PDF(Pubmed)
Abstract：
Current microbiome signatures for chronic diseases such as diabetic kidney disease (DKD) are mainly based on low-resolution taxa such as genus or phyla and are often inconsistent among studies. In microbial ecosystems, bacterial functions are strain specific, and taxonomically different bacteria tend to form co-abundance functional groups called guilds. Here, we identified guild-level signatures for DKD by performing in-depth metagenomic sequencing and conducting genome-centric and guild-based analysis on fecal samples from 116 DKD patients and 91 healthy subjects. Redundancy analysis on 1,543 high-quality metagenome-assembled genomes (HQMAGs) identified 54 HQMAGs that were differentially distributed among the young healthy control group, elderly healthy control group, early-stage DKD patients (EDG), and late-stage DKD patients (LDG). Co-abundance network analysis classified the 54 HQMAGs into two guilds. Compared to guild 2, guild 1 contained more short-chain fatty acid biosynthesis genes and fewer genes encoding uremic toxin indole biosynthesis, antibiotic resistance, and virulence factors. Guild indices, derived from the total abundance of guild members and their diversity, delineated DKD patients from healthy subjects and between different severities of DKD. Age-adjusted partial Spearman correlation analysis showed that the guild indices were correlated with DKD disease progression and with risk indicators of poor prognosis. We further validated that the random forest classification model established with the 54 HQMAGs was also applicable for classifying patients with end-stage renal disease and healthy subjects in an independent data set. Therefore, this genome-level, guild-based microbial analysis strategy may identify DKD patients with different severity at an earlier stage to guide clinical interventions.
OBJECTIVE: Traditionally, microbiome research has been constrained by the reliance on taxonomic classifications that may not reflect the functional dynamics or the ecological interactions within microbial communities. By transcending these limitations with a genome-centric and guild-based analysis, our study sheds light on the intricate and specific interactions between microbial strains and diabetic kidney disease (DKD). We have unveiled two distinct microbial guilds with opposite influences on host health, which may redefine our understanding of microbial contributions to disease progression. The implications of our findings extend beyond mere association, providing potential pathways for intervention and opening new avenues for patient stratification in clinical settings. This work paves the way for a paradigm shift in microbiome research in DKD and potentially other chronic kidney diseases, from a focus on taxonomy to a more nuanced view of microbial ecology and function that is more closely aligned with clinical outcomes.
OBJECTIVE: Traditionally, microbiome research has been constrained by the reliance on taxonomic classifications that may not reflect the functional dynamics or the ecological interactions within microbial communities. By transcending these limitations with a genome-centric and guild-based analysis, our study sheds light on the intricate and specific interactions between microbial strains and diabetic kidney disease (DKD). We have unveiled two distinct microbial guilds with opposite influences on host health, which may redefine our understanding of microbial contributions to disease progression. The implications of our findings extend beyond mere association, providing potential pathways for intervention and opening new avenues for patient stratification in clinical settings. This work paves the way for a paradigm shift in microbiome research in DKD and potentially other chronic kidney diseases, from a focus on taxonomy to a more nuanced view of microbial ecology and function that is more closely aligned with clinical outcomes.
摘要：
目前糖尿病肾病（DKD）等慢性疾病的微生物组特征主要基于低分辨率分类群，如属或门，并且在研究中通常不一致。在微生物生态系统中，细菌功能是菌株特异性的，和分类学上不同的细菌倾向于形成称为行会的共富官能团。这里,我们通过对116例DKD患者和91例健康受试者的粪便样本进行深度宏基因组测序和以基因组为中心和基于行会的分析,确定了DKD的行会水平特征.对1,543个高质量宏基因组组装基因组（HQMAGs）的冗余分析鉴定出54个HQMAGs在年轻健康对照组中差异分布，老年健康对照组,早期DKD患者（EDG），和晚期DKD患者（LDG）。共丰度网络分析将54个HQMAG分为两个行会。与公会2相比，公会1含有更多的短链脂肪酸生物合成基因，而编码尿毒症毒素吲哚生物合成的基因更少，抗生素耐药性,和毒力因子。公会指数,源于公会成员的丰富程度及其多样性，将DKD患者与健康受试者以及不同严重程度的DKD之间进行划分。经年龄调整的部分Spearman相关分析显示，行会指数与DKD疾病进展及预后不良的风险指标相关。我们进一步验证了使用54个HQMAG建立的随机森林分类模型也适用于在独立数据集中对终末期肾病患者和健康受试者进行分类。因此,这个基因组水平，基于公会的微生物分析策略可以在早期阶段识别不同严重程度的DKD患者，以指导临床干预。
目标：传统上，微生物组研究受到对分类学分类的依赖的限制，这些分类可能无法反映微生物群落内的功能动态或生态相互作用。通过以基因组为中心和基于行会的分析来超越这些限制，我们的研究揭示了微生物菌株与糖尿病肾病(DKD)之间复杂而特异的相互作用.我们公布了两个不同的微生物协会，对宿主健康有相反的影响，这可能会重新定义我们对微生物对疾病进展的贡献的理解。我们的发现的含义不仅仅是关联，提供潜在的干预途径，并为临床环境中的患者分层开辟新的途径。这项工作为DKD和潜在的其他慢性肾脏疾病的微生物组研究的范式转变铺平了道路。从对分类学的关注到对微生物生态学和功能的更细致的看法，这与临床结果更密切相关。
目标：传统上，微生物组研究受到对分类学分类的依赖的限制，这些分类可能无法反映微生物群落内的功能动态或生态相互作用。通过以基因组为中心和基于行会的分析来超越这些限制，我们的研究揭示了微生物菌株与糖尿病肾病(DKD)之间复杂而特异的相互作用.我们公布了两个不同的微生物协会，对宿主健康有相反的影响，这可能会重新定义我们对微生物对疾病进展的贡献的理解。我们的发现的含义不仅仅是关联，提供潜在的干预途径，并为临床环境中的患者分层开辟新的途径。这项工作为DKD和潜在的其他慢性肾脏疾病的微生物组研究的范式转变铺平了道路。从对分类学的关注到对微生物生态学和功能的更细致的看法，这与临床结果更密切相关。
