Perturbation of effector and regulatory T cell subsets in Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS), by Ece Karhan, Courtney Gunter, Vida Ravanmehr, Meghan Horne, Lina Kozhaya, Stephanie Renzullo, Lindsey Placek, Joshy George, Peter N Robinson, Suzannne D Vernon, Lucinda Bateman, Derya Unutmaz in bioRxiv 2019.12.23. 887505
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disorder of unknown etiology, and diagnosis of the disease is largely based on clinical symptoms. We hypothesized that immunological disruption is the major driver of this disease and analyzed a large cohort of ME/CFS patient or control blood samples for differences in T cell subset frequencies and functions. We found that the ratio of CD4+ to CD8+ T cells and the proportion of CD8+ effector memory T cells were increased, whereas NK cells were reduced in ME/CFS patients younger than 50 years old compared to a healthy control group.
Remarkably, major differences were observed in Th1, Th2, Th17 and mucosal-associated invariant T (MAIT) T cell subset functions across all ages of patients compared to healthy subjects. While CCR6+ Th17 cells in ME/CFS secreted less IL-17 compared to controls, their overall frequency was higher. Similarly, MAIT cells from patients secreted lower IFNgamma;, GranzymeA and IL-17 upon activation. Together, these findings suggest chronic stimulation of these T cell populations in ME/CFS patients.
In contrast, the frequency of regulatory T cells (Tregs), which control excessive immune activation, was higher in ME/CFS patients. Finally, using a machine learning algorithm called random forest, we determined that the set of T cell parameters analyzed could identify more than 90% of the subjects in the ME/CFS cohort as patients (93% true positive rate or sensitivity).
In conclusion, these multiple and major perturbations or dysfunctions in T cell subsets in ME/CFS patients suggest potential chronic infections or microbiome dysbiosis.
These findings also have implications for development of ME/CFS specific immune biomarkers and reveal potential targets for novel therapeutic interventions.
…In this detailed study, we analyzed the immunological differences between ME/CFS patients and healthy controls within a large cohort and found several major differences in T cell subset frequencies and functions between the two groups.
… the ratio of two major subsets of T cells, namely the CD4+ to CD8+ T cell ratio, was increased in ME/CFS patients compared to healthy controls.
… There was also a major difference seen between healthy controls and ME/CFS patients in the Th17 cell subset, which is involved in responding to bacteria and is also a culprit in several autoimmune and chronic inflammatory conditions… we think this suggests a chronic activation of Th17 cells in ME/CFS which induces an ‘exhausted’ state where the cells are more dysfunctional due to their chronic stimulation.
…There was also a major difference seen in the mucosal-associated invariant T (MAIT) cells in ME/CFS patients compared to healthy controls… It is possible that these changes in Th17 and MAIT cells are associated with differences in the composition of the microbiota of the ME/CFS patients, and that a disruption in the microbiota causes chronic activation of these subsets and an exhausted state in ME/CFS patients.
Interestingly, we also noted that regulatory T cells (Tregs) were increased in ME/CFS patients compared to controls. Tregs function to suppress excessive chronic immune responses, so this is consistent with our finding that there appears to be chronic activation of major T cell subsets in ME/CFS patients.
Finally and importantly, we utilized these immune profiling parameters in a machine learning classifier and were able to correctly identify ME/CFS patients from healthy controls with high sensitivity and accuracy. As patients often wait years to receive an ME/CFS diagnosis since there are currently no clear diagnostic tools to identify the disease, the development of an immune profile classier could aid as a biomarker to diagnose the disease.