Nutrition & microbiome

In this episode, Alice talks to Prof. Hannelore Daniel about the joint evolution of nutrition research and omics, common misconceptions about the microbiome, and the influence of diet and exercise on the metabolome.

Hannelore Daniel

Hannelore Daniel @TUM (German)

Favorite metabolite

Discussed paper by Krug et. al.
The dynamic range of the human metabolome revealed by challenges

Cited paper on sugar metabolomics by Mack et. al.
Exploring the Diversity of Sugar Compounds in Healthy, Prediabetic, and Diabetic Volunteers

Other resources
Talk on Gut Microbiome: Myths and Metabolites by Prof. Hannelore Daniel
(given at the 8th Munich Metabolomics Symposium – November 2021)

More about The Metabolomist podcast

Episode Transcript

Alice: Welcome to this podcast. And thank you for accepting this invitation. You studied biochemistry in the context of nutrition, from what I understand at the University of Giessen.

And in 1992, you became professor of biochemistry also in Giessen. After working in different places, you worked in the UK, you worked in the US in different research institutions.

And in 1998, you joined the Technical University of Munich as Professor for nutrition in physiology. And since 2018, you’re now retired. From what I understand, you’re a professor emeritus. Correct? But you’re still very active in the field and very interested in nutrition and biochemistry. – And what interests us today also metabolomics. So would you like to tell us a bit more. What are other points that you would like to point out in your career? And maybe tell us a bit about your research focus in the field of nutrition.

Hannelore: Thank you very much for this invitation to join this series of podcasts and this nice intro. I’m a nutritionist in background and yet I started early in my career with microbiome research in 1978 during my diploma project and treated rats with high concentration of antibiotics to bring the microbiome (at that time called gut flora) down and study the effect on nitrogen metabolism.

So that was my intro into the science world and I was fortunate to become a PhD student and could do a PostDoc et cetera. What has been guiding my entire career is to get a hand on very fundamental processes that relate to human nutrition.

So I was mainly interested in proteins that are important for uptake of nutrients in the intestine. Also for reabsorption of nutrients in kidney that you don’t lose essential amino acids. So I very early in my career started in cloning genes and trying to express these proteins in all kinds of weird organisms.

My peers all thought she is lost for nutritional science because I was doing such fundamental work. And, I also got interested in the genes and the genome and I was fortunate to have a microarray, which was very early. I could do my first microarray study in 2000, 22 years ago. I was fortunate enough to have money to buy equipment to do proteomics and buy equipment to do metabolomics.

That all was driven by very fundamental questions. What makes up human metabolism how do we get nutrients in and waste products out of cells. And that later became more holistic in that few of the entire human system. Not only in cell culture or in model organisms.

I have been working in yeast, I have been working in worms. I have been working in mice, in rats, in strange fish. And towards the end of my career, it was humans. And I did my first human studies pretty late in my career. And that was actually when I got interested in metabolomics and I saw what kind of fantastic tools do we get in hand now, to look in a more comprehensive manner into human metabolism.

Although that may be a little bit limited because we have mainly only access to plasma, urine, maybe some other body fluids.

Alice: There are lots of interesting points you’re making. One thing I noticed is, so is it right that then omics whether it’s genomics, transcriptomics, proteomics, metabolomics really changed the power of the types of studies you were doing? Or what did it change for your research?

Hannelore: It changed completely how nutrition science was perceived in the science community. It was such a privilege to have been in science when that happened. Because nutrition science was considered kitchen science. It didn’t worry about diets and body shape and things like that. So it was never considered to be real science.

Alice: And the omics helped with this?

Daniel: Talking omics – We were suddenly in the heart of all this life sciences talking omics.

Alice: Interesting.

Daniel: And it was fantastic. So that was really a game changer, for the entire science community that deals with food and nutrition and health.

Alice: And metabolomics specifically, what kind of relevance would it have then? I guess it’s particularly relevant for nutrition research. Right?

Daniel: Yeah, it is particularly relevant for nutrition research. And it has in essence two dimensions that made it so important. One is that you indeed can look into various metabolic states easily.

In the past we were limited to measure cholesterol, glucose, we measured with very specific means such as an amino acid analyzer – just 20 or 30 different amino acids. And here you look at hundreds of metabolites in a sample and what makes it even more attractive is that you can have tiny volumes and you can measure hundreds of metabolites in 10 or 20 micro liters. Fantastic. Absolutely fantastic. And even you can do that from a fingertip here. Put a drop of blood on a filter paper or little sponge and get hundreds of metabolites. It’s a dream.

Alice: It sounds like it.

Daniel: The second line is that we can measure metabolites that relate to the intake of particular foods. Where we can at least get a rough idea of what have volunteers, what have consumers really consumed?  Because when we do human studies and ask volunteers what did you eat in last week? They don’t tell us the truth. We know that. And yet here we have now a toolkit that allows by some of the characteristic components of individual food items that we can measure in blood or in urine of whether that food item has been consumed.

We’re not yet there that we can really give numbers in terms of how much was consumed. But at least if somebody tells us, that he doesn’t drink any alcohol at all and we found a ethyl glucuronite in urine. Then we know he didn’t tell us the truth. I call them not biomarkers. I just call them exposure markers.

Alice: That makes sense. But what would be your definition of a biomarker then?

Daniel: Biomarker has been defined by a clinical chemistry in terms of predictive markers in the health-disease trajectory. And if I find proline-betaine which is a marker metabolite for consumption of orange juice. It doesn’t have any bioactivity. It’s there. And it tells us orange juice has been consumed.

Alice: And it’s interesting the parallels you can make. Because of course you speak of exposure markers in the field of nutrition. But I come from the field of toxicology and we also have exposure markers. You see traces of the chemicals you’ve been exposed to – It’s the exact same topic. It’s there, it’s present. It doesn’t mean it’s having an effect or it’s a proof of the effect. But it’s a proof of it’s present. Yeah, absolutely. And you mentioned that you started your career with microbiome research. At which point did you reconnect with microbiome research?

Daniel: It came with this hype that came around the corner. I mean, yeah. Jeff Gordon’s paper changed almost the world. And it seems like you cannot get any paper published if you don’t include any microbiome analysis. I have seen trends in sciences for 40 years, coming and going. But I’ve never seen a trend like this microbiome hype like it is now present in all the areas of the life sciences and even other areas. 

Alice: I attended a talk that you gave a few months ago where you discussed some of the myths about the microbiome.

It was really interesting. For example, how we always hear that we have at least as many, if not more bacteria in and on ourselves than cells. It was really interesting to hear you speak about these myths and debunk this myths about the microbiome. Would you like to point to a few of them and maybe tell the audience. Which things they shouldn’t believe anymore?

Daniel: As I said, I have been working in gut functions throughout my entire career – From 1978 on. I would argue that I know the gut in the different regions from duodenum to anus quite well.

I could live with the knowledge that there are bacteria in the large intestine quite well for over 30 years. Because Nature, Science, Cell, New England Journal, Lancet – you name it. I have not read the word microbiome or gut flora for 30 years. And yet, science was explained to me at all levels. And now suddenly, the microbiome is brought into context of almost everything. That cannot be – frankly.

Alice: This contribute to some aspects, especially when you look at the metabolome, right?

Daniel: No doubt. If you ever have cut open a mouse or a rat and have taken out the contents of the intestine. You get a rough idea of how much is there. And I was even privileged to collect the samples from human tissues in ancient times. So I went to autopsy and you could get some samples. I was suspicious when I was reading that the human large intestine contains 1.5 to 2 kilo of, let’s call it biomass.

And it just can’t be. It was actually old literature that used antique technologies, a balance and it took out the intestine from sudden death victims, collected the content and put the organ per se and the tissue and the content separately on the balance. [The scientists] came to the conclusion that with large variability, young large intestine had a total volume of contents of roughly 230 milliliters. So large variability, no doubt. But the mean, or the median was 230 milliliters. They put it in a freeze dryer and put it again on the balance. And there were 36 grams of dry matter left, of the entire human colonic content.

And yet the literature was full of this 1.5 – 2 kilos, and, funny enough, there was a series of papers, by Milo and Sender. They traced this information on the volume or the weight of the biomass in large intestine. And at the same time also with respect to the numbers of bacteria because it was claimed that the human gut microbiome contained 10 times more cells, bacterial cells, than human body cells. So Milo and Sender traced it back to a study or a review published in 1972, by Luckey in American Journal of Clinical Nutrition, taking the number of bacteria in a colonic sample and protecting this number to the entire intestine – Not accepting at that time, that there is a huge gradient in terms of the bacterial density. So the numbers of 1.5 to 2 kilos, all originate from this one paper with a misconception about the density of bacteria and different regions of the intestine but it made it into Science and Nature and all top notch journals and was quoted and quoted and quoted. Then came Milo and Sender a couple of years ago and said that all is wrong. They did a very careful analysis of all the old literature and came to the conclusion that the total weight of bacterial biomass residing on and in humans is likely to be something like 200 grams.

Alice: Big difference. And without making a bad joke, it takes a lot of guts to write that paper to that goes against everything that everyone is saying. So it’s really brave.

Daniel: That’s why always emphasized the great work they did. And I strongly recommend to look up those papers.

Alice: Yeah. You find the link in the shownotes. This is really interesting resource for people who are interested in microbiome. Thank you. Are there other aspects of the microbiome you’d like to discuss maybe myths that you really want to fight?

Daniel: I know it’s not a myth that. I’m just very credible when it comes to mouse work. I have been studying mice. I’ve been studying rats. I don’t know how many animals went through my lab. I sometimes feel so sad about that. But you know, the mouse is not a little human. The mouse is still a mouse. And the black six mouse, whether J or N that is mainly studied is also only one mouse strain, of literally hundreds. And even if you measure some very fundamental things in these different mouse strains, you can see, they are so different.

It’s just spectacular. And we studied mainly one strain and we explain the world based on one strain. One mouse strain is like studying more or less one human.

And would you conclude that all humans are the same if you study just one?

Alice: No.

Daniel: A human is a human, two arms, usually, and two ears and things like that. So in this respect, yes, mouse is a mouse.

But for example if we look into the abdomen of a mouse you see a real fermentation chamber. That is a cecum. The cecum in humans is the appendix. It is a tiny thing. We have a colon that is structured in three regions. It’s completely different in terms of anatomical size and also in ultra-structure and the mouse cecum and large intestine relative to body mass, is almost twice the size of what the ratio would be in humans.

Alice: Are these things that can be compensated for with bioinformatics/ modeling? Or there’s no point in going there you think? (because it’s so different that it would be like comparing apples and oranges).

Daniel: I think it is even more complicated. Two things.

  1. There’s a very nice paper that came out that struck me because I had been asking a number of colleagues of mine: Whenever you have a large fermenter in a biotechnology company. One of the biggest problems is get rid of the heat produced by bacteria. So I was asking myself of whether the gut microbiome in particular in this rodents contributes to body temperature management. Hard to measure.

Couple of months ago a nice paper was published. Doing exactly that and measuring the contribution of the bacterial heat that got produced in the cecum of a mouse and yet it is a substantial amount of total energy turnover. But that should not be used as a conclusion that in the human intestine heat production by the bacteria contributes in a substantial amount to overall energy expenditure. Again, organ mass in the mouse relative to total body mass is twice of what it would be in humans.

  • And moreover, we feed all those rodents that are sitting in facilities diets that are prudent by any means because they are not cooked. The starch is raw. They are either irradiated with Cobald 16 (that is dry irradiation) or autoclaved, that is also dry heat. The starch is not swollen. Everything is raw.

Alice: Very different.

Daniel: Very different. And if you cut open the cecum of a mouse that just receive chow diet. You see the big chunks of the chow in the cecum. So I think we’re not taking into account many of these factors whenever we look at mice as a model for microbiome-host interaction. I’m not saying mice are useless.

Alice: You have to be aware of the limits of every model. Like every model has its limits and this is also true for mice.

And this is also true when we work with humans. As you said, you can make very interesting studies in humans. But if they lie about what they eat even without meaning it. If they give you false information that can really then make it difficult to use the data or to interpret it correctly. These are really important points yes. Were there other points you would like to discuss about the microbiome.

Daniel: Well, in terms of the reference, we are talking not about that gut microbiome and we talk about the stool microbiome. Only a very few studies, but this will change that’s for sure. Look at different sections.

Alice: So that means you have to take the animal and then…

Daniel: I’m talking about humans.

Alice: Are you talking about humans. Okay. How do you get…

Daniel: You can use tubings that you go in either from the rectal side (that’s what gastroenterologists do), and you can also use nasal gastro tubing. And you can go through these small intestine and collect at different sites. And little devices that can collect samples as they are traveling down the intestine. And you even can then identify where it is located and can activate it to collect a sample.

Alice: Wow. I didn’t know that this existed. This is really interesting.

Daniel: I mean, it’s not standard now yet. There are more studies now. Why is that relevant? There are some studies that have been doing that already showing that the microbiome is different in different regions of the small and large intestine.

And even in the large intestine, whether you go into the colon; transversum, there are differences. Some of the differences in comparison to stool are remarkable. So yeah, you have a spacial microbiome if you go from proximal to distal and the stool just does not represent the entire diversity that you find in small or large intestine. So that’s not only regional;  it’s also then if you go radial, which means from the lumen of the intestine towards the wall.

Because people think that the bacteria all the time in contact with the epithelium. That’s not correct. We have a mucus layer that in the large intestine, is between roughly 500 and 900 micrometers thickness. And it has an inner layer, it’s produced from the mucus proteins producing cells.

It’s a pretty sticky layer and the inner layer, is a mesh that is almost tight. So there are no bacteria, if you stain the inner mucus layer. You don’t see any bacteria, if you have a damage intestine for example in Crohn´s disease. It’s looking different. But in a healthy individual, this inner mucus layer is in essence sterile.

The outer mucus layer is a bit fluffy and bacteria can be found in there. Yet the density of bacteria is much lower than in the lumen. And at the same time you have, because a tissue is nicely oxygenated, you have oxygen gradient from the tissue surface into the lumen. Which means for all the anerobic bacteria, this mucus layer region is horrible (because it has a certain oxygen tension). So in essence, it would kill the bacteria. Nevertheless, bacteria that can tolerate certain pO2 levels may well sit there. So I’m just saying, even in terms of density, and in terms of the different species, if you look into the substructures (radial and longitudinal) microbiomes are not identical.

Alice: So there are many dimensions that most studies don’t look into.

Daniel: Right. So, and then we take usually one stool sample. We sequence (shotgun or whatever you do) and then we start telling stories. We even report entire movies by taking one snapshot. Be careful. Because it’s well known, that the microbiome beyond all the technical problems in terms of standardization, there’s a large variability in how often people go to toilet. The total volume of stool, the color and consistency of stool, the frequency of stool emptying. And that all has been shown to affect the microbiome in terms of the diversity of the bacteria or relative abundance of bacteria.

Those information would be essential to give some meaning to the data is usually not collected.

Alice: And so to get this wealth of different dimensions in space and also in time would be one way to bring stories that are more meaningful about the microbiome

Daniel:  A single snapshot cannot deliver any information.

Alice: Yeah, of course it’s similar to metabolomics. It’s similar with metabolomics and this is the topic of the paper we wanted to discuss together. That’s in a study, like the one we’re going to talk about now: If you have a single snapshot or if you have 65 different time points with different challenges and changes over time.

You can tell different stories and you can go more in depth into what’s going and how the system is changing over time. So maybe we go now to the paper, if you don’t mind. Let’s discuss this. So for the audience the paper we are going to discuss today is a paper entitled:
The dynamic range of the human metabolome revealed by challenges.

It has five first authors. The first of them is Susanna Krug. It was published in 2012 in the FASEB journal. And it’s a really interesting study. You suggested this paper for us to discuss and I think people will learn a lot also about designing ambitious studies for metabolomics or for any omics. And how to interpret the results, how to organize and structure your work when you have such rich data. I think there’s a lot we can discuss. Before we look at the results, I just want to explain a bit the study design and also to ask you, how fun that was to design? Because it looks like an enormous amount of work.

Is 15 human participants you asked them under supervision to go through six different challenges in different series over four days. It was two times, two days and it started with fasting. And what I really like is that over the 36 hours of fasting there were eight or nine collection points already. So you didn’t just let them fast and then look before and after you really wanted to see the dynamics over time. Then you had different typesof challenges related to diet. So the type of liquid diet, glucose tolerance test and lipid tolerance test, and then an exercise challenge with cycling for 30 minutes and finally a stress challenge where people had to put their hand in cold water for a few minutes. Which can elicit metabolic responses as well.

And so besides the many time points where you collected samples you didn’t just limit yourself to plasma. You created plasma, urine and then breath gas or the breath of the participants and EBC, which is exhaled, breath, condensate I think. And you performed different types of metabolomics on these different materials and then did all sorts of analysis that slowly, progressively take us to what might be going on and also how it’s going on.

Before we look at the results and the interesting findings in this, can you comment on the organization of this study, the planning, how much of a challenge was it? Because it’s really ambitious.

Daniel: It was demanding  and it brought a number of emotional responses. We did it with a number of colleagues and said, you know, how can we at best get a picture of this?

Alice: Did you try to do everything in one study?

Daniels: Yeah. The idea was to use volunteers that are as homogeneous as possible. I mean, it would’ve been nice to have 15 clones.

Alice: It’s yeah. You can see like the sex is the same, the BMI similar, the age is similar.

Daniels: Any means we did super phenotyping. We put them into the sports facility to measure. How they perform? Then we housed them for four days in a cabinet. If you like.

Alice: You decide what they eat, you decide when they sleep, you decide everything.

Daniels: And they had catheters sitting in their arms while sleeping; and the exercise was really demanding as well. It was 75% vO2, which meant some of the youngsters that were not so well trained lost about the liter of sweat. Anyway, it was fun. But it also was a huge exercise to find an agreement amongst the experts of, you know, what can we do and what can we learn if we do that?

Alice: Then, so you perform this experiment, collected the samples, measured them, and then there’s a huge bioinformatic study that goes behind it. So you first you look at the data and then you do correlations to see what moves together and so on. And then you realize, okay, the acylcarnitines and carnitines are changing significantly. There’s probably something going on with beta oxidation.

And then you make a mathematical model of beta oxidation. If you can be totally honest about this, did you expect this to happen? Had you already planned to do this or did you really let the data tell you where to look?

Daniels: The latter. So let me, put it in a perspective. We thought we have the most beautiful metabolomic study done on the planet. We submitted to top notch journals. And they all didn’t like it. So what’s new here. It was shocking. It was really shocking. It was so frustrating.

Alice: Because it’s not just descriptive. Like you really show this dynamic range that you put in the title. Like you really show that something is happening.

Daniels: I mean it was only 15 people. And we know, we know, we know. We probably should have included microbiome that it probably would’ve flowed.

Alice: In retrospect that would’ve been simpler.

Daniels: So in the end, it ended up in FASEB. But we have a repository and we are still working, on the data. Because we have metabolites from, I don’t know, six different platforms. NMR; non-targeted, targeted, GC-MS/MS, LC-MS/MS; Two commercial platforms, two homemade platforms. And then PTR-MS for breath. A wealth of metabolitesroughly 800 for plasma in each sample. And our friends at the Helmholtz Center, particularly Gabi Kastenmüller (podcast episode with Gabi) are still working on the dataset. And make the entire dataset, which is already in the public domain more easily accessible, that people can study under which conditions for example, do we see an increase in metabolite X or Y or Z.

And I’m positive there will be two or three more papers now coming pretty late from the study. But in the end, I’m still excited in every time I look back into these profiles. There is something that catches my attention. I did this flat before outcomes.

Alice: Yeah. And this, I think, is a really interesting point to mention – I think it’s a bit of pressure you have even when you have a single data set of any omic (Metabolomics or other) you always have the pressure of saying, did I exploit this to the fullest? Did I find everything? And the answer is almost always “no”. And that’s okay I think. Because, of course, here you have an enormously rich dataset. But there are always things that you can only understand later or things that you can only focus your attention on now. And then you will look at the rest later.

And thing is an important thing to mention to people, to take off a bit of the pressure and you can always publish papers a couple years later or even 10 years later. It’s still relevant. As long as you have an up to date analysis of it. Why not?

Daniels: I can tell you, I got all the profiles for the individual metabolites (And the 60 samples over the four days of all the volunteers). Which means I had about 800 different traces. And I was sitting in planes and trains and just push the button from metabolite to metabolite, to metabolite, to metabolite. Just look at the profile.

I’ve seen this profile before. So I went back and I could recognize patterns easily. So I found four or five metabolites that show exactly the same profile. And how are they interconnected, linked to each other. So that was and still is an enjoyment.

Alice: That’s good to hear. And so could you tell us in a few sentences, what for you are the biggest messages from this paper? What did you find out? That was so interesting?

Daniels: Well, first I would argue that a single fasting blood sample is not enough to tell a movie.

Alice: And this is what we recommend also in the clinics, when you do tests and stuff. It’s all like, take you’d come fasted. We take your blood, then you can have a sandwich and go home.

Daniels: I mean, the question is, who decided that the morning blood sample after an overnight fast is the reference for everything on the planet. And that is one of the stories, because we were feeding the volunteers the night before a highly standardized real food. And what is really funny, because we were then putting them into this 36 hour of fasting. – We have now the complete washout, if you like of the food derived metabolites. And that is fantastic. And I’ve never seen that before. So you have individual metabolites that were ingested (it was a chicken meal with some veggies, garlic and onions and some other spices). And you can identify the metabolites derived from garlic and onion and you even see the entire kinetics.

And if you do a log transformation, you see they all follow first order kinetics. And I’ve never seen that before for so many metabolites. And I think that is a treasure. Because it also allows you to measure elimination half time. And you can ask why do different metabolites have different elimination, half lifes?

And I can give you a nice example: Methylhistidine derived from the chicken meat. Possibly first order kinetics and pretty fast eliminated. For other metabolites you have a completely flat elimination rate. Which means they’re probably protein bound and go around for much longer. Because you also see and in it, since we collected 25 urine samples. You see in essence the same pattern then with the appearance in urine. So I’m just saying that was the first learning curve. The second learning curve, 36 hours of fasting is catabolic state per excellence.

You see the push on lipolysis. You see the push on beta oxidation. You see the ketone bodies going up – So everything like in textbooks. There were two volunteers and particular one volunteer. It looked completely different. In essence you would argue: “he must be dead”. That is not possible that somebody can fast for 36 hours and not showing any substantial increase in ketone bodies.

But we could be sure that the guy was not eating anything ‘illegally’.

Alice: Because you are monitoring them closely!

Daniels: The other metabolites were down. So he must be in deep fasting.

Alice: But he didn’t have this capacity to produce ketone body.

Daniels: He is a mystery. And usually you would say, hey, we have 15 volunteers and that is a outlier. And you know, I don’t have to tell you, there are smart approaches to get rid of these outliers. Life sciences got rid of all the interesting phenotypes.

Alice: Here you want tosee this outlier. You want to ask her “why are you different? Tell me!”

Daniels: Exactly. That was discouraging. But he survived the four days, like all the others. But I cannot tell you how he managed to look so different. And what he relied in terms of his metabolism. I mean, he must have had tons of glycogen in liver.

Alice: This is a possibility.

Daniels: That is not easy to measure, of course, not too many people can measure glycogen non-invasively by MRS. I’m just saying that it was a learning curve as well. Coming back to acylcarnitines. I heard of the acylcarnitines because I was teaching biochemistry to students for 30 years of my career. So you have for the fatty acids, you have a CoA pool in the cytosol and you have a CoA pool in the mitochondria. And you have the inner mitochondrial membrane which does not allow CoAs to be shuttled there. – You need the carnitine to take the fatty acid, cleave off the CoA, put the fatty acid on the carnitine. Then you have a shuttle protein that translocates the acylcarnitine into the mitochondria. And then you reverse it and you hook up again CoA.

I never would have expected that in essence, every single fatty acid from the acetyl to the most long chain, appears as a carnitine derivative or conjugate in peripheral blood. They are all there?

Alice: This is a question always – Why do these things are supposed to be at the heart of the cells? What do we find them in the plasma? This is often a question.

Daniel: Because it’s a water soluble form of fatty acids. Fatty acids as fatty acids are nasty molecules. They go into each membrane you cannot control that they move in, put in their long hydrophobic tail and screw up the entire structure. So you better protect the cell from an overflow of free fatty acids. And that is possible, if you make them more water soluble by hooking up a carnitine and get rid of them. So we use the extracellular space – the plasma as a distribution space.

Because beta oxidation cannot be increased if you increase lipolysis. Because lipolysis goes on in catabolic state. But at the same token your production of ATP is limited by the reduction equivalents. And that is not very well coordinated. I don’t know why nature did not better connect the capacity of lipolysis to beta oxidation and citric acid cycle and respiration.

Alice: But there must be a reason cause usually there is.

Daniels: So if you have this overflow of fatty acids. You can expand the volume of distribution with the acylcarnitines. If you have a limited capacity for beta oxidation, even in mitochondria, you can get rid of the medium chain and or short chain derivatives. You put them out into plasma again. It is, in essence, a protection mechanism. It protect cells from an accumulation of fatty acids and of all the CoAs that cannot be on time be oxidized via beta oxidation. What I’m really interested in is which protein in the plasma membrane mediates this import-export business.

Alice: Back to the transporters.

Daniels: Yes. Back to the transporters. I mean, we know how it is done in the inner mitochondrial membrane which protein in the plasma membrane. And it must be then in adipose tissue. But also in muscle cells all over the place in essence. But it looked over the four days, we always saw this reciprocal change in acylcarnitine over free carnitine. So whenever the acylcarnitines increased in plasma, the free carnitine went down. It was a mirror like behavior.

Alice: It’s a beautiful figure. It’s also a really nice starting point for interpretation. It was really well made.

Daniel: That would argue in a naive manner that it is an exchange mechanism at the plasma cell membrane, as it is an exchange mechanism in a mitochondrial membrane: For each acylcarnitine put out, a free carnitine is taken up. And I would explain in a naive manner, at least, how this mirror like behavior can be seen? That was a nice piece of biochemistry, which I hadn´t seen before in this beautiful manner.

Alice: And then you show the variability between the participants increases as you put them under stress, right? This is another one of the findings of the paper.

Daniels: So we had this exercise built in – 30 minute on a bike at high force. As I mentioned, some of the guys really sweated. We were collecting blood sample before and after 15 minutes and after 30 minutes and then they stepped down from the bike. But we collected further blood samples. And there were of course metabolites that only show up in the exercise. First baseline, then -boom- going up, and -boom- going down back to baseline. That’s amazing.

That’s only for this two time points, amongst the 60 blood samples or time points. We had this huge increase and of course, if the first one that jumps to the eye is lactate. No surprise. But going from figure to figure what’s going on here! I found fumarate, I found oxalacetate, I found the entire citric acid cycle in plasma. It should not be in plasma.

Alice: So what is it doing there? Do you know?

Daniels: The exercise was so harsh. Just that they disrupted muscle cells. So they washed in essence  cellular contents into the plasma.

Alice: Yeah. Makes sense.

Daniels: And the strongest evidence was on one of the platforms – We had cAMP (cyclic AMP) as a metabolite and of course you wouldn’t expect cAMP to be in plasma. But here in the exercise, it suddenly popped up in plasma. Including some phosphorylated intermediates that should not be there either.

So exercise can be modest. Exercise can be harsh and that was harsh exercise. And obviously, the best explanation is that you have a real rupture of muscle cells.

Alice: Which is expected with intense sports. I mean, it really makes sense.

Daniels: So that was beautiful.

Alice: It is, there are lots of beautiful things in this and I’m happy to hear you still working with this data. So we probably get more beautiful papers out of it.

So it’s good to know.

Daniels: I hope so. 

Alice: So I think we’re already at the end of the hour. Do you have a favorite metabolite and why?

Daniels: I do have various favorite metabolites in particular those where you don’t find too much in literature. 

Alice: The interesting one.

Daniels: I give you an example. We were feeding the volunteers the standard liquid diet. Which was in essence a commercial product, used in clinical nutrition. In people that have impaired digestibility of nutrients.

So it’s an enteral nutrition solution. To bring in calories, to bring in all the essential nutrients. And of course it is defined – No other food item is so well defined as this kind of liquid diet and you can put it in a freezer. And you can use the same batch of a liquid diet a year later and challenge the volunteers again.

That was actually the dream that we do the whole thing. Then in a consecutive manner, yeah know, use the same volunteers five years later.

Alice: But they all said, no?

Daniels: No, no. They’re all over the world now.  Got to know one last year. Made his career in industry. So coming back. I think it was a good idea. Because it is really specified in terms of all the fatty acids and the micros, the vitamins, et cetera. When we looked then at the appearance of all those metabolites. I found lanthionine and I’d never heard of lanthionine. And I don’t know where it comes from! I know where it comes from. But I still didn’t know whether it is, or it was contained in the liquid diet or whether it is produced.

On its way from the intestine into circulation and lanthionine is a strange molecule. It’s a non-proteinogenic amino acid.  It has two carboxyl groups. It has two amino groups and it’s connected from with a sulfur atom that comes from cystine. So it must be a cystine and serine fused.  

Alice: Interesting metabolite. You mentioned, you’re not sure it came from the liquid diet. Isn’t that? Isn’t the composition controlled of this liquid diet. You should know, shouldn´t you?

Daniel: It’s well controlled. But you don’t know whether it is produced by heat. Process, things like that. Because I can also, I think that is a learning curve as well.

We did an oral glucose tolerance test. Like millions of oral glucose tolerance tests have been done. And then I found metabolites popping up in plasma where I said that just cannot be. I mean, it’s pure glucose that is administered. And I learned that our people didn’t use pure glucose.

They went to a pharmacy, they bought a commercial OGTT solution. That is used by physicians for diet.

Alice: Which contains preservatives, flavorings and whatever.

Daniels: Yeah. Shocking enough. There was ferulic acid in there. There was hippuric acid coming in. Because hippuric acid we think microbiome, microbiome, microbiome. And there was benzoic acid used as preservative in this commercial OGTT solutions. So that was a learning curve as well.

Alice: That it also shows yeah. That you should really be careful.

Daniels: OGTT solutions. That you can buy either in the US or in Europe. Of course they contain glucose. But they also contain other things – be careful.

Alice: Especially when you use methods that are so sensitive to whatever is in it. At least you should know what’s in it. But if you can remove some of the noise it’s even better.

Daniels: Yeah, absolutely. So coming back. I don’t know whether the lanthionine is produced by heat treatment to preserve this liquid diet.  I was not familiar with the chemical structure. I’d never heard lanthionine before.

And let me come to one point that I would like to make or two points. I’m following now, metabolomics for 15 years. And I’m surprised it’s still almost all the studies report only fold changes. Metabolomics the technology allows to get real concentrations. I know that it is not trivial to do whenever I talked to young people and said, why did you not go for a quantitative analysis?

The answer was – it’s difficult. They said, yes, that’s your problem. You’re born too late. All the simple things I’ve done. Take on the challenge. So I would love to see more quantitative measurements. And however, if we go into clinical chemistry and you get your blood analyzed, you get such a leaflet with a hundred things measured in millimoles in milligrams per liter units.

If metabolomics wants to get more into diagnostics I think in the end it has to come with quantitative measurements. And my second point is the methods measure what they can measure. Each technology has limits and advantages.

It’s amazing how the number of metabolites has been increased over the years with combining platforms from NMR to GCMS et cetera, et cetera yet. I would love to see some more targeted platforms that go really into metabolic pathways and try to cover as many metabolites in a known pathway. We usually have a substratethat goes in. Maybe one intermediate and maybe one product or another intermediate, make ratio. And we draw conclusions and explain our data.

Alice: I guess it’s particularly difficult cause the structures are very similar I guess.

Daniel: We have been developing in a joint effort. Semiquantitative – It’s now on the way of being quantitative for sugar and sugar derivatives. Because I don’t have to tell you how you report the sugars!

Alice: You don’t.

Daniels: Yeah, because you have so many isomers and they’re hard to distinguish. Carina Mack has a developed a GC method and she ends up with about 50 sugars and sugar derivatives in urine and about 40 in plasma.

Alice: Nice.

Daniels: Sugar, sugar alcohols, sugar acids. And amazingly disaccharides in plasma and urine. If a student would tell me that sucrose is absorbed and appears in plasma, I would’ve said, no way. The sucrose is cleaved and the glucose and the fructose appears. No! The sucrose appears in plasma, who may choose it as well.

And sucrose appears in urine and lactose appears in plasma and lactose appears in urine. So many disaccharides, many other monosaccharides (trehalose, mannose, sucrose) – you name it. So, the world of carbohydrates in plasma or sugar and sugar derivatives is completely underreported.

We also have a paper where we demonstrate that there are some sugars that behave like glucose. You can see the same signature of a pre-diabetic state and a type-2 diabetes. It’s not only glucose. There are other sugars showing the same profiles. And they may be even more useful than glucose in predicting type-2 diabetes.

So that is an example where I say, you know, it would be good to go more into the study of these metabolites.

Alice: So if you were to push the development of metabolomics you would go in that direction at the moment. Thank you very much.