Directly to content
  1. Publishing |
  2. Search |
  3. Browse |
  4. Recent items rss |
  5. Open Access |
  6. Jur. Issues |
  7. DeutschClear Cookie - decide language by browser settings

Transcriptomics data analysis from renal fibrosis

Kim, Hyojin

[thumbnail of Hyojin_Kim_Phd_thesis_defence_8.Nov.pdf] PDF, English
Achtung, Restricted access: Repository staff only until 7 November 2022.
Login+Download (12MB) | Terms of use

Citation of documents: Please do not cite the URL that is displayed in your browser location input, instead use the DOI, URN or the persistent URL below, as we can guarantee their long-time accessibility.

Abstract

Around 10 % of the world population suffers from chronic kidney disease. While the initial stimulus of kidney injury may vary, fibrosis represents the common end-stage of nearly all kidney diseases. However, the pathogenesis of renal fibrosis remains not well understood due to the complexity of the kidney tissue, heterogeneity of kidney cells as well as high heterogeneity across patients. The kidney is one of the most complex organs and consists of a multitude of different cell types such as podocytes, proximal tubular cells, distal tubular cells, endothelial cells, pericytes, fibroblasts and myofibroblasts. Myofibroblasts have been previously identified as the central conductors in fibrosis. After kidney injury, myofibroblasts expand and produce excess extracellular matrix, which in return can lead to pathologic tissue remodeling and loss of kidney function.

In order to better understand the pathogenesis of renal fibrosis on a cellular level, I analyzed single-cell RNA sequencing (RNA-seq) data of renal perivascular cells, which were isolated by fluorescence activated cell sorting (FACS) using the cell markers Gli1, Ng2, Myh11, Pdgfrb and Cd31. FACS, as well as single cell library generation was performed in collaboration by Christoph Kuppe, MD, PhD of the RWTH Uniklinik Aachen. The cell markers Gli1, Ng2, Myh11, Pdgfrb are common markers for identification of fibroblasts, pericytes, endothelial cells and epithelial cells, while Cd31 is used as a marker of endothelial cells within the perivascular niche of the kidney. Based on the sorted cells, I performed cell-type specific functional studies including analysis of pathway and transcription factor activity as well as ligand receptor interaction analysis. When interpreting outputs, I integrated renal fibrosis-related ligands, receptors, pathways and transcription factors all together. Based on the integrated data, I was able to identify 6 key biological motives in fibrosis, which were supported by prior studies. Additionally, I identified several driver genes of myofibroblast differentiation in renal fibrosis. Literature studies confirmed that 40 of these genes were previously identified as driver genes of myofibroblast differentiation or fibrosis-related genes.

In a second step, I conducted bulk-level microarray data analysis of chronic kidney disease samples to identify potential candidates for drug repositioning. By reversely matching the disease signatures to datasets of drug-treated cell lines, I identified 20 small molecules as drug-repositioning candidates for 9 different kidney diseases. One of the drugs, “Nilotinib”, was already approved by the FDA. Nilotinib is known to ameliorate renal fibrosis in rats by inhibiting Pdgfr signaling. Consistent with this, the single-cell study also identified that the Pdgfa-Pdgfrb interaction with subsequent JAK-STAT downstream signaling is a key pathway leading to renal fibrosis.

In summary, I analyzed renal fibrosis-causing biological pathways, transcription factors, ligand receptor interaction and cell differentiation on a single cell level. To better understand the pathogenesis of fibrosis, I interpreted results by combining biological pathways, transcription factors and ligand receptor interaction analysis, and collapsed these into well-known pathological motives, which are consistent with prior studies on kidney fibrosis. Additionally, I identified several novel candidate genes that may play a central role in pericyte (or fibroblast) to myofibroblast differentiation. Some of these genes will be validated experimentally. At a bulk data level, I performed drug repositioning analysis for 9 different chronic kidney diseases and identified the FDA-approved drug “Nilotinib” as a candidate for drug repositioning for kidney fibrosis. This work opens up new possibilities to understand the pathogenesis of renal fibrosis on a single cell level and enables drug-repositioning for renal fibrosis on single cell level.

Document type: Dissertation
Supervisor: Brors, Prof. Dr. Benedikt
Place of Publication: Heidelberg
Date of thesis defense: 8 November 2021
Date Deposited: 17 Nov 2021 10:36
Date: 2022
Faculties / Institutes: The Faculty of Bio Sciences > Dean's Office of the Faculty of Bio Sciences
DDC-classification: 570 Life sciences
About | FAQ | Contact | Imprint |
OA-LogoDINI certificate 2013Logo der Open-Archives-Initiative