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Kidney Week

Abstract: PO0624

Cell Type-Specific Chromatin Architecture Reveals Target Genes for Kidney Disease Risk Variants

Session Information

  • CKD Mechanisms - 2
    October 22, 2020 | Location: On-Demand
    Abstract Time: 10:00 AM - 12:00 PM

Category: CKD (Non-Dialysis)

  • 2103 CKD (Non-Dialysis): Mechanisms

Authors

  • Yang, Jingping, National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing, Jiangsu, China
  • Duan, Aiping, National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing, Jiangsu, China
  • Liu, Zhihong, National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing, Jiangsu, China
Background

Although GWAS studies have identified hundreds of genetic variants associated with kidney diseases, the identification of causal variants and their target genes are rather limited. Most of disease-associated variants locate in non-coding elements. The roles of these elements are cell type-specific. Additionally, due to the non-linear regulation of the elements, the identification of causal variants as well as their target genes is even more challenging.

Methods

In order to understand the genetic risk of kidney diseases, we generated a cell type-specific set of epigenetic landscape including transcription-centered 3D chromatin organization, histone modifications distribution and transcriptome with HiChIP, ChIP-seq and RNA-seq respectively, in kidney tubule cells. We integrated the epigenetic annotation to identify causal variants and target genes which were further tested by CRISP techniques in zebrafish.

Results

We identified genome-wide functional elements and thousands of interactions between the distal elements and target genes. The results revealed that risk variants for renal tumor and chronic kidney disease were enriched in kidney tubule cells. We further pinpointed the target genes for the variants and validated two target genes by CRISP techniques in zebrafish, demonstrating that SLC24A1 and MTX1 were indispensable genes to maintain kidney function.

Conclusion

Our results produce valuable multi-omic resource and establish a bioinformatic pipeline in dissecting functions of kidney diseases-associated variants based on cell type-specific epigenetic landscape.