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Estrogen metabolites increase nociceptor hyperactivity in a mouse model of uterine pain
Zili Xie, … , Sarah K. England, Hongzhen Hu
Zili Xie, … , Sarah K. England, Hongzhen Hu
Published April 14, 2022
Citation Information: JCI Insight. 2022;7(10):e149107. https://doi.org/10.1172/jci.insight.149107.
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Research Article Neuroscience

Estrogen metabolites increase nociceptor hyperactivity in a mouse model of uterine pain

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Abstract

Pain emanating from the female reproductive tract is notoriously difficult to treat, and the prevalence of transient pelvic pain has been placed as high as 70%–80% in women surveyed. Although sex hormones, especially estrogen, are thought to underlie enhanced pain perception in females, the underlying molecular and cellular mechanisms are not completely understood. Here, we showed that the pain-initiating TRPA1 channel was required for pain-related behaviors in a mouse model of estrogen-induced uterine pain in ovariectomized female mice. Surprisingly, 2- and 4-hydroxylated estrogen metabolites (2- and 4-HEMs) in the estrogen hydroxylation pathway, but not estrone, estradiol, or 16-HEMs, directly increased nociceptor hyperactivity through TRPA1 and TRPV1 channels, and picomolar concentrations of 2- and 4-hydroxylation estrone (2- or 4-OHE1) could sensitize TRPA1 channel function. Moreover, both TRPA1 and TRPV1 were expressed in uterine-innervating primary nociceptors, and their expression was increased in the estrogen-induced uterine pain model. Importantly, pretreatment with 2- or 4-OHE1 recapitulated estrogen-induced uterine pain-like behaviors, and intraplantar injections of 2- and 4-OHE1 directly produced a TRPA1-dependent mechanical hypersensitivity. Our findings demonstrated that TRPA1 is critically involved in estrogen-induced uterine pain-like behaviors, which may provide a potential drug target for treating female reproductive tract pain.

Authors

Zili Xie, Jing Feng, Tao Cai, Ronald McCarthy, Mark D. Eschbach II, Yuhui Wang, Yonghui Zhao, Zhihua Yi, Kaikai Zang, Yi Yuan, Xueming Hu, Fengxian Li, Qin Liu, Aditi Das, Sarah K. England, Hongzhen Hu

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