Preprint IgG Antibody Responses to Epstein-Barr Virus in ME/CFS:Their Effective Potential for Disease Diagnosis & Pathological Antigenic Mimicry, 2023, Fonseca

https://www.preprints.org/manuscript/202311.1523/v1

IgG Antibody Responses to Epstein-Barr Virus in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Their Effective Potential for Disease Diagnosis and Pathological Antigenic Mimicry
André Fonseca
,
Mateusz Szysz
,
Hoang Thien Ly
,
Clara Cordeiro
,
Nuno Sepúlveda
*

Version 1 : Received: 22 November 2023 / Approved: 23 November 2023 / Online: 23 November 2023 (11:10:59 CET)

How to cite: Fonseca, A.; Szysz, M.; Ly, H.T.; Cordeiro, C.; Sepúlveda, N. IgG Antibody Responses to Epstein-Barr Virus in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Their Effective Potential for Disease Diagnosis and Pathological Antigenic Mimicry. Preprints 2023, 2023111523. https://doi.org/10.20944/preprints202311.1523.v1


Abstract

The diagnosis and the pathology of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) remain under debate. However, there is a growing body of evidence for an autoimmune component in ME/CFS caused by the Epstein-Barr virus (EBV) and other viral infections.

In this work, we took advantage of a large public dataset on the IgG antibodies to 3,054 EBV peptides to understand whether these immune responses could be used as putative biomarkers for disease diagnosis and triggers of pathological autoimmunity in ME/CFS patients using healthy controls (HCs) as a comparator cohort.

We then aimed at predicting disease status of study participants using a Super Learner algorithm targeting an accuracy of 85% when splitting data into train and test datasets.

When we compared data of all ME/CFS patients or data of a subgroup of these patients with non-infectious or unknown disease trigger to the dataset of HC, we could not find an antibody-based classifier that would meet the desired accuracy in the test dataset.

In contrast, we could identify a 26-antibody classifier that could distinguish ME/CFS patients with an infectious disease trigger from HCs with 100% and 90% accuracies on the train and test sets, respectively.

We finally performed a bioinformatic analysis of the EBV peptides associated with these 26 antibodies.

We found no correlation between the importance metric of the selected antibodies in the classifier and the maximal sequence homology between human proteins and each EBV peptide recognized by these antibodies.

In conclusion, these 26 antibodies against EBV have an effective potential for disease diagnosis of a subset of patients, but they are less likely to trigger pathological autoimmune responses that could explain the pathogenesis of ME/CFS.

Keywords
Biomarker discovery; disease pathogenesis; autoimmunity; antigenic mimicry; machine learning

 

Perhaps @Jonathan Edwards wants to comment on this.

Does this method seem of much use?

From what I’ve understood we usually understand very little about autoimmunity and from what researchers understand often the process appears to be very dynamic and complexly involved and intertwined which would be something that would be missed by such a method only looking at: “We found x elevated, now we look at the cross-activity of x to human proteins” when the true answers might involve “We found x elevated, x sets of y, now there’s some stochasticity involved we don’t understand and we end up with some cross-activity to human proteins”.

I feel like such a study could only answer straightforward things that one is already likely to know but not something that would be less straightforward than a direct cross-reactivity of an EBV antibody to a human protein that would somehow also have to correlate with symptoms of ME/CFS.

 

Are they saying that the 26 antibodies are just a downstream effect of ME/CFS and are not causing the disease?

 

I doubt it means anything. Antibodies to peptides are likely to be very misleading. In vivo antibodies recognise domains of intact proteins. It is hard to know why 26 peptides should be of interest.

 

Aaron Ring (Yale University) autoantibody/antigen work he's using genetically modified fungi to produce target protein - but not sure even that has really worked i.e. to identifying autoantibodies. Possibly Aaron's approach is better than peptides though.
Aaron's looked at ME (Nath) and I think long covid.

 

I confess I am a bit bemused by the broad indifference to IgG values that characterizes the greater medical community.

It is my simple and arguably simplistic understanding that when infected, the body's first defensive response is IgMs. This lasts roughly 30 days. It can involve fever and lethargy and muscle aches and pains etc. If still infected after that 30 days or so, IgG's kick into gear. These can last a long time. Conventional wisdom is they can show in testing for years, supposedly well beyond the time they evoke their own cluster of symptoms.

The thing is, in my experience, most doctors ignore IgG's simply on the basis of that conventional wisdom. They think any labs involving IgG's are simply misleading, without value other than to demonstrate past exposure. They don't even consider that any sequelae may be a result of that IgG response - despite that sequential antibody response being not just typical, but to be expected.

 

Full study released.

https://www.mdpi.com/1648-9144/60/1/161

 

Looks to be a revised abstract.

Abstract
Background and Objectives: The diagnosis and pathology of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) remain under debate. However, there is a growing body of evidence for an autoimmune component in ME/CFS caused by the Epstein-Barr virus (EBV) and other viral infections.

Materials and Methods: In this work, we analyzed a large public dataset on the IgG antibodies to 3054 EBV peptides to understand whether these immune responses could help diagnose patients and trigger pathological autoimmunity; we used healthy controls (HCs) as a comparator cohort. Subsequently, we aimed at predicting the disease status of the study participants using a super learner algorithm targeting an accuracy of 85% when splitting data into train and test datasets.

Results: When we compared the data of all ME/CFS patients or the data of a subgroup of those patients with non-infectious or unknown disease triggers to the data of the HC, we could not find an antibody-based classifier that would meet the desired accuracy in the test dataset. However, we could identify a 26-antibody classifier that could distinguish ME/CFS patients with an infectious disease trigger from the HCs with 100% and 90% accuracies in the train and test sets, respectively. We finally performed a bioinformatic analysis of the EBV peptides associated with these 26 antibodies. We found no correlation between the importance metric of the selected antibodies in the classifier and the maximal sequence homology between human proteins and each EBV peptide recognized by these antibodies.

Conclusions: In conclusion, these 26 antibodies against EBV have an effective potential for disease diagnosis in a subset of patients. However, the peptides associated with these antibodies are less likely to induce autoimmune B-cell responses that could explain the pathogenesis of ME/CFS.