Proteome-wide copy-number estimation from transcriptomics

• Published in: Molecular systems biology Vol. 20; no. 11; pp. 1230 - 1256
• Main Authors: Sweatt, Andrew J, Griffiths, Cameron D, Groves, Sarah M, Paudel, B Bishal, Wang, Lixin, Kashatus, David F, Janes, Kevin A
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 04.11.2024; Springer Nature
• Subjects: Biomedical and Life Sciences; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; CCLE; Cell Line, Tumor; CVB3; EMBO10; EMBO56; Female; Gene Dosage; Gene Expression Profiling - methods; Gene Regulatory Networks; Humans; Life Sciences; Method; Pinferna; Proteome - genetics; Proteome - metabolism; Proteomics - methods; RNA, Messenger - genetics; RNA, Messenger - metabolism; SWATH; Systems Biology; TMT; Transcriptome; TMT; CVB3; SWATH; CCLE; Pinferna
• ISSN: 1744-4292; 1744-4292
• DOI: 10.1038/s44320-024-00064-3

Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein’s median copy number and hierarchically appends mRNA–protein and mRNA–mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26–29% of luminal tumors. By adopting a gene-centered perspective of mRNA–protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics. Synopsis A simple, data-driven method links transcript abundance from RNA-seq to per-cell protein abundance from mass spectrometry. Pinferna outperforms existing approaches, accurately parameterizes systems biology models, and reclassifies tumor subtypes. Pinferna uses three quantitative formalisms that capture measured RNA-protein relationships for 4366 human genes in 369 cancer cell lines. Pinferna consistently yields a more accurate inferred proteome than random sampling of existing proteomes. Using Pinferna-derived initial conditions, a systems-biology model correctly predicts a viral-receptor abundance threshold for infectability. Pinferna reclusters canonical subtypes of breast cancer and predicts new abundance dependencies for a cyclin-dependent kinase that are experimentally validated. A simple, data-driven method links transcript abundance from RNA-seq to per-cell protein abundance from mass spectrometry. Pinferna outperforms existing approaches, accurately parameterizes systems biology models, and reclassifies tumor subtypes.