Jennifer Kirwan
Jennifer Kirwan is Head of the Metabolomics Platform at the Berlin Institute of Health at Charité (Germany)
Favorite metabolite
Melatonin, a close relative of tryptophan, Jennifer Kirwan’s favorite metabolite at MetSoc last year and the metabolite chosen by our previous guest David Wishart!
Papers we discussed in this episode
Translating metabolomics into clinical practice
Don’t forget to send your constructive criticism on this opinion piece in a comment via LinkedIn
Her past and present activities in the metabolomics community include
- mQACC consortium, promoting best QA/QC practices in untargeted metabolomics. Learn more in the consortium’s recommendations here.
- Precision medicine and pharmacometabolomics task group of the Metabolomics Society
- German society for metabolome research (DGMet)
Sign up for The Metabolomist mailing list to be the first to hear about the latest episodes and news around metabolomics at https://themetabolomist.com
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 to this new episode of the Metabolomic podcast. Today my guest is Jennifer Kirwan.
Jennifer: Thanks for having me.
Alice: Thanks for being here. You are the head of the Metabolomics platform at the Berlin Institute for Health in Germany. Can you tell us a little bit more about your background and the work that you do there?
Jennifer: Yes. My original background is as a veterinarian and I worked as a clinician for a few years before moving by accident into metabolomics where I’ve stayed ever since. I discovered I love statistics and really enjoyed the work. So I moved out to Berlin from the UK about six years ago. And the Berlin Institute of Health became the third pillar of the Charity teaching hospital a couple of years ago, where we’ve remained ever since.
We are working mainly on translational medicine. And our primary role is effectively to support other people to answer their research questions in metabolomics. We also do some of our own research. Biologically we are very interested in the heart-gut-brain triage and how different metabolites are communicating between these three organs and how they may be involved in health and disease.
We also do a lot of research on quality management, quality assurance, and quality control in metabolomics applications.
Alice: Thank you. You are also involved in certain working groups in the metabolomics community? I think you’re quite active here as well.
Jennifer: We are extremely active. I’m a central committee member for the metabolomics quality assurance and quality control consortium. This is an international consortium dedicated to furthering better quality management in metabolomics. I’m an active member of the Precision Medicine Working Group for the International Metabolomics Society, and I’m a founding member and former board member of the “Deutsche Gesellschaft fuer Metabolomforschung” which translates into English as the German Metabolome Research Society.
Alice: As you mentioned, you didn’t start out in metabolomics like many people because metabolomics is a relatively recent technique, but, you really had a kind of indirect path to it. Do you remember if there was something that you found particularly challenging when you met this method in your work?
Jennifer: I found all sorts of things challenging. I’d been working as a clinician and I’d been used to equipment that just works. And when you move into a scientific sphere, you are asking yourself why you’ve spent half a million or a million euros on Massspec equipment and then you spend most of your time troubleshooting.
Alice: Troubleshooting. It’s a really interesting perspective.
Jennifer: Yes, and we had lots of problems with a new instrument when I first started a PhD and I spent a lot of time staring at chromatograms, trying to work out what the problem was. In the end, I think it really developed my scientific skills and my lateral thinking skills, and it’s made me into a better scientist.
Alice: Yeah, I guess that comes from the fact that metabolomics is a very multidisciplinary technique you can’t be an expert at everything, but you need a language that’s understood by many different types of experts that that kind of opens your horizons.
Jennifer: So this is a really interesting perspective that you’re offering here. When I started in metabolomics, we were expected to be experts in everything. And I did everything from study design right through to my own bioinformatics and data analysis. And as I’ve stayed in the field, I’ve seen how it has diversified more and more into very specific fields where you have one person responsible perhaps for the analytical side and another person is doing only bioinformatics and another person may only be doing statistical analysis. This has tremendous advantages, especially as we are getting more and more complex in what analytics we can do. But you are absolutely correct that unless we learn each other’s languages and unless we learn something about the background as to how this data was collected, how this data was processed and how it was analyzed, it’s very easy unwittingly for there to be communication problems. – And this leads to misinterpretation of data. Absolutely.
Alice: And this is exactly the main topic of our discussion today. Because if, let’s say the analytical chemist not aware of certain details about or important things about the measurements and this is not communicated later on to the people who process the data, either the bioinformaticians or the people who do the biological interpretation of the data – this can have huge repercussions. We will have a very good example of this in the second paper we’ll discuss today.
Jennifer: I think this is a time really to be celebrating different specialties in teams. It’s very easy to join a team and see what somebody else can without realizing what you are bringing to the team and I think we need everybody on board bringing their own specialities to get the best possible results. And that’s analytical chemists, biochemists, statisticians, bioinformaticians. We need everybody at the table.
Alice: That means, when we want to apply metabolomics to the clinics or to clinical research, we also need to get the clinicians on board, the regulatory people on board and so on. Right?
Jennifer: So this is absolutely a big theme that we need to be discussing with clinicians and the regulatory bodies more. And we shouldn’t leave out other important groups such as patients, ethical advisors and privacy experts. Because I think the more that we move into the clinic, the more we start stepping out of our own comfort zone and we are going to need to bring more experts on board to make sure that we do a good job of this.
Alice: Thanks for this. That’s really true. So let’s move on then to the opinion piece: The first paper I wanted to discuss with you is relatively short in size but I really enjoyed reading it – it’s titled “Translating Metabolomics into Clinical Practice”. The goal is clear. You’ve published this very recently in nature reviews and amongst several things that I really enjoyed in this paper was that you write that metabolomics is on the precipice of transforming from a research tool to powerful clinical platform to improve pre precision medicine. You begin with the application of metabolomics to precision medicine. Why did you choose to focus on precision medicine specifically?
Jennifer: I suppose because this is where I see the real power of metabolomics. We know that medical doctors are experts in diagnosing multiple diseases but we still have diseases that it’s difficult to predict outcome. It’s difficult to predict response to medication. And there are still a number of diseases which we label these as clinicians as diseases of exclusion. So we’ve done every other test and we can’t find anything else wrong. And I think that by having a metabolomics approach and possibly a multi omics approach, we may be able to start subcategorizing diseases and subcategorizing groups of patients in terms of who’s likely to respond best to treatment A and who’s likely to respond best to treatment B. I think this is going to be of benefit to everybody as long as we make sure that we think about the ethical implications of cost and accessibility from the very beginning.
Alice: Very true. And there’s also the aspect of how metabolomics is responsive to the peculiarities of each patient – to the intra-individual variability and the inter-individual variability as well. So how we differ from each other and how our own metabolome will change over time. This is something that even other omics usually don’t give us access to. So does that bring extra power to metabolomics for precision medicine?
Jennifer: It brings extra power and it brings extra frustration. One of the beauties of metabolomics is that you have this large inter-individual variability, and we can use metabolomics effectively as measure of phenotype of the individual. At the same time it’s a very sensitive method and we’ll see differences purely down to age, down to diet, down to ethnicity, down to social lifestyle. These all need to be teased out when we’re thinking about diagnostic tests. This brings us to an additional challenge of what needs to be considered as a confounding factor and what we could actually consider as a co risk factor. So is age a confounding factor or if a particular disease is associated with an aging process, is it actually an important part of the equation when you’re analyzing the data?
Alice: That’s a really interesting point. You discuss several diseases throughout this paper and I found it particularly interesting when you talked about psychiatric diseases that are not necessarily an example that people would go to because it’s maybe not the most classical diseases. -Of course you also talk about cancer, you talk about things that are usually in the foreground for many papers that discuss metabolomics. I really like that you chose psychiatric diseases like depression, like schizophrenia to demonstrate also where metabolomics can help and where maybe we need more help because other tools have less potential to give us information. Can you explain why you chose to discuss these diseases?
Jennifer: It comes from a very personal history and I should be clear that we have worked on one or two projects looking at psychiatric diseases, but it’s not our major focus. Several years ago I read an article in a newspaper about an individual who’d spent time in a psychiatric institution and she talked about how she’d been treated as scary as other by the staff. Then one day a psychiatrist thought to do a clock test on her and discovered that she’d lost half of her vision. And from there they actually did some physical tests. – I think MRI can’t remember what the eventual diagnosis was but the conclusion of this article was how staff started relating to her differently once her disease was perceived as physical rather than psychiatric. Now we all know people who have depression. Some of us know people who have schizophrenia and many of us also will know people personally who have Alzheimer’s. I think when we stop trying to divide diseases into psychiatric and physical it gives us a new perspective on how we can look for treatments and possibly even cures. We are finding out more and more about the microbiome and the gut microbiome in particular and it is clear evidence that there’s a direct brain-gut link there through the vagus nerve, but there also seems to be other roots. And I think as time goes on we’ll find that psychiatric illnesses will have a biochemical and possibly a microbiological component to them as much as a sociological and genetic component to them.
Alice: Absolutely. There are many places where metabolomics helps us for this type of diseases and others also. There is the understanding, the mechanisms and the etiology, there’s the diagnostics, but there’s also treatment for choosing which treatment to give to people. Knowing who will be responsive to what. I worked a lot on depression last year and it’s still amazing to me that most people won’t respond to the first line treatment. They won’t respond to the second line treatment. And so we keep giving drugs to people and we just test it directly on them without knowing what effect it will have. When we start looking at the signatures of the response of two treatments in patients in their blood or in other matrices, you start to see that we might be able to predict who will respond to which treatment. At the minimum, avoid giving something that has really serious side effects to someone that we know will not respond to it. And also to directly give people what will be effective for them. And this is important for any disease, but it’s also very interesting for psychiatric diseases.
Jennifer: And I think this knowledge will also start spreading into physical diseases. So people with heart disease, for instance, have a much higher risk of depression than the general population. I think many people have just assumed it’s because they’re sick but there’s some evidence to suggest that there may be microbiome signatures of that, which may be playing into the depressive symptoms, and then the depression is actually potentially part of the disease not as a result of the disease. I’m careful what I say here because this is not my my specialty.
Alice: It also reminds me of what you said earlier, also when we plan greater projects maybe this is also a place where discussing with patient groups and with other parts of society that helps us to identify what are the important points to look at is really important because the perspective of the patients, of the families of the patients, of all sorts of people and institutions can really help to address this better.
Following up on the opinion piece that we’re discussing, I noticed there were quite a few mentions of artificial intelligence or machine learning that you suggest they can be really useful for different uses and applications of metabolomics in the clinics. Especially, there is one I really liked where you talk about regulation validation, which is often an issue that people face where they’re like – I have a great signature, I have really good ideas for biomarkers for this disease I’m interested in, but either I don’t know how to get into the clinics or it’s too expensive for me or there are many kinds of barriers on the way to, the clinics. You made an interesting suggestion where you write that, there could be a way to have only disease specific biomarker algorithms that would have to be validated and not the chemistry itself.
Jennifer: I should say that the initial idea of having a non-validated test was not mine. It came from a couple of discussions. One of which was with Annie Evans who has been working with Baylor Children’s Hospital – this is a paper by Lou et al. – And what they did to overcome this regulatory hurdle was they started running tests on blood samples using non clinically validated tests to direct where they send the tests to do the clinically validated tests, if that makes sense. Basically, you run a non-validated test and you have an answer that would suggest that it’s disease X. Now you can take a blood sample and you can run a validated clinical test to test for disease X. I think it’s a great idea because it saves time and money diagnosing sick patients quickly. What you can then also do is you can start building up databases. The more samples you run to start categorizing your unknown samples into metabolic categories that you may then in the future be able to diagnose with specific clinical tests. It allows you effectively to identify new subgroups of rare diseases. Now, this doesn’t stop you needing to validate your metabolomics test. But it lowers the barrier because now you are doing this as an in-house validation rather than necessarily needing to do a full regulatory FDA approved validation. However, I have a greater vision, which I hope others will share. That if we could start being able to validate wide targeted metabolomics tests, then we could start using these in a similar way. Where now we’ve got one test for multiple diseases and we are now just having to validate the algorithm. – That’s what I was explaining in the paper.
Alice: So this is the dream – You have a set of „pre-biomarkers“ that would cover a large ground of diseases that you would look for in the patients and then it would point you towards the ones that might be happening in the patient. Did I get that right?
Jennifer: In newborn screening, which is what Baylor is using this for, they’ve often got fairly extreme metabolic changes but we’ve all been in the situation where we are finding the same metabolites popping up again and again in various different diseases.
I’m actually part of a larger consortium. It’s a EU Horizon Grant looking at inflammatory diseases. It’s called IMMEDIATE. And we’re looking at how inflammation affects the metabolome and may be attenuated by microbiome changes. If we can pass some of these regulatory hurdles to have an analytical test accepted as the data is valid. This gives a lower barrier to just needing the data to prove that your algorithm is now valid.
Alice: Thank you. One follow up question on what you said. – You mentioned that it’s often the same metabolites that are seen to be changing in different diseases:
Can’t machine learning also help us with this? So instead of looking for metabolites X, Y, Z, that we would look for different patterns of change in these metabolites that might be different from disease to disease. And so we can have the same set of metabolites but the algorithm is the thing that tells us what is really happening, even though the data seems to be the same for all the diseases.
Jennifer: This is exactly the message that I was trying to get across. You can do it via machine learning and I think we need to be embracing machine learning more and more. We’ve got data to show that you may only need 5, 6, 7 metabolites to diagnose certain diseases. Which then in theory makes it possible to do by a hand. But, I’d say certainly from the discovery stage that machine learning is a very powerful tool when used correctly. One of the challenges that we have as a community is to make sure that our data for our machine learning tools is not only robust but also sufficient in quantity to actually make good machine learning tools.
Alice: This is gonna be one of the challenges. I think everyone is getting interested in the topic but there is a trade off between the type of models you use and the amount of data you will need. We need to have enough data to feed most of those algorithms. Yes. And it’s also about how complex you need to make your machine learning tool. Principle components analysis is often described as machine learning and it’s not particularly challenging to use. There’s always a temptation to use much more complex tools like neural networks, for instance. The more complex your tool, potentially, the more you are able to find patterns that are more difficult to find by hand, but you are more at risk of not being able to explain the decision making by the tool. And this can lead to unforeseen biases in your data analytics. I know the machine learning community is working hard on making sure that tools are explainable.
Alice: And you discuss also in the paper these biases and I guess in the application of machine learning to clinical applications we have the same kind of bias that we would get in any other applications of machine learning, for example, based on sex differences or ethnicity or also age differences that will have a large impact on the metabolism.
Jennifer: Then we would also have to have training sets that represent a large enough proportion of the population, or at least that define which part of the population was addressed, that we know where to apply it afterwards. So we have have these biases – And you also have the unexpected biases. Yes. There’s a famous example of a machine learning tool for, I think it was pneumonia and they wanted to predict which patients should spend time in the ICU and which patients could just remain on the normal ward. They found to their horror when they started implementing it, that it was saying that the sickest patients could stay on the ward. I think particularly patients with asthma. When they started investigating why, it turned out that their machine learning was using data were effectively the doctors were making a decision early on to send patients with asthma to the ICU so they were getting better and the machine learning tool was misinterpreting that as they shouldn’t have been in the ICU in the first place. It’s a beautiful example of why you really have to think about the data and not just accept what the computer’s telling you.
Alice: Yes. It’s a great example. Are there other aspects of the paper you would like to discuss?
Jennifer: So one of the things that we as a metabolomics community are enthusiastic about is data sharing. For this vision of transferring metabolomics into the clinic, to be realized we’re going to need large amounts of data. That is both going to probably need to be a community effort so that we can try and overcome some of these biases and get enough data – but it gives us the additional challenge then – how do we assimilate and compare data collected in different labs on different instruments. And we have this challenge of transferability of data. As a community, I think that we need to be fighting for standards of measurement with mass spectrometry, in particular. It doesn’t necessarily have to be absolutely quantitative, but we need a standard by which we can give a real number to our measurements that is meaningful.
What do I mean by that? Well, absolutely quantitative is quoting something as, micrograms per microliter or something similar. What may be more realistic may be to have a known standard that we can give a relative quantification to – That is validated.
Alice: I think it’s a good point, but isn’t there another way to address this? You have quantitative measurements, and of course you have variability for a given individual and between individuals, but then you can compare that to reference ranges as well for metabolites that would be quantified in large populations. – Then you could have ideas if you’re within the usual range or not, it’s actually what is commonly done in the clinics for all the metabolites that we measure in the clinics at the moment.
Jennifer: Yeah. So where you are able to quantify metabolites then absolute quantification is definitely the way forward. We know for instance that in the clinical labs there are well designed protocols to make sure that clinical labs are measuring things within a certain era of other clinical labs and this is definitely the way forward. I’m also thinking about much larger quantity of metabolites and particularly lipids which we have no standards for and for which absolute quantification suddenly becomes much harder.
Alice: Yes. My last question about that paper was – Did you get any feedback from the metabolomics community or the medical community on this opinion piece? Have you had a lot of feedback yet?
Jennifer: I had a lot of feedback. I had a lot of people asking me for access to it. I had it advertised on my LinkedIn and I got a lot of positive comments. I don’t know whether people are just very nice. So far I haven’t had any constructive criticism. I’m actually going to open to the community that if there is constructive criticism on my opinion, then I’m very open to hearing it. Because I think that science is about always being open to improving.
Alice: Yes. And it’s the point of an opinion piece. You give your opinion and then you hear the different opinions, but exactly that’s how the conversation gets going. It’s good. I would move on to our next topic, which is Biobanking. And especially considerations for biobanking of different types of samples for precision medicine. I think you have a lot of advice for this especially for people who want to use samples from biobanks and should maybe think of what to look out for and of course, for people who will be involved in collecting and storing samples and maybe other activities.
What are points that are important; that we should all know about?
Jennifer: I think if you are planning a study, then talk to the experts early. And this sounds obvious, but I think most of our listeners today are probably from the metabolomics community and will be very used to people turning up with samples that are 10 years old and want them analyzed – And they were not collected with metabolomics in mind. To get the best quality data you want the least technical variation. And if you want the least technical variation, you need to think about it in the planning – We all know that the challenge with metabolomics is that the people collecting the samples are often not the metabolomics experts and they’re often busy study nurses or sometimes general nurses who have been asked to collect this as part of their everyday job when they’ve got X number of other more urgent tasks to do. And so I think the first place to start is to engage with the people that are working on your team, sit down, discuss what you are after and why it’s important.
Have the discussion about what’s most important, what’s going to affect results, and hopefully foster a sense of community of engagement and of enthusiasm, so that everybody’s on board to follow protocols – And has a good understanding of what that protocol means. Because it’s one thing to write a protocol, it’s another to follow what someone else has written in the same way. Protocols are essential. They should be written – I think if you have videos, it’s even better because then people can follow them and understand exactly what you mean. And as an individual, it’s very important that you understand what your study design requires. By which I mean: If you want your perfect metabolomics research where you want something that is going to be as representative as possible of your sample, then you need very careful biobanking techniques:
You need to think about temperatures. You need to think about how long you keep blood as blood, how long you keep plasma out of the fridge, or preferably the liquid nitrogen for. But if your overall aim is to have something that’s really robust as a biomarker, then you may be having a conversation with the clinicians that they’re more interested in biomarkers that can survive real life clinical conditions. I, as the metabolomics person, would always rather have perfect conditions. That’s clear, but we have studies ongoing that for cost and practicality reasons we made a clear decision that we are looking at a different scientific question. This may surprise people, but there has to be an element of reality of clinical life in your decision making.
Alice: Absolutely. It’s really interesting also from that perspective to consider the planning as a very broad way of looking at it. It’s not just planning the details of the experiment, but really planning what you hope that your work will turn into maybe 15 years down the line. Maybe one day this will be used as a biomarker in the clinics. It’s a very different question indeed than to look for the perfect signature in perfectly preserved samples. That’s a really good point. I like it a lot.
Of course I can’t, not mention this cuz you talk about the importance of project planning and also in the story principle, that is my very first step. The first step is you sit down and you plan your experiment from beginning to end. From thinking, I want to perform this experiment, get this kind of samples, and do this kind of analysis because I want to answer that question. And then this whole process is gonna be very important to determine if you have the luxury to really work on your study from beginning to end including collecting the samples, which is not always the case, but if you can plan it as the person who does the metabolomics or the team who does the metabolomics, if you can plan it, also considering which samples you want, how old they should get, should you get them from a biobank, are you collecting yourself? – These kind of things. It’s really crucial because for the quality of the samples it’s gonna have an impact, but also for the results of your interpretation it’s gonna have an impact. If you end up not having exactly the samples that you wanted to have or that you would’ve needed to answer your question, then the whole project is a miss. Do you have experience ordering samples from a biobank yourself? – Is there something that people should be careful about you think, or that they should ask the biobank?
Jennifer: Definitely how the samples were collected and aliquoted. And the time points and temperature points across the sample collection chain. I think this is crucial. For all I’m saying that we’ve got projects ongoing where we are looking for more robust biomarkers that can survive clinical practice. The success with metabolomics is often about how much you can reduce technical variability and noise. And the more variability you have, the more you end up having to throw out metabolites because they’re too technically variable. I think there are some ISO standards for metabolomics collection now. To the best of my knowledge, they’re not yet widely adopted. I’m going to be interested to see in the future how widely adopted they are by Biobanks. I think having some standardization of sample collection across Biobank is certainly going to be useful because it comes back to this intercom comparability of samples.
Alice: For people who are interested in samples from Biobanks – My next guest will be someone who did a study using brain samples from a Biobank. He insisted to discuss what you know about your samples because he saw some interesting things on the brain samples that he was using.
Jennifer: We basically worked with our biobank to design a protocol that’s suitable for metabolomics and proteomics collection. I think this is now in use for most studies that go into the biobank.
Alice: So let’s go to the second paper we wanted to discuss. The one focused on quality assurance and quality control. We also will be looking into a very interesting case where one of your post docs found that one metabolite was not what the world thought it was. You can find the link on the shownotes for this episode:
It’s called „Identification, validation of Small Molecule Analytes in Mouse Plasma by LCMS, a case study of misidentification of a short-chain fatty acid acid with a keto body“. The first author is Marielle Garcia Rivera. You wanted to discuss this paper today. So can you begin maybe by telling me why this is the paper that came to mind?/ Why you wanted to discuss this one specifically.
Jennifer: I really like this paper because of what it says about thinking. Maryelle, she’s a former postdoc of ours and a very talented chemist. She was given the job of implementing short chain fatty acid method from another lab and getting it working within our lab. As she was working on it, she noticed some inconsistencies with some of her results. In particular she was looking at adding a couple of extra keto bodies and when she looked at the 3-hydroxy butyric acid, she found that there was an extra peak where there shouldn’t be. She’s somebody who is naturally curious and really thinks about data. And so she investigated this and this peak happened to match with the same transition peak as acetic acid, which was one of the metabolites that the on short-chain fatty acids that we were interested in. Sorry. HBA is actually a keto body. She investigated this and found that not only the seemed HBA to be suffering from in-source fragmentation in the mass spectrometer but when we were analyzing the plasma samples, there was a strong probability that our quantification of the acetic acid was now being affected by the this HBA transition – the [putative] in-source fragment. I really love what she did here because she found an interesting result, she followed it through and ended up just adjusting the method so that we could detect both the HBA keto body and the acetic acid much more accurately and with confidence. And it’s a great example of how quality management and good scientific observation, really pays dividends in terms of improving the quality of your data.
Alice: And also I think of taking ownership of the experiment that you make. There are some people who would say – this is the protocol. I’m just following the protocol. But it’s really important when you see something that doesn’t quite fit to look into it and to try to understand why it’s not what you expect.
Jennifer: I think we need to be celebrating analytical chemists more. They are central to metabolomics and it’s where it all begins. We are not always appreciating the hard work that they’re putting in.
Alice: I’m not an analytical chemist, so I rely on the data that is provided to me by the people who measure it. And I like how the paper begins because of course you start a scientific paper, you always want to demonstrate the relevance of what you’re discussing. So you always start with health and diseases. This paper begins by saying both of those metabolite classes are very important, metabolites for immune responses, diabetes and cardiovascular disease. And, of course, that’s true of both short fatty acid and keto bodies. And from the point of view of the work that I do, in majority, which is the biological interpretation of the results, if you give me a concentration and you tell me this is this short chin fat acid. I’m gonna make a story based on an increase in this metabolites in the in the sample. And if you tell me we have both a short chain fatty acid and a keto body, it’s going to be a different story. So the implications are huge when we think of the applications we’re going to make of the data at the end. It’s really important.
Jennifer: That’s one aspect of it. And the other aspect you can imagine if you’ve got a 10 or 20% noise level because there’s an additional compound there. You could end up missing that there’s a change in either or both compounds because there’s too much variation in the data.
Alice: And that’s why it’s really a good example of the importance of QA and QC in metabolomics. What are your main recommendations in that regards? You’re, involved in this – Working group or it’s a consortium.
Jennifer: It’s a consortium. International consortium of people who are really engaged in quality management and metabolomics. I’m going to be honest and say: I got into quality management by accident. It’s not something that the majority of people wake up one morning and say, „I’m going to be a quality manager“.
Alice: You did it by accident or by necessity?
Jennifer: Well, by necessity. But I’d also say, and here I have to thank my ex-boss Mark Viant, another really talented chemist. He infused me about thinking about data in another way. So not just the biological interpretation, but how the technical variability and the way that it was collected may be influencing the final results. Because of his guidance I started getting very deeply involved in this subject. And the more involved you get, the more excited you get by what it’s then possible to do with good quality management techniques. This is a huge task. We wrote a white paper on the subject and we restricted ourselves just to quality control samples because we decided that if we did any more then the paper would basically be too large.
It would be a book, probably. I think that every stage of the process benefits from a good quality management strategy – every stage. There’s this wonderful lecture on statistics by David Broadhurst where he talks about marginal gains and this is the idea that he uses the British Olympic Cycling Team as an example. This is a fantastic example to use because effectively what they did was they looked at everything. They looked at everything from the saddles that they were using to the pillows they were sleeping on so that they could get the best night’s sleep. And they tried so save 1% of our time here, and we save 0.2% of our time here, and we saved perhaps 3% by doing this and as an overall result of these different things and these tiny incremental time savings they ended up with the most gold medals that’d ever won. And we are doing the reverse with quality management. We’re saying, okay, if we have a very tightly time controlled collection procedure for our samples at biobanking; if we have this very rigorously timed and controlled sample preparation procedure for our analytical preparation; if we make sure that our batch lengths are less than X number of samples, then we may at each step be reducing the variability in any individual metabolite by an incremental amount. It’s obviously different from metabolite to metabolite. But over the course of the entire pipeline of metabolomics this is going to massively improve your technical variability and that improves your statistical power – easiest and cheapest way of doing it.
Alice: Wow. And so for someone who is interested in improving their quality management, do they go to your paper or do they have other resources that they can learn from?
Jennifer: So I would say for quality management – I would never rely on one single resource. This is partly because I think we are still in the process of learning ourselves. But if you’re interested in quality management, our paper’s obviously a good place to start. We’re writing more papers. The mQACC consortium and if you’re really interested then go onto the mQACC website and apply to become a member.
Alice: Okay. We can put a link to the website in the show notes – then people can find it easily. Good. I think that takes us to your favorite metabolite. So you’ve actually already contributed your favorite metabolite last year, because you were so kind to speak to me in Valencia at the last metabolomics society conference. So I’ve asked you to come up with a new favorite metabolite and tell us why it’s so great. So what have you chosen today?
Jennifer: My second favorite metabolite is melatonin, also known as N-Acetyl-5-methoxy tryptamine. But life is too short – so most of you will already know that melatonin, is produced by the pineal gland in the brain and it’s one of the mechanisms that we use for circadian rhythm and sleep wake cycles. It tends to increase in most people’s as it gets dark so that we are ready to sleep. One of the interesting things is how much is produced as extra pineal melatonin? The retina produces some melatonin. That’s perhaps not so surprising given it’s link to circadian rhythms. But so does the gut, the reproductive system, and certain immune cells in particular macrophages and mast cells. This becomes particularly intriguing when you look at melatonin’s other actions in the body. It acts as an antioxidant and it’s a fantastic three radical scavenger. It’s anti-inflammatory. It has a really interesting effect on the immune system because it can act on cytokines when we need to ramp up the immune system. It acts via receptors when the defense is no longer needed and it can actually also calm down the immune system. It’s also been shown at pharmacological levels to potentially be oncostatic and it’s now being explored as an adjunct in cancer treatments. It’s apotrophic. – And it’s even been shown by its own metabolite, AMK to potentially be a memory booster. It’s got all of these different mechanisms in the body and it’s produced in various parts of the body. And I think that as we explore the tryptophan pathway more and as we explore the gut microbiome more, we are going to find that melatonin starts popping up in some very interesting metabolic processes.
Alice: Thank you for this. It was really interesting. We discussed this the last time about tryptophan as well. I always find it really interesting that tryptophan is an essential amino acid. So we get it through a food and so the microbiome gets the first pick. If we think of now all the things that melatonin does we’re still a bit reliant on our microbiome to leave enough of the tryptophan to us so that we can have all these actions as well.
Jennifer: Well, I didn’t mention that melatonin is also consumed through the diet as well.
Alice: Of course. That makes sense. Then you can also take it in directly. Then we have more chances.
Jennifer: It’s also really interesting chemically because it’s an indole alkaloid and it’s an amphiphilic molecule – So it can pass over plasma membranes very easily. I think, we’re going to find more and more roles for melatonin and physiological processes. But I also suspect that it will start popping up as adjunct or even full treatment for certain conditions.
Alice: It’s really interesting. I also find it really interesting how metabolites, as we learn more about their functions, get completely different kind of personalities. If you hear melatonin until recently, you just say – „ah, it’s about sleep“. Now we learned with you that there are many, many other roles of melatonin. I like this a lot about metabolomics and about the study of metabolites that we realize they can do so many different things. The single metabolites has so many roles. It’s wonderful. Various groups have discovered that it seems to inhibit viruses entering cells and it’s been studied as a potential treatment for Covid 19. Again – Another use; and it’s antidepressant. I’d like to bring us back to biobanking because one of the things with melatonin is, of course, it’s light sensitive. So if you’re interested in studying this, then you need to think about how you are collecting your samples. And it’s also phasic. So again, experimental design. If you’re collecting it in different people, then you need to make sure that it’s similar time of the day.
Alice: Thank you very, very much. It was a lovely discussion and thank you for being on the podcast with us.
Jennifer: Thank you very much for inviting me.
