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Article Transcriptome−Based Identification and Characterization of Genes Associated with Resistance to Beta−Cypermethrin in Rhopalosiphum padi (Hemiptera: Aphididae) 1,2,† 2,3,† 2 2 2 2 4 1, Qiuchi Li , Xinan Li , Yulin Sun , Xujun Tian , Saige Zhu , Yanbo Wang , Haifeng Gao , Caihua Shi * 2, and Xun Zhu * MARA Key Laboratory of Sustainable Crop Production in the Middle Reaches of the Yangtze River (Co−Construction by Ministry and Province), College of Agriculture, Yangtze University, Jingzhou 434025, China State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China Henan Engineering Research Center of Green Pesticide Creation & Intelligent Pesticide Residue Sensor Detection, School of Resource and Environmental Sciences, Henan Institute of Science and Technology, Xinxiang 453003, China Key Laboratory of Integrated Pest Management on Crop in Northwestern Oasis, Institute of Plant Protection, Xinjiang Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Urumqi 830091, China * Correspondence: email@example.com (C.S.); firstname.lastname@example.org (X.Z.) † These authors contributed to the work equally and should be regarded as co-first authors. Abstract: Beta−cypermethrin is one of the most widely used pyrethroid insecticides. However, its continuous and extensive use developed pests’ resistance to beta−cypermethrin. To identify candi- date genes for potential resistance phenotypes and facilitate more targeted studies, we screened out Citation: Li, Q.; Li, X.; Sun, Y.; a wide range of resistance−related genes by comparing multiple transcripts before and after the Tian, X.; Zhu, S.; Wang, Y.; Gao, H.; induction of multiple agents. In this study, transcriptomes were compared to elucidate the mecha- Shi, C.; Zhu, X. nisms and genetic basis of potential resistance between Rhopalosiphum padi (R. padi) sensitive (SS) Transcriptome−Based Identification and resistant (Beta−R) strains to beta−cypermethrin (resistance ratio: 4588.48). These two strains of and Characterization of Genes aphids were treated with a spray solution of lethal beta−cypermethrin concentration (i.e., LC50). To Associated with Resistance to obtain diverse transcripts, we obtained 17,985,440–25,478,353 clean data from different transcript Beta−Cypermethrin in Rhopalosiphum groups, of which 17,183 genes were annotated. Subsequently, these transcripts were divided into padi (Hemiptera: Aphididae). multiple groups for comparison purposes to obtain more comprehensive genes related to resistance. Agriculture 2023, 13, 235. https:// There were 178 to 2856 differentially expressed genes (DEGs) in these transcript groups. The DEGs, doi.org/10.3390/agriculture13020235 including the enriched ones, were classified according to the GO and KEGG Pathway databases. Academic Editor: Nikoletta G. Ntalli Besides, some drug−resistant DEGs were related to cuticle proteins and detoxification metabolic Received: 18 November 2022 processes. Among them, 17 genes related to cuticle protein were upregulated and 20 were down- Revised: 9 January 2023 regulated, 11 genes related to P450 were upregulated and 25 were downregulated, 7 genes related Accepted: 17 January 2023 to UGT were upregulated and 15 were downregulated, 2 genes related to ABC transporter were Published: 18 January 2023 upregulated and 4 were downregulated, 2 genes related to trypsin were upregulated and 1 were downregulated. Finally, qRT−PCR by DEGs confirmed the observed trend in the RNA sequencing expression profile, and most of the results were consistent between qRT−PCR and RNA sequencing Copyright: © 2023 by the authors. (RNA−seq). The results of this study are highly significant in understanding the resistance phenom- Licensee MDPI, Basel, Switzerland. enon in R. padi and other similar wheat aphids, establishing the valuable basis for further research This article is an open access article in the complex mechanism of R. padi resistance to beta−cypermethrin. distributed under the terms and conditions of the Creative Commons Keywords: beta−cypermethrin; Rhopalosiphum padi; transcriptome comparison; resistance Attribution (CC BY) license mechanism (https://creativecommons.org/license s/by/4.0/). Agriculture 2023, 13, 235. https://doi.org/10.3390/agriculture13020235 www.mdpi.com/journal/agriculture Agriculture 2023, 13, 235 2 of 19 1. Introduction Due to their relatively low toxicity to humans and high insecticidal efficiency, pyre- throids are among the most widely used insecticides . Beta−cypermethrin is one of the most commonly used synthetic pyrethroids to protect crops by reducing pest infestations, thereby minimizing yield losses. However, with the extensive use of pyrethroids, many pests have developed varying degrees of resistance to these insecticides [2,3]: for example, Spodoptera litura , Musca domestica , the maize fall armyworm  and the pic- ture−winged flies . Pyrethroids have also been used for a long time to control aphids such as Aphis glycines , Aphis gossypii  and wheat aphids . However, Gong et al. monitored the resistance of wheat aphids to pyrethroids in 15 provinces of China in the period from 2018 to 2019, finding numerous wheat aphids’ moderate or higher resistance to pyrethroids; the most notable among these aphids include Sitobion avenae and Rhopalosiphum padi . In light of such previous studies, novel research on the resistance mechanism of beta−cypermethrin to pest is highly significant when such a study ensures the rational use of pyrethroids as well as suggesting ways of delaying pests’ early re- sistance to pyrethroids . Resistance to insecticides is a common adaptive strategy. The increased expression of metabolic resistance−related protein family genes and the decreased penetration rate of insect epidermis are two very important resistance mechanisms . Penetration resistance refers to modifications in the cuticle that eventually slows down the penetration of insecticide molecules into insects’ body. So far, two mechanisms of penetration resistance have been described: cuticle thickening and altering cuticle com- position. Cuticular modifications occur due to the overexpression of diversified genes or exoskeleton proteins such as cuticular proteins, enzymes−catalyzed enzymatic reactions (CYP4G16 and laccase 2) or ABC transporters that promote cuticular translocation . Many experiments have demonstrated that epidermal penetration is integral to insects’ resistance to pyrethroids. For example, Blattella Germanica, a beta−cypermethrin−resistant insect, has lower epidermal penetration than that of sensitive strains . According to additional research, cuticular penetration is a significant factor in the development of fen- valerate resistance in common house mosquitoes such as Culex pipiens pallens . Insects exhibit pyrethroid−resistance as a result of an increase in the expression levels of multiple metabolic detoxification enzymes. Pyrethroid insecticides are mainly metabo- lized by the oxidation of methyl, methylene, alkyl and aryl substituents, and the resulting metabolites are hydrolyzed by the corresponding alcohols, phenols, acids and glycine, sulfate, glucuronic acid or glucoside and ester bonds, which can be excreted by insects . Oxidation requires phase I detoxifying enzymes of Cytochromes P450 (P450s) , phase II detoxifying enzymes of UDP–glucose–transferase (UGTs)  and glutathione– s–transferase (GSTs) , and ATP binding cassette transporters (ABCs) , while ester bond breaking requires esterases such as carboxyl/cholinesterase (CCEs) . Among them, P450s are the superfamily of detoxifying enzymes, and they play a crucial role in the detoxification of many exogenous substances found in insects . Many related stud- ies have linked high expression levels of the cytochrome P450 gene with pyrethroid re- sistance in the aphid. For example, P450 plays an important role in pyrethroid resistance in Aphis gossypii, Myzus persicae and Aphis glycines [22-24]. Further studies on the resistance of UGT−related genes to insects are also in progress. For instance, the major phase II de- toxification enzymes—Uridine diphosphate (UDP) and −glycosyltransferases (UGTs)— have been implicated in the glycosylation of lipophilic endobiotics and xenobiotics and thus, potentially lead to the evolution of insecticide resistance . Moreover, some UGTs have also been found to be associated with insecticide resistance [26-28]. Rhopalosiphum padi (Hemiptera: Aphididae) (R. padi) is among the dominant wheat aphid populations found in wheat . At present, the use of pyrethroids insecticides is the major technique to control the growth and spread of R. padi . Thus, it is very nec- essary to study the resistance of aphids to pyrethroids. We assumed that insect resistance to insecticides might be the result of the co−regulation of multiple genes, and a significant Agriculture 2023, 13, 235 3 of 19 number of genes can be searched more comprehensively through the transcriptome. Therefore, we used multiple transcriptome comparisons to compare the differentially ex- pressed genes in multiple dimensions to expose key insecticide−related resistance genes more easily. For example, Feng et al. used short−term induction of agents to select re- sistance−related genes in their insecticide resistance study on Tetranychus cinnabarinus , and Wang et al. also used short−term induction of agents to obtain one of the tran- scripts in their insecticide resistance study on R. padi . In this study, a highly beta−cypermethrin−resistant R. padi population was found in the field while high−throughput RNA−seq was used to determine the transcriptome pro- file of beta−cypermethrin−resistant (Beta−R) and beta−cypermethrin−sensitive (SS) R. padi. We treated the two R. padi populations (SS and Beta−R) with a spray solution of lethal beta−cypermethrin concentration (i.e., LC50) to produce different transcripts; then, we compared the transcripts of beta−cypermethrin−sensitive and beta−cypermethrin−re- sistant strains of R. padi, and we also compared the transcripts of beta−cypermethrin−sus- ceptible and −resistant strains of R. padi before and after their exposure to beta−cyperme- thrin. Through this process, effective resistant genes were screened out. In consequence, the results of the study are extremely useful for understanding the beta−cypermethrin resistance mechanism of R. padi and developing more effective management strategies. 2. Materials and Methods 2.1. Aphid Strain For the current study, the R. padi population used in the experiments was collected from Shizuishan in Ningxia (NXS) in 2018 (N39°05′57″, E106°44′51″), and the sensitive strain (SS) and beta−cypermethrin resistant strain (Beta−R) were obtained from different single−female populations. After rearing the population for a specific period of time, the lethal beta−cypermethrin concentration (i.e LC50) of the single female population to beta−cypermethrin (95% purity, Beijing Green Agricultural Science and Technology Group Co., Ltd., Beijing, China) was evaluated; this measurement showed the lowest LC50 as the sensitive strain (SS), and the sensitive strain was not exposed to any chemical agent during the rearing period. The population with the highest LC50 was selected as the pri- mary resistant strain, and selection for the beta−cypermethrin resistance strain was per- formed via spraying the aqueous solution of beta−cypermethrin once a week with con- centrations their current lethal concertation (i.e., LC50) for the pyrethroid insecticide. Meanwhile, the lethal concertation (i.e., LC50) of R. padi resistance strain to beta−cyperme- thrin was evaluated every third generation. The high resistance strain (Beta−R) was estab- lished after continuous culture for multiple generations. 2.2. Bioassay The LC50 of the pyrethroids (beta−cypermethrin) was tested with leaf−dip bioassays . Beta−cypermethrin was dissolved in acetone and used to prepare five serial concen- trations in water containing 0.1% Tween 80, respectively. Wheat leaves with about 60 ap- terous adult R. padi were dipped into insecticide solutions for 3–5 s; the residual droplets of the solution on the leaves were absorbed by dry filter paper and subsequently placed in a 90 mm diameter plastic Petri dish. The control was treated with water containing 0.1% Tween 80 and 0.1% acetone alone. Three replicates of approximately 60 individuals were used for each insecticide concentration, and 5 serial concentrations were used for each insecticide and a control. The mortality was checked after 24 h. During and after treat- ment, the aphids were maintained at 22–24 °C with a photoperiod of 16:8 (L:D) h and a relative humidity of 60 ± 10%. Agriculture 2023, 13, 235 4 of 19 2.3. RNA Isolation We diluted beta−cypermethrin to the LC50 of the sensitive and resistant strains. The sensitive strain (SS) was divided into two groups; one group was treated with SS without any external treatment, while the other group was treated for short−term spray treatment with SS strain LC50 (3.22 mg/L) and it was named SS−T. The resistant strain Beta−R was also divided into two groups. One group was treated with Beta−R alone, while the other received a short−term spray treatment with Beta−R strain LC50 (14,774.89 mg/L) and was designated Beta−R−T. Later, the samples (10 wingless adults from each treatment along with four replicates per treatment) were collected after 24 h. Altogether, 16 samples were collected for RNA−seq. Total RNA was isolated by using RNA Easy Fast Tissue/Cell Kit (Tiangen, Beijing, China). The RNA concentration and purity were measured using a Nano Photometer N50 Touch (IMPLEN, München, Germany). The integrity of RNA was confirmed using Aga- rose gel electrophoresis. 2.4. Library Preparation and Sequencing Illumina−based RNA−seq was performed on 4 treatments of R. padi and its 4 replicas for each treatment. Based on synthesis and sequencing technology, cDNA libraries were sequenced, and numerous reads were produced. These ‘raw reads’ are usually provided in FASTQ (FQ) format. To obtain clean reads, the adapter, ploy−N, and low−quality se- quences were removed from raw reads. Q score, GC content, and sequence duplication level were calculated to obtain clean, high−quality reads, which were then utilized for all subsequent analyses. The clean reads were mapped to the R. padi genome (GenBank no. GCA_020882245.1), and the expression levels of the genes were calculated by using frag- ments per kilobase per million reads (FPKM) values . 2.5. Functional Annotation KOBAS 2.0 software, and topGO R packages were used to compare selected tran- script sequences against two databases: Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). 2.6. Differentially Expressed Genes (DEGs) Analysis and Annotation We evaluated four transcription profiles: “Beta−R−T compared with Beta−R,” “SS−T compared with SS,” “Beta−R compared with SS−T,” and “Beta−R compared with SS” in order to identify the mechanism of R. padi resistance to beta−cypermethrin. Then, these four transcription profiles were divided into two groups for comparative analysis of DEGs. The first group (“Beta−R−T compared with Beta−R”, “SS−T compared with SS”) was the resistant group and sensitive group; when the aphids were briefly induced by beta−cypermethrin, the changes were observed in the various functions of resistant and sensitive strains. The second group (“Beta−R−T compared with SS−T” and “Beta−R com- pared with SS”) was the beta−cypermethrin−induced group and the non−beta−cyperme- thrin−induced control group, to locate and screen a large number of drug−resistance genes. Differential expression analysis was performed, using edgeR , to calculate the p−value of differential expression. The greater the significance of the gene expression dif- ference, the smaller the p−value; hence, genes were identified as differentially expressed genes (DEGs). In order to control the false detection rate (FDR), it is necessary to filter genetic variations using a combination of p−value and FoldChange, with the screening condition being p−value 0.05 and log2 |FoldChange| > 1. The functional annotation and classification of the genes were performed through the GO database, while biological pathway annotations were obtained by using the KEGG database. Agriculture 2023, 13, 235 5 of 19 2.7. Quantitative Real−Time PCR Analysis Quantitative real−time PCR (qRT−PCR) analysis was conducted to validate the ex- pression profiles of 10 randomly selected DEGs and one reference gene, glyceralde- hyde−3−phosphate dehydrogenase (GAPDH) genes . DNAMAN was used to design specific primers to confirm the assembled sequences, followed by the use of a simple tech- nique of reverse transcription PCR (the primers used in qRT−PCR are summarized in Ta- ble 1). Quantitative reverse transcription PCR (qRT−PCR) reactions were performed by using the reagent SuperReal PreMix Plus (SYBP Green) (Tiangen, Beijing, China) on a CFX Connect™ Fluorescent quantitative PCR detection system (Bio−Rad, Hercules, CA, USA). All qRT−PCR experiments were performed in triplicate using independent samples. The −△△Ct expression levels were determined by the 2 method , using the geometric mean of the reference gene for normalization . Table 1. Primers used for qRT−PCR validation of differentially expressed genes. Gene Name Forward Primer (5′−3′) Reverse Primer (5′−3′) g9940 ACACATCGTTGGGAATCG GCTCTGTAACCACGCTTTC g24565 GCAGTTTGCTTTGCTTGC TTCGGCTGTCTTGTAATCG g488 CCGCACTTGCCCAATAC AACCGCTGTGGTCATCG g19171 TGACGCCACATACGACATC CGATTCTCTTGGTGCCATC g11992 GGTGGCCCAAGAGGTTGTATAGG TGGACACTGAAGTTTCGCAAGAATG g7371 TAAGGATTTCCCAGCGGA CGTTTCTCGCCTGTATCAAG g9824 GTTACAAGTTCGCTGGTTTC CATTGCTGATGCCTCAAG g7368 ATCACTACTCGCATCGCAC AGATTCGGGTTCAACACC g4263 ACAACAAGCCGTTCCCAAG ATGGACCTTCGGAGACGAGT g4506 CCTGTTCTTGGATTCCCTC TGACATCGGTCGGTCTTT g16174 TCCAGAGGTTCGTCAATG GTTCAGAAAGATAACTGCCG g12284 TGATTCGTCGCACCCAAAC ACACGCCTTCCGTTGTCTTG g7359 GTCATCAGGTTCTATCTATGGC TCAGTGTGCCTTCCAAAC g8765 TCCAGAGACATAGCGTTGC AGACCACACACTGACTGACG GAPDH(reference gene) GCTCCATTAGCCAAGGTTATTC CAGCACCTCTACCATCTCTCC 3. Results 3.1. Bioassay As can be seen from Table 2, the LC50 of R. padi to SS was 3.22 mg/L of indoor. At the same time, the LC50 of R. padi to Beta−R increased continuously after continuous agent selection. At the 24th generation, the resistant strain reached 14,774.89 mg/L, and the re- sistance ratio was 4588.48. Table 2. Sensitivity of resistant and sensitive strains of R. padi to beta−cypermethrin. 1 2 3 Strain Generation LC50 (95%Cl; mg/L) RR SS − 3.22 (1.97–5.26) − Beta−R G24 14,774.89 (5808.17–37,603.98) 4588.48 1 2 Generation number of spraying tide for R. padi resistant strains. Median lethal concentration (LC50), 95% confidence interval (95%CI). Resistance ratio (RR). 3.2. Transcriptome Data Analysis Illumina sequencing data are shown in Table 3. Following filtration, 22,191,059 to 25,478,353 clean readings were obtained from the SS strain, 19,307,241 to 22,881 clean read- ings were obtained from the Beta−R strain, 20,228,267 to 26,040 clean readings were ob- tained from the SS−T strain, and 17,985,440 to 22,214,579 clean readings were obtained from the Beta−R−T strain. In the Digital Gene Expression (DGE) library, clean readings’ Agriculture 2023, 13, 235 6 of 19 Q30 percentage and GC content ranged from 92.75% to 98.92% and 35.73% to 39.52%, re- spectively. On average, 77.51% of the clean reading was drawn to the R. padi genome da- tabase (https://bipaa.genouest.org/sp/rhopalosiphum_padi/analysis/16, accessed on 8 January 2022). Statistics were made on the distribution of reads compared to genomes in the samples (Figure 1). The distribution in the Exon region and the Intron region of the comparison sequences ranged from 84.4 to 91.9% and 3.6 to 7.1, respectively. Distribution in the Intergenic region was between 4.5% and 10.7%. Table 3. Summary of Illumina RNA−sequencing data. 1 2 3 4 5 6 7 Samples Raw Reads Clean Reads Q30 (%) GC Content (%) Total Mapped Mapped Ratio (%) SS−1 25,645,404 25,388,540 92.75 38.53 25,388,540 96.99 SS−2 25,744,355 25,439,416 94.81 39.17 25,439,416 95.81 SS−3 25,802,229 25,478,353 94.75 38.64 25,478,353 95.79 SS−4 22,427,557 22,191,059 95.58 39.19 22,191,059 97.43 Beta−R−1 19,606,609 19,307,241 98.15 38.47 42,874,386 73.73 Beta−R−2 23,305,059 22,881,481 98.23 36.95 22,881,481 48.97 Beta−R−3 19,873,732 19,350,373 98.91 37.11 19,350,373 45.28 Beta−R−4 19,816,890 19,333,166 98.18 38.67 19,333,166 64.92 SS−T−1 20,228,267 19,995,591 95.81 39.52 19,995,591 97.38 SS−T−2 26,040,187 25,614,225 95.97 38.83 25,614,225 97.47 SS−T−3 25,027,865 24,448,408 95.72 39.14 24,448,408 97.24 SS−T−4 20,847,432 20,497,212 95.87 39.11 20,497,212 97.40 Beta−R−T−1 17,985,440 17,587,297 98.81 35.73 38,741,343 48.04 Beta−R−T−2 21,079,387 20,600,348 98.92 37.03 20,600,348 28.55 Beta−R−T−3 24,195,778 23,528,216 98.21 38.02 23,528,216 74.14 Beta−R−T−4 22,214,579 21,623,496 98.82 38.98 21,623,496 81.06 Sample name: there were four groups of samples, SS, Beta−R, SS−T, and beta−R−T, with four rep- licates in each group. SS was a R. padi−sensitive strain not induced by beta−cypermethrin, Beta−R was a R. padi−resistant strain not induced by beta−cypermethrin, and SS−T was a R. padi−sensitive strain induced by beta−cypermethrin. Beta−R−T was a R. padi−resistant strain induced by beta−cy- 2 3 permethrin. The number of sequenced Raw reads. The number of Clean reads obtained after 4 5 filtering. The percentage of bases with Phred values greater than 30 in the total base population. 6 7 G/C bases as a percentage of the total number of bases. Total number of filtered Clean reads. The proportion of the genome that is compared. Agriculture 2023, 13, 235 7 of 19 Figure 1. Distribution of unique mapping reads in exon, intron, and intergenic regions. SS was the R. padi−sensitive strain transcriptome without beta−cypermethrin induction, Beta−R was the R. padi−resistant strain transcriptome without beta−cypermethrin induction, and SS−T was the R. padi−sensitive strain transcriptome with beta−cypermethrin induction. Beta−R−T was the R. padi−sensitive strain transcriptome with beta−cypermethrin induction, with four replicates in each group. 3.3. Functional Annotation and Classification Annotation results show that 17,183 (64.76%) of the 26,535 total unigenes were anno- tated against different databases. For different databases, 10,775 (40.61%) and 6408 (24.15%) unigenes were annotated in the GO and KEGG databases, respectively. Gene ontology (GO) showed that 10,775 single genes were divided into 3706 subclasses, of which 2157 (58.20%) were biological process (BP) items, 276 (7.45%) were cellular compo- nent (CC), and (MF) 1273 (34.35%) were molecular function (Supplementary File S1). Most of the unigenes in the BP category were presumed to be the oxidation−reduction process (644), regulation of transcription, DNA templated (471), and proteolysis (423). The uni- genes of the CC category were presumed to be an integral component of the membrane (3090), nucleus (904), and membrane (541). Those in the MF category were presumed to be nucleic acid binding (1479), ATP binding (1051), and Zinc Ion binding (749) (Figure 2A, Supplementary File S1). In the KEGG mapped results, the pathways were divided into six main categories; among these, genetic information processing (2598), metabolism (1597), and signaling and cellular processes (1113) accounted for the top three (Figure 2B, Sup- plementary File S2). Agriculture 2023, 13, 235 8 of 19 Figure 2. Results of unigenes against gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. (A) Gene ontology (GO) annotation and classification of the Rhopalosiphum padi transcriptome. BP: biological process, CC: Cellular component, MF: Molecular Function. (B) Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation and pathways of the R. padi transcriptome. 3.4. Differentially Expressed Genes Analysis Based on the p−value and log2 |Foldchange| value, DEGs of insecticide treatment samples and contrast samples identification comparison results are presented in Table 3. The number of DEGs obtained was different in different treatments. The least number of DEGs was obtained when resistant strains were induced and resistant strains were not induced (Beta−R−T compared with Beta−R) (178). The greatest number of DEGs was ob- tained when resistant strains were not induced. Meanwhile, sensitive strains were not in- duced (Beta−R compared with SS) (2856) (Table 4, Figure S1, Supplementary File S3). Agriculture 2023, 13, 235 9 of 19 Table 4. The number of upregulated and downregulated DEGs in each sample was compared. Compare Up Down Beta−R−T vs. Beta−R 147 31 SS−T vs. SS 584 297 Beta−R−T vs. SS−T 1518 930 Beta−R vs. SS 1758 1098 DEGs obtained by comparing the transcriptional profiles of resistant R. padi strains were induced by beta−cypermethrin with those of resistant R. padi strains not induced by beta−cypermethrin. DEGs obtained by comparing the transcriptional profiles of sensitive R. padi strains were induced by beta−cypermethrin with those of sensitive R. padi strains not induced by beta−cypermethrin. DEGs obtained by comparing the transcriptional profiles of resistant R. padi strains were induced by beta−cypermethrin with those of sensitive R. padi strains induced by beta−cypermethrin. DEGs obtained by comparing the transcriptional profiles of resistant R. padi strains were not induced by beta−cypermethrin with those of sensitive R. padi strains not induced by beta−cypermethrin. 3.5. GO Classification and KEGG Pathway Identification of DEGs We analyzed the 178 annotated DEGs in Beta−R−T compared with the Beta−R group and 881 annotated DEGs in the SS−T compared with SS group; and 64 DEGs were found in the two beta−cypermethrin−treated groups; 114 DEGs were unique to the Beta−R strain, and 817 DEGs were unique to the SS strain (Figure 3). Based on results obtained using GO annotation and KEGG pathway analysis for the specific genes in the different groups, we found that the functional classification of DEGs was very different between them. The number of DEGs in the SS experimental group was greater than that in the Beta−R exper- imental group for GO terms. Figure 3. Venn map of differentially expressed genes (DEGs) in Beta−R−T vs. Beta−R group and SS−T vs. SS group. Beta−R−T vs. Beta−R: DEGs obtained by comparing the transcriptional profiles of resistant R. padi strains were induced by beta−cypermethrin with those of resistant R. padi strains not induced by beta−cypermethrin. SS−T vs. SS: DEGs obtained by comparing the transcriptional profiles of sensitive R. padi strains were induced by beta−cypermethrin with those of sensitive R. padi strains not induced by beta−cypermethrin. Under the induction of beta−cypermethrin, in the Beta−R−T compared with the Beta−R group, a total of 73 DEGs were found to be annotated in 541 GO terms. The 762 Agriculture 2023, 13, 235 10 of 19 DEGs, found in the SS strain experimental group (SS−T compared with SS), were mapped with 1881 GO terms (Supplementary File S4). Later, the 64 GO terms were shared with the DEGs of the two transcriptome comparison groups. Comparing the log2 Fold Change of 64 common genes between the two groups, we found that the expression levels of most genes in the resistant group were higher than those in the sensitive group (Supplementary File S5). Additionally, one unique GO term (structural constituent of cuticle) was found among the DEGs of the Beta−R experimental group. The change of cuticle structure may be the reason for the resistance of R. padi to beta−cypermethrin (Figure S2, GO terms were sorted by p−values from small to large, Supplementary File S4). When the Beta−R−T group was compared with the Beta−R group, a total of 21 DEGs were annotated into 45 KEGG pathways. The 160 DEGs, found in the SS strain experi- mental group (SS−T compared with SS), were mapped with 194 KEGG pathways; and the number of DEGs in the sensitive group was the largest. As the bubble chart indicates, most of the top pathways belong to the categories of metabolism and organismal systems among which typical pathways include “Ascorbate and aldarate metabolism”, “Starch and sucrose metabolism”, and “Pentose and glucuronate interconversions”. This indicates that the SS strain has a great impact when stimulated by chemical agents, and more genes are activated to synthesize energy and metabolize energy to help worms survive the crisis. In addition, the pathway annotations of the resistance and sensitive groups indicate that the pathways are marked with drug metabolism. There is also an ABC transporters path- way in the resistance group that seems responsible for R. padi’s resistance. The mechanism is worthy of further study in this context (Figure 4, Supplementary File S6). Agriculture 2023, 13, 235 11 of 19 Figure 4. KEGG pathways annotation classification map of DEGs in SS and beta−R groups before and after beta−cypermethrin induction. (A) Beta−R−T vs. Beta−R: DEGs obtained by comparing the transcriptional profiles of resistant R. padi strains were induced by beta−cypermethrin with those of resistant R. padi strains not induced by beta−cypermethrin. (B) SS−T vs. SS: DEGs obtained by com- paring the transcriptional profiles of sensitive R. padi strains were induced by beta−cypermethrin with those of sensitive R. padi strains not induced by beta−cypermethrin. 3.6. Analysis of DEGs Associated with Drug Resistance and qRT− PCR Validation of DEGs The insecticide−treated DEGs and control samples, which were considered to be in- secticide tolerance−related genes, are listed in Table 5 and Supplementary File S7. Using beta−R−t vs. SS−t, Beta−R vs. SS transcripts as the standard, beta−r−t vs. Beta−R vs. SS Agriculture 2023, 13, 235 12 of 19 transcripts were compared for all the DEGs associated with drug resistance in the auxil- iary. We found that 17 out of 37 genes associated with cuticle protein were upregulated and 20 were downregulated; 11 cytochrome P450−related genes were upregulated while 25 were downregulated; 7 UDP−glycosyl transferase (UGT)−related genes were upregu- lated and 15 were downregulated (Supplementary File S7). We further found that there were 6 ABC transporter−related genes, of which 2 were upregulated and 4 were down- regulated, and 3 trypsin−related genes, of which 2 were upregulated and 1 was downreg- ulated (Supplementary File S7). Overall, we found that the total number of downregulated genes was more than the number of upregulated genes, and the number of DEGs, associ- ated with insecticide tolerance in the “Beta−R vs. SS“ group, was the highest, followed by cuticle protein, cytochrome P450, and UGTs. Lastly, four cuticle proteins, four cytochrome P450s, four UGTs, one ABC transporter, and one trypsin−related DEG were selected for qRT−PCR verification (Table 6). Finally, we performed a qRT−PCR analysis of 14 DEGs associated with drug resistance and confirmed the expression trends observed in the RNA−Seq results (Figure 5), suggesting that the DEGs profiles were reliable. Table 5. Insecticide tolerance−related genes. 1 2 3 4 Beta−R−T vs. Beta−R SS−T vs. SS Beta−R−T vs. SS−T Beta−R vs. SS Up Down Up Down Up Down Up Down Cuticle protein 4 0 7 2 7 12 11 14 Cytochrome P450 0 0 6 5 4 16 6 11 UDP−glucosyltransferases UGT 0 1 6 7 3 6 5 10 ATP−binding cassette (ABC) transporter 0 0 0 0 1 4 1 1 Trypsin 0 0 0 0 2 0 2 1 DEGs obtained by comparing the transcriptional profiles of resistant R. padi strains were induced by beta−cypermethrin with those of resistant R. padi strains not induced by beta−cypermethrin. DEGs obtained by comparing the transcriptional profiles of sensitive R. padi strains were induced by beta−cypermethrin with those of sensitive R. padi strains not induced by beta−cypermethrin. DEGs obtained by comparing the transcriptional profiles of resistant R. padi strains were induced by beta−cypermethrin with those of sensitive R. padi strains induced by beta−cypermethrin. DEGs obtained by comparing the transcriptional profiles of resistant R. padi strains were not induced by beta−cypermethrin with those of sensitive R. padi strains not induced by beta−cypermethrin. Table 6. DEGs associated with insecticide resistance. Gene ID Accession Number Description g9940 XM_026951046.1 cuticle protein 19−like g24565 XM_026952080.1 cuticle protein 7−like g488 XM_027990008.1 cuticle protein 7−like g19171 XM_026961146.1 cuticle protein−like g11992 XM_027984718.1 P450 4C1−like g7371 MW287284.1 cytochrome P450 6CY8 (CYP6CY8) g9824 MN764890.1 cytochrome P450 CYP6DC1 (CYP6DC1) g7368 MK534505.1 cytochrome P450 CYP6CZ1 (CYP6CZ1) g4263 XM_027994410.1 UDP−glucuronosyltransferase 2C1−like g4506 XM_026960509.1 UDP−glucuronosyltransferase 2B2−like g16174 XM_026962565.2 UDP−glucuronosyltransferase 2C1−like g12284 XM_026954189.1 UDP−glucuronosyltransferase−like g7359 XM_026956515.1 ABC transporter G family member 23−like g8765 XM_026949522.1 trypsin−1−like Agriculture 2023, 13, 235 13 of 19 Figure 5. Quantitative real−time PCR (qRT−PCR) validation of the expression of differentially ex- pressed genes identified using RNA sequencing. The expression levels were normalized to GAPDH genes. (A) Beta−R−T vs. SS−T: DEGs obtained by comparing the transcriptional profiles of resistant R. padi strains were induced by beta−cypermethrin with those of sensitive R. padi strains induced by beta−cypermethrin. (B) Beta−R vs. SS: DEGs obtained by comparing the transcriptional profiles of resistant R. padi strains were not induced by beta−cypermethrin with those of sensitive R. padi strains not induced by beta−cypermethrin. Agriculture 2023, 13, 235 14 of 19 4. Discussion A common approach in studying the physiological adaptations that lead to insect resistance to insecticides is to compare the transcriptome of resistant strains to that of sen- sitive strains . In our transcriptome experiment, to screen more comprehensive re- sistance−related genes, we treated the existing R. padi’s beta−cypermethrin resistant and sensitive strains with short−term agent treatment, respectively, to obtain drug−induced strains of resistant and sensitive strains. Firstly, we conducted expression analysis and functional enrichment of GO and KEGG by comparing the two transcriptomes of DEGs before and after induction of antag- onistic strains and sensitive strains. In this way, it is easier to expose the rapid changes in expression and function of resistance genes after encountering agents to identify the func- tions that lead to aphid resistance to insecticides. We found that the DEGs of the inducible resistant strain and non−inducible resistant strain treatment comparison groups were sig- nificantly less in number than those in the inducible sensitive strain treatment and non−in- ducible sensitive strain treatment comparison group. Furthermore, almost no DEGs, re- lated to resistance, appeared in the inducible resistance strain treatment and non−induci- ble resistance strain comparison groups. This result contradicts Wang’s obtained results, where the number of differentially expressed genes in resistant strains was higher than that in sensitive strains . However, our research found that the expression level of common genes in the resistant groups was significantly higher than sensitive groups. This phenomenon requires further study of the specific reasons and real mechanisms. The functional annotation showed that these genes had different functions, most of which were related to the enhanced activity of detoxification enzymes and structural changes of cuticle proteins. We then conducted two transcriptome comparisons between uninduced resistant and sensitive strains, and between induced resistant and sensitive strains. We found a large number of resistance−related genes. We found that most DEGs appeared in the com- parison of two transcriptomes between non−induced resistant strains and sensitive strains and induced resistant strains and sensitive strains, and the expression levels of some genes changed in the multi−group comparison, and the expression levels changed in the same trend. For example, regarding the cuticle protein gene g9944 and UGT gene g16174, this kind of gene should be studied as the key gene of insecticide resistance. We also found a large number of downregulated genes, which may be a balance to compensate for the fitness cost. This phenomenon is very interesting and requires further research. The findings could indicate that the mechanism of drug resistance in R. padi is com- plex, resulting from the joint action of multiple resistance mechanisms. From the contact of the drug with the epidermal protein of the insect body to the digestion of trypsin after entering the insect body, to the final function of the detoxification enzyme gene oxidation hydrolysis upon exiting the body, the process is rigorous and scientific. The cuticle protein exhibits unique properties and noticeable changes during all kinds of comparisons. The insect cuticle is the first line of defense against external chemi- cals, specifically the proteinaceous and chitinous procuticle . Cuticular protein (CP) genes play an important role in insect development and defense . As such, cuticular proteins have been reported to play crucial roles in the insecticide resistance and tolerance of a variety of insect species, including Myzus persicae , Aphis gossypii , and Plutella xylostella . Our results demonstrated that epidermal protein−related genes were the most numerous and varied among the insecticide−related DEGs, as shown in Table. 4. The downregulation of epidermal protein genes, as in this study, conforms with previous studies that reported the downregulation of several cuticle protein genes in the imidaclo- prid−resistant Myzus persicae . The cuticular changes underlying insecticide resistance involve two main parameters: thickness and composition of the cuticle . More signifi- cantly, cuticular thickening has been more commonly associated with resistance ; but there are recent reports that link cuticular composition to decreased xenobiotic penetra- tion . Therefore, on the basis of the results, it is suggested that cuticular reorganization Agriculture 2023, 13, 235 15 of 19 could play a role in insecticide resistance for D. citri, though the specific roles of the CPs involved in alterations of cuticle structure or composition as specifically related to insec- ticide penetration require further research. Cytochrome P450 is an enzyme with a variety of metabolic functions, and it is in- volved in detoxifying foreign chemicals such as pesticides . The overexpression of the P450 monooxygenase enzyme is the most common mechanism of resistance to beta−cy- permethrin in many insects [47-49]. In aphids, the P450 monooxygenase enzyme also plays an important role in insecticide detoxification and resistance [46,50,51]. In the pre- sent study, different treatments generated different DEGs; among these, the most P450s−related downregulated single genes were generated in the inducible resistant strain treatment and inducible sensitive strain treatment comparison group (17). Most of the P450−related upregulated single genes were generated in non−inducible resistant strain treatment and non−inducible sensitive strain treatment comparison group (6) (most of which were labelled as CYP4 and CYP6 family genes, as presented in Supplementary File S8). Both genes (CYP4 and CYP6) were more associated with pesticides than any other P450 family, suggesting that these genes play an important role in pesticide metabolism and detoxification [52,53]. Therefore, overexpression of these two genes may be related to the resistance and detoxification process of R. padi to beta−cypermethrin. For example, the CYP4 family gene of Aedes albopictus plays an important role in pyrethroid detoxification . In houseflies, the CYP6 family gene was overexpressed in pyrethroid−resistant pop- ulations, which proved to be related to the pyrethroid resistance of houseflies . Simi- larly, transcriptional overexpression of CYP6 family genes, associated with pyrethroid re- sistance, was also found in Helicoverpa zea as collected from the field . Although our results demonstrated that overexpression of CYP4 and CYP6 family genes is associated with the detoxification of beta−cypermethrin in R. padi, further research is required to de- termine whether these P450s can metabolize imidacloprid and chlorpyrifos. UDP−glucosyltransferases (UGTs) are detoxification enzymes that are involved in in- secticide metabolism . The phase Ⅱ enzymes, UGTs act on the toxic by−products of the phase Ⅰ metabolism; UGT−catalyzed conjugation of small lipophilic compounds with sug- ars is an important detoxification and homeostatic function in all living organisms . For example, in thiamethoxam−resistant strains of A. gossypii, UGT was significantly up- regulated relative to the susceptible strains , and UGTs were also implicated in the resistance of Meteorus pulchricornis to commonly used insecticides . In our experiment, we used qRT−PCR to verify the expression levels of UGTs genes (g4263 g15391, g13098, g16174) that were differentially expressed in transcriptome data; it suggests the involve- ment of UGTs in beta−cypermethrin metabolism and transport. In the qRT−PCR assay of the annotated UGT detoxification enzymes, we found that g4263 expression was highly upregulated by the qRT−PCR assay of the UGT detoxification enzyme. UGTs play an im- portant role in a variety of physiological and biochemical processes in insects, including the detoxification of substrates (such as plant allelochemicals and insecticides) [61,62]; our results indicated that UGTs may play a role in the tolerance and detoxification of insecti- cides. ATP−binding cassette (ABC) transporters are a family of transmembrane proteins that mainly rely on ATP to generate energy for substrates. Here, ABC transporters, as the phase Ⅲ detoxification enzymes, play a vital role in the detoxification and defense of var- ious tissues and organs of the insect [63,64]. ABC transporters have been associated with pyrethroids resistance in insects . In this study, insecticide−resistant DEGs were asso- ciated with ABC transporters, and most were downregulated single genes. Thus, the ABC transporter may play an important role in the detoxification process and insecticide toler- ance of R. padi. In addition, trypsin−related genes, which accounted for a large group of DEGs in S. avenae , were upregulated in Caligus rogercresseyi, following exposure to deltamethrin . In our study, trypsin DEGs were also found to be related to pyrethroid resistance, indicating that the trypsin enzyme is vital in the detoxification process and insecticide tolerance of R. padi. Agriculture 2023, 13, 235 16 of 19 Through this experiment, we found a large number of genes related to insecticide resistance, and we should screen out the key genes of resistance for further study in the follow−up experiments. We found that metabolites are the direct defense line of insects against the harm of drugs and can directly respond to the attack of drugs, to enhance insect resistance. Thus, metabolome analysis can assist transcriptome analysis to more ac- curately identify key genes associated with resistance . Therefore, metabolism−related experiments should be included in subsequent experiments to assist in verifying the re- sults of transcriptome analysis. 5. Conclusions This study used RNA−seq to describe genes associated with beta−cypermethrin re- sistance in R. padi. The results indicate that aphid response patterns, during the develop- ment of beta−cypermethrin resistance, are complex as evidenced by changes observed in epidermal structure, binding, metabolic processes, and expression of genes associated with oxidative phosphorylation. Additional research is required to evaluate the specific role of these genes in the response of R. padi to pesticide exposure stress. As carried out in the current study, transcriptome analysis reveals resistance and analyses insecticide−re- lated genes by transcriptome map; and it provides updated ideas regarding the resistance of R. padi. Moreover, understanding how insects develop resistance to insecticides and evaluating the related mechanisms of insecticide tolerance are useful steps for preventing and delaying the development of insecticide resistance in insects. Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture13020235/s1, File S1: Functional items annotated to genes in the GO database and the number of genes enriched for each item, File S2: Genes anno- tated to the KEGG database for functional classification and the number of genes enriched to that function, File S3: DEGs up− or downregulated in response to different treatment, File S4: Compari- son of resistance group and sensitive group, File S5: DEGS GO enrichment in resistant and sensitive groups, File S6: DEGS KEGG enrichment in resistant and sensitive groups, File S7: Screening of different resistance−related genes, File S8: P450 DEGs family classification. Figure S1. Differential expression unigene (DEGs) analysis of beta−cypermethrin−high−resistant and beta−cyperme- thrin−susceptible R. padi. (A) Beta−R−T vs. Beta−R: The R. padi−resistant strains induced by beta−cy- permethrin were compared with the R. padi−resistant strains not induced by beta−cypermethrin, (B) SS−T vs. SS: The R. padi−sensitive strains induced by beta−cypermethrin were compared with the R. padi−sensitive strains not induced by beta−cypermethrin, (C) Beta−R−T vs. SS−T: The R. padi−re- sistant strains induced by beta−cypermethrin were compared with the R. padi−sensitive strains in- duced by beta−cypermethrin, (D) Beta−R vs. SS: The R. padi−resistant strains not induced by beta−cy- permethrin were compared with the R. padi−sensitive strains not induced by beta−cypermethrin. (a) Volcanic map of DEGs. Dots represent individual genes. Red dots represent upregulated genes and green dots represent downregulated genes. The blue dots indicate genes that are not differentially expressed. (b) Heat map analysis of DEGs hierarchical clustering. Red and blue indicate high and low expression in beta−cypermethrin resistant strains, respectively. The colors range from dark red to dark blue, indicating the size of the log2FPKM value. Figure S2. GO annotation classification map of DEGs in SS and beta−R groups before and after beta−cypermethrin induction. (A) Beta−R−T vs. Beta−R: The R. padi−resistant strains induced by beta−cypermethrin were compared with the R. padi−resistant strains not induced by beta−cypermethrin, (B) SS−T vs. SS: The R. padi−sensitive strains induced by beta−cypermethrin were compared with the R. padi−sensitive strains not induced by beta−cypermethrin. GO terms are sorted by p−values from small to large. Author Contributions: Conceptualization, X.Z. and C.S.; methodology, X.Z. and Q.L.; software, Q.L.; formal analysis, Q.L. and X.L.; investigation Q.L., X.L., Y.S., X.T., S.Z., and Y.W.; writing— original draft preparation, Q.L.; writing—review and editing, X.Z., C.S., H.G., and X.L. 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Agriculture – Multidisciplinary Digital Publishing Institute
Published: Jan 18, 2023
Keywords: beta−cypermethrin; Rhopalosiphum padi; transcriptome comparison; resistance mechanism
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