- Background: Gibberella ear rot (GER) is one of the most economically important fungal diseases of maize in the temperate zone due to moldy grain contaminated with health threatening mycotoxins. - Results: RNA-Seq-derived transcriptome profiles of fungal- and mock-inoculated developing kernel tissues of two maize inbred lines were used to identify differentially expressed transcripts and propose candidate genes mapping within GER resistance quantitative trait loci (QTL). - A greater number of transcripts were up regulated in the former (1174) than the latter (497) and increased as the infection progressed from 1 to 2 days after inoculation.. - Conclusion: By screening global gene expression profiles for differentially expressed genes mapping within resistance QTL regions, we have identified candidate genes for gibberella ear rot resistance on several maize chromosomes which could potentially lead to a better understanding of Fusarium resistance mechanisms.. - thus affecting the productivity of the most important food crops cultivated in the temperate zone [2]. - Full list of author information is available at the end of the article. - 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0. - Genotypes with complete resistance have never been re- ported but genotypes that could retard the growth and development of the disease and consequently reduce dis- ease severity and mycotoxin level at harvest have been described [4]. - To improve breeding efficiency for GER resistance and understand host-pathogen interaction at the molecular level, advances in the next generation sequencing tech- nologies offer powerful tools. - These QTL could be even more useful if we could identify which of those genes within the QTL regions are involved in the resistance.. - RNA-Seq analysis of differential gene expression could serve as an alternative method to discover candidate genes together with the QTL regions we recently identi- fied. - The state of a given cell/tissue can be captured through the detection of the transcribed RNAs at any time point. - RNA-Seq provides whole genome scanning of expressed genes, including splice variants, uncalled genes, and noncoding RNAs [10].. - Second- ary metabolites such as phenolics, phytoalexins and anti- microbial proteins have been suggested as mechanisms of resistance [13, 14], but the regulation and identifica- tion of genes involved in the biosynthesis of the second- ary metabolites is still an active area of research. - attack through the activity of the terpene synthases ZmTPS6 and ZmTPS11 [14].. - The objectives of the current study were therefore to identify differentially expressed genes (DEG) between fungal and mock (sterile fungal medium only) inoculated maize ears and compare the DEG between a susceptible and a resistant inbred line. - We further assessed the possibility of detecting candidate genes by exploring the DEG within the recently identified silk and kernel GER resistance QTL regions and validating their gene expression profiles by quantitative PCR.. - Plant materials used in the current study were two in- breds with contrasting GER resistance. - For the RNA-Seq experiment, the two inbred lines were inoculated under field conditions over the years 2004 and 2006, with overhead irrigation to promote dis- ease development. - We prepared the inocu- lum using a modified version of the method used by Reid et al. - Maize plants were selected based on their similarity in days to silking in each of the experimental years, to en- sure uniformity in stage of kernel development and avoid environmental influence when we compare gene. - expression between fungal and mock treatments of the same inbred. - For the RNA-Seq experiment, 6–8 sib- crossed primary ears from each inbred were inoculated with 1 ml of fungal inoculum on the same day prior to 10 am and a similar numbers of ears were injected with Bilay’s media as a mock treatment. - RNA was extracted in bulk (3–4 ears per treatment, per testing year) from 2004 and 2006 field samples for the RNA-Seq and initial ddPCR experiments while individual ears from the 2013 field season were used as biological replicates for further ddPCR validation.. - RNA-Seq data analysis. - The RNA-Seq data was analyzed using CLC Genomics workbench version 9 (Qiagen Corp. - Prior to the map- ping and gene expression analysis, the raw data was trimmed based on quality scores that were determined by the base caller error probability level (P <. - Gene expression levels were estimated as transcripts per million (TPM) [21] which was calculated as: TPM = (RPKM × 10 6. - An empirical analysis of differential gene expression or the ‘exact test’ according to Robinson and Smyth [22]. - a package also incorporated in EdgeR Bioconductor [23], is similar to Fisher’s exact test but also accounts for over dispersion caused by bio- logical variability which makes it most suited for RNA-Seq data that has few biological replicates per experimental group.. - Transcripts were considered as significantly differen- tially expressed when fold change was ≥2.0, False Discovery Rate (FDR) [24] corrected P ≤ 0.05 among groups and TPM ≥ 5 in at least one of the groups compared.. - Functional enrichment analysis of significantly up reg- ulated genes were performed using one of the g:Profiler web tool set, g:GOSt [25, 26]. - Additional interpretation of the significantly up and/or down regulated genes was performed through visualization of the genes’ involvement in known meta- bolic pathways and other biological processes using MAPMAN software [27] and the genome visualization tool Circos [28].. - We validated selected candidate gene expression profiles of fourteen genes using RNA from the same 2004 and 2006 samples as was used for RNA-Seq. - To validate gene expression patterns over a broader number of time points and treatments, we sampled three biological replicates of B73 and CO441 developing kernels under untreated, mock- and fungal-inoculated condi- tions (1, 2, 4 and 5 DAI [days after inoculation]) in the 2013 field season.. - To check the specificity of the se- lected primers, forward and reverse primer sequences were submitted to the National Center for Biotechnol- ogy Information (NCBI) Blast program [30] to search nucleotide databases. - Primers were designed to avoid transcript regions which contained insertions or dele- tions between inbreds, as visualized in RNA-Seq alignment data. - In addition, primers were located within regions that were common between the differ- ent transcript isoforms and represented the overall gene expression.. - This high level of partitioning serves to significantly in- crease precision and sensitivity while eliminating many of the requirements for optimization and validation that is associated with standard quantitative PCR. - mock inoculated samples using RNA-Seq. - The experiment was conducted with at least three or four biological repli- cates per experimental group in each of the two testing years, 2004 and 2006. - RNA extractions were performed from pooled tissues samples for each of the testing years and hence each year was considered as a biological replicate for data analysis.. - This may be due to our less than stringent alignment parameters and some genomic di- vergence between our B73 seed stock and the source of the B73 genome sequence. - ≥2 fold change) response to fungal infection of which 1255 were up regulated in either of the inbred lines tested (Fig. - We assigned the DEG to functional categories based on gene ontology and assessed their representation for each treatment relative to their representation in the genome.. - In the more resistant inbred CO441, genes involved in secondary metabolism, cell wall biosynthesis/modifica- tion and chitin catabolism, oxidation reduction pro- cesses, immune response and nutrient reservoir activity were significantly up-regulated (P <. - 0.05) and are also expressed at higher levels in a maize inbred resistant to Aspergillus flavus infection relative to the susceptible B73 [34]. - This was followed by more up regulated genes in each of the biotic stress re- sponse categories including PR proteins and respira- tory burst or hypersensitive responses at 2 DAI suggesting earlier host cell death in the susceptible relative to the resistant inbred.. - Validation of candidate gene expression. - Using RNA from the same 2004 and 2006 samples, droplet digital PCR quantifica- tion of the selected DEGs and their RNA-Seq TPM values showed similar trends of expression in 12 of the. - However, two (AC208897.3_FG004 and GRMZM2G119150) exhibited very low gene expression by ddPCR (despite testing multiple sets of primers) and appeared to show inconsistent expression patterns be- tween the two methods of gene transcript quantifica- tion (Additional file 2).. - 5) in order to expand our gene expression profile information over a third field season. - (GRMZM2G086430) was induced at higher levels in the resistant inbred relative to B73 regardless of the treatment. - The ability to ultimately reduce disease severity ap- peared to be determined by the stronger, more consti- tutive expression of such defensive genes in the resistant inbred CO441 (Additional file 1: Table S4).. - Our RNA-Seq experiment complemented what had been previously documented by protein profiling of maize kernel tissue during F. - When we compared the RNA-seq DEG with protein profiles [36] of the same two inbred lines, only. - 4.3% of the DEG encoded proteins (52 of 1223) were detected in at least two biological replicates using iTRAQ protein profiling methods. - 4 Distribution of DEG and GER resistance QTL regions across the maize genome. - 0.05) and QTL regions detected for silk (E) and kernel (F) GER resistance. - We considered genes mapping within resistance QTL associated with both kernel inoculation and silk channel inoculation, as both inoculation methods ultimately lead to colonization of the developing ear. - How- ever, mechanisms contributing to silk inoculation resist- ance could be found in the silk or other tissues in the path to colonize the ear including the developing kernels.. - One of the major QTL regions we identified on chromosome 2 har- bors a cytochrome P450 gene (GRMZM2G164074;. - Although this particular maize gene has not been characterized yet, members of the CYP94 family in Arabidopsis are involved in jasmonic acid turnover, medi- ating jasmonic acid inactivation and thereby controlling hormone levels [43, 44]. - As this maize CYP94B12 gene was found in a QTL region with the highest genetic effect for silk and second highest for kernel GER resistance [9] and both RNA-Seq and ddPCR data supported higher expression levels in CO441, it is possible that modulation of jasmonic acid may determine the efficacy of resistance. - 5 Validation of candidate gene expression profiles by droplet digital PCR. - Candidate gene expression pattern 1, 2, 4 and 5 days after non-treated (NT), mock (M) and fungal (FG) inoculation of B73 and CO441 developing kernels using ddPCR. - After hormone biosynthesis, the downstream reaction of fungal infected host tissue would be either cell death of the infected tissue (hypersensitive response) and/or the production of secondary metabolites such as alkaloids, flavonoids, terpenoids (collectively known as phyto- alexins) and pathogenesis related (PR) proteins. - On the other hand, phytoalexins have been reported to directly exert antimicrobial activity on the fungus or create structural barriers by lignification of the plant cell wall [47].. - One of the QTL regions we detected for GER resist- ance to silk channel inoculation on chromosome 9 har- bored an uncharacterized gene, GRMZM2G423331, encoding a protein with 67% sequence similarity to nari- genin 7-O-methyltransferase, an enzyme responsible for the biosynthesis of a rice phytoalexin called sakuranetin [48]. - Another candidate gene, GRMZM2G010909, was dis- covered in the same QTL region as the PR-10 gene clus- ter. - This gene encodes a protein with 64% amino acid similarity with a wound induced protein 1 in rice Oryza sativa Japonica and an orthologue was reported to pro- vide structural reinforcement by accumulating in the cell. - wall of the ice plant [55]. - One of the closest orthologues to GRMZM2G334336 (encoded proteins share 70% amino acid identity) is the barley UDP-glucosyltransferase HvUGT13248 which was reported to convert DON to the less harmful compound DON-3-O-glucoside [60]. - The current study characterized the plant response to Gibberella ear rot disease in maize using gene expression profiling of two inbred lines with contrasting levels of resistance and identified fungal responsive genes map- ping within chromosomal regions associated with GER resistance. - This information helped us to identify candi- date genes that are possibly relevant in the defense re- sponse to help understand GER resistance mechanisms.. - Our study focused on genes represented in the B73 gen- ome sequence and therefore any novel genes present in this germplasm were not considered as they could not be easily mapped to the genome. - In particular, four genes on chromosome 2, which were consistently expressed at higher levels in the resistant inbred and lying within kernel resistance QTL regions, seemed the most promising, namely a MFS transporter, a cytochrome P450, an aldehyde dehydrogenase and a lectin domain gene. - Further validation of the associ- ation of these genes with GER resistance QTLs is ne- cessary to improve our understanding of maize resistance to F. - Mapping of RNA-Seq reads to the reference genome B73 V2. - Upregulated transcripts mapping within GER resistance QTL regions. - Comparison between ddPCR and RNA-Seq expression profiles of selected genes. - The Y-axis scale corresponds to transcripts per million (TPM) for RNA-Seq data and copies/ μ l for ddPCR. - Tissue samples from the 2004 and 2006 field season were used for both gene expression quantitation methods. - GER: Gibberella ear rot. - Funding bodies had no role in the design of the study, collection of data, data analysis and interpretation, and the writing of the manuscript.. - The RNA sequencing dataset is available from the NCBI Gene Expression Omnibus (GSE92448) (https://www.ncbi.nlm.nih.gov/geo. - in maize – a review. - Mapping and quantifying mammalian transcriptomes by RNA-Seq. - Phases of infection and gene expression of Fusarium graminearum during crown rot disease of wheat. - RNA-Seq gene expression estimation with read mapping uncertainty. - edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. - Molecular phylogenetic and expression analysis of the complete WRKY transcription factor family in maize. - Cytochrome P450 CYP94B3 mediates catabolism and inactivation of the plant hormone jasmonoyl-L-isoleucine. - Role of the pepper cytochrome P450 gene CaCYP450A in defense responses against microbial pathogens. - A network approach of gene co-expression in the Zea mays/. - The role of the cell wall in plant immunity. - Environmental and developmental regulation of the wound-induced cell wall protein WI12 in the halophyte ice plant. - Lipid peroxidation and antioxidant defense impairment in the hearts of chick embryos induced by in ovo exposure to Fusarium mycotoxin butenolide. - Transcriptome analysis of the barley-deoxynivalenol interaction: evidence for a role of glutathione in deoxynivalenol detoxification.. - Ectopic expression of the cotton non-symbiotic hemoglobin gene GhHbd1 triggers defense responses and increases disease tolerance in Arabidopsis
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