Proteome-wide copy-number estimation from transcriptomics

• Published in: Molecular systems biology
• 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: Germany 27.09.2024
• Subjects: 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.