A Molecular network approach reveals shared cellular and molecular signatures between CFS and other fatiguing illnesses, 2021, Comella et al

Full title:
A Molecular network approach reveals shared cellular and molecular signatures between chronic fatigue syndrome and other fatiguing illnesses

https://www.medrxiv.org/content/10.1101/2021.01.29.21250755v1

 

CFS patients did an exercise test, with blood samples taken before and on the following 3 days. RNA-seq, followed by differential expression analysis of sorted immune cells showed little difference.

That sounds like a failure to replicate PEM. They suggest it may be due to an insufficiently intense exercise challenge. I'm not sure, maybe they didn't recruit enough participants with PEM.

Viral load distribution differed between cases and controls.


T and B cell clones:

They did a complicated analysis and after comparing the results to a database of various other fatiguing illness found this which is interesting:

And this is also interesting:

They discuss some further similarities between these and other illnesses.

Moving on, they tried to identify "key driver genes".

 

Starting to skim, pretty big team. Just want to post for now that they also published a companion website to visualize the data: https://irenefp.github.io/bcellm7.html.

I see no major red flags other than there being no changes following CPET. But otherwise this is out of my league. Which is nice in a way, refreshing compared to the toddlerism of BPS.

 

I’m waiting for my @Simon M explainer....Didn’t know that Mt.Sinai had a group working on this.

 

What MXD1 does: it seems to suppress the process of making ribosomes (which in turn make proteins).

STX3: involved in exocytosis (a way to transport molecules in bulk out of the cell). Also involved in the growth of axons and dendrites of neurons.

DYSF: involved in skeletal muscle repair. Repairs wounds in the cell membrane. Defects in this gene lead to various types of muscle dystrophy.

LYN: a key role in cell activation. Especially B cells. Also participates in insulin signalling.

MLL2: The protein co-localizes with lineage determining transcription factors on transcriptional enhancers and is essential for cell differentiation and embryonic development. It also plays critical roles in regulating cell fate transition, metabolism, and tumor suppression.

NCOA2: is a transcriptional co-activator of the glucocorticoid receptor and interferon regulatory factor 1 (IRF1).

TUBB1: structural constituent of the cell cytoskeleton.

Too tired to do the rest.

 

A lot of work just to make the visualization animations.

 

Senior author

https://en.m.wikipedia.org/wiki/Eric_Schadt

 

Silent no more...

 

I still haven't had a deeper look yet.

 

I'm happy that we're finally getting a taste of 21st century science, as well as a comparison with other fatiguing illnesses which smaller ME/CFS research teams often aren't able to provide.

The participants were well characterized, they had to fulfill 3 diagnostic criteria, 2 of which require PEM:

It seems that the moderate intensity of the CPET is the culprit. Maybe they didn't want participants to crash badly? (Glad they were able to get so many timepoints / blood draws though.)

However, the methodology for CPET in ME/CFS requires attaining maximal effort according to Workwell (highlights mine) [1]:

I think they would have found biological changes had they repeated the CPET, even at moderate intensity. That's been the case for Maureen Hanson's team at Cornell who does 2-day CPET. This was a small study but I wish the investigators had had the possibility (funds) to do a 2-day CPET. I should note that from the text, it seems to me that they're not aware of the difference between single and repeated CPETs? The IOM report says:

[1] Stevens S, Snell C, Stevens J, Keller B, VanNess JM. Cardiopulmonary Exercise Test Methodology for Assessing Exertion Intolerance in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Front Pediatr. 2018;6:242. Published 2018 Sep 4. doi:10.3389/fped.2018.00242

 

Tiny study:

To provide some insight into the molecular processes that underlie CFS, we carried out a study on 15 patients diagnosed with CFS and 15 age, sex, and BMI matched controls (Supplemental Table-2). CFS was formally diagnosed using the Fukuda Criteria (Supplemental Table-4)[19], Canadian Consensus Guidelines [20], and updated international consensus criteria,

While I like the comparison with other 13 illnesses, I think sample is just way too small to justify such a broad analysis (even though they do seem to have taken multiple comparisons seriously). So I am going to pass on this unless someone gives me a good reason to take a deeper look.

 

The exercise wasn't trivial:

The maximal heart rate function was from another paper:
eMHR = 179 + 0.29 x age - 0.011 x age(2).

This function underestimated my (1st CPET) maximal heart rate by about 25BPM!
70% of the predicted eMHR for me is significantly below the first ventilatory threshold (as measured on 1st CPET), but the workload at that heartrate is not trivial, at least for ME/CFS patients.

 

I think impact scales do not take into account the way you can struggle and still do things if you are feeling worse with ME. There is a point when you can't do anything but it is commoner to do it but just feel bad about it. When I was moderate, I often felt that ME did not stop me doing things it just took all the fun out of them.

The other thing is how much they measured things like sore throats, swollen glands, pain and itches, not to mention cognitive problems.

After all these years I still have trouble describing how I feel if I do too much. Often, I only realise how bad things were when I get back to my more normal state. I am more likely to say I feel fine when I feel bad!

 

Way out of my depth here. If somebody could translate into plain English?

What to make of this: significant differences in viral loads between groups? The graphs don't help, I can't make head nor tail of of Fig2a-c. Is the viral load higher in patients or in controls? Either way, what I recall from other studies looking for viruses is that they didn't find any very significant differences one way or the other, possibly a little less virus in patients? Are they measuring something different here to get a significant difference? Or just an artifact of the small numbers?

Looking at the references there does seem to be some consistency to the null results though one paper [22] also speculates that blood may not have been the best choice to test this on.

[21] Keech, A., et al., Gene Expression in Response to Exercise in Patients with Chronic Fatigue Syndrome: A Pilot Study.Front Physiol, 2016. 7: p. 421
https://pubmed.ncbi.nlm.nih.gov/27713703/ (an A Lloyd paper, have only read the abstract; not discussed on S4ME yet)

[22] Bouquet, J., et al., Whole blood human transcriptome and virome analysis of ME/CFS patients experiencing post-exertional malaise following cardiopulmonary exercise testing.PLoS One, 2019. 14(3): p. e0212193.
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0212193

Discussed here: https://www.s4me.info/threads/whole...monary-exercise-testing-2019-chiu-et-al.8692/

 

So very pleased that Eric Schadt has remained involved. I have heard his name a few years ago. We were hoping he would stay involved.

https://icahn.mssm.edu/profiles/eric-schadt

 

The Kolmogorov–Smirnov test is a nonparametric test (does not assume parameterised statistical distributions). The figure does show higher viral load (log10cpm), but the Wilcoxon Ranked Test figure still shows largely overlapping distributions. The difference might still indicate something about the group of patients, but the test is not useful as a specific biomarker.

 

Although your post referred to a couple of specific sections of the paper, I thought it would be useful to mention that Cort Johnson has written an article about this paper that he published today entitled Study Suggests Similar Processes Are Driving Long COVID and ME/CFS. The article is in many ways an extended summary of the paper, and I think that it covers the main points very well. Cort also includes a short interview he had with the paper's lead author, Phillip Comella.

Although Cort's article is only a summary, I think it gives a very good idea of what the paper is all about without having to wade through the technically dense paper itself. As a result, I think that his article makes the results of this work available to a much wider audience, especially those of us with moderate or severe brain fog.

A version of the paper that has the illustrations inline, making it somewhat easier to read, can be found here.

As for the content of the paper, it certainly makes a lot of sense to me, especially since my particular version of ME/CFS clearly involves significant immune dysfunction.

The sample size is definitely problematic here; this is what happens when there's not enough research money to go around. But I think that the authors have done an excellent job with what they have here, and their work appears to be solid enough to put a stake in the ground for future research. As the numbers of people affect by long COVID are only going to grow over time, it should be easier to get money for more extensive studies along these lines than it has been for ME/CFS. I have high hopes that our illness will be a real beneficiary of the work done on long COVID, as I expect the funding for the latter to far exceed what we have been able to get for ME/CFS.

 

To use your analogy; what we have, over the last 30 years or so, is enough stales in the ground to lay out a reasonable sized town, but we still have no buildings, not even a single brick has been laid, all we have is 'stakes in the ground' - most of them so covered in vegetation/piles of crud as to be invisible.

One of the consequences of very small stakes and a lot of time.

 

Back in the day when I was a BMS(biomedical scientist with genetics, immunology and haematology background) a sample size n=15 was often used as a pilot study to cheaply test the water.

I am hopeful that the progress of technology will slowly move in this direction to enable real time dynamic monitoring of transcriptome. Then my understanding is longitudinal study might produce answers?

I am an eternal optimist.

Maybe this paper will be another little piece of the puzzle?

I am fascinated by the paper but it is very technically dense as someone else commented. I reckon it will keep me off the streets for months and absorbed trying to understand it