An Interview with Chris Armstrong, PhD, and Wenzhong Xiao, PhD Transcript

An Interview with Chris Armstrong, PhD, and Wenzhong Xiao, PhD: Transcript

Dr. Meadows: Hi everyone and thank you for listening to our interview today. I am Dr. Danielle Meadows, OMF’s VP of Research Programs and Operations, and I’m joined today by Dr. Chris Armstrong, the Director of our Melbourne ME/CFS Collaboration, and Dr. Wenzhong Xiao, the Director of our Computational Research Center for Complex Diseases.

And just as a reminder for this series of interviews, as I’m talking to our sirectors, we’re really trying to get at some of the bigger picture and draw some connections between different areas of research that our directors specialize in. So, Chris, I’m going to start with you today, can you give us just a really brief overview of your hypothesis of ME/CFS?

Dr. Armstrong: Yes, I can give you a brief hypothesis. In fact, we have a few different ideas of what causes ME/CFS, but in a general sense, what we think about is, where we come from, is to think about the symptoms that the patients experience and the findings that we have in this field and how these two things may marry up.

And in a general sense, we think about energy efficiency or the efficiency at which ATP can be produced. And when I say something like that, people may think, well immediately mitochondria. So, the way the mitochondria, how effective they function, their ability to produce energy, ATP molecules. But there are a lot of things go into what makes something efficient.

And if you were to think of the human body, like you would think of a car, you have mitochondria, which may be the engine. But you have fuels, right? So, mitochondria needs substrate to be able to produce energy. And the provision of those fuels comes through blood flow. It’s not just substrates like that you eat from your diet that go through your gut, and into the bloodstream. It’s also oxygen. And it’s not just the provision of fuels to these tissues, but it’s also the removal of byproducts from those fuels. So, exhaust and things like that.

And so this combination of factors, if you think about it as a general whole, you start to see that, you know, issues in the gut, issues with blood flow, mitochondrial inefficiency, they all lead to like this idea of a general inefficiency or inefficient energy production system, in combination with the fact that you have comorbidities or things like immune activations or inappropriate release of adrenaline for POTS and kind of MCAS, these sort of situations that produce or use, sorry, I should say a lot of ATP rapidly outside of the control of the patients. And so, when patients are pacing, they’re thinking about muscles and things that use ATP for using energy in ways that they can control. But there are obviously things outside of their control where energy also gets utilized.

And so, we think this culmination of factors in together as an interplay, that the outcome of that is a generally an energy or an energy inefficient system with added things that weigh that down and reduce the general energy profile within that patient population. That’s about as simple as I can make it for a general hypothesis overview.

Dr. Meadows: That’s great. Thank you. And maybe now I’ll turn quickly to you Wenzhong, in the realm of, you know, kind of drawing connections, you obviously kind of do a lot of that actually as a computational person. You’re looking at different data sources. So maybe you can give us a quick look at your hypothesis of the disease based on, you know, the bigger picture that you have.

Dr. Xiao: Yeah. Thank you. So, I think Chris mentioned you know, one of these very attractive hypotheses, which I agree. And that’s what we’ve been studying as well, computationally. And we looked at muscle biopsies of patients and identified some of these potential problems seen in patients, and right now we’re doing verifications in collaboration with other groups to see whether modulating those points, as Chris outlined elegantly, would actually be able to help patients.

So, that’s one part of our computational work. The other two parts I like to mention, which are a little bit harder to study. One is the immune system. You know, you might already have discussed with some of the groups you know, there a few potential causes for perturbing that system.

Right now, we’re collaborating with, one of the groups at NIH to look at one particular part of this. And you know, again whether modulating that in, for example, disease models could potentially restore the immune system. And the third one is I think the most difficult, which is the central nervous system. Because directing, studying patient central nervous system is just very difficult. So, the evidence that we see so far, for example, from the whole genome sequencing data from Mike Snyder’s group at the Stanford you know, point to some of the potential involvement in the central nervous system.

And we’re still trying to figure out whether there are ways to potentially probe the central nervous system. So, the short summary is that these are probably the three systems that I think a lot of researchers are working on, you know, either one of them or combination of them. And we’re working with them together to look at this computationally.

Dr. Meadows: That’s awesome. Thanks, Wenzhong. And you know, so in your work you’ve been able to, you know, pull in disparate sources of data and kind of come up with some of these computational findings that you’re talking about. And so I want to maybe take a second to draw some connections from your work to maybe some of the human studies that Chris, you’re performing.

And I’ll throw out an idea or two here and there, but you know, I’ll let you take it from there. So, maybe going back to the first bit that you were talking about Wenzhong, I think you mentioned looking at the muscle samples and I think you’ve published a paper that’s looking at the shared metabolic dysregulation between ME/CFS and Long COVID.

And I know Chris, you’ve also published on, you know, looking at the metabolic profiles to differentiate ME/CFS as part of that like, you know, diagnostic kind of approach to things. So maybe we can start there, talk about your connections in the metabolic realm and then we can go elsewhere from there. Either one of you can take it.

Dr. Armstrong: Yeah. Okay. So, either one! Yeah, so I think you know, both our groups and I guess across the different groups in Open Medicine Foundation supported centers, we have probably a general interest in metabolism, I would say across the board is pretty consistent and I think what we’ve found, and I guess, identified is, I guess amino acid changes or alterations, lipid changes, as well, as those two kind of components seem to stand out, at least from our perspective, as a general sense. And that has been for across, for a number of different studies in this field. So, trying to understand kind of them as, I guess, fuels, but also there are other roles that they kind of relied upon is kind of an important strategy for us moving forward.

The work that we did in terms of trying to identify, you know, biomarkers or diagnostics is ongoing. And as people will know, Open Medicine Foundation has begun the Bioquest study as well. So this is kind of all fitting together as an idea around differential diagnosis and working out whether we can develop, without actually knowing the mechanism itself can utilize large amounts of data and machine learning or other AI tools to differentiate or separate out ME, utilizing these tools to effectively see things that we can’t utilizing our standard statistical models or standard statistical tools that we use and making something fit that may not be so obvious.

And so that’s the idea in some of the large data projects that we’re doing. Is effectively trying to circumvent the typical workflow that’s required to lead to biology and sorry to diagnostics and treatments, which typically moves by first identifying what the pathological mechanism is. And I think that’s really important because that pathological mechanism we have research that’s trying to uncover what that is, but I think it’s also really important to have other studies trying to identify ways around under having to understand that to get to outcomes for patients sooner.

Dr. Meadows: Yeah. Really kind of working in parallel as opposed to in series to move things along as quickly as possible.

Dr. Armstrong: Yeah, exactly.

Dr. Meadows: Wenzhong, anything you want to add to this point?

Dr. Xiao: Yeah, I just want to say I concur with what Chris said, and his group has done a number of studies that are really you know, very interesting in this field.

As you mentioned earlier, we did computational modeling of the muscle of patients, and we saw some of the changes. But again Chris already mentioned, which are consistent with that you know, what his group had found as well. So then the next question would be, you know, how can we potentially correct for that. The particular publication that you mentioned where we thought that we would specifically look at supplements.

The reason, it’s just because there’s no FDA approval necessary on the supplements. And then the issue with supplements is that the efficacy versus you know, safety which we all know because we have supplements typically through oral route. And in order to make it effective give to the right tissue at the right dose, you know, that itself would require further development. So, so far what we heard is that you know, roughly half of the patients that wrote to me that said that, you know, the particular supplement helped them, but still you get half of them that basically said that it doesn’t work.

So we’re trying to figure out whether that’s a dose problem or, you know, we should actually try to modulate it. You know, along the lines, Chris mentioned, for example, enhance amino acid metabolism or enhanced lipid metabolism using some of the other molecules. But when we go with you know, some of these other more potent molecules, you get into the prescription drugs and that would require, obviously clinical trials. I know that’s what Open Medicine Foundation is actively planning.

Dr. Meadows: I think that you know, gets to a good point where you know, you mentioned, you know, not everybody is responding to these things and that you know, I think kind of steers us toward the precision medicine conversation pretty nicely. And you both have kind of done some work in or, and are doing some work in that area. So, you know, maybe Wenzhong, you’ve talked about the TreatME Survey before, which is you know, conducted by the patient researcher Martha Ecky, and you were able to analyze the data from these patient responses and show some clusters based off of symptoms where you know, patients were responding differently to different medications and having them you know, basically having these four subgroups based off of the symptoms.

And I know Chris on your side, you’re working on this personalized treatment trial and some other things in the precision medicine realm too, where you’re kind of similarly taking that clinical picture and trying to work backward to the molecular level and kind of figure out what’s potentially going to be some prognostic indicators that could be used for precision medicine. So, there’s, I think, some potential for synergy in that work there. So maybe I’ll open the floor there.

Dr. Armstrong: Yeah, definitely! This space is again, a way to try and move around that, that roadblock of not understanding that central pathological mechanism. And I think this very well could be a heterogeneous disease. It is defined by a set of symptoms. And so because it’s not defined by a pathology in itself, we can’t be sure that it’s all the one pathology. Not saying that it is not, but it’s more about how you desire or think about studies where you could look at both, you know, you could look at it if it was separate pathologies or separate things and in different individuals that present with similar symptoms or whether it is just one.

And so thinking about that is a way to kind of maybe even characterize patients, maybe their response to different treatments and thinking about, well, if you could look at, utilize that to work out the response, the things that have actually improved, and then potentially the drugs, their mechanisms where they have overlapping. If they say certain subsets of different treatments are able to that kind of function or work on different, the same areas of the body.

Come and produce similar maybe outcomes, then you are looking to see whether that may be a mechanism around that. So we’re kind of working backwards, as you say. But there’s a lot of kind of exciting things in this space, I think. We haven’t really scratched the surface of what would be possible at all within this area. I think the TREATME survey was excellent, really providing a large, like a large scale data to be able to give you numbers and ideas around what patients maybe have trialed and how they may have affected them.

That’s the kind of region you want to kind of get into. All that we’re kind of adding on top with some of our work that we’re doing at the moment is to collect biofluids along with that process and be more prospective, rather than retrospective, in terms of those studies. And, but in essence, yeah, they’re going to lead to a similar kind of understanding, I think, which is trying to utilize large amounts of data from individuals based on the interventions, they’re already naturally trying to see whether we could understand the biology of their disease better, but also look to whether you can predict a bevvy of different treatments that might be useful for individuals. And so there’s only a few groups I think, that I know of in this whole field that are looking to do this sort of work to predict treatments that would be useful for individual patients.

And yeah, I think that’s obviously a really important area to really look into, especially with maybe more what’s more coming of age and is becoming more accessible, like things like generative AI and the capabilities of this technology for making predictions and understanding really complicated kind of, data sets that may not be, that have a lot of missing data or all sorts of different things to kind of make sense of this.

Yeah. So, I think that’s be it’s becoming an area that has a lot of potential. That’s what I would say.

Dr. Xiao: Yeah. I agree.

Dr. Meadows: Yeah. And I think, Wenzhong, kind of in this area with you know, some of the more network based analyses that you do, you’re also able to kind of learn from similar diseases as well and potentially pull in some, you know, useful information on even specific symptoms that are associated with ME/CFS and what might be more effective for those symptoms.

So it’s, you know, looking at the combination treatment regimen kind of thing as opposed to, Chris like you said, finding one thing that’s going to hit that central problem that we have yet to really hone in on fully.

Dr. Xiao: Yeah, I agree. I agree. Again, you know, I agree completely with what Chris mentioned that, you know, in the ideal world, we would be able to find you know, one particular treatment that would help.

A particular patient. I think that would be, you know, the common goal that every group in Open Medicine research network is trying to achieve. But you mentioned you know, a good point as well, that the, perhaps a combo would be also something that we should look at. There are a number of diseases we know that are currently effectively treated by using some of these combo treatments.

So that’s certainly something that we should take into consideration in using, for example some of these AI machine learning tools. It should be something that we can put into consideration.

Dr. Meadows: All right. Well, I think I will go ahead and wrap us up there. Thank you both so much for your time and agreeing to, to chat about some connections between your research. It’s always great to get some good discussion. So, thank you again.

Dr. Xiao: Thank you.

Dr. Armstrong: Thank you, Danielle.