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Abstract: SA-PO1009

Gene Expression and Regulatory Activity Disruptions in Glomerular Disease Patients

Session Information

Category: Glomerular Diseases

  • 1403 Podocyte Biology

Authors

  • Sokolova, Ksenia, Princeton University, Princeton, New Jersey, United States
  • Theesfeld, Chandra L., Princeton University, Princeton, New Jersey, United States
  • Wang, Chen, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, United States
  • Kiryluk, Krzysztof, Columbia University Vagelos College of Physicians and Surgeons, New York, New York, United States
  • Mariani, Laura H., University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
  • Kretzler, Matthias, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
  • Troyanskaya, Olga, Princeton University, Princeton, New Jersey, United States

Group or Team Name

  • CureGN Consortium Genetics Working Group.
Background

The role of noncoding variants in glomerular disease is not well understood. In this work we use deep learning to analyze contributions of these variants to gene expression and regulatory activity disruptions in the CureGN cohort, which includes patients with minimal change disease (MCD), membranous nephropathy (MN), IgA nephropathy (IgAN) and focal segmental glomerulosclerosis (FSGS).

Methods

To systematically assess the functional impact of the variants, we used deep learning models to predict gene expression changes in primary human cell types and the underlying biochemical disruptions to chromatin regulators across millions of patient variants.

Results

Gene expression changes were predicted for over 3 million variants in 25 kidney cell types. Effects of rare variants were accumulated per gene. We found multiple genes and pathways (Reactome) that were significantly dysregulated across all patients. For example, a gene with significant gene expression disruption was RABGGTB (p-val=1.42e-74), which is prognostically unfavorable in renal cancer. Others include RREB1 (p-val=1.178e-88) and MAPK7 (p-val=2.738e-220). Top variants from each diagnosis group impacted distinct sets of partially overlapping genes and pathways.
In addition to gene expression, we obtained predictions for regulatory activity disruption for over 25 million gene proximal patient variants. Predicted chromatin biochemical disruption was more severe for strongly conserved variants and variants associated with disease-related pathway genes. A subset of genes and pathways were significantly disrupted between and within patient groups. For example, within variants found in MCD patients, genes involved in PCSK9 reactions had stronger chromatin profiling disruptions (p-val=6.36e-05) than the other variants of MCD patients.

Conclusion

Through comprehensive analysis of the non-coding variants in the CureGN cohort, we discovered variants, genes, and biological pathways disproportionately perturbed across the CureGN patients. Gene expression predictions provided functional effects, while chromatin profiling predictions offered additional insight into disruption of molecular mechanisms. This analysis provides hypotheses for disease-contributing biology and a resource for the future experimental follow-up.

Funding

  • NIDDK Support