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In planta Identification of Putative Pathogenicity Factors from the Chickpea Pathogen Ascochyta rabiei by De novo Transcriptome Sequencing Using RNA-Seq and Massive Analysis of cDNA Ends.

Fondevilla S, Krezdorn N, Rotter B, Kahl G, Winter P - Front Microbiol (2015)

Bottom Line: Since pathogenicity factors are usually secreted, we predicted the A. rabiei secretome, yielding 550 putatively secreted proteins.MACE identified 596 transcripts that were up-regulated during infection.An analysis of these genes identified a collection of candidate pathogenicity factors and unraveled the pathogen's strategy for infecting its host.

View Article: PubMed Central - PubMed

Affiliation: Plant Molecular Biology, Institute for Molecular Bioscience, Goethe-University of Frankfurt Frankfurt am Main, Germany.

ABSTRACT
The most important foliar diseases in legumes worldwide are ascochyta blights. Up to now, in the Ascochyta-legume pathosystem most studies focused on the identification of resistance genes in the host, while very little is known about the pathogenicity factors of the fungal pathogen. Moreover, available data were often obtained from fungi growing under artificial conditions. Therefore, in this study we aimed at the identification of the pathogenicity factors of Ascochyta rabiei, causing ascochyta blight in chickpea. To identify potential fungal pathogenicity factors, we employed RNA-seq and Massive Analysis of cDNA Ends (MACE) to produce comprehensive expression profiles of A. rabiei genes isolated either from the fungus growing in absence of its host or from fungi infecting chickpea leaves. We further provide a comprehensive de novo assembly of the A. rabiei transcriptome comprising 22,725 contigs with an average length of 1178 bp. Since pathogenicity factors are usually secreted, we predicted the A. rabiei secretome, yielding 550 putatively secreted proteins. MACE identified 596 transcripts that were up-regulated during infection. An analysis of these genes identified a collection of candidate pathogenicity factors and unraveled the pathogen's strategy for infecting its host.

No MeSH data available.


Related in: MedlinePlus

Workflow followed to identify transcripts up-regulated “in planta” vs. “in medium” treatments.
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Figure 2: Workflow followed to identify transcripts up-regulated “in planta” vs. “in medium” treatments.

Mentions: A summary of the workflow employed for identification of transcripts differentially expressed during the infection process is shown in Figure 2. The reduced A. rabiei transcriptome was used for assignment of the transcripts. Transcript frequencies were calculated using a custom workflow. Briefly, MACE reads were mapped to the reduced A. rabiei transcriptome using the SOAP2.2 software (Li et al., 2008) allowing five base mismatches. Normalization and test for differential expression were performed using the DEseq package (Anders and Huber, 2010). To identify transcripts over-expressed during infection, normalized expression values were compared between “in planta” libraries and “in medium” libraries. A transcript was considered to be up-regulated during infection when the normalized expression value of the transcript was statistically significantly different (padjusted ≤ 0.05), and at least two times higher, “in planta” compared to “in medium” treatment for at least one time point after inoculation, or when the transcript was only sequenced in “in planta” libraries. As the number of transcripts from the fungus in “in planta” libraries were markedly smaller than “in medium” libraries, the absence or lower frequency of a transcript “in planta” compared to “in medium” was not considered to be accurate enough to define a transcript as down-regulated during infection. Therefore, only transcripts over-expressed “in planta” are reported in this study.


In planta Identification of Putative Pathogenicity Factors from the Chickpea Pathogen Ascochyta rabiei by De novo Transcriptome Sequencing Using RNA-Seq and Massive Analysis of cDNA Ends.

Fondevilla S, Krezdorn N, Rotter B, Kahl G, Winter P - Front Microbiol (2015)

Workflow followed to identify transcripts up-regulated “in planta” vs. “in medium” treatments.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4664620&req=5

Figure 2: Workflow followed to identify transcripts up-regulated “in planta” vs. “in medium” treatments.
Mentions: A summary of the workflow employed for identification of transcripts differentially expressed during the infection process is shown in Figure 2. The reduced A. rabiei transcriptome was used for assignment of the transcripts. Transcript frequencies were calculated using a custom workflow. Briefly, MACE reads were mapped to the reduced A. rabiei transcriptome using the SOAP2.2 software (Li et al., 2008) allowing five base mismatches. Normalization and test for differential expression were performed using the DEseq package (Anders and Huber, 2010). To identify transcripts over-expressed during infection, normalized expression values were compared between “in planta” libraries and “in medium” libraries. A transcript was considered to be up-regulated during infection when the normalized expression value of the transcript was statistically significantly different (padjusted ≤ 0.05), and at least two times higher, “in planta” compared to “in medium” treatment for at least one time point after inoculation, or when the transcript was only sequenced in “in planta” libraries. As the number of transcripts from the fungus in “in planta” libraries were markedly smaller than “in medium” libraries, the absence or lower frequency of a transcript “in planta” compared to “in medium” was not considered to be accurate enough to define a transcript as down-regulated during infection. Therefore, only transcripts over-expressed “in planta” are reported in this study.

Bottom Line: Since pathogenicity factors are usually secreted, we predicted the A. rabiei secretome, yielding 550 putatively secreted proteins.MACE identified 596 transcripts that were up-regulated during infection.An analysis of these genes identified a collection of candidate pathogenicity factors and unraveled the pathogen's strategy for infecting its host.

View Article: PubMed Central - PubMed

Affiliation: Plant Molecular Biology, Institute for Molecular Bioscience, Goethe-University of Frankfurt Frankfurt am Main, Germany.

ABSTRACT
The most important foliar diseases in legumes worldwide are ascochyta blights. Up to now, in the Ascochyta-legume pathosystem most studies focused on the identification of resistance genes in the host, while very little is known about the pathogenicity factors of the fungal pathogen. Moreover, available data were often obtained from fungi growing under artificial conditions. Therefore, in this study we aimed at the identification of the pathogenicity factors of Ascochyta rabiei, causing ascochyta blight in chickpea. To identify potential fungal pathogenicity factors, we employed RNA-seq and Massive Analysis of cDNA Ends (MACE) to produce comprehensive expression profiles of A. rabiei genes isolated either from the fungus growing in absence of its host or from fungi infecting chickpea leaves. We further provide a comprehensive de novo assembly of the A. rabiei transcriptome comprising 22,725 contigs with an average length of 1178 bp. Since pathogenicity factors are usually secreted, we predicted the A. rabiei secretome, yielding 550 putatively secreted proteins. MACE identified 596 transcripts that were up-regulated during infection. An analysis of these genes identified a collection of candidate pathogenicity factors and unraveled the pathogen's strategy for infecting its host.

No MeSH data available.


Related in: MedlinePlus