Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

A Dugourd, C Kuppe, M Sciacovelli, E Gjerga, A Gabor, KB Emdal, V Vieira, DB Bekker-Jensen, J Kranz, Eric Bindels, ASH Costa, A Sousa, P Beltrao, M Rocha, JV Olsen, C Frezza, Rafael Kramann, J Saez-Rodriguez

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Abstract

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.

Original languageEnglish
Article numbere9730
JournalMolecular Systems Biology
Volume17
Issue number1
DOIs
Publication statusPublished - 1 Jan 2021

Bibliographical note

Funding Information:
A.D. and E.G. were Marie‐Curie Early Stage Researchers supported by the European Union's Horizon 2020 research and innovation program (675585 Marie‐Curie ITN “SymBioSys”) to J.S.R. A.D. was funded by German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung BMBF) MSCoreSys research initiative research core SMART‐CARE (031L0212A). This work was further supported by the JRC for Computational Biomedicine which was partially funded by Bayer AG, and the Medical Research Council (MC_UU_12022/6 to C.F. and M.S.). The Novo Nordisk Foundation Center for Protein Research is supported by Novo Nordisk Foundation grant number NNF14CC0001. J.V.O. was funded by a grant from Danish Council for Independent Research (8020‐00100B) to partly support K.B.E. who was also supported in part by the Lundbeck Foundation (R193‐2015‐243). R.K. was supported by grants of the German Research Foundation (DFG: SFBTRR57, P30; SFBTRR219 C05, CRU344, P1), by a Grant of the European Research Council (ERC‐StG 677448), a Grant of the State of North Rhine‐Westphalia (Return to NRW), the BMBF eMed Consortia Fibromap, the ERA‐CVD Consortia MEND‐AGE, the Else Kroener Fresenius Foundation (EKFS) and the Interdisciplinary Centre for Clinical Research (IZKF) within the faculty of Medicine at the RWTH Aachen University (O3‐11). C.K. was supported by the German Society of Internal Medicine (DGIM). Thanks to Hyojin Kim for her contribution to the original COSMOS logo design. Thanks to Denes Turei for his help with putting the meta PKN online. We thank E. Ruppin and R. Katzir for helping us with the breast cancer dataset from Katzir (2019). Open Access funding enabled and organized by ProjektDEAL. et al

Funding Information:
The authors declare that they have no conflict of interest. C.F. is a member of the scientific advisory board of Owlstone and scientific adviser of Istesso. JSR has received funding from GSK and expects to receive funding from Sanofi and consultant fees from Travere Therapeutics.

Funding Information:
A.D. and E.G. were Marie-Curie Early Stage Researchers supported by the European Union's Horizon 2020 research and innovation program (675585 Marie-Curie ITN “SymBioSys”) to J.S.R. A.D. was funded by German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung BMBF) MSCoreSys research initiative research core SMART-CARE (031L0212A). This work was further supported by the JRC for Computational Biomedicine which was partially funded by Bayer AG, and the Medical Research Council (MC_UU_12022/6 to C.F. and M.S.). The Novo Nordisk Foundation Center for Protein Research is supported by Novo Nordisk Foundation grant number NNF14CC0001. J.V.O. was funded by a grant from Danish Council for Independent Research (8020-00100B) to partly support K.B.E. who was also supported in part by the Lundbeck Foundation (R193-2015-243). R.K. was supported by grants of the German Research Foundation (DFG: SFBTRR57, P30; SFBTRR219 C05, CRU344, P1), by a Grant of the European Research Council (ERC-StG 677448), a Grant of the State of North Rhine-Westphalia (Return to NRW), the BMBF eMed Consortia Fibromap, the ERA-CVD Consortia MEND-AGE, the Else Kroener Fresenius Foundation (EKFS) and the Interdisciplinary Centre for Clinical Research (IZKF) within the faculty of Medicine at the RWTH Aachen University (O3-11). C.K. was supported by the German Society of Internal Medicine (DGIM). Thanks to Hyojin Kim for her contribution to the original COSMOS logo design. Thanks to Denes Turei for his help with putting the meta PKN online. We thank E. Ruppin and R. Katzir for helping us with the breast cancer dataset from Katzir et al (2019). Open Access funding enabled and organized by ProjektDEAL.

Publisher Copyright:
© 2021 The Authors. Published under the terms of the CC BY 4.0 license

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