ArticleCurrent microbiology2025
Deciphering Autoantigen Signatures in Chikungunya Virus Infection Using Machine Learning: A Data-Driven Approach to Understand Host Immunity.
Article in Current microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Chikungunya virus (CHIKV), a mosquito-borne alphavirus, causes acute febrile illness and often progresses to chronic arthritis-like symptoms for which the underlying molecular mechanisms remain elusive. This study identifies key biomarkers of CHIKV-host interactions, shedding light on potential mechanisms underlying virus-induced joint pathology. RNA sequencing data from peripheral blood samples of paediatric patients with natural Chikungunya infection (15-17 days post-symptom onset; GSE99992: Severe cases = 42, Non-Severe cases = 44) was analysed using binary classification models with StratifiedKFold validation, ensuring a robust and reliable approach to feature selection. A panel of 20 gene features selected by recursive feature elimination with cross-validation (RFECV) showed overlap with known autoantigens and were differentially expressed in CHIKV infection. Network analysis revealed interactions among host biomarkers-THG1L, SLC44A5, KCNN3-and viral components such as nsp4 (CHIKV RNA polymerase) and BCL2-like 11 (an apoptosis facilitator), highlighting a multifactorial virus-host interplay. Fibronectin 1 (FN1) emerged as a central hub gene, known for its role in osteoblast mineralization, skeletal development and its association with renal pathologies. These findings provide novel insights into CHIKV-induced immune dysregulation and offer a foundation for future experimental validation and therapeutic exploration.
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