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.

Kidney International
|November 21, 2025
PubMed

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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