{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Sweatt AJ"],"funding":["HHS | National Institutes of Health","Human Frontier Science Program","HHS | National Institutes of Health (NIH)","NIAID NIH HHS","David and Lucile Packard Foundation (PF)","NHLBI NIH HHS","Human Frontier Science Program (HFSP)","NCI NIH HHS","David and Lucile Packard Foundation","NIGMS NIH HHS"],"pagination":["1230-1256"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11535397"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["20(11)"],"pubmed_abstract":["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."],"journal":["Molecular systems biology"],"pubmed_title":["Proteome-wide copy-number estimation from transcriptomics."],"pmcid":["PMC11535397"],"funding_grant_id":["U54-CA274499","T32 GM145443","2009-34710","LT000469/2021-L","R50-CA265089","T32-HL007284","R50 CA265089","T32 HL007284","R01 AI186222","U54 CA274499","P30 CA044579"],"pubmed_authors":["Sweatt AJ","Kashatus DF","Paudel BB","Griffiths CD","Janes KA","Groves SM","Wang L"],"additional_accession":[]},"is_claimable":false,"name":"Proteome-wide copy-number estimation from transcriptomics.","description":"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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2026-06-01T20:31:41.733Z","creation":"2025-04-04T11:43:02.276Z"},"accession":"S-EPMC11535397","cross_references":{"pubmed":["39333715"],"doi":["10.1038/s44320-024-00064-3"]}}