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Kidney Week

Abstract: SA-OR27

3D Genome Architecture of Human Renal Cortex and Medulla

Session Information

Category: Genetic Diseases of the Kidneys

  • 1002 Genetic Diseases of the Kidneys: Non-Cystic

Authors

  • Muthusamy, Selvaraj, University of Washington, Seattle, Washington, United States
  • Akilesh, Shreeram, University of Washington, Seattle, Washington, United States
Background

Genomic DNA is organized in a non-random manner within the mammalian nucleus. How this three-dimensional genome architecture influences cell-type specific phenotypes is poorly understood. Genome-wide methods such as Hi-C can systematically map out 3D genome architecture. However until now, technical and cost limitations have prevented these powerful approaches from being applied to intact human kidney tissues.

Methods

We performed global genome conformation (Hi-C) analysis on macrodissected human renal cortex and medulla from the same individual. Since existing algorithms to identify intra and inter-chromosomal interactions in Hi-C sequencing data are plagued by low concordance, we developed a novel machine learning algorithm used in the domain of computer vision to identify significant contacts in our Hi-C data.

Results

Each kidney Hi-C sample was deeply sequenced to >400 million mapped contacts enabling visualization of topologically associated domains (TADs) and contacts at 10kb resolution. Comparing even these highly similar samples, our novel algorithm identified significantly different genome conformation at multiple intra-chromosomal contacts in renal cortex (n=1789) and medulla (n=1841) (figure). Further validation by DNA-FISH and comparison to orthogonal functional genomic data sets (ATAC-seq, RNA-seq) are ongoing.

Conclusion

These high-resolution chromatin conformation maps of intact human kidney will provide an valuable resource for the study of kidney genome regulation. Our novel loop-calling algorithm enabled identification of fine genome architectural differences between renal cortex and medulla. Our data can also be used to link genetic risk loci to target genes in genome-wide association studies.

Hi-C contact matrices for cortex and medulla for chr2:117,860,000-118,850,000; Green circle = differential loop.