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Deciphering the Pathobiome: Intra- and Interkingdom Interactions Involving the Pathogen Erysiphe alphitoides

Overview of attention for article published in Microbial Ecology, May 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#41 of 2,195)
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
twitter
25 X users
googleplus
1 Google+ user

Citations

dimensions_citation
90 Dimensions

Readers on

mendeley
138 Mendeley
Title
Deciphering the Pathobiome: Intra- and Interkingdom Interactions Involving the Pathogen Erysiphe alphitoides
Published in
Microbial Ecology, May 2016
DOI 10.1007/s00248-016-0777-x
Pubmed ID
Authors

Boris Jakuschkin, Virgil Fievet, Loïc Schwaller, Thomas Fort, Cécile Robin, Corinne Vacher

Abstract

Plant-inhabiting microorganisms interact directly with each other, forming complex microbial interaction networks. These interactions can either prevent or facilitate the establishment of new microbial species, such as a pathogen infecting the plant. Here, our aim was to identify the most likely interactions between Erysiphe alphitoides, the causal agent of oak powdery mildew, and other foliar microorganisms of pedunculate oak (Quercus robur L.). We combined metabarcoding techniques and a Bayesian method of network inference to decipher these interactions. Our results indicate that infection with E. alphitoides is accompanied by significant changes in the composition of the foliar fungal and bacterial communities. They also highlight 13 fungal operational taxonomic units (OTUs) and 13 bacterial OTUs likely to interact directly with E. alphitoides. Half of these OTUs, including the fungal endophytes Mycosphaerella punctiformis and Monochaetia kansensis, could be antagonists of E. alphitoides according to the inferred microbial network. Further studies will be required to validate these potential interactions experimentally. Overall, we showed that a combination of metabarcoding and network inference, by highlighting potential antagonists of pathogen species, could potentially improve the biological control of plant diseases.

X Demographics

X Demographics

The data shown below were collected from the profiles of 25 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 138 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
France 2 1%
South Africa 1 <1%
Unknown 133 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 38 28%
Student > Ph. D. Student 34 25%
Student > Master 14 10%
Student > Bachelor 10 7%
Student > Doctoral Student 4 3%
Other 14 10%
Unknown 24 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 68 49%
Biochemistry, Genetics and Molecular Biology 12 9%
Environmental Science 11 8%
Mathematics 3 2%
Medicine and Dentistry 3 2%
Other 9 7%
Unknown 32 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 29. 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 28 August 2023.
All research outputs
#1,329,887
of 25,369,304 outputs
Outputs from Microbial Ecology
#41
of 2,195 outputs
Outputs of similar age
#22,259
of 312,333 outputs
Outputs of similar age from Microbial Ecology
#4
of 42 outputs
Altmetric has tracked 25,369,304 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,195 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has done particularly well, scoring higher than 98% 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 312,333 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.