 All right, welcome to all 15 by 4, everyone. Today, I'm going to be talking about the science of metabolomics. A little bit about myself first, my name is Aaron. I'm a student at the Technical University of Munich. And currently, I specialize in what is my passion is time series metabolomics research and translating that into usable theory and being able to come up with functional conclusions from this data. So without further ado, we're just going to dive right into metabolism and see what it really is. So here is metabolism, right? We're diving into the deep end right now. So I hope you guys are ready to swim. I don't know if you guys can see. We have different sections over here. I have to use this blue lighter. So there's different little categories here. We have carbohydrate metabolism, lipid metabolism, vitamins, and minerals. And it's really all connected. We have these little nodes here. As you can see, these little circular dots, which represent metabolites. And the lines represent interconversions and conversions that you'll see that really create a network of metabolism within the human cell. So what metabolomics does is it tries to explain a little bit of this. But first, definition I'll be using quite a bit, or the word I'll be using quite a bit is metabolite. And so what is a metabolite? A metabolite is a substance that occurs within metabolism. It's part of the metabolic process that allows it to continue and allows it to follow through. And we can represent a metabolite through these convenient colored circles here. And we can call these primary metabolites. Primary metabolites are metabolites that are critical for the processes to continue. Without these metabolites, you'll likely probably die. So we need these. These are good. But within metabolism, a common theme is interconversion and conversion to smaller and lesser known metabolites. And we can call these secondary metabolites. These are useful for completing metabolic processes for specific pathways. And as these conversions continue, we can form other primary metabolites and other secondary metabolites through the action of enzymes, which are little protein components in the cell that allow this to occur. And we form a large network of metabolites and reactions. And remember the first map that I showed you? That's actually a very simplified version of what metabolism is, believe it or not. Because the number of metabolites that actually exist within the human cell is about 18,000. And the number of reactions that are possible to make this occur is 11,000. So metabolomics tries to explain this. And it's no easy test. So we'll jump right into what is metabolomics then. So I'm sure you guys have slept once in your life or ran or been stressed a little bit. And you kind of wondered, how does this affect your metabolism? And I've also wondered that too. And so what metabolomics does, it allows us to take either blood samples or cell either samples after these certain challenges, maybe after sleep, maybe after exercise, or anything you can think of. And we hand these samples over to different analytic techniques where we're able to identify the metabolites that are within these samples, as well as the concentration values of them. And from here, we are able to provide a snapshot of what actually is occurring within metabolism at that specific time point. And what actually separates metabolomics from other analytic techniques and traditional experiments is that usually we take a look at maybe one, two, or three metabolites or components of a cell. With metabolomics, we're able to measure multiple metabolites at once. So we're really able to provide a true picture of what metabolism is. And so when we have this map, and this is the camera, this represents metabolomics over here, so we're able to take pictures of the metabolism and really incorporate and be able to understand what's really going on at certain time points by the different concentration changes in which metabolites are present. And so we can take different sections, and we can also take the whole thing if you want. You just need enough money to do it. So that's a major challenge, actually. And so with any complicated science, I feel that we should always be able to break it down to cake consumption, something very simple. So I'm sure you're at one point in your life, you've had cake, and you've wanted to eat this cake, as this guy does too. So we're going to start with cake. During the first parts of metabolism is chewing. We have enzymes in our mouth that break this cake down to more fundamental components. And as we chew, and we chew and these enzymes work, it's going to start going through the digest process. And then from here, and also a little background in biology, it's going to travel through the suffocates and through the stomach. And it's going to start breaking down into further and further smaller, more fundamental bits. And once it reaches a fundamental bit, such as glucose or lactose or something of this nature, it's able to then be transported from the intestine into the blood, as you can see. And then this is where we are introduced to our first metabolomics technique. So as the metabolites are traveling through the blood, we're able to take a picture and see what's really going on. So we can also quantify which the levels of the metabolites that are in this picture. With metabolite one here, metabolite two, metabolite three, we are able to see how much is within. And then this is for identifying biomarkers. And what is a biomarker? A biomarker is a molecular indicator of an outcome. So as we can say, and this is a very simple example, these are biomarkers of cake consumption. Elevated metabolite one, and elevated metabolite three are biomarkers of cake consumption. And maybe a more interesting study, an actual study this time, was done by Dr. Serge Rezzi. And what he wanted to find out is can we predict if someone likes chocolate without them even eating chocolate yet? And so what he did is he took plasma profiles of a couple of subjects, more than a couple. And he ran this profile and ran these analytic techniques and decided to find if there's any biomarkers that are outstanding between the two. And what he found is that in chocolate lovers, there is elevated citrate and phenylacetyl glutamate. And in chocolate, in different people who really just don't really care about chocolate, there's higher levels of carnitine and anacetyl carnitine. And these have functional implications, but it's going to take quite a while to explain this in probably another talk, so I'll just leave it at here. But nonetheless, these are biomarkers of chocolate lovers or people of chocolate indifference. So what's next? So as these metabolites travel through the blood, they are able to enter the cell, and then all those enzymatic processes that we talked about earlier in the map are able to occur. And then this brings us to our next metabolomics technique called single cell metabolomics. So now on a more serious note, we are talking about chocolate cake and chocolate, which is fun, but there's also real practical use for this. Dr. Zimboni here at ETH-Jerk wanted to find out if he can differentiate breast cancer from normal cancer. These cells metabolically, if they're metabolically different. And so he more or less takes a picture of the cell and then finds the biomarkers. And the biomarkers that he found are citrate, aconitate, fumarite, malate, acetyl-COA, and lactate, which separate the two. And to give understanding to this and to be able to further expand on the potential of metabolomics, I'm going to give a quick biology course real quick. So listen up. So here's a healthy cell. We have the nucleus. We have the endoplasmic reticulum. We have the mitochondria and some lysosomes hanging around. These first five metabolites, citrate, aconitate, fumarite, malate, and acetyl-COA are very much found mostly in mitochondria. So they're usually not found outside of it. So in normal metabolism, we have this plasma membrane, the cellular plasma membrane right here, and the mitochondrial membrane right here. And so we're going to do an example of metabolism with glucose, which is the primary fundamental sugar. So normally, glucose enters the cell, is converted into pyruvate, and then is able to get into the mitochondria. From here, we'll have energy production. This is the energy that really gives us life. We're able to think, breathe, basically do all our functions from this energy. With a cancer cell, such as breast cancer, there's something missing. And I don't know if you guys can notice, but the mitochondria is not there anymore. And so the implications of this are when glucose enters the cell, it becomes pyruvate, and it wants to get to this mitochondria. But since there isn't one, it can't. So it gets converted into lactate, and then it's shuttled outside of the cell. So there's a large increase in lactate in and outside of the cell. So we know that lactate increases. And what do you guys think these five metabolites do? They probably decrease, and that's what they do. They decrease because there is no mitochondria, but lactate does increase. And this is actually a well-known effect. It's called the Warburg effect. This is one of the principles of cancer biology. And we can, which is the loss of mitochondria and the increase in lactate. We can represent this effect by this new black dot that will then join our friends over here, the blue, orange, and red dot. So what if we want to find out why this occurs? What is the context behind this? We want more details. We know the biomarkers, but what's really going on? Well, we can also measure metabolomics in real time. This is brand new technology that's coming out right now. And as our capabilities increase, it's going to be more prominent. So this is kind of a look into the future, as well as some fringe technology right now. But so we're able to look at it in time. We're able to do a time series metabolic profile of cells or of the human body. So we can more or less take a picture of each concentration level over time. So we'll have observation point time point one, where we have these four groups of metabolites measured. And we can start to form a system, which is the basis of system biology. And then we can then show time point one, which will be evidenced by these four lines that are representative of these concentrations. And then we can create a trend line from each of these. And as you can see, at time point three, this is where the real action occurs. We see the black dot decreasing quite a bit, as is expected, because those five metabolites decrease. And then we have orange and red here increasing, which we'll find out later. Then we also have time point four. So here's the interesting part. So we have our trend lines here. And we have our five metabolites and lactate here. We know that these are the black dots. This is the black dot. Which one do you guys think lactate is? By a show of hands, do you think lactate would be represented by the orange dot or the red dot? First, we'll go with the red dot. Just raise your hand if you guys think it's a red dot. Looks like the majority. And the orange dot, I guess, is not the others. But what it actually is, is the orange dot. And this is because, and you would assume that it would be the red dot, because it's increasing, lactate is increasing, but there is some metabolomics complications with this. So what we find is that the orange dot, which is lactate, actually is able to get converted into other metabolites within the cell. And from there, that is what the red dot is. So these extra lactate derivatives that we see are actually represented by the red dot. And this is new. So we have been referencing the Warburg effect recently, which is one of the foundations of cancer biology. And the idea with the Warburg effect is that glucose comes into the cell, it's converted to lactate, and then gets thrown out of the cell as a waste product. But what we're showing here now is that lactate doesn't necessarily act that way. It's kind of overturning what cancer biology, overturning one of the principles of cancer biology. And we'll get into that more real quick. But so, just a quick overview then of what time series metabolomics is allowing us to do, it's giving us a better understanding of the complete mechanisms. And it really connects the metabolism to the phenotype and provides a full picture of the biochemistry that leads up to these kinds of events that are occurring. And so now instead of the Warburg effect occurring where glucose enters the cell and then leaves the cell as a waste product, there is through external studies as well, we find that there's little, many mitochondria still left in cancer cells. And even though they can't metabolize glucose and there's still a large increasing lactate, glucose comes in, gets converted into lactate, and lactate enters into the mitochondria. And then mitochondria are able to then convert lactate into lipid derivatives, which then are used for the structure of the cancer cell and the survival of the cancer cell. And all this is very new. This is 2017 research, and I think it's fascinating really. But so this is all the possible due to time series metabolomics. So just back to the metabolomics research itself. One of the goals is to identify what metabolism is on a system level. So we can use plasma metabolomics or we could use single cell metabolomics to really characterize the human body. But there are some pros and cons. For example, in the plasma profiles, we don't know where the metabolites are coming from because they could be coming from the lungs, they could be coming from the liver, from adipose tissue, from anything really. So when we're taking a picture of the plasma, we know it's there, but we don't know where it's coming from. But with the cell, we're able to identify with cellular metabolomics, we're able to identify the reactions that are occurring, but then we can't also connect it to a systemic level. So really the conclusion here is that we need both to be able to best understand biology. And the last slide here is the main goal of metabolomics is to identify a metabolotype. And metabolotypes are a very interesting subject. So we have our green people here and we have our gray and we have our red. What metabolites metabolotypes do is it categorizes people based off metabolism. Everyone has their own unique metabolism, but that uniqueness is shared among other people as well. So you're able to create categories of people who have similar metabolisms but are different from other people. And with that, we're able to create the metabolotype one, metabolotype two, and metabolotype three. And then we can start really understanding on a populational basis what we're really dealing with. And with this technique, even though we're not quite there yet, but we're still working on it, we will finally be able to accomplish personalized health in a matter that is more genuine and accurate than we've ever done before. So that's it.