Complementation of <i>CTB7</i> in the Maize Pathogen <i>Cercospora zeina</i> Overcomes the Lack of In Vitro Cercosporin Production

Swart, Velushka; Crampton, Bridget Genevieve; Ridenour, John B.; Bluhm, Burt H.; Olivier, Nicholas Abraham; Meyer, J.J.M.; Berger, David Kenneth

doi:10.1094/mpmi-03-17-0054-r

Cited by 30 publications

(45 citation statements)

References 50 publications

(35 reference statements)

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“…In contrast, the gene was expressed at a lower level in B73 plants and was not induced by C. zeina in the glasshouse experiment ( Figure 6) (Supplementary Figure S5A). This is consistent with a previous RNAseq study of B73 field-grown plants that did not show differential expression of the gene between leaves with high and low C. zeina content ( Supplementary Table S4) (Swart et al, 2017).…”

Section: The Cml444 Copy Of the Lectinsupporting

confidence: 93%

“…This site is a hotspot for GLS disease. GLS disease and RNAseq data from the B73 plants in this field trial have been reported by Christie et al (2017) and Swart et al (2017) (NCBI GEO accession GSE94442; RRID:SCR_006472); however, the current study used data from the B73-QTL plants.…”

Section: Gls Disease Assessment Of Field-grown Maizementioning

confidence: 99%

“…Another aim of the differential gene expression analysis was to compare global gene expression of maize genes between B73 and B73-QTL plants challenged with C. zeina. A previous study (Swart et al, 2017) had examined differential expression in B73 control and B73 C. zeina-challenged plants in the same field trial (GEO database GSE94442). This data was compared to the expression profiles of B73 genes in B73-QTL plants that were found to be differentially expressed in response to C. zeina.…”

Section: Bioinformatics Pipeline 2: Differential Expression Analysismentioning

confidence: 99%

“…The maize glasshouse inoculation experiment with C. zeina isolate CMW25467 was conducted using the protocol described in Swart et al (2017). Maize B73 and B73-QTL plants were inoculated at the V8 stage of development (7 weeks after planting).…”

Section: Maize Inoculations With C Zeina In the Glasshousementioning

confidence: 99%

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De novo Assembly of Transcriptomes From a B73 Maize Line Introgressed With a QTL for Resistance to Gray Leaf Spot Disease Reveals a Candidate Allele of a Lectin Receptor-Like Kinase

et al. 2020

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Gray leaf spot (GLS) disease in maize, caused by the fungus Cercospora zeina, is a threat to maize production globally. Understanding the molecular basis for quantitative resistance to GLS is therefore important for food security. We developed a de novo assembly pipeline to identify candidate maize resistance genes. Near-isogenic maize lines with and without a QTL for GLS resistance on chromosome 10 from inbred CML444 were produced in the inbred B73 background. The B73-QTL line showed a 20% reduction in GLS disease symptoms compared to B73 in the field (p = 0.01). B73-QTL leaf samples from this field experiment conducted under GLS disease pressure were RNA sequenced. The reads that did not map to the B73 or C. zeina genomes were expected to contain novel defense genes and were de novo assembled. A total of 141 protein-coding sequences with B73-like or plant annotations were identified from the B73-QTL plants exposed to C. zeina. To determine whether candidate gene expression was induced by C. zeina, the RNAseq reads from C. zeina-challenged and control leaves were mapped to a master assembly of all of the B73-QTL reads, and differential gene expression analysis was conducted. Combining results from both bioinformatics approaches led to the identification of a likely candidate gene, which was a novel allele of a lectin receptor-like kinase named L-RLK-CML that (i) was induced by C. zeina, (ii) was positioned in the QTL region, and (iii) had functional domains for pathogen perception and defense signal transduction. The 817AA L-RLK-CML protein had 53 amino acid differences from its 818AA counterpart in B73. A second "B73like" allele of L-RLK was expressed at a low level in B73-QTL. Gene copy-specific RT-qPCR confirmed that the l-rlk-cml transcript was the major product induced fourfold by C. zeina. Several other expressed defense-related candidates were identified, including a wall-associated kinase, two glutathione s-transferases, a chitinase, a glucan Frontiers in Plant Science | www.frontiersin.org 1 March 2020 | Volume 11 | Article 191Welgemoed et al.Maize de novo Transcriptome QDR GLS beta-glucosidase, a plasmodesmata callose-binding protein, several other receptorlike kinases, and components of calcium signaling, vesicular trafficking, and ethylene biosynthesis. This work presents a bioinformatics protocol for gene discovery from de novo assembled transcriptomes and identifies candidate quantitative resistance genes.

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Section: The Cml444 Copy Of the Lectinsupporting

confidence: 93%

Section: Gls Disease Assessment Of Field-grown Maizementioning

confidence: 99%

Section: Bioinformatics Pipeline 2: Differential Expression Analysismentioning

confidence: 99%

Section: Maize Inoculations With C Zeina In the Glasshousementioning

confidence: 99%

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De novo Assembly of Transcriptomes From a B73 Maize Line Introgressed With a QTL for Resistance to Gray Leaf Spot Disease Reveals a Candidate Allele of a Lectin Receptor-Like Kinase

et al. 2020

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“…The initial set of RNA expression features included data from 2-3 replicates of 79 distinct tissue types in the maize inbred B73 (222 total samples) (Stelpflug et al, 2016) and 52 samples from biotic and biotic stress studies of B73 in different labs (Opitz et al, 2014;Makarevitch et al, 2015;Swart et al, 2017) for a total of 274 distinct samples. Normalized (FPKM: fragments per kilobase of exon per million aligned reads) expression values for each gene in each experiment were obtained from (Hoopes et al, 2019).…”

Section: Composition Of the Prediction Variable Datasetmentioning

confidence: 99%

Non-Homology-Based Prediction of Gene Functions

Dai

Liang

et al. 2019

Preprint

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Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations.

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Non‐homology‐based prediction of gene functions in maize (Zea mays ssp. mays)

Dai

Liang

et al. 2020

The Plant Genome

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Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, randomforest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated "gold standard" GO annotation set. Non-homology

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Complementation of CTB7 in the Maize Pathogen Cercospora zeina Overcomes the Lack of In Vitro Cercosporin Production

Cited by 30 publications

References 50 publications

De novo Assembly of Transcriptomes From a B73 Maize Line Introgressed With a QTL for Resistance to Gray Leaf Spot Disease Reveals a Candidate Allele of a Lectin Receptor-Like Kinase

De novo Assembly of Transcriptomes From a B73 Maize Line Introgressed With a QTL for Resistance to Gray Leaf Spot Disease Reveals a Candidate Allele of a Lectin Receptor-Like Kinase

Non-Homology-Based Prediction of Gene Functions

Non‐homology‐based prediction of gene functions in maize (Zea mays ssp. mays)

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