Preprint Toward a Disease Module for ME/CFS: A Network-Based Gene Prioritization, 2025, Maccallini

https://www.medrxiv.org/content/10.1101/2025.04.13.25325733v1

Abstract
Background: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating condition with unclear etiology and no FDA-approved treatment. Recent studies suggest a possible genetic contribution to its pathogenesis.

Objective: This study aims to identify candidate genes for ME/CFS using both empirical evidence from genome-wide and next-generation sequencing studies on monogenic cases and computational expansion based on protein-protein interaction networks.

Methods: Twenty-two genes associated with ME/CFS were identified from relevant literature, including both common and rare variants. These genes were used as seeds in the STRING database to retrieve high-confidence interacting genes. A Random Walk with Restart (RWR) algorithm ranked 1063 candidate genes by their similarity to the seeds. The top 250 ranking genes were selected to define a disease module termed the ME/CFS module. This module was analysed for enrichment in metabolic pathways and disease associations.

Results: Enrichment analysis identified significant overlaps with sphingolipid metabolism and signaling, and energy-related pathways. Heme degradation, TP53-regulated metabolic genes, and thermogenesis were also identified as possibly contributing to the pathogenesis of ME/CFS. Overlaps with metabolic and neurodegenerative diseases were observed.

Conclusion: The ME/CFS module captures biologically plausible mechanisms underlying ME/CFS, with a particular focus on lipid and energy metabolism. It also provides a tool for filtering exome and genome data for the study of Mendelian cases of ME/CFS.

 

Can you add the author’s name and the year to the title for searchability? Done

Paolo Maccallini might be this guy:
https://paolomaccallini.wordpress.com/category/myalgic-encephalomyelitischronic-fatigue-syndrome/

 

@paolo has an account here but I don't know if he still visits.

He's written a number of blogs about tracking his ME/CFS with data through the seasons as well as looking at data from research papers. He was the person that found the issue (link) with Chris Pontings P4HA1 gene blog. I mention this not to belittle Chris, but to point out that Paolo is very smart and has dug deep into the ME/CFS genetic data before.

In my view using the STRING database is a great technique and I hope it can be applied to the DecodeME data.

 

Here is @paolo's blog that contains slides from a talk about re-analysing the data from the UK Biobank ME/CFS data. According to one of the slides he has written 4 papers in this area.
https://paolomaccallini.wordpress.c...stics-generated-from-the-uk-biobank-database/

 

Last seen oct’24. He might still browse ingocnito?

 

It sounds like half of studies find an increase and half find a decrease in sphingomyelins. Through what mechanism could multiple studies find opposite changes in a molecule that is actually meaningful in a disease?

 

This is the exact rabbit hole I went down a few weeks ago! I still only have vague guesses, but my best guess would be that we’re seeing a downstream effect of serine metabolism, since many (but not all) of those studies also found similar directionality with other downstream metabolites (e.g. ceramides)

You could explain transient fluctuations in serine pretty easily via the serine-formate shuttle. In this shuttle, serine is imported into the mitochondria to produce formate, which is released into the cytosol and can become serine again.

Uptake of serine into the mitochondria is one of the many alternative methods for regenerating mitochondrial NAD(P)H from NAD(P) (which would be required for oxidative phosphorylation).

Serine production in the cytosol also requires NAD+. If there’s an issue with shuttling of H- into the mitochondria (sorry to bring up my malate hypothesis yet again, but it is why I was looking into this) you’d end up with more of your cytosolic NAD in its reduced form (NADH) and staying that way. Meaning less serine.

So transient reliance on serine would explain how sphingolipids might end up at low levels, comparatively. What I can’t exactly figure out is what might be happening to end up with higher serine—though since it’s the same cycle, intervention at any other point could presumably result in exactly that.

Perhaps you have a temporary situation where more of the formate is still being exported from the mitochondria but serine isn’t being imported at the same rate?

 

I'm open to the idea of a molecule being high at times and low at times within patients. But between studies, assuming the groups are similar, you should be getting the same results. My only guess is different studies are looking at dissimilar groups. Maybe mild vs severe? Short vs long duration? Different comorbidities?

 

I think it would be transient within eavh individual patient, probably based on other factors that would require upregulation of that shuttle. So it could swing wildly based on when you happen to capture the snapshot.

one of my biggest concerns with studies such as these is we don’t know how much participants exerted themselves to get there. So we would have no idea what metabolic state the participants are in when they give samples.

Did they just come from the bus? Did they get driven there? Were they waiting for an hour to give a sample? Each of those scenarios would look different metabolically based on the person’s own functional capacity and recent history of activity if my theory is right.

 

All that’s to say, you’d only need a difference of whether the participants [added: generally] had to wait an hour to give samples or not to potentially explain the difference in high or low sphingolipids across studies

 

The idea that they swing between very high and very low based on exertion is intriguing. Would that have been picked up in any of the exercise studies?

 

I suspect the earlier studies did not control food intake or have rapid sample stabilization and processing after collection. Many metabolites will change over time and I think this could lead to a lot of discrepancies. For example a quick search showed Sphingosine-1-phosphate (S1P), a sphingolipid, has a half life of 15mins. Not saying that is the cause here.......

 

That’s a great question! The Hanson lab’s exercise metabolomics paper didn’t mention sphingolipids at all, which surprised me since it was a major finding in their previous non-exercise studies.

Two potential confounders might be thag we don’t actually know how much exertion they were already in at baseline, and they’ve also mentioned that sample processing was done differently across their studies to explain other inconsistencies. If it is the latter is true, then it’s also possible that sphingolipid differences are due more to sample processing than my proposed answer.

I think to accurately assess this you’d need to essentially do an exercise study where the participants were on bed rest for several days beforehand. I don’t know of a study that had the resources to do that so far.

 

That’s a good point, if the sample processing wasn’t randomized adequately it could explain studies showing lower levels at least.

The one thing that wouldn’t be explained is higher levels in other studies, though perhaps there’s some chemistry I’m not aware of that encourages sphingolipids production in certain conditions.

[edit: nevermind, I’m being silly. It would explain higher levels if the randomization was skewed in the other direction]

 

Well maybe visiting severe patients at home might be best, which @DMissa is making happen and it looks like lipids will be involved.

Edit: Oh sorry you said exercise study, I was just thinking testing in general.

 

Thanks, that’s exciting! I’d be really interested to see those results.

I think it might still be informative for that despite not being an exercise study for the reason you note. The only thing is that you wouldn’t know if it’s due to lack of earlier exertion or just to them being more severe.

I suppose it could be possible to just ask most participants to rate how much they exerted themselves when giving the sample—wouldn’t be a perfect measure but could at least be used as a regression covariate.

 

Maccallini is a smart guy, but I can't help but see these sorts of studies simply throwing up noise. He needs access to better data.