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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Updated: Jun 11, 2025

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
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Proteome-wide copy-number estimation from transcriptomics.

Andrew J Sweatt1, Cameron D Griffiths1, Sarah M Groves1

  • 1Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA.

Molecular Systems Biology
|September 27, 2024
PubMed
Summary
This summary is machine-generated.

This study develops a statistical method to infer protein copy numbers from messenger RNA (mRNA) levels, outperforming existing approaches. The findings enhance understanding of gene regulatory networks and disease classification.

Keywords:
CCLECVB3PinfernaSWATHTMT

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Area of Science:

  • Systems Biology
  • Genomics
  • Proteomics

Background:

  • Protein abundance data is crucial for understanding biological regulation but is less available than RNA sequencing (RNA-seq) data.
  • Existing methods for inferring protein levels from mRNA have limitations.

Purpose of the Study:

  • To develop and validate a statistical model for inferring protein copy numbers from mRNA expression data.
  • To assess the accuracy of inferred protein levels against existing benchmarks.
  • To apply the method to biological questions in viral infection and cancer.

Main Methods:

  • Statistical modeling integrating quantitative proteomics and transcriptomics data from 4366 genes across 369 cell lines.
  • Hierarchical model building to link mRNA and protein levels, considering gene-specific dependencies.
  • Validation against null models, protein-abundance repositories, empirical ratios, and a proteogenomic challenge winner.

Main Results:

  • Inferred protein levels from mRNA significantly outperformed various null models and existing methods.
  • The mRNA-to-protein relationships captured biological processes and protein complexes.
  • The method identified a viral-receptor abundance threshold for coxsackievirus B3 susceptibility.
  • Inferred protein copy numbers re-classified 26-29% of luminal breast cancer tumors.

Conclusions:

  • The developed gene-centered approach accurately infers protein abundance from mRNA, achieving accuracy comparable to proteomics reproducibility.
  • This method provides a valuable tool for systems biology, particularly when direct proteomic data is limited.
  • Inferred protein levels have significant implications for understanding disease mechanisms and improving cancer diagnostics.