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Integration of Thermal Proteome Profiling with phosphoproteomic and transcriptomic data via mechanistic network models decodes the molecular response to PARP inhibition

GSE243208 Homo sapiens Expression profiling by high throughput sequencing 6 samples Submitted 2024/04/10 Platform GPL16791
Summary
The deregulation of complex diseases often spans multiple molecular processes. A multimodal functional characterization of these processes can shed light on the disease mechanisms and the effect of drugs. Thermal Proteome Profiling (TPP) is a mass-spectrometry based technique assessing changes in thermal protein stability that can serve as proxies of functional changes of the proteome. These unique insights of TPP can complement those obtained by other omics technologies. Here, we show how TPP can be integrated with phosphoproteomics and transcriptomics in a network-based approach using COSMOS, a framework for causal integration of multi-omics, to provide an integrated view of transcription factors, kinases and proteins with altered thermal stability. This allowed us to recover known mechanistic consequences of PARP inhibition in ovarian cancer cells on cell cycle and DNA damage response in detail and to uncover new insights into drug response mechanisms related to interferon and hippo signaling. We found that TPP complements the other omics data and allowed us to obtain a network model with higher coverage of the main underlying mechanisms. These results illustrate the added value of TPP, and more generally the power of network models to integrate the information provided by different omics technologies. We anticipate that this strategy can be used to broadly integrate functional proteomics with other omics to study complex molecular processes.
Published in
Network integration of thermal proteome profiling with multi-omics data decodes PARP inhibition
Burtscher ML, Gade S, Garrido-Rodriguez M et al. · Molecular systems biology 2024 · PMID 38454145 · doi:10.1038/s44320-024-00025-w
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Also filed as BioProject PRJNA1017402 and SRA study SRP460549. Searching any of these in the dataset finder brings you back here.

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