Study on Revealing Peanut-Related Disease Prevention Gene Clusters via Whole Transcriptome Sequencing

: Root rot caused by Fusarium oxysporum is a serious root disease that affects the growth and yield of peanut crops worldwide. Identifying resistance genes is of great signiﬁcance for both conventional and molecular breeding. At present, there are a few identiﬁed disease resistance genes, and the defense response mechanism against this pathogen remains unclear. Using transcriptome analysis techniques, 21 differentially expressed genes (DEGs) were identiﬁed in plants treated with Bacillus thuringiensis TG5 and carbendazim. Among these DEGs, the TIFY10A protein gene, pyruvate decarboxylase, and UDP glycosyltransferase 74B1 genes play an important role in early resistance to root rot in peanut plants, leading to regulatory factors that trigger immunity. Our study provides important information on the response mechanism of peanut plants to F. oxysporum -induced root rot and the protective beneﬁts of colonization by the microbial pesticide, B. thuringiensis .


Introduction
Peanut (Arachis hypogaea L.) is one of the most important cash crops in the world. During its entire growth cycle, it is vulnerable to various pathogenic microorganisms; therefore, cultivating and selecting resistant varieties are some of the most economical and effective ways to prevent and control diseases. Disease resistance genes are one of the important means by which peanut plants are protected against pathogenic microorganisms. Current research shows that the early and timely activation of plant-defense-related genes in peanut are key to an effective response to pathogenic attack. Transcriptomic techniques are increasingly used to screen plant transcriptomes for resistance-related genes. Previous studies have shown that the positive regulation of the phosphorylation of calmodulinbinding protein 60-LIKE G, mediated by calcium-dependent protein kinase, can improve the resistance of peanut plants to soil-borne fungal pathogens and increase the expression of immune response-related genes [1,2]. The overexpression of peanut pathogenesis-related protein genes, in response to pathogen stress or abiotic stimuli, plays an important role in the defense system of peanut plants [3,4]. Furthermore, oxidative lipids, lipoxygenase (LOX), and related oxidative metabolites in peanut plants are involved in the specific defense mechanism of peanut plants against fungal infection [5,6]. More research is needed to better understand the response of peanut plants to pathogens and the disease resistance genes involved.
Many transcription factors in plants, especially the JASMONATE ZIM-domain, PEA-POD, zinc finger inflorescence, and TIFY, are crucial in regulating plant stress responses [7][8][9]. Several pathways, including the pyruvic acid decarboxylase-mediated plant metabolic pathway [10], the cytochrome P450 monooxygenase-regulated triterpene modification pathway [11], and the cinnamic acid 4-hydroxylase (SdC4H, CYP73A111)-regulated biosynthetic pathway of secondary metabolites [12], also play important roles in providing a host defense response in plants. Various environmental challenges, including exposure to high levels of salt, drought, chemical pesticides, pathogenic microorganisms, and intense light, can cause an increase in the number of reactive oxygen species (ROS), high levels of which are harmful to plants. The accumulation of antioxidant substances in peanut plants is shown to have positive effects on the defense mechanism of peanut plants against glyphosate stress [13][14][15][16]. Further research is required to better understand the interaction between transcription factors [17], metabolic pathways, and various environmental factors in the stress response of peanut plants against pathogenic microorganisms.
Root rot in peanut plants caused by F. oxysporum is a serious root disease that affects the growth and yield of this crop worldwide. Existing research shows that F. oxysporum produces fusaric acid, which reduces host resistance, and cell wall-degrading enzymes are also produced that degrade the host cell wall and compromise tissue protection, releasing polysaccharides that promote the growth of pathogenic fungi [18]. Some Bacillus have a strong antagonistic effect on peanut powdery mildew [19]. Bacillus pumilus is reported to have an inhibitory effect on peanut blight in South China [20] and Pseudomonas fluorescens is a good biocontrol inhibitor in plants [21]. Furthermore, a commercialized preparation of B. thuringiensis (Bt) inhibits the damage to leaves caused by Spodoptera frugiperda and is an efficient substitute for chemical pesticides [22,23]. However, the mechanism of B. thuringiensis-induced peanut plant resistance to root rot caused by F. oxysporum infection requires further research.
The mechanism of microbial pesticides and traditional chemical pesticides that combat F. oxysporum in peanut plants is poorly understood, and thus far, no studies have explored the changes in the expressions of various functional gene clusters following F. oxysporum infection in peanut plants, as well as the changes in effector levels that mediate resistance responses. In this paper, based on the Illumina high-throughput sequencing transcriptome method, we examined the mechanism of microbial pesticides that forces peanut plants to cope with F. oxysporum stress by identifying new genes and metabolic pathways. This study provided a putative candidate gene cluster for differential expression in peanut plant cells as well as the metabolic pathways associated with resistance to F. oxysporum infection and resistance gene expression. It can further verify the potential candidate gene cluster and further develop marker detection methods to reduce the contamination level of F. oxysporum in peanut production.

Experimental Setup
We used 18 flowerpots (22 cm in diameter) filled with 2500 g of industrial sand, and six seeds were planted in each pot. The pots were watered thoroughly and seedlings emerged, usually after approximately 14 d. Three tests groups, CK (control), B. thuringiensis (TG5; NCBI SUB12195433, access number CP110119.1), and carbendazim (Car) treatments, were set up, and each treatment was repeated thrice. Nine pots of peanut seedlings were inoculated with F. oxysporum (25 mL of fermentation liquid) via irrigation of the roots. After 7 d of normal culture, 62.5 µL of 5 × 10 6 CFU of B. thuringiensis was added to three pots of the TG5 treatment group, 62.5 µL of carbendazim (1:800 dilution) was added to the Car treatment group, and 62.5 µL of purified water was added to the CK treatment group. The root transcriptome, chlorophyll, catalase (CAT), and superoxide dismutase (SOD) levels in peanut plant cells were measured at 0, 24, and 72 h, respectively. This treatment was performed with each sample in triplicate.

Determination of Chlorophyll Content
We collected leaf tips from the same position for each treatment group. Chlorophyll was extracted from 0.2 g of material and the absorbance measured at 665 and 649 nm using 95% ethanol as a blank control.

Determination of Soil Enzyme Activity
The activity of SOD in peanut plant leaf tissue was measured using the nitroblue tetrazole (NBT) method (the amount of enzyme required to inhibit a 50% photochemical reduction of NBT was taken as the enzyme activity unit, U). The activity of peroxidase was measured using the guaiacol method (A470 enzyme changed 0.01 times per minute when measured as the activity unit of peroxidase conversion, U).

Transcriptome Sequencing of Peanut Root Tissue
Peanut root tissue DNA was extracted using a reagent kit CTAB + Adlai RN40 (manufacturer: Adlai, model: RN40). The Huada Gene DNBSEQ platform was used for sequencing. The sequencing data were filtered with SOAPnuke (v1.5.2) [24] by: removing reads containing sequencing adapter; removing reads whose low-quality base ratio (base quality less than or equal to 5) is more than 20%; and removing reads whose unknown base ('N' base) ratio is more than 5%. Afterward clean reads were obtained and stored in FASTQ format. The clean reads were mapped to the reference genome using HISAT2 (v2.0.4) [25]. Bowtie2 (v2.2.5) [26] was applied to align the clean reads to the reference coding gene set, and then, the expression level of the gene was calculated using StringTie (v2.1.2) [27]. Clean reads were compared with the reference genome based on Hisat [28] to obtain positional information on the reference genome or gene, as well as the unique sequence feature information of the sequencing sample. Based on the Hisat2 alignment results between the reads of each sample and the reference genome sequence, GATK4 software [28] was used to identify single-base mismatches between the sequencing sample and the reference genome as well as potential SNP sites. Furthermore, it was possible to analyze whether these SNP loci affected the expression level of genes or the types of protein products.

Transcriptome Data Analysis Method
To detect differentially expressed genes (DEGs), fold change ≥ 2 and FDR < 0.05 were used as the screening criteria. Hierarchical clustering analysis was performed on the screened DEGs and cluster analysis on genes with the same or similar expression patterns. Then, we further classified and conducted an enrichment analysis on the three major functional classes of gene ontology (GO), molecular function (MF), cellular component (CC), and biological process (BP).
In a pathway significance enrichment analysis, the KEGG Pathway database was used as a reference, and hypergeometric tests were conducted to identify pathways that were substantially enriched in DEGs compared to the entire genome background. A DEG interaction network was constructed based on the results of differential expression analysis and the interaction relationships included in the database. For species included in the database, the interactions between the target gene set were directly extracted from the database to construct the interaction network. For species not included in the database, BLAST (Version0.5.1) software was used to sequence and compare the target gene with the proteins in the database, search for homologous proteins, and construct an interaction network based on the interactions between homologous proteins. The transcription factor analysis was based on AnimalTFDB and PlantTFDB, which are classifications and annotations whole-genome transcription factors and cofactors containing 97 animal and 165 plant genomes, respectively. Transcription factors were divided into 73 families and cofactors were divided into 83 families and 6 major categories based on DNA binding domains. The corresponding species was found using the corresponding transcription factors.

Statistical Analysis
Data were analyzed using Excel 2003 and IBM SPSS 23. Duncan's multiple range test (5% probability level) was used for one-way analysis of variance (ANOVA) to compare the average value of different treatment methods (p < 0.05). Single-factor ANOVA was used to test the homogeneity of variance for real-time data. If necessary, the description (D) test was used to compare the average values between different treatments (p < 0.05). Data represent mean ± SEM.

Picture of Peanut Plant Disease Resistance
After inoculation with F. oxysporum and cultivation for 7 d, compared with the control, Car treatment initially thickened and blackened the peanut plants leaves; however, the leaves soon recovered. After TG5 treatment, the leaves became thicker and the leaf veins grew stronger, resulting in a significant increase in the number of root nodules, root area, leaf area, and net length of the peanut roots. Notably, the control effect of Car on root rot was not significant in the medium to long term ( Figure 1); however, the damage to peanut roots was significant, such that it was difficult for the plant to recover in a short growth cycle.

Statistical Analysis
Data were analyzed using Excel 2003 and IBM SPSS 23. Duncan's multiple range test (5% probability level) was used for one-way analysis of variance (ANOVA) to compare the average value of different treatment methods (p < 0.05). Single-factor ANOVA was used to test the homogeneity of variance for real-time data. If necessary, the description (D) test was used to compare the average values between different treatments (p < 0.05). Data represent mean ± SEM.

Picture of Peanut Plant Disease Resistance
After inoculation with F. oxysporum and cultivation for 7 d, compared with the control, Car treatment initially thickened and blackened the peanut plants leaves; however, the leaves soon recovered. After TG5 treatment, the leaves became thicker and the leaf veins grew stronger, resulting in a significant increase in the number of root nodules, root area, leaf area, and net length of the peanut roots. Notably, the control effect of Car on root rot was not significant in the medium to long term ( Figure 1); however, the damage to peanut roots was significant, such that it was difficult for the plant to recover in a short growth cycle.

Analysis of Chlorophyll and Protective Enzymes in Peanut Leaves
Chlorophyll is the main pigment that characterizes the photosynthetic capacity of plants. Compared with the control, a change in chlorophyll content in peanut leaves was observed after TG5 treatment ( Figure 2). The chlorophyll content in peanut leaves after TG5 treatment was superior to the control, although the change in the number of individual leaves was not very significant. Given the number of leaves, there was a significant difference in chlorophyll content between the entire plant and the entire plot, resulting in

Analysis of Chlorophyll and Protective Enzymes in Peanut Leaves
Chlorophyll is the main pigment that characterizes the photosynthetic capacity of plants. Compared with the control, a change in chlorophyll content in peanut leaves was observed after TG5 treatment ( Figure 2). The chlorophyll content in peanut leaves after TG5 treatment was superior to the control, although the change in the number of individual leaves was not very significant. Given the number of leaves, there was a significant difference in chlorophyll content between the entire plant and the entire plot, resulting in the significant accumulation of substances caused by photosynthesis. The accumulation of ROS caused membrane lipid peroxidation and the loss of membrane differential permeability in plants, leading to defense reactions such as allergic necrosis and phytoalexin production. SOD and CAT eliminated ROS and superoxide anion in plants, thus providing plant disease resistance. Compared with the control, after 72 h of TG5 treatment, the contents of SOD and CAT in peanut plants increased by 23.2 and 14.0%, respectively, significantly increasing the stress resistance of the peanut plants (p < 0.05). Compared with Car treatment, the effect of TG5 on SOD and peroxidase in peanut plants was similar and less harmful to the environment. production. SOD and CAT eliminated ROS and superoxide anion in plants, thus providing plant disease resistance. Compared with the control, after 72 h of TG5 treatment, the contents of SOD and CAT in peanut plants increased by 23.2 and 14.0%, respectively, significantly increasing the stress resistance of the peanut plants (p < 0.05). Compared with Car treatment, the effect of TG5 on SOD and peroxidase in peanut plants was similar and less harmful to the environment.

Sample Clustering and Principal Component Analysis
A total of 27 samples were tested in this project, resulting in 287.29 Gb of data with a Q30 base percentage of 90.19% or higher. Raw data obtained via sequencing were filtered, and these filtered clean reads were matched to the reference sequences. Sequence alignment was performed between the clean reads of each sample and the designated reference genome, with an alignment efficiency ranging from 74.39 to 97.93%. After comparing the sequencing reads to the reference genome and reconstructing the transcript, a total of 5955 new genes were detected, of which 5599 were functionally annotated.

Gene Expression Level Analysis
Fragments per kilobase of transcript per million fragments mapped (FPKM), as an indicator measure of transcript or gene expression level, can be directly used to compare differences in gene expression between different samples. Transcriptome data were used to detect gene expressions with high-sensitivity sequencing, as well as FPKM values of protein coding gene expression levels across six orders of magnitude. As shown in the quadrant diagram, we intuitively observed the dispersion of the expression levels of individual sample genes compared to the overall gene expression levels of different samples (Figure 3).

Sample Clustering and Principal Component Analysis
A total of 27 samples were tested in this project, resulting in 287.29 Gb of data with a Q30 base percentage of 90.19% or higher. Raw data obtained via sequencing were filtered, and these filtered clean reads were matched to the reference sequences. Sequence alignment was performed between the clean reads of each sample and the designated reference genome, with an alignment efficiency ranging from 74.39 to 97.93%. After comparing the sequencing reads to the reference genome and reconstructing the transcript, a total of 5955 new genes were detected, of which 5599 were functionally annotated.

Gene Expression Level Analysis
Fragments per kilobase of transcript per million fragments mapped (FPKM), as an indicator measure of transcript or gene expression level, can be directly used to compare differences in gene expression between different samples. Transcriptome data were used to detect gene expressions with high-sensitivity sequencing, as well as FPKM values of protein coding gene expression levels across six orders of magnitude. As shown in the quadrant diagram, we intuitively observed the dispersion of the expression levels of individual sample genes compared to the overall gene expression levels of different samples (Figure 3).   Figure 4 shows the gene expressions in different groups over different time sequences. There were significant gene differences in different time sequences and between different groups. A hierarchical clustering analysis was performed on the screened DEGs to cluster genes with the same or similar expression patterns.

Gene Ontology (GO) Analysis between TG5 Strains and Carbazim Treatment Groups
GO comprehensively demonstrates the attributes of genes and gene products in organisms. GO was divided into three functional categories: MF, CC, and BP. GO classification statistics of DEGs are shown in Figure S1.

Gene Regulation Analysis in Biochemical Process
Our data show that, after 72 h, TG5 treatment increased the expression levels of NADPH cytochrome P450 reductase 2, L-ascorbate peroxidase, calmodulin, and NADPH    Figure 4 shows the gene expressions in different groups over different time sequences. There were significant gene differences in different time sequences and between different groups. A hierarchical clustering analysis was performed on the screened DEGs to cluster genes with the same or similar expression patterns.

Gene Ontology (GO) Analysis between TG5 Strains and Carbazim Treatment Groups
GO comprehensively demonstrates the attributes of genes and gene products in organisms. GO was divided into three functional categories: MF, CC, and BP. GO classification statistics of DEGs are shown in Figure S1.

Gene Regulation Analysis in Biochemical Process
Our data show that, after 72 h, TG5 treatment increased the expression levels of NADPH cytochrome P450 reductase 2, L-ascorbate peroxidase, calmodulin, and NADPH

Gene Ontology (GO) Analysis between TG5 Strains and Carbazim Treatment Groups
GO comprehensively demonstrates the attributes of genes and gene products in organisms. GO was divided into three functional categories: MF, CC, and BP. GO classification statistics of DEGs are shown in Figure S1.

Gene Regulation Analysis in Biochemical Process
Our data show that, after 72 h, TG5 treatment increased the expression levels of NADPH cytochrome P450 reductase 2, L-ascorbate peroxidase, calmodulin, and NADPH Agriculture 2023, 13, 1608 7 of 16 oxidase genes by 184, 205, 180, and 303% (p < 0.05), respectively, compared with the control values ( Figure 5A). The expression levels of calmodulin and NADPH oxidase genes increased by 147 and 154%, respectively, after treatment with Car treatment (p < 0.05).

Pathogenesis-Related Regulatory Genes Analysis
Pathogenesis-related proteins are produced when plants respond to stress, pathogen attacks, or abiotic stimulation, and play an important role in plant defense systems [3]. Our data show that, after 72 h, TG5 treatment decreased the expression of pathogenesis-related protein 1 gene by 12.0% (p < 0.05) and increased the expression of PTI6 pathogenesis-related gene by 316% (p < 0.05) compared with the control. Meanwhile, Car treatment increased the gene expression levels of pathogenesis-related protein 1 and pathogenesis-related gene transcriptional activator PTI6 by 268 and 668%, respectively (p < 0.05; Figure 6A).

Difference Analysis in KEGG Metabolic Pathway between TG5 and Car Treatment Groups
Our data show the gene pathways of influence during host-pathogen interactions (Figure 7). A pathway check was performed to examine DEGs during host-pathogen interactions. These pathways represent three types of metabolic paths, MF, CC, and BP, as well as other metabolic pathways, including linoleic acid 13S LOX, NADPH cytochrome P450 reductase 2, and 12-oxo plant dienoic acid reductase 11. In this case, the metabolic pathway was Arabidopsis K000591 (linoleic acid metabolism). The metabolic pathway of abscisic acid receptor PYL4 and NADPH oxidase was Arabidopsis K004016 (MAPK signaling pathway plant), and for xylanase endoglucanase/hydrolase protein 22 and protein TIFY10A, it was the Arabidopsis K004075 (plant hormone signal transduction) pathway. The pathway for disease progression-related protein 1, disease-related gene transcriptional activator PTI6, and calmodulin metabolism was Arabidopsis K004626 (plant-pathogen interaction). The metabolic pathway of L-ascorbate peroxidase and abscisic acid 8'-hydroxylase was Arabidopsis K000906 (carotenoid biosynthesis); for phospholipid hydrogen peroxide and glutathione peroxidase, it was Arabidopsis K000480 (glutathione metabolism); and for UDP glycosyltransferase 74B1, it was Arabidopsis K000966 (glucoside biosynthesis). The H1 metabolic channel of alcohol dehydrogenase 1 and aldehyde dehydrogenase family 3 was Arabidopsis K000620 (pyruvate metabolism).
The pathogenic mechanism of peanut root rot caused by F. oxysporum has not yet been clearly defined, and the use of high-quality reference genomes has created a huge opportunity to improve our current understanding of this mechanism. Current research has found that there are 145 metabolic pathways related to peanut pathogenicity. Among these, linoleic acid metabolism, plants with the MAPK signaling pathway, plant hormone signaling transduction, plant-pathogen interactions, carotenoid biosynthesis, glutathione metabolism, glucoside biosynthesis, and acetone acid metabolism are the main metabolic pathways.
Plant stress-resistant gene clusters mainly include members of the LOX gene cluster, transcription factor gene cluster, protective enzyme gene cluster [5], secondary metabolism, and the associated regulation gene cluster. LOX produces fatty acid metabolites in plants, including hydroxy fatty acids, epoxy hydroxy fatty acids, and trihydroxy fatty acids that participate in pathogen-specific defense mechanisms against fungal infection. An increase in LOX activity in infected rice plants regulates the oxidation of linoleic acid to 9-
The pathogenic mechanism of peanut root rot caused by F. oxysporum has not yet been clearly defined, and the use of high-quality reference genomes has created a huge opportunity to improve our current understanding of this mechanism. Current research has found that there are 145 metabolic pathways related to peanut pathogenicity. Among these, linoleic acid metabolism, plants with the MAPK signaling pathway, plant hormone signaling transduction, plant-pathogen interactions, carotenoid biosynthesis, glutathione metabolism, glucoside biosynthesis, and acetone acid metabolism are the main metabolic pathways.
Plant stress-resistant gene clusters mainly include members of the LOX gene cluster, transcription factor gene cluster, protective enzyme gene cluster [5], secondary metabolism, and the associated regulation gene cluster. LOX produces fatty acid metabolites in plants, including hydroxy fatty acids, epoxy hydroxy fatty acids, and trihydroxy fatty acids that participate in pathogen-specific defense mechanisms against fungal infection. An increase in LOX activity in infected rice plants regulates the oxidation of linoleic acid to 9-hydroperoxy-10, 12, 15-octadecatrienoic acid, and its metabolites, which exhibit an-tibacterial activity against various pathogens [6]. An increase in lipid peroxidase levels (represented by malondialdehyde content) and CAT content in peanut plant leaves under glyphosate stress induces changes in plant antioxidant status, serving as an effective defense mechanism against glyphosate toxicity [4]. The continuous and cumulative induction of phenylalanine ammonia lyase, peroxidase, polyphenol oxidase, superoxide dismutase, CAT, LOX, and other defense enzymes, as well as pathogenesis-related proteins, enhanced the resistance of peanut plants to leaf miner and ear rot [29]. This study confirms the above results. After the peanut plants were treated with Car and TG5, NADPH oxidase expression increased, which induced and enhanced the stress response of the plants to F. oxysporum. WRKY transcription factors are found to participate in growth and development but are also involved in plant defense and stress responses via regulating a variety of downstream stress response genes [30,31]. In contrast to the above studies, transcription factor protein TIFY10A and pathogenesis protein activator PTI6 jointly mediate the effects of carbendazim and the biological pesticide TG5 agent, inducing the resistance of peanut plants to F. oxysporum stress.
As an important glycosylation catalyst, UDP flavonoid 3-O-glucosyltransferase responds to plant stress by activating plant hormones and increasing related metabolites. UDP glycosyltransferases genes play an important role in plant secondary metabolism [32,33]. They can serve as structural genes in the final step of anthocyanin biosynthesis, significantly increasing plant anthocyanin content by inhibiting the feedback of jasmonate signaling [34], as well as participating in plant detoxification processes via interactions with transcription factors [35,36]. This study found that UDP glycosyltransferase and its related gene cluster played an important role in resisting the stress caused by F. oxysporum infection in peanut plants.
The traditional chemical fungicide carbendazim is known to have an effective antibacterial effect on F. oxysporum [8,37]. Therefore, using plant growth-promoting rhizobacteria and other microbial technology to partially or completely replace carbendazim pesticide is an essential method for the prevention and control of F. oxysporum-induced root rot in peanut plants.

Conclusions
We believe that the overexpression of transcription factor TIFY protein, pathogenesis transcription activator PTI6, pathogenesis-related protein 1, and NADPH-oxidase-related genes in peanut plants is central to the mechanism of Car-induced resistance in peanut plants in response to root rot caused by F. oxysporum. Simultaneously, the colonization of peanut plants with the TG5 strain can improve antibacterial properties through the overexpression of phospholipid hydroperoxides, glutathione peroxidase, and NADPH oxidase, which work to eliminate free radicals and antioxidant substances. The overexpression of protein TIFY10A and pathogenesis-related transcriptional activator PTI6 genes in peanut plants helps to improve their bacterial resistance. The UDP glycosyltransferase 74B1 gene and its related gene cluster is the main control gene cluster elicited by B. thuringiensis TG5 in peanut plants, increasing their resistance to F. oxysporum-induced root rot.