eprintid: 35323
rev_number: 17
eprint_status: archive
userid: 8403
dir: disk0/00/03/53/23
datestamp: 2024-09-12 15:19:49
lastmod: 2024-09-23 11:54:02
status_changed: 2024-09-12 15:19:49
type: doctoralThesis
metadata_visibility: show
creators_name: Tüchler, Nadine
title: Dynamic multi-omics and mechanistic modeling of kidney fibrosis progression
subjects: ddc-570
divisions: i-140001
adv_faculty: af-14
keywords: Multi-omics, mesenchymal cells
cterms_swd: Niereninsuffizienz
cterms_swd: Fibrose
cterms_swd: Extrazelluläre Matrix
cterms_swd: Biomarker
cterms_swd: Zielstruktur
cterms_swd: RNS-Interferenz
cterms_swd: Mikroskopie
cterms_swd: Modellierung
cterms_swd: Datenanalyse
cterms_swd: Transkriptionsfaktor
abstract: Chronic kidney disease (CKD) affects more than 10% of the world’s population and causes millions of deaths annually. Organ fibrosis is the main driver of the pathology underlying CKD, myofibroblasts are the cellular correlate of disease progression and TGF-β is considered as a master regulator of the underlying molecular processes. Despite progress in understanding the disease, there is no specific treatment available, and current diagnostic indicators neither facilitate early detection of CKD nor correlate with actual renal damage. Therefore, there is an urgent need for more time-sensitive biomarkers and a better understanding of diseases progression to provide the basis for new treatment options.
The aim of this thesis was to provide detailed mechanistic insights into CKD progression. To study fibrosis progression, a cellular model was used, consisting of human kidney derived PDGFRβ+ mesenchymal cells stimulated with TGF-β. Initial characterization of the model aligned with existing knowledge in the literature, indicating morphological and transcriptional changes along with increased ECM accumulation.
Following this, time-resolved multi-omics data, including transcriptomics, proteomics, phosphoproteomics and secretomics were acquired. The findings revealed previously proposed biomarkers and drug targets for kidney fibrosis, shown at all omics levels. These include SERPINE1, CCN2, CDH11, and integrins, alongside novel factors like LTBP2 and ADAM12, that have not been studied in the context of kidney fibrosis yet. Proteins like carboxypeptidase CPA4, with unknown implications in fibrosis, require further investigation to demonstrate their potential as therapeutic target or marker.
In addition, an integrative analysis approach was employed, using mechanistic modeling combined with footprint methods to estimate transcription factor (TF), kinase and phosphatases activities. TFs implicated in fibrosis were identified and experimentally validated using siRNA knock-down. This underscored the role of E2F1, FLI1 and NR4A1 in modulating fibrosis and ECM deposition. Nevertheless, further exploration is needed to elucidate the role of these TFs in collagen gene regulation and ECM accumulation during fibrosis.
Furthermore, mechanistic modeling generated new hypotheses regarding pathway dynamics over time in the fibrotic context. Overall, this integrative approach contributes to a deeper understanding of the molecular mechanisms driving kidney fibrosis, offering potential biomarkers and therapeutic targets for clinical translation. Additionally, the dataset generated serves as a valuable resource for further research in the field of CKD.
date: 2024
id_scheme: DOI
id_number: 10.11588/heidok.00035323
ppn_swb: 1903304490
own_urn: urn:nbn:de:bsz:16-heidok-353231
date_accepted: 2024-07-22
advisor: HASH(0x55a9a638cea0)
language: eng
bibsort: TUCHLERNADDYNAMICMUL
full_text_status: public
place_of_pub: Heidelberg
citation:   Tüchler, Nadine  (2024) Dynamic multi-omics and mechanistic modeling of kidney fibrosis progression.  [Dissertation]     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/35323/1/Tuechler_Thesis_20240506_.pdf