{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T21:44:26Z","timestamp":1791841466983,"version":"4.3.4"},"reference-count":73,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:00:00Z","timestamp":1747872000000},"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. Immunol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Influenza infection is a significant threat to public health, and identifying dynamic biomarkers that influence disease progression is crucial for effective intervention.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We conducted a comprehensive evaluation of physiological and pathological parameters in Balb\/c mice infected with H1N1 influenza over a 14-day period. We employed the DIABLO multi-omics integration method to analyze dynamic changes in the lung transcriptome, metabolome, and serum metabolome from mild to severe stages of infection.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Our analysis highlighted the critical importance of intervention within the first 6 days post-infection to prevent severe disease. We identified several novel biomarkers associated with disease progression, including <jats:italic>Ccl8, Pdcd1, Gzmk<\/jats:italic>, kynurenine, L-glutamine, and adipoyl-carnitine. Additionally, we developed a serum-based influenza disease progression scoring system.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>This study provides new insights into the molecular mechanisms underlying influenza progression and identifies potential targets for therapeutic intervention. The developed scoring system serves as a valuable tool for early diagnosis and prognosis of severe influenza.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1566690","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T05:27:02Z","timestamp":1747891622000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Multiomics analysis unveils key biomarkers during dynamic progress of IAV infection in 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