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Advisor(s)
Abstract(s)
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.
Description
Keywords
Biomarker discovery Disease pathogenesis Autoimmunity Antigenic mimicry Machine learning
Citation
Medicina 60 (1): 161 (2024)
Publisher
MDPI