Evidence map›Paper›PMID 26261351›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2015

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 and 6 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

  1. Article
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  15. Proteogenomic Analysis ofFrontiers in microbiology · 2018
    Article
  16. Article
  17. Thermosensitivity of growth is determined by chaperone-mediated proteome reallocation.Proceedings of the National Academy of Sciences of the United States of America · 2017
    Article
  18. Global transcriptional regulatory network forProceedings of the National Academy of Sciences of the United States of America · 2017
    Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Laurence YangDepartment of Bioengineering, University of California at San Diego, La Jolla, CA 92093;
Justin TanDepartment of Bioengineering, University of California at San Diego, La Jolla, CA 92093;
Edward J O'BrienDepartment of Bioengineering, University of California at San Diego, La Jolla, CA 92093;
Jonathan M MonkDepartment of Bioengineering, University of California at San Diego, La Jolla, CA 92093;
Donghyuk KimDepartment of Bioengineering, University of California at San Diego, La Jolla, CA 92093;
Howard J LiDepartment of Bioengineering, University of California at San Diego, La Jolla, CA 92093;
Pep CharusantiDepartment of Bioengineering, University of California at San Diego, La Jolla, CA 92093;
Ali EbrahimDepartment of Bioengineering, University of California at San Diego, La Jolla, CA 92093;
Colton J LloydDepartment of Bioengineering, University of California at San Diego, La Jolla, CA 92093;
James T YurkovichDepartment of Bioengineering, University of California at San Diego, La Jolla, CA 92093;
Bin DuDepartment of Bioengineering, University of California at San Diego, La Jolla, CA 92093;
Andreas DrägerDepartment of Bioengineering, University of California at San Diego, La Jolla, CA 92093; Center for Bioinformatics Tuebingen, University of Tuebingen, 72076 Tübingen, Germany;ORCID http://orcid.org/0000-0002-1240-5553
Alex ThomasDepartment of Bioengineering, University of California at San Diego, La Jolla, CA 92093; Novo Nordisk Foundation Center for Biosustainability, 2970 Hørsholm, Denmark;
Yuekai SunInstitute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305;
Michael A SaundersDepartment of Management Science and Engineering, Stanford University, Stanford, CA 94305.
Bernhard O PalssonDepartment of Bioengineering, University of California at San Diego, La Jolla, CA 92093; Novo Nordisk Foundation Center for Biosustainability, 2970 Hørsholm, Denmark; palsson@ucsd.edu.

Funding

GENOMICALLY BASED MODELS FOR ANTIBIOTIC DEVELOPMENTR01GM057089 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PALSSON, BERNHARD O · 1998 to 2021
$6.9M
Model-guided Identification of Synthetic Lethal Genes for Drug Target DevelopmentR01GM098105 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI PALSSON, BERNHARD O · 2011 to 2013
$1.8M
Multiscale Molecular Systems Biology: Reconstruction and Model OptimizationU01GM102098 · NIGMS · STANFORD UNIVERSITY · PI SAUNDERS, MICHAEL ALAN · 2012 to 2016
$1.8M
NIGMS NIH HHS 1R01GM098105NIGMS NIH HHS R01 GM057089NIGMS NIH HHS R01 GM098105NIGMS NIH HHS U01 GM102098
6 · The paper itself

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.

Indexed as

Gene Expression Regulation, BacterialGenes, BacterialHigh-Throughput Screening AssaysMetabolomeProteomeSystems BiologyBuchneraComputer SimulationDatasets as TopicEscherichia coliEscherichia coli ProteinsModels, BiologicalMultigene FamilyMycoplasma genitaliumTranscriptomeEscherichia coli ProteinsProteomeconstraint-based modelingcore proteomegene expressionmetabolismminimal genome

Identifiers

PMID26261351
PMCPMC4553782

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.