Flux metabolomics & cancer

In this episode, Alice Limonciel and Gary Patti discuss the applications of flux metabolomics to the study of cancer, the benefits of various biological models, and the potential of nutritional intervention to study and influence cancer metabolism.

Garry Patti

Garry Patti is Professor of Chemistry and of Genetics and Medicine at Washington University in St.Louis MO (USA)

Favorite metabolite

Paper we discussed in this episode
Isotope tracing in adult zebrafish reveals alanine cycling between melanoma and liver

“Cancer cells demonstrate a remarkable degree of metabolic plasticity.”

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Finally available – Alices first book
The STORY principle – A guide to the biological interpretation of metabolomics
Also featuring some of the Metabolomists from Season 1
Available on Amazon and the biocrates webshop

Episode Transcript

Alice: Welcome back the Metabolomist. Today I’m joined by Gary Patti. Hello.

Gary: Good morning.

Alice: Gary, you’re a professor at the School of Medicine at Washington University in St. Louis. Would you like to tell us about your expertise and the topics that your research focuses on?

Gary: Sure. Happy to. First, let me just thank you for having me today. It’s a pleasure to be here and look forward to this interview. My lab is a metabolism lab. We do a lot of work in the metabolomics technology development space, but that space is complimented by a lot of applications. In addition to doing mass spectrometry experiments, we do a lot of biochemistry. We work with various different animal models. We do a lot of classical biochemistry types of experiments.
We’re primarily focusing on two different areas:

  1. Trying to understand what the impact of chemicals in our environment is on metabolism. So you’re probably aware that we’re exposed to a lot of chemicals throughout our daily lives, whether it be from food, which are kind of obvious instances but also from somewhat unexpected sources such as hygiene products like shampoos or toothpaste, et cetera. And these contain a lot of chemicals; most of which we really don’t understand how they impact health and disease.
  2. Trying to understand the development of cancer and tumors. And really in kind of a broad space – not just one specific kinds of cancer but cancer in general and specifically potentially how some of these exposures in our environment, whether it be dietary exposures or carcinogens to smoke, power plants, all of those types of exposures potentially contribute to the development of cancer.

Alice: Thanks. That’s really interesting. I noticed also in your work that you use a variety of models.
On the podcast, I’ve had a lot of guests who were working primarily with human samples, either blood samples or maybe tissue samples. I noticed in your lab you work also with cells and with animal models. In the way that you use them, do you have any pros and cons on using some of those models for different applications?
How do you choose which one you will use for a specific study?

Gary: Yeah, it’s a great question. We use, cells, organoids, various animal models ranging from zebra fish to mice to rats and also human subjects. They all have pros and, and cons. The cells are the easiest to work with, of course. They allow us to do certain types of experiments that aren’t feasible in human subjects. Data interpretation can be a little simpler in cells compared to some of the more complicated systems.
Later we might talk a bit about isotope tracing. Isotope tracing is certainly much more complicated in human subjects. Not impossible; but much harder, more expensive for reasons we’ll discuss – So the cells have real advantage. The way I like to think of it is that by using cells we can define the highways in some sense. The analogy that I always think about is that metabolism is much like a street map.

What we’re trying to do when we think about metabolism in the context of a particular application is understand what the density of traffic is on any given street and how a particular cell gets from destination A to destination B. How we approach it in the lab is that cells provide a really good opportunity to define the roadmap, to define what streets there and how traffic is activated on the streets. What we find is that the ways in which cells transform nutrients in cell culture can look very different than the ways that cells might transform nutrients in animals or in humans. I do think using a combination of all of those different models is important. because, you really want to validate everything in human subjects ultimately, I would say is the endpoint.

Alice: And we’re going to have also a really great example of this added value of having a whole organism in the paper discussed, because we’re going to look at interactions between different organs or different tissues within the animals that you studied. Even though I’m a big defender of in vitro experiments, I think there is really a lot of great information you can get from cell culture. There are certain things that you cannot do or that you need to know to start to model them. If you want to do co-cultures, for example, you would have to have the idea in your, in the case we’re talking about, or we will talk about to grow the tumor cells with liver cells than see if they interact or not in the way that you found out. But you use the animal model really well for this in the paper we’ll discuss.

Garry: Thank you. Yeah, we try to go back and forth. I see it as kind of a ladder. It’s not unidirectional where you have to start themselves. I mean, sometimes you can start in human subjects and go backwards. Sometimes you start themselves, generate a question and validate. It’s really bidirectional.

Alice: Yeah. I think when you have the variety then you can use each one of them for what it is good for, and then combine them in the best way that you can think of.

Garry: Yeah, I totally agree.

Alice: You said most of your work is focused on metabolomics or you do a lot of your work with metabolomics. How did you discover metabolomics? Do you remember your first encounter with this method and how that was for you?

Garry: My path to metabolomics was certainly non-linear. I’m, old enough to say that when I was doing my work, metabolomics was not the buzzword. Now, I think, metabolomics has almost become a synonym with a lot of the work in metabolism. Most people that do in-depth metabolic analyses in some way or another are using. That wasn’t the case when I was starting in metabolism, my initial areas of interest were actually related to bacterial resistance to antibiotics. I was trying to understand how certain bacteria particularly noscomial pathogens develop resistance to different kinds of antibiotics that are used in the clinic, such as penicillin. And as it turns out, perhaps not surprising to you, a lot of these drugs have a strong metabolic component. They have a big impact on the metabolism and it’s specifically the metabolism of a particular structure in bacteria called the cell wall. So these antibiotics that we were looking at interfered with the production of the cell wall by the bacteria. I was using at the time nuclear magnetic resonance (NMR) to study bacterial metabolism. Bacteria are a little different from the other types of systems that we were talking about moments ago with cancer because the physiology is less important. You don’t have the type of interactions with a tumor. Of course; you have a bunch of cells that constitute tumor. Tumors aren’t just one type of cells, but bacteria are a little simpler in the sense that you can grow them in culture and it’s a little easier to understand whether it is relevant.

Alice: One could argue that now, especially if you talk about common bacteria, that you have this rather complex network now with the host metabolites and all different kinds of signals that are going to influence. So similar to what you would do with antibiotics, but in this kind of exchange. So we found a way to make bacteria complicated, haven’t we?

Garry: Yeah, that’s a great, point. Certainly in the gut microbiome there’s a lot of important interactions there and the context of Penicillin resistance or antibiotic resistance, least in what we were studying. – It was a little simpler. We weren’t trying to probe those types of questions, but absolutely. The simple thing is that NMR isn’t as sensitive as mass spectrometry, which is another major analytical tool that one can apply to study metabolism. But because we were looking at bacteria, we could grow them up in huge flasks of many liters. I was doing experiments sometimes with 10 liters of bacteria. That´s a lot of bacteria and analyzing them by NMR provided a lot of insight, as we matured in those studies, I became increasingly interested in intermediates and pathways that NMR couldn’t resolve. It seems natural to me that one would try to turn to a different technology to look at those intermediates to better characterize those pathways and start thinking about flux. We were using isotope tracing at the time, not really to trace pathways, but to increase sensitivity in NMR. Not all nuclei or NMR active. We added C 13 to bacteria samples in those days because C 13 is NMR active; C 12 isotope is not. This increased our signals. The idea for me was moving towards other technologies that would have better sensitivities that would allow us to probe those metabolic pathways. So it made sense to start thinking about mass spectrometry. At the time this wasn’t really put under the word metabolomics. We were using mass spectrometry to start thinking about those pathways. It was just biochemistry in those days. Now, of course, we’d recognize that as being metabolomics.

Alice: From what you just said, we can see that you have quite some experience with the method and you have quite a few years of working with metabolomics behind you. So maybe for the younger audience, the people who are starting with metabolomics, is there anything you can think of that you wish you had known that maybe you could share with them to make something a bit less difficult for them in the near future?

Garry: I think in those early days when I first started doing mass spectrometry analysis of metabolic extracts, we would do much like people do today: You isolate, get rid of all the macromolecules and you isolate all the small molecules and analyze them by mass spectrometry. – And I was just initially astonished at how many signals there were in the datasets. As a trained biochemist, I was familiar with central carbon metabolism and the handful of pathways that surrounding them. But we were seeing a couple thousand metabolites. When we first started doing those experiments and we were seeing, 10,000, 20,000, 25,000 peaks it was really shocking. And I thought at the time – extremely exciting. The computational resources that we had at the time were much less developed than they are now. So most of the things we couldn’t identify, I guess you could argue that we still can’t identify a lot of those different signals, but I was under the impression, at least initially, that a lot of those represented new molecules that had never been reported before. That those were so-called unknowns or novel metabolites that maybe weren’t in biochemistry textbooks yet, but that were present in cells and that served an important biochemical function.
One thing that I vastly underappreciated at the time was how complex mass spectrometry metabolomics data are. You could take one standard. Glutamate or whatever your favorite metabolite is and put it in a beaker and analyze it by mass spectrometry. In a perfect world you might think you get one peak for glutamate, but can see a hundred or 200 or more peaks that are derived from that one metabolite. The metabolite can break into pieces. It can adduct, it can fragment; there’s contaminants in the samples. And at the time I didn’t appreciate all that because I had underestimated the complexity of the data and I also overestimated how many molecules we were actually measuring. I think that led to much more sophisticated and complicated interpretations of the data. That were probably needed. So I would say, for those starting the journey: Assuming that something that you can’t identify as an unknown is probably not a novel metabolite. I think that shouldn’t be necessarily the default hypothesis.

Alice: Thank you. So let’s move on to the main topic that will be related to the paper we’ll discuss today.  The paper about a model of melanoma. Let us discuss a bit cancer and metabolomics. When we think of cancer and metabolism, we think of energy metabolism. And this is a big part of the paper that we’ll discuss and carbon metabolism. But could you give us an overview also from the work that you’ve been doing with your group? So what are the key elements of this energy metabolism, but also of other parts of metabolism important for cancer that you would like to highlight?

Garry: Absolutely. Energy metabolism is particularly fascinating in cancer cells because cancer cells employ a particularly surprising metabolic program. What characterizes cancer cells is an uncontrolled capacity for proliferation. So that is to say that we have one cell that rapidly turns into two cells and four cells et cetera. And so if you look at what characterizes cell proliferation or cell division, for a cell to turn into another cell or multiple cells, it has to replicate its contents. And so that means that it has to remake all of its genetic material; it has to remake all of its plasma membranes; it has to remake all of its proteins. So that is actually a substantial anabolic load or synthetic burden that’s associated with cell proliferation. So when we think about that, it seems that that would require a lot of energy. Making things generally seems like it should be associated with an energy demand. But what’s fascinating about cancer cells is over a hundred years ago now, one of the pioneers of biochemistry, researcher by the name of Otto Warburg in the early 19 hundreds, discovered that cancer cells do in fact take up a lot of glucose, which is one of the most prominent nutrients that’s available in our circulation.
What is surprising is not that they take a lot of glucose but that most of the glucose is transformed into a metabolite called lactate. – Generally lactate is recognized as a waste product of cells. If you transform glucose into lactate it only yields two ATP through glycolysis.
If you take glucose and you oxidize it in mitochondria, in contrast, it yields on the order of 30 to 38 ATP. So it would seem intuitively that a cancer cell with all of these synthetic demands would be trying to maximize the amount of energy that they can produce. The reality is that most of the glucose that they metabolize gets metabolized in a very inefficient way so that it yields a relatively small amount of ATP per glucose. And so for over a hundred years, cancer biologists have been trying to understand why in the world cancer cells would do this.
It’s not just in vivo, by the way we were talking about that – the difference between in vivo settings and in vitro settings. You see the same thing if you take a cancer cell and you put it in an oxygenated cell culture. Do the same thing where it takes up a lot of glucose and transforms the majority of it into lactate. I think that’s one of the really interesting energetic paradoxes. When these cells seem to need so much energy, why would they be yielding so much? Why would they be getting such a low output of energy from glucose?

Alice: Also one way that we’ve been looking at cancer for the last decades is through the angle of genetics or transcriptomics or genetic changes. Because we always think of the mutations in the genes that are at the origin of the problem with the cancer cells. We see more and more that metabolomics is a very interesting tool to study cancer. So do you have any arguments as to why metabolomics is a good omics to study cancer compared to other ones like genomics or proteomics?

Garry: I would say that the other omics are equally important. I wouldn’t say that metabolomics is any more insightful than the others. You could, in fact, make arguments in any of the omics, depending on what kind of context using one is more important than the other. I would say that most cancer cells are rapidly dividing. That does require metabolic alterations. It is more of a universal hallmark of cancer. There are metabolic properties, this phenomena that I described where most of the glucose taken up is similar across most cancers. That makes it nice because what you’re describing is certainly true, that there are a lot of different genetic changes across different types of cancers, but many of the metabolic properties are conserved. That is attractive in the sense that it at least invites the possibility that there might be ways therapeutically to approach cancer that could be more universal. Now in practice, whether or not that’s possible is a totally different story. I think the other point that I would mention is that metabolomics provides a nice biochemical readout of what’s happening with the phenotype. Doing Transcriptomics, for example, is a powerful type of analysis that a lot of people do. It’s very standardized. There’s a lot of public data that’s available and you get better coverage than you do with technology such as metabolomics. – But at the end of the day it doesn’t necessarily tell you what’s happening phenotypically: Just because you have an increased or decreased expression of a particular transcript doesn’t necessarily mean that that pathway is more or less active. Furthermore, even if a transcript does correlate with a change in biochemical activity how much the metabolic pathway changes? You can’t infer that from transcripts, at least not reliably. And so metabolomics is really a necessary  extension to understand biochemistry if you want to understand and be very precise with some of those types of assessments.

Alice: I think in cancer, as in other diseases, we often see the thinking work going in both directions – from the genetics to the metabolomics or from the metabolism to the genetics. And it works really well both ways, but you can’t really predict. I just had this conversation with another guest about a study on asthma where you can’t really predict which direction is going to be – they’re both very valid ways of looking at the data.

Garry: I think, now, there’s a lot more genomics information available. So more often we tend to start there because particularly in the clinic, there’s more information readily accessible.

Alice: When we prepared the episode together we touched on the topic of nutritional intervention – How do you see that being a promising tool for either studying or finding therapeutic ways to address cancer or maybe other diseases as well?

Garry: I think nutritional intervention is a very intriguing area right now and we are finding that cancer cells require certain nutrients. It’s a very provocative idea to say “what would happen if we deprived cancer cells of these nutrients? Would that prevent them from proliferating and thereby mitigate disease?”