{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T19:29:55Z","timestamp":1774380595256,"version":"3.50.1"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Genet."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Pre-eclampsia (PE) is a specific type of gestational hypertension associated with high morbidity and mortality. This study aims to identify mitochondria-related regulatory molecules in PE through bioinformatics analysis, which will help pinpoint potential therapeutic targets and elucidate potential mechanisms of action in PE.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This study integrated three PE placental transcriptome datasets (n = 103\/157) to screen for mitochondrial-related hub genes. Key gene screening was performed by combining three machine learning algorithms\u2014Random Forest, LASSO, and SVM\u2014followed by the construction of a diagnostic neural network model. Additionally, single-cell sequencing data were utilized to analyze the cellular expression patterns of candidate genes in the placenta. To further elucidate the underlying mechanisms, functional validation was conducted both in PE rat model and\n                      <jats:italic>in vitro<\/jats:italic>\n                      using HTR-8 cells, supplemented by multi-omics correlation analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Machine learning analysis identified three key genes (GCLM, SNAP23, RHOT2), and the diagnostic model built upon them demonstrated excellent performance (training set AUC = 0.907; validation set AUC = 0.875). Single-cell analysis revealed the expression patterns of these genes within specific cell subtypes, consistent with the transcriptional features of trophoblast cell populations. In the PE rat model, downregulation of GCLM and SNAP23 and upregulation of RHOT2 were significantly correlated with clinical phenotypes such as hypertension and proteinuria, as well as changes in placental inflammatory factor levels (TNF-\u03b1, IL-1\u03b2, IL-6). Specifically, SNAP23 and GCLM showed negative correlations with inflammatory cytokines but positive correlations with fetal weight, while RHOT2 expression positively correlated with disease severity.\n                      <jats:italic>In vitro<\/jats:italic>\n                      experiments confirmed that overexpression of SNAP23 restored mitochondrial membrane potential, reduced reactive oxygen species levels, and suppressed cytokine release in lipopolysaccharide (LPS)-treated HTR-8 cells. Multi-omics analysis further indicated that these genes are involved in immune dysregulation and mitochondrial dysfunction during PE progression.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study establishes GCLM, SNAP23, and RHOT2 as mechanistically important biomarkers for preeclampsia. Among them, modulation of SNAP23 shows therapeutic potential in alleviating mitochondrial damage and inflammatory responses in PE, providing a new direction for intervention strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fgene.2026.1723592","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:10:57Z","timestamp":1774368657000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying mitochondrial genes and potential biological functions in pre-eclampsia: bioinformatics and experimental insights"],"prefix":"10.3389","volume":"17","author":[{"given":"Yaohui","family":"Wang","sequence":"first","affiliation":[{"name":"School of Traditional Chinese Medicine, Henan University of Chinese Medicine","place":["Zhengzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixin","family":"Du","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Henan University of Chinese Medicine","place":["Zhengzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liping","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Traditional Chinese Medicine, Henan University of Chinese Medicine","place":["Zhengzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junlin","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Traditional Chinese Medicine, Henan University of Chinese Medicine","place":["Zhengzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Traditional Chinese Medicine, Henan University of Chinese Medicine","place":["Zhengzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyang","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Traditional Chinese Medicine, Henan University of Chinese Medicine","place":["Zhengzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenyang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Traditional Chinese Medicine, Henan University of Chinese Medicine","place":["Zhengzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhua","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Henan University of Chinese Medicine","place":["Zhengzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Henan University of Chinese Medicine","place":["Zhengzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingling","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Henan University of Chinese Medicine","place":["Zhengzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengbei","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Traditional Chinese Medicine, Southern Medical University","place":["Guangzhou, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1002\/uog.26105","article-title":"Machine\u2010learning\u2010based prediction of pre\u2010eclampsia using first\u2010trimester maternal characteristics and biomarkers","volume":"60","author":"Ansbacher\u2010Feldman","year":"2022","journal-title":"Ultrasound in Obstetrics and Gynecol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"gaac026","DOI":"10.1093\/molehr\/gaac026","article-title":"Transcriptional profiles of genes related to mitochondrial aging in placental pathologies","volume":"28","author":"Bartho","year":"2022","journal-title":"Mol. 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