Preprint Comparing DNA Methylation Landscapes in Peripheral Blood from [ME/CFS] and Long COVID Patients, 2025, Peppercorn et al

Abstract
Post-viral conditions, Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Long COVID (LC), share >95% of their symptoms, but the connection between disturbances in their underlying molecular biology is unclear. This study investigates DNA methylation patterns in peripheral blood mononuclear cells (PBMC) from patients with ME/CFS, LC, and healthy controls (HC). Reduced Representation Bisulphite Sequencing (RRBS) was applied to the DNA of age- and sex-matched cohorts: ME/CFS (n=5), LC (n=5) and HC (n=5).

The global DNA methylomes of the three cohorts were similar and spread equally across all chromosomes, except the sex chromosomes, but there were distinct minor changes in the exons of the disease cohorts towards more hypermethylation. A principal component analysis (PCA) analysing significant methylation changes (p<0.05) separated the ME/CFS, LC and HC cohorts into three distinct clusters. Analysis with a limit of >10% methylation difference and at P<0.05 identified 214 Differentially Methylated Fragments (DMF) in ME/CFS, and 429 in LC compared to HC. Of these 118 DMFs were common to both cohorts. Those in promoters and exons were mainly hypermethylated, with a minority hypomethylated.

There were rarer examples with either no change in methylation in ME/CFS but a change in LC, or a methylation change in ME/CFS but in the opposite direction in LC. The differential methylation in a number fragments was significantly greater in the LC cohort than in the ME/CFS cohort. Our data reveal a generally shared epigenetic makeup between ME/CFS and LC but with specific distinct changes. Differences between the two cohorts likely reflect the stage of the disease from onset (LC 1 year vs ME/CFS 12 years) but specific changes imposed by the SARS-COV-2 virus in the case of the LC patients cannot be discounted.

These findings provide a foundation for further studies with larger cohorts at the same disease stage and for functional analyses to establish clinical relevance.

Link (MDPI Preprint, May 2025, open access)

 

That's pretty impressive separation on the PCA

 

Yeah exactly, it looks like it's a negative finding to me. If they've done 73239 tests which is how it seems, they have a huge multiple testing problem and it seems like they probably haven't multiple test corrected?

By chance, with this many tests you would expect 0.05x73239 = 3661 false positive results. In the paper it looks as though they find 3363 p<0.05 significant differentially methylated fragments. I think it's all noise, and that PCA result is just a reflection of the number of tests they've done. If there is a methylation pattern associated with ME, this kind of analysis wouldn't be able to find it because of the number of tests being far greater than the sample size.

 

Caramba!!!
Always nice to capitalise on a picked cherry. But it is nice nevertheless.

 

I agree. Run this sort of study again with a bit more finesse and we might see something very interesting.

 

Is it surprising to find effects with such minuscule numbers of people - five in each group? Does this mean that if there's an effect, it's massive? I'm surprised they thought the study worth doing on only five each in the first place.

 

If you have thousands of data points and a very small group.

It’s pretty likely you’ll find effects like that just through randomness.

 

I think there is a discrepancy between what the text says and what the figure legend says. I initially had the same concern as you that they might be pre-selecting the features that would make the findings look best, so I checked the text and saw this:

This certainly suggested to me that all 70K sites were used for this PCA, which assuaged my concerns. However, I didn’t check the figure legend, which seems to tell an entirely different story. That is highly concerning as that particular detail changes the whole narrative of that figure.

That wouldn’t necessarily be a concern as the plot only shows PC1 and PC2. PCA starts with the axis of greatest variation and then proceeds orthogonally until the pre specified number of components, so you’d expect that the first two carry the most information regardless of how many data points were considered. The main issue would be if the largest variation between all the data points was something other than your feature of interest (i.e. sex or age or some other confounding factor), in which case that would dominate the first PCs. Typically studies will show the PCA on all considered sites precisely to show what features are driving variation in the cohort and whether it is a confounder.

So either they did exactly what I would expect from a good analysis and simply made a mistake on the figure legend (which is a bit concerning but can be addressed in good faith), or they’re being quite dishonest by not also showing the PCA on all sites.

[Edit: I will reach out to the authors and let them know about the discrepancy so hopefully it can be fixed in the published manuscript. In the mean time, I’ll give them some benefit of the doubt since the rest of the study seems to be honest to my eye.]

 

I haven’t done a DNA methylation analysis like this paper but I do have comparable experience from ATAC-seq which looks at open chromatin (another epigenetic modification that uses similar protocols for sequencing). That study used 3 replicates on old vs. young mice. Even using genetically identical mice raised in the same environment, there was significant variation. The amount of sites that passed initial QC was comparable to this study. Which is interesting since with humans you expect massively more variation. Though much of that may be chalked up to a difference in methodology, I can’t exactly say what you’d expect from BSS.

I should have clarified in my first comment that it was a rather curiously good separation, as @Hutan caught on to, I just didn’t have time then to dig into the details. It’s not completely unexpected, you’d certainly hope to see a stark difference between healthy and disease states, but also quite impressive for 5 human replicates each if you only take it at face value. I suspect some of the “cleanness” is driven by their stringent selection of sites across replicates right at the beginning, which means that they are only choosing sites they are highly confident in (good thing), but also only sites that have a strong signal in the first place. Further replication would absolutely be necessary, which the authors themselves acknowledge.

 

Yes that piqued my interest for the same reason, though I’m definitely trying not to get too excited when this is a very preliminary study.

 

The platform SequenceME are hoping to use seems to do methylation detection I think. Oxford Nanpore had a conference this week and mentioned it along with other improvements to their platform, I caught some bits of announcements from it, but am not sure of all the details.

relevant posts on bluesky:

https://bsky.app/profile/nanoporetech.com/post/3lpp5etxocc24
https://bsky.app/profile/surroundscience.bsky.social/post/3lpon72xbe22h