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Comparison of Fusarium graminearum Transcriptomes on Living or Dead Wheat Differentiates Substrate-Responsive and Defense-Responsive Genes

Overview of attention for article published in Frontiers in Microbiology, July 2016
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Title
Comparison of Fusarium graminearum Transcriptomes on Living or Dead Wheat Differentiates Substrate-Responsive and Defense-Responsive Genes
Published in
Frontiers in Microbiology, July 2016
DOI 10.3389/fmicb.2016.01113
Pubmed ID
Authors

Stefan Boedi, Harald Berger, Christian Sieber, Martin Münsterkötter, Imer Maloku, Benedikt Warth, Michael Sulyok, Marc Lemmens, Rainer Schuhmacher, Ulrich Güldener, Joseph Strauss

Abstract

Fusarium graminearum is an opportunistic pathogen of cereals where it causes severe yield losses and concomitant mycotoxin contamination of the grains. The pathogen has mixed biotrophic and necrotrophic (saprophytic) growth phases during infection and the regulatory networks associated with these phases have so far always been analyzed together. In this study we compared the transcriptomes of fungal cells infecting a living, actively defending plant representing the mixed live style (pathogenic growth on living flowering wheat heads) to the response of the fungus infecting identical, but dead plant tissues (cold-killed flowering wheat heads) representing strictly saprophytic conditions. We found that the living plant actively suppressed fungal growth and promoted much higher toxin production in comparison to the identical plant tissue without metabolism suggesting that molecules signaling secondary metabolite induction are not pre-existing or not stable in the plant in sufficient amounts before infection. Differential gene expression analysis was used to define gene sets responding to the active or the passive plant as main impact factor and driver for gene expression. We correlated our results to the published F. graminearum transcriptomes, proteomes, and secretomes and found that only a limited number of in planta- expressed genes require the living plant for induction but the majority uses simply the plant tissue as signal. Many secondary metabolite (SM) gene clusters show a heterogeneous expression pattern within the cluster indicating that different genetic or epigenetic signals govern the expression of individual genes within a physically linked cluster. Our bioinformatic approach also identified fungal genes which were actively repressed by signals derived from the active plant and may thus represent direct targets of the plant defense against the invading pathogen.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 100 100%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 25 25%
Student > Ph. D. Student 20 20%
Researcher 12 12%
Student > Bachelor 10 10%
Student > Master 8 8%
Other 10 10%
Unknown 15 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 40%
Biochemistry, Genetics and Molecular Biology 33 33%
Chemistry 4 4%
Engineering 2 2%
Immunology and Microbiology 1 1%
Other 3 3%
Unknown 17 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 11 August 2016.
All research outputs
#15,381,002
of 22,882,389 outputs
Outputs from Frontiers in Microbiology
#15,227
of 24,913 outputs
Outputs of similar age
#236,235
of 365,301 outputs
Outputs of similar age from Frontiers in Microbiology
#297
of 459 outputs
Altmetric has tracked 22,882,389 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 24,913 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one is in the 30th percentile – i.e., 30% 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 365,301 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 459 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.