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

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 "B73-like" 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 four-fold by C. zeina. Several other expressed defense-related candidates were identified, including a wall-associated kinase, two glutathione s-transferases, a chitinase, a glucan beta-glucosidase, a plasmodesmata callose-binding protein, several other receptor-like 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

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

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 “B73- like” 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 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.This work was based on research supported in part by the National Research Foundation of South Africa (Grant Number 98977) and the Genomics Research Institute of the University of Pretoria (UP), South Africa.http://www.frontiersin.org/Plant_Scienceam2020BiochemistryForestry and Agricultural Biotechnology Institute (FABI)GeneticsMicrobiology and Plant PathologyPlant Production and Soil Scienc

Next generation sequencing reveals past and current widespread occurrence of maize yellow mosaic virus in South Africa

Maize yellow mosaic virus (MaYMV) is a single-stranded RNA polerovirus first identified in China. MaYMV was recently reported from West and East Africa, however it had not yet been reported from southern Africa. RNA-seq data from South African field-grown fungal-infected maize was mined for viral sequences by de novo assembly of reads that did not map to the maize or fungal genomes. Predicted proteins from the de novo-assembled unmapped reads matched MaYMV proteins with regions of 96–100% identity. MaYMV was detected in maize RNAseq data from 2009, 2012 and 2013. Complete South African MaYMV genome sequences (5642 nt) were determined by RT-PCR and Sanger sequencing of samples from two different maize genotypes, years, and sites. Phylogenetic analysis confirmed the species identity as MaYMV, and showed separate clustering of isolates between Africa, Asia and South America. Some MaYMV positive samples had reads matching Potyviridae (Johnson grass mosaic virus and Sugarcane mosaic virus), and mycoviruses (Setosphaeria turcica hypovirus 1, Bipolaris maydis partitivirus 1, and Pleospora typhicola fusarivirus 1). A 2016/2017 RT-PCR survey of maize plants exhibiting virus-like symptoms, such as yellowing and streaking patterns, revealed MaYMV in 39 samples from six provinces in South Africa. This report documents the earliest known MaYMV infection world-wide, and indicates that the virus is widespread throughout Africa.DATA AVAILABILITY: All data is available in the manuscript and Electronic Supplementary Material. The MaYMV RSA BR1A and MaYMV RSA SCM genome sequences have been deposited in Genbank (Accessions MG570476; MN943641, respectively). RNA-seq data has been deposited at the NCBI GEO (Gene Expression Omnibus) repository (Accessions GSE94442, GSE99005).Online Resource 1. Bioinformatics pipeline based on de novo assembly of unmapped reads used for discovery of maize yellow mosaic virus in maize RNA‐seq data. (a) Raw reads were assessed with FastQC, trimmed with Trimmomatic based on the FastQC results, and aligned to the reference genomes of maize and C. zeina to collect unmapped reads. (b)Unmapped reads were assembled using Trinity, their protein sequences predicted with TransDecoder and compared against the NCBI nr database using BLASTP.Online Resource 2. Oligonucleotide primers used to amplify and Sanger sequence the complete genomes of Maize yellow mosaic virus isolates RSA BR1A and RSA SCM.Online Resource 3. Percent nucleotide identity for complete genomes between maize yellow mosaic virus from South Africa and other worldwide isolates.Online Resource 4. Comparison of predicted maize yellow mosaic virus proteins of RSA BR1A and RSA SCM from South Africa and MaYMV Yunnan 11 from China.Online Resource 5. RNA-dependent RNA polymerase-based phylogenetic analysis of maize yellow mosaic virus and related Poleroviruses. The evolutionary history was inferred from the RNA-dependent RNA polymerasenucleotide sequences by using the Maximum Likelihood method with the GTRGAMMA model. Bootstrap consensus values are shown at the nodes. The sequences were extracted from complete genome sequences with the following NCBI accession numbers: RSA BR1A (MG570476), RSA SCM (MN943641), Kenya KALRO (MH205607), Kenya MYDV-like (MF974579), Tanzania 76 (MG664790.1), Ethiopia (MF684369), Nigeria (KY684356.1), China Y11 (KU248489.1), China Y1 (KU179221.1), China MYDV-RMV2 (KT992824.1), China SC (MK652149), Brazil (KY940544.1), Ecuador (KY052793), BVG Gimje (KT962089.1) and MYDV-RMV (KC921392.1). The latter two were used as outgroups. The scale bar indicates the number of nucleotide substitutions per site.Online Resource 6. Confirmation by Sanger sequencing that the expected maize yellow mosaic virus RT-PCR product was amplified from maize inbred B73 sample BR1B. Sequences derived from sequencing the 753bp RT-PCR product with the MaYMV-F or MaYMV-R primers were named BR1B.MaYMV.F or BR1B.MaYMV.R, respectively. These sequences were aligned to the corresponding sequence from the assembled MaYMV RSA BR1A genome sequence determined by 5’RACE, 3’RACE, RT-PCR and Sanger sequencing [BR1A (MG570476)], and the reference sequence MaYMV Yunnan 11 from China [KU248489]. The MaYMV-F and MaYMV-R primer sequences are reported in Chen et al. (2016). Non-consensus sites are shown by an asterisk.Online Resource 7. Representative maize leaf symptoms of samples that were positive for maize yellow mosaic virus with the RT‐PCR assay using MaYMV‐F and MaYMV‐R primers. A, B: Mosaic symptoms; C‐D: Yellow streaks; E‐H: Narrow yellow streaks. A‐H samples: 17‐4263, 17‐4267, 17‐4245, 17‐4135, 17‐4275, 17‐4261, 17‐4284, 17‐4172, respectively. Maizegenotypes are not known.Online Resource 8. Actin RT-PCR to confirm RNA and cDNA integrity for samples that were RT-PCR negative for maize yellow mosaic virus. RT-PCR products amplified using the primer pair actinF and actinR were visualized by agarose gel electrophoresis, with an expected 169 bp cDNA actin product. Lane M, O’GeneRuler 100 bp DNA Ladder (Thermo-Fischer, Waltham, USA); lane 1, RT no template control; lane 2, PCR no template control; lane 3, maize healthy control/RNA positive control; lane 4, 16-3308; lane 5, 16-3328; lane 6, 16-3224; lane 7, 16-3252; lane 8, 16-3256. The additional 277 bp product in lanes 3-8 is the actin gDNA product, since the primers flank an intron, indicating presence of some gDNA in the samples.Online Resource 9. Maize B73 reads corresponding to RNA viruses.The National Research Foundation, South Africa, the Department of Agriculture Forestry and Fisheries Research and Technology Fund, South Africa, and the University of Pretoria, South Africa.https://link.springer.com/journal/10658hj2021BiochemistryForestry and Agricultural Biotechnology Institute (FABI)GeneticsMicrobiology and Plant PathologyPlant Production and Soil Scienc

First report of maize yellow mosaic virus (MaYMV) on maize (Zea mays) in Tanzania

Maize yellow mosaic virus (MaYMV) has been reported from China, Ecuador, Brazil and Burkino Faso on maize since 2016 and appears to be an emerging virus with a wide global distribution. Thirty-five maize samples with varying degrees of mosaic and stunting symptoms, were collected in May/June 2015 from the regions of Mara, Arusha, Manyara, Kilimanjaro, Morogoro and Pwani in Tanzania. Total RNA was extracted from leaf material, which was used to prepare RNAtag libraries according to Shishkin et al. (2015). Sequencing was carried out on an Illumina HiSeq2500 instrument. The reads from each dataset were taxonomically classified using the Kaiju software package (Menzel et al. 2016), with thirty datasets having reads showing homology to MaYMV, as well as either/both Maize chlorotic mottle virus and potyviruses. Trimmed datasets were assembled using CLC Genomics Workbench 9 de Novo assembly tool on default settings, with the exception of the following: “minimum contig length” (2000 bp), “length fraction” (0.9), “similarity fraction” (0.9). This yielded seven full/near full genomes with GenBank accession numbers MG664788 – MG664794, sharing 97.9–99.9% sequence homology. These sequences shared a 96.3–96.5% (KU291105) and 97–97.3% (KU291103) nucleotide identity with those from China. Seven positive samples were tested with RT-PCR, using the PCR primers MaYMV-F and MaYMV-R (Chen et al. 2016). Six samples produced the expected RT-PCR amplicon of 750 bp, with one potentially below the detection limit for RT-PCR. An RNA-seq negative sample did not produce the MaYMV amplicon but was positive for a maize control RT-PCR (actin), as expected. The identities of the amplicons were confirmed in four samples using Sanger sequencing. Next generation sequencing and PCR confirmed MaYMV positive samples in all sampled regions except Pwani. The broad geographical distribution of MaYMV in samples from this study suggests that the virus is well established in Tanzania.https://link.springer.com/journal/421612020-02-01am2019Forestry and Agricultural Biotechnology Institute (FABI)Microbiology and Plant PathologyPlant Production and Soil Scienc

IMA genome‑F17 : draft genome sequences of an Armillaria species from Zimbabwe, Ceratocystis colombiana, Elsinoë necatrix, Rosellinia necatrix, two genomes of Sclerotinia minor, short‑read genome assemblies and annotations of four Pyrenophora teres isolates from barley grass, and a long-read genome assembly of Cercospora zeina

AVAILABILITY OF DATA AND MATERIALS : The datasets generated during the current study are available in the NCBI repository, https://www.ncbi.nlm.nih.gov/bioproject/PRJNA 355276.No abstract available.The National Research Foundation, South Africa, the Hans Merensky Chair in Avocado Research, the Department of Science and Technology (DSI)/National Research Foundation (NRF) Centre of Excellence in Plant Health Biotechnology (CPHB), South Africa, and the DSI-NRF SARChI chair in Fungal Genomics.http://www.imafungus.orgam2023BiochemistryForestry and Agricultural Biotechnology Institute (FABI)GeneticsMicrobiology and Plant PathologyPlant Production and Soil ScienceSDG-15:Life on lan