Informatics Inference of Exercise-Induced Modulation of Brain Pathways Based on Cerebrospinal Fluid Micro-RNAs in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Vaishnavi Narayan, Narayan Shivapurkar, James N Baraniuk, Vaishnavi Narayan, Narayan Shivapurkar, James N Baraniuk

Abstract

Introduction: The post-exertional malaise of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) was modeled by comparing micro-RNA (miRNA) in cerebrospinal fluid from subjects who had no exercise versus submaximal exercise. Materials and Methods: Differentially expressed miRNAs were examined by informatics methods to predict potential targets and regulatory pathways affected by exercise. Results: miR-608, miR-328, miR-200a-5p, miR-93-3p, and miR-92a-3p had higher levels in subjects who rested overnight (nonexercise n=45) compared to subjects who had exercised before their lumbar punctures (n=15). The combination was examined in DIANA MiRpath v3.0, TarBase, Cytoscape, and Ingenuity software® to select the intersection of target mRNAs. DIANA found 33 targets that may be elevated after exercise, including TGFBR1, IGFR1, and CDC42. Adhesion and adherens junctions were the most frequent pathways. Ingenuity selected seven targets that had complementary mechanistic pathways involving GNAQ, ADCY3, RAP1B, and PIK3R3. Potential target cells expressing high levels of these genes included choroid plexus, neurons, and microglia. Conclusion: The reduction of this combination of miRNAs in cerebrospinal fluid after exercise suggested upregulation of phosphoinositol signaling pathways and altered adhesion during the post-exertional malaise of ME/CFS. Clinical Trial Registration Nos.: NCT01291758 and NCT00810225.

Keywords: cerebrospinal fluid; informatics; micro-RNA (miRNA); myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS); pathway analysis.

Conflict of interest statement

No competing financial interests exist.

© Vaishnavi Narayan et al., 2020; Published by Mary Ann Liebert, Inc.

Figures

FIG. 1.
FIG. 1.
Informatics workflow explaining the flow of miRNA data from qPCR to targets and pathways. miRNA, micro-RNA; qPCR, quantitative polymerase chain reaction.
FIG. 2.
FIG. 2.
IPA MicroRNA Target Filter® Network formed with three miRNAs miR-92a-3p, miR-328-3p, and miR-200a-5p.
FIG. 3.
FIG. 3.
Target protein interaction networks. The top networks that included linker proteins were drawn with (A) IPA and (B) Cytoscape ReactomeFI. (C) STRING showed networks without linkers. IPA, Ingenuity Pathway Analysis®.
FIG. 3.
FIG. 3.
Target protein interaction networks. The top networks that included linker proteins were drawn with (A) IPA and (B) Cytoscape ReactomeFI. (C) STRING showed networks without linkers. IPA, Ingenuity Pathway Analysis®.
FIG. 4.
FIG. 4.
Compilation of KEGG pathways from DIANA and IPA® target lists. Targets found by searching through DIANA were in white boxes, while IPA targets were highlighted in light yellow. Representative members of PLC, PKC, cAMP, PIK3R3–AKT, Adherens junction, and classical MAPK and MAPK–JNK were interconnected. Cytoscape classified 15 other DIANA targets to adhesion and integrin pathways (Table 6). KEGG, Kyoto Encyclopedia of Genes and Genomes.
FIG. 5.
FIG. 5.
Proposed modulation of miR-93-3p, MCM7, SMAD7, and TGFBR1 by exercise (cfs0>CFS condition).

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