Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data

Laurence Yang, Justin Tan, Edward J. O'Brien, Jonathan M. Monk, Donghyuk Kim, Howard J. Li, Pep Charusanti, Ali Ebrahim, Colton J. Lloyd, James T. Yurkovich, Bin Du, Andreas Draeger, Alex Thomas, Yuekai Sun, Michael A. Saunders, Bernhard O. Palsson

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Finding the minimal set of gene functions needed to sustain life is of both fundamental and practical importance. Minimal gene lists have been proposed by using comparative genomics-based core proteome definitions. A definition of a core proteome that is supported by empirical data, is understood at the systems-level, and provides a basis for computing essential cell functions is lacking. Here, we use a systems biology-based genome-scale model of metabolism and expression to define a functional core proteome consisting of 356 gene products, accounting for 44% of the Escherichia coli proteome by mass based on proteomics data. This systems biology core proteome includes 212 genes not found in previous comparative genomics-based core proteome definitions, accounts for 65% of known essential genes in E. coli, and has 78% gene function overlap with minimal genomes (Buchnera aphidicola and Mycoplasma genitalium). Based on transcriptomics data across environmental and genetic backgrounds, the systems biology core proteome is significantly enriched in nondifferentially expressed genes and depleted in differentially expressed genes. Compared with the noncore, core gene expression levels are also similar across genetic backgrounds (two times higher Spearman rank correlation) and exhibit significantly more complex transcriptional and posttranscriptional regulatory features (40% more transcription start sites per gene, 22% longer 5'UTR). Thus, genome-scale systems biology approaches rigorously identify a functional core proteome needed to support growth. This framework, validated by using high-throughput datasets, facilitates a mechanistic understanding of systems-level core proteome function through in silico models; it de facto defines a paleome.
Original languageEnglish
JournalProceedings of the National Academy of Sciences of the United States of America
Volume112
Issue number34
Pages (from-to)10810-10815
Number of pages6
ISSN0027-8424
DOIs
Publication statusPublished - 2015

Keywords

  • Constraint-based modeling
  • Metabolism
  • Gene expression
  • Minimal genome
  • Core proteome

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