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Updated: Jan 10, 2026

Comparative Proteomic Analysis of Whole Kidney, Medulla, and Cortical Tubules in Diabetic Pathogenesis of Kidney Injury in Mice
Published on: May 2, 2025
Molecular pathways of kidney development and their applications to clinical research
Friederike Ehrhart1, Helge Martens2, Norman D Rosenblum3
1Department of Translational Genomics, NUTRIM/MHeNs, Maastricht University, Maastricht, The Netherlands.
Abstract:
Congenital anomalies of the kidney and urinary tract (CAKUT) are the major cause of childhood chronic kidney disease and an antecedent cause of adult-onset cardiovascular and kidney failure. Both genetic and environmental factors have been implicated in human kidney malformations, with pathogenic variants or DNA copy number variations identified in ∼16% and 10% of cases, respectively. To date, >60 CAKUT-associated genes have been identified, most of which are established regulators of organogenesis. Although excellent reviews covering the genetic bases of CAKUT exist, new approaches for automated analysis and machine learning require formats that can be easily read and interpreted by computers. Here, we develop and describe fully machine-readable, well-annotated pathways to visualize and analyze key events during kidney development. Pathways include genes controlling nephrogenesis, including glomerulotubular development, the GDNF/RET signaling axis driving ureter branching, the development of the ureteric bud-derived collecting system, and lineage dependencies of all kidney cell types with marker gene expression. These pathways are published on the WikiPathways database. Furthermore, we provide 3 examples of how to apply these molecular pathways to translational clinical research. We demonstrate how they (i) inform the discovery of new CAKUT-associated candidate genes, (ii) illuminate the aberrant transcriptomic panorama in a specific genetic kidney malformation, and (iii) help understand how environmental perturbations may cause kidney malformations. Taken together, this review summarizes and visualizes current knowledge informing kidney maldevelopment and genetic causes of CAKUT and facilitates future advanced data analyses and data integration approaches.
Insights
Machine-readable pathways for congenital anomalies of the kidney and urinary tract (CAKUT) development were created. These pathways aid in discovering new CAKUT genes and understanding environmental impacts on kidney malformations.
Area of Science:
- Developmental Biology
- Genetics
- Bioinformatics
Background:
- Congenital anomalies of the kidney and urinary tract (CAKUT) are a leading cause of chronic kidney disease in children and kidney failure in adults.
- Genetic factors, including pathogenic variants and copy number variations, are implicated in a significant portion of CAKUT cases.
- Existing reviews on CAKUT genetics lack machine-readable formats for advanced computational analysis.
Purpose of the Study:
- To develop machine-readable, annotated pathways detailing key events in kidney development.
- To provide tools for visualizing and analyzing genetic and environmental factors in CAKUT.
- To facilitate the application of pathway data in translational clinical research.
Main Methods:
- Creation of machine-readable pathways for nephrogenesis, including glomerulotubular development and ureter branching (GDNF/RET signaling).
- Inclusion of data on collecting system development and cell-type lineage dependencies with marker gene expression.
- Publication of pathways on the WikiPathways database for accessibility.
Main Results:
- Developed comprehensive, machine-readable pathways for kidney development.
- Demonstrated pathway utility in identifying novel CAKUT-associated genes.
- Showcased application in analyzing transcriptomic data and understanding environmental causes of kidney malformations.
Conclusions:
- The developed pathways provide a valuable resource for understanding kidney maldevelopment and CAKUT.
- These pathways facilitate advanced data analysis, integration, and discovery in CAKUT research.
- The machine-readable format supports computational approaches, including machine learning, for CAKUT gene discovery and etiological research.
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