Integrative analysis of multi-omics data reveals importance of collagen and the PI3K AKT signalling pathway in CAKUT

被引:0
作者
Jumamurat R. Bayjanov [1 ]
Cenna Doornbos [1 ]
Ozan Ozisik [2 ]
Woosub Shin [3 ]
Núria Queralt-Rosinach [4 ]
Daphne Wijnbergen [4 ]
Jean-Sébastien Saulnier-Blache [5 ]
Joost P. Schanstra [6 ]
Bénédicte Buffin-Meyer [5 ]
Julie Klein [6 ]
José M. Fernández [5 ]
Rajaram Kaliyaperumal [6 ]
Anaïs Baudot [5 ]
Peter A. C. ’t Hoen [6 ]
Friederike Ehrhart [7 ]
机构
[1] Department of Medical BioSciences, Radboud University Medical Centre, Nijmegen
[2] Aix Marseille Univ, INSERM, MMG, Marseille
[3] Department of Bioinformatics-BiGCaT, NUTRIM/MHeNs, Maastricht University, Maastricht
[4] Department of Human Genetics, Leiden University Medical Center, Leiden
[5] Institut National de la Santé et de la Recherche Médicale (INSERM), U1297, Institute of Cardiovascular and Metabolic Disease, Toulouse
[6] Université Toulouse III Paul-Sabatier, Toulouse
[7] Barcelona Supercomputing Center (BSC), Barcelona
[8] CNRS, Marseille
基金
欧盟地平线“2020”;
关键词
D O I
10.1038/s41598-024-71721-8
中图分类号
学科分类号
摘要
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. © The Author(s) 2024.
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