Dr. Josch K. Pauling

LipiTUM - Computational Systems Medicine on Lipids and Metabolism

Our Projects

Precision Medicine integrating Lipidomics

Molecular disease subtying and patient stratification

Precision medicine requires tools for molecular patient stratification to find disease subtypes and develop specialized treatments. We are developing computational tools to stratify patients based on lipidomics or other omics profiles. Our approaches are based on biclustering algorithms, which we modify and extend to improve performance. This allows us to identify molecular signatures that characterize patient subgroups, which can serve as markers to classify patients or used as hypotheses, to further investigate the molecular mechanisms of diseases.

LipiTUM team members: Tim Rose, Thibault Bechtler, Octavia Ciora, Florian Molnar, Kim Anh Lilian Le

 The Lipid Network

Multi-level omics data is getting available for an increasing number of experiments. This requires computational tools being able to integrate and datamine heterogeneous types of data and produce insights going beyond the separate analysis of each data set. Such tools usually require databases, with functional interactions, such as biological networks. While methods are already available and used in other omics disciplines, they are only partially applicable to lipidomics data. We are utilizing the content of different lipid-protein interaction databases to generate lipid interactions networks. Based on those we develop dedicated algorithms to mechanistically understand lipidome alterations and functionally integrate lipidomics with other omics data.

LipiTUM team members: Tim Rose, Lisa Falk, Lucie Klischat, Nikolai Koehler

Liver Systems Medicine: Lipotyping non-alcoholic fatty liver disease 

We are collaborating with the LiSyM consortium to analyze clinical lipidomics samples from patients with non-alcoholic fatty liver disease and nonalcoholic steatohepatitis. By applying established and our novel machine learning methods, we are seeking to identify disease specific markers in the lipid markers and patient subgroups. Molecular lipidomics data could help to provide early detection of these widespread diseases and allow personalized treatments.

LipiTUM team members: Tim Rose

 Lipotyping pancreas

 In a collaboration with the Max Planck Institute of Molecular Cell Biology and Genetics and the Paul Langerhans Institute in Dresden, we investigate mechanisms and markers for diabetes in different pancreatic tissues. With our computational expertise we extract relevant molecular signatures to unravel disease mechanisms and their interplay with cellular lipidomes.

LipiTUM team members: Tim Rose

CoVex: The Corona Virus Explorer

 We participated in the development of the Coronavirus Explorer (CoVex), a systems medicine platform for the analysis of host-virus interactions. With the integration of multiple databases and novel algorithms, new drug targets and drug repurposing candidates can be predicted using experimentally validated interactions of the host and viral proteins. This can help to get a better understanding of the viral mechanisms and assist in the search for effective treatments.

Link to publication: https://www.nature.com/articles/s41467-020-17189-2

LipiTUM team members: Tim Rose


Selected Publications

1. Lipid imaging: Ellis SR et al. Nature Methods (2018). in press.
2. Lipidomics standardization: Pauling JK et al. PLOS ONE. (2017) 12(11): e0188394.
3. Quantitative lipidomics: Gallego SF et al. Biochimica et Biophysica Acta. (2017) 1862(2):145-155.
4. Computational lipidomics: Pauling JK et al. J. Integr. Bioinform. (2017) 13(1): 34-51.
5. MSn lipid profiling: Almeida R et al. J. Am. Soc. Mass Spectrom. (2015) 26(1):133-148.
6. Multi-omics and systems biology: Pauling JK et al. Integr Biol. (2014) 6(11):1058-1068.
7. Multi-omics and networks: Alcaraz N et al. BMC Syst. Biol. (2014) 8:99.
8. Clinical microbiota analysis: Zakharkina T et al. PLOS ONE (2013) 8(7): e68302.
9. Genome transfer: Pauling JK et al. Nucleic Acids Res. (2012) 40(D1):D610-4.
10. Metabolomics: Hauschild AC et al. Metabolites (2012) 2, 733-755.