Proteome allocation is linked to transcriptional regulation through a modularized transcriptome.

Area of Science:

• Microbiology
• Systems Biology
• Bioinformatics

Background:

• Quantitatively linking the proteome (all proteins) to the transcriptome (all RNA transcripts) on a per-gene basis remains challenging.
• Recent data analytics advances allow meaningful modularization of the bacterial transcriptome.

Purpose of the Study:

• To investigate if matched transcriptome and proteome datasets from bacteria under diverse conditions can be modularized similarly.
• To reveal novel relationships between bacterial proteome and transcriptome compositions.

Main Methods:

• Modularization of bacterial transcriptome and proteome datasets.
• Comparative analysis of module compositions.
• Statistical modeling to infer proteome allocation from transcriptome data.

Main Results:

• Proteome and transcriptome modules share similar gene product compositions.
• Proteome modules often integrate multiple transcriptome modules.
• Differences between proteome and transcriptome modules reflect known regulatory mechanisms.
• Absolute proteome allocation can be statistically inferred from transcriptome data alone.

Conclusions:

• A quantitative and knowledge-based genome-scale relationship exists between the bacterial proteome and transcriptome.
• Modularization provides a framework for understanding proteome-transcriptome connections.
• Transcriptome data holds potential for predicting proteome allocation.