Department of Medical BioSciences, Radboud University Medical Centre, Nijmegen, The Netherlands.
Aix Marseille Univ, INSERM, MMG, Marseille, France.
Sci Rep. 2024 Sep 5;14(1):20731. doi: 10.1038/s41598-024-71721-8.
Congenital Anomalies of the Kidney and Urinary Tract (CAKUT) is the leading cause of childhood chronic kidney failure and a significant cause of chronic kidney disease in adults. Genetic and environmental factors are known to influence CAKUT development, but the currently known disease mechanism remains incomplete. Our goal is to identify affected pathways and networks in CAKUT, and thereby aid in getting a better understanding of its pathophysiology. With this goal, the miRNome, peptidome, and proteome of over 30 amniotic fluid samples of patients with non-severe CAKUT was compared to patients with severe CAKUT. These omics data sets were made findable, accessible, interoperable, and reusable (FAIR) to facilitate their integration with external data resources. Furthermore, we analysed and integrated the omics data sets using three different bioinformatics strategies: integrative analysis with mixOmics, joint dimensionality reduction and pathway analysis. The three bioinformatics analyses provided complementary features, but all pointed towards an important role for collagen in CAKUT development and the PI3K-AKT signalling pathway. Additionally, several key genes (CSF1, IGF2, ITGB1, and RAC1) and microRNAs were identified. We published the three analysis strategies as containerized workflows. These workflows can be applied to other FAIR data sets and help gaining knowledge on other rare diseases.
先天性肾及尿路畸形(CAKUT)是儿童慢性肾衰竭的主要病因,也是成年人慢性肾脏病的重要病因。遗传和环境因素被认为会影响 CAKUT 的发展,但目前已知的发病机制仍不完整。我们的目标是确定 CAKUT 中的受影响途径和网络,从而帮助更好地了解其病理生理学。为此,我们比较了 30 多个非严重 CAKUT 患者和严重 CAKUT 患者的羊水样本中的 microRNA 组、肽组和蛋白质组。这些组学数据集是可发现、可访问、可互操作和可重复使用(FAIR)的,以促进它们与外部数据资源的整合。此外,我们使用三种不同的生物信息学策略分析和整合了这些组学数据集:使用 mixOmics 的综合分析、联合降维和通路分析。这三种生物信息学分析提供了互补的特征,但都指向胶原蛋白在 CAKUT 发展和 PI3K-AKT 信号通路中的重要作用。此外,还鉴定了几个关键基因(CSF1、IGF2、ITGB1 和 RAC1)和 microRNAs。我们将这三种分析策略作为容器化工作流程发布。这些工作流程可以应用于其他 FAIR 数据集,并有助于了解其他罕见疾病。