Evidence map›Paper›PMID 25112473›Full record

ArticleJournal of bacteriology2014

Towards an informative mutant phenotype for every bacterial gene.

Adam Deutschbauer, Morgan N Price, Kelly M Wetmore, Daniel R Tarjan, Zhuchen Xu, Wenjun Shao, Dacia Leon, Adam P Arkin, Jeffrey M Skerker

Abstract read
In one paragraph

Article in Journal of bacteriology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 citing papers in PubMed.

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  9. Cell Aggregation and Aerobic Respiration Are Important forApplied and environmental microbiology · 2019
    Article
  10. A Markerless Method for Genome Engineering inFrontiers in microbiology · 2019
    Article
  11. Article
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  13. Article
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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

9 authors.

Adam DeutschbauerPhysical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Morgan N PricePhysical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Kelly M WetmorePhysical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Daniel R TarjanEarth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA Energy Biosciences Institute, University of California, Berkeley, California, USA.
Zhuchen XuDepartment of Bioengineering, University of California, Berkeley, California, USA.
Wenjun ShaoPhysical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Dacia LeonEarth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA Energy Biosciences Institute, University of California, Berkeley, California, USA.
Adam P ArkinPhysical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA Energy Biosciences Institute, University of California, Berkeley, California, USA Department of Bioengineering, University of California, Berkeley, California, USA APArkin@lbl.gov skerker@berkeley.edu.
Jeffrey M SkerkerPhysical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA Energy Biosciences Institute, University of California, Berkeley, California, USA Department of Bioengineering, University of California, Berkeley, California, USA APArkin@lbl.gov skerker@berkeley.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mutant phenotypes provide strong clues to the functions of the underlying genes and could allow annotation of the millions of sequenced yet uncharacterized bacterial genes. However, it is not known how many genes have a phenotype under laboratory conditions, how many phenotypes are biologically interpretable for predicting gene function, and what experimental conditions are optimal to maximize the number of genes with a phenotype. To address these issues, we measured the mutant fitness of 1,586 genes of the ethanol-producing bacterium Zymomonas mobilis ZM4 across 492 diverse experiments and found statistically significant phenotypes for 89% of all assayed genes. Thus, in Z. mobilis, most genes have a functional consequence under laboratory conditions. We demonstrate that 41% of Z. mobilis genes have both a strong phenotype and a similar fitness pattern (cofitness) to another gene, and are therefore good candidates for functional annotation using mutant fitness. Among 502 poorly characterized Z. mobilis genes, we identified a significant cofitness relationship for 174. For 57 of these genes without a specific functional annotation, we found additional evidence to support the biological significance of these gene-gene associations, and in 33 instances, we were able to predict specific physiological or biochemical roles for the poorly characterized genes. Last, we identified a set of 79 diverse mutant fitness experiments in Z. mobilis that are nearly as biologically informative as the entire set of 492 experiments. Therefore, our work provides a blueprint for the functional annotation of diverse bacteria using mutant fitness.

Indexed as

Genetic FitnessBacterial ProteinsGene Expression Regulation, BacterialMutationShewanellaZymomonasBacterial Proteins

Identifiers

PMID25112473
PMCPMC4187699

What OpenQuestion holds

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Registered trials

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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.