Gut and respiratory microbiota landscapes in IgA nephropathy: a cross-sectional study
收藏Taylor & Francis Group2025-05-14 更新2026-04-16 收录
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https://tandf.figshare.com/articles/dataset/Gut_and_respiratory_microbiota_landscapes_in_IgA_nephropathy_a_cross-sectional_study/26969393/1
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资源简介:
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 <i>Bacteroides</i>, <i>Escherichia-Shigella,</i> and <i>Parabacteroides</i>, and decreases in <i>Parasutterella</i>, <i>Dialister</i>, <i>Faecalibacterium</i>, and <i>Subdoligranulum</i> were notable. In the respiratory microbiota, increases in <i>Neisseria</i>, <i>Streptococcus, Fusobacterium</i>, <i>Porphyromonas</i>, and <i>Ralstonia</i>, and decreases in <i>Prevotella</i>, <i>Leptotrichia</i>, and <i>Veillonella</i> were observed. Post-immunosuppressive therapy, <i>Oxalobacter</i> and <i>Butyricoccus</i> levels were significantly reduced in the gut, while <i>Neisseria</i> and <i>Actinobacillus</i> levels decreased in the respiratory tract. <i>Veillonella</i> and <i>Fusobacterium</i> appeared to influence IgAN through dual immune loci, with <i>Fusobacterium</i> 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.
提供机构:
Yan, Yan; Qing, Jianbo; Li, Yafeng; Zhi, Wenqiang; Wu, Feng; Yuan, Xiaoli
创建时间:
2024-09-09



