Proteome Based Identification of Potential RNAi Targets for Cotton Mealybug (Phenacoccus Solenopsis Tinsley) Management

doi:10.21203/rs.3.rs-4857038/v1

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Proteome Based Identification of Potential RNAi Targets for Cotton Mealybug (Phenacoccus Solenopsis Tinsley) Management

https://doi.org/10.21203/rs.3.rs-4857038/v1

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Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae), commonly known as cotton mealybug, regarded as an invasive pest worldwide, particularly in the tropics and subtropics. Despite imposing significant economic threat on vast number of agricultural crops a promising, environment-friendly control strategy against this crop pest is lacking. Additionally, molecular aspects of this insect pest are under-studied. This is the pioneer study providing the proteome data of four different developmental stages of cotton mealybug. Differential expression of proteins (DEPs) was studied among six different groups of which, maximum DEPs (550 up-regulated and 1118 down- regulated) were obtained when the quantifiable proteins of Egg + first nymphal were compared with second nymphal instar (FC ≥ 2, P < 0.05). From the generated proteomics data potential target genes were selected for cotton mealybug management. Further, these genes were explored and evaluated for RNAi-based pest control and optimisation of dsRNA delivery system in cotton mealybug. RNAi-based pest management analysis signified that dsRNA of Ferritin-like precursor (Psfer) gene (TRINITY_DN17055_c1_g1_i1) caused a significant amount of ~ 69% mortality followed by dsRNA of probable cytochrome P450 6a14-like (Psp450 6a14) gene (TRINITY_DN47081_c0_g1) and odorant-binding protein 2 precursor (Psobp) gene (TRINITY_DN11547_c0_g1). This investigation proposes potential alternate, green strategy for management of cotton mealybug and related pest population. And this study offers valuable insights into proteome of Cotton mealybug and hemipterans further providing avenues of proteome-based identification of RNAi targets for pest management and crop improvement.

Integrated pest management

Cotton mealybug

dsRNA

RNAi

Industrial Crops

Proteome

Sustainable pest control

Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae), commonly known as cotton mealybug, are small-sized, sap-sucking insect pest species severely impairing plant growth and physiology (Arya et al., 2018). The infested plants display general symptoms of distorted morphology, stunted growth, compromised yield, all this make the cotton mealybug a major pest of economic repercussions (Shivakumara et al.,2022). Additionally, cotton mealybug secrets honeydew which promotes the growth of sooty moulds on the infested plants. These moulds alter the photosynthetic and physiological properties of the plants (Shivakumara et al.,2024). Owing to the loopholes in the international quarantine system of quarantine regulations, P. solenopsis infestation has spread to all the major cotton producing countries (Bakry et al., 2024, Hussain et al., 2024, Di Serio et al., 2021). It is regarded as an invasive pest worldwide, particularly in the tropics and subtropics. Over 200 plant genera (of various industrial and fiber crops) from more than 51 distinct families are infested by mealybug (Tong et al., 2022, Abd El-Ghany et al., 2022, García Morales et al., 2016, Nagrare et al., 2011) and the list is expanding, recently, a research group published first report on P. solenopsis Tinsley, 1898 (Hemiptera: Pseudococcidae) infestation in Greece (Kapantaidaki et al., 2024).

Globally, cotton is one of the most significant crops both, economically and socially. It is often referred to as "white gold" or the "king of fibers" (Priyadarshini et al., 2022). After China, India is the world's second-largest cotton grower (Statista, 2023). The yield quantity and quality are susceptible to various biotic and abiotic factors. The entomological profile of cotton is characterized by a remarkably diverse and complex insect pest spectrum, comprising at least 1326 identified species globally (Kumar et al., 2024). This highlights the crop's susceptibility to a wide range of insect threats, necessitating comprehensive and integrated pest management strategies to mitigate potential yield losses and ensure sustainable production. With the failure of Bt cotton in India, secondary insect pests emerged predominantly sucking pests (Nagrare et al., 2009). Mealybugs alone destroyed between 30–60% of India's cotton harvest (Thangavel & Ganapathy, 2017). Given to the waxy exoskeleton, high reproduction rate, and significant number of alternate hosts cotton mealybug Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae), became highly invasive pest in India, eventually accounting as one of the major pests of cotton.

Given to the waxy exoskeleton, high reproduction rate, and significant number of alternate hosts cotton mealybug Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae), became highly invasive pest in India, eventually accounting as one of the major pests of cotton (Eldesouky et al.,2023). Presently, majority of the pest control methods are chemical pesticide-based, that are ineffective against the sap-sucking category of insect pests as the contact area (probing area) is much less (Waqas et al., 2021). To counter the spread of cotton mealybug population a highly effective pest management approach is required (Nagrare et al., 2020). Even after the availability of toxins like δ-endotoxins isolated from Bacillus thuringiensis, the possibility of applying a transgenic approach for pest control seems less effective against this sap-sucking insect (Chougule et al., 2012). Hence, there is an urgent need of devising alternative environment-friendly techniques for P. solenopsis management. RNA interference (RNAi) has been investigated as a viable method for controlling various insect pest population (Hunter & Wintermantel 2021; Jain et al., 2021; Yang et al., 2022; Finetti et al., 2023). RNAi is a natural process in which small RNA molecules regulate gene expression, via sequence specific degradation of target mRNA or by altering the translation process (Mannsperger et al., 2010). RNAi-based gene expression regulation for pest control requires the selection of potential genes and the functional knowledge of the selected genes. The study of insect omics has been demonstrated to be a promising strategy for identifying and selecting novel and species-specific target molecules. RNAi has been successfully applied for P. solenopsis management using the transcriptome data for target gene selection (Khan et al., 2018; Singh et al., 2019; Arya et al., 2021).

The data acquired from the transcriptome and proteome together provide invaluable resources for a systematic investigation of any organism (Ma et al., 2014). The proteome data can serve as a more potent pool of potential targets for development of bio-rational pesticide. Advances in mass spectrometry-based proteomics have made the investigation of complex organisms a lot easier (Hewes et al., 2001; Tamhane et al., 2021). Proteomics-based analysis has considerably enhanced our knowledge of the molecular mechanisms underlying insect metamorphosis, diapause, embryogenesis, vitellogenesis, and their control (Zhai et al., 2013; Zhnag et al., 2001; Sehrawat et al., 2014). A better perspective on signaling, regulatory, and metabolic networks driving insect-specific processes would be possible by integrating proteomics findings with the other molecular approaches. The above information served as an inspiration for this study. Authors here have established protein isolation protocol for cotton mealybug and proposed the proteomics data of all four developmental stages and followed by identification of promising candidate genes for pest management of cotton mealybug and related insects are the main foci of this study. We have combined and compared proteomics data generated during this study with the transcriptomic data (previously published by our lab; Arya et al., 2018). Further, potential target genes were selected from the omics data for RNAi-based gene expression regulation of the target pest.

2.1 Insect culture

P. solenopsis population was maintained at CSIR-National Botanical Research Institute (Lucknow, India) from a colony started in 2015 on cotton (Gossypium hirsutum) and grown at 28°C temperature and L14:D10 photoperiod. Climate-controlled growth chamber at 28°C, L14:D10 photoperiod, and 60–70% relative humidity (RH) under laboratory conditions were maintained throughout the experiment as followed by Arya et al., 2021.

2.2 Sample Preparation

Four different developmental stages (Egg + first nymphal instar, second nymphal instar, third nymphal instar, and female adult) of P. solenopsis were collected for protein isolation. Insects were washed using 1% Triton X-100. This step was repeated until the waxy coating was completely removed. Washing of the insect was repeated using deionised water, until no frothing was seen in the tube.

2.3 Protein extraction, estimation & digestion

500 mg of insect tissue was finely grinded using liquid nitrogen. The powdered tissue was homogenized in extraction buffer (0.5% SDS, 20 mM EDTA, 10 mM DTT, 100mM Tris-HCl (pH 6.8) and 2 mM PMSF). Incubated at 4°C for 2 hours followed by centrifugation (13,000g, 4°C, 10 mins). The supernatant was transferred to fresh tubes. Cold 10% TCA/Acetone and 5mM β-mercaptoethanol (β-ME) were mixed thoroughly to each tube. This mixture was incubated (at -20°C for 2 hours), centrifuged (at 13,000 g for 5 mins), and the supernatant was discarded (Cilia et al., 2009; Niu et al., 2018). The protein pellet was vigorously disrupted using a glass rod, washed with 5mL of 80% ice-cold acetone, and air-dried. The pellet was stored at − 80°C until further use. Protein pellet was solubilised in rehydration buffer (7M Urea, 2M Thiourea, 2% CHAPS, 100mM DTT) and dialyzed against 20 mM TrisCl, pH 8.0. Protein estimation was performed using BSA method, spectrophotometrically (Supplementary Fig. 1) (He F., 2011). The extracted protein was alkylated using 20 mM IAA, and reduced using 10 mM DTT. These procedures were performed at 25°C for 45 min at 37°C for 1 hour, respectively. Further, trypsin digestion was done by adding 100 mM TEAB (Triethylammonium bicarbonate) (Sigma-Aldrich, Darmstadt, Germany) to the protein sample. This step was repeated till the concentration of urea reached < 2 M. Trypsin was added such that protein:trypsin ratio of 50:1 ( w/w) was maintained and the solution was incubated overnight. An additional digestion was done with a protein-trypsin ratio of 100:1 for 4 hours. The tryptic peptides were desalted using a C18 SPE column (Phenomenex, Torrance, CA, USA) and vacuum-dried.

2.4 LC-MS/MS analysis

The enriched peptides were dissolved in solvent A (0.1% formic acid) and loaded onto a reversed-phase analytical column. The gradient of solvent B (0.1% formic acid in 98% acetonitrile) increased (from 6 to 23% in 26 min, 23 to 35% in 8 min, shot to 80% in 3 min, and held at 80% for 3 min). The experiment was operated on the Acquity Waters UPLC system at a constant flow rate of 400 nL/min. The peptides were then exposed to an NSI source and were analyzed by tandem mass spectrometry (MS/MS) coupled online to the UPLC supplied with 2.0 kV electrospray voltage. (Skipp, et al., 2005, Lagrain, et al., 2013, Possenti, et al., 2013) (Supplementary Fig. 2).

2.5 Protein identification

The RAW data files were analysed using the Mascot search engines of Proteome Discoverer (Thermo Scientific, Version 1.2). The MS/MS search criteria were as follows: Mass tolerance of 20 ppm for MS and 0.25 Da for MS/MS mode, trypsin as the enzyme with one missed cleavage allowed, carbamidomethylation of cysteine as static, and methionine oxidation as dynamic modifications, respectively. High-confidence peptides were used for protein identifications by setting a target false discovery rate (FDR) threshold of 1% at the peptide level. Only unique peptides with high confidence were used for protein identifications. Further, for peptide ion accounting informatics, PLGS was used as it mines deeper into UPLC/MSE data. And identifies more peptides/proteins with better statistical rigor and sequence coverage than conventional LC/MS/MS methods. Details are listed in supplementary Fig. 2.

2.6 Validation of proteomic data via transcriptomic data and Bioinformatics analysis

The in-house generated transcriptome data of all four different developmental stages of cotton mealybug was used as a frame for proteome data validation. For this validation, the identified peptides were searched/queried against our published transcriptome data of cotton mealybug using PLGS search engine. Further, with the obtained results, bioinformatics analysis was performed. agriGO v2.0 database (http://systemsbiology.cau.edu.cn/agriGOv2/) was used to perform GO annotations. KAAS (http://www.genome.jp/kaas-bin/ kaas_main, version 2.0) were used to perform KEGG analysis.

2.7 Differentially expressed proteins (DEPs)

To analyse the differential expression levels of proteins between the major developmental stages, up and down-regulated proteins were estimated between six different groups of developmental stages of P. solenopsis. Peptides with two-fold variation in expression with statistical significance between two or more developmental stages were denoted as differentially expressed.

2.8 Functional enrichment

(a) GO enrichment analysis

As per the GO annotation, proteins were categorized in three groups based on their involvement in biological processes, cellular components, and molecular functions. The enrichment of differentially abundant proteins was performed for each class. The identified proteins were detected using a double-tailed Fisher’s precision test. GO with a P value < 0.05 is considered significant.

(b) Pathway enrichment analysis

According to the KEGG database, the differentially expressed protein was enriched against all identified proteins using a two-tailed Fisher’s exact test. The pathway was regarded as significant with a corrected p-value < 0.05. These enriched pathways were hierarchically classified with the help of the KEGG website.

2.9 Selection and Sequence analysis of the potential target genes

Peptides present among all the four developmental stages of cotton mealybug were shortlisted, their respective gene sequences were retrieved from the in house generated transcriptome data of P. solenopsis. The selected genes were studied for their functional annotation, and those involved in vital processes like development, chitin metabolism, digestion and nutrition, detoxification, immunity, pesticide resistance absorption, hormone homeostasis, energy metabolism, cellular and cytoskeletal components were selected for dsRNA efficacy evaluation. dsRNA fragments were designed for the selected genes. For this, online available dsRNA check software was utilized and similarities between the non-model and closely related species were removed (Naito et al., 2009). NCBI-BLAST (National Centre for Biotechnology Information- Basic Local Alignment Search Tool) was used for nucleotide match search for nearly perfect and short sequences were performed against P. solenopsis sequence and with the and the Homo sapiens genome. GFP was amplified from the binary vector pCAMBIA1303 and, dsGFP was used as negative control. Sequence analysis of the selected genes was done using the blastX tool to blast sequences with a non-redundant database of NCBI to annotate their functions. Data validation was done using the in-house generated transcriptome data. Full length gene sequences of the gene fragments that showed higher mortality reported from previous studies and transcripts showing the highest ubiquitous expression were used for further analysis. A list of the selected fifteen genes is given in the supplementary table 3 (sequences provided in supplementary file).

2.11 Amplification of target genes and dsRNA preparation

The specific dsRNA sequences of selected fifteen target genes are finalized using cDNA of the third nymphal instar of P. solenopsis as a template. The primers designed to amplify target genes had additional T7 promoter sequences (GCGTAATACGACTCACTATAGGGAGA) at the 5’-end and 3’-end. The amplified and purified gene fragments is were further proceeded for dsRNA synthesis expression cassette was created using the MEGA-script kit (Ambion), following manufacturer’s instructions.

2.12 Screening of target gene by feeding bioassay

Insect bioassays were performed with the dsRNA of selected fifteen target genes and the negative control (GFP). The third nymphal instar were used. Experiments were performed as described earlier by Arya et al., 2021.

( a) dsRNA feeding bioassays: soaking method

Tobacco leaves were dipped in 200uL solution of 150 ng/µL of dsRNA (and GFP dsRNA as negative control) and nuclease-free water. Third nymphal instar of cotton mealybug (n = 15) were allowed to feed on dsRNA-soaked Tobacco leaves for 36 hours. A parallel experiment was run for negative control using dsGFP. Mortality data was evaluated 36 hours post-treatment. The experiment was performed in biological triplicates. SPSS 15.0 software was used for performing statistical analysis.

(b) dsRNA feeding bioassays: oral delivery and validation by qRT-PCR

From the soaking method, ten genes that exhibited mortality more than 55% were selected for oral delivery-based bioassay. In the oral delivery method, dsRNA of the selected ten genes were separately mixed with in-house developed and optimized mealybug diet; the third nymphal instars (n = 15) were fed on this mixture. Initially, bioassay was performed 200µL dsRNA of concentration 150ng/µL, the best performing dsRNAs were further evaluated for their potency at low concentrations ranging from 30µg dsRNA/mL of diet, 40µg dsRNA/mL diet and 50µg dsRNA/mL diet. Mortality was recorded after 72 hours of treatment and statistical analysis was performed. The four genes (Ferritin-like precursor (PsFer), Cytochrome P450 6a14-like (PsP450 6a14), Cytochrome P450 303a1 (PsP450 303a1), and Odorant-binding protein 2 precursor (Psobp)) displaying the highest mortality at the lowest time-intervals (low exposure time) were further selected for were selected for downstream processing. Quantitative RT-PCR analysis was performed to observe relative gene expression of PsFer, PsP450 6a14, PsP450 303a1, and PsOBP in both dsRNA treated and non-treated insects (control) after 72 hours of the treatment (Yang et al., 2022). GFP was used as a negative control. RNA isolation was done using TRIzol reagent (Sigma Aldrich, USA) (Arya et al.,2021) at zero hours (untreated) and 72 hours of feeding of 100µg of dsRNA. cDNA was synthesised using the SuperScript III Reverse Transcriptase (Ambion, USA). Using gene specific primers, quantitative RT-PCR was performed. Primer 3.0 program was used for primer synthesis. Expression level estimation of target genes in the treated insects was done using the 2-∆∆CT method. Normalization was done with α-tubulin and β-tubulin. The selection of housekeeping gene for normalization was influenced from the previously published by Arya et al., 2018. The evaluation was done by taking average of three technical replicates per biological duplicate.

2.13 PITGS for in-planta transient gene silencing and qRT-PCR

On the basis of above results, best-performing three dsRNAs; dsPsFer, dsPsP450 6a14, and dsPsOBP were selected for in-planta transient gene silencing analysis via Agrobacterium infiltration as report by Arya et al., 2021. Details of the gene constructs used for Agrobacterium infiltration is provided in the supplementary data. Gene expression down-regulation PsFer, PsP450 6a14, and PsOBP was validated by qRT-PCR. For transient gene silencing analysis RNA isolation and cDNA synthesis were done (as discussed above) of the both treated third nymphal instar insects (fed on infiltrated tobacco leaves for 72 hours) and non-treated third nymphal instar (fed on control tobacco leaves for 72 hours). Analysis was done at two different time points (after 12 and 24 hours of treatment) of dsRNA feeding. The primers for gene-specific portions were synthesized using the Primer 3.0 program. The expression of PsFerritin-like precursor (PsFer), PsCytochrome P450 6a14-like (PsP450 6a14), and, PsOdorant-binding protein 2 precursor (PsOBP) and GFP in third instar mealybug fed on infiltrated tobacco leaves was compared with the third instar mealybug fed on control plants. Values for each developmental stage were taken as an average of three technical replicates of each biological replicate. Statistical analysis was done with using SPSS 16.0.

2.14 Data analysis

All experiments were performed in triplicate. Graph Pad Prism 5.0 software was used for statistical analysis. The mortality data was recorded and subjected to one-way ANOVA using GraphPad Prism 8.0 (San Diego, California, USA) for each bioassay-response. The standard error mean of the three replicates is represented by an error bar. Asterisk on the graph indicates significance (*P < 0.05, **P < 0.01, ***P < 0.001), among different treatments. Student’s t-test was used for statistical comparisons between groups, and P < 0.05 was considered statistically significant.

3.1 LC-MS/MS and identification of differentially expressed proteins

The outline of the steps followed in Nanoscale liquid chromatography coupled to tandem mass spectrometry (nano LC-MS/MS) and bioinformatics analysis is displayed in Fig. 1(a). Differential expression of proteins among different samples of developmental stages of cotton mealybug was observed. Nano-LC-MS/MS technology was utilised and quantifiable proteins obtained from each stage are listed in supplementary table 1. For accessing DEPs six different combinations (Egg + 1st nymphal instar Vs 2nd nymphal instar, Egg + 1st nymphal instar Vs 3rd nymphal instar, Egg + 1st nymphal instar Vs Adult female, 2nd nymphal instar Vs 3rd nymphal instar, 2nd nymphal instar Vs Adult female, 3rd nymphal instar Vs Adult female) were studied. The results of DEPs are represented in Fig. 1 (b), 1(c), and 1(d). Significant difference in the protein expression profile of Egg + first nymphal and second nymphal instar was found pointing towards the involvement of different molting and metamorphosis-related pathways in both developmental stages. The DEPs were categorized on the basis of functions performed. For gene ontology, agriGO v2.0 database was used, and the DEPs were divided into three classes: biological process (BP), cellular component (CC), and molecular function (MF). A total of, 171 biological processes, 110 molecular functions, and 44 cellular components genes were mapped. The biological pathways of the DEPs were explored using KEGG. In KEGG, 3,914 quantifiable DEPs were mapped to 307 different pathways. Among the annotated sequences in KEGG, 15.917% of pathways classified were metabolism-related, majorly involved in carbohydrate metabolism (100 proteins), lipid metabolism (55 proteins), and protein families: metabolism (198), signal transduction (390), cellular processes (258), environmental adaptation (431). A detailed view of the KEGG enrichment is depicted in Fig. 1 (e) and 1(f) and Supplementary Table 2.

3.2 Insect bioassays and qRT-PCR based validation

Potential target genes for inducing RNAi-based mortality in P. solenopsis were identified from the proteome data. The proteome data was rigorously studied and annotation was done using the previously published transcriptomic data of P. solenopsis (Arya et al., 2018). Further, these genes were matched with the Database of Essential Genes (http://www.essentialgene.org/) with the vital genes of T. castaneum (100) and D. melanogaster (339) for RNAi-mediated silencing. The genes were filtered as reported by Thakur et al., 2016, for selecting RNAi targets in insects. Fifteen genes were selected namely, Odorant receptor (ODR), Cytochrome C oxidase (COX), Major Hsp 70(HSP70), Odorant-binding protein 2 precursor (OBP), Importin (IMPRT), Cdc 42 small effector protein (CDC42), probable cytochrome P450 6a14-like (P450 6a14), probable cytochrome P450 303a1-like (P450 303a1), HSP90 cochaperone CDC37-like protein (HSP90), protein Wnt-4 precursor, putative (WNT), chitin binding peritrophin-A, putative (CBP), Ferritin-like precursor (Fer), chitin-binding domain containing protein (CBD), peroxisomal membrane protein PMP22-like (PMP22), and sugar transporter SWEET1-like (SWEET1). These genes were involved in key processes like energy metabolism, reproduction, detoxification, nutrition absorption, defence and others. GFP was selected as negative control. A detailed list of selected fifteen genes is provided in supplementary table 3. dsRNA against vital genes of P. solenopsis were delivered inside the target insect by feeding them on dsRNA (150 ng/mL) soaked Tobacco leaves, mortality was recorded, and graph for percentage mortality was plotted (Fig. 2(a,b)). Maximum mortality was shown by the dsRNAs of PsFer (75%), PsCBD (74%), PsOBP (70%), PsP450 6a14 (67%), and minimum mortality was projected by dsRNAs of Pshsp90 (45%), Pscdc42(34%), Pspmp22 (32%), Psimportin (30%) and GFP (21%). Among these target genes, the ten exhibiting maximum mortality (more than 50%) were proceeded for the downstream evaluation. Soaking the Tobacco leaves with dsRNA is an effective method for initial screening but required dsRNA in large quantities. Hence, another bioassay was optimized with an artificial diet. The dsRNAs against selected ten genes were mixed separately with artificial mealybug diet. Details of these ten selected genes is given in Table 1. The third instar of P. solenopsis was fed on these dsRNA supplemented artificial diet. Mortality data was recorded after 72 hours of treatment and LC₅₀ was calculated (Table 2). Maximum mortality at the lowest concentration was displayed by dsRNA targeting the PsFer gene (11.78 ± 0.00) followed by, PsOBP gene (17.34 ± 2.28), and PsP450 6a14 gene (19.83 ± 0.00). Highest LC₅₀ was shown by dsRNAs targeting Pshsp90 gene (37.54 ± 0.89), Pswnt gene (38.8 ± 4.99), and Pspmp22 gene (39.78 ± 0.70). Based upon the previous observations, the gene PsP450 303a1 was expected to show significant mortality even at low concentrations, surprisingly, the LC₅₀ of PsP450 303a1 (24.03 ± 0.00) was higher than PsP450 6a14 (19.83 ± 0.00) (the other cytochrome variant used in this study). LC₅₀ of dsRNA GFP was high (39.71 ± 0.00) (as per the expectations). The gene GFP is not found in insects; hence, any mortality caused by its dsRNA was minimal and was not due to the downregulation of GFP expression but rather because of unmeasurable and unknown variables having biased influence on the results. Based on LC₅₀ of recorded for all the ten target dsRNAs, it was proposed that PsFer (11.78 ± 0.00), PsOBP (17.34 ± 2.28), PsP450 6a14 (19.83 ± 0.00), and PsP450 303a1 (24.03 ± 0.00) were the four highly potent target genes for inducing lethality in insect pests. The efficacy of selected four genes was further evaluated at three different lower concentrations to find the optimum dsRNA concentration with highest efficacy. Three different concentrations (lower than the previously examined) were used (30µ g dsRNA/mL diet, 40µ g dsRNA/ml diet, and 50µ g dsRNA/mL) at three different time points (after 12 hours, 24 hours and 36 hours of treatment) (Fig. 3(a)). Mortality data and RNAi efficacy data were validated using quantitative RT-PCR and relative gene expression studies were done (Fig. 3(b)). dsRNA of GFP was taken as negative control. The expression of both, PsFer and PsP450 6a14 genes was down regulated by more than five times and PsOBP gene was down-regulated by ~ 3.3 times. However, the transcript levels of gene PsP450 303a1 were nearly unaffected. For downtream analysis, PsFer, PsOBP and PsP450 6a14 were targeted.

Table 1

The above table is showing the details of the genes selected for dsRNA feeding bioassays by oral delivery.
Target gene annotation	Codes used	TRINITY ID	Primer Sequence	Amplicon Size (base pairs)
probable cytochrome P450 6a14-like (Acyrthosiphon pisum)	PsP450 6a14	TRINITY_DN47081_c0_g1	F-ATTTTCAAAGGCTCTGATCG R- AAGATACCTTGGAGATGAAATAT	301bp
probable cytochrome P450 303a1-like (Acyrthosiphon pisum)	PsP450 303a1	TRINITY_DN22608_c0_g1	F- GTGCCTTTATGCTTAGA R- CTTTCCCAGTCTACATTTTG	240bp
odorant-binding protein 2 precursor (Acyrthosiphon pisum)	PsOBP	TRINITY_DN11547_c0_g1	F- ATCCATCACCATTTTCGCA R- AAAGAGCAGAACAAAATAT	252bp
HSP90 cochaperone CDC37-like proteinue (Danaus plexippus)	PsHSP90	TRINITY_DN14858_c0_g1	F- ATGCAGGTAACTGAACTTTA R- CATTTTAATAGGAATTTTAT	371bp
protein Wnt-4 precursor, putative (Pediculus humanus corporis)	PsWNT	TRINITY_DN20502_c0_g1	F- TTTACGACCTCTTTTTCGCTC R- CCACTACCCCCATCGAC	260bp
chitin binding peritrophin-A, putative (Pediculus humanus corporis)	PsCBP	TRINITY_DN9540_c0_g2	F- ATAATAAATCAATATGATTT R- AATGATAATAAATGTTATTT	295bp
Ferritin-like precursor (Acyrthosiphon pisum)	PsFer	TRINITY_DN17055_c1_g1_i1	F-GGTATCGCGCGAGAAATGA R-AGTTCAGATGCCCGACGATT	256bp
chitin-binding domain containing protein (Triatoma infestans)	PsCBD	TRINITY_DN1509_c0_g1	F- ATTAATTGCAAGTTAGTTGTG R- TTTTGCAAAAACTGAAGCCCC	266bp
peroxisomal membrane protein PMP22-like (Acyrthosiphon pisum)	PsPMP22	TRINITY_DN44137_c0_g1	F- GCCAGTAGAATTTATCATTGA R- TCAATGATAAATTCTACTGGC	344bp
sugar transporter SWEET1-like (Acyrthosiphon pisum)	PsSWEET1	TRINITY_DN22636_c0_g1	F- CACCATCGAAACCAGCTTTTAGTC R- GATGCCTTCGGTTTTTCACTGA	325bp
Green Fluorescent Protein (GFP)	GFP	N/A (negative control)	F- TGGCCAACACTTGTCACTACTTT R-TATACATCATGGCCGACAAG	300bp

Table 2

The above table represents LC₅₀ (µg dsRNA/mL diet) of dsRNAs after 72 hours of dsRNA feeding treatment with third instar nymphs of cotton mealybug.
TARGET GENE	LC₅₀ (µg dsRNA/ mL diet)	N	Slope + SE	95% CI	R²
CBD	27.40 ± 2.00	300	0.161 ± 0.031	0.08980 to 0.2393	0.6441
P450 303a1	24.03 ± 0.00	300	0.166 ± 0.022	0.09553 to 0.3268	0.6432
OBP	17.34 ± 2.28	300	0.160 ± 0.04	0.05870 to 0.2433	0.4436
HSP90	37.54 ± 0.89	300	0.116 ± 0.050	0.03303 to 0.2200	0.4543
WNT	38.8 ± 4.99	300	0.121 ± 0.062	0.07481 to 0.4306	0.6004
CBP	36.09 ± 0.32	300	0.180 ± 0.90	0.0273 to 0.21215	0.6143
Fer	11.78 ± 0.00	300	0.156 ± 0.040	0.04981 to 0.2136	0.6122
P450 6a14	19.83 ± 0.00	300	0.118 ± 0.022	0.03373 to 0.2025	0.6941
PMP22	39.78 ± 0.70	300	0.145 ± 0.37	0.05781 to 0.2346	0.5994
SWEET1	33.58 ± 3.20	300	0.128 ± 0.012	0.03833 to 0.2205	0.6376
GFP	39.71 ± 0.10	300	0.142 ± 0.17	0.03313 to 0.2115	0.6446

3.3. in-planta transient gene silencing

From the in-vitro bioassay it was inferred that out of the tested fifteen genes Ferritin-like precursor (PsFer), Cytochrome P450 6a14-like (PsP450 6a14), and, Odorant-binding protein 2 precursor (PsOBP) exhibited maximum mortality with significant down-regulation of target transcript level. Therefore, these three genes were selected for in-planta validation via transient gene silencing. Plant binary vector pBI101 was used for designing Ferritin-like precursor (PsFer), Cytochrome P450 6a14-like (PsP450 6a14), and, Odorant-binding protein 2 precursor (PsOBP), and GFP silencing construct under the control of double enhancer CamV35S promoter as previously reported by our group (Thakur et al., 2014). All four (pFERi, pP450 6a14i, pOBPi and, pGFPi) silencing constructs were confirmed by restriction digestion analysis (Supplementary Fig. 3(a)). Supplementary Fig. 3(b) is showing graphical representation of gene silencing construct maps of all the four selected genes. All four (pFERi, pP450 6a14i, pOBPi, and, pGFPi) silencing constructs were transformed in Agrobacterium tumefaciens LBA4404 and these transformed Agrobacterium cells were finally used for agro-infiltration in Tobacco leaves (Arya et al 2022). Experimental set-up with pGFPi infiltrated tobacco leaves was taken as negative control. Bioassay was performed with a third nymphal instar and mortality data was recorded (Table 3). After 24 hours of treatment pFERi infiltrated plants exhibited maximum mortality of ~ 69% followed by pP450 6a14i, and pOBPi infiltrated plants both showing mortality rate of ~ 58%. The quantitative RT-PCR results showed significant down regulation of respective target genes. In case of pFERi fed insects, after 12 hours the drop in the transcript level was half and after 24 hours the transcript level got reduced to around 5 times. After 24 hours of treatment a significant reduction was seen in the transcript levels of both, pP450 6a14i, and pOBPi fed insects (Fig. 3 (c)).

Table 3

**Insect bioassay of the third instar fed on pFERi, pP450 6a14i, pOBPi and, pGFPi infiltrated leaves.** Mean ± SE was used for calculating mortality percentage and analysis was done with one-way ANOVA (P < 0.05). Means were compared using the DMRT at (P < 0.05) (SPSS software); means superscripted with the same letter within a column are not significantly different.
Time interval	Mortality by pFERi agro-infiltrated leaves	Mortality by pP450 6a14i agro-infiltrated leaves	Mortality by pOBPi agro-infiltrated leaves	Mortality by pGFPi agro-infiltrated leaves (Control)
12 hours	62.56 ± 2.86%^a	55.77 ± 6.86%^c	52.60 ± 5.86%^c	10.16 ± 3.60%
24 hours	68.76 ± 3.11%^a	58.76 ± 3.86%^b	58.09 ± 6.88%^b	15.76 ± 5.86%

Phenacoccus solenopsis Tinsley, a notorious pest in global cotton production, has been primarily managed through chemical pesticide application (Cui et al., 2024). However, the prolonged use of these chemicals poses significant environmental and ecological risks, including the development of insecticide resistance, collateral damage to beneficial organisms, and contamination of water and soil ecosystems (Wang et al., 2024). In response to these concerns, the development of sustainable, eco-friendly control strategies for P. solenopsis population management is a pressing research priority. RNA interference (RNAi) technology has emerged as a promising tool for functional genomics research in insects, enabling the targeted knockdown of essential genes. Specifically, RNAi has been successfully employed to investigate key functional genes in P. solenopsis and other hemipteran insects, offering a potential avenue for the development of novel, environmentally benign pest control methods (Khan et al., 2018; Singh et al., 2019; Arya et al., 2021).

Despite the potential of exogenously applied RNAi biopesticides, their commercialization has been hindered by limitations in the efficacy and consistency of double-stranded RNA (dsRNA) triggers, as well as variability in RNA interference (RNAi) suppression levels (Hunter & Wintermantel 2021). These challenges have necessitated further research into enhancing delivery methodologies for efficient uptake of exogenous dsRNA by plants and insects, with the ultimate goal of optimizing RNAi-based management strategies for pests and pathogens. Investigations are focused on overcoming barriers to dsRNA delivery, such as cellular uptake, systemic transport, and environmental stability, to achieve reliable and robust RNAi-mediated pest control. Apart from delivery system selection of target gene is also an essential step. The selection of target genes for RNAi based insect pest management is majorly done from transcriptomic data. Even though transcriptome offers a detailed overview of global gene expression, proteomic data provides a comprehensive insight into the protein profile of an organism hence, used as a complementary approach for better understanding molecular interactions (Bali et al., 2022). In this research study, proteome of four different developmental stages of P. solenopsis was performed and validated using the in-house produced transcriptome libraries. Further, the vital genes were identified and evaluated for RNAi efficacy. Ultimately, this study provided three promising gene targets for gene silencing-based control of P. solenopsis and related pest species.

This report aims to provide the basis for development of a sustainable and alternative control technique for regulating the cotton mealybug population. The lack of proteome data of the target pest was one of the most significant challenges faced during this investigation. The proteome database generated in this study was used for selection of putative target genes. Different dsRNA delivery methods were optimized and evaluated for cotton mealybug mortality during this study.

Iron is essential for cell viability as it is a co-factor in many cellular processes (Andrews, N. C., 2008). Ferritin has a well reported role in iron absorption, metabolism and homeostasis. Altering the iron metabolism in mosquito can be utilised as a control strategy for regulating fecundity, populations, and transmission rates of mosquito and thereby the vector-borne diseases (Geiser et al., 2019). In Whitefly, salivary ferritin is reported to have role in host exploitation by suppressing the plant defence (Su et al., 2019). Additionally, insect iron metabolism is significantly different from mammalian ferritins. Insect ferritins are heavier mass, their subunits are usually glycosylated and they composed of a signal peptide. The crystal structure of insect ferritin exhibits tetrahedral symmetry and that of mammalian ferritin shows an octahedral symmetry. The primary association of insect ferritins is with the vacuolar system and iron transport. On the contrary the mammalian ferritins, are cytoplasmic are involved in iron storage (Pham & Winzerling.,2010). Hence, targeting ferritin for pest population management could be a promising approach with minimal non-target effects (especially in mammals). From the present study, it is inferred that RNAi-based downregulation of Psfer (TRINITY_DN17055_c1_g1_i1) can induce significant mortality in the target pest. This report proposes a sustainable pest management strategy for controlling the invasive pest of cotton, P. solenopsis. The data and information from this study can further be utilized to deliver substantial outcomes in the field of integrated pest-management strategies.

This report aims to provide a sustainable and alternative control technique for regulating the cotton mealybug population. This study concludes that down-regulation of iron storage, transportation, and iron homeostasis maintaining protein Ferritin-like precursor (PsFer) can be used for controlling cotton mealybug population. This is the pioneer study proposing the protein isolation protocol for cotton mealybug, proteomics data of all four developmental stages. Also, this is the first report targeting Psfer (TRINITY_DN17055_c1_g1_i1) gene for insect pest management in the family Pseudococcidae.

Conflict of Interest

The authors report no conflict of interest.

Credit authorship contribution statement

P.C.V designed and supervised this study. G.S. designed and edited the first draft of the paper. S.R. and G.J. assisted in writing the first draft of the paper. S.R. performed the gene construct preparation. S.P. assisted in conducting experiments. S.S. - Formal analysis, Data curation, Methodology, Conceptualization, Investigation, writing original draft, review and editing. P.C.V- Funding acquisition, verified and reviewed the manuscript.

Acknowledgement

We thank and acknowledge Dr. Manisha Mishra for helping in the data analysis and CSIR- National Botanical Research Institute, India. Institutional Manuscript ID- CSIR-NBRI_MS/2024/02/13.

Availability of data and materials

Other data and materials could be requested from the corresponding author.

Consent for publication

The authors agreed to the publication of the manuscript in this journal.

Ethics approval and consent to participate

This manuscript is an original paper and has not been published in other journals. The authors agreed to keep the copyright rule.

Funding

No funding

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No competing interests reported.

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Proteome Based Identification of Potential RNAi Targets for Cotton Mealybug (Phenacoccus Solenopsis Tinsley) Management

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Abstract

Figures

1. Introduction

2. Materials & Methods

2.1 Insect culture

2.2 Sample Preparation

2.3 Protein extraction, estimation & digestion

2.4 LC-MS/MS analysis

2.5 Protein identification

2.6 Validation of proteomic data via transcriptomic data and Bioinformatics analysis

2.7 Differentially expressed proteins (DEPs)

2.8 Functional enrichment

(a) GO enrichment analysis

(b) Pathway enrichment analysis

2.9 Selection and Sequence analysis of the potential target genes

2.11 Amplification of target genes and dsRNA preparation

2.12 Screening of target gene by feeding bioassay

2.14 Data analysis

3. Results

3.1 LC-MS/MS and identification of differentially expressed proteins

3.2 Insect bioassays and qRT-PCR based validation

4. Discussion

Conclusion

Declarations

References

Additional Declarations

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