Integrating genomics and AI to uncover molecular targets for mRNA vaccine development in lupus nephritis

• Published in: Frontiers in immunology Vol. 15; p. 1381445
• Main Authors: Mou, Lisha, Lu, Ying, Wu, Zijing, Pu, Zuhui, Wang, Meiying
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 04-10-2024
• Subjects: Adult; Artificial Intelligence; Female; Gene Expression Profiling; genomics; Genomics - methods; Humans; lupus nephritis; Lupus Nephritis - genetics; Lupus Nephritis - immunology; Machine Learning; Male; mRNA Vaccines; non-negative matrix factorization (NMF); RNA, Messenger - genetics; single cell; Single-Cell Analysis; single-cell RNA sequencing; systemic lupus erythematosus; Transcriptome; Vaccine Development; single-cell RNA sequencing; single cell; systemic lupus erythematosus; non-negative matrix factorization (NMF); genomics; lupus nephritis; machine learning; functional network analysis
• ISSN: 1664-3224; 1664-3224
• DOI: 10.3389/fimmu.2024.1381445

Lupus nephritis (LN), a complex complication of systemic lupus erythematosus, requires in-depth cellular and molecular analysis for advanced treatment strategies, including mRNA vaccine development. In this study, we analyzed single-cell RNA sequencing data from 24 LN patients and 10 healthy controls, supplemented by bulk RNA-seq data from additional LN patients and controls. By applying non-negative matrix factorization (NMF), we identified four distinct leukocyte meta-programs in LN, highlighting diverse immune functions and potential mRNA vaccine targets. Utilizing 12 machine learning algorithms, we developed 417 predictive models incorporating gene sets linked to key biological pathways, such as MTOR signaling, autophagy, Toll-like receptor, and adaptive immunity pathways. These models were instrumental in identifying potential targets for mRNA vaccine development. Our functional network analysis further revealed intricate gene interactions, providing novel insights into the molecular basis of LN. Additionally, we validated the mRNA expression levels of potential vaccine targets across multiple cohorts and correlated them with clinical parameters such as the glomerular filtration rate (GFR) and pathological stage. This study represents a significant advance in LN research by merging single-cell genomics with the precision of NMF and machine learning, broadening our understanding of LN at the cellular and molecular levels. More importantly, our findings shed light on the development of targeted mRNA vaccines, offering new possibilities for diagnostics and therapeutics for this complex autoimmune disease.