Center for Computational Biology, Flatiron Institute, New York, New York.
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey.
J Am Soc Nephrol. 2022 Jun;33(6):1208-1221. doi: 10.1681/ASN.2021060784. Epub 2022 Apr 27.
Molecular characterization of nephropathies may facilitate pathophysiologic insight, development of targeted therapeutics, and transcriptome-based disease classification. Although membranous nephropathy (MN) is a common cause of adult-onset nephrotic syndrome, the molecular pathways of kidney damage in MN require further definition.
We applied a machine-learning framework to predict diagnosis on the basis of gene expression from the microdissected kidney tissue of participants in the Nephrotic Syndrome Study Network (NEPTUNE) cohort. We sought to identify differentially expressed genes between participants with MN versus those of other glomerulonephropathies across the NEPTUNE and European Renal cDNA Bank (ERCB) cohorts, to find MN-specific gene modules in a kidney-specific functional network, and to identify cell-type specificity of MN-specific genes using single-cell sequencing data from reference nephrectomy tissue.
Glomerular gene expression alone accurately separated participants with MN from those with other nephrotic syndrome etiologies. The top predictive classifier genes from NEPTUNE participants were also differentially expressed in the ERCB participants with MN. We identified a signature of 158 genes that are significantly differentially expressed in MN across both cohorts, finding 120 of these in a validation cohort. This signature is enriched in targets of transcription factor NF-κB. Clustering these MN-specific genes in a kidney-specific functional network uncovered modules with functional enrichments, including in ion transport, cell projection morphogenesis, regulation of adhesion, and wounding response. Expression data from reference nephrectomy tissue indicated 43% of these genes are most highly expressed by podocytes.
These results suggest that, relative to other glomerulonephropathies, MN has a distinctive molecular signature that includes upregulation of many podocyte-expressed genes, provides a molecular snapshot of MN, and facilitates insight into MN's underlying pathophysiology.
肾病的分子特征可促进病理生理学的深入了解、靶向治疗的发展和基于转录组的疾病分类。尽管膜性肾病（MN）是成人肾病综合征的常见病因，但 MN 导致肾脏损伤的分子途径仍需要进一步明确。
我们应用机器学习框架，根据 NEPTUNE 队列中参与者的肾脏组织的基因表达来预测诊断。我们试图在 NEPTUNE 和欧洲肾脏 cDNA 库（ERCB）队列中鉴定出 MN 与其他肾小球肾炎患者之间差异表达的基因，在肾脏特异性功能网络中找到 MN 特异性基因模块，并使用参考肾切除术组织的单细胞测序数据确定 MN 特异性基因的细胞类型特异性。
单独的肾小球基因表达即可准确地区分 MN 患者与其他肾病综合征病因患者。NEPTUNE 参与者的顶级预测分类器基因在 ERCB 中 MN 患者中也有差异表达。我们在两个队列中鉴定出 158 个 MN 中差异显著表达的基因，在验证队列中鉴定出 120 个。该特征与转录因子 NF-κB 的靶标富集。将这些 MN 特异性基因在肾脏特异性功能网络中聚类发现，具有功能富集的模块，包括离子转运、细胞突起形态发生、粘附调节和创伤反应。参考肾切除术组织的表达数据表明，这些基因中有 43%主要由足细胞表达。
这些结果表明，与其他肾小球肾炎相比，MN 具有独特的分子特征，包括许多足细胞表达基因的上调，为 MN 提供了一个分子快照，并有助于深入了解 MN 的潜在病理生理学。
