Abstract
Chronic kidney disease exacts an enormous and inequitable toll on the health of the population. Improvements to the existing therapeutic armamentarium are critically needed. Single cell sequencing is a powerful research technique that has yielded exciting advancements in the understanding of kidney physiology and disease. In this dissertation, I review how single cell sequencing has facilitated the use of kidney organoid systems in modeling kidney diseases, revealed key disease-associated cell types and states, and enabled mechanistic annotation of kidney disease-associated genetic variants. I focus on a profibrotic, proinflammatory proximal tubule state that is implicated in maladaptive repair following acute kidney injury and may promote fibrotic processes in chronic kidney disease. I hypothesize that changes in gene regulatory mechanisms drive the formation of this distinct cell state and that single cell multiomic sequencing, in which simultaneous gene expression and chromatin accessibility profiles are generated for each individual cell, offers an unprecedented opportunity to computationally predict the involved gene regulatory networks. I explore how to apply a regularized regression approach to a single cell multiomic dataset generated from human adult kidney samples to predict cell type-specific cis-regulatory elements and transcription factors for all major kidney cell types. Applying this model to study the formation of the maladaptive, disease-associated proximal tubule cell state identifies key cis-regulatory elements and transcription factors predicted to regulate the healthy-maladaptive proximal tubule transition. I show that gene regulatory networks driving maladaptive proximal tubule gene expression are strongly enriched for chronic kidney disease heritability, implicating the cell state in disease pathogenesis. Transcription factors predicted to promote or inhibit the formation of the maladaptive proximal tubule state include novel therapeutic candidates, such as NFAT5, and previously implicated regulators, including ESRRG and PPARA. I validate the model predictions through a combination of computational and experimental approaches. I also detail a computational framework to analyze a single cell multiome dataset of kidney organoid differentiation, characterizing maturation and computationally predicting developmentally important transcription factors. This dissertation concludes with a discussion of future directions in modeling gene regulation with single cell datasets and possible applications to studying mechanisms of disease.
Committee Chair
Benjamin Humphreys
Committee Members
Andrew Malone; Jeffrey Miner; Kory Lavine; Sidharth Puram; Tychele Turner
Degree
Doctor of Philosophy (PhD)
Author's Department
Biology & Biomedical Sciences (Molecular Genetics & Genomics)
Document Type
Dissertation
Date of Award
5-6-2025
Language
English (en)
DOI
https://doi.org/10.7936/rzjd-s612
Author's ORCID
https://orcid.org/0000-0001-7957-2180
Recommended Citation
Ledru, Nicolas, "The Gene Regulatory Landscape of Proximal Tubule Sub-populations in Kidney Disease" (2025). Arts & Sciences Theses and Dissertations. 3556.
The definitive version is available at https://doi.org/10.7936/rzjd-s612