{"title":"Mapping fatigue: discovering brain regions and genes linked to fatigue susceptibility.","authors":"Yifei Zhang, Zehan Zhang, Qingqian Yu, Yutong Jiang, Chenyu Fei, Fengzhi Wu, Feng Li","doi":"10.1186/s12967-025-06284-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The relationship between the brain and fatigue is gaining increasing attention, with numerous studies indicating that certain specific brain regions may be closely linked to fatigue. Our study aimed to identify brain regions exhibiting significant causal relationships to fatigue and discover potential neurotherapeutic targets associated with fatigue, in the pursuit of seeking new approaches for fatigue treatment.</p><p><strong>Methods: </strong>A bidirectional two-sample Mendelian randomization (TSMR) method was employed to investigate causal relationships between cortical and subcortical gray matter volumes in 83 regions and fatigue. Then, we utilized frontal cortex expression Quantitative Trait Loci data, employing the methods of Summary-data-based Mendelian Randomization (SMR) and Bayesian colocalization to identify genes that exhibit significant association with fatigue. Finally, the transcription levels of candidate genes were assessed in a central fatigue rat model using RT-qPCR.</p><p><strong>Results: </strong>The results of the TSMR analysis revealed that an increased in the volume of the right lateral orbitofrontal, left caudal middle frontal, right caudal middle frontal, and right rostral middle frontal cortices may be correlated with a diminished susceptibility to fatigue. The SMR and Bayesian colocalization analysis identified ECE2, GPX1, METTL21EP, RP11-665J16.1, and SNF8 as candidate genes associated with fatigue. RT-qPCR results confirmed significantly elevated transcription levels of Ece2, Gpx1, and Snf8 in the frontal cortex of central fatigue model rats compared to controls.</p><p><strong>Conclusions: </strong>Our findings afford substantial theoretical support for the connection between the brain and fatigue, while also providing novel insights into the genetic mechanisms and therapeutic targets for fatigue, particularly central fatigue.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"293"},"PeriodicalIF":6.1000,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Translational Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12967-025-06284-x","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Background: The relationship between the brain and fatigue is gaining increasing attention, with numerous studies indicating that certain specific brain regions may be closely linked to fatigue. Our study aimed to identify brain regions exhibiting significant causal relationships to fatigue and discover potential neurotherapeutic targets associated with fatigue, in the pursuit of seeking new approaches for fatigue treatment.
Methods: A bidirectional two-sample Mendelian randomization (TSMR) method was employed to investigate causal relationships between cortical and subcortical gray matter volumes in 83 regions and fatigue. Then, we utilized frontal cortex expression Quantitative Trait Loci data, employing the methods of Summary-data-based Mendelian Randomization (SMR) and Bayesian colocalization to identify genes that exhibit significant association with fatigue. Finally, the transcription levels of candidate genes were assessed in a central fatigue rat model using RT-qPCR.
Results: The results of the TSMR analysis revealed that an increased in the volume of the right lateral orbitofrontal, left caudal middle frontal, right caudal middle frontal, and right rostral middle frontal cortices may be correlated with a diminished susceptibility to fatigue. The SMR and Bayesian colocalization analysis identified ECE2, GPX1, METTL21EP, RP11-665J16.1, and SNF8 as candidate genes associated with fatigue. RT-qPCR results confirmed significantly elevated transcription levels of Ece2, Gpx1, and Snf8 in the frontal cortex of central fatigue model rats compared to controls.
Conclusions: Our findings afford substantial theoretical support for the connection between the brain and fatigue, while also providing novel insights into the genetic mechanisms and therapeutic targets for fatigue, particularly central fatigue.
期刊介绍:
The Journal of Translational Medicine is an open-access journal that publishes articles focusing on information derived from human experimentation to enhance communication between basic and clinical science. It covers all areas of translational medicine.