Fibrosis is a process that can arise from injury and inflammation. Under normal conditions, an injury remits by wound healing and scarring. However, chronic injury or inflammation generates an excess of connective tissue of proliferating fibrotic cells which secrete extracellular matrix (ECM) and may affect the correct performance of the involved organ or tissue. Both excess of fibrotic cells and aberrant remodelling of ECM empowers further inflammatory signals that lead to chronic fibrosis, damages of organ/tissue architecture and, ultimately, to its loss of function. Regardless of the different conditions leading to fibrosis and the diverse organs that can be afflicted by it, the main driving process involves transformation of various cell types into fibroblasts and myofibroblasts, which produce excessive amounts of ECM components.
Prevalence of chronic fibrotic diseases, mainly in the form of chronic kidney disease (CKD), liver cirrhosis or lung fibrosis, is high especially in the developed world. Hence, understanding the molecular mechanisms orchestrating them alongside with the identification of early biomarkers and development of efficient therapies is essential (Friedman et al., Sci Transl Med., 2013). We focus on kidney fibrosis, in collaboration with the Division of Nephrology and Clinical Immunology at the University Hospital Aachen, in particular with Rafael Kramann. We also work in liver fibrosis in the context of the LiSyM network.
In order to gain deeper insight into the mechanisms at the core of the fibrotic process and to find possible markers and treatments, we consider several types of data and a variety of modeling approaches. In the case of transcriptomic data we try to reduce the vast amount of single gene information into a functional context by applying functional genomics methods. We use our tools PROGENy (Schubert et al., Nat Commun, 2018) and DoRothEA (Garcia-Alonso et al., Cancer Res, 2017) as well as publicly available tools. PROGENy infers pathway activity from gene expression signatures and DoRothEA estimates the activity of transcription factors by performing gene set enrichment analysis on a collection of manually curated human regulons.
Our research focuses on liver and kidney fibrosis. From these, we obtain different types of data as well as curated prior knowledge on already well-studied mechanisms involved from our tool Omnipath (Türei et al., Nat Methods, 2016) and other resources. We integrate them using available or in house tools to gain further insight into the underlying processes driving fibrosis. We hope to leverage the obtained knowledge to find potential new markers and therapeutic targets.
We have so far focused on public transcriptomics data from human CKD samples and functional genomic techniques to characterize the differences accross CKD subtypes (Tajti et al., bioRxiv, 2018). We currently aim to relate molecular data with clinical parameters such as the decline of the filtering function of the kidney using machine learning-driven approaches capable of exploiting several feature selection and dimensionality reduction methods. Furthermore, we are trying to combine biological information of drug-response with deconvolution of gene expression (Shen-Orr et al., Curr Opin Immunol, 2013) to both in tissue and cell-culture data.
In parallel, we are integrating whole genome sequencing data from bulk and single-cell measurements to deconvolute heterogeneity across CKD samples. Finally, we use microfluidics together with the group of Christoph Merten in EMBL to do high-throughput screenings of drug combinations and characterize response of specific cell-types. Our long term aim is to develop multi-cellular models of the kidney tissue to unveil the mechanisms driving fibrosis in multiple scales, namely from the intracellular point of view to the tissue level.
In the context of chronic liver diseases, fibrosis frequently leads to cirrhosis. The number of global cirrhosis deaths is steadily growing, resulting in over 1 million deaths worldwide (Mokdat et al., BMC Med, 2014). Our contribution to understand this condition is to investigate the dynamic profile of chronic liver disease progression at RNA level in mouse models. Functional analysis of the transcriptome will decipher disease-stage associated molecular changes which can help us identify critical parameters for liver fibrosis progression.