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

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.