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Genomics

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Related Experiment Video

Updated: May 23, 2025

Comparative Proteomic Analysis of Whole Kidney, Medulla, and Cortical Tubules in Diabetic Pathogenesis of Kidney Injury in Mice

Comparative Proteomic Analysis of Whole Kidney, Medulla, and Cortical Tubules in Diabetic Pathogenesis of Kidney Injury in Mice

Published on: May 2, 2025

Leveraging complementary multi-omics data integration methods for mechanistic insights in kidney diseases.

Fadhl Alakwaa¹, Vivek Das², Arindam Majumdar³

¹Department of Internal Medicine, Division of Nephrology, University of Michigan, Ann Arbor, Michigan, USA.

|March 10, 2025

Summary

Integrating multi-omics data reveals key urinary proteins and pathways linked to chronic kidney disease (CKD) progression. This approach aids in understanding CKD mechanisms and identifying potential therapeutic targets.

Keywords:

Chronic kidney disease Expression profiling Nephrology Proteomics

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Using 2-Photon Microscopy to Quantify the Effects of Chronic Unilateral Ureteral Obstruction on Glomerular Processes

Using 2-Photon Microscopy to Quantify the Effects of Chronic Unilateral Ureteral Obstruction on Glomerular Processes

Published on: March 4, 2022

Area of Science:

Nephrology
Genomics
Proteomics
Metabolomics

Background:

Chronic kidney diseases (CKDs) represent a significant global health challenge.
Understanding the complex pathophysiology of CKD is crucial for effective management.
High-dimensional multi-omics data offers potential for novel insights into disease mechanisms.

Purpose of the Study:

To explore the utility of complementary multidimensional -omics data integration methods.
To elucidate the mechanisms underlying CKD progression.
To identify robust biomarkers associated with CKD outcomes.

Main Methods:

Integration of tissue transcriptomic, urine and plasma proteomic, and urine metabolomic data.
Application of two orthogonal multi-omics data integration approaches (unsupervised and supervised).
Validation of identified urinary proteins in an independent cohort using survival analysis.

Main Results:

Both integration methods identified 8 urinary proteins significantly associated with long-term CKD outcomes.
These findings were replicated in an independent validation group.
Three shared enriched pathways were identified: complement and coagulation cascades, cytokine-cytokine receptor interaction, and JAK/STAT signaling.

Conclusions:

Multiscalar data integration strategies effectively identify and prioritize disease mechanisms in CKD progression.
The identified urinary proteins and pathways offer potential as biomarkers and therapeutic targets.
This integrated multi-omics approach is valuable for advancing kidney disease research with increasing data complexity.