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Funded Projects

Our research is supported by several national and international funding agencies. We are part of the Collaborative Research Centers (CRC) 1453 [2021-2024], 992 [2020-2024], 1479 [2021-2025], and 1597 [2023-2027] of the German Research Foundation at the University of Freiburg, Germany (https://www.sfb1453.uni-freiburg.dehttp://www.sfb992.uni-freiburg.dehttps://www.sfb1479.uni-freiburg.de/, https://www.smalldata-initiative.de/). These CRCs aim to understand mechanisms underlying complex (epi)genetic diseases and generate optimal synergies with the work carried out in the Institute of Genetic Epidemiology.

German Research Foundation (CRC 1453 “Nephrogenetics (NephGen)”): Speaker; Projects P15, P16, S1; 2021-2024

Kidney disease represents a global public health challenge. Chronic kidney disease alone affects 10% of adults. Despite the high prevalence and the great costs associated with treating kidney diseases, the low number of clinical trials and specific treatments in nephrology attests to a shortage of therapeutic targets. The identification of druggable targets has been complicated by an incomplete understanding of the underlying pathophysiological mechanisms. Pharmacological compounds that operate on proteins or pathways connected to a given disease by human genetic evidence are at least twice as likely to successfully move through the clinical development pipeline, compared to those with no genetic support. Therefore, NephGen will use evidence from both monogenic and complex genetic kidney diseases to identify and characterize molecules and pathways that represent targets to improve the prevention and treatment of kidney disease. To this end, NephGen researchers have assembled large patient- and population-studies, and established a variety of model organisms and state-of-the-art methods for genome editing, (single-cell) sequencing, structural biology, diverse omics technologies, whole animal live imaging as well as integrative analyses and modeling of high-dimensional data. To facilitate clinical translation, NephGen will use both modern statistical approaches and modify the implicated molecules and pathways in disease-specific model organisms through genetic and pharmacological approaches. 

More information can be found here: https://www.sfb1453.uni-freiburg.de

German Research Foundation (SCHL 2292/3–1): „Machine learning aided causal inference: Harnessing Multidimensional Omics Data to Improve Understanding of Complex Diseases”, 2024-2030

This research project outlines a machine learning-driven approach to analyze the interconnectedness between molecular traits and health conditions, focusing on proteins and metabolites. By leveraging large-scale population and patient data, the project aims to uncover associations between groups of molecular phenotypes and diseases in an unsupervised manner. The methodology will be scalable and extendable to new data types, including mitochondrial DNA and cell-type-specific molecular intermediates. The primary applications will be broad phenome-wide analyses, followed by detailed investigations into the relationship between mitochondria, metabolites, and kidney function. The ultimate goal is to identify potential therapeutic targets and prioritize follow-up studies.

German Research Foundation (CRC 1597 "SmallData"): Project B06, 2023-2027; together with Johannes Hertel

Summary: We hypothesize that combining knowledge-driven constraint-based whole-body modeling with hypothesis-generating research on the genetics of human metabolism will facilitate novel mechanistic insights, overcoming the limitations of each paradigm alone in small data situations. We will transfer information from genetic association analyses into in silico models of human metabolism, and vice versa. Specifically, we will utilize whole-body models based on the constraint-based modeling approach as an independent validation resource for metabolite-gene associations. The associations will be obtained from gene-based tests of rare, deleterious variants and their effect on metabolite levels in plasma and urine. Novel knowledge about human physiology and metabolism originating from these association studies will be fed back and continuously improve the whole-body models.

German Research Foundation (CRC 1479 „OncoEscape – Oncogen-driven immune escape“): Project S1 „Data analysis, integration, and modelling“, 2021-2025; together with Prof. M. Boerries and Prof. O. Schilling

Summary: S1 will provide standardised and advanced workflows for the analysis of next generation sequencing (NGS) data of human tumour samples for oncogenic mutations and scRNA-seq data of immune and tumour cells derived from primary human tumour samples as well as quantitative and functional proteomics workflows and data analyses for human tumour samples. Besides its focus on project support, S1 also aims to identify metabolite biomarkers of immune cell function and to develop novel proteomics analysis methods.

German Research Foundation (KO 3598/6-2): „Identification and Characterization of Novel Imaging Biomarkers of Kidney Function and Disease ”, 2024-2026; together with Elias Keller und Maximilian Russe

The project aims to generate new, complementary markers of kidney function and disease, derived from whole-body MR images using machine learning approaches, that will aid the diagnosis, classification and monitoring of chronic kidney disease.

German Research Foundation (SE 2407/3-1): „Discovery and Characterization of Risk Factors for Chronic Kidney Disease Progression, Cardiovascular Diseases and Death”, 2021-2023

Utilizing the data available from the German Chronic Kidney Disease (GCKD) study, we thus aim to assess baseline metabolite levels and their ability to predict adverse outcomes in chronic kidney disease (CKD) patients as well as to model CKD progression in the presence of non-fatal events. The three specific objectives in this project are:

(1) To evaluate associations of metabolite levels from plasma and urine with the prospective endpoints all-cause mortality, KF, and CV events. We expect to find known as well as novel associations.

(2) To evaluate the prognostic value of selected endpoint-associated metabolites, while accounting for known prognostic factors. The performance will be compared in relation to the performance of clinical benchmark models. An external validation step using independent data is pursued.

(3) Finally, we will explore the specific effects of non-fatal events such as MI or AKI on KF risk using the statistical framework of multi-state models. 

EU Marie Sklodowska Curie Training Network TRAINCKDis (Multidisciplinary Training in Chronic Kidney Disease: from genetic modifiers to drug discovery)

The goal of the Research Training Network TrainCKDis is to provide high-level training in chronic kidney disease (CKD) - a global public health burden - to a new generation of highly achieving early stage researchers. TrainCKDis will develop scientific skills necessary for thriving careers in an expanding area that underpins innovative technological development across a range of diverse disciplines including nephrology, epidemiology, genetics, cell biology, and drug discovery at the interface of basic molecular, genetic and clinical research. This will be achieved by a unique combination of “hands-on” research training, non-academic placements, courses and workshops on scientific and transferable skills, facilitated by the consortium’s academic/non-academic composition. The EU Training Network is coordinated by Dr. Fabiola Terzi at the Université Paris Descartes.

Bundesministerium für Bildung und Forschung (e:Med Juniorverbünde in der Systemmedizin): CKDNapp A toolbox for monitoring and tailoring treatment of chronic kidney disease patients - a personalized systems medicine approach, 2020-2024

Summary: Chronic Kidney Disease (CKD) can arise from multiple causes. It is characterized by a variable course of diseases and a high burden of cardiovascular and metabolic comorbidities, complicating optimal treatment. To provide optimal, personalized medical care for each patient, physicians need to obtain a detailed, comprehensive picture of that patient’s state. For this purpose, he/she integrates different levels of data, e.g., clinical/demographic parameters, biomarkers, and drug information, with medical knowledge. Because CKD is a complex disease, this data integration process is extremely challenging.

We (collaboration partners: Ulla T. Schultheiß, Helena U. Zacharias, Michael Altenbuchinger und Johannes Raffler) propose based on data from the German Chronic Kidney Disease study (1) to computationally model this complex CKD system, (2) to enrich these models with novel omics data, (3) to discover novel biomarkers, and (4) to build a clinical decision support (CDS) software based on these models assisting physicians in personalized everyday CKD patient care. Our CDS software, called CKDNapp (CKD Nephrologists’ app), will (i) predict adverse events and disease progression, (ii) refine diagnosis of CKD staging, (iii) return transparent reasoning for all predictions and recommendations, (iv) offer in silico modification of patient parameters by the physician, and (v) deliver comprehensive literature support. It will be available as an easy-to-use software for smartphones, tablets, and desktop computers.

German Research Foundation (CRC 992 “MEDEP: Medical Epigenetics”): Project C07 “Role of DNA methylation in human chronic kidney disease”, 2020-2024

Summary: Chronic kidney disease (CKD) is a complex disease affecting ~10% of adults and has a clear heritable component. Small cohort human and animal studies have linked altered DNA methylation to CKD. Therefore we will carry out epigenome-wide association studies (EWAS) of incident CKD and EWAS of CKD progression among patients with specific CKD etiologies. This will be followed by integration of results with kidney cell-type specific gene expression and chromatin annotation maps and genome-wide DNA sequence variants. Together, these approaches will provide new insights into the contribution of epigenetic modifications to CKD in humans.

Previously funded projects

German Research Foundation (SCHL 2292/2–1): „Proteome-wide Association Studies of Metabolites and Kidney Function”, 2023

Subcontract to “Identifying Novel Biological Pathways for Gout using DNA Methylation and Genetics”, NIAMS R01-AR073178 (to Dr. Adrienne Tin, PI), 2018-2023

German Research Foundation (KO 3598/4-2): “Renal Metabolite Handling: from Gene to Function to Disease”, 2019-2022

German Research Foundation (KO 3598/5-1): Heisenberg Professorship of Genetic Epidemiology

German Research Foundation (CRC 1140 “KIDGEM: Kidney Disease – From Genes to Mechanisms”): Project A05 “Genetic risk variants for chronic kidney diseases in a prospective cohort of 5,217 patients”, 2015-2019

KfH Stiftung Präventivmedizin: The German Chronic Kidney Disease (GCKD) Study, 2009-2019

Else Kröner Forschungskolleg NAKSYS (Nierenfunktionsstörungen als Komplikation von Systemerkrankungen): Project 08 Relationship of Thyroid Function and Chronic Kidney Disease, 2015-2020

Subcontract to “Epigenetic Landscape of Chronic Kidney Disease”, NIDDK 5R01DK087635-07 (to Dr. Katalin Susztak, PI), 2014-2019

German Research Foundation (KO 3598/3-1): Heisenberg Professorship, 2016-2018

German Research Foundation (KO 3598-4/1): “Renal Metabolite Handling: from Gene to Function to Disease”, 2014-2018