Proteome-wide copy-number estimation from transcriptomics.

Area of Science:

• Systems Biology
• Genomics
• Proteomics

Background:

• Absolute protein copy numbers are crucial for understanding regulatory networks but are difficult to measure.
• Quantitative proteomic data is scarce compared to transcriptomic data from RNA sequencing.

Approach:

• Developed a statistical method to link messenger RNA (mRNA) levels to protein copy numbers using existing quantitative proteomics and transcriptomics data.
• Created gene-specific models that incorporate mRNA-protein and mRNA-mRNA dependencies to predict protein abundance.
• Validated the approach using independent cell lines and primary prostate samples, comparing it against null models and empirical ratios.

Key Points:

• Inferred protein abundances from mRNA accurately predict biological processes and protein-protein interaction complexes.
• The method was used to estimate viral receptor abundances for coxsackievirus B3 infection modeling.
• Applied to breast cancer RNA sequencing data, inferred protein levels reclassified 26% of Luminal A and 29% of Luminal B tumors, identifying key therapeutic targets like CDK4 and CTTNA2.

Conclusions:

• The mRNA-guided protein inference method achieves accuracy comparable to contemporary proteomics.
• This approach provides a valuable tool for systems biology, enabling mRNA-based prediction of absolute protein abundance.
• A web tool is available for researchers to access these gene-specific predictive models.