Dr. Manuel Nietert

Group Leader Chemoinformatics and Imaging

Telephone: 0551/39-61789

E-Mail: manuel.nietert@med.uni-goettingen.de

Location: Goldschmidtstr. 1, 2. OG, Room: 2.112

ORCID ID: https://orcid.org/0000-0001-5443-7943

Group of Chemoinformatics And Imaging

Curriculum Vitae

Since 2018 Group Leader of Applied Bio(chem)informatics and Image Analysis in the Department of Medical Bioinformatics
Since 2019   Project Leader, AutoBuSTeD, UMG, Germany
Since 2017   Project Leader, CandActCFTR, UMG, Germany 
2013 - 2016  Researcher & Project manager, MetastaSys, UMG, Germany
2010 - 2013  Researcher & Project manager, BreastSys, UMG, Germany 
2007 - 2009  Research Assist., Dept. of Microbiology and Genetics, TU Darmstadt, Germany
08/2008  PhD in Chemistry, Goethe University, Frankfurt a. M. - Germany
08/2004  Master Level Degree in Biochemistry, Goethe University, Frankfurt a. M., Germany

Awards/ Academic Roles

Christiane Herzog-Foundations “Forschungsförderpreis für wissenschaftliche Nachwuchsförderung 2018”, initial funding for the Automatic bubble sweat test diagnostic project – AutoBuSTeD

Vice spokesperson of the working group Data quality and transparency in medical research of the TMF e.V. - the umbrella organization for networked medical research in Germany. https://s.gwdg.de/70QJZO 

Member of the FAIRDOM Systems Biology Developers Foundry since 2012 https://s.gwdg.de/K3Ywvu

Research focus

Major Research Interests

For the past years we focus our research in the field of cystic fibrosis (CF), where we still see an unmet demand for IT solutions to help this field. In general our work group aims at providing IT based solutions for biochemical aspects of the systems biology and systems medicine projects, but if necessary can even venture out to help improve the acquisition setup. We thus develop and adapt software solutions to provide the required tools for medical research.

At a first glance, CF is a monocausal disease in which over 2000 putative mutations leading to various forms of phenotypes have been identified. Among these, about 300 variants define the more common types.
The CFTR protein is only effective as an integral membrane protein, and as such, it is affected by transcription, translation, folding and degradation, as well as protein traficking processes. Thus, this monogenic disease has multiple sites for potential drug intervention during its life cycle and covers also protein structure variants. This makes it an interesting target for a system medicine approach. The life cycle of the protein offers also multiple modes to obtain information to annotate the system and the existing literature offers various annotations for specific combinations of mutations and read-outs (e.g. protein expression, functional patch clamp measurements, up to structure models and molecular dynamic simulations).

Current Projects

Automated Bubble Sweat Test Diagnostics – AutoBuSTeD -


AutoBuSteD is an example of the work we do on the automation of image analysis workflows, e.g. the AutoBuSTeD project, and we also cover various other image input sources for other projects as well. E.g. microscopy data in the project Pulmonary transplantation of macrophages as a cellbased therapy to treat chronic infections in the cystic fibrosis lung, where we are the collaboration partner to automate the LysoSensor image analysis.

Curated database of candidate therapeutics for the activation of CFTR-mediated ion conductance – CandActCFTR –


CandActCFTR is a curated compound database which annotates the chemical structure library with information on where and how in the protein life cycle a compound likely interacts, thus comprising a good starting point for modelling the disease and enhancing ligand based approaches. In the upcoming extension of CandActCFTR, this ligand-based approach will be complemented by structure-based annotations, including the means to predict the interactions between CandActCFTR substances and CFTR by using existing molecular dynamics trajectories, and by adding more organisation and annotation modules

Relevant publications

Nietert M.; Vinhoven L.; Auer F.; Hafkemeyer S.; Stanke F.
Comprehensive analysis of chemical structures that have been tested as CFTR activating substances in a publicly available database CandActCFTR
Frontiers in Pharmacology,2021. https://www.frontiersin.org/articles/10.3389/fphar.2021.689205; accepted Nov 2021

Pallenberg S, T, Junge S, Ringshausen C, F., Sauer-Heilborn A, Hansen G, Dittrich A M, Tümmler B, Nietert M.
CFTR modulation with elexacaftor-tezacaftor-ivacaftor in people with cystic fibrosis assessed by the β-adrenergic sweat rate assay
Journal of Cystic Fibrosis,2021. https://doi.org/10.1016/j.jcf.2021.10.005

Vinhoven L, Voskamp M, Nietert M M.
Mapping Compound Databases to Disease Maps—A MINERVA Plugin for CandActBase. 
J. Pers. Med. 2021, 11

, 1072. doi.org/10.3390/jpm11111072

Vinhoven, L.; Stanke, F.; Hafkemeyer, S.; Nietert, M.M.
CFTR Lifecycle Map—A Systems Medicine Model of CFTR Maturation to Predict Possible Active Compound Combinations.
Int. J. Mol. Sci. 2021, 22(14), 7590. 

Jo P, Bernhardt M, Nietert M, ..., Ströbel P, Schildhaus HU, Gaedcke J.
KRAS mutation status concordance between the primary tumor and the corresponding metastasis in patients with rectal cancer.
PLoS One. 2020 Oct 1;15(10):e0239806. doi: 10.1371/journal.pone.0239806. PMID: 33002027

Uhlig J, Biggemann L, Nietert MM, ..., Uhlig A.
Discriminating malignant and benign clinical T1 renal masses on computed tomography: A pragmatic radiomics and machine learning approach.
Medicine (Baltimore). 2020 Apr;99(16):e19725. doi: 10.1097/MD.0000000000019725. PMID: 32311963

Lowes M, Kleiss M, Lueck R, Detken S, Koenig A, Nietert M, ..., Ghadimi M, Conradi LC, Homayounfar K.
The utilization of multidisciplinary tumor boards (MDT) in clinical routine: results of a health care research study focusing on patients with metastasized colorectal cancer.
Int. journal of colorectal disease. 2017; PMID: 28779354, PMCID: PMC5596058

Linke F, Zaunig S, Nietert M, ..., Wilting J, Kube D.
WNT5A: a motility-promoting factor in Hodgkin lymphoma.
Oncogene. 2017; 36(1):13-23. PMID: 27270428

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