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Combining Shigella Tn-seq data with gold-standard E. coli gene deletion data suggests rare transitions between essential and non-essential gene functionality

Overview of attention for article published in BMC Microbiology, September 2016
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Title
Combining Shigella Tn-seq data with gold-standard E. coli gene deletion data suggests rare transitions between essential and non-essential gene functionality
Published in
BMC Microbiology, September 2016
DOI 10.1186/s12866-016-0818-0
Pubmed ID
Authors

Nikki E. Freed, Dirk Bumann, Olin K. Silander

Abstract

Gene essentiality - whether or not a gene is necessary for cell growth - is a fundamental component of gene function. It is not well established how quickly gene essentiality can change, as few studies have compared empirical measures of essentiality between closely related organisms. Here we present the results of a Tn-seq experiment designed to detect essential protein coding genes in the bacterial pathogen Shigella flexneri 2a 2457T on a genome-wide scale. Superficial analysis of this data suggested that 481 protein-coding genes in this Shigella strain are critical for robust cellular growth on rich media. Comparison of this set of genes with a gold-standard data set of essential genes in the closely related Escherichia coli K12 BW25113 revealed that an excessive number of genes appeared essential in Shigella but non-essential in E. coli. Importantly, and in converse to this comparison, we found no genes that were essential in E. coli and non-essential in Shigella, implying that many genes were artefactually inferred as essential in Shigella. Controlling for such artefacts resulted in a much smaller set of discrepant genes. Among these, we identified three sets of functionally related genes, two of which have previously been implicated as critical for Shigella growth, but which are dispensable for E. coli growth. The data presented here highlight the small number of protein coding genes for which we have strong evidence that their essentiality status differs between the closely related bacterial taxa E. coli and Shigella. A set of genes involved in acetate utilization provides a canonical example. These results leave open the possibility of developing strain-specific antibiotic treatments targeting such differentially essential genes, but suggest that such opportunities may be rare in closely related bacteria.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Argentina 1 2%
Canada 1 2%
Unknown 48 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 28%
Student > Ph. D. Student 12 24%
Student > Master 5 10%
Student > Bachelor 5 10%
Professor 3 6%
Other 4 8%
Unknown 7 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 16 32%
Agricultural and Biological Sciences 13 26%
Immunology and Microbiology 7 14%
Business, Management and Accounting 2 4%
Veterinary Science and Veterinary Medicine 2 4%
Other 2 4%
Unknown 8 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 October 2017.
All research outputs
#6,912,401
of 23,308,124 outputs
Outputs from BMC Microbiology
#765
of 3,238 outputs
Outputs of similar age
#105,929
of 336,159 outputs
Outputs of similar age from BMC Microbiology
#16
of 77 outputs
Altmetric has tracked 23,308,124 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 3,238 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done well, scoring higher than 76% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 336,159 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 77 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.