Saez-Rodriguez Group

Systems Biomedicine

Unveiling the molecular mechanisms driving fibrosis

Background and motivation

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 and proliferating fibroblasts which build up an excess 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 loss of function. Regardless of the different conditions leading to fibrosis and the diverse organs that can be afflicted by it, the main mechanism involves various cell types mainly (myo)fibroblasts, which produce excessive amounts of ECM components. Fbrosis-related diseases, mainly in the form of chronic kidney disease (CKD), liver cirrhosis or lung fibrosis, are highly prevalent 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. 


Data and approaches

To gain deeper insight into the mechanisms driving fibrosis and to find possible markers and treatments, we analyse several types of data with a variety of modeling approaches. In the case of transcriptomic data, we try to reduce the vast amount of the transcriptome into a functional context by applying our functional genomics footprint methods (PROGENy, DoRothEA and CARNIVAL). Using single cell RNA-seq data, we are identifying cell-type-based molecular signatures, and study cell-cell interactions. Furthermore, we integrate different layers of omics data and approaches in order to unveil the signaling mechanisms and metabolism driving fibrotic processes.


Our research focuses on kidney, heart and liver 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 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 [1]

Kidney fibrosis

Research with systems biology approaches is relatively scarce when compared with other diseases, in particular in the context of CKD [1]. We work on kidney and heart fibrosis with Rafael Kramann at the University Hospital Aachen, and within the Molecular Medicine Partnership Unit (MMPU) on CKD with EMBL, together with Rainer Pepperkok and Christoph Merten. In a preliminary study, we focused on the meta-analysis of public transcriptomics data from human CKD samples with different functional genomic techniques to characterize the differences across CKD subtypes [2]. We also contributed to the analysis of single-cell RNA seq data to dissect the key cells involved in scar-formation in the kidney [3].  We are integrating multiple omics data in order to create a comprehensive model of kidney fibrosis. 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. 


Heart fibrosis and myocardial infarction

Together with Rafael Kramann and Ivan Costa in Aachen we have analyzed single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of different physiological zones of human myocardial infarction and fibrosis [4]. Using different tools, including MISTy for spatial data [5], we identified and validated mechanisms that drive cardiac fibrosis. 


Liver Fibrosis and Chronic Liver Disease 

In the liver, fibrosis frequently leads to cirrhosis, which leads to over 1 million deaths per year worlwide. Our contribution to understand this condition is to investigate the dynamic profile of chronic liver disease progression at RNA level in mouse models provided by Marie-Luise Berres from University Hospital Aachen and Ahmed Ghallab from Technical University Dortmund. This is part of the Lisym (Liver Systems Medicine) network. Functional analysis of the transcriptome will decipher disease-stage associated molecular changes which can help us identify critical parameters for liver fibrosis progression.

In a collaboration with Jan Hengstler also from Technical University Dortmund we investigated the influence of liver fibrosis on lobular zonation with the outcome that during fibrosis pericentral regions adopts periportal features [6]



  1. Saez-Rodriguez J, Rinschen MM, Floege J, Kramann R. Big science and big data in nephrology. Kidney Int. 2019. doi:10.1016/j.kint.2018.11.048
  2. Tajti F, Kuppe C, Antoranz A, Ibrahim MM, Kim H, Ceccarelli F, et al. A Functional Landscape of CKD Entities From Public Transcriptomic Data. Kidney Int Rep. 2020;5: 211–224. doi:10.1016/j.ekir.2019.11.005
  3. Kuppe C, Ibrahim MM, Kranz J, Zhang X, Ziegler S, Perales-Patón J, et al. Decoding myofibroblast origins in human kidney fibrosis. Nature. 2021;589: 281–286. doi:10.1038/s41586-020-2941-1
  4. Kuppe C, Flores ROR, Li Z, Hannani M, Tanevski J. Spatial multi-omic map of human myocardial infarction. bioRxiv. 2020. Available:
  5. Tanevski J, Gabor A, Flores ROR, Schapiro D, Saez-Rodriguez J. Explainable multi-view framework for dissecting inter-cellular signaling from highly multiplexed spatial data. doi:10.1101/2020.05.08.084145
  6. Ghallab A, Myllys M, Holland CH, Zaza A, Murad W, Hassan R, et al. Influence of Liver Fibrosis on Lobular Zonation. Cells. 2019;8. doi:10.3390/cells8121556