Preprint Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis, 2025, Zhang+

I made a quick note about this at some point much earlier in the thread—it looks like they redid the clustering on the scRNA-seq data set, and I was already quite skeptical of the celltype labeling on the original paper.

Both this paper and the original scRNA-seq did not show feature or violin plots affirming that their clusters are what they say they are. In the text, this paper mentioned CCL5 and GZMB markers—I can be reasonably confident that this cluster is cytotoxic T cells based on those findings alone.

However, I did not see any convincing evidence that they were in fact CD4s. The original paper did not look like it got good separation between their CD4s and CD8s, so it’s possible this cluster could be partly, or mostly, cytotoxic CD8s.

I’m hoping they might provide cluster markers as additional supplementary material if a reviewer brings up my same points. Though it wasn’t brought up as a critique for the Hanson group paper so I’m pessimistic.

 

Here is the plot from supplementary figure 6. Is that a "feature plot" or "cluster markers"? The cells are labelled "8. CTL CD4"
https://www.medrxiv.org/content/medrxiv/early/2025/04/16/2025.04.15.25325899/DC6/embed/media-6.pdf

This is the first paper that came up when searching CD4 CTL
CD4 CTL, a Cytotoxic Subset of CD4+ T Cells, Their Differentiation and Function

 

Unfortunately that’s just showing their annotations, not the evidence for it. What you’re looking for is something like the attached which shows the expression levels of CD4 and CD8A/B.
Or a violin plot showing expression levels separated by their cluster labels. I didn’t see any evidence in any of their supplementals. cytotoxic CD4s do exist, they just haven’t shown that the cells they claim are cytotoxic CD4s are exclusively CD4s

 

I think it may be worth remembering that, as far as I know, absolutely nothing informative has been gleaned from looking at T cell subsets in the blood in the known autoimmune diseases. There may be some shifts but nobody has shown them to mean anything relevant to the disease mechanism. Even in the diseases that really are likely to be T cell driven - psoriasis and Reiter's - nothing useful has been found as far as I know. Looking at lymphocyte function using PBMC is probably a waste of time in chronic disorders of immune regulation. AIDS shows itself in CD4 cytopenia but that is something rather different.

I think it would have been better if the paper focused on the genetics pure and simple and did not try to interpret the findings mixed in with scRNA studies.

 

NLGN2 links to a rare disease https://www.malacards.org/card/senior_loken_syndrome_7
Autosomal recessive genetic disease characterized by progressive wasting of the filtering unit of the kidney , with or without medullary cystic renal disease, and progressive eye disease

Thin basement membrane disease

A few of those genes are a target of FOXO1a

 

I don't really know how to read these figures, but it looks as if the strongest associations are with really common things that aren't part of the ME/CFS syndrome—but are female-weighted like ME/CFS, e.g. gall bladder removal, depression, and likelihood of having had surgery?

 

I think the second one should say 'Covid19:C2_v2'. But I agree, I can't think of what it could mean to be associated with both the controls and the cases for COVID (assuming that's what that code stands for)

 

"COVID19:C2_v2" and "COVID19:C2_v2_England_controls" phenotypes in Genebass:

https://app.genebass.org/gene/undefined/phenotype/categorical-COVID19-both_sexes-C2_v2-04162021

https://app.genebass.org/gene/undef...19-both_sexes-C2_v2_england_controls-04162021

 

Oh, so "C2_v2_england_controls" means "COVID-19 positive (controls include untested), only patients from centers in England". So they had a COVID illness but not everyone was confirmed with a lab test? I guess that means both groups were COVID positive and having both associations makes sense.

 

I think Leptin (LEP in figure 2B) is also showing up again, as in the study by Beentjes et al?

 

Good catch! Leptin seems to have come up in quite a few ME/CFS studies if I remember right. MEpedia (link) has some links to those.

 

I'm not sure I follow. The GWAS returned genes significant between people who have and have not had COVID. Since they're genes, they won't have anything to do with age. Maybe there are genes that affect what occupation they got, but that's still on the causal pathway between genes and getting COVID. I think it's fair to point out that's it's one of the highest associations out of 4000 conditions, even if getting to the bottom of why they're associated will have to come later.

Though I'm not sure this correlation is very interesting anyway, if it turns out there are post-COVID ME/CFS participants in this study, since it's likely the reason some of them have ME/CFS is because they got COVID which would be more likely if they have genes for COVID susceptibility. [Edit: But it'd at least be a sort of replication of the genes for COVID susceptibility in this case.]

I don't know exactly what this methodology is, but I'm thinking it's something along the lines of seeing if the p-values for these genes in other GWAS were significant. So it would only let them know if the gene is correlated, but not which direction.

 

I think figures A and B are looking at rare variants and C and D are looking at common variants, so there should be some differences. And I think Fig. D is using genes from a specific other study (ref 38, Lammi 2023) that used data other than the UK Biobank, which was used in Figs. A and B.

 

You're saying the genes for COVID in the UK Biobank are essentially mostly random and meaningless? "Bad luck" is randomness, which is what significance tests are used to rule out. If they were split into COVID and control only or mainly based on random chance, then there should be minimal findings from the GWAS. Maybe a few irrelevant genes by chance would pass the threshold, but it's no different from any other disease in any other GWAS.

Yes, some people in the control group were probably asymptomatic. That doesn't really change the findings. In that case the genes are associated with COVID which is bad enough to cause symptoms.

Apart from that, I think it would be quite the coincidence for COVID (and depression) to show up in the top 10 out of over 4000 conditions when compared to ME/CFS, considering the connections that can be made between these and ME/CFS, if the genes for COVID in the UK Biobank were effectively random.

 

I think one of the most interesting parts of the study will be seeing what genes matched between these participants with ME/CFS and Biobank data on depression and COVID.

Considering the similarities between ME/CFS and depression, and how the distinction for accurate diagnosis can be tricky, so cohorts will have some misdiagnosed, seeing depression come out as number one out over >4000 traits is essentially a replication and shows that there are probably actually genes found here that cause ME/CFS and/or depression. Or the same genes cause both conditions. Unless I'm missing something, getting 1st out of 4000 traits would be highly unlikely for such a similar condition if those genes were random.

Similarly, if these are people with long COVID ME/CFS, finding a high association with COVID is essentially indicating that those COVID susceptibility genes probably actually do cause COVID.

 

Ok, I see you mean that there may be less interesting reasons on the causal pathway between the genes and being a member of the COVID cohort, not so much the randomness of an invisible hand rolling dice for whether a person will or won't get COVID with no regard for what genes they have. Yeah, maybe.

It's a good point about severe COVID not being one of the most significant traits if the genes code for immune susceptibility.

Good question. I don't think that this study tried to answer that, and I don't think I know enough about genetic studies to know how to figure that out.