Molecular Medicine Partnership Unit

The MMPU is a joint venture between the Medical Faculty of the University of Heidelberg and the European Molecular Biology Laboratory (EMBL).

Chronic Kidney Disease

Rainer Pepperkok, Julio Saez-Rodriguez and Matias Simons

Our research interest

Our research aims to identify and understand the signalling events that lead to uncontrolled growth of myofibroblasts and ultimately the onset of chronic kidney disease.


Chronic kidney disease (CKD) affects around 10% of the population in the Western world, has no specific treatment, and leads to kidney failure. Its pathomechanism is organ destruction through fibrosis, a process mainly driven by uncontrolled growth of myofibroblasts that produce large amounts of extracellular matrix. However, its molecular basis is poorly understood. We want to identify and understand the signalling events that lead to uncontrolled growth of myofibroblasts and ultimately the onset of CKD.

We will develop cutting-edge microscopy and microfluidics technologies to study the response of patient biopsies to large numbers of biological perturbations. We will expand our existing readouts to phenotypes and RNA expression. We will analyse the data computationally and link the results to the patient’s pathological status and clinical outcome. For this, we will adapt our methodology (Eduati et al., Nat Commun 2018) to build mechanistic signalling models. These models will be built from the microfluidics perturbation data and existing signalling pathway knowledge (Türei _et al., Nat Methods_2016).

A powerful tool to understand and treat pathophysiologies is the generation of large-scale datasets that report the responses of cells to biological perturbations. A new technology we have developed (Eduati et al., Nat Commun 2018) allows us to perform such studies ex vivo on small biological samples obtained from patients. We used a caspase-3 reporter to the samples as a proxy for apoptosis. Although limited, the reporter data can be used to build mechanistic and predictive models of these pathways. By also including higher content readouts via microscopy and RNA sequencing, we will drastically increase the number of processes that can be analysed.


Our goal is to understand the pathophysiology of CKD. The immediate outcome will be an understanding of which factors and signalling pathways drive kidney fibrosis, ultimately leading to CKD.

Furthermore, it will reveal common features and differences between the multiple CKD types, and eventually provide biomarkers and novel drug targets that can be taken further into clinical studies.

More generally, we aim to develop a high-content platform for personalised therapy of kidney (and other) diseases.


Claudia Sommerer, Kidney Center Heidelberg

Martin Zeier, Kidney Center Heidelberg

Important Publications

Decoding myofibroblast origins in human kidney fibrosis.
Kuppe C, Ibrahim MM, Kranz J, Zhang X, Ziegler S, Perales-Patón J, Jansen J, Reimer KC, Smith JR, Dobie R, Wilson-Kanamori JR, Halder M, Xu Y, Kabgani N, Kaesler N, Klaus M, Gernhold L, Puelles VG, Huber TB, Boor P, Menzel S, Hoogenboezem RM, Bindels EMJ, Steffens J, Floege J, Schneider RK, Saez-Rodriguez J, Henderson NC, Kramann R.
Nature. 2021 Jan;589(7841):281-286. doi: 10.1038/s41586-020-2941-1. Epub 2020 Nov 11. PMID: 33176333

The tissue proteome in the multi-omic landscape of kidney disease.
Rinschen MM, Saez-Rodriguez J.
Nat Rev Nephrol. 2020 Oct 7. doi: 10.1038/s41581-020-00348-5. Online ahead of print. PMID: 33028957 Review.

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies.
Eduati F, Jaaks P, Wappler J, Cramer T, Merten CA, Garnett MJ, Saez-Rodriguez J.
Mol Syst Biol. 2020 Feb;16(2):e8664. doi: 10.15252/msb.20188664. PMID: 32073727

The authors reply.
Saez-Rodriguez J, Rinschen MM, Floege J, Kramann R.
Kidney Int. 2019 Dec;96(6):1422-1423. doi: 10.1016/j.kint.2019.09.011.
PMID: 31759488 No abstract available.

Metabolic rewiring of the hypertensive kidney.
Rinschen MM, Palygin O, Guijas C, Palermo A, Palacio-Escat N, Domingo-Almenara X, Montenegro-Burke R, Saez-Rodriguez J, Staruschenko A, Siuzdak G.
Sci Signal. 2019 Dec 10;12(611):eaax9760. doi: 10.1126/scisignal.aax9760. PMID: 31822592

A microfluidics platform for combinatorial drug screening on cancer biopsies.
Eduati F, Utharala R, Madhavan D, Neumann UP, Longerich T, Cramer T, Saez-Rodriguez J, Merten CA.
Nat Commun. 2018 Jun 22;9(1):2434. doi: 10.1038/s41467-018-04919-w. PMID:29934552

Big science and big data in nephrology.
Saez-Rodriguez J, Rinschen MM, Floege J, Kramann R.
Kidney Int. 2019 Jun;95(6):1326-1337. doi: 10.1016/j.kint.2018.11.048. Epub 2019 Mar 5. Review. PMID:30982672

OmniPath: guidelines and gateway for literature-curated signaling pathway resources.
Türei D, Korcsmáros T, Saez-Rodriguez J.
Nat Methods. 2016 Nov 29;13(12):966-967. doi: 10.1038/nmeth.4077. No abstract available. PMID: 27898060