An analytical method integrating small RNA and DNA methylomics to study crop continuous cropping adaptability

Abstract

This invention discloses an analytical method integrating small RNA and DNA methylmics to study crop continuous cropping adaptation, comprising the following steps: collecting wheat samples under W1 and WM conditions; identifying 21-24nt-siRNA clusters under W1 and WM conditions; performing differential expression analysis on 24-nt siRNA clusters; for differentially expressed 24-nt siRNAs, screening for clusters overlapping with differentially methylated regions (DMRs), retaining only pairs with consistent regulatory patterns, defining them as RdDM action sites, and then predicting siRNA target genes. This analytical method can systematically identify differentially expressed siRNAs under continuous cropping conditions; combine whole-genome methylation data (WGBS) to analyze the association between siRNAs and differentially methylated regions (DMRs); identify key RdDM target genes and their functional pathways; and construct a multi-level regulatory network of siRNA–DNA methylation–gene expression, providing a theoretical basis and application foundation for crop continuous cropping adaptation and molecular improvement.

Description

Technical Field

[0001] This invention relates to the fields of plant molecular biology and agricultural biotechnology, and in particular to an analytical method that integrates small RNA and DNA methylomics to study crop continuous cropping adaptability. Background Technology

[0002] Long-term continuous cropping generally leads to yield reduction, and even in the absence of obvious disease infection, problems such as abnormal root development, hindered nutrient absorption, and increased soil microbial stress are still observed. Studies have shown that crops undergo extensive transcriptional and epigenetic reprogramming under continuous cropping conditions, among which 24-nt siRNA is an important factor in epigenetic silencing and DNA methylation.

[0003] Studies have shown in model plants such as Arabidopsis thaliana, rice, and maize that:

[0004] 24-nt siRNA regulates gene silencing through the RdDM pathway, maintaining genome stability;

[0005] siRNA and DNA methylation work together to participate in stress response and developmental regulation, thereby affecting the expression of neighboring genes.

[0006] However, existing research is mostly limited to model plants, lacking systematic studies on continuous cropping conditions of important food crops such as wheat. Current technologies largely employ single smallRNA-seq or WGBS sequencing, which can only identify siRNA or methylation levels separately, and are insufficient to reveal the complex regulatory networks behind yield reduction caused by continuous cropping. Summary of the Invention

[0007] The purpose of this invention is to provide an analytical method for studying crop continuous cropping adaptability by integrating small RNA and DNA methylomics. By integrating small RNA sequencing and whole genome bisulfite sequencing, the interaction mechanism between siRNA and DNA methylation is systematically revealed, elucidating its role in the RNA-mediated DNA methylation (RdDM) pathway and its impact on gene expression and crop continuous cropping adaptability.

[0008] To achieve this objective, the present invention adopts the following technical solution:

[0009] An analytical method integrating small RNA and DNA methylomics to study crop continuous cropping adaptation includes the following steps:

[0010] Step S1 involves collecting wheat samples under conditions W1 and WM, where W1 represents rapeseed-wheat rotation and WM represents continuous wheat cultivation. Specifically...

[0011] 11) Wheat flag leaves and roots were collected during the stem elongation stage, and RNA and DNA were extracted, respectively.

[0012] 12) Perform small RNA sequencing on the extracted RNA: Construct a small RNA library from samples with an integrity RIN > 4.0 in the extracted RNA;

[0013] 13) Perform whole-genome bisulfite sequencing on the extracted DNA to establish a WGBS library;

[0014] Step S2: Identify the 21-24 nt-siRNA clusters under W1 and WM conditions;

[0015] Step S3: Differential expression analysis of the 24-nt siRNA cluster;

[0016] In step S4, for differentially expressed 24-nt siRNAs, clusters overlapping with differentially methylated regions (DMRs) are screened out, and only pairs with consistent regulatory patterns are retained, which are defined as RdDM action sites. Then, the target genes of the siRNAs are predicted.

[0017] As a specific implementation method, step 12) also includes quality control of the data in the small RNA library after the small RNA library is established. This involves removing poly-N sequences, 5' adapter contamination, sequences with missing 3' adapters or inserted tags, reads with an uncertain base ratio exceeding 10% (N > 10%), and reads with a base quality value < 5 accounting for more than 50%. High-quality clean reads are then generated, and these high-quality clean reads are compared with the wheat reference genome to retain sequences that match perfectly.

[0018] As a specific implementation, step 13) also includes quality control of the data in the established WGBS library, removing adapter sequences; removing the entire read if the average base quality value of the entire read is less than 15; truncating bases with a base quality value less than 5 from the beginning and end of the sequence; removing sequences shorter than 80 bp after cleaning to finally obtain high-quality clean reads; aligning the high-quality clean reads with the wheat reference genome, allowing a maximum of two mismatches; removing repetitive sequences; and extracting cytosine methylation levels in the CG, CHG, and CHH backgrounds.

[0019] As a specific implementation method, the detailed process of differential expression analysis of the 24-nt siRNA cluster in step S3 is as follows:

[0020] First, the expression level of the 24-nt siRNA cluster was quantified using the annotatePeaks.pl script in the HOMER software with the parameters -size given, -fpkm, -len 1, -d.

[0021] Then, differential expression analysis was performed. In this process, the same script was first used to calculate the raw counts of the 24-nt siRNA clusters, with the parameters set to -noadj, -size given, -len 1, -d. Then, differential expression analysis was performed using the R package edgeR, with a p-value < 0.05 and |log2(fold change)| > 0 as the screening threshold, thereby identifying differentially expressed 24-nt siRNA clusters.

[0022] Compared with existing technologies, the beneficial effects of this invention are as follows: the analytical method of integrating small RNA and DNA methylomics to study crop continuous cropping adaptability can systematically identify differentially expressed siRNAs under continuous cropping conditions; combined with whole genome methylation data (WGBS), the association between siRNA and differentially methylated regions (DMRs) can be analyzed; key RdDM target genes and their functional pathways can be identified; and a multi-level regulatory network of siRNA-DNA methylation-gene expression can be constructed, providing a theoretical basis and application foundation for crop continuous cropping adaptability and molecular improvement. Attached Figure Description

[0023] Figure 1 Flowchart for RdDM site identification and target gene prediction of differentially expressed 24-nt siRNA;

[0024] Figure 2 The length distribution of small RNAs in wheat leaves under W1 and WM planting conditions;

[0025] Figure 3 The length distribution of small RNAs in wheat roots under W1 and WM planting conditions;

[0026] Figure 4 The length distribution of siRNA clusters in wheat leaves under W1 and WM planting conditions;

[0027] Figure 5 The length distribution of siRNA clusters in wheat roots under W1 and WM planting conditions;

[0028] Figure 6 The length distribution of 24-nt siRNA clusters in wheat leaves under W1 and WM planting conditions;

[0029] Figure 7 The length distribution of 24-nt siRNA clusters in wheat roots under W1 and WM planting conditions;

[0030] Figure 8To identify differentially expressed siRNAs (DE-siRNAs) in leaves under WM versus W1 planting conditions;

[0031] Figure 9 To identify differentially expressed siRNAs (DE-siRNAs) in the root system under WM versus W1 planting conditions;

[0032] Figure 10 A scatter plot showing the correlation between the overlapping regions of differentially expressed siRNAs (DE-siRNAs) and differentially methylated regions (DMRs) in leaves;

[0033] Figure 11 A scatter plot showing the correlation between the overlapping regions of differentially expressed siRNAs (DE-siRNAs) and differentially methylated regions (DMRs) in the root system;

[0034] Figure 12 The number of target genes corresponding to the differentially upregulated and downregulated 24-nt siRNA was determined.

[0035] Figure 13 GO enrichment analysis of target genes in leaves;

[0036] Figure 14 GO enrichment analysis of target genes in roots;

[0037] Figure 15 Venn diagram for integration analysis of predicted 24-nt siRNA target genes and differentially expressed genes (DEGs). Detailed Implementation

[0038] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0039] This invention provides an analytical method for studying crop continuous cropping adaptability by integrating small RNA and DNA methylomics, comprising the following steps:

[0040] Step S1 involves collecting wheat samples under conditions W1 and WM, where W1 represents rapeseed-wheat rotation and WM represents continuous wheat cultivation. Specifically...

[0041] 11) Collect wheat flag leaves and roots during the stem elongation period, and extract RNA and DNA respectively;

[0042] 12) Perform small RNA sequencing on the extracted RNA: Construct small RNA libraries from samples with a completeness RIN > 4.0 in the extracted RNA. Perform quality control on the data in the small RNA libraries to remove poly-N sequences, 5' adapter contamination, sequences with missing 3' adapters or inserted tags, reads with an uncertain base ratio exceeding 10% (N > 10%), and reads with a base quality value < 5 accounting for more than 50%. Generate high-quality clean reads. Align the high-quality clean reads with the wheat reference genome and retain sequences that match perfectly.

[0043] 13) Perform whole-genome bisulfite sequencing on the extracted DNA to build a WGBS library. Then, perform quality control on the data in the WGBS library, remove adapter sequences; if the average base quality value of the entire read is less than 15, remove the entire read; remove bases with a base quality value less than 5 from the beginning and end of the sequence; remove sequences shorter than 80 bp after cleaning to obtain high-quality clean reads. Align the high-quality clean reads with the wheat reference genome, allowing a maximum of two mismatches, remove repetitive sequences, and extract cytosine methylation levels in the CG, CHG, and CHH backgrounds.

[0044] Step S2: Identify 21-24-nt siRNA clusters under W1 and WM conditions;

[0045] Step S3 involves differential expression analysis of the 24-nt siRNA cluster, as detailed below:

[0046] First, the expression level of the 24-nt siRNA cluster was quantified using the annotatePeaks.pl script in the HOMER software with the parameters -size given, -fpkm, -len 1, -d.

[0047] Then, differential expression analysis was performed. In this process, the same script was first used to calculate the raw counts of the 24-nt siRNA clusters, with the parameters set to -noadj, -size given, -len 1, -d. Then, the edgeR package was used to perform differential expression analysis, with a p-value < 0.05 and |log2 (fold change)| > 0 as the screening threshold, so as to identify differentially expressed 24-nt siRNA clusters.

[0048] In step S4, for differentially expressed 24-nt siRNAs, clusters overlapping with differentially methylated regions (DMRs) are screened out, and only pairs with consistent regulatory patterns are retained, which are defined as RdDM action sites. Then, the target genes of the siRNAs are predicted.

[0049] The following detailed description, in conjunction with embodiments, illustrates the analytical method for studying crop continuous cropping adaptability using integrated small RNA and DNA methylomics provided by this invention. However, these descriptions should not be construed as limiting the scope of protection of this invention. Example 1

[0050] Plant materials and growing conditions

[0051] This experiment was conducted in a long-term crop rotation field in Harste, Germany. Wheat ( Triticum aestivum L. Rapeseed was cultivated under two cropping regimes: (1) rapeseed-wheat rotation (W1); and (2) continuous wheat monoculture (WM). Three independent plots were set up for each cropping regime as biological replicates. At the wheat stem elongation stage (EC31), 5-10 wheat plants were randomly selected from each plot, and flag leaf and root tissues were collected. After washing, the samples were mixed by plot, cryogenically ground into a fine powder for RNA extraction.

[0052] RNA extraction and small RNA sequencing

[0053] Total RNA was extracted using TRIzol® reagent (Thermo Fisher Scientific, Germany) according to the manufacturer's instructions. RNA quality and concentration were determined by agarose gel electrophoresis and a NanoVue Plus spectrophotometer (GE Healthcare Life Science). Only samples with an RNA integrity index (RIN) greater than 4.0 were used for small RNA library construction. Small RNA libraries were prepared according to Illumina standard procedures and sequenced at 50 bp (SE50) at one end using the Illumina HiSeq platform in Cambridge, UK, yielding 12 libraries, as shown in Table 1.

[0054] Table 1. Alignment data of sRNA in each library

[0055]

[0056] The raw data underwent quality control to remove poly-N sequences, 5' adapter contamination, sequences lacking 3' adapters or inserted tags, reads with an uncertain base ratio exceeding 10% (N > 10%), and reads with a base quality value < 5 accounting for more than 50% of the reads, resulting in high-quality clean reads. These high-quality clean reads were aligned to the wheat reference genome (IWGSC RefSeqv2.1) using Bowtie (see Langmead, B., Trapnell, C., Pop, M. and Salzberg, SL, 2009. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome biology, 10(3), p.R25.), retaining only perfectly matching sequences for subsequent analysis.

[0057] Whole-genome bisulfite sequencing (WGBS)

[0058] Genomic DNA was extracted from the same mixed sample of flag leaves and roots using the DNeasy Plant Mini Kit (Qiagen, Germany). DNA integrity was assessed by agarose gel electrophoresis, and concentration was determined using a Qubit 3.0 fluorometer (Invitrogen, USA). 500 ng of high-quality DNA from each sample was sonicated to approximately 200 bp using a Covaris sonicator. WGBS library preparation was performed using the EZ DNA Methylation-Gold™ Kit (Zymo Research, USA), including end repair, A-tailing, adapter ligation, bisulfite conversion, and PCR amplification. The library was sequenced at 150 bp paired ends (PE150) on an Illumina NovaSeq 6000 platform, a procedure performed by Shenzhen EasyGene Technology Co., Ltd.

[0059] The raw sequencing data underwent quality control, and adapter sequences were removed. If the average base quality of the entire read was below 15, the entire read was discarded. Bases with a base quality of less than 5 were truncated from the beginning and end of the sequence. Sequences shorter than 80 bp after cleaning were removed, resulting in high-quality clean reads. The clean reads were aligned to the wheat reference genome (IWGSC RefSeq v2.1) using Bismark (see Krueger, F. and Andrews, SR, 2011. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. bioinformatics, 27(11), pp.1571-1572.), allowing a maximum of two mismatches. SAMtools (see Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R. and 1000 Genome Project DataProcessing Subgroup, 2009. The sequence alignment / map format and SAMtools. bioinformatics, 25(16), pp.2078-2079.) was used to remove repetitive sequences. Cytosine methylation levels in CG, CHG, and CHH backgrounds were extracted using the Bismarkmethylation extractor. Differentially methylated regions (DMRs) were analyzed by DSS (Park, Y. and Wu, H., 2016. Differential methylation analysis for BS-seq data under general experimental design. Bioinformatics, 32(10), pp.1446-1453.), with the threshold set as |methylation difference| > 0.25 and FDR < 0.05.

[0060] siRNA cluster identification

[0061] After removing reads that were aligned with miRNAs, the remaining sRNA sequences were used for siRNA cluster identification. Reads were aligned to the wheat reference genome (IWGSC RefSeq v2.1) using ShortStack (v3.8.5) (reference Axtell, MJ, 2013. ShortStack: comprehensive annotation and quantification of small RNA genes. Rna, 19(6), pp.740-751.) with parameters set to --mismatches 0 and --mmap f. Fully matched reads were clustered with parameters set to --mincov 1rpmm, --pad 200, --nohp, --dicermin 21, and --dicermax 24. 21–24-nt siRNA clusters were extracted using a custom Perl script (see Zhou, M., Palanca, AMS and Law, JA, 2018. Locus-specific control of the de novo DNA methylation pathway in Arabidopsis by the CLASSY family. Nature genetics, 50(6), pp.865–873.). A tag directory was generated using the makeTagDirectory script (with the -format sam and -keepAll options) of the HOMER software (reference Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, YC, Laslo, P., Cheng, JX, Murre, C., Singh, H. and Glass, CK, 2010. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cellidentities. Molecular cell, 38(4), pp.576-589.). Then, a custom script was used to split the subdirectories according to the siRNA size (20–25nt).Overlapping clusters among biological repeats were identified using HOMER's mergePeaks script (-venn parameter), retaining only regions appearing in at least two repeats as the final 24-nt siRNA cluster sets for W1 and WM in each tissue type. Different sets were then merged using Bedtools (see Quinlan, AR and Hall, IM, 2010. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6), pp.841-842.) to generate a list of clusters without redundancy.

[0062] siRNA differential expression analysis

[0063] Expression levels of the 24-nt siRNA cluster were calculated using the HOMER script annotatePeaks.pl with options -size given, -fpkm, -len 1, -d. Differential expression analysis was performed using raw counts (-noadj, -size given, -len 1, -d) and with the aid of edgeR (3.22.5) (refer to Robinson, MD, McCarthy, DJ and Smyth, GK, 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. bioinformatics, 26(1), pp.139-140.), with P < 0.05 and |log2 Fold Change| > 0.

[0064] siRNA target gene prediction

[0065] For differentially expressed 24-nt siRNAs, clusters overlapping with differentially methylated regions (DMRs) are first screened. Only pairs with consistent regulatory patterns are retained (i.e., siRNA upregulation paired with hypermethylated DMRs, or siRNA downregulation paired with hypomethylated DMRs), defined as candidate RdDM sites. Target genes are determined based on their genomic location: DMRs located within a gene are defined as direct target genes; those located in intergenic regions and close to the gene promoter (within 50 kb), if their promoters contain DMRs with consistent methylation patterns, are marked as potential indirect target genes. This prediction process is as follows: Figure 1 As shown.

[0066] Functional analysis of sRNA targets

[0067] Functional enrichment analysis of target genes was performed using the R package clusterProfiler (see Xu, S., Hu, E., Cai, Y., Xie, Z., Luo, X., Zhan, L., Tang, W., Wang, Q., Liu, B., Wang, R. and Xie, W., 2024. Using clusterProfiler to characterize multiomics data. Natureprotocols, 19(11), pp.3292-3320.) and the online platform agriGO (see Tian, ​​T., Liu, Y., Yan, H., You, Q., Yi, X., Du, Z., Xu, W. and Su, Z., 2017. agriGO v2. 0: a GOanalysis toolkit for the agricultural community, 2017 update. Nucleic acidsresearch, 45(W1), pp.W122-W129.). GO entries with a p-value <0.05 after correction are considered significantly enriched.

[0068] The results obtained from the above analysis are as follows:

[0069] Deep sequencing identification of small RNAs in wheat under different planting systems

[0070] To investigate the role of small RNAs in yield reduction during continuous wheat cultivation, we constructed 12 small RNA libraries (Table S1) from wheat flag leaves and root tissues under rapeseed-wheat rotation (W1) and continuous wheat monoculture (WM) conditions. Three biological replicates were set up for each condition and tissue. Illumina high-throughput sequencing generated 10.2 million to 17 million raw reads per library. After adapter removal and quality control, 3.4 million to 10.2 million clean reads with lengths of 18–30 nt were obtained. Alignment results showed that 91.92%–93.88% of the small RNAs in the leaf samples could map to the wheat reference genome, while only 44.81%–54.44% in the root tissue (Table 1) were mapped, suggesting that there may be a large number of exogenous or unannotated small RNAs in the root tissue. Length distribution analysis showed that small RNAs were mainly concentrated in the 18–24 nt range, with 22-nt small RNAs being the most abundant in leaves and 21-nt small RNAs being the most abundant in roots, followed by 24-nt small RNAs. Figure 2 , 3 As shown in the figure, W1 and WM represent crop-rotated wheat and continuously cropped wheat, respectively; L and R represent leaf and root tissues, respectively. These distribution patterns were largely consistent between the W1 and WM treatments, showing only slight differences. However, the changes in the relative abundance of 24-nt small RNAs suggest that transcriptional silencing (TGS) may be regulated under continuous planting conditions, and siRNAs may be involved in yield-reducing molecular processes.

[0071] Differential expression analysis of 24-nt siRNA clusters under continuous wheat planting conditions

[0072] To assess the role of 24-nt siRNAs in genome stability and gene expression regulation, we analyzed siRNA clusters after removing known and newly predicted miRNAs. The results showed that 24-nt siRNAs were the dominant class in leaf and root tissues, consistent with their functions in RNA-mediated DNA methylation (RdDM) and transcriptional silencing, such as... Figure 4 , 5 As shown, 55,465 and 38,418 24-nt siRNA clusters were identified in leaves and roots, respectively, with most clusters ranging in length from 24 to 250 nt. Figure 6 , 7 As shown. Approximately 8% of the clusters showed significantly different expression in the two tissues, such as... Figure 8 , 9 As shown. In the leaves, the number of up-regulation and down-regulation clusters are close (2261 and 2141 respectively), as... Figure 8 As shown, in the root tissue, the number of upregulated clusters (2662) is much greater than that of downregulated clusters (334), such as Figure 9 As shown, this upregulated pattern indicates that extensive siRNA regulatory reprogramming occurred in the root system under continuous planting stress.

[0073] By integrating differentially expressed siRNAs with genome-wide methylation data (WGBS), candidate RdDM sites were identified, which are co-localized differentially methylated regions (DMRs) with consistent change patterns (e.g., upregulated siRNAs correspond to hypermethylated DMRs). For details, see [link to documentation]. Figure 1 As shown in the figure. Correlation analysis further confirmed the positive correlation between changes in siRNA abundance and DNA methylation levels, as shown in the figure. Figure 10 , 11 As shown, Figure 10 , 11 The results show the relationship between changes in siRNA abundance (Y-axis, log2 fold change) and changes in DNA methylation level (X-axis), supporting the involvement of siRNA in epigenetic regulation via the RdDM pathway.

[0074] Target gene prediction and functional analysis of differentially expressed siRNAs

[0075] Target genes of differentially expressed siRNAs were predicted based on RdDM sites and categorized into direct targets (intra-gene DMRs) and potential indirect targets (promoter region DMRs) according to genomic location. A total of 456 candidate target genes were identified, such as... Figure 12 As shown, more genes in the root tissue were predicted to be upregulated by siRNA, consistent with the trend of upregulation of siRNA in roots; while in the leaves, more siRNAs were downregulated, suggesting that some genes may have been de-repressed. These results indicate that siRNA-mediated transcriptional silencing is stronger in the root tissue, while partial unsilencing may occur in the leaves.

[0076] GO enrichment analysis showed that upregulated siRNA target genes were enriched in cuticle and floral organ development-related functions (GO:0042335, GO:0048438), while downregulated siRNA target genes were enriched in fungal defense response-related genes (GO:0050832). Figure 13 As shown. Furthermore, specific enrichment patterns were observed in different tissues. In leaves, siRNA target genes were mainly involved in hormone signaling pathways, including abscisic acid (ABA) metabolism (GO:0009687), jasmonic acid (JA), and ethylene (ET) signaling pathways (GO:0009867, GO:0009873). In root tissues, upregulated siRNA target genes were involved in developmental and growth processes, such as... Figure 14As shown, it is also significantly enriched in immune responses, such as hypersensitivity regulation (GO:0010363) and innate immunity (GO:0045087). These results suggest that siRNA-mediated gene regulation plays a complex role in wheat's response to continuous cropping stress.

[0077] Integration analysis of siRNA with transcriptome and methylome reveals candidate regulatory targets

[0078] To further identify key regulatory factors associated with continuous cropping, we integrated the target genes of differentially expressed siRNAs with transcriptome data, focusing on analyzing cases where siRNAs and their target genes exhibit opposite expression patterns. (See [link to relevant documentation]). Figure 15 , Figure 15 Differentially expressed genes (DEGs) with expression patterns opposite to their corresponding siRNAs were screened as candidate genes. Although the number of predicted targets was large, only a small portion overlapped with the differentially expressed genes (DEGs), as shown in Tables 2 and 3, indicating that the transcriptional response is complex under long-term monoculture conditions. Most candidate genes in leaves were upregulated, while most were suppressed in roots, consistent with the bias in siRNA expression. Figure 8 , 9 As shown.

[0079] Table 2. List of differentially expressed genes regulated by differentially expressed siRNAs in leaves.

[0080]

[0081] Table 3. List of differentially expressed genes regulated by differentially expressed siRNAs in roots.

[0082]

[0083] Table 2 shows that in leaves, the upregulated siRNA silenced the photosystem II and chlorophyll biosynthesis-related genes LPA1 and CHLP; the downregulated 24-nt siRNA cluster corresponds to the wax synthesis gene GL1-2, which plays an important role in cuticle defense. Furthermore, another stress-induced gene, PER56, was also suppressed by siRNA, suggesting that under continuous planting conditions, plants may undergo resource redistribution, reflecting a trade-off between growth and defense. Table 3 shows that the candidate genes in the root system are mostly defense-related genes suppressed by the upregulated siRNA. These widespread suppression effects indicate that under continuous planting conditions, root physiological activities are inhibited on a larger scale.

[0084] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made in accordance with the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. An analysis method for integrating small RNA and DNA methylation omics studies for crop adaptation to continuous cropping, characterized by, The method comprises the following steps: Step S1, collecting wheat samples under W1 and WM conditions, wherein W1 is rape-wheat rotation, and WM is continuous wheat cultivation, specifically, 11) collecting wheat flag leaves and root systems during the stem elongation period, and respectively performing RNA and DNA extraction, 12) performing small RNA sequencing on the extracted RNA: establishing a small RNA library for samples with an integrity RIN > 4.0 in the extracted RNA; 13) performing whole genome bisulfite sequencing on the extracted DNA to establish a WGBS library; Step S2, identifying 21-24-nt siRNA clusters under W1 and WM conditions; Step S3, performing differential expression analysis on the 24-nt siRNA clusters; Step S4, for the differentially expressed 24-nt siRNA, screening out clusters overlapping with differential methylation regions DMRs, retaining only pairs with consistent regulation modes, and defining them as RdDM action sites, and then predicting siRNA target genes, In step 12), after the small RNA library is established, the data in the small RNA library is subjected to quality control, and sequences with poly-N sequences, 5' adapter contamination, missing 3' adapters or inserted tags, reads with a proportion of uncertain bases exceeding 10% and reads with a proportion of base quality values < 5 exceeding 50% are removed, high-quality clean reads are generated, the high-quality clean reads are aligned with a wheat reference genome, and sequences that completely match are retained; Step 13) further comprises performing quality control on the data in the established WGBS library, cutting off adapter sequences; if the average base quality value of the entire read is lower than 15, the entire read is removed; the base quality value of the sequence is less than 5 from the beginning and end; sequences shorter than 80 bp in length are removed after cleaning, and finally high-quality clean reads are obtained, the high-quality clean reads are aligned with a wheat reference genome, allowing up to two mismatches, removing repetitive sequences, and extracting cytosine methylation levels in CG, CHG and CHH backgrounds.

2. The method according to claim 1, wherein the method is used for analyzing the crop continuous cropping adaptability by integrating the small RNA and DNA methylation omics. The specific process of differential expression analysis on the 24-nt siRNA clusters in step S3 is as follows: First, the expression level of the 24-nt siRNA cluster is quantified by using the annotatePeaks.pl script in the HOMER software with parameters-size given, -fpkm, -len1, -d; Then, differential expression analysis is performed, in which the raw count value of the 24-nt siRNA cluster is first calculated by using the above annotatePeaks.pl script with parameters set as-noadj, -size given, -len 1, -d; and then differential expression analysis is performed by using the R package edgeR, with P value < 0.05 and |log2(fold change)| > 0 as the screening threshold, so as to identify differentially expressed 24-nt siRNA clusters.

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