2) Integrative Omics in myalgic encephalomyelitis (ME/CFS):
ME/CFS patients suffer from a range of debilitating conditions with significant pain and fatigue. Given the lack of diagnostic molecular markers for ME/CFS and a very limited understanding of its etiology, there is critical need to understand the etiology and mechanisms of ME/CFS predisposition and severity, as well as to define set of ME/CFS clinical ontology for stratification of the disease.
While early studies showed promise in identifying different metabolic, immunologic, or microbial biomarkers of ME/CFS, these studies were limited in scope, sample size, or, importantly, integration across datatypes.
A prospective cohort has been designed to address our overarching hypothesis that the immune system’s etiological role in ME/CFS is predicated on two major factors: first, that immune cells themselves are programmed to respond aberrantly to environmental stimuli, and second, that ME/CFS patients harbor microbes that aberrantly stimulate immune cells.
This prospective cohort includes ME/CFS patients from which clinical metadata and immune, metabolome and gut microbiome markers analyzed longitudinally. Acquiring these datatypes simultaneously allows us to test our hypothesis in an integrated fashion: first, we can evaluate whether alterations in metabolism can instigate aberrant inflammation or reflect altered immune activity, and second, whether microbial dysbiosis is associated with specific ME/CFS inflammatory markers or markers of immune dysfunction.
We will develop and apply advanced bioinformatic analyses to identify homogeneous, therapeutically actionable subgroups of ME/CFS patients.
We hypothesize that clustering of patients across more than one type of molecular or genomic investigation will provide a particularly strong indication. We will: 1) computationally stratify clinical aspects of ME/CFS; 2) use biclustering techniques to identify candidate ME/CFS subgroups in each of the genomic modalities; and 3) use integrative multimodality clustering to identify significant subgroups defined by more than one genomic investigation.
