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Deciphering Autoantigen Signatures in Chikungunya Virus Infection Using Machine Learning: A Data-Driven Approach to
Chaitra Mallasandra Krishnappa1, Shanker Govindaswamy2, Anjali Ganjiwale3
1Department of Life Science, Bangalore University, Bangalore, Karnataka, 560056, India.
Current Microbiology
|September 15, 2025
Summary
Researchers identified key host biomarkers involved in Chikungunya virus (CHIKV) infection, revealing molecular mechanisms behind chronic joint pain and offering targets for future therapies.
Area of Science:
- Virology
- Immunology
- Genomics
Background:
- Chikungunya virus (CHIKV) causes acute illness and chronic arthritis, but molecular mechanisms are unclear.
- Identifying host-pathogen interactions is crucial for understanding CHIKV pathogenesis.
Purpose of the Study:
- To identify key host biomarkers and molecular mechanisms in Chikungunya virus infection.
- To elucidate potential pathways underlying virus-induced joint pathology.
Main Methods:
- Analysis of RNA sequencing data from pediatric patients with CHIKV infection.
- Utilized binary classification models with StratifiedKFold validation for feature selection.
- Employed recursive feature elimination with cross-validation (RFECV) to identify 20 gene features.
Main Results:
- Identified 20 differentially expressed gene features, overlapping with known autoantigens.
- Network analysis revealed interactions between host biomarkers (THG1L, SLC44A5, KCNN3) and CHIKV components (nsp4, BCL2-like 11).
- Fibronectin 1 (FN1) emerged as a central hub gene, implicated in skeletal development and renal pathologies.
Conclusions:
- Discovered key biomarkers of CHIKV-host interactions, providing insights into immune dysregulation.
- Findings illuminate potential mechanisms of CHIKV-induced joint pathology.
- Results lay the groundwork for future experimental validation and therapeutic strategies.
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