Network integration of thermal proteome profiling with multi-omics data decodes PARP inhibition.
Mira L BurtscherStephan GadeMartín Garrido-RodriguezAnna RutkowskaThilo WernerH Christian EberlMassimo PetretichNatascha KnopfKatharina ZirngiblPaola GrandiGiovanna BergaminiMarcus BantscheffMaria Fälth-SavitskiJulio Saez-RodriguezPublished in: Molecular systems biology (2024)
Complex disease phenotypes often span multiple molecular processes. Functional characterization of these processes can shed light on disease mechanisms and drug effects. Thermal Proteome Profiling (TPP) is a mass-spectrometry (MS) based technique assessing changes in thermal protein stability that can serve as proxies of functional protein changes. 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 multi-omics integration framework, to provide an integrated view of transcription factors, kinases and proteins with altered thermal stability. This allowed us to recover consequences of Poly (ADP-ribose) polymerase (PARP) inhibition in ovarian cancer cells on cell cycle and DNA damage response as well as interferon and hippo signaling. We found that TPP offers a complementary perspective to other omics data modalities, and that its integration allowed us to obtain a more complete molecular overview of PARP inhibition. We anticipate that this strategy can be used to integrate functional proteomics with other omics to study molecular processes.
Keyphrases
- single cell
- mass spectrometry
- cell cycle
- dna repair
- dna damage response
- dna damage
- transcription factor
- electronic health record
- cell proliferation
- big data
- single molecule
- liquid chromatography
- multiple sclerosis
- high resolution
- binding protein
- emergency department
- ms ms
- amino acid
- high performance liquid chromatography
- gas chromatography
- protein protein
- machine learning
- artificial intelligence
- tandem mass spectrometry
- dna binding
- deep learning