Gut and respiratory microbiota landscapes in IgA nephropathy: a cross-sectional study

• Published in: Renal failure 2024-12, Vol.46 (2), p.2399749
• Main Authors: Yuan, Xiaoli, Qing, Jianbo, Zhi, Wenqiang, Wu, Feng, Yan, Yan, Li, Yafeng
• Format: Article
• Language: English
• Subjects: 16SrRNA; Adult; Artificial Intelligence and Machine Learning; Bacteria - classification; Bacteria - genetics; Bacteria - isolation & purification; Case-Control Studies; Cross-Sectional Studies; Feces - microbiology; Female; Fusobacterium; Gastrointestinal Microbiome; Glomerulonephritis, IGA - microbiology; Humans; IgA nephropathy; Machine Learning; Male; Microbiota; Middle Aged; Mucosal immune response; RNA, Ribosomal, 16S - genetics; Young Adult
• ISSN: 0886-022X; 1525-6049; 1525-6049
• DOI: 10.1080/0886022X.2024.2399749

IgA nephropathy (IgAN) is intimately linked to mucosal immune responses, with nasopharyngeal and intestinal lymphoid tissues being crucial for its abnormal mucosal immunity. The specific pathogenic bacteria in these sites associated with IgAN, however, remain elusive. Our study employs 16S rRNA sequencing and machine learning (ML) approaches to identify specific pathogenic bacteria in these locations and to investigate common pathogens that may exacerbate IgAN. In this cross-sectional analysis, we collected pharyngeal swabs and stool specimens from IgAN patients and healthy controls. We applied 16SrRNA sequencing to identify differential microbial populations. ML algorithms were then used to classify IgAN based on these microbial differences. Spearman correlation analysis was employed to link key bacteria with clinical parameters. We observed a reduced microbial diversity in IgAN patients compared to healthy controls. In the gut microbiota of IgAN patients, increases in , and , and decreases in , , , and were notable. In the respiratory microbiota, increases in , , , and , and decreases in , , and were observed. Post-immunosuppressive therapy, and levels were significantly reduced in the gut, while and levels decreased in the respiratory tract. and appeared to influence IgAN through dual immune loci, with abundance correlating with IgAN severity. This study revealing that changes in flora structure could provide important pathological insights for identifying therapeutic targets, and ML could facilitate noninvasive diagnostic methods for IgAN.