bioRxivMicrobiology
Multi-omics of host-microbiome interactions in short- and long-term Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)
r. xiong, C. Gunter, E. Fleming, S. Vernon, L. Bateman, D. Unutmaz, J. Oh
Abstract
Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex, multi-system, debilitating disability manifesting as severe fatigue and post-exertional malaise. The chronic dysfunctions in ME/CFS are increasingly recognized as significant health factors with potential parallels with long COVID. However, the etiology of ME/CFS remains elusive with limited high-resolution human studies. In addition, reliable biomarker-based diagnostics have not been well-established, but may assist in disease classification, particularly during different temporal phases of the disease. Here, we performed deep multi- omics (shotgun metagenomics of gut microbiota and plasma metabolomics) and clinical phenotyping of healthy controls (n=79) vs. two cohorts of ME/CFS patients - those with short-term disease (10y, n=79). Overall, ME/CFS was characterized by reduced gut microbiome diversity and richness with high heterogeneity, and depletion of sphingomyelins and short-chain fatty acids in the plasma. We found significant differences when stratifying by cohort; short-term ME/CFS was associated with more microbial dysbiosis, but long-term ME/CFS was associated with markedly more severe phenotypic and metabolic abnormalities. We identified a reduction in the gene-coding capacity (and relative abundance of butyrate producers) of microbial butyrate biosynthesis together with a reduction in the plasma concentration of butyrate, especially in the short-term group. Global co-association and detailed gene pathway correlation analyses linking the microbiome and metabolome identified additional potential biological mechanisms underlying host-microbiome interactions in ME/CFS, including bile acids and benzoate pathways. Finally, we built multiple state-of-the-art classifiers to identify microbes, microbial gene pathways, metabolites, and clinical features that individually or together, were most able to differentiate short or long-term MECFS, or MECFS vs. healthy controls. Taken together, our study presents the highest resolution, multi-cohort and multi- omics analysis to date, providing an important resource to facilitate mechanistic hypotheses of host-microbiome interactions in ME/CFS.
- organism: human
- assay: metagenomics
- assay: metabolomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
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