| Autor: |
Andrew J Sweatt, Cameron D Griffiths, Sarah M Groves, B Bishal Paudel, Lixin Wang, David F Kashatus, Kevin A Janes |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Molecular Systems Biology, Vol 20, Iss 11, Pp 1230-1256 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1744-4292 |
| DOI: |
10.1038/s44320-024-00064-3 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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