newborn screening. There are more people who’ve had and will have metabolomic tests than will ever have genetic tests. And it saves lives! It makes a profound difference. So metabolomics is already in the clinic. Metabolomics is already having a profound impact in people’s lives. At the beginning of life, it’s, playing a role. I think metabolomics can also play a role, in midlife or towards the end of life as we try and identify some of the things that scare us all: Things like cancer, Alzheimer’s or diabetes. And I think there’s a real role for identifying the first trends towards pre-diabetes, before Alzheimer’s there’s mild cognitive impairment before sort of full-blown cancer, there’s stage one cancers.
My focus over the last number of years is to look at identifying metabolic biomarkers that are predictive or that identify the earliest stages of disease. Because I think the role for metabolomics can be in prevention. Genes tell you what you might have metabolites tell you what you do have. Being able to monitor over a person’s life course and about changes allows you to pick up conditions before they manifest. I think this is the role that metabolomics really needs to play. Whether you call it in vitro diagnostics, predictive diagnostics, or preventative medicine – I think metabolomics can play a real role in a shift from reactive medicine to preventative medicine. I think it can play a real role in wellness and health as opposed to treating disease. And I’d like to see people use metabolomics different from a standard diagnostic test, “one chemical one disease” to more like hundreds of chemicals to assess your general health state. We are complicated organisms and our health is also made up of many components. And, I think metabolomics gives you that opportunity. Our focus is on preventive early stage, clinical markers. In all cases, it’s really important to have quantitative values, because without those you can’t translate them into reference values.
You can’t compare them to reference values, you can’t translate them into clinical diagnostic tests. You can’t use them in any kind of medical field. This is why we’ve always worked in quantitative targeted metabolomics. It’s made it very easy for discoveries we’ve made to translate into clinical applications. I think it’s been quite productive for us.
Alice: You mentioned, having, let’s say a hundred metabolites for one disease. So does that mean you would favor more patterns or like larger signatures as. Biomarker for maybe more for complex diseases than others.
David: I think from a diagnostic perspective you want a small number of biomarkers. It makes it more useful from a testing perspective but the concept with metabolomics is to go to more than one marker per disease. So you could have two or three or four, and that gives you greater specificity and sensitivity. But from a health monitoring perspective, I would view it as important to be able to look at several hundred metabolites at a time because you don’t know what someone’s is going to get. And so if you’re forcing someone to do a hundred blood tests to do a hundred different single marker tests you’ll bleed them dry so if you could with a drop of blood measure 500 compounds, which are reflective of most of the common disorders then you have a way of measuring health, not necessarily measuring a specific disease, but measuring health and that’s why that broad coverage is useful from the wellness perspective. Whereas from the disease diagnostic, you just want to measure a couple metabolites at a time.
Alice: And maybe also it helps, because if you have a single metabolite, it might be changing for different reasons as well in a given individual and this takes us also to this idea of the variability between different individuals.
That’s is something that sometimes people see as an issue with metabolomics. So that also for a given individual, if I eat something different I might have different levels of amino acids, which are used for many conditions. But also, this variability is something that can be very useful and that can give us a lot of information, and that is one of the reasons why metabolomics is a great tool for precision medicine, maybe more interesting than other types of omics. You’ve written about precision medicine in metabolomics quite a lot. What, what’s your position on this? Why is metabolomics interesting for precision medicine and what works for metabolomics and what maybe makes it more difficult?
David: You brought up the point about variability. There is a fair bit of human variability, and I think this is from the cross-sectional side. When we look at people just at one time point we see variability. Over a longitudinal level we don’t vary a whole lot from our set points. And so I think the central advantage of metabolomics is this ability to measure over time and to measure those changes over time and to have a reference point potentially even say “today I’m very healthy”. That’s my reference point. If you’re in your twenties, that’s probably when you’re healthiest – that’s a good reference point. As things drift up or down good or bad, that’s something you can track.
Your genome doesn’t change. Maybe you’re born with a bad genome, or a good genome, but it’s just hard to know when those things might suddenly lead to a problem or maybe it’s just a threat you live with for your entire life and it doesn’t happen. So I think from precision medicine perspective, the fact that the metabolome can be detected first days of life, that’s when we do newborn screening to the end of life allows us to see how things have evolved. I think it’s ability to predict conditions or prognosticate makes it unique. Proteomics can help, but the advantage that that metabolomics has over proteomics right now is that metabolomics is much more quantitative and the assays are much cheaper. They’re faster to perform. And when you look at the number of biomarkers that are used in the clinic, the overwhelming number are small molecules. The fact that they’re quantitatively measurable and fast reproducible gives an advantage. ELISA tests are reproducible within a lab but not across labs and very tremendously with matches of antibodies other conditions. So that metabolomics is the spawn of analytical chemistry. Analytical chemists are focused on, not only accurately identifying, but accurately quantifying things. If we remember where we came from as analytical chemistry, I think that it opens the door for much more useful clinical work and much more precise measurements for precision medicine.
Alice: Besides the new newborn screening, have you already seen from your work interesting new applications of metabolomics for diagnostic or for screening? Are there already things out there for those diseases where the work is still a bit difficult? Like, for example, to detect a disease a few years before it’s really too late? Do we already have good examples of the application of metabolomics?
David: We have applications that are published but not in use. And I think this is a real problem for the field of metabolomics and for the field of medicine. I think it could be regulatory problem but there’s also an inertia. It’s very expensive to go from a discovery or a publication to a validated biomarker that is used in the clinic. The funding systems that we have don’t really encourage people to do that. If, as a scientists, your worth is measured by your publications, not by the number of biomarkers or even the number of drugs you’ve developed.
Biomarker identification isn’t a highly profitable business. In some cases, if you identify a biomarker that is better and cheaper, is not accepted by a lot of people because the people who are making a lot of money from the more expensive biomarker are not happy. I think there’s a lot of constraints that undermine the ability to translate really useful biomarkers into practice. And some of them are structural and institutional and it’s a real shame. I don’t know if there’s things we can do to change that. But if there are funding agencies listening, it would be nice if they appreciated the importance or created mechanisms to help translate biomarkers because without biomarkers, precision medicine really isn’t possible.
Alice: Nope. Maybe we need a specialty task force for this. Yes. To push metabolomics forward. Then I only asked you to think about your favorite metabolite a few minutes ago. So have you found one and can you tell us why it would be your favorite metabolite of the day? Because I guess you have a lot.
David: Yeah, there’s 250,000 to choose from! One, that I found really fascinating, it surprises me every day, is the largest amino acid Tryptophan. And it was the only one I could remember the single letter for, because its last name. It’s one that has remarkable roles. It plays so many regulatory and signaling roles in the body. So it’s not just simply a proteogenic amino acid. It’s an amino acid that’s used in controlling the immune system. It’s an amino acid that plays a role in mood and neuronal signaling. It is an amino acid plays a role in formation of melanin and melatonin and pigmentation. It seems to play such a central role, but it is not only a good amino acid, it can also be turned into something bad. And the conversion of tryptophan into indole and indoxole sulfate which is a pretty serious toxin. It seems to have a role in anxiety and depression and chronic kidney disease and probably Alzheimer’s disease. The fact that you can have a single molecule with use and utility spanning so many things that’s both good and bad. That is fascinating to me and I think it embodies what I think we will find out about most metabolites – that they have all these roles and they’re not just simply fuel or they’re not just simply building blocks.
Alice: Absolutely. I completely agree. And probably the next revolution (it’s already started) but you started with expecting a few hundred metabolites and found out that there are much more, and probably now we think each metabolite has a few functions, but then we’re going to figure out that it’s all linked together and everything can do anything if it’s in the right context.
David: Exactly.
Alice: This is going to be fun. Especially for those doing the interpretation. Yeah. Thank you very much for taking part in this podcast. It was a pleasure to speak with you.
David: Thank you, Alice.
