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Transcriptional profiling of Klebsiella pneumoniae defines signatures for planktonic, sessile and biofilm-dispersed cells

Overview of attention for article published in BMC Genomics, March 2016
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Title
Transcriptional profiling of Klebsiella pneumoniae defines signatures for planktonic, sessile and biofilm-dispersed cells
Published in
BMC Genomics, March 2016
DOI 10.1186/s12864-016-2557-x
Pubmed ID
Authors

Cyril Guilhen, Nicolas Charbonnel, Nicolas Parisot, Nathalie Gueguen, Agnès Iltis, Christiane Forestier, Damien Balestrino

Abstract

Surface-associated communities of bacteria, known as biofilms, play a critical role in the persistence and dissemination of bacteria in various environments. Biofilm development is a sequential dynamic process from an initial bacterial adhesion to a three-dimensional structure formation, and a subsequent bacterial dispersion. Transitions between these different modes of growth are governed by complex and partially known molecular pathways. Using RNA-seq technology, our work provided an exhaustive overview of the transcriptomic behavior of the opportunistic pathogen Klebsiella pneumoniae derived from free-living, biofilm and biofilm-dispersed states. For each of these conditions, the combined use of Z-scores and principal component analysis provided a clear illustration of distinct expression profiles. In particular, biofilm-dispersed cells appeared as a unique stage in the bacteria lifecycle, different from both planktonic and sessile states. The K-means cluster analysis showed clusters of Coding DNA Sequences (CDS) and non-coding RNA (ncRNA) genes differentially transcribed between conditions. Most of them included dominant functional classes, emphasizing the transcriptional changes occurring in the course of K. pneumoniae lifestyle transitions. Furthermore, analysis of the whole transcriptome allowed the selection of an overall of 40 transcriptional signature genes for the five bacterial physiological states. This transcriptional study provides additional clues to understand the key molecular mechanisms involved in the transition between biofilm and the free-living lifestyles, which represents an important challenge to control both beneficial and harmful biofilm. Moreover, this exhaustive study identified physiological state specific transcriptomic reference dataset useful for the research community.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
Unknown 72 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 25%
Researcher 16 22%
Student > Master 10 14%
Student > Doctoral Student 5 7%
Student > Bachelor 4 5%
Other 8 11%
Unknown 12 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 30%
Biochemistry, Genetics and Molecular Biology 16 22%
Immunology and Microbiology 11 15%
Medicine and Dentistry 5 7%
Engineering 3 4%
Other 4 5%
Unknown 12 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 18 March 2016.
All research outputs
#8,552,680
of 14,207,170 outputs
Outputs from BMC Genomics
#4,725
of 8,338 outputs
Outputs of similar age
#133,940
of 265,420 outputs
Outputs of similar age from BMC Genomics
#1
of 2 outputs
Altmetric has tracked 14,207,170 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,338 research outputs from this source. They receive a mean Attention Score of 4.2. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
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 265,420 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them