Back

Preferential innervation of endometriosis by hyperexcitable Ret/GFRα1+ nociceptors associates with target GDNF and clinical pain

Dourson, A.; Fluegel, M.; Kim, A.; Mwirigi, J.; Morales, M. E.; Borja, R.; Golden, J.; Bardawil, E.; Ross, W.; Nahman-Averbuch, H.; Gereau, R.

2026-08-26 neuroscience
10.64898/2026.08.21.744503 bioRxiv
Show abstract

Endometriosis is a prevalent condition characterized by chronic pelvic pain that is frequently refractory to treatment. While the mechanisms underlying this pain remain poorly defined, clinical evidence often indicates that lesion innervation, but not disease stage (e.g. number and depth of lesions), correlate with pelvic pain severity. However, characterization of lesion-innervating neurons is incomplete, revealing an opportunity to identify novel, disease-modifying therapeutics. Here, we coupled functional analyses of lesion-innervating neurons in a mouse model with concurrent identification and characterization of lesion-innervating neurons from pain-phenotyped endometriosis patients. Following the confirmation of abdominal-directed pain-like behaviors in the mouse model, electrophysiological analysis revealed that lesion-innervating dorsal root ganglion (DRG) neurons are hyperexcitable compared to matched controls. These neurons are predominantly small-diameter and bind Isolectin B4, an established marker of the GDNF Family Ligand receptor, Ret. GDNF is concentrated within the stromal layer of both mouse and human lesions, adjacent to axons expressing the GDNF co-receptor, GFR1. Critically, clinical pain correlates with lesion GDNF level, axonal density, and neuronal GFR1 levels. These data provide evidence that endometrial lesions may recruit the Ret-positive subpopulation of nociceptors where they become sensitized and increase patient pain.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.