{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T09:54:21Z","timestamp":1782986061676,"version":"3.54.5"},"reference-count":73,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T00:00:00Z","timestamp":1721520000000},"content-version":"vor","delay-in-days":20,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["American J Rep Immunol"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Problem<\/jats:title><jats:p>The vaginal microbiome has a substantial role in the occurrence of preterm birth (PTB), which contributes substantially to neonatal mortality worldwide. However, current bioinformatics approaches mostly concentrate on the taxonomic classification and functional profiling of the microbiome, limiting their abilities to elucidate the complex factors that contribute to PTB.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method of study<\/jats:title><jats:p>A total of 3757 vaginal microbiome 16S rRNA samples were obtained from five publicly available datasets. The samples\u00a0were divided into two categories based on pregnancy outcome: preterm birth (PTB) (<jats:italic>N<\/jats:italic> = 966) and term birth (<jats:italic>N<\/jats:italic> = 2791). Additionally, the samples were further categorized based on the participants\u2019 race and trimester. The 16S rRNA reads were subjected to taxonomic classification and functional profiling using the Parallel\u2010META 3 software in Ubuntu environment. The obtained abundances were analyzed using an integrated systems biology and machine learning approach to determine the key microbes, pathways, and genes that contribute to PTB. The resulting features were further subjected to\u00a0statistical analysis to identify the top nine features with the greatest effect sizes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We identified nine significant features, namely <jats:italic>Shuttleworthia<\/jats:italic>, <jats:italic>Megasphaera<\/jats:italic>, <jats:italic>Sneathia<\/jats:italic>, proximal tubule bicarbonate reclamation pathway, systemic lupus erythematosus pathway, transcription machinery pathway, <jats:italic>lepA<\/jats:italic> gene, <jats:italic>pepX<\/jats:italic> gene, and <jats:italic>rpoD<\/jats:italic> gene. Their abundance variations were observed through the trimesters.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Vaginal infections caused by <jats:italic>Shuttleworthia<\/jats:italic>, <jats:italic>Megasphaera<\/jats:italic>, and <jats:italic>Sneathia<\/jats:italic> and altered small metabolite biosynthesis pathways such as lipopolysaccharide folate and retinal may increase the susceptibility to PTB. The identified organisms, genes, pathways, and their networks may be specifically targeted for the\u00a0treatment of bacterial infections that increase\u00a0PTB risk.<\/jats:p><\/jats:sec>","DOI":"10.1111\/aji.13905","type":"journal-article","created":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T16:20:15Z","timestamp":1721578815000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Biomarker Identification for Preterm Birth Susceptibility: Vaginal Microbiome Meta\u2010Analysis Using Systems Biology and Machine Learning Approaches"],"prefix":"10.1111","volume":"92","author":[{"given":"Sudeepti","family":"Kulshrestha","sequence":"first","affiliation":[{"name":"Systems Biology and Data Analytics Research Lab Centre for Computational Biology and Bioinformatics Amity Institute of Biotechnology Amity University  Noida Uttar Pradesh India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priyanka","family":"Narad","sequence":"additional","affiliation":[{"name":"Division of Biomedical Informatics (BMI) Indian Council of Medical Research  Ansari Nagar New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brojen","family":"Singh","sequence":"additional","affiliation":[{"name":"School of Computational and Integrative Sciences Jawaharlal Nehru University  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Somnath S.","family":"Pai","sequence":"additional","affiliation":[{"name":"Amity Institute of Virology &amp; 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