Identification of miRNAs associated with Aspergillus flavus infection and their targets in groundnut (Arachis hypogaea L.)

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Pushpesh Joshi, Vinay Sharma, Arun K. Pandey, Spurthi N. Nayak, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4607193/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2025 Read the published version in BMC Plant Biology → Version 1 posted 9 You are reading this latest preprint version Abstract Background : Groundnut is the major oilseed crop that suffers substantial post-harvest losses due to aflatoxin contamination by the fungus Aspergillus flavus . The interaction between A . flavus and groundnut microRNAs in combating aflatoxin contamination remains unclear. This study was carried out to identify microRNAs (miRNAs) to enhance the understanding of in vitro Seed Colonisation (IVSC) resistance mechanism in groundnut. Result : In this study, resistant (J 11) and susceptible (JL 24) genotypes of groundnut were treated with toxigenic A. flavus (strain AF-11-4), and total RNA was extracted at 1 day after inoculation (1 DAI), 2 DAI, 3 DAI and 7 DAI. Seeds of JL 24 showed higher mycelial growth than J 11 at successive days after inoculation. A total of 208 known miRNAs belonging to 36 miRNA families, with length varying from 20-24 nucleotides, were identified, along with 27 novel miRNAs, with length varying from 20-22 nucleotides. Using psRNATarget server, 952 targets were identified for all the miRNAs. The targeted genes function as disease resistant proteins encoding, auxin responsive proteins, squamosa promoter binding like proteins, transcription factors, pentatricopeptide repeat-containing proteins and growth regulating factors, etc. Through differential expression analysis, seven miRNAs (aly-miR156d-3p, csi-miR1515a, gma-miR396e, mtr-miR2118, novo-miR-n27, ptc-miR482d-3p and ppe-miR396a) were found common among 1 DAI, 2 DAI, 3 DAI and 7 DAI in J 11, whereas ten miRNAs (csi-miR159a-5p, csi-miR164a-3p, novo-miR-n17, novo-miR-n2, osa-miR162b, mtr-miR2118, ptc-miR482d-3p, ptc-miR167f-3p, stu-miR319-3p and zma-miR396b-3p) were found common among 1 DAI, 2 DAI, 3 DAI and 7 DAI in JL 24. Two miRNAs, ptc-miR482d-3p and mtr-miR2118, showed contrasting expression at different time intervals between J 11 and JL 24. These two miRNAs were found to target those genes with NBS-LRR function, making them potential candidates for marker development in groundnut breeding programs aimed at enhancing resistance against A. flavus infection. Conclusion : This study enhances our understanding of the involvement of two miRNAs namely, ptc-miR482d-3p and mtr-miR2118, along with their NBS-LRR targets, in conferring resistance against A. flavus -induced aflatoxin contamination in groundnut under in vitro conditions. Aspergillus flavus Differential expression Genes Groundnut MicroRNA. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Groundnut ( Arachis hypogaea L.) is an important legume crop that possesses abundant protein and oil content. It is cultivated across approximately 30.5 million hectares worldwide, resulting in an annual production of approximately 54.2 million tonnes [1]. The centre of origin of groundnut is reported in Gran Panatanal (Mato Grosso, Brazil) and on the eastern slopes of the Bovilian Andes [2]. It plays a vital role in enhancing food security by promoting nutrition in many developing countries [3]. Groundnut stands apart from other leguminous crops due to its unique characteristic of developing a gynophore that form pod underground. The wild species of groundnut are diploids, whereas cultivated species are allotetraploid with 2n = 4x = 40, AABB-type genome. Groundnut breeding aims to achieve maximized yield, enhanced nutritional content, resistance to biotic and abiotic stresses, and ensuring compatibility with mechanized farming and harvesting practices. The yield of groundnut is severely affected by biotic and abiotic stresses. Among biotic stresses, aflatoxin contamination poses major pre- and post-harvest losses upto 13-59% worldwide [4]. Aflatoxins are carcinogenic mycotoxin with immunosuppressive properties produced by fungi genus Aspergillus [5]. Moreover, aflatoxin B1, B2, G1, and G2 are most toxic mycotoxin naturally occurring in groundnut. These toxins hold greater significance compared to other fungal toxins due to their carcinogenic effects and potential for acute poisoning [6]. A. flavus is the most common species producing aflatoxin [7]. However, other species such as A. parasiticus and A. nomius might be source of contamination in some localities [8,9]. Aflatoxin came into focus in 1960s when large number of Turkey birds died in UK due to aflatoxin-contaminated feed. Apart from groundnut, these fungi also contaminate the other commodities such as rice, chilli pepper, wheat, maize and tree nut [10,11,12]. Chronic exposure to high level of aflatoxin has adverse impact on human and is considered a growth retardant factor in young individuals, and increasing the vulnerability to auto-immune deficiency symptoms [13,14]. The fungus tends to invade groundnut crop at three stages: pre-harvest, during crop development and post-harvest stages [15,16]. Developing groundnut varieties resistant to A. flavus infection is considered as an economically viable solution to mitigate aflatoxin contamination in areas where groundnuts are cultivated. However, this task presents several challenges for breeders. One major challenge is the lack of reliable resistance resources available. It requires significant time, effort, and financial investment to identify and introgress genomic regions that confer resistance to A. flavus infection into groundnut varieties. Another challenge lies in the complex and often hidden interactions between the plant and the fungus. The mechanisms by which groundnut plants resist or tolerate A. flavus infection are not fully understood, making it difficult to accurately select and breed for resistance traits. Furthermore, environmental factors play a crucial role in the development and spread of A. flavus infection. Temperature, humidity, soil conditions, and other environmental variables can influence the severity and prevalence of the fungus. Thus, these environmental effects must account when developing resistant varieties, adding another layer of complexity to the breeding process [17]. Groundnut is known to exhibit three resistance mechanism: namely resistance to in vitro seed colonization (IVSC), pre-harvest aflatoxin contamination and resistance to post-harvest aflatoxin production in seeds [18]. Investigating the molecular components of aflatoxin resistance is necessary to explore the origins of resistance through three distinct mechanisms. Unravelling the molecular mechanisms and identifying genes associated with IVSC resistance holds the promise of transforming the control of fungal colonization and aflatoxin contamination in groundnut. The progress made in genomics resources has facilitated the sequencing of several groundnut genomes, included three cultivated allotetraploid varieties (Tiffrunner, Shitouqi, and Fuhuasheng) alongside two ancestral diploid species ( A. duranensis and A. ipaensis ) [19,20,21,22]. These sequencing efforts have opened up avenues for comprehensive explorations into the resistance against A. flavus infection, presenting new opportunities for in-depth investigations. However, several studies have been reported for breeding against aflatoxin contamination. Comparative transcriptome analysis and weighted gene co-expression network analysis were employed to investigate the resistance mechanism of groundnut against A. flavus . Their findings suggest that pathogenesis-related proteins, serine/threonine kinase, MAPK kinase, and pattern recognition receptors play crucial roles in groundnut’s ability to resist A. flavus [23]. The significance of miRNAs in various biological processes such as counteracting environmental effects, developmental transitions, stabilizing genome and defence response against various pathogens, has been reported in eukaryotes [24]. The microRNAs (miRNAs) are small non-coding RNA comprised of 21-24 nucleotides (nt) present in both plants and animals. Historically, the first miRNA was Lin-4, identified in Caenorhabditis elegans , while the first miRNA in plant was discovered in Arabidopsis [25,26,27]. Moreover, the first miRNA in groundnut was discovered in 2010 using high-throughput Solexa sequencing [28]. Several miRNAs have been discovered to control abiotic stress factors such as drought, salinity, cold, and heat, as well as biotic stress factors such as pathogenesis of bacteria, fungi, nematodes through post-transcriptional regulations [29,30]. The significance of miRNAs in plant against biotic stress factors has been extensively explored. miRNAs exert their influence on target genes by binding specifically to targeting sites on gene transcripts. This sequence-specific binding can result in either degradation of the target mRNA or translational repression, mediated by proteins associated with the miRNA [31]. Moreover, miRNAs play critical role in leaf morphogenesis, floral development and root initiation and development [26]. Several known and novel miRNAs were identified that induced or inhibited upon infection by Ralstonia solanacearum through high-throughput genotyping in susceptible and resistant cultivars of groundnut [27]. The miRNA family (miR family) (miR2118) found to be associated with NBS-LRR gene whose expression was upregulated in resistant cultivar. The miRNAs, miR472/RDR6 proven to modulate PAMP-triggered immunity (PTI) and effector triggered immunity (ETI) through the post-transcriptional regulation in Arabidopsis [32]. The occurrence of multiple miRNAs in defence response against blast causing fungus Magnaporthe oryzae has already been reported in rice [33,34]. With this background, we attempted to identify miRNAs to enhance the understanding of IVSC resistance mechanism in groundnut. MATERIALS AND METHODS Plant material, stress treatment and RNA extraction The miRNA study aimed to examine the resistance to IVSC by using resistant (J 11) and susceptible (JL 24) genotypes of groundnut, alongside a highly toxigenic strain of A. flavus (AF 11-4), identified at the Groundnut Pathology Unit of ICRISAT. The strain was cultivated in a pure culture on Potato Dextrose Agar for seven days, after which a conidial suspension was prepared at a concentration of 10 6 spores/ml. Screening of in vitro seed colonization Surface sterilization of 100 healthy seeds each of the J 11 and JL 24 genotypes were carried out using 0.1% HgCl 2 for 3 minutes. Subsequently, the seeds underwent three rinses with sterile distilled water. For each genotype, two distinct sets were made, comprising a control group and an infected group. Approximately 50 sterilized seeds of each genotype were placed on sterile filter papers in petri dishes to serve as control samples. The remaining 50 seeds were exposed to a spore suspension of the toxigenic strain 'AF 11-4' of A. flavus at an optimal concentration of 10 6 colony forming units/ml. Both sets were incubated in a dark, and humid chamber at temperature of 28°C with 100% relative humidity. RNA samples were collected from both the infected and control groups of J 11 and JL 24 genotypes at 1 day, 2 days, 3 days, and 7 days after inoculation (1 DAI, 2 DAI, 3 DAI, and 7 DAI). During each time interval, a few seeds were used for microscopic examination of the seed coat and for estimating the aflatoxin level. The experiment was carried out twice, and each set was considered as a separate biological replicate. To estimate the aflatoxin concentration, 16 samples were analysed, consisting of two genotypes, four stages, and two treatments. Aflatoxin quantification and microscopic observation of seed coat The quantitative estimation of total aflatoxins accumulated under both control and infected treatments was carried out using an indirect competitive enzyme-linked immunosorbent assay (ELISA). The assay employed polyclonal antibodies produced against Aflatoxin B1 (AFB1) as explained by Waliyar et al. [16]. The seed coats of both infected and control genotypes were observed under a stereomicroscope. RNA isolation and sequencing The "NucleoSpin® RNA Plant" kit (Macherey-Nagel, Germany) was utilized to isolate total RNA from the seeds. The quality and quantity of RNA were assessed using a Nanodrop 1000 spectrophotometer (Thermo Fisher Scientific Inc, USA). For the construction of the cDNA library, approximately 5 μg of total RNA was used, which was pooled together in equal quantities from two biological replicates. RNA samples that were sequenced on the Illumina HiSeq 2500 platform met the following quality criteria: a 260/280 ratio between 1.8 to 2.1, a 260/230 ratio between 2.0 to 2.3, and a RIN (RNA integrity number) value greater than 7.0. Paired-end reads of 2 × 100 bp were generated from the samples, and subsequent to running a quality control (QC) analysis with NGS-QC box, filtered reads were obtained. miRNA sequencing and data pre-processing Illumina TruSeq Small RNA Library Prep Kit (Illumina Inc., San Diego, CA) was used to construct small RNA libraries according to the manufacturer’s instructions. After separating 1 µg of RNA from each sample using polyacrylamide gel electrophoresis (PAGE), 18-30 nt long RNA fragments were enriched, followed by ligation of 30 and 50 adapter using T4 RNA ligase. After adaptor ligation of RNA molecules, cDNA was synthesized, amplified, and subsequently sequenced on the Illumina HiSeq 4000. “Trimmomatic v0.35” was used to perform several quality control steps on the raw reads obtained from sequencing, including the removal of low-quality reads, reads with adaptor or primer contamination, and those with a poly-A tail. Those reads were rejected which are shorter than 18 nt and longer than 35 nt. After obtaining clean reads from each sample, they were subjected to additional screening to remove any rRNA, tRNA, snoRNA, or repeat sequences. Once the filtering was done, the repeated reads were converted into distinct sequences, which were assigned read counts to facilitate miRNA prediction. Identification of known and novel miRNA Conserved miRNAs were identified using miRBase [35] by mapping filtered unique reads of each sample onto plant miRNAs. The alignment procedure involved using the Bowtie alignment tool v1.1.2 with a tolerance of two mismatches. Any unaligned unique reads were subsequently subjected to novel miRNA prediction. The unique reads that remained were mapped onto the groundnut genome using Bowtie, with no allowance for mismatches. Subsequently, putative precursor sequences, spanning 250 bp, were extracted for the aligned reads. Using miRDeep-P, a probabilistic model-based software, novel miRNAs were identified from the identified precursor sequences [36]. MiRDeep-P introduces a novel prediction approach that takes into account various factors including the secondary structure, the presence of a 30-overhang, evidence of star miRNA, the length difference between mature and star miRNA (which should be less than six nucleotides), the Dicer cleavage site, and the minimum free energy of the small RNA reads [37]. Moreover, the miRNAs that were identified were grouped into families using CD-HIT [37] with a 90% identity threshold, based on their sequence similarity. Afterwards, the psRNATarget server [38] was utilized with default parameters to predict the mRNA targets of the identified miRNAs. Expression analysis of miRNAs To evaluate miRNA expression levels and normalize raw reads count, DESeq2 was used [39]. A miRNA was considered significantly expressed if it possessed log2 fold change ≥1 or ≤ 1 and a P-value ≤ 0.05. Genes Expression The A. hypogaea gene expression atlas (AhGEA) specific to the fastigiata sub-species (BioProject ID: PRJNA484860) was utilized to examine the tissue-specific expression patterns of the selected genes [40]. RESULTS Microscopic observation and aflatoxin estimation Using stereomicroscope, mycelial growth was considerably low at first days after inoculation (1 DAI) in J 11 and JL 24 genotypes. The seeds of JL 24 showed higher mycelial growth than J 11 at 2 DAI and at subsequent periods (3 DAI and 7 DAI). The genotype J 11 showed no fungal colonization, whereas JL 24 noticed with considerable colonization. The germination of both genotypes was uniform at controlled condition. However, the seeds JL 24 couldn’t germinate after inoculation due to heavy fungal growth and colonization ( Figure 1(A) ). High-throughput miRNA sequencing The resistant (J 11) and susceptible (JL 24) genotypes exhibited significant difference in the aflatoxin content upon A. flavus infection on seeds ( Figure 1B) . To study the variations of miRNAs during A. flavus infection in seeds, four inoculation periods were selected: namely, 1 day after inoculation (1 DAI), 2 days after inoculation (2 DAI), 3 days after inoculation (3 DAI) and 7 days after inoculation (7 DAI). In this way, 4 infected days (ID) and 4 controlled days (CD) for J 11 and JL 24 were made in the experiment. A total of 16 small RNA libraries were constructed from aflatoxin infected seeds (J 11_ID1, J 11_ID2, J 11_ID3, J 11_ID7, JL 24_ID1, JL 24_ID2, JL 24_ID3, JL 24_ID7) and seeds at controlled condition (J 11_CD1, J 11_CD2, J 11_CD3, J 11_CD7, JL 24_CD1, JL 24_CD2, JL 24_CD3, JL 24_CD7) and sequenced using Illumina/Solexa 500 platform to identify the aflatoxin-related miRNA in groundnut. Total of 782.65 million reads were generated with an average of 48.91 million reads per sample (Table 1) . After subsequent steps of filtering low quality reads and trimming, a set of 543.85 million high quality reads was retained for further analysis. Around 509.36 million clean reads with length 15-nt ≤ Reads ≤ 30-nt were obtained. The length distribution of unique miRNA indicated that 21 nt (62.97%) were the most abundant class followed by 22-nt (27.65%), 20-nt (3.82%), 23-nt (2.97%) and 24-nt (2.55%) ( Figure 2A ). The length of miRNAs within the range of 20–24 nt is in line of DCL cleaved product (Reinhart et al. , 2002). Most of the miRNA sequences, especially of 20-nt, 21-nt, 22-nt and 23-nt length, initiate with uridine (U). The 24-nt long miRNAs has adenine (A) as first nucleotide at 5' end ( Figure 2B and Figure 2C ). Table 1: Statistical analysis of small RNAs mapped in groundnut genome Treatment RW QFR FN 15 ≤ Reads ≤ 30 nt FRNC FC FR FER J 11-CD1 54733510 38290230 38289935 37607501 2773759 2730265 2307710 1282733 J 11-CD2 34160093 25581901 25581710 24315116 2212974 2180634 1855181 1156020 J 11-CD3 38339938 32633469 32633132 24721003 4916621 4829220 4135336 2422427 J 11-CD7 25631514 15375955 15375860 15073897 740328 725596 608319 381023 JL 24-CD1 37556017 31438065 31437804 28912948 1826128 1807981 1571559 931563 JL 24-CD2 96122457 63723184 63722354 60796033 15275895 14849698 11950811 7234406 JL 24-CD3 70078765 57731276 57731226 53277043 6723254 6616518 5577768 3729203 JL 24-CD7 34872344 27533632 27533600 25547125 3754098 3678154 3207151 2160496 J 11-ID1 43370573 27052365 27052357 26588590 1055798 1042247 870828 475804 J 11-ID2 36088471 20740745 20740319 18139228 4378778 4294535 3516043 2122107 J 11-ID3 34220378 20622878 20622349 16947807 3988497 3905385 3213088 1868655 J 11-ID7 52216928 35637927 35637918 34723693 2803845 2749333 2244547 1323987 JL 24-ID1 102952235 68520018 68519053 65035009 8681231 8536505 6770784 3587796 JL 24-ID2 39858694 26175601 26175592 25559301 2868239 2841539 2340381 1388715 JL 24-ID3 47355756 30784013 30784006 30363484 2405071 2375130 1864742 1097799 JL 24-ID7 35093653 22009701 22009696 21759049 2868239 2841539 2340381 1388715 Total 782651326 543850960 509366827 RW: Raw reads, QFR: Quality filtered reads, FN: Filtered for Ns, FRNC: Filtered reads for ncRNA, FC: Filtered for chloroplast, FR: Filtered for repeats, FER: Filtered for exonic region, CD: Controlled day, ID: Infected day Identification of known and novel miRNA miRNAs are known to play critical roles in response to both biotic and abiotic stresses. To identify known and novel miRNAs, filtered reads were mapped against miRNAs of related species through miRBase. A total of 50.9 million reads were mapped to miRBase, enabling the identification of 208 known miRNAs belonging to 36 miR families ( Additional file 1 ). In addition to known miRNAs, plants also possess unique miRNAs for which unmapped reads were subjected to miRNA prediction processed through miRDeep-P. Briefly, mapped reads were used to obtain precursor sequences, which folded into possible stem-loop structures using the “Vienna” package and further filtered and processed. A total of 27 potential novel miRNAs with length ranged from 20 to 22 nt were obtained after the removal of those miRNA which could not meet the miRNA criteria. The average GC content of groundnut miRNAs was found to be 51.05%, similar to chickpea (48%) and soybean (46%). The known miRNAs were grouped into 36 families based on similarity-based clustering. Among these, miR166 was the largest family with 28 miRNA members, followed by miR156 (24 members) and miR167 (23 members). However, novel miRNAs could not fit into any of the conserved miRNA families. Differentially expressed miRNAs during A. flavus infection in groundnut seed To identify differentially expressed miRNAs, expression patterns of identified miRNAs were evaluated in all libraries. The majority were found in more than one sample. The criteria of adjusted p-value <0.05 and fold change 1 was used to identify differentially expressed miRNAs. We found 79, 102, 97 and 56 differentially expressed miRNAs in J 11 between controlled and infected day 1, 2, 3 and 7, respectively. Seven miRNAs namely aly-miR156d-3p, csi-miR1515a, gma-miR396e, mtr-miR2118, novo-miR-n27, ptc-miR482d-3p and ppe-miR396a were common in resistant variety at 1 DAI, 2 DAI, 3 DAI and 7 DAI and accounting 3.6% of total differentially expressed miRNA in J 11. In JL 24, the numbers of differentially expressed miRNAs were 87, 122, 83 and 103 between controlled and infected days 1, 2, 3, and 7, respectively. A total of ten miRNAs (csi-miR159a-5p, csi-miR164a-3p, novo-miR-n17, novo-miR-n2, osa-miR162b, mtr-miR2118, ptc-miR482d-3p, ptc-miR167f-3p, stu-miR319-3p and zma-miR396b-3p) were common in susceptible variety at 1 DAI, 2 DAI, 3 DAI and 7 DAI, and accounting 4.8% of differentially expressed miRNAs in JL 24. In between the J 11 and JL 24, the numbers of differentially expressed miRNAs were 31, 62, 25 and 37 at infected day 1, 2, 3 and 7, respectively ( Figure 2(D, E, F) ). Two miRNAs (mtr-miR2118 and ptc-miR482d-3p) were common between J 11 and JL 24 at 1 DAI, 2 DAI, 3 DAI and 7 DAI, and accounting 0.017% of total differentially expressed miRNAs. Upon comparison between controlled and infected samples, the numbers of upregulated miRNAs were more than downregulated miRNAs in J 11 and JL 24. However, number of downregulated miRNAs were higher when comparison was made between J 11 and JL 24 at different infected days ( Table 2 ) ( Additional file 2 ). Table 2: Summary of numbers of down and up-regulated miRNAs in different combinations Genotypes Treatments No. of downregulated miRNAs No. of upregulated miRNAs J 11 CD1-ID1 46 33 J 11 CD2-ID2 41 61 J 11 CD3-ID3 53 44 J 11 CD7-ID7 25 31 Total 165 169 JL 24 CD1-ID1 33 54 JL 24 CD2-ID2 64 58 JL 24 CD3-ID3 37 46 JL 24 CD7-ID7 36 67 Total 170 225 CD1: Controlled day 1, ID1: Infected day 1, CD2: Controlled day 2, ID2: Infected day 2, CD3: Controlled day 3, ID3: Infected day 3, CD7: Controlled day 7, ID7: Infected day 7 In silico target identification Identification of target of miRNAs was conducted using psRNATarget server, based on complementarity between miRNAs and target sequence. Total 952 unique targeted genes were identified for 235 (27 novel and 208 known) miRNAs. In total, 1742 targets were found for 83.8% (197) miRNAs and maximum target were found for members of family miR159 (238), followed by miR482 (235) and miR396 (231) ( Figure 3 ). The target annotations grouped the genes into several categories, including disease resistance genes, cellular enzymes (kinase, methyltransferase, β-galactosidase, etc.), transcription factors, proteasome assembly, proton transmembrane transport, meristem development and maintenance. The majority of annotated genes were responsible for disease resistance protein (22.8%), followed by transcription factors (3.6%), auxin responsive protein coding genes (3.5%), and pentatricopeptide repeat protein (3.5%). To understand the possible involvement of miRNA targets in the groundnut’s response to aflatoxin stress, a Gene Ontology (GO) enrichment analysis was carried out. A total of 1609 biological processes, 401 cellular components, and 946 molecular functions were allocated uniformly among the targets. Among biological process, the most significant GO terms were cellular process, metabolic process, response to stimulus, and biological regulations. Similarly, binding has most significant GO term followed by catalytic activity and transcription regulator activity among molecular function. In cellular component category, cellular anatomical entity has maximum GO term followed by protein-containing complex ( Figure 4 ). Common miRNA in two groundnut genotypes at 1 DAI, 2 DAI, 3 DAI and 7 DAI On comparing different days after inoculation for the same genotype, we found more miRNAs were expressed at 2 DAI in J 11 as well as in JL 24. This indicated that the two genotypes responded in more similar manner at 2 DAI. However, slope of number of miRNAs was raised at 7 DAI in JL 24 comparable to J 11 where slope diminished after 2 DAI. This showed the both genotypes had started to respond to A. flavus infection from 2 DAI but susceptible genotype showed different response at 7 DAI. Large number of differentially expressed common miRNAs were observed in both J 11 and JL 24 at 1 DAI, 2 DAI, 3 DAI and 7 DAI. Total 31 differentially expressed miRNAs were common between J 11 and JL 24 at 1 DAI. Among them, 11 miRNAs showing contrasting expression between J 11 and JL 24. Sixty-two differentially expressed miRNAs were common between J 11 and JL 24 at 2 DAI. Among them, 50 miRNAs showed contrasting expression between J 11 and JL 24. Similarly, 16 from 25 miRNAs and 16 from 31 miRNAs were showing contrasting expression between J 11 and JL 24 at 3 DAI and 7 DAI, respectively. The known miR families such as miR166, miR167 and miR156 were identified more frequently and abundantly expressed, consistent with previous studies ( Figure 5 ). To identify the miRNAs involved in aflatoxin resistance, we interrogated into those common differentially expressed miRNAs which have contrasting expression patterns in resistant genotype under 1 DAI, 2 DAI, 3 DAI and 7 DAI. Interestingly, two miRNAs namely mtr-miR2118 and ptc-miR482d-3p from miR2118 and miR482 families, respectively, were showed contrasting expression pattern between J 11 and JL 24 at all the infected days. Both miRNAs, ptc-miR482d-3p and mtr-miR-2118 showed downregulation in resistant genotype, J 11 whereas it become upregulated in susceptible genotype, JL 24 (Table 3) . However, miRNAs such as zma-miR396b-3p and osa-miR162b also showed upregulation in JL 24 but didn’t express in J 11. These miRNAs were further looked for targeted genes, there were seven genes for mtr-miR2118 and 62 targets for ptc-miR482d-3p. The annotation of these targeted genes was found to be associated with disease resistance mechanism. The gene expression atlas (AhGEA) of A. hypogaea ssp. fastigiata was utilized to detect the expression of genes specific to certain tissues [40]. For mtr-miR2118, the expression of only four genes were found in different tissues in AhGEA. These genes were found in chromosome 9, 19, 5 and 15. Similarly, total 43 genes showed tissue specific expression associated with ptc-miR482d-3p in AhGEA. Among 43, maximum genes (21) were found on chromosome 12 followed by chromosome 2 (13 genes), chromosome 14 (4 genes), chromosome 4 (4 genes) and chromosome 3 (1 gene) (Table 4) . The insilico expression of these genes using AhGEA were shown in Figure 6 . For zma-miR396b-3p, six targets were identified, linked with LRR receptor-like serine/threonine-protein kinase metabolism. Similarly, osa-miR162b had 23 targeted genes associated with metabolism of beta amylase, UDP-glycosyltransferase, endoribonuclease Dicer homolog and pentatricopeptide repeat-containing proteins. Table 3: Differential expression of miRNAs common among 1 DAI, 2 DAI, 3 DAI and 7 DAI in J 11 and JL 24 Genotypes Differential Expression Pattern miRNAs 1 DAI 2 DAI 3 DAI 7 DAI J 11 aly-miR156d-3p 1.2333638 -1.904056 2.1020817 2.507183 csi-miR1515a 2.7652149 -1.9477172 1.313454 1.1852549 gma-miR396e -2.1416757 1.1874424 -3.5345429 -1.263646 mtr-miR2118 1.0282493 -1.9824826 -2.6600738 -3.0291357 novo-miR-n27 1.7720686 -1.1817914 1.5628696 1.2441486 ppe-miR396a -1.9717507 1.3534523 -3.4090121 -1.8653712 ptc-miR482d-3p -2.1416757 -1.6160782 -1.7824791 -1.8851344 JL 24 csi-miR159a-5p 1.4456028 1.8431715 2.9909445 3.1198553 csi-miR164a-3p 1.7575468 -2.2442914 2.0200909 1.1666936 novo-miR-n17 -1.2676821 1.2468739 -1.2670718 -2.1762961 novo-miR-n2 1.1136906 5.7691709 1.0030173 4.0842314 osa-miR162b 1.0026593 1.2092994 1.0315865 2.821197 ptc-miR167f-3p 1.5876218 3.4908697 1.1146716 3.2541564 stu-miR319-3p 2.1725843 -1.418384 2.3671895 -1.2011708 zma-miR396b-3p 2.1725843 1.1062059 1.5175905 3.7597965 ptc-miR482d-3p -1.8048254 -1.0048254 2.7943508 4.6891405 mtr-miR2118 1.3013652 1.258209 1.0253275 2.8391189 1 DAI: first day after inoculation; 2 DAI: second day after inoculation; 3 DAI: third day after inoculation; 7 DAI: seventh day after inoculation Table 4: Summary of targeted 47 genes of miRNAs miRNA Targeted Gene model Chromo-some Start position End position Annotations miR2118 Arahy.H8JIAA.1 15 154617270 154625699 Disease resistance protein (TIR-NBS-LRR class) family Arahy.7J0RKL.1 09 6874895 6880710 Disease resistance protein (TIR-NBS-LRR class) family Arahy.VF2B86.1 19 8244428 8250223 Disease resistance protein (TIR-NBS-LRR class) family Arahy.HVB0T8.1 05 90137426 90144450 Disease resistance protein (TIR-NBS-LRR class) family ptc-miR482d-3p Arahy.LVW2ZC.1 02 2165965 2179064 LRR and NB-ARC domain disease resistance protein Arahy.SLVW9F.1 12 1845409 1846752 Disease resistance protein (TIR-NBS-LRR class) family Arahy.Y2F96D.1 12 2634170 2637760 LRR and NB-ARC domain disease resistance protein Arahy.KL7N91.1 12 2857891 2861052 LRR and NB-ARC domain disease resistance protein Arahy.77RH3X.1 12 2840034 2841879 LRR and NB-ARC domain disease resistance protein Arahy.104ZDW.1 04 126965165 126971113 Disease resistance protein (TIR-NBS-LRR class) family Arahy.NUHQ9Q.1 04 126980888 126986798 Disease resistance protein (TIR-NBS-LRR class) family Arahy.6V6NN7.1 14 141400695 141406643 Disease resistance protein (TIR-NBS-LRR class) family Arahy.1VN7JI.1 14 141416418 141422328 Disease resistance protein (TIR-NBS-LRR class) family Arahy.CA6E74.1 12 1892963 1907959 LRR and NB-ARC domain disease resistance protein Arahy.V6X2E8.1 12 2827095 2838123 Disease resistance protein (TIR-NBS-LRR class) family Arahy.8HLD5E.1 12 2752265 2757090 LRR and NB-ARC domain disease resistance protein Arahy.I5JPYW.1 12 2768839 2780577 LRR and NB-ARC domain disease resistance protein Arahy.ZKR6CY.1 02 2041262 2044900 LRR and NB-ARC domain disease resistance protein Arahy.UZFH7Q.1 14 141463237 141467397 Disease resistance protein (TIR-NBS-LRR class) family Arahy.REWL7K.1 04 127027707 127031867 Disease resistance protein (TIR-NBS-LRR class) family Arahy.51RKDV.1 12 2485257 2492769 LRR and NB-ARC domain disease resistance protein Arahy.M55R6K.1 02 16662936 16665269 Disease resistance protein (TIR-NBS-LRR class) family Arahy.DLTR3L.1 12 2445028 2450508 LRR and NB-ARC domain disease resistance protein Arahy.II44X3.1 04 127073261 127076829 Disease resistance protein (TIR-NBS-LRR class) family Arahy.388Y5C.1 14 141508791 141512359 Disease resistance protein (TIR-NBS-LRR class) family Arahy.N06FBV.1 02 1506683 1516435 LRR and NB-ARC domain disease resistance protein Arahy.15H21N.1 02 509289 513987 LRR and NB-ARC domain disease resistance protein Arahy.K68I1Q.1 12 2901005 2904685 LRR and NB-ARC domain disease resistance protein Arahy.YM09LB.1 02 2011567 2015247 LRR and NB-ARC domain disease resistance protein Arahy.JUY39I.1 03 123658764 123662492 LRR and NB-ARC domain disease resistance protein Arahy.VMD6HC.1 02 14047304 14048911 Disease resistance protein (TIR-NBS-LRR class) family Arahy.2HN52Q.1 12 2535411 2539172 LRR and NB-ARC domain disease resistance protein Arahy.3TW696.1 02 2455813 2460791 LRR and NB-ARC domain disease resistance protein Arahy.7S97YI.1 12 2915861 2919511 LRR and NB-ARC domain disease resistance protein Arahy.25Q9K5.1 12 2793908 2797486 LRR and NB-ARC domain disease resistance protein Arahy.73AA2K.1 12 2922302 2926138 LRR and NB-ARC domain disease resistance protein Arahy.MZFR22.1 02 1565683 1568877 LRR and NB-ARC domain disease resistance protein Arahy.U1TKV1.1 02 1543715 1549934 LRR and NB-ARC domain disease resistance protein Arahy.0PVT6F.1 12 2588797 2592594 LRR and NB-ARC domain disease resistance protein Arahy.3G3XAR.1 12 3267043 3271844 Disease resistance protein (TIR-NBS-LRR class) family Arahy.G3725Q.1 02 2193339 2196848 LRR and NB-ARC domain disease resistance protein Arahy.83LA0K.1 12 1862247 1868553 LRR and NB-ARC domain disease resistance protein Arahy.8LIU0E.1 12 2662012 2665677 LRR and NB-ARC domain disease resistance protein Arahy.TKN2M5.1 12 3074253 3079786 LRR and NB-ARC domain disease resistance protein Arahy.FXRP5B.1 02 2153742 2160389 Disease resistance protein (TIR-NBS-LRR class) family Arahy.IS7D7R.1 02 1869681 1883608 LRR and NB-ARC domain disease resistance protein Arahy.ZBPZ8H.1 12 13220251 13221241 Disease resistance protein (TIR-NBS-LRR class) family DISCUSSION Groundnut is a protein rich leguminous crop with chief source of income in many developing nations [41]. Beyond its nutritious value (oil, protein, sugar, vitamins and minerals), it also has important role in sustainable agriculture due to its ability to thrive in marginal soil, withstand drought and fix nitrogen [42]. However, groundnut is susceptible to aflatoxin during both pre-harvest and post-harvest stages [43]. An integrated management strategy is crucial to minimize the risk of aflatoxin contamination. Aflatoxin are generally secondary metabolites produced by soil-borne saprophytic group of genus Aspergillus which affect groundnut and other food commodities. Environmental parameters such as high soil temperature, moisture stress, relative humidity influence the A. flavus infection and subsequent aflatoxin accumulation. The lack of genetic resistance in groundnut, along with these environmental factors, limited progress in this direction, makes this trait very complex [44]. At the post-transcriptional level, small non-coding RNAs such as miRNAs have been identified as significant regulators of gene expression [45]. miRNAs are non-coding RNAs that play vital roles in developmental processes and stress responses through negative regulations [46]. In this study, A. flavus infected and controlled small RNA libraries were prepared to identify known and novel miRNAs from resistant and susceptible cultivars. Among 208 known miRNA, 25 miRNAs were first reported in groundnut by Zhao et al . [27], 42 miRNAs were reported by Chi et al. [23] and 66 miRNAs were identified by Zhao et al. [47]. Previous studies reported that known miRNAs are majorly involved in developmental processes, whereas novel miRNAs were the part of species-specific gene regulatory functions [48,49]. Some features of miRNAs such as length and GC content were in concordance with the previous studies [50,51]. The known miR families such as miR166, miR167 and miR156 were identified more frequently and abundantly expressed, consistent with previous studies [23,52,53]. Total 186 and 199 variants of known miRNAs were identified from different treatments of resistant and susceptible cultivars. The abundance of variants for most of known miRNA families were higher in JL 24 than J 11, and miR166 having more variants than other families. All novel miRNAs were not registered miRBase which support the evidence to declare them as novel miRNAs. The known and novel miRNAs were search against Nucleotides, GSS, ESTs and TSA sequence of Arachis using the psRNATarget server. The identified targets were annotated by BLAST against NCBI Nr Database. A total of 1742 targets were identified, with some miRNAs having more than one targets. Functional annotation and classification showed that 22.8% of the targets were found to be associated with disease resistance proteins such as RPP and TMV resistance proteins, 5.6% with unknown proteins, 3.6% with transcription factors such as TCP4, TCP3, MYB52, MYB 97, and remaining were associated with receptors like serine/threonine protein kinase, mitogen activated protein, growth regulating factor, etc. Interestingly, most miRNAs were predicted to target resistance genes analog. The number of disease resistance proteins-coding genes were the target of miRNAs such as Recognition of Peronospora parasitica 13 (RPP) known to encode 820 amino acids which were believed to reside within cytoplasm and function in LRR (Leucine rich repeat) synthesising [54]. Similarly, RFL1 protein which was domain in NBS-LRR [55], TAO1 contributes to disease resistance in response to Pseudomonas syringae pathovars of tomato [56], Dominant Suppressor of Camta 3 number 1 (DSC1) which is immune receptor of TIR-NB-LRR [57], leucine-rich repeat receptor-like protein kinase and leaf rust disease-resistance locus receptor-like protein kinase, TMV resistance protein and LRR receptor-like serine/threonine-protein kinase are the putative protein encoded by the targeted genes found in this study. The NBS-LRRs are composed of a nucleotide-binding domain located at the center, which is connected to a leucine-rich repeat (LRR) domain at the C-terminal end. Additionally, there is a variable N-terminal domain that can either be a coiled-coil (CC) domain or a Toll-interleukin-1 receptor (TIR)-like domain [58]. Apart from disease resistance proteins, transcription factors like bHLH155, bHLH19, DUO1, GAMYB, MYB33, MYB52, MYB97, TCP2 were also target of identified miRNAs. Total 229 miRNAs showed the differential expression in at least one treatment, and it was noted that all novel miRNAs were among the differentially expressed miRNAs. Moreover, it was seen that members of the same miRNA families responded differently to A. flavus infection. For example, miRNAs from family miR156 namely aly-miR156d-3p showed upregulation (Log 2 FC= 2.51) at DAI7 in J 11 whereas ath-miR156b-3p was downregulated (Log 2 FC= -1.64) at DAI 7 in J 11. Similar trend was previously reported in other crops such as chick pea where miR171 showed differential expression pattern under Ascochyta blight infection [59] and soybean in which miR396 showed differential expression at high cadmium concentration [60]. Total seven miRNAs in J 11 were continuously expressed at DAI1, DAI2, DAI3 and DAI7, while ten miRNAs were commonly expressed in JL 24. Similarly, commonly expressed miRNAs (Osa-miR156d, Osa-miR159b, Osa-miR820c, and Osa-miR1876) were identified between susceptible and resistant rice cultivars [61]. The miRNAs, mtr-miR2118 and ptc-miR482d-3p were belongs to miR2118 and miR482 families. The members of these families are known to play major roles in stress response [62]. The targets of mtr-miR2118 were found on chromosome 05, 09, 15 and 19 whereas targets of ptc-miR482d-3p located on chromosome 02, 03, 04, 12 and 14, and all these targets were associated with TIR-NBS-LLR encoding domains. CONCLUSION In this study, we identified 208 known and 27 novel miRNAs in J 11 and JL 24 groundnut genotypes. Total 1742 targets were identified for these miRNAs, which were found to encode disease resistant proteins, transcription factor involved in several metabolic pathways, transmembrane receptors, and protein kinase family proteins, etc. There were only two (mtr-miR2118 and ptc-miR482b-3p) differentially expressed miRNAs which expressed at all days after inoculations in both resistant, (J 11) and susceptible, (JL 24) genotypes. Further, the insilico expression analysis revealed the tissue specific expression of target genes of these two miRNAs. Functional annotation of these genes, showed that the genes were known to be involved in disease resistance mechanism by regulating the expression of various proteins like TIR-NBS-LRR, TMV resistance protein and serine/threonine protein kinase. These targets of miRNAs in resistance against A. flavus can be used in the development of markers for groundnut breeding program to enhance resistance against A. flavus . Declarations Ethics approval and consent to participate : Not applicable. Consent for publication : Not applicable Availability of data and materials : The datasets generated and/or analysed during the current study are available in the NCBI repository, BioProject ID PRJNA355201. Competing interests : The authors declare that they have no competing interests. Funding : This research was partially funded by the USAID-US University Collaboration Grant, Peanut & Mycotoxin Innovation Lab (PMIL), MARS-Wrigley, USA; and Bill & Melinda Gates Foundation (BMGF), USA through Tropical Legumes III project. Authors' contributions : R.K.V. and M.K.P conceived and supervised the project. P.J. written the main manuscript. PB analysed the RNA data. VS edited the manuscript. AKP, SNN, SS, HS, MKP and RKV contributed to generating the data, reviewing and improving the manuscript. All authors read and approved the final manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4607193","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":323960590,"identity":"b7c1b989-1ef6-4f3e-971d-849b3e60ffad","order_by":0,"name":"Pushpesh Joshi","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Pushpesh","middleName":"","lastName":"Joshi","suffix":""},{"id":323960591,"identity":"9b747289-e6d4-4313-8771-a4f80d31eef1","order_by":1,"name":"Vinay Sharma","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Vinay","middleName":"","lastName":"Sharma","suffix":""},{"id":323960592,"identity":"2d6189d6-bed6-486c-a132-5f52feb980fe","order_by":2,"name":"Arun K. Pandey","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Arun","middleName":"K.","lastName":"Pandey","suffix":""},{"id":323960593,"identity":"210be896-9a05-44cc-a8bf-39f86ebcc9dd","order_by":3,"name":"Spurthi N. Nayak","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Spurthi","middleName":"N.","lastName":"Nayak","suffix":""},{"id":323960594,"identity":"dab2bd32-2304-4029-b12e-8b911291976b","order_by":4,"name":"Prasad Bajaj","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Prasad","middleName":"","lastName":"Bajaj","suffix":""},{"id":323960595,"identity":"ddf60a66-66d9-4950-82fb-d3ae1206166e","order_by":5,"name":"Hari K. Sudini","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Hari","middleName":"K.","lastName":"Sudini","suffix":""},{"id":323960596,"identity":"072b4697-5bd5-43aa-bd94-c5dfc309df40","order_by":6,"name":"Shailendra Sharma","email":"","orcid":"","institution":"Chaudhary Charan Singh University","correspondingAuthor":false,"prefix":"","firstName":"Shailendra","middleName":"","lastName":"Sharma","suffix":""},{"id":323960597,"identity":"1fce686d-918b-4796-8e30-8d1c9e0d1e70","order_by":7,"name":"Rajeev K. Varshney","email":"","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":false,"prefix":"","firstName":"Rajeev","middleName":"K.","lastName":"Varshney","suffix":""},{"id":323960598,"identity":"e7228288-0701-4d05-aa85-41ba2a078c72","order_by":8,"name":"Manish K. Pandey","email":"data:image/png;base64,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","orcid":"","institution":"International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)","correspondingAuthor":true,"prefix":"","firstName":"Manish","middleName":"K.","lastName":"Pandey","suffix":""}],"badges":[],"createdAt":"2024-06-19 16:26:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4607193/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4607193/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-025-06322-2","type":"published","date":"2025-03-18T15:57:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60340304,"identity":"9c5d88c6-1132-41f9-8e96-2e63971871b0","added_by":"auto","created_at":"2024-07-15 18:24:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":897580,"visible":true,"origin":"","legend":"\u003cp\u003eMicroscopic observations and aflatoxin quantification of J 11 and JL 24; \u003cstrong\u003eA)\u003c/strong\u003e Growth of mycelium on seed coat after 7\u003csup\u003eth\u003c/sup\u003e day of inoculation in susceptible variety (JL 24) and resistant variety (J 11);\u003cstrong\u003e B) \u003c/strong\u003ePhenotypic observations of seeds of J 11 and JL 24 and graphical representation of aflatoxin contamination at different time points (1 DAI, 2 DAI, 3 DAI and 7 DAI).\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4607193/v1/69569c8b9356f6583c211c06.jpg"},{"id":60340311,"identity":"a964590c-09e0-4c7d-9a40-27f3d3df0410","added_by":"auto","created_at":"2024-07-15 18:24:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":414338,"visible":true,"origin":"","legend":"\u003cp\u003eTotal counts of miRNAs;\u003cstrong\u003e A) \u003c/strong\u003eLength distribution of miRNAs;\u003cstrong\u003e B) \u003c/strong\u003eRelative proportion of bases in known miRNAs;\u003cstrong\u003e C) \u003c/strong\u003eRelative proportion of bases in novel miRNAs;\u003cstrong\u003e D) \u003c/strong\u003eNumber of differentially expressed miRNAs between controlled and infected days in J 11; \u003cstrong\u003e(E)\u003c/strong\u003e Differentially expressed miRNAs between controlled and infected days in JL 24;\u003cstrong\u003e (F)\u003c/strong\u003e Differentially expressed miRNAs between J 11 and JL 24 at different days after inoculation\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4607193/v1/919cffc8fe97dc42eb77c7d2.jpg"},{"id":60340653,"identity":"925cbdce-f523-4b34-9139-7620d41b2b8d","added_by":"auto","created_at":"2024-07-15 18:32:13","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":228435,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of number of targets from different miRNA families\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4607193/v1/cfdbaa49de0c5c086c788d72.jpg"},{"id":60340308,"identity":"346964d2-941b-4145-8e62-d0d1a6896137","added_by":"auto","created_at":"2024-07-15 18:24:13","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":330904,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology enrichment analysis of all identified differentially expressed miRNAs involved in \u003cstrong\u003e(A) \u003c/strong\u003eBiological processes; \u003cstrong\u003e(B)\u003c/strong\u003e Molecular processes;\u003cstrong\u003e (C) \u003c/strong\u003eCellular components.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4607193/v1/903de0e2fb9d2b4f369cd0a6.jpg"},{"id":60340313,"identity":"ef01400c-b9db-405c-bedc-311ba3296677","added_by":"auto","created_at":"2024-07-15 18:24:13","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":195492,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of numbers of differentially expressed miRNAs from different miR families.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4607193/v1/c9a31489464215780cf6e066.jpg"},{"id":60340310,"identity":"cd46cf3d-76e1-4bf8-acb3-6c9b52f4cae8","added_by":"auto","created_at":"2024-07-15 18:24:13","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":508744,"visible":true,"origin":"","legend":"\u003cp\u003eTissue-specific expression pattern of targeted 47 genes of miRNAs (43 for miR-482d-3p and four for mtr-miR2118) across 20 different tissues;\u003cstrong\u003e \u003c/strong\u003eCp: Coleoptile, Cd: Cotyledon; ER: Emerging radicle; Fr: Flower; Immature bud; LS: Leaves senescence; LV: Leaves vegetative; PWI: Pod wall immature; PWM: Pod wall mature; PSS: Pre-soaked seeds; RS: Root seedling; RV: Root vegetative; S_15: Seeds 15; S_25: Seeds 25; S_5: Seeds 5; SS: Shoot seedling; SV: Stem vegetative.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4607193/v1/977204dfd92238f878ece553.jpg"},{"id":79120401,"identity":"ab35de0e-b836-44b2-8b84-04d9a3db9530","added_by":"auto","created_at":"2025-03-24 16:06:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4051149,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4607193/v1/e888a032-049f-4ed6-9b8e-78e71cf79cb5.pdf"},{"id":60340305,"identity":"7173fad9-581a-402c-ac4f-bb9fdf2fcc1a","added_by":"auto","created_at":"2024-07-15 18:24:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1474285,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4607193/v1/a9d77296d935a7085089ebcc.pdf"},{"id":60340312,"identity":"0449b95d-d51a-48bd-be3d-ed3bc344b97a","added_by":"auto","created_at":"2024-07-15 18:24:13","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":28480,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4607193/v1/0bc9ec53f397cb3a7704dc0a.xlsx"},{"id":60340309,"identity":"b0668549-48b7-4207-92bc-302a02eb384e","added_by":"auto","created_at":"2024-07-15 18:24:13","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":1474285,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4607193/v1/c97b75daef8c8c84f3f4be3e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of miRNAs associated with Aspergillus flavus infection and their targets in groundnut (Arachis hypogaea L.)","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGroundnut (\u003cem\u003eArachis hypogaea\u003c/em\u003e L.) is an important legume crop that possesses abundant protein and oil content. It is cultivated across approximately 30.5 million hectares worldwide, resulting in an annual production of approximately 54.2 million tonnes [1]. The centre of origin of groundnut is reported in Gran Panatanal (Mato Grosso, Brazil) and on the eastern slopes of the Bovilian Andes [2]. It plays a vital role in enhancing food security by promoting nutrition in many developing countries [3]. Groundnut stands apart from other leguminous crops due to its unique characteristic of developing a gynophore that form pod underground. The wild species of groundnut are diploids, whereas cultivated species are allotetraploid with 2n = 4x = 40, AABB-type genome. Groundnut breeding aims to achieve maximized yield, enhanced nutritional content, resistance to biotic and abiotic stresses, and ensuring compatibility with mechanized farming and harvesting practices. The yield of groundnut is severely affected by biotic and abiotic stresses. Among biotic stresses, aflatoxin contamination poses major pre- and post-harvest losses upto 13-59% worldwide [4]. Aflatoxins are carcinogenic mycotoxin with immunosuppressive properties produced by fungi genus \u003cem\u003eAspergillus\u003c/em\u003e [5]. Moreover, aflatoxin B1, B2, G1, and G2 are most toxic mycotoxin naturally occurring in groundnut. These toxins hold greater significance compared to other fungal toxins due to their carcinogenic effects and potential for acute poisoning [6].\u0026nbsp;\u003cem\u003eA. flavus\u003c/em\u003e is the most common species producing aflatoxin [7]. However, other species such as \u003cem\u003eA. parasiticus\u003c/em\u003e and \u003cem\u003eA. nomius\u003c/em\u003e might be source of contamination in some localities [8,9]. Aflatoxin came into focus in 1960s when large number of Turkey birds died in UK due to aflatoxin-contaminated feed. Apart from groundnut, these fungi also contaminate the other commodities such as rice, chilli pepper, wheat, maize and tree nut [10,11,12]. Chronic exposure to high level of aflatoxin has adverse impact on human and is considered a growth retardant factor in young individuals, and increasing the vulnerability to auto-immune deficiency symptoms [13,14].\u003c/p\u003e\n\u003cp\u003eThe fungus tends to invade groundnut crop at three stages: pre-harvest, during crop development and post-harvest stages [15,16]. Developing groundnut varieties resistant to \u003cem\u003eA. flavus\u003c/em\u003e infection is considered as an economically viable solution to mitigate aflatoxin contamination in areas where groundnuts are cultivated.\u0026nbsp;However, this task presents several challenges for breeders. One major challenge is the lack of reliable resistance resources available. It requires significant time, effort, and financial investment to identify and introgress genomic regions that confer resistance to \u003cem\u003eA. flavus\u003c/em\u003e infection into groundnut varieties. Another challenge lies in the complex and often hidden interactions between the plant and the fungus. The mechanisms by which groundnut plants resist or tolerate \u003cem\u003eA. flavus\u003c/em\u003e infection are not fully understood, making it difficult to accurately select and breed for resistance traits.\u0026nbsp;Furthermore, environmental factors play a crucial role in the development and spread of \u003cem\u003eA. flavus\u003c/em\u003e infection. Temperature, humidity, soil conditions, and other environmental variables can influence the severity and prevalence of the fungus. Thus, these environmental effects must account when developing resistant varieties, adding another layer of complexity to the breeding process [17]. Groundnut is known to exhibit three resistance mechanism: namely resistance to \u003cem\u003ein vitro\u003c/em\u003e seed colonization (IVSC), pre-harvest aflatoxin contamination and resistance to post-harvest aflatoxin production in seeds [18]. Investigating the molecular components of aflatoxin resistance is necessary to explore the origins of resistance through three distinct mechanisms. Unravelling the molecular mechanisms and identifying genes associated with IVSC resistance holds the promise of transforming the control of fungal colonization and aflatoxin contamination in groundnut. The progress made in genomics resources has facilitated the sequencing of several groundnut genomes, included three cultivated allotetraploid varieties (Tiffrunner, Shitouqi, and Fuhuasheng) alongside two ancestral diploid species (\u003cem\u003eA. duranensis\u003c/em\u003e and \u003cem\u003eA. ipaensis\u003c/em\u003e) [19,20,21,22]. These sequencing efforts have opened up avenues for comprehensive explorations into the resistance against \u003cem\u003eA. flavus\u003c/em\u003e infection, presenting new opportunities for in-depth investigations. However, several studies have been reported for breeding against aflatoxin contamination. Comparative transcriptome analysis and weighted gene co-expression network analysis were employed to investigate the resistance mechanism of groundnut against \u003cem\u003eA. flavus\u003c/em\u003e. Their findings suggest that pathogenesis-related proteins, serine/threonine kinase, MAPK kinase, and pattern recognition receptors play crucial roles in groundnut\u0026rsquo;s ability to resist \u003cem\u003eA. flavus\u003c/em\u003e [23].\u003c/p\u003e\n\u003cp\u003eThe significance of miRNAs in various biological processes such as counteracting environmental effects, developmental transitions, stabilizing genome and defence response against various pathogens, has been reported in eukaryotes [24]. The microRNAs (miRNAs) are small non-coding RNA comprised of 21-24 nucleotides (nt) present in both plants and animals. Historically, the first miRNA was Lin-4, identified in \u003cem\u003eCaenorhabditis elegans\u003c/em\u003e, while the first miRNA in plant was discovered in \u003cem\u003eArabidopsis\u003c/em\u003e [25,26,27]. Moreover, the first miRNA in groundnut was discovered in 2010 using high-throughput Solexa sequencing [28]. Several miRNAs have been discovered to control abiotic stress factors such as drought, salinity, cold, and heat, as well as biotic stress factors such as pathogenesis of bacteria, fungi, nematodes through post-transcriptional regulations [29,30].\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe significance of miRNAs in plant against biotic stress factors has been extensively explored. miRNAs exert their influence on target genes by binding specifically to targeting sites on gene transcripts. This sequence-specific binding can result in either degradation of the target mRNA or translational repression, mediated by proteins associated with the miRNA [31]. Moreover, miRNAs play critical role in leaf morphogenesis, floral development and root initiation and development [26]. Several known and novel miRNAs were identified that induced or inhibited upon infection by \u003cem\u003eRalstonia solanacearum\u003c/em\u003e through high-throughput genotyping in susceptible and resistant cultivars of groundnut [27]. The miRNA family (miR family) (miR2118) found to be associated with \u003cem\u003eNBS-LRR\u003c/em\u003e gene whose expression was upregulated in resistant cultivar. The miRNAs, miR472/RDR6 proven to modulate PAMP-triggered immunity (PTI) and effector triggered immunity (ETI) through the post-transcriptional regulation in \u003cem\u003eArabidopsis\u0026nbsp;\u003c/em\u003e[32]. The occurrence of multiple miRNAs in defence response against blast causing fungus \u003cem\u003eMagnaporthe\u003c/em\u003e \u003cem\u003eoryzae\u003c/em\u003e has already been reported in rice [33,34]. With this background, we attempted to identify miRNAs to enhance the understanding of IVSC resistance mechanism in groundnut.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003ePlant material, stress treatment and RNA extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe miRNA study aimed to examine the resistance to IVSC by using resistant (J 11) and susceptible (JL 24) genotypes of groundnut, alongside a highly toxigenic strain of \u003cem\u003eA. flavus\u003c/em\u003e (AF 11-4), identified at the Groundnut Pathology Unit of ICRISAT. The strain was cultivated in a pure culture on Potato Dextrose Agar for seven days, after which a conidial suspension was prepared at a concentration of 10\u003csup\u003e6\u003c/sup\u003e spores/ml.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScreening of \u003cem\u003ein vitro\u003c/em\u003e seed colonization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurface sterilization of 100 healthy seeds each of the J 11 and JL 24 genotypes were carried out using 0.1% HgCl\u003csub\u003e2\u003c/sub\u003e for 3 minutes. Subsequently, the seeds underwent three rinses with sterile distilled water. For each genotype, two distinct sets were made, comprising a control group and an infected group. Approximately 50 sterilized seeds of each genotype were placed on sterile filter papers in petri dishes to serve as control samples. The remaining 50 seeds were exposed to a spore suspension of the toxigenic strain \u0026apos;AF 11-4\u0026apos; of \u003cem\u003eA. flavus\u003c/em\u003e at an optimal concentration of 10\u003csup\u003e6\u003c/sup\u003e colony forming units/ml. Both sets were incubated in a dark, and humid chamber at temperature of 28\u0026deg;C with 100% relative humidity. RNA samples were collected from both the infected and control groups of J 11 and JL 24 genotypes at 1 day, 2 days, 3 days, and 7 days after inoculation (1 DAI, 2 DAI, 3 DAI, and 7 DAI). During each time interval, a few seeds were used for microscopic examination of the seed coat and for estimating the aflatoxin level. The experiment was carried out twice, and each set was considered as a separate biological replicate. To estimate the aflatoxin concentration, 16 samples were analysed, consisting of two genotypes, four stages, and two treatments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAflatoxin quantification and microscopic observation of seed coat\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe quantitative estimation of total aflatoxins accumulated under both control and infected treatments was carried out using an indirect competitive enzyme-linked immunosorbent assay (ELISA). The assay employed polyclonal antibodies produced against Aflatoxin B1 (AFB1) as explained by Waliyar et al. [16]. The seed coats of both infected and control genotypes were observed under a stereomicroscope.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA isolation and sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;NucleoSpin\u0026reg; RNA Plant\u0026quot; kit (Macherey-Nagel, Germany) was utilized to isolate total RNA from the seeds. The quality and quantity of RNA were assessed using a Nanodrop 1000 spectrophotometer (Thermo Fisher Scientific Inc, USA). For the construction of the cDNA library, approximately 5 \u0026mu;g of total RNA was used, which was pooled together in equal quantities from two biological replicates. RNA samples that were sequenced on the Illumina HiSeq 2500 platform met the following quality criteria: a 260/280 ratio between 1.8 to 2.1, a 260/230 ratio between 2.0 to 2.3, and a RIN (RNA integrity number) value greater than 7.0. Paired-end reads of 2 \u0026times; 100 bp were generated from the samples, and subsequent to running a quality control (QC) analysis with NGS-QC box, filtered reads were obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003emiRNA sequencing and data pre-processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIllumina TruSeq Small RNA Library Prep Kit (Illumina Inc., San Diego, CA) was used to construct small RNA libraries according to the manufacturer\u0026rsquo;s instructions. After separating 1 \u0026micro;g of RNA from each sample using polyacrylamide gel electrophoresis (PAGE), 18-30 nt long RNA fragments were enriched, followed by ligation of 30 and 50 adapter using T4 RNA ligase.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter adaptor ligation of RNA molecules, cDNA was synthesized, amplified, and subsequently sequenced on the Illumina HiSeq 4000. \u0026ldquo;Trimmomatic v0.35\u0026rdquo; was used to perform several quality control steps on the raw reads obtained from sequencing, including the removal of low-quality reads, reads with adaptor or primer contamination, and those with a poly-A tail. Those reads were rejected which are shorter than 18 nt and longer than 35 nt. After obtaining clean reads from each sample, they were subjected to additional screening to remove any rRNA, tRNA, snoRNA, or repeat sequences. Once the filtering was done, the repeated reads were converted into distinct sequences, which were assigned read counts to facilitate miRNA prediction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of known and novel miRNA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConserved miRNAs were identified using miRBase [35] by mapping filtered unique reads of each sample onto plant miRNAs. The alignment procedure involved using the Bowtie alignment tool v1.1.2 with a tolerance of two mismatches. Any unaligned unique reads were subsequently subjected to novel miRNA prediction. The unique reads that remained were mapped onto the groundnut genome using Bowtie, with no allowance for mismatches. Subsequently, putative precursor sequences, spanning 250 bp, were extracted for the aligned reads. Using miRDeep-P, a probabilistic model-based software, novel miRNAs were identified from the identified precursor sequences [36]. MiRDeep-P introduces a novel prediction approach that takes into account various factors including the secondary structure, the presence of a 30-overhang, evidence of star miRNA, the length difference between mature and star miRNA (which should be less than six nucleotides), the Dicer cleavage site, and the minimum free energy of the small RNA reads [37]. Moreover, the miRNAs that were identified were grouped into families using CD-HIT [37] with a 90% identity threshold, based on their sequence similarity. Afterwards, the psRNATarget server [38] was utilized with default parameters to predict the mRNA targets of the identified miRNAs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpression analysis of miRNAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate miRNA expression levels and normalize raw reads count, DESeq2 was used [39]. A miRNA was considered significantly expressed if it possessed log2 fold change \u0026ge;1 or \u0026le; 1 and a P-value \u0026le; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenes Expression\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eA. hypogaea\u003c/em\u003e gene expression atlas (AhGEA) specific to the \u003cem\u003efastigiata\u003c/em\u003e sub-species (BioProject ID: PRJNA484860) was utilized to examine the tissue-specific expression patterns of the selected genes [40].\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eMicroscopic observation and aflatoxin estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing stereomicroscope, mycelial growth was considerably low at first days after inoculation (1 DAI) in J 11 and JL 24 genotypes. The seeds of JL 24 showed higher mycelial growth than J 11 at 2 DAI and at subsequent periods (3 DAI and 7 DAI). The genotype J 11 showed no fungal colonization, whereas JL 24 noticed with considerable colonization. The germination of both genotypes was uniform at controlled condition. However, the seeds JL 24 couldn\u0026rsquo;t germinate after inoculation due to heavy fungal growth and colonization (\u003cstrong\u003eFigure 1(A)\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh-throughput miRNA sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe resistant (J 11) and susceptible (JL 24) genotypes exhibited significant difference in the aflatoxin content upon \u003cem\u003eA. flavus\u003c/em\u003e infection on seeds (\u003cstrong\u003eFigure 1B)\u003c/strong\u003e. To study the variations of miRNAs during \u003cem\u003eA. flavus\u003c/em\u003e infection in seeds, four inoculation periods were selected: namely, 1 day after inoculation (1 DAI), 2 days after inoculation (2 DAI), 3 days after inoculation (3 DAI) and 7 days after inoculation (7 DAI). In this way, 4 infected days (ID) and 4 controlled days (CD) for J 11 and JL 24 were made in the experiment. A total of 16 small RNA libraries were constructed from aflatoxin infected seeds (J 11_ID1, J 11_ID2, J 11_ID3, J 11_ID7, JL 24_ID1, JL 24_ID2, JL 24_ID3, JL 24_ID7) and seeds at controlled condition (J 11_CD1, J 11_CD2, J 11_CD3, J 11_CD7, JL 24_CD1, JL 24_CD2, JL 24_CD3, JL 24_CD7) and sequenced using Illumina/Solexa 500 platform to identify the aflatoxin-related miRNA in groundnut. Total of 782.65 million reads were generated with an average of 48.91 million reads per sample \u003cstrong\u003e(Table 1)\u003c/strong\u003e. After subsequent steps of filtering low quality reads and trimming, a set of 543.85 million high quality reads was retained for further analysis. Around 509.36 million clean reads with length 15-nt \u0026le; Reads \u0026le; 30-nt were obtained.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe length distribution of unique miRNA indicated that 21 nt (62.97%) were the most abundant class followed by 22-nt (27.65%), 20-nt (3.82%), 23-nt (2.97%) and 24-nt (2.55%) (\u003cstrong\u003eFigure 2A\u003c/strong\u003e). The length of miRNAs within the range of 20\u0026ndash;24 nt is in line of DCL cleaved product (Reinhart \u003cem\u003eet al.\u003c/em\u003e, 2002). Most of the miRNA sequences, especially of 20-nt, 21-nt, 22-nt and 23-nt length, initiate with uridine (U). The 24-nt long miRNAs has adenine (A) as first nucleotide at 5\u0026apos; end (\u003cstrong\u003eFigure 2B and Figure 2C\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Statistical analysis of small RNAs mapped in groundnut genome\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"721\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRW\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eQFR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e15 \u0026le; Reads \u0026le; 30 nt\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFRNC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFER\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJ 11-CD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e54733510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e38290230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e38289935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e37607501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2773759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2730265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2307710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e1282733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJ 11-CD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e34160093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e25581901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e25581710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e24315116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2212974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2180634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e1855181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e1156020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJ 11-CD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e38339938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e32633469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e32633132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e24721003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e4916621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e4829220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e4135336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e2422427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJ 11-CD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e25631514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e15375955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e15375860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e15073897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e740328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e725596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e608319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e381023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJL 24-CD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e37556017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e31438065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e31437804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e28912948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e1826128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e1807981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e1571559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e931563\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJL 24-CD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e96122457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e63723184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e63722354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e60796033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e15275895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e14849698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e11950811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e7234406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJL 24-CD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e70078765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e57731276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e57731226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e53277043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e6723254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e6616518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e5577768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e3729203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJL 24-CD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e34872344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e27533632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e27533600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e25547125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e3754098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e3678154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e3207151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e2160496\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJ 11-ID1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e43370573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e27052365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e27052357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e26588590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e1055798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e1042247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e870828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e475804\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJ 11-ID2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e36088471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e20740745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e20740319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e18139228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e4378778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e4294535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e3516043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e2122107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJ 11-ID3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e34220378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e20622878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e20622349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e16947807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e3988497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e3905385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e3213088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e1868655\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJ 11-ID7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e52216928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e35637927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e35637918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e34723693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2803845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2749333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2244547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e1323987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJL 24-ID1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e102952235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e68520018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e68519053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e65035009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e8681231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e8536505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e6770784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e3587796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJL 24-ID2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e39858694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e26175601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e26175592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e25559301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2868239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2841539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2340381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e1388715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJL 24-ID3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e47355756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e30784013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e30784006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e30363484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2405071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2375130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e1864742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e1097799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003eJL 24-ID7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e35093653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e22009701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e22009696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e21759049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2868239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2841539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e2340381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e1388715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e782651326\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e543850960\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.977715877437326%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e509366827\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.86350974930362%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.749303621169917%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eRW:\u003c/strong\u003e Raw reads, \u003cstrong\u003eQFR:\u003c/strong\u003e Quality filtered reads, \u003cstrong\u003eFN:\u003c/strong\u003e Filtered for Ns, \u003cstrong\u003eFRNC:\u003c/strong\u003e Filtered reads for ncRNA, \u003cstrong\u003eFC:\u003c/strong\u003e Filtered for chloroplast, \u003cstrong\u003eFR:\u003c/strong\u003e Filtered for repeats, \u003cstrong\u003eFER:\u003c/strong\u003e Filtered for exonic region, \u003cstrong\u003eCD:\u003c/strong\u003e Controlled day, \u003cstrong\u003eID:\u003c/strong\u003e Infected day\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of known and novel miRNA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003emiRNAs are known to play critical roles in response to both biotic and abiotic stresses. To identify known and novel miRNAs, filtered reads were mapped against miRNAs of related species through miRBase. A total of 50.9 million reads were mapped to miRBase, enabling the identification of 208 known miRNAs belonging to 36 miR families (\u003cstrong\u003eAdditional file 1\u003c/strong\u003e). In addition to known miRNAs, plants also possess unique miRNAs for which unmapped reads were subjected to miRNA prediction processed through miRDeep-P. Briefly, mapped reads were used to obtain precursor sequences, which folded into possible stem-loop structures using the \u0026ldquo;Vienna\u0026rdquo; package and further filtered and processed. A total of 27 potential novel miRNAs with length ranged from 20 to 22 nt were obtained after the removal of those miRNA which could not meet the miRNA criteria. The average GC content of groundnut miRNAs was found to be 51.05%, similar to chickpea (48%) and soybean (46%). The known miRNAs were grouped into 36 families based on similarity-based clustering. Among these, miR166 was the largest family with 28 miRNA members, followed by miR156 (24 members) and miR167 (23 members). However, novel miRNAs could not fit into any of the conserved miRNA families.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferentially expressed miRNAs during \u003cem\u003eA. flavus\u003c/em\u003e infection in groundnut seed\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify differentially expressed miRNAs, expression patterns of identified miRNAs were evaluated in all libraries. The majority were found in more than one sample. The criteria of adjusted p-value \u0026lt;0.05 and fold change \u0026lt;-1 and \u0026gt;1 was used to identify differentially expressed miRNAs. We found 79, 102, 97 and 56 differentially expressed miRNAs in J 11 between controlled and infected day 1, 2, 3 and 7, respectively. Seven miRNAs namely aly-miR156d-3p, csi-miR1515a, gma-miR396e, mtr-miR2118, novo-miR-n27, ptc-miR482d-3p and ppe-miR396a were common in resistant variety at 1\u003csup\u003e\u0026nbsp;\u003c/sup\u003eDAI, 2 DAI, 3 DAI and 7 DAI and accounting 3.6% of total differentially expressed miRNA in J 11. In JL 24, the numbers of differentially expressed miRNAs were 87, 122, 83 and 103 between controlled and infected days 1, 2, 3, and 7, respectively. A total of ten miRNAs (csi-miR159a-5p, csi-miR164a-3p, novo-miR-n17, novo-miR-n2, osa-miR162b, mtr-miR2118, ptc-miR482d-3p, ptc-miR167f-3p, stu-miR319-3p and zma-miR396b-3p) were common in susceptible variety at 1 DAI, 2 DAI, 3 DAI and 7 DAI, and accounting 4.8% of differentially expressed miRNAs in JL 24. In between the J 11 and JL 24, the numbers of differentially expressed miRNAs were 31, 62, 25 and 37 at infected day 1, 2, 3 and 7, respectively (\u003cstrong\u003eFigure 2(D, E, F)\u003c/strong\u003e). Two miRNAs (mtr-miR2118 and ptc-miR482d-3p) were common between J 11 and JL 24 at 1 DAI, 2 DAI, 3 DAI and 7 DAI, and accounting 0.017% of total differentially expressed miRNAs. Upon comparison between controlled and infected samples, the numbers of upregulated miRNAs were more than downregulated miRNAs in J 11 and JL 24. However, number of downregulated miRNAs were higher when comparison was made between J 11 and JL 24 at different infected days (\u003cstrong\u003eTable 2\u003c/strong\u003e) (\u003cstrong\u003eAdditional file 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Summary of numbers of down and up-regulated miRNAs in different combinations\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.397435897435898%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.166666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of downregulated miRNAs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.80128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of upregulated miRNAs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003eJ 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.397435897435898%\" valign=\"top\"\u003e\n \u003cp\u003eCD1-ID1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.166666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.80128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003eJ 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.397435897435898%\" valign=\"top\"\u003e\n \u003cp\u003eCD2-ID2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.166666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.80128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003eJ 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.397435897435898%\" valign=\"top\"\u003e\n \u003cp\u003eCD3-ID3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.166666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.80128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003eJ 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.397435897435898%\" valign=\"top\"\u003e\n \u003cp\u003eCD7-ID7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.166666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.80128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.032051282051285%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.166666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.80128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003eJL 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.397435897435898%\" valign=\"top\"\u003e\n \u003cp\u003eCD1-ID1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.166666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.80128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003eJL 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.397435897435898%\" valign=\"top\"\u003e\n \u003cp\u003eCD2-ID2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.166666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.80128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003eJL 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.397435897435898%\" valign=\"top\"\u003e\n \u003cp\u003eCD3-ID3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.166666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.80128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.634615384615383%\" valign=\"top\"\u003e\n \u003cp\u003eJL 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.397435897435898%\" valign=\"top\"\u003e\n \u003cp\u003eCD7-ID7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.166666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.80128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.032051282051285%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.166666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.80128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eCD1:\u003c/strong\u003e Controlled day 1, \u003cstrong\u003eID1:\u003c/strong\u003e Infected day 1, \u003cstrong\u003eCD2:\u003c/strong\u003e Controlled day 2, \u003cstrong\u003eID2:\u003c/strong\u003e Infected day 2, \u003cstrong\u003eCD3:\u003c/strong\u003e Controlled day 3, \u003cstrong\u003eID3:\u003c/strong\u003e Infected day 3, \u003cstrong\u003eCD7:\u003c/strong\u003e Controlled day 7, \u003cstrong\u003eID7:\u003c/strong\u003e Infected day 7\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIn silico\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;target identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIdentification of target of miRNAs was conducted using psRNATarget server, based on complementarity between miRNAs and target sequence. Total 952 unique targeted genes were identified for 235 (27 novel and 208 known) miRNAs. In total, 1742 targets were found for 83.8% (197) miRNAs and maximum target were found for members of family miR159 (238), followed by miR482 (235) and miR396 (231) (\u003cstrong\u003eFigure 3\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe target annotations grouped the genes into several categories, including disease resistance genes, cellular enzymes (kinase, methyltransferase, \u0026beta;-galactosidase, etc.), transcription factors, proteasome assembly, proton transmembrane transport, meristem development and maintenance. The majority of annotated genes were responsible for disease resistance protein (22.8%), followed by transcription factors (3.6%), auxin responsive protein coding genes (3.5%), and pentatricopeptide repeat protein (3.5%). To understand the possible involvement of miRNA targets in the groundnut\u0026rsquo;s response to aflatoxin stress, a Gene Ontology (GO) enrichment analysis was carried out. A total of 1609 biological processes, 401 cellular components, and 946 molecular functions were allocated uniformly among the targets. Among biological process, the most significant GO terms were cellular process, metabolic process, response to stimulus, and biological regulations. Similarly, binding has most significant GO term followed by catalytic activity and transcription regulator activity among molecular function. In cellular component category, cellular anatomical entity has maximum GO term followed by protein-containing complex (\u003cstrong\u003eFigure 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCommon miRNA in two groundnut genotypes at 1 DAI, 2 DAI, 3 DAI and 7 DAI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn comparing different days after inoculation for the same genotype, we found more miRNAs were expressed at 2 DAI in J 11 as well as in JL 24. This indicated that the two genotypes responded in more similar manner at 2 DAI. However, slope of number of miRNAs was raised at 7 DAI in JL 24 comparable to J 11 where slope diminished after 2 DAI. This showed the both genotypes had started to respond to \u003cem\u003eA. flavus\u003c/em\u003e infection from 2 DAI but susceptible genotype showed different response at 7 DAI. Large number of differentially expressed common miRNAs were observed in both J 11 and JL 24 at 1 DAI, 2 DAI, 3 DAI and 7 DAI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTotal 31 differentially expressed miRNAs were common between J 11 and JL 24 at 1 DAI. Among them, 11 miRNAs showing contrasting expression between J 11 and JL 24. Sixty-two differentially expressed miRNAs were common between J 11 and JL 24 at 2 DAI. Among them, 50 miRNAs showed contrasting expression between J 11 and JL 24. Similarly, 16 from 25 miRNAs and 16 from 31 miRNAs were showing contrasting expression between J 11 and JL 24 at 3 DAI and 7 DAI, respectively.\u0026nbsp;The known miR families such as miR166, miR167 and miR156 were identified more frequently and abundantly expressed, consistent with previous studies (\u003cstrong\u003eFigure 5\u003c/strong\u003e). To identify the miRNAs involved in aflatoxin resistance, we interrogated into those common differentially expressed miRNAs which have contrasting expression patterns in resistant genotype under 1 DAI, 2 DAI, 3 DAI and 7 DAI. Interestingly, two miRNAs namely mtr-miR2118 and ptc-miR482d-3p from miR2118 and miR482 families, respectively, were showed contrasting expression pattern between J 11 and JL 24 at all the infected days. Both miRNAs, ptc-miR482d-3p and mtr-miR-2118 showed downregulation in resistant genotype, J 11 whereas it become upregulated in susceptible genotype, JL 24 \u003cstrong\u003e(Table 3)\u003c/strong\u003e. However, miRNAs such as zma-miR396b-3p and osa-miR162b also showed upregulation in JL 24 but didn\u0026rsquo;t express in J 11. These miRNAs were further looked for targeted genes, there were seven genes for mtr-miR2118 and 62 targets for ptc-miR482d-3p. The annotation of these targeted genes was found to be associated with disease resistance mechanism. The gene expression atlas (AhGEA) of \u003cem\u003eA. hypogaea\u003c/em\u003e ssp. \u003cem\u003efastigiata\u003c/em\u003e was utilized to detect the expression of genes specific to certain tissues [40]. For mtr-miR2118, the expression of only four genes were found in different tissues in AhGEA. These genes were found in chromosome 9, 19, 5 and 15. Similarly, total 43 genes showed tissue specific expression associated with ptc-miR482d-3p in AhGEA. Among 43, maximum genes (21) were found on chromosome 12 followed by chromosome 2 (13 genes), chromosome 14 (4 genes), chromosome 4 (4 genes) and chromosome 3 (1 gene) \u003cstrong\u003e(Table 4)\u003c/strong\u003e. The insilico expression of these genes using AhGEA were shown in \u003cstrong\u003eFigure 6\u003c/strong\u003e. For zma-miR396b-3p, six targets were identified, linked with LRR receptor-like serine/threonine-protein kinase metabolism. Similarly, osa-miR162b had 23 targeted genes associated with metabolism of beta amylase, UDP-glycosyltransferase, endoribonuclease Dicer homolog and pentatricopeptide repeat-containing proteins.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Differential expression of miRNAs common among 1 DAI, 2 DAI, 3 DAI and 7 DAI in J 11 and JL 24\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.859766277128548%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84.14023372287146%\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifferential Expression Pattern\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003e\u003cstrong\u003emiRNAs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1 DAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2 DAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3 DAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7 DAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.833333333333334%\" rowspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eJ 11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.166666666666668%\"\u003e\n \u003cp\u003ealy-miR156d-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.5%\"\u003e\n \u003cp\u003e1.2333638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.5%\"\u003e\n \u003cp\u003e-1.904056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.5%\"\u003e\n \u003cp\u003e2.1020817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.5%\"\u003e\n \u003cp\u003e2.507183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003ecsi-miR1515a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e2.7652149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-1.9477172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.313454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.1852549\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003egma-miR396e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-2.1416757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.1874424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-3.5345429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-1.263646\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003emtr-miR2118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.0282493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-1.9824826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-2.6600738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-3.0291357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003enovo-miR-n27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.7720686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-1.1817914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.5628696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.2441486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003eppe-miR396a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-1.9717507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.3534523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-3.4090121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-1.8653712\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003eptc-miR482d-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-2.1416757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-1.6160782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-1.7824791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-1.8851344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.833333333333334%\" rowspan=\"10\"\u003e\n \u003cp\u003e\u003cstrong\u003eJL 24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.166666666666668%\"\u003e\n \u003cp\u003ecsi-miR159a-5p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.5%\"\u003e\n \u003cp\u003e1.4456028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.5%\"\u003e\n \u003cp\u003e1.8431715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.5%\"\u003e\n \u003cp\u003e2.9909445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.5%\"\u003e\n \u003cp\u003e3.1198553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003ecsi-miR164a-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.7575468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-2.2442914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e2.0200909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.1666936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003enovo-miR-n17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-1.2676821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.2468739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-1.2670718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-2.1762961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003enovo-miR-n2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.1136906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e5.7691709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.0030173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e4.0842314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003eosa-miR162b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.0026593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.2092994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.0315865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e2.821197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003eptc-miR167f-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.5876218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e3.4908697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.1146716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e3.2541564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003estu-miR319-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e2.1725843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-1.418384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e2.3671895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-1.2011708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003ezma-miR396b-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e2.1725843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.1062059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.5175905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e3.7597965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003eptc-miR482d-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-1.8048254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e-1.0048254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e2.7943508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e4.6891405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.336633663366335%\"\u003e\n \u003cp\u003emtr-miR2118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.3013652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.258209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e1.0253275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.415841584158414%\"\u003e\n \u003cp\u003e2.8391189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e1 DAI: first day after inoculation; 2 DAI: second day after inoculation; 3 DAI: third day after inoculation; 7 DAI: seventh day after inoculation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Summary of targeted 47 genes of miRNAs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"752\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.652463382157125%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003emiRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.842876165113182%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTargeted Gene model\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.252996005326231%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChromo-some\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.451398135818907%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStart\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eposition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.451398135818907%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEnd\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eposition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.348868175765645%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnotations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.652463382157125%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003emiR2118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.842876165113182%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.H8JIAA.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.252996005326231%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.451398135818907%\" valign=\"top\"\u003e\n \u003cp\u003e154617270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.451398135818907%\" valign=\"top\"\u003e\n \u003cp\u003e154625699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.348868175765645%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.7J0RKL.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e6874895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e6880710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.VF2B86.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e8244428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e8250223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.HVB0T8.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e90137426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e90144450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.652463382157125%\" rowspan=\"43\" valign=\"top\"\u003e\n \u003cp\u003eptc-miR482d-3p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.842876165113182%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.LVW2ZC.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.252996005326231%\" valign=\"top\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.451398135818907%\" valign=\"top\"\u003e\n \u003cp\u003e2165965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.451398135818907%\" valign=\"top\"\u003e\n \u003cp\u003e2179064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.348868175765645%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.SLVW9F.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e1845409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e1846752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.Y2F96D.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2634170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2637760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.KL7N91.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2857891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2861052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.77RH3X.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2840034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2841879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.104ZDW.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e126965165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e126971113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.NUHQ9Q.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e126980888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e126986798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.6V6NN7.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e141400695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e141406643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.1VN7JI.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e141416418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e141422328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.CA6E74.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e1892963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e1907959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.V6X2E8.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2827095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2838123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.8HLD5E.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2752265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2757090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.I5JPYW.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2768839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2780577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.ZKR6CY.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2041262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2044900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.UZFH7Q.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e141463237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e141467397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.REWL7K.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e127027707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e127031867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.51RKDV.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2485257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2492769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.M55R6K.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e16662936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e16665269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.DLTR3L.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2445028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2450508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.II44X3.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e127073261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e127076829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.388Y5C.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e141508791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e141512359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.N06FBV.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e1506683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e1516435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.15H21N.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e509289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e513987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.K68I1Q.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2901005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2904685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.YM09LB.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2011567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2015247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.JUY39I.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e123658764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e123662492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.VMD6HC.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e14047304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e14048911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.2HN52Q.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2535411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2539172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.3TW696.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2455813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2460791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.7S97YI.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2915861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2919511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.25Q9K5.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2793908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2797486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.73AA2K.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2922302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2926138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.MZFR22.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e1565683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e1568877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.U1TKV1.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e1543715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e1549934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.0PVT6F.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2588797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2592594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.3G3XAR.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e3267043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e3271844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.G3725Q.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2193339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2196848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.83LA0K.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e1862247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e1868553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.8LIU0E.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2662012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2665677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.TKN2M5.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e3074253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e3079786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.FXRP5B.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2153742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e2160389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.IS7D7R.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e1869681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e1883608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eLRR and NB-ARC domain disease resistance protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.970193740685545%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eArahy.ZBPZ8H.1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.475409836065573%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e13220251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816691505216095%\" valign=\"top\"\u003e\n \u003cp\u003e13221241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.92101341281669%\" valign=\"top\"\u003e\n \u003cp\u003eDisease resistance protein (TIR-NBS-LRR class) family\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eGroundnut is a protein rich leguminous crop with chief source of income in many developing nations [41]. Beyond its nutritious value (oil, protein, sugar, vitamins and minerals), it also has important role in sustainable agriculture due to its ability to thrive in marginal soil, withstand drought and fix nitrogen [42]. However, groundnut is susceptible to aflatoxin during both pre-harvest and post-harvest stages [43]. An integrated management strategy is crucial to minimize the risk of aflatoxin contamination. Aflatoxin are generally secondary metabolites produced by soil-borne saprophytic group of genus \u003cem\u003eAspergillus\u003c/em\u003e which affect groundnut and other food commodities. Environmental parameters such as high soil temperature, moisture stress, relative humidity influence the \u003cem\u003eA. flavus\u003c/em\u003e infection and subsequent aflatoxin accumulation. The lack of genetic resistance in groundnut, along with these environmental factors, limited progress in this direction, makes this trait very complex [44]. \u0026nbsp; At the post-transcriptional level, small non-coding RNAs such as miRNAs have been identified as significant regulators of gene expression [45]. miRNAs are non-coding RNAs that play vital roles in developmental processes and stress responses through negative regulations [46]. In this study, \u003cem\u003eA. flavus\u003c/em\u003e infected and controlled small RNA libraries were prepared to identify known and novel miRNAs from resistant and susceptible cultivars. Among 208 known miRNA, 25 miRNAs were first reported in groundnut by Zhao et\u003cem\u003e\u0026nbsp;\u003c/em\u003eal\u003cem\u003e.\u003c/em\u003e [27], 42 miRNAs were reported by Chi et al. [23] and 66 miRNAs were identified by Zhao et al. [47]. Previous studies reported that known miRNAs are majorly involved in developmental processes, whereas novel miRNAs were the part of species-specific gene regulatory functions [48,49]. Some features of miRNAs such as length and GC content were in concordance with the previous studies [50,51]. The known miR families such as miR166, miR167 and miR156 were identified more frequently and abundantly expressed, consistent with previous studies [23,52,53]. Total 186 and 199 variants of known miRNAs were identified from different treatments of resistant and susceptible cultivars. The abundance of variants for most of known miRNA families were higher in JL 24 than J 11, and miR166 having more variants than other families. All novel miRNAs were not registered miRBase which support the evidence to declare them as novel miRNAs. The known and novel miRNAs were search against Nucleotides, GSS, ESTs and TSA sequence of \u003cem\u003eArachis\u003c/em\u003e using the psRNATarget server. The identified targets were annotated by BLAST against NCBI Nr Database. A total of 1742 targets were identified, with some miRNAs having more than one targets. Functional annotation and classification showed that 22.8% of the targets were found to be associated with disease resistance proteins such as RPP and TMV resistance proteins, 5.6% with unknown proteins, 3.6% with transcription factors such as TCP4, TCP3, MYB52, MYB 97, and remaining were associated with receptors like serine/threonine protein kinase, mitogen activated protein, growth regulating factor, etc. Interestingly, most miRNAs were predicted to target resistance genes analog. The number of disease resistance proteins-coding genes were the target of miRNAs such as Recognition of \u003cem\u003ePeronospora parasitica\u0026nbsp;\u003c/em\u003e13 (RPP) known to encode 820 amino acids which were believed to reside within cytoplasm and function in LRR (Leucine rich repeat) synthesising [54]. Similarly, RFL1 protein which was domain in NBS-LRR [55], TAO1 contributes to disease resistance in response to \u003cem\u003ePseudomonas syringae\u003c/em\u003e pathovars of tomato [56], Dominant Suppressor of Camta 3 number 1 (DSC1) which is immune receptor of TIR-NB-LRR [57], leucine-rich repeat receptor-like protein kinase and leaf rust disease-resistance locus receptor-like protein kinase, TMV resistance protein and LRR receptor-like serine/threonine-protein kinase are the putative protein encoded by the targeted genes found in this study. The NBS-LRRs are composed of a nucleotide-binding domain located at the center, which is connected to a leucine-rich repeat (LRR) domain at the C-terminal end. Additionally, there is a variable N-terminal domain that can either be a coiled-coil (CC) domain or a Toll-interleukin-1 receptor (TIR)-like domain [58]. Apart from disease resistance proteins, transcription factors like bHLH155, bHLH19, DUO1, GAMYB, MYB33, MYB52, MYB97, TCP2 were also target of identified miRNAs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTotal 229 miRNAs showed the differential expression in at least one treatment, and it was noted that all novel miRNAs were among the differentially expressed miRNAs. Moreover, it was seen that members of the same miRNA families responded differently to \u003cem\u003eA. flavus\u003c/em\u003e infection. For example, miRNAs from family miR156 namely aly-miR156d-3p showed upregulation (Log\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eFC= 2.51) at DAI7 in J 11 whereas ath-miR156b-3p was downregulated (Log\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eFC= -1.64) at DAI 7 in J 11. Similar trend was previously reported in other crops such as chick pea where miR171 showed differential expression pattern under Ascochyta blight infection [59] and soybean in which miR396 showed differential expression at high cadmium concentration [60]. Total seven miRNAs in J 11 were continuously expressed at DAI1, DAI2, DAI3 and DAI7, while ten miRNAs were commonly expressed in JL 24. Similarly, commonly expressed miRNAs (Osa-miR156d, Osa-miR159b, Osa-miR820c, and Osa-miR1876) were identified between susceptible and resistant rice cultivars [61]. The miRNAs, mtr-miR2118 and ptc-miR482d-3p were belongs to miR2118 and miR482 families. The members of these families are known to play major roles in stress response [62]. The targets of mtr-miR2118 were found on chromosome 05, 09, 15 and 19 whereas targets of ptc-miR482d-3p located on chromosome 02, 03, 04, 12 and 14, and all these targets were associated with TIR-NBS-LLR encoding domains.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn this study, we identified 208 known and 27 novel miRNAs in J 11 and JL 24 groundnut genotypes. Total\u0026nbsp;1742 targets were identified for these miRNAs, which were found to encode disease resistant proteins, transcription factor involved in several metabolic pathways, transmembrane receptors, and protein kinase family proteins, etc. There were only two (mtr-miR2118 and ptc-miR482b-3p) differentially expressed miRNAs which expressed at all days after inoculations in both resistant, (J 11) and susceptible, (JL 24) genotypes. Further, the insilico expression analysis revealed the tissue specific expression of target genes of these two miRNAs. Functional annotation of these genes, showed that the genes were known to be involved in disease resistance mechanism by regulating the expression of various proteins like TIR-NBS-LRR, TMV resistance protein and serine/threonine protein kinase. These targets of miRNAs in resistance against \u003cem\u003eA. flavus\u003c/em\u003e can be used in the development of markers for groundnut breeding program to enhance resistance against \u003cem\u003eA. flavus\u003c/em\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: The datasets generated and/or analysed during the current study are available in the NCBI repository, BioProject ID PRJNA355201.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This research was partially funded by the USAID-US University Collaboration Grant, Peanut \u0026amp; Mycotoxin Innovation Lab (PMIL), MARS-Wrigley, USA; and Bill \u0026amp; Melinda Gates Foundation (BMGF), USA through Tropical Legumes III project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e: R.K.V. and M.K.P conceived and supervised the project. P.J. written the main manuscript. PB analysed the RNA data. VS edited the manuscript. AKP, SNN, SS, HS, MKP and RKV\u0026nbsp;contributed to generating the data, reviewing and improving the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: The authors are thankful for partial financial assistance received from the Indian Council of Agricultural Research (ICAR) through ICAR-ICRISAT collaborative project, Department of Biotechnology (DBT), Government of India and MARS-Wrigley and Bill and Melinda Gates Foundation (BMGF), USA. PJ and VS acknowledges Chaudhary Charan Singh University (CCSU), Meerut, for collaborating with ICRISAT and the opportunity given as a student to pursue this investigation at ICRISAT. VS acknowledges the Council of Scientific and Industrial Research (CSIR), Govt. of India for the award of CSIR-SRF Direct fellowship (File No: 09/0800(18433)/2024-EMR-I) for PhD.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFAOSTAT. Food and Agriculture Organization of the United Nations, Rome. 2022. https://www.fao.org/faostat/en/#data/QCL.\u003c/li\u003e\n\u003cli\u003eFAOSTAT. Food and Agriculture Organization of the United Nations, Rome. 2018; URL: http://faostat.fao.org.\u003c/li\u003e\n\u003cli\u003eVarshney RK, Mohan SM, Gaur PM, Gangarao NVPR, Pandey MK, Bohra A, et al. Achievements and prospects of genomics-assisted breeding in three legume crops of the semi-arid tropics. Biotechnol Adv. 2013;31(8):1120-1134.\u003c/li\u003e\n\u003cli\u003eKodape AR, Raveendran A, Babu CSV. Aflatoxins: A Postharvest Associated Challenge and Mitigation Opportunities. Aflatoxins-Occurrence, Detection and Novel Detoxification Strategies. 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New Phytol. 2012;233(5):2047-2057.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Aspergillus flavus, Differential expression, Genes, Groundnut, MicroRNA. ","lastPublishedDoi":"10.21203/rs.3.rs-4607193/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4607193/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Groundnut is the major oilseed crop that suffers substantial post-harvest losses due to aflatoxin contamination by the fungus \u003cem\u003eAspergillus flavus\u003c/em\u003e. The interaction between \u003cem\u003eA\u003c/em\u003e. \u003cem\u003eflavus\u003c/em\u003e and groundnut microRNAs in combating aflatoxin contamination remains unclear. This study was carried out to identify microRNAs (miRNAs) to enhance the understanding of \u003cem\u003ein\u003c/em\u003e \u003cem\u003evitro\u003c/em\u003e Seed Colonisation (IVSC) resistance mechanism in groundnut.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e: In this study, resistant (J 11) and susceptible (JL 24) genotypes of groundnut were treated with toxigenic \u003cem\u003eA. flavus\u003c/em\u003e (strain AF-11-4), and total RNA was extracted at 1 day after inoculation (1 DAI), 2 DAI, 3 DAI and 7 DAI. Seeds of JL 24 showed higher mycelial growth than J 11 at successive days after inoculation. A total of 208 known miRNAs belonging to 36 miRNA families, with length varying from 20-24 nucleotides, were identified, along with 27 novel miRNAs, with length varying from 20-22 nucleotides. Using psRNATarget server, 952 targets were identified for all the miRNAs. The targeted genes function as disease resistant proteins encoding, auxin responsive proteins, squamosa promoter binding like proteins, transcription factors, pentatricopeptide repeat-containing proteins and growth regulating factors, etc. Through differential expression analysis, seven miRNAs (aly-miR156d-3p, csi-miR1515a, gma-miR396e, mtr-miR2118, novo-miR-n27, ptc-miR482d-3p and ppe-miR396a) were found common among 1 DAI, 2 DAI, 3 DAI and 7 DAI in J 11, whereas ten miRNAs (csi-miR159a-5p, csi-miR164a-3p, novo-miR-n17, novo-miR-n2, osa-miR162b, mtr-miR2118, ptc-miR482d-3p, ptc-miR167f-3p, stu-miR319-3p and zma-miR396b-3p) were found common among 1 DAI, 2 DAI, 3 DAI and 7 DAI in JL 24. Two miRNAs, ptc-miR482d-3p and mtr-miR2118, showed contrasting expression at different time intervals between J 11 and JL 24. These two miRNAs were found to target those genes with NBS-LRR function, making them potential candidates for marker development in groundnut breeding programs aimed at enhancing resistance against \u003cem\u003eA. flavus\u003c/em\u003einfection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: This study enhances our understanding of the involvement of two miRNAs namely, ptc-miR482d-3p and mtr-miR2118, along with their NBS-LRR targets, in conferring resistance against \u003cem\u003eA. flavus\u003c/em\u003e-induced aflatoxin contamination in groundnut under \u003cem\u003ein vitro\u003c/em\u003e conditions.\u003c/p\u003e","manuscriptTitle":"Identification of miRNAs associated with Aspergillus flavus infection and their targets in groundnut (Arachis hypogaea L.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-15 18:24:08","doi":"10.21203/rs.3.rs-4607193/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-08T06:50:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-07T08:30:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-05T12:53:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103708393599187748035033105109558896845","date":"2024-07-01T04:17:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282337750504496878759180191432645719422","date":"2024-06-29T23:25:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-29T12:19:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-24T11:31:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-24T11:29:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2024-06-19T16:24:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b83c051-6343-4ed3-996b-4ebf1cfa3d3b","owner":[],"postedDate":"July 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T15:59:39+00:00","versionOfRecord":{"articleIdentity":"rs-4607193","link":"https://doi.org/10.1186/s12870-025-06322-2","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-03-18 15:57:05","publishedOnDateReadable":"March 18th, 2025"},"versionCreatedAt":"2024-07-15 18:24:08","video":"","vorDoi":"10.1186/s12870-025-06322-2","vorDoiUrl":"https://doi.org/10.1186/s12870-025-06322-2","workflowStages":[]},"version":"v1","identity":"rs-4607193","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4607193","identity":"rs-4607193","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}