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Bar-Yoseph, H.

Publications and source records attributed to Bar-Yoseph, H..

2 recordsLinked to original sources

Gut microbiota promotes pain in fibromyalgia

Fibromyalgia is a chronic syndrome characterized by widespread pain in the absence of evident tissue injury or pathology, making it one of the most mysterious chronic pain conditions. Despite affecting 2-4% of the population, primarily women1, the cause and underlying mechanisms of fibromyalgia remain elusive, and effective targeted treatments are currently unavailable. The gut microbiota of women with fibromyalgia differs from healthy controls2,3. However, it is unknown whether changes in gut microbiota have a causal role in mediating pain and other symptoms of fibromyalgia. Here, we show that fecal microbiota transplantation (FMT) from individuals with fibromyalgia, but not from healthy controls, into germ-free mice induces persistent pain hypersensitivity. FMT from fibromyalgia patients led to a reduction in intraepidermal nerve fiber density and alterations in the peripheral immune profile, and induced activation of spinal microglia, which contributed to the development of pain in mice. Notably, the pain hypersensitivity in mice that were administered microbiota from fibromyalgia patients resolved after FMT from healthy controls. Consistent with these findings, an open-label pilot study showed that transplanting microbiota from healthy individuals to humans with fibromyalgia alleviated pain and reduced overall symptom severity. Thus, altered gut microbiota has a causal role in fibromyalgia pain, highlighting it as a promising target for therapeutic interventions.

neuroscience↗

A personalized network framework reveals predictive axis of anti-TNF response across diseases

Personalized treatment of complex diseases has been mostly predicated on biomarker identification of one drug-disease combination at a time. Here, we used a novel computational approach termed Disruption Networks to generate a new data type, contextualized by cell-centered individual-level networks, that captures biology otherwise overlooked when performing standard statistics. The new data-type extends beyond the feature level space, to the relations space, by quantifying individual-level breaking or rewiring of cross-feature relations. Applying disruption network to dissect high-dimensional blood data, we discover and validate that the RAC1-PAK1 axis is predictive of anti-TNF response in inflammatory bowel disease. Intermediate monocytes, which correlate with the inflammatory state, play a key role in the RAC1-PAK1 responses, supporting their modulation as a therapeutic target. This axis also predicts response in rheumatoid arthritis, validated in three public cohorts. Our findings support blood-based drug response diagnostics across immune-mediated diseases, implicating common mechanisms of non-response.

systems biology↗