The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, China.
Department of Nephrology, Shanxi Provincial People's Hospital (Fifth Hospital), Shanxi Medical University, Taiyuan, China.
Ren Fail. 2024 Dec;46(2):2399749. doi: 10.1080/0886022X.2024.2399749. Epub 2024 Sep 9.
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.
IgA 肾病（IgAN）与黏膜免疫反应密切相关，鼻咽和肠道淋巴组织对其异常黏膜免疫至关重要。然而，这些部位与 IgAN 相关的特定致病细菌仍不清楚。我们的研究采用 16S rRNA 测序和机器学习（ML）方法来鉴定这些部位的特定致病细菌，并研究可能加重 IgAN 的常见病原体。
在这项横断面分析中，我们收集了 IgAN 患者和健康对照者的咽拭子和粪便标本。我们应用 16SrRNA 测序来鉴定差异微生物种群。然后，使用 ML 算法根据这些微生物差异对 IgAN 进行分类。采用 Spearman 相关分析将关键细菌与临床参数联系起来。
与健康对照组相比，IgAN 患者的微生物多样性降低。在 IgAN 患者的肠道微生物群中， 、 、 和 增加， 、 、 和 减少。在呼吸道微生物群中， 、 、 、 和 增加， 、 和 减少。免疫抑制治疗后，肠道中 和 的水平显著降低，而呼吸道中 和 的水平降低。 和 似乎通过双重免疫位点影响 IgAN，其丰度与 IgAN 的严重程度相关。
本研究揭示了菌群结构的变化可能为确定治疗靶点提供重要的病理见解，而 ML 可能有助于为 IgAN 提供非侵入性诊断方法。
