Multi omics analysis of fibrotic kidneys in two mouse models

Area of Science:

• Nephrology
• Molecular Biology
• Bioinformatics

Background:

• Kidney fibrosis is a significant clinical challenge lacking effective treatments and a deep understanding of its molecular basis.
• Current research requires comprehensive datasets to elucidate the complex mechanisms driving kidney fibrosis.

Purpose of the Study:

• To generate and present a comprehensive, integrated multi-omics dataset of fibrotic kidneys.
• To develop a user-friendly web application for accessing and interrogating this multi-omics data.
• To facilitate research into the molecular pathogenesis of kidney fibrosis.

Main Methods:

• Utilized two established mouse models of kidney fibrosis: folic acid (FA)-induced nephropathy and unilateral ureteral obstruction (UUO).
• Performed mRNA and small RNA sequencing alongside 10-plex tandem mass tag (TMT) proteomics on kidney samples across various time points.
• Developed a bioinformatics workflow for data processing, validation, and integration of multi-omics datasets.

Main Results:

• Generated a comprehensive temporal multi-omics dataset (proteomics, mRNA, and small RNA transcriptomics) from fibrotic mouse kidneys.
• Integrated diverse omics data into a searchable web application, the Mouse Kidney Fibromics browser.
• Provided a validated, searchable resource for transcriptome and proteome data relevant to kidney fibrosis research.

Conclusions:

• The Mouse Kidney Fibromics browser offers a valuable, accessible resource for the scientific community.
• This integrated multi-omics data advances the understanding of kidney fibrosis molecular pathways.
• The dataset and browser tool are expected to accelerate the development of novel therapeutic strategies for kidney fibrosis.