Proteome allocation is linked to transcriptional regulation through a modularized transcriptome

Area of Science:

• Microbiology and Systems Biology
• Genomics and Proteomics

Background:

• Quantitatively relating bacterial proteomes to transcriptomes per gene remains challenging.
• Recent data analytics advances allow meaningful modularization of bacterial transcriptomes.

Approach:

• Investigated matched transcriptome and proteome datasets from bacteria under diverse conditions.
• Applied modularization techniques to both datasets to identify interrelationships.
• Utilized statistical modeling to infer proteome allocation from transcriptome data.

Key Points:

• Proteome and transcriptome modules share similar gene product compositions.
• Proteome modules often integrate multiple transcriptome modules.
• Differences between modules reflect known transcriptional and post-translational regulation, enabling knowledge mapping.
• Absolute proteome allocation can be inferred from transcriptome data alone.

Conclusions:

• Established quantitative and knowledge-based genome-scale relationships between bacterial proteomes and transcriptomes.
• Demonstrated the utility of modularization for understanding gene expression regulation.
• Opened avenues for predicting protein abundance from RNA sequencing data.