Dr. Manuel Nietert
Group Leader Chemoinformatics and Imaging
Location: Goldschmidtstr. 1, 2. OG, Room: 2.112
ORCID ID: https://orcid.org/0000-0001-5443-7943
Position and Employment
Since 2018 Group Leader of Applied Bio(chem)infomatics and Image Analysis in the Department of Medical Bioinformatics
Since 2019 Project Leader, AutoBuSTeD, UMG, Germany
Since 2017 Project Leader, CandActCFTR, UMG, Germay
2013 - 2016 Researcher & Project manager, MetastaSys, UMG, Germany
2010 - 2013 Researcher & Project manager, BreastSys, UMG, Germany
2007 - 2009 Research Assist., Inst. of Mircobiologiy and Genetics, TU Darmstadt, Germany
Education and Training
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
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).
Automated Bubble Sweat Test Diagnostics – AutoBuSTeD -s.gwdg.de/xv5Uzr
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 – s.gwdg.de/xvtIlC
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
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