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

Abstract

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.

EASTR: Correcting systematic alignment errors in multi-exon genes

“…To demonstrate the versatility and applicability of EASTR, we applied it to three model organisms with distinct genomic repeat content and to samples with different library preparation methods. The organisms and tissues we selected were human brain [9], Zea mays (maize) leaves [10] and pollen [11], and Arabidopsis thaliana [12]. Our objective was to highlight the effectiveness of EASTR in enhancing alignment accuracy across a broad range of organisms and experimental designs.…”

High-density mapping for gray leaf spot resistance using two related tropical maize recombinant inbred line populations

“…Meanwhile, the increase of SA, carotenoids and reactive oxygen species has positive correlation with the defense response [33]. Studies by de novo assembly of transcriptomes of B73 containing a GLS resistance QTL derived from CML444 have identi ed several likely candidate genes, including a lectin receptor-like kinase, a wall-associated kinase, two glutathione s-transferases [34]. Armadillo (ARM) repeat proteins also response to biotic and abiotic stress [35], one gene GRMZE2G476902 linked to a signi cantly associated SNP which was associated with GLS encodes an ARM repeat protein [20].…”

Reorienting research investments toward under‐researched crops for sustainable food systems

“…Studies on food systems have increasingly involved biotechnology and genomics (Agarwal et al., 2021; Welgemoed et al., 2020), crop improvement and sustainable production (Quaye et al., 2021; Wahab et al., 2020), plant pathogens (Livoi et al., 2021; Nguetti et al., 2019), and crop modeling (Adisa et al., 2018; Ogbazghi et al., 2019). Other studies have looked at gender (Akrong et al., 2020; Torvikey, 2021), climate change, and policy (Beinah & Kunyanga, 2020; Bii et al., 2020) to enhance food system sustainability.…”