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BSocial: Deciphering Social Behaviors within Mixed Microbial Populations

Overview of attention for article published in Frontiers in Microbiology, May 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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Title
BSocial: Deciphering Social Behaviors within Mixed Microbial Populations
Published in
Frontiers in Microbiology, May 2017
DOI 10.3389/fmicb.2017.00919
Pubmed ID
Authors

Jessica Purswani, Rocío C. Romero-Zaliz, Antonio M. Martín-Platero, Isabel M. Guisado, Jesús González-López, Clementina Pozo

Abstract

Ecosystem functionality depends on interactions among populations, of the same or different taxa, and these are not just the sum of pairwise interactions. Thus, know-how of the social interactions occurring in mixed-populations are of high interest, however they are commonly unknown due to the limitations posed in tagging each population. The limitations include costs/time in tediously fluorescent tagging, and the number of different fluorescent tags. Tag-free strategies exist, such as high-throughput sequencing, but ultimately both strategies require the use of expensive machinery. Our work appoints social behaviors on individual strains in mixed-populations, offering a web-tool (BSocial http://m4m.ugr.es/BSocial.html) for analyzing the community framework. Our quick and cheap approach includes the periodic monitoring of optical density (OD) from a full combinatorial testing of individual strains, where number of generations and growth rate are determined. The BSocial analyses then enable us to determine how the addition/absence of a particular species affects the net productivity of a microbial community and use this to select productive combinations, i.e., designate their social effect on a general community. Positive, neutral, or negative assignations are applied to describe the social behavior within the community by comparing fitness effects of the community against the individual strain. The usefulness of this tool for selection of optimal inoculum in biofilm-based methyl tert-butyl ether (MTBE) bioremediation was demonstrated. The studied model uses seven bacterial strains with diverse MTBE degradation/growth capacities. Full combinatorial testing of seven individual strains (triplicate tests of 127 combinations) were implemented, along with MTBE degradation as the desired function. Sole observation of highest species fitness did not render the best functional outcome, and only when strains with positive and neutral social assignations were mixed (Rhodococcus ruber EE6, Agrobacterium sp. MS2 and Paenibacillus etheri SH7), was this obtained. Furthermore, the use of positive and neutral strains in all its combinations had a significant higher degradation mean (x1.75) than exclusive negative strain combinations. Thus, social microbial processes benefit bioremediation more than negative social microbial combinations. The BSocial webtool is a great contributor to the study of social interactions in bioremediation processes, and may be used in other natural or synthetic habitat studies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 28%
Student > Master 5 13%
Researcher 5 13%
Student > Doctoral Student 3 8%
Student > Bachelor 3 8%
Other 5 13%
Unknown 8 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 23%
Immunology and Microbiology 7 18%
Biochemistry, Genetics and Molecular Biology 5 13%
Environmental Science 3 8%
Business, Management and Accounting 2 5%
Other 4 10%
Unknown 10 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 19 October 2017.
All research outputs
#3,637,560
of 22,974,684 outputs
Outputs from Frontiers in Microbiology
#3,314
of 25,034 outputs
Outputs of similar age
#65,134
of 313,660 outputs
Outputs of similar age from Frontiers in Microbiology
#124
of 514 outputs
Altmetric has tracked 22,974,684 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 25,034 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has done well, scoring higher than 86% 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 313,660 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 514 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.