open access publication

Article, 2024

Proteome allocation is linked to transcriptional regulation through a modularized transcriptome

Nature Communications, ISSN 2041-1723, Volume 15, 1, Page 5234, 10.1038/s41467-024-49231-y

Contributors

Patel, Arjun 0000-0003-1959-0445 [1] Mcgrosso, Dominic M 0000-0001-6623-6268 [1] Hefner, Ying [1] Campeau, Anaamika 0000-0002-0241-8908 [1] Sastry, Anand V 0000-0002-8293-3909 [1] Maurya, Svetlana Rajkumar 0000-0002-4700-2711 [1] Rychel, Kevin 0000-0002-4769-2804 [1] Gonzalez, David J 0000-0003-1423-5970 [1] Palsson, Bernhard Orn 0000-0003-2357-6785 (Corresponding author) [1] [2]

Affiliations

  1. [1] University of California, San Diego
  2. [NORA names: United States; America, North; OECD];
  3. [2] Technical University of Denmark
  4. [NORA names: DTU Technical University of Denmark; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

It has proved challenging to quantitatively relate the proteome to the transcriptome on a per-gene basis. Recent advances in data analytics have enabled a biologically meaningful modularization of the bacterial transcriptome. We thus investigate whether matched datasets of transcriptomes and proteomes from bacteria under diverse conditions can be modularized in the same way to reveal novel relationships between their compositions. We find that; (1) the modules of the proteome and the transcriptome are comprised of a similar list of gene products, (2) the modules in the proteome often represent combinations of modules from the transcriptome, (3) known transcriptional and post-translational regulation is reflected in differences between two sets of modules, allowing for knowledge-mapping when interpreting module functions, and (4) through statistical modeling, absolute proteome allocation can be inferred from the transcriptome alone. Quantitative and knowledge-based relationships can thus be found at the genome-scale between the proteome and transcriptome in bacteria.

Keywords

allocation, analytes, bacteria, bacterial transcriptomes, combination, combination of modules, composition, conditions, data, data analytics, dataset, datasets of transcriptomes, diverse conditions, function, gene products, genes, genome-scale, model, modularity, modulation, modulation function, post-translational regulation, production, proteome allocation, proteomics, regulation, relationship, sets, sets of modules, statistical model, transcriptional regulation, transcriptome

Funders

  • National Institute of General Medical Sciences
  • Novo Nordisk Foundation

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