Heterogenous circulating miRNA changes in ME/CFS converge on a unified cluster of target genes: A computational analysis

Abstract

Myalgic Encephalomyelitis / Chronic Fatigue Syndrome is a debilitating, multisystem disease of unknown mechanism, with a currently ongoing search for its endocrine mediators. Circulating microRNAs (miRNA) are a promising candidate for such a mediator and have been reported as significantly different in the patient population versus healthy controls by multiple studies. None of these studies, however, agree with each other on which specific miRNA are under- or over-expressed. This discrepancy is the subject of the computational study presented here, in which a deep dive into the predicted gene targets and their functional interactions is conducted, revealing that the aberrant circulating miRNAs in ME/CFS, although different between patients, seem to mainly target the same specific set of genes (p ≈ 0.0018), which are very functionally related to each other (p ≲ 0.0001). Further analysis of these functional relations, based on directional pathway information, points to impairments in exercise hyperemia, angiogenic adaptations to hypoxia, antioxidant defenses, and TGF-β signaling, as well as a shift towards mitochondrial fission, corroborating and explaining previous direct observations in ME/CFS. Many transcription factors and epigenetic modulators are implicated as well, with currently uncertain downstream combinatory effects. As the results show significant similarity to previous research on latent herpesvirus involvement in ME/CFS, the possibility of a herpesvirus origin of these miRNA changes is also explored through further computational analysis and literature review, showing that 8 out of the 10 most central miRNAs analyzed are known to be upregulated by various herpesviruses. In total, the results establish an appreciable and possibly central role for circulating microRNAs in ME/CFS etiology that merits further experimental research.

Fig 1. Methodology flowchart.

Fig 2. Functional interactions between implicated genes via STRING analysis.

(A-C) Genes most targeted by elevated miRNA. (D-F) Genes most targeted by downregulated miRNA.

Fig 3. Directional protein-protein interaction networks.

(A) Both activating and inhibiting edges present. (B) Activating edges only; used for Katz centrality analysis.

Fig 4. Ranking of topmost inhibited genes.

(A) Genes ranked by their Katz centrality score (scores > 1 pictured). (B) Genes ranked by miRTarBase frequency score (for comparison; same as Table 3; only scores ≥ 6 pictured). (C) Top 50 genes with the highest final score (miRTarBase score multiplied by Katz centrality score). Full list available in S2 Data.