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

I haven't had a closer look yet but Tate had previously published a similar study. I wouldn't be surprised if there is some overlap.

 

A previous study by this group was Changes in DNA methylation profiles of myalgic encephalomyelitis/chronic fatigue syndrome patients reflect systemic dysfunctions (Helliwell et al., 2020). From a quick glance: very similar methodology (although DMAP + single-CpG MethylKit vs DMAP2 in the more recent one). n=20 (10 pwME, 10 controls); RRBS (146575 frags); DMAP found 76 DMFs (52% hypo), MethylKit 349 DMCs (56% hypo), highest rep in intergenic (40%) & intronic (25%) regions. (The abstract states 394, but the results section states 349.); fragment-level statistics reported without FDR correction.

This group's RRBS pipeline was documented in Chapter 9 of the new Springer Protocol (link).

 

For what it’s worth, the full PCA does actually tell an interesting story along PC2—the fact that there are distinct “Neapolitan stripes” between the three groups, despite some messy outliers, is impressive since it is actually taking into consideration all 70K sites (even despite the fact that the 5/5 selection to 70K will introduce some skewing to begin with, it’s been a very lively debate in epigenomic sequencing for years as to whether you can pre-select sites/peaks based on any a-priori knowledge)

In my opinion, the story that the full PC2 tells is that people with longer disease are trending back towards healthy control in terms of DNA methylation. If it was simply a difference of “LC or ME/CFS is different from everything else,” you wouldn’t see neopolitan, you’d see one stripe amidst a soup of the other 2 groups.

I’m certainly not going to call that a definitive finding on the basis of n=15, but it could absolutely be the basis of a future study comparing people who got LC at the beginning of the pandemic to those recently afflicted to assess impact of disease duration with the same trigger.

So I’m absolutely in agreement with you @Hutan that as it is used, the PCA ends up being circular. Which is a shame in my opinion, since there is an interesting story here despite the tiny sample size. They do go into it in the later analysis, but it almost loses the punch.

I do understand why they didn’t show it like that though. If you don’t know to look along each axis separately, it just all looks like soup. But I think there were possible ways to finesse this—using only the PC2 score as its own latent variable and showing it as a box plot, for example.

 

I’m also realizing just now that I have a tendency to use food metaphors when discussing science close to dinner time. I’m a caricature of myself

 

I would agree with Utsikt. Peer review is a lottery and generally far less fair and rigorous than the attention of members here. If the analysis seems misleading that needs to be understood by all.

 

I think you're right, but it's not just the PCA it's also the fact they don't multiple test correct and find basically exactly the number of positive results you'd expect by chance. It's cool there are genes that could make sense, but as it stands I don't feel I can trust any of it.

However, if what @jnmaciuch says about the PCA done on all 70k+ fragments is true then that's a different story. If the groups really look like they're separating out on that PCA then that would be encouraging. Unlike the PCA on only the significant fragments you wouldn't expect to see group differences there I think based on noise alone.

 

Here is how it looks with simulated random data. From 72k features in each of three groups consisting of 5 individuals sure enough you get here ~3.6k (3575) positive hits with anovas and a PCA that look very similar to theirs on the significant fragments.


Note that doing the PCA on all the simulated results doesn't separate the groups; if Peppercorn/Tate's does that would be of interest. I hope we can get to see that PCA soon.

 

I'd be a bit suprised if these people are not just a subset of people that had been previously analysed in:
A pilot study on the immune cell proteome of long COVID patients shows changes to physiological pathways similar to those in myalgic encephalomyelitis/chronic fatigue syndrome

I find repeated publications of small samples slightly worrisome. Hopefully the next study can be significantly larger.

 

A little over and under 10%, respectively. So there’s a lot of noise even when selecting down to 70K, probably to be expected

 

[edit: sorry, hit post too soon] Yes if you pulled more of the red dots into the middle and pushed the green and blue further apart, it wouldn’t look too far off. It’s a weak signal, so probably would come down to ANOVA between groups on PC2 scores. Definitely would like to see replication on a larger cohort