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• W4292155183 abstract "HomePlant DiseaseVol. 106, No. 10Integrated Sequencing Data, Annotation, and Targeting Analysis of mRNAs and MicroRNAs from Tea Leaf During Infection by Tea Leaf Spot Pathogen, Epicoccum nigrum PreviousNext RESOURCE ANNOUNCEMENT OPENOpen Access licenseIntegrated Sequencing Data, Annotation, and Targeting Analysis of mRNAs and MicroRNAs from Tea Leaf During Infection by Tea Leaf Spot Pathogen, Epicoccum nigrumQin Tang, Chen Huang, Hongke Huang, Zhongqiu Xia, Yuqin Yang, Xinyue Jiang, Delu Wang, and Zhuo ChenQin TangKey Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, ChinaSearch for more papers by this author, Chen HuangKey Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, ChinaSearch for more papers by this author, Hongke HuangKey Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, ChinaCollege of Tea Science, Guizhou University, Guiyang, Guizhou 550025, ChinaSearch for more papers by this author, Zhongqiu XiaKey Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, ChinaCollege of Tea Science, Guizhou University, Guiyang, Guizhou 550025, ChinaSearch for more papers by this author, Yuqin YangKey Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, ChinaCollege of Tea Science, Guizhou University, Guiyang, Guizhou 550025, ChinaSearch for more papers by this author, Xinyue JiangKey Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, ChinaSearch for more papers by this author, Delu Wang†Corresponding authors: D. Wang; E-mail Address: dlwang@gzu.edu.cn, and Z. Chen; E-mail Address: gychenzhou@aliyun.comCollege of Forestry, Guizhou University, Guiyang, Guizhou 550025, ChinaSearch for more papers by this author, and Zhuo Chen†Corresponding authors: D. Wang; E-mail Address: dlwang@gzu.edu.cn, and Z. Chen; E-mail Address: gychenzhou@aliyun.comhttps://orcid.org/0000-0001-7130-8457Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, ChinaSearch for more papers by this authorAffiliationsAuthors and Affiliations Qin Tang1 Chen Huang1 Hongke Huang1 2 Zhongqiu Xia1 2 Yuqin Yang1 2 Xinyue Jiang1 Delu Wang3 † Zhuo Chen1 † 1Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang, Guizhou 550025, China 2College of Tea Science, Guizhou University, Guiyang, Guizhou 550025, China 3College of Forestry, Guizhou University, Guiyang, Guizhou 550025, China Published Online:17 Aug 2022https://doi.org/10.1094/PDIS-04-22-0761-AAboutSectionsView articlePDFSupplemental ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmailWechat View articlemRNA and microRNA Sequence AnnouncementTea leaf spot, caused by the fungal phytopathogen Epicoccum nigrum, can negatively affect the productivity and quality of tea leaves in tea plantations in Guizhou Province, southwestern China. The sequences of the messenger RNAs (mRNAs) and microRNAs (miRNAs) from tea leaves, the response of the abundance of these sequences to challenge by E. nigrum, and the prediction of the targeting between miRNAs and mRNAs from tea leaves during the infection process could contribute to our understanding of this plant–pathogen interaction. Here, we report the sequences of the differentially expressed mRNA (DEmRNAs) and the differentially expressed miRNAs (DEmiRNAs) between infected and uninfected leaves, using the Illumina NovaSeq 6000 platform and the Illumina HiSeq 2500 platform. Infection by E. nigrum significantly up- or downregulated expression of 819 or 338 DEmRNAs, respectively, as well as 4 of 7 DEmiRNAs, respectively, at the significance levels of P = 0.01. In terms of defense response in the biological process aspect of gene ontology, the DEmRNAs zeamatin (TEA032349.1, TEA005276.1, and TEA026507.1) and endochitinase 4 (TEA021819.1) were upregulated, whereas the DEmRNA CURL3, BRI1, SR160, tBRI1 (TEA020365.1) was downregulated. The miRNAs lus-MIR160b-p3_2ss12CT17CG, gma-MIR10423-p3_1ss3AC, mes-MIR477f-p5_1ss19AG, and ath-miR396b-5p_1ss1TA targeted the most genes (namely, 37) at 39, 16, and 24 genes, respectively. Twenty-two target genes of these miRNAs were enriched in the pathway of plant–pathogen interaction. Such comprehensive expression profiling of mRNAs and miRNAs from E. nigrum-challenged tea leaves will provide a resource for future research into host–pathogen interactions.The Didymellaceae family consists of 19 monophyletic generic clades, each representing an individual genus, in which exists a number of important phytopathogens (Chen et al. 2017). E. nigrum can cause leaf spot on Lablab purpureus, loquat, cowpea, and tea (Deepika et al. 2021; Mahadevakumar et al. 2014; Wu et al. 2017; Yin et al. 2022). A minor pathogen on its own, E. nigrum acts as part of a disease complex with the primary pathogens Alternaria alternata and Diaporthe ilicicola sp. nov. to cause fruit rot of deciduous holly (Lin et al. 2018).miRNAs are small, noncoding RNAs of 18 to 25 nucleotides (nt), which function posttranscriptionally by base pairing to mRNAs to repress protein synthesis (Fabian et al. 2010). miRNAs of the plant host can defend against infection by fungal pathogens through inhibiting the expression of fungal genes associated with pathogenesis (Islam et al. 2018; Zhang et al. 2016). Furthermore, miRNA from phytopathogenic fungus can target mRNA from the host, to downregulate the resistance response during infection (Feng et al. 2021). To date, no research has been published on the miRNAs and mRNAs of tea leaves during challenge by the pathogen E. nigrum. In this study, we conducted the sequencing of mRNAs and miRNAs from infected and uninfected tea leaves, and carried out functional annotation on the DEmRNAs and on target genes associated with the DEmiRNAs from infected tea leaves during infection. These data will provide a valuable resource for future research into this host–pathogen interaction.Single-spore E. nigrum strain ACCC39733 isolated from leaf-spot-infected tea leaves in a tea plantation in Guizhou Province, China, was incubated on potato dextrose agar in the dark for 20 days at 28°C. Five-year-old potted plants of tea (Camellia sinensis L. O. Kuntze ‘Fuding-dabaicha’) were grown in a greenhouse and maintained at 25°C during the day and at 20°C at night, with cycles of 14 h of light and 10 h of darkness and a relative humidity of 70 to 80%. The third or fourth leaf on each detached healthy twig was inoculated with an aqueous conidial suspension (106 conidia/ml, 20 to 30 μl per inoculation site) or mycelial plugs (6 mm in diameter) onto physically wounded tea leaves. On either side of the main vein, the adaxial surface of tea leaves was punctured twice using sterilized syringe needles to provide inoculation sites, with the four holes per leaf being clustered within a 6-mm-diameter range. For the uninfected treatment, sterile water was used to inoculate the tea leaves. For the infected or uninfected treatments, 15 healthy twigs were pooled to represent a replicate and each treatment was represented by three biological replicates. By 48 h postinoculation, a lesion had formed at each inoculation site on the tea leaf in the infected treatment but not in the uninfected treatment. The area neighboring the inoculation sites of each leaf was harvested using a sterile hole punch at 2 days after inoculation. In total, 15 tea leaves, one from each twig, were pooled to represent each sample, then ground into fine powder followed by snap-freezing in liquid nitrogen.Total RNA from tea leaves from each of the three biological replicates from the infected or uninfected treatments was extracted using TRIzol reagent (Invitrogen, Carlsbad, CA, U.S.A.), following the manufacturer’s procedure. Separate cDNA libraries were prepared from each of the three biological replicates of each treatment using the mRNA-Seq Sample Preparation Kit (Illumina, San Diego, CA, U.S.A.), and were sequenced on an Illumina NovaSeq 6000 platform at Lc-bio Technologies Co., Ltd. (Hangzhou, China), following the manufacturer’s recommended protocol. The low-quality reads, including adapters, low-quality bases, and undetermined bases, were removed using cutadapt and in-house Perl script (Martin 2011). For the Q20 and Q30 indexes and the GC content of the clean data, the sequence quality was further verified to generate the clean data of high quality using FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Unigenes were assembled using StringTie software (https://ccb.jhu.edu/software/stringtie/) and analysis of the DEmRNAs was conducted using the R package Empirical Analysis of Digital Gene Expression Data in R (edgeR) (version 3.13) (https://bioconductor.org/packages/release/bioc/html/edgeR.html) (threshold value of significant difference with log2 [foldchange] ≥ 1 or ≤ −1, P < 0.05) (Pertea et al. 2015). All assembled unigenes were annotated using the databases of gene ontology (GO; http://geneontology.org/), the Kyoto Encyclopedia of Genes and Genomes (KEGG; https://www.genome.jp/kegg/), the Protein Families (http://pfam.xfam.org/), the Swiss-Prot (https://www.expasy.org/resources/uniprotkb-swiss-prot), eggNOG (http://eggnog5.embl.de/#/app/home), and the nonredundant (Nr) protein (https://www.ncbi.nlm.nih.gov/), using DIAMOND software (http://ab.inf.uni-tuebingen.de/software/diamond) (Buchfink et al. 2015).The library of small RNAs was prepared using TruSeq Small RNA Sample Prep Kits (Illumina), then sequenced using the single-end sequencing method by the Illumina HiSeq 2500 platform (Illumina), with a sequencing read length of 1 × 50 bp. Raw reads were analyzed using the ACGT101-miR program (LC Sciences, Houston, TX, U.S.A.) to remove adapter dimers and low-complexity sequences, and filtered by aligning the databases of mRNA, RFam (containing ribosomal RNA, transfer RNA, small nuclear RNA, and small nucleolar RNA) (http://rfam.janelia.org), and Repbase (https://www.girinst.org/repbase). Reads with lengths of 18 to 25 nt, known as miRNA-like, were retained, and unique sequences with lengths in the range 18 to 25 nt were mapped to species-specific precursors in miRBase 22.0 (https://www.mirbase.org/) by BLAST search, to identify known miRNAs and novel 3p- and 5p-derived miRNAs. The remainder of the sequences were aligned through the databases of mRNA, RFam (http://rfam.janelia.org), and Repbase (https://www.girinst.org/repbase), and filtered to form valid data. The significance of the DEmiRNAs through the infected treatment compared with the uninfected treatment was determined using Student’s t test. Target genes of DEmiRNAs were predicted using TargetFinder (http://targetfinder.org/), and their enrichment analysis was conducted using GO and KEGG databases (Dai and Zhao 2011).The number of annotated genes from tea leaves was 33,932. Through comparison of the mRNA abundances in infected and uninfected samples, the number of DEmRNAs from tea leaves with trends of up- or downregulation was 819 or 338, respectively (Table 1). GO enrichment analysis of DEmRNAs indicated that the total number of genes in GO database (TB gene number) and the number of genes with significantly different expressions through infected treatment compared with uninfected treatment (TS gene number) was 12,160 and 397, respectively. In terms of defense response in the biological process aspect of GO, the number of genes with significantly different expression annotated in special GO terms (S gene number) and the number of genes annotated in the specified GO term (B gene number) was 8 and 58, respectively. Expression of DEmRNAs Zeamatin (TEA032349.1, TEA005276.1, and TEA026507.1), uncharacterized LOC104900389 (TEA032973.1), endochitinase 4 (TEA021819.1), and POPTRDRAFT_771920 (TEA001010.1 and TEA012343.1) was upregulated in the infected treatment, whereas the abundance of the DEmRNA CURL3, BRI1, SR160, tBRI1 (TEA020365.1) was downregulated. KEGG enrichment analysis of DEmRNAs indicated that the TB gene number and the TS gene number was 14,631 and 476, respectively. For the pathway of the plant–pathogen interaction, the number of genes with significantly different expression annotated in the special pathway (S gene number) and the number of genes annotated in the specified pathway (B gene number) was 53 and 1,637, respectively. The abundance of 27 DEmRNAs was upregulated in the tea host infected by the pathogen. These DEmRNAs were further divided into three classes: (i) encoding proteins associated with disease resistance such as pathogenesis-related protein PR-1 (TEA022585.1), pathogenesis-related leaf protein 6 (TEA004542.1), and basic form of pathogenesis-related protein 1 (TEA004541.1); (ii) encoding transcription factors such as probable WRKY transcription factor 33 (TEA007197.1, TEA001873.1, and TEA023233.1), ethylene-responsive transcription factor 1 (TEA004127.1), and myb-related protein Myb4-like (TEA029352.1); and (iii) kinases such as probable serine/threonine-protein kinase Cx32, chloroplastic (TEA021406.1), and kinase family protein with leucine-rich repeat domain, putative (TEA005926.1).Table 1. Statistics of messenger RNA (mRNA) and microRNA (miRNA) sequences of tea leaves during infection by Epicoccum nigrumSequence analysisaData typeSample sourcebComparison (P)eTarget genesInfectedcUninfecteddmRNAsRaw reads53,550,25954,947,715−−Valid reads51,479,41450,713,737−−Valid bases7.72 G7.61 G−−DEmRNAsUpregulation−−819−Downregulation−−338−miRNAsRaw reads10,719,53112,036,591−Valid reads6,537,0787,753,858−DEmiRNAsUpregulation−−4 (P = 0.01)−Downregulation−−7 (P = 0.01)−Upregulation−−28 (P = 0.05)−Downregulation−−44 (P = 0.05)−PredictionfPC-3p-88161_31−−−disease resistance protein RPM1-like (TEA006342.1, TEA012432.1, TEA020737.1, TEA020574.1, TEA014729.1, TEA014730.1, and TEA024091.1)PC-3p-146815_14−−−disease resistance protein RPM1-like (TEA004787.1 and TEA007941.1)ppe-MIR535b-p3_1ss11TC−−−disease resistance protein At4g27190-like (TEA025099.1 and TEA016990.1)ath-miR159a−−−transcription factor GAMYB (TEA025308.1 and TEA021145.1)gma-MIR10423-p3_1ss3AC−−−transcription factor MYB39 (TEA026389.1)gma-MIR10423-p3_1ss3AC−−−transcription factor RAX1 (TEA033855.1)PC-3p-108003_23−−−LRR receptor-like serine/threonine-protein kinase FLS2 (TEA015472.1)PC-5p-65439_46−−−probable LRR receptor-like serine/threonine-protein kinase At3g47570 (TEA011072.1)−−−Eix2 (TEA015465.1)−−−putative receptor-like protein kinase At3g47110 (TEA001867.1)ath-miR396b-5p_1ss1TA−−−Eix2 (TEA027243.1)aDE = differentially expressed.bSamples consisted of tea leaves in which four holes clustered within a 6-mm-diameter range were punctured per leaf using sterilized syringe needles.cInfected treatment = mycelial plugs (6 mm in diameter) applied to the inoculation sites.dUninfected treatment = sterile water.eInfected treatment versus uninfected treatment (P value in parenthesis).fPrediction of miRNAs and corresponding target genes enriched in the pathway of plant–pathogen interactions.Table 1. Statistics of messenger RNA (mRNA) and microRNA (miRNA) sequences of tea leaves during infection by Epicoccum nigrumView as image HTML miRNAs were identified and their target genes were predicted, then divided into gp1, gp2a, gp2b, gp3, and gp4. Upregulation or downregulation of the abundance of DEmiRNAs in infected relative to uninfected samples was 4 of 7 (P < 0.01) and 28 of 44 (P < 0.05), respectively. For instance, the abundance of miRNAs csi-miR167a-3p_2ss8CA15TC, PC-5p-65439_46, PC-3p-192418_10, and stu-miR482a-3p_1ss8TG was upregulated during infection by the pathogen (P < 0.01), whereas the abundance of miRNAs PC-3p-199354_9, PC-3p-222060_7, PC-5p-67878_44, PC-5p-111855_22, PC-5p-110530_22, PC-5p-111770_22, and PC-3p-100444_25 was downregulated (P < 0.01). The target genes of the miRNAs were predicted and annotated using GO and KEGG databases; miRNAs lus-MIR160b-p3_2ss12CT17CG, gma-MIR10423-p3_1ss3AC, mes-MIR477f-p5_1ss19AG, and ath-miR396b-5p_1ss1TA targeted the highest number of genes, with the number of target genes for each miRNA being 37, 39, 16, and 24, respectively. Both miRNAs lus-MIR160b-p5_2ss12CT17CG and lus-MIR160b-p3_2ss12CT17CG targeted the same mRNA; namely, POPTRDRAFT_771920 (TEA033373.1). Twenty-two of the target genes of the miRNAs were enriched in the pathway of plant–pathogen interaction. The target genes were divided into several classes, as follows: (i) disease resistance proteins such as disease resistance protein RPM1-like (TEA006342.1, TEA012432.1, TEA020737.1, TEA020574.1, TEA014729.1, TEA014730.1, TEA024091.1, TEA004787.1, and TEA007941.1) and disease resistance protein At4g27190-like (TEA025099.1 and TEA016990.1) and (ii) transcription factors such as transcription factor MYB39 (TEA026389.1), transcription factor RAX1 (TEA033855.1), and transcription factor GAMYB (TEA021145.1 and TEA025308.1) (Table 1).The sequences of the transcriptome and the miRNAs of tea leaves will provide an important resource for researchers studying pathogenic mechanisms, disease resistance responses, and plant–pathogen interaction.Data AvailabilityThe sequences of the mRNAs and miRNAs of tea leaves have been deposited in GenBank under Sequence Read Archive (SRA) accession number PRJNA810983 and SRA accession number PRJNA811108. 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Plants 2:16153. https://doi.org/10.1038/nplants.2016.153 Crossref, ISI, Google ScholarFunding: This project was supported by the Post-subsidy project of National Key Research Development Program of China (2018-5262), National Natural Science Foundation of China (numbers 31860515 and 21977023), Program of Introducing Talents to Chinese Universities (D20023), and China Agriculture Research System of MOF and MARA (D09).The author(s) declare no conflict of interest.DetailsFiguresLiterature CitedRelated Vol. 106, No. 10 October 2022SubscribeISSN:0191-2917e-ISSN:1943-7692 Download Metrics Article History Issue Date: 27 Sep 2022Published: 17 Aug 2022Accepted: 10 May 2022 Pages: 2741-2745 Information© 2022 The American Phytopathological SocietyFundingNational Key Research Development Program of ChinaGrant/Award Number: 2018-5262National Natural Science Foundation of ChinaGrant/Award Number: 31860515Grant/Award Number: 21977023Program of Introducing Talents to Chinese UniversitiesGrant/Award Number: D20023China Agriculture Research System of MOF and MARAGrant/Award Number: D09Keywordscash cropfungileaf spotmessenger RNAmicroRNAprediction of target geneteaThe author(s) declare no conflict of interest.PDF download" @default.
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