Expression profile analysis of maize in response to Setosphaeria turcica

Accepted Manuscript Expression profile analysis of maize in response to Setosphaeria turcica

Fengmei Shi, Yunhua Zhang, Keqin Wang, Qinglin Meng, Xinglong Liu, Ligong Ma, Yichu Li, Jia Liu, Ling Ma PII: DOI: Reference:

S0378-1119(18)30268-3 doi:10.1016/j.gene.2018.03.030 GENE 42652

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Gene

Received date: Revised date: Accepted date:

20 June 2017 27 February 2018 12 March 2018

Please cite this article as: Fengmei Shi, Yunhua Zhang, Keqin Wang, Qinglin Meng, Xinglong Liu, Ligong Ma, Yichu Li, Jia Liu, Ling Ma , Expression profile analysis of maize in response to Setosphaeria turcica. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Gene(2017), doi:10.1016/j.gene.2018.03.030

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ACCEPTED MANUSCRIPT Expression Profile Analysis of Maize in Response to Setosphaeria turcica Fengmei Shi a,b, Yunhua Zhangb, Keqin Wang b, Qinglin Meng b, Xinglong Liu b, Ligong Ma b, Yichu Li b, Jia Liu b, Ling Ma a* a School of Forestry, Northeast Forestry University, Harbin 150040, PR China b Institute of Plant Protection, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, PR China

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Corresponding author E-mail address: [email protected]

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ACCEPTED MANUSCRIPT Abstract

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Northern corn leaf blight (NCLB), caused by the hemibiotrophic fungal pathogen Setosphaeria turcica, is one of the major foliar diseases of maize. The use of resistant cultivars is the most effective, economical, and environmentally friendly means to control NCLB. At present, the molecular mechanisms of maize resistance to S. turcica is not clear. Elucidating the molecular resistance mechanisms of maize response to S. turcica would aid breeding for a maize variety with fungal tolerance. In this study, maize leaves before and after infection with S. turcica were sequenced by RNA-seq, and 5903 differentially expressed genes (DEGs) were screened. Among them, 950 and 2245 genes were up-regulated 12 h and 60 h (samples H12 and H60, respectively) after infection, 752 and 1956 genes were down-regulated in H12 and H60, respectively. The GO and KEGG enrichment analysis of the DEGs showed that the GO and Pathway with the most annotation sequences were closely related to plant resistance. The expression of eight randomly selected DEGs was analyzed using qRT-PCR, and expression was consistent with the RNA-seq data. The expression patterns of four categories of genes were analyzed namely, genes involved in plant and pathogen interactions, transcription factors related to plant stress-tolerance, genes related to plant hormones and plant antioxidant. Many resistant signaling pathways were initiated such as the MAPK signal transduction pathway and the expression of multiple antioxidant-related genes [Peroxidase (POD), Catalase (CAT), Glutathione-S-transferase (GST) and Superoxide Dismutase (SOD)] following S. turcica infection. Many disease resistance signal transduction pathways and defense response pathways were induced following maize infection by S. turcica, suggesting a multiple gene network system. To the best of our knowledge, this is the first time that RNA-seq technology has been used to perform transcription analysis of maize in response to S. turcica stress. Taken together, these data provide novel and valuable information that will help understand the resistance mechanism in maize against S. turcica and locate candidate genes related to maize resistance against S. turcica.

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Keywords: DEGs, defense response, disease resistance, northern corn leaf blight, RNA-seq, Zea mays

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1.Introduction Northern corn leaf blight (NCLB), caused by the hemibiotrophic fungal pathogen Setosphaeria turcica (anamorph Exserohilum turcicum, formerly known as Helminthosporium turcicum), is one of the major foliar diseases of maize (Vivek et al. 2010; Jiang et al. 2012; Chen et al. 2016; Debela et al. 2017). NCLB can dramatically reduce maize yield by destroying the photosynthetically active leaf during the grain-filling period (Raymundo 1981； Sibiya et al. 2013; Chen et al. 2016). In the year of serious occurrence of NCLB, the yield of susceptible varieties decreased by about 50%. (Wilson et al. 2009; Sibiya et al. 2013). Resistant cultivars are the most effective, economical, and environmentally friendly means to control NCLB (Jiang et al. 2012). Both dominant genes (Ht1, Ht2, Ht3, HtN, Htm1, Htn1, and HtP) and recessive genes (ht4 and rt) conferring resistance to specific races of S. turcica have been identified (Welz and Geiger 2000; Ogliari et al. 2005), and several Ht genes have been mapped with molecular markers. There are many reports on molecular mapping of NCLB resistance genes Ht1 (Bentolila et al. 1991; Van et al. 2001; Ogliari et al. 2007), Ht2 (Van et al. 2001; Yin et al. 2003), Ht3 (Van et al. 2001); Htn1 (Van et al. 2001), and rt (Ogliari et al. 2007). Recently, Htn1 was cloned using a map-based cloning approach. Htn1 encodes a wall-associated receptorlike kinase that acts as an important component of the plant innate immune system by perceiving pathogen- or host-derived elicitors (Hurni et al. 2015). Various studies have been conducted to map QTLs for resistance to NCLB, and these QTLs seem to be distributed throughout the genome (reviewed by Welz and Geiger 2000; Wisser et al. 2006; Ali and Yan 2012; Chen et al. 2016). However, the molecular mechanism of maize resistance to S. turcica is not clear. Elucidating the molecular resistance mechanisms of maize response to S. turcica would aid maize breeding programs for NCLB resistance improvement. Large studies on model species have clarified some crucial events in plant resistance. Plants have evolved an armory of defense mechanisms against pathogen invasion, and the defense system can be generally divided into two types, i.e., pathogen-associated molecular pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) (Jones et al. 2006; Dodds et al. 2010). On the extracellular face of the host cell, pathogen associated molecular patterns (PAMPs) are recognized by pattern recognition receptors (PPRs) and the subsequent stimulation of PPRs leads to PTI (Lanubile et al. 2014). PTI is considered as based defense responses, preventing pathogen further spread (WU Xiao-jun et al. 2015). PTI induces mitogen-activated protein kinases (MAPKs) and calcium signaling, the transcription of pathogen-responsive genes, the production of reactive oxygen species (ROS) and the deposition of callose to reinforce the cell wall at sites of infection (Nürnberger et al. 2004). Furthermore, plants have evolved a more specialized defense mechanism towards successful pathogens, ETI, which acts largely inside the cell and involves the recognition of pathogen delivered effectors contributing to pathogen virulence by plant resistance (R) proteins (Wang et al. 2016). ETI is an accelerated and amplified PTI response, resulting in disease resistance and, usually, a hypersensitive cell death response at the infection site. Following the early signaling events activated by pathogen attack, elicitor signals are often amplified through the generation of secondary signal molecules, such as salicylic acid(SA), ethylene(ET) and jasmonic acid(JA). In addition, the defense response in plant-fungal interactions is also closely related to the accumulation of many secondary metabolites, such as flavonoids, phenolic compounds and phytoalexins (Sekhon et al. 2006; Lanubile et al. 2014; Wang et al. 3

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2016). The maize inbred line B73 has been sequenced (Li et al. 2010) assisting our research into gene expression of maize under pathogen stress at the transcriptional level. RNA-seq is increasingly being used for global gene expression profiling in plants (Perazolli et al. 2012; Zhu et al. 2013). There are many transcriptomic studies of maize under pathogen stress based on RNA-seq technology. Lanubile et al. (2014) used RNA-seq techniques to analyze functional genomic of constitutive and inducible defense responses to Fusarium verticillioides infection in maize genotypes with contrasting ear rot resistance. WU Xiao-junet al. (2015) used RNA-Seq techniques to compare transcriptome profiling of two maize near-isogenic lines differing in the allelic state for bacterial brown spot disease resistance. Wang et al. (2016) reveal the disease-resistance mechanism of the maize inbred line of BT-1 which displays high resistance to ear rot using RNA high throughput sequencing. Meyer et al. (2017) revealed a role for kauralexins in resistance to grey leaf spot disease through RNA-Seq analysis of resistant and susceptible sub-tropical maize lines, caused by Cercospora zeina. However, there have been no reports on maize transcriptome profiling using RNA-seq analysis following S. turcica infection. The aim of this study was to conduct Digital Gene Expression (DGE) analysis on the resistant maize inbred line C103HtN carrying the HtN gene to elucidate the transcriptome changes under S. turcica stress using RNA-seq techniques. Furthermore, we used bioinformatic analysis to identify the expression patterns of genes and the critical pathways in resistance response to S. turcica stress. This study will help to further elucidate the molecular resistance mechanism of maize to S. turcica infection and provide valuable candidate genes that could be used to develop resistant maize genotypes against S. turcica.

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2.Materials and Methods 2.1 Plant material, induction treatment and sample collection The resistant maize inbred parental line C103HtN carrying the dominant HtN gene was used as plant material and a NCLB1 race was used as the tested strain (all provided by the Institute of Plant Protection of Heilongjiang Academy of Agricultural Sciences). The seeds were put in a nutrition bowl and placed in a light incubator at 25°C / 18°C (day / night), light time 14 h, light intensity 3000 Lx. The induction experiment was carried out at the 5-leaf stage of maize (Barakat et al. 2010; Chia-Lin Chung et al. 2011). After the NCLB1 strain was activated for 7 days on PDA medium. 5 pieces of infected PDA medium (diameter 1 cm) were inoculated onto the fourth leaf of per plant, and the medium was attached to the same side of the main vein. Approximately 1 g of leaves that were the other side of the fourth leaves were cut along the main vein after incubation at 0 h (control), 12 h and 60 h, and rapidly placed in liquid nitrogen and stored at -80°C. There were three biological replicates, labeled as H0-1, H0-2, H0-3, H12-1, H12-2, H12-3, H60-1, H60-2, H60-3. 2.2 RNA extraction, cDNA library construction and sequencing Total RNA of nine samples was extracted separately from frozen material using the TRIzol reagent (Invitrogen, USA) according to the manufacturer’s instructions. DNA was removed using DNase (Invitrogen) and then cleaned using the RNAeasy Mini Kit (Qiagen). The RIN (RNA integrity number) values (>8.0) of these samples were assessed using an Agilent 2100 4

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Bioanalyzer (Santa Clara, CA, USA). Nine cDNA libraries were constructed separately and were sequenced by Sangon Biotech Co., Ltd (Shanghai, China). Paired-end sequencing was performed using a Solexa mRNA-seq platform according to the manufacturer’s instructions (Illumina San Diego, CA, USA). Briefly, we used magnetic beads to isolate total RNA from Zea mays kernels. Second-strand cDNA was synthesized using appropriate buffers, dNTPs, RNase H, and DNA polymerase I. Sangon Biotech Co., Ltd (Shanghai, China) prepared the rt fragments, which were depurated with a QiaQuick PCR extraction kit (Qiagen, Hilden, Germany), and resolved with an elution buffer for end repair and by the addition of poly(A). For PCR amplification, we selected suitable fragments as templates based on the results of agarose gel electrophoresis. The library was sequenced using an Illumina HiSeqTM2500 (Paired, 125 nt). Sequencing-received raw image data were transformed by base culling into sequence data. Prior to mapping reads to the reference database, adaptor sequences and low-quality sequences were trimmed using a sliding window to scan the sequence for low quality regions (low quality score cut-off was 20 with a 1% false rate, sliding window size was 10 bp, length score cut-off was 35 bp). At the same time, the Q20, Q30 and QC contents of the clean reads were calculated. All downstream analyses were based on clean reads with high quality.

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2.3 Bioinformatics Analysis The clean reads were aligned to the maize B73 reference genome (ZmB73_RefGen_v32) using TopHat v2.0.6 (Trapnell et al. 2009). Alignments were processed with Cufflinks 2.0.2 (Trapnell et al., 2010) to assemble transcript isoforms and quantify expression values such as fragments per kilobase of exon model per million mapped reads (FPKM) of known and novel genes using the maize working gene set as the reference annotation (AGPv32) and to guide the RABT assembly using default parameters. Prior to differential gene expression analysis, for each sequenced library, the read counts were adjusted using the edgeR program package through one scaling normalized factor (Robinson et al., 2010). Differential expression analysis was performed with the DESeq package (Anders et al. 2010), with a P value threshold of 0.05. Sequences of differentially expressed genes (DEGs) were compared with the NCBI non-redundant (NR) database using the BLAST algorithm with an E-value of 10-3 and were functionally annotated using Blast2GO. This assigned GO terms and the metabolic pathway in the Kyoto Encyclopedia of Genes and Genomes (KEGG) to the query sequences. The clustering of FPKM expression values of DEGs was performed using a Euclidean distance measure with complete linkage. 2.4 Real-time PCR (qRT-PCR) To verify the reliability of the RNA-Seq results, four up-regulated and down-regulated genes detected by RNA-Seq were confirmed by qRT-PCR random detection. Primer6.0 software was used for primer design of eight DEGs (Table S1). The internal reference gene was the housekeeping gene, actin. The first strand of cDNA was synthesized using AMV First Strand cDNA Synthesis Kit (Sangon, Shanghai, China). An SG Fast qPCR Master Mix (2X) reagent was used in a reaction system of 20 μL with three replicates. The qRT-PCR reaction was run on the LightCycler480 Software Setup. Reverse transcription reaction system: SybrGreen qPCR Master Mix (2X) 10 μL, primer F (10 μM) 0.4 μL, primer R (10 μM) 0.4 μL, ddH 7.2 5

ACCEPTED MANUSCRIPT μL, Template (cDNA) 2 μL, Total (cDNA) 200.4 μL. Reaction procedure: 40 cycles of 95°C 7 s, 55°C 10 s, 72°C 15 s followed by pre-denaturation at 95°C for 3 min. Three biological replicates and three experimental replicates were prepared for each sample. Ct values were determined based on three technical replicates of each sample, and were transformed into relative quantification data using the 2–ΔΔCt relative quantitative method (Kenneth et al. 2001). Experimental errors were calculated from the standard deviation among the three biological replications.

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3.Results 3.1 Digital gene expression (DGE) profile of maize To investigate the global transcriptome in maize under S. turcica stress, we performed DGE analysis on maize leaves infected with S. turcica for 0 h (H0), 12 h (H12), and 60 h (H60). Between 17.4 and 2.04 million raw reads were generated for each library, and more than 98% of the raw reads were clean reads. Clean reads generated from the nine DGE libraries were mapped onto the maize inbred B73 reference genome sequence. At least 78.51% of the clean reads were mapped to the reference database, and the ratio of uniquely mapped reads was more than 68.72% (Table 1). The number of expressed reads was calculated and then normalized to FPKM to evaluate the gene expression level. The FPKM values of 70–80% of the reads in each sample were below 15, indicating most genes in each sample had low expression. The FPKM values for each gene in each sample are shown in Table S2.

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3.2 Analysis of DEGs (DEGs) To facilitate the global identification of responsive genes in the resistant maize inbred line of C103HtN treated with S. turcica, gene expression levels were quantified using maize B73 as the reference genome, and the abundance of each transcript was expressed as FPKM as implemented by Cuinks (Trapnell et al. 2010). Genes were defined as DEGs with a P value threshold of 0.05. (Anders et al. 2010). The results are shown in Figure 1. The comparison of H12 with H0 revealed that 950 genes were significantly up-regulated and 752 genes were down-regulated. Furthermore, 2245 genes were up-regulated and 1956 genes were down-regulated in H60 compared with H0. In addition, the number of DEGs at H60 were much more than H12 for both up- and down-regulated genes. For the up-regulated genes, most were unique toward each time point, with the exception of 601 genes which were persistently altered at both time points. Similarly, for the down-regulated genes, only 503 genes were common to both H12 and H60 (Figure 1 and Table S3). The genes only expressed at one time point are presented in Table S4. Among the DEGs, the 5 most abundant expressed genes up-regulated and down-regulated in the two stress treatments are listed in Table 2. In H12, laccase family protein (GRMZM2G400390), HLH DNA-binding domain superfamily protein (AC193786.3_FG005), and wound induced protein (GRMZM2G106393) gene were significantly up-regulated. Photosystem II 47 kDa protein (GRMZM5G808939), and maturase-related protein (GRMZM5G851769) gene expression were down-regulated. In H60, the anionic peroxidase (GRMZM2G108219) gene was significantly up-regulated, while the bifunctional inhibitor / lipid-transfer protein / seed storage 2S albumin superfamily protein (GRMZM2G000221) was 6

ACCEPTED MANUSCRIPT significantly down-regulated.

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3.3 Functional annotation of DEGs in clusters The GO database was used to annotate the DEGs. As a result, 1702 DEGs became 3758 GO IDs in H12, and 4201 DEGs became 5788 GO IDs in H60. These annotated sequences belonged to three main categories (cellular component, molecular function, and biological process), with 16, 12 and 23 functional groups in H12, respectively (Fig. S1) and 17, 14 and 23 functional groups in H60, respectively (Fig. S2). The top 30 categories in each sample are shown in Fig. 2 and 3. The response to stimulus (GO:0050896), intrinsic to membrane (GO:0031224), integral to membrane (GO:0016021) and single-organism metabolic process (GO:00447100) were the main categories in H12 and H60. The defense response (GO:0006952) category dominated in H12. The transferase activity (GO:0016740) category dominated in H60. It was interesting that "biological process" was a high-percentage of the top 30 categories for H12 and H60. To identify the biological pathways active in the DGE libraries, we mapped all annotated genes to terms in the KEGG database to search for significantly enriched genes involved in metabolic or signal transduction pathways. When comparing H12 with H0, we assigned 923 DEGs to 252 KEGG pathways. The 2507 DEGs identified in the comparison of H60 with H0 were assigned to 301 KEGG pathways. Among all KEGG annotations, the top 5 pathways in the two stress treatments are listed in Table 3. DEGS involved in plant hormone signal transduction was present in both H12 and H60. There were DEGs involved in four pathways in H12, including plant-pathogen interaction, phenylpropanoid biosynthesis, circadian rhythm – plant, and glutathione metabolism. There were DEGs involved in four main pathways in H60, including carbon metabolism, biosynthesis of amino acids, starch and sucrose metabolism, and glycolysis gluconeogenesis.

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3.4 Quantitative validation of DEGs by qRT-PCR In order to validate the RNA-Seq expression profiles of DEGs, real-time RT-PCR was performed on 8 DEGs randomly selected for high or low FPKM expression levels, namely GRMZM2G026780, GRMZM2G028393, GRMZM2G126900 and GRMZM2G349791 (up-regulated), GRMZM2G031354, GRMZM2G147221, GRMZM2G147221 and GRMZM6G617209 (down-regulated). The results of qRT-PCR of 8 DEGs were compared with the results of Illumina RNA-seq sequencing. The results are shown in Fig. 4. The trend of expression changes of these selected genes based on qRT-PCR was similar with those detected by Illumina-sequencing method. However, the change folds of these gene expression levels responding to S. turcica-infection detected by qRT-PCR had some difference with those detected by Illumina sequencing (Fig. 4). 3.5 Analysis of the expression patterns of DEGs 3.5.1 Genes associated with plant pathogen interaction We found 89 genes associated with plant and pathogen interaction were differentially expressed after S. turcica infection (Table S5 and S6). These were further categorized as pathogenesis-related (PR) proteins (8.99%), chitinase (8.99%), disease resistance protein (11.24%), MAP (1.12%), MAPK (8.99%), MAPKK (2.25%), MAPKKK (3.37%) and 7

ACCEPTED MANUSCRIPT cytochrome P450 (55.06%). Of these DEGs, 14 (15.73%) were only differentially expressed in H12, and 43 (48.31%) of the DEGs were only differentially expressed in H60.

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3.5.2 Transcription factors related to plant stress resistance The results showed that 154 transcription factors (Table S7 and S8) were up-regulated after S. turcica infection and belong to seven transcription factor families: the ZIP family (12.34%), the WRKY family (25.97%), the NAC family (15.58%), the MYB family (25.32%), the HLH family (12.34%) and the AP2 / EREBP family (8.44%). Of these transcription factors, 18 transcription factors were only up-regulated in H12, and 54 transcription factors were only up-regulated in H60.

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3.5.3 Plant hormone Plant hormones play an important role in plant development and are also involved in plant resistance to pathogens. A total of 61 plant hormone-related DEGs (Table S9 and S10) were identified. There were 45 auxin related genes (73.77%), of which seven genes were only differentially expressed in H12, and 20 genes were only differentially expressed in H60. Twelve CTK related genes (19.67%), of which two genes were only differentially expressed in H12, and four genes were only differentially expressed in H60. Finally, four ethylene pathway related genes (6.56%) were identified, of which one genes were both differentially expressed in H12 and H60.

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3.5.4 Plant Antioxidant Related Genes We analyzed the global expression profiles of genes related to 4 antioxidant-related gene families (Table S11 and S12), including POD (58.82%), CAT (5.88%), GST (32.35%) and SOD (2.94%). Among 34 antioxidant-related DEGs, two genes were only differentially expressed in H12, and 14 genes were only differentially expressed in H60.

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4.Discussion RNA-seq is a technique used to study the differences in gene expression with characteristics such as large information coverage, accurate analysis, low data redundancy, and detection of low expression (Alagna et al. 2009). In this study, nine samples from maize leaves infected with S. turcica were sequenced using RNA-Seq; 5903 DEGs were screened, and eight DEGs were randomly selected for qRT-PCR verification. GO and KEGG enrichment analysis showed that the GO and Pathway closely related to the maize stress response were salicylic acid mediated signaling pathway (GO: 0009863), systemic acquired resistance / salicylic acid mediated signaling pathway (GO: 0009862), ethylene metabolic process (GO: 0009692), ethylene mediated signaling pathway (GO: 0009873), induced systemic resistance / jasmonic acid mediated signaling pathway (GO: 0009864), jasmonic acid metabolic process (GO: 0009694), response to fungus (GO:0009620) which was directly associated with maize defense response (Kim et al. 2015), MAPK signaling pathway-fly (ko04013), MAPK signaling pathway (ko04010), plant-pathogen interaction (ko04626), phenylpropanoid biosynthesis (ko00940), phenylalanine metabolism (ko00360), flavonoid biosynthesis (ko00941), flavone and flavonol biosynthesis (ko00944), linoleic acid metabolism (ko00591), isoquinoline alkaloid biosynthesis (ko00950), terpenoid backbone biosynthesis (ko00900) and 8

ACCEPTED MANUSCRIPT glutathione metabolism (ko00480), plant hormone signal transduction (ko04075). Analysis of these DEGs leads to greater understanding of the molecular mechanisms of maize response to S. turcica and lay the foundation for further study of candidate gene function.

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Plants have a complex array of defense mechanisms that act against pathogen attack, involving structural and chemical barriers and the production of inducible defense-related proteins (PR proteins) (Sels et al. 2008). PR proteins are a component of PTI and may act as flags for systemic defense or can directly combat pathogenic invasion (Miranda et al. 2017). The expression of ZmPR1 gene, ZmPR2 gene, ZmPRm3 gene, ZmPR6 gene, ZmPR9 gene were activated in the resistant near-isogenic line to defense Pseudomonas syringae pv. syringae van Hall infention in maize (WU Xiao-jun et al. 2015). In this study, PR protein gene (GRMZM2G456997) was up-regulated in H12, two PR protein genes (GRMZM2G465226 and GRMZM2G402631) were up-regulated in H60, and PR protein gene (GRMZM2G075283) was up-regulated in both H12 and H60. It was indicated that these genes may be involved in defense of maize against NCLB.

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The basic assembly of a mitogen-activated protein kinase (MAPK) cascade is a three-kinase module, MAPK, MAPK kinase (MAPKK) and MAPKK kinase (MAPKKK) (Chang et al. 2001; Whitmarsh et al. 2007). The MAPK cascades play important roles in responsing a broad variety of biotic and abiotic stimuli. Moreover, MAPK cascades are key players in ROS signaling. Several studies have revealed that MAPK signaling pathways can not only be induced by ROS but also can in turn regulate ROS production (Pitzschke et al. 2009). Recent studies have suggested that many protein kinases in maize are involved in stress resistance in plants. ZmMKK3 mediates osmotic stress and abscisicacid (ABA) signal responses in transgenic tobacco (Zhang et al. 2012). ZmMKK4 regulates osmotic stress through reactive oxygen species scavenging in transgenic tobacco and confers salt and cold tolerance in Arabidopsis (Kong et al. 2011). The expression of ZmMKK1 was induced by various biotic and abiotc stresses and signal molecules, and over expression of ZmMKK1 in tobacco enhanced the tolerance to chilling stress and altered the plants’ resistance to biotrophic and necrotrophic pathogens (Soderlund et al. 2009; Cai et al. 2014). The homologs of the AtMKK2 (GRMZM2G400470) was found more induced in the resistant CO441 maize genotype, supporting its involvement in CO441-mediated resistance to Fusarium verticillioides (Lanubile et al. 2014). In this study, MAPK family gene (GRMZM2G053987) was up-regulated in both H12 and H60, three MAPK family genes (GRMZM2G007848, GRMZM2G392737 and GRMZM2G180555) were up-regulated in H60, MAPKK family gene (GRMZM2G400470) was up-regulated in both H12 and H60, and MAPKKK family gene (GRMZM2G140726) was up-regulated in H60. The differential expression of these genes indicates that the MAPK signaling pathway may be involved in maize defense response to S. turcica. WRKY transcription factors play important roles in plant defense responses (Pandey et al. 2009). Microarray analyses of transcriptional responses to drought stress and fungal infection showed that maize WRKY proteins are involved in stress responses and stress tolerance (Wei et al. 2012). The genes ZmWRKY19, ZmWRKY53 and ZmWRKY67 were found to possess 9

ACCEPTED MANUSCRIPT elevated expression in TZAR101 (resistant maize inbred line), and these findings indicated that WRKY transcription factors are involved in resistance (Fountain et al. 2015). Our results showed that 15 WRKY transcription factors genes were up-regulated in both H12 and H60. Thus, WRKY family members are very important genes in maize under S. turcica stress.

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Auxins play an integral role in plant growth and development, including transcription, signal transduction, metabolism, and transport (Jain et al. 2009). Auxins have also been implicated in the plant stress response but display complex behaviour in plant-pathogen interactions (Domingo et al. 2009; Kazan et al. 2009). A number of genes involved in auxin signaling pathway were identified as Pss-responsive genes, suggesting potential roles of auxin response in the regulation of resistance to BBS pathogens (WU Xiao-jun et al. 2015). Seven auxin response transcription factors (ARF; ARF1, 3, 4, 6, 7, 15 and 20) were highly expressed inthe resistant maize RIL387 in the response to Cercospora zeina (Meyer et al. 2017). In this study, 45 auxin related genes were differentially expressed. The differential expression of these genes indicates that the auxin signaling mediated pathway may be involved in maize defense response to S. turcica.

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During pathogen stress, many reactive oxygen species (ROS) are released. On the one hand, ROS can improve the resistance of plants to pathogens, and on the other hand, they destroy the cell structure of the plant itself. Plants often alleviate oxidative damage by producing active oxygen scavengers to remove excess ROS (Foyer et al. 2011). The study was extended to the analysis of enzymes involved in removing ROS, namely ascorbate peroxidase (APX), catalase (CTA), peroxidase (POD) and superoxide dismutase (SOD). In resistant seedlings, before infection, AXP and SOD enzyme activities were higher compared to the susceptible ones, while after 5 days, they remained unchanged. On the other hand, in the susceptible seedlings all enzymes assayed were activated only after F. verticillioides inoculation. These findings supported the hypothesis of a basal defense response provided by the resistant genotype in maize both in kernels and seedlings (Lanubile et al.2012). Induction of GST is a widely recognized marker for ROS accumulation during defense (Dixon et al. 2009). A suite of twenty-three GST genes were up-regulated at 72 hours post inoculation in both genotypes maize (Lanubile et al. 2014). In this study, following S. turcica, the expression levels of many genes involved in active oxygen scavenging in the leaves of maize changed; seven POD genes were up-regulated and 13 down-regulated; two CAT genes were up-regulated; one SOD gene was down-regulated; seven GST genes were up-regulated and four down-regulated; and one respiratory burst oxidase-like protein gene was up-regulated. The expression of several antioxidant-related genes changed, suggesting expression of active oxygen scavengers may increase following S. turcica infection to maintain the balance of active oxygen and enhance maize resistance to S. turcica. ETI constitutes the second layer of pathogen-sensing mechanism in plants, which is featured by the intracellular recognition of effector protein by a particular type of receptor prtein (R-genes) (Jones et al. 2006; Dodds et al. 2010). Six R-gene homologs (RFO1, MLA6, ER, MLO1, AT1 and Rp1-D) exhibited significantly higher expression levels in the resistant maize lines during Pss attacks (WU Xiao-jun et al. 2015). In this study, ER 10

ACCEPTED MANUSCRIPT (GRMZM2G132212) showed increased expressions in both H12 and H60, and At1-like (GRMZM2G137868) was up-regulated in H60. Thus, these homolog genes serve as the potential candidate R-genes specially involved in NCBL resistance.

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Plants defend themselves through a variety of mechanisms, including the production of secondary metabolites (Jones et al. 2006). Phytoalexins are antimicrobial molecules synthesised de novo after pathogen attack and act to inhibit the growth of the invading pathogen (Vanetten et al. 1994). Gibberellin (GA), a ubiquitous, diterpenoid plant hormone responsible for growth and development, and diterpenoid phytoalexins share a common biosynthetic step catalyzed by ent-copalyl diphosphate synthases（CPS）in rice. Syn-CPS exclusively produces diterpenoid phytoalexins (Prisic et al. 2004). Similar to rice, maize kauralexins are diterpenoid phytoalexins that are fungal-induced and occur as a result of the action of the ent-CPS, ZmAn2. The ZmAn2 gene product shares 60% amino acid sequence identity with the maize ZmAn1 enzyme that functions in GA biosynthesis (Harris et al. 2005; Schmelz et al. 2011; Lanubile et al. 2014). Diterpenoid kauralexins were shown to be induced in maize roots by drought, Fusarium verticillioides and Phytophthora cinnamomi, in maize leaves by C. zeina and in maize stems by F. graminearum and Rhizopus microsporus infection (Harris et al. 2005; Schmelz et al. 2011; Allardyce et al. 2013; Vaughan et al. 2015; Christie et al. 2017). Kauralexin accumulation was correlated to expression of the kauralexin biosynthetic gene, ZmAn2 and a candidate biosynthetic gene, ZmKSL2 (Meyer et al. 2017). Our results showed that the diterpenoid kauralexin biosynthetic gene, ZmAn2 (GRMZM2G044481) was up-regulated in both H12 and H60, suggesting suggesting kauralexin may be induced in maize leaves by S. turcica.

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The maize (Zea mays L.) caffeic acid O-methyl-transferase (COMT) is a key enzyme in the biosynthesis of lignin (Fornalé et al. 2006; Wang et al. 2016). The accumulation of lignins in plant cell walls increases the strength and stiffness of fibers, improves the efficiency of water transport through the vascular system, and protects plants from pathogen attack (Mellerowicz et al. 2001; Boerjan et al. 2003; Boudet et al. 2003; (Fornalé et al.2006). The study has demonstrated that ZmCCoAOMT2, which encodes a caffeoyl-CoA O-methyltransferase associated with the phenylpropanoid pathway and lignin production, is the gene within qMdr9.02 conferring quantitative resistance to both southern leaf blight and gray leaf spot (Qin et al. 2017). In this study, a predicted O-methyltransferase (GRMZM2G349791) had higher expression in H60, and also confirmed by real-time RT-PCR analyses. Thus, this gene putatively associated with maize resistance to S. turcica. Analysis of DEG function suggest maize defense response to S. turcica is regulated by a multi-gene network system. Further research is required on the DEGs involved in the defense of maize against S. turcica, the signaling pathways involved, and whether these DEGs are involved in defense against other maize diseases. 5.Conclusion In this study, host-pathogen interaction transcriptome profiling was carried out in maize during S. turcica infection using RNA-seq. There were 5903 DEGs identified in response to 11

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infection with S. turcica, among which 3195 were up-regulated and 2708 were down-regulated. GO and KEGG enrichment analysis defined the biological function and metabolic pathways of the identified DEGs. Expression characteristics of eight randomly selected DEGs before and after stress using qRT-PCR were consistent with the results of the RNA-seq. The expression patterns of four categories of genes were analyzed, and showed that some key regulatory gene families involved in biotic stress were differentially expressed. To the best of our knowledge, this was the first publication using RNA-seq technology to analyze the transcriptome of maize in response to S. turcica. Taken together, these data provide novel and valuable information that will help understand the resistance mechanism in maize against S. turcica and locate candidate genes related to maize resistance against S. turcica.

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Acknowledgments We thank the Institute of Plant Protection of Heilongjiang Academy of Agricultural Sciences in China for kindly providing maize seeds (C103HtN) and a NCLB1 race. This work was supported by the National Key Research and Development Project of China [grant number 2016YFD0300704], the Nature Scientific Foundation of Heilongjiang Province, China [grant number C201311], and the Science and Tchnology Innovation Engineering of Heilongjiang Academy of Agricultural Sciences [grant number 2012QN036].

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H0

18649280

H12_1

20434682

H12_2

19925840

H12_3

19935368

H12

20098630

H60_1

19782648

H60_2

19586326

H60_3

19891792

H60

19753589

19593204 (99.0%) 19387158 (99.0%) 19705230 (99.1%) 19561864 (99.0%)

119.83 118.71 117.82 119.18 118.02

15383382 (78.51%)

14021863 (71.56%)

15585712 (80.39%) 15762604 (79.99%) 15577233 (79.63%)

13412947 (69.18%) 13894661 (70.51%) 13776490 (70.42%)

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11911235 (69.35%) 13614136 (68.72%) 13236648 (71.80%) 12920673 (69.96%) 14698599 (72.70%) 14553256 (73.75%) 14089353 (71.31%) 14447069 (72.59%)

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117.72

118.34 119.29 119.28 119.31

119.29

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Unique mapped reads

15568552 (90.64%) 16014893 (80.84%) 14866986 (80.65%) 15483477 (84.04%) 16050661 (79.38%) 15801595 (80.08%) 15628178 (79.09%) 15826811 (79.52%)

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118.58

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17175526 (99.0%) 19810128 (99.0%) 18434930 (99.2%) 18473528 (99.1%) 20219194 (98.9%) 19733440 (99.0%) 19758834 (99.1%) 19903823 (99.0%)

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Good Mean Mapped Reads lengh (bp)

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H60-up

3.0139 2.73594 -3.66578 -3.64705 -3.54268 -3.18175

GRMZM5G851769

-3.16186

GRMZM2G061126 GRMZM2G074307 GRMZM5G892774 GRMZM2G587231 GRMZM2G108219 GRMZM2G085711

5.54416 5.28034 4.72834 4.71556 4.54886 -4.57686

GRMZM2G003558

-4.04374

GRMZM2G040858

-3.54098

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GRMZM2G106393 GRMZM2G170734 GRMZM2G040858 GRMZM5G808939 GRMZM2G046392 GRMZM2G157517

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3.09341

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H60-down

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hypothetical protein TPA: putative laccase family protein putative HLH DNA-binding domain superfamily protein TPA: Wound induced protein TPA: hypothetical protein hypothetical protein ZemaCp066 photosystem II 47 kDa protein hypothetical protein ZEAMMB73_947591 hypothetical protein ZEAMMB73_117146 maturase-related protein, partial (mitochondrion) hypothetical protein ZEAMMB73_502571 hypothetical protein hypothetical protein ZEAMMB73_243275 uncharacterized protein LOC100383178 TPA: anionic peroxidase putative apyrase family protein TPA: hypothetical protein ZEAMMB73_265069 hypothetical protein ZemaCp066 TPA:putative bifunctional inhibitor/lipid-transfer protein/seed storage 2S albumin superfamily protein hypothetical protein ZEAMMB73_012062

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H12-down

GRMZM2G179462 GRMZM2G400390

Nr-annotation

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H12-up

Log2.Fol d change 3.65755 3.42726

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Gene ID

-3.26543

GRMZM2G042867

-3.21004

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GRMZM2G000221

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ko04075

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0.01033305

Plant hormone signal transduction

ko04626

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0.005867484

Plant-pathogen interaction

ko00940

14

0.03574594

Phenylpropanoid biosynthesis

ko04712

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3.40E-06

Circadian rhythm - plant

ko00480

9

0.0456797

Glutathione metabolism

ko01200

65

0.000222542

Carbon metabolism

ko01230

58

0.01017529

Biosynthesis of amino acids

ko04075

56

0.004323374

Plant hormone signal transduction

ko00500

42

0.02299892

Starch and sucrose metabolism

ko00010

38

0.001048177

Glycolysis / Gluconeogenesis

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DEGs num

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H12 vs h0

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Figure 2 GO enrichment of H12 Figure 3 GO enrichment of H60 Figure 4 qRT-PCR validation of DEGs

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Supplementary Tables Supplementary Table 1 Primers for real-time quantitative PCR Supplementary Table 2 The FPKM values for each gene in each sample Supplementary Table 3 DEGs common to both H12 and H60 Supplementary Table 4 Genes expressed in one time point Supplementary Table 5 DEGs related to plant pathogen interaction Supplementary Table 6 Data resource of DEGs related to plant pathogen interaction Supplementary Table 7 Differentially expressed transcription factors related to plant stress resistance Supplementary Table 8 Data resource of differentially expressed transcription factors related to plant stress resistance Supplementary Table 9 DEGs related to plant hormones Supplementary Table 10 Data resource of DEGs related to plant hormones Supplementary Table 11 DEGs related to antioxidant Supplementary Table 12 Data resource of DEGs related to antioxidant

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Supplementary Figure Legends Supplementary Figure 1 GO classification of H12 Supplementary Figure 2 GO classification of H60

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Abbreviations NCLB Northern corn leaf blight S. Turcica Setosphaeria turcica DEGs Differentially expressed genes DGE Digital Gene Expression FPKM Fragments Per Kilobase of exon model per Million mapped reads PR proteins Pathogenesis-related proteins PTI pathogen-associated molecular pattern-triggered immunity ETI effector-triggered immunity MAPK mitogenactivated protein kinase ROS Reactive oxygen species POD Peroxidase CAT Catalase GST Glutathione-S-transferase SOD Superoxide Dismutase

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ACCEPTED MANUSCRIPT Expression Profile Analysis of Maize in Response to Setosphaeria turcica

Highlights

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 Transcriptional response of maize to Setosphaeria turcica infection

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 Maize leaves before and after infection with S. turcica were sequenced by RNA-seq

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 qRT-PCR analysis of eight genes were consistent with RNA-seq results  MAPK signal transduction and antioxidant-related genes differentially expressed

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 Highlight candidate genes as potential markers of tolerance to Setosphaeria turcica

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Figure 1

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Figure 3

Figure 4

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Figure 6