The ZcVgR gene is involved in the regulation of reproduction and lifespan in female Zeugodacus cucurbitae (Coquillett) mediated by short-term high temperature

doi:10.21203/rs.3.rs-3342618/v1

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The ZcVgR gene is involved in the regulation of reproduction and lifespan in female Zeugodacus cucurbitae (Coquillett) mediated by short-term high temperature

https://doi.org/10.21203/rs.3.rs-3342618/v1

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Zeugodacus cucurbitae (Coquillett) is an important pest of fruit and vegetable crops. Previous studies have shown that differential expression of the vitellogenin receptor (ZcVgR) gene mediated by short-term high temperature causes a decrease in egg number and lifespan in female Z. cucurbitae. To comprehensively understand the molecular mechanism of the ZcVgRgene in the regulation of reproduction and lifespan, Illumina high-throughput sequencing and Tandem Mass Tags (TMT) quantitative proteomics methods were used to determine the transcriptional and proteomic information of female Z. cucurbitae after silencing the target gene (ZcVgR) by siRNA in this paper. The results showed that after silencing the target gene, the expression of the ZcVgRgene was significantly downregulated at the transcriptional level, there was no significant difference at the protein level, and the correlation between the transcriptome and proteome was low; silencing the ZcVgRgene resulted in the downregulation of vitellogenin-1 (ZcVg1), juvenile hormone epoxide hydrolase (JHEH), troponin C (TnC), heat shock protein 70 (HSP70) and other related genes at the transcriptional level, which reduced egg number and heat tolerance in adult females, leading to reduced reproductive capacity and short lifespan; silencing the ZcVgR gene activated immune-related pathways and inhibited energy metabolism-related pathways at the transcriptional level; activated Glycometabolism and Phagosome pathways at the protein level, and inhibited Phototransduction-fly and Autophagy-animal pathways. The results of this study may provide a theoretical basis for integrated control of Z. cucurbitae during the hot season.

Zeugodacus cucurbitae (Coquillett)

ZcVgR gene

reproduction

lifespan

transcriptome

proteome

Zeugodacus cucurbitae (Coquillett) is one of the most destructive fruit and vegetable pests in tropical and subtropical regions and is extremely damaging to cucurbit crops such as bitter melon (Puri et al., 2022). Female adults pierce the fruit with their ovipositor to lay eggs, which hatch into larvae that feed on the fruit, causing it to rot. It has been reported that Z. cucurbitae can damage different crops by up to 30–100% in different seasons, causing huge losses to the agricultural economy (Hadapad et al., 2019). Temperature is an important climatic factor affecting the growth and development of Z. cucurbitae, and rearing Z. cucurbitae under high temperature conditions reduces its survival and reproductive rates, causing serious effects on its physiological and biochemical functions, development, and reproduction (Du Yao et al., 2007). However, Z. cucurbitae can still maintain high population densities during the hot season, causing persistent damage to melons and vegetables (Mkiga and Mwatawala, 2015). With global climate change, the frequency and intensity of extreme heat events are increasing (Ma Gang and Ma Chunsen, 2016), and Z. cucurbitae are affected by the fact that they tend to experience short-term and fluctuating extreme high temperatures rather than constant high temperatures under natural conditions (Ma et al., 2021), which may elicit a different response than under constant high temperature conditions. High temperatures of 33℃, 37℃, 41℃ and 45℃ were applied to newly emerged adult Z. cucurbitae for 1 h. It was found that 45℃ for 1 h caused a fluctuation in the number of eggs in females, with a high rate of egg malformation and only 61.6% of eggs able to hatch; however, when the ambient temperature was suitable, the surviving offspring were still able to establish a population and continue to infest the plant (Zeng et al., 2022). However, it is not clear how short-term high temperature affects female Z. cucurbitae reproduction at the molecular level, and it is important to study the reproductive mechanism of females in the context of short-term high temperature for integrated control of Z. cucurbitae.

To elucidate the molecular mechanism by which short-term high temperature affects female Z. cucurbitae reproduction, transcriptomic and proteomic analyses of female Z. cucurbitae treated with short-term high temperature revealed that the vitellogenin receptor (ZcVgR) gene was differentially expressed at both the transcript and protein levels (Zeng et al., 2019). VgR specifically binds to vitellogenin (Vg) through endocytosis and transports it to the oocyte to further form vitellin, which provides nutrients for embryo development. VgR is essential for insect reproduction (Tufail and Takeda, 2009). Therefore, the ZcVgR gene was selected as a target gene to investigate the molecular mechanism of female reproduction in Z. cucurbitae under a high temperature background. The relative expression of the ZcVgR gene was measured in newly emerged female Z. cucurbitae treated at 33°C, 37°C, 41°C and 45°C for 1 h. It was concluded that short-term high temperature treatment drove the downregulation of the ZcVgR gene, and the expression of the ZcVgR gene increased progressively with increasing number of days, and it increased rapidly from 5 d to reach the peak at 7 d (Lian et al., 2022b). To investigate the function of the ZcVgR gene in female Z. cucurbitae mediated by short-term high temperature, newly emerged females were treated at 45°C for 1 h, and then injected with siRNA to silence the ZcVgR gene at 5 d of age, and the egg number and lifespan of females were significantly reduced (Lian et al., 2022a), suggesting that the ZcVgR gene is involved in the regulation of reproduction and lifespan in females mediated by short-term high temperature. However, how does the ZcVgR gene regulate short-term high temperature-mediated reproduction and lifespan in female Z. cucurbitae? What are the specific regulatory mechanisms? Which related genes (or pathways) are involved in the regulation? These questions remain unanswered.

In this study, we used Illumina high-throughput sequencing and Tandem Mass Tags (TMT) quantitative proteomics technology to obtain histological information on transcript and protein levels in female Z. cucurbitae after silencing the ZcVgR gene. Transcriptome sequencing is based on the Illumina sequencing platform to examine all mRNAs transcribed from a given tissue or cell at a given time to obtain the gene expression pattern of an organism at different developmental stages and in different physiological states (Wang et al., 2010). With the advantages of high throughput, high sensitivity, and wide range of applications, it has been applied in various fields of biology and plays an important role in understanding the development of organisms and the occurrence of diseases (Wang et al., 2009). Transcriptome assembly of female and male adult Helicoverpa armigera tentacles identified the olfactory gene pool of H. armigera, providing a new idea for green control of H. armigera (Liu et al., 2012). The proteome is defined as the study of the sum of all proteins of an organism, organ or tissue at a given time (Wilkins et al., 1996). TMT quantitative proteomics analysis is an in vitro isotope-labelled relative and absolute quantification technique that uses a variety of isotope reagents to label the N-terminal or lysine side chain motifs of protein peptides, which are analysed in tandem using a high-precision mass spectrometer to obtain the amount of protein expression in a given sample, in order to study the molecular networks that control the life activities of organisms at the protein level (Zhang and Elias, 2017). Using this technique, the molecular weight of proteins can be accurately determined by mass spectrometry analysis, and multiple samples can be analysed simultaneously for high-throughput protein determination. The technique has high sensitivity and a wide dynamic range, allowing for the detection of proteins with large differences in abundance between samples and multiple comparisons between samples (Zhang and Xie, 2019). Xinxin et al. (2020) used TMT quantitative proteomics technology to determine the proteome of overwintering and summer adult Lissorhoptrus oryzophilus and screened for proteins closely associated with overwintering L. oryzophilus. The immune response in the midgut of Plutella xylostella larvae to the Bt8010 strain was analysed using transcriptomics and TMT quantitative proteomics, providing a theoretical basis for better biological control of P. xylostella (Lin et al., 2020). The correlation analysis of transcriptomics and proteomics can not only penetrate the laws and nature of life activities at different levels, but also reveal the mutual regulatory roles between the two. In recent years, transcriptomics and proteomics have been widely used in entomological research (Barah et al., 2015). The combined study of RNA interference, transcriptomics and proteomics technologies can further unravel the functions of insect genes, reveal the genetic mechanisms of insect morphology, physiology, behaviour, development, environmental adaptation, and evolution, etc., and provide a rich theoretical basis for integrated pest management. In this study, Illumina high-throughput sequencing and TMT quantitative proteomics methods were used to perform the transcriptional and proteomic and correlation analyses of female Z. cucurbitae after silencing the ZcVgR gene, and proposed to elucidate the mechanism by which short-term high temperature mediates the involvement of the ZcVgR gene in the regulation of reproduction and lifespan of female Z. cucurbitae, with the expectation that it will provide a reference for integrated control of Z. cucurbitae during the hot season.

2.1. Insect sources and feeding

Z. cucurbitae were collected from the balsam pear field in Nada Town, Danzhou City, Hainan Province, China (109°29' E, 19°30' N) and reared in the laboratory at 25°C ± 1°C, with a relative humidity of 70 ± 5% and a 14:10 h (light: dark) light cycle. The larval and adult diets were prepared as described previously (Zhou, 2016), and the materials needed to prepare the larval and adult diets were purchased from Hainan Qingfeng Biotechnology Co., Ltd. (China).

2.2. Synthesis and design of siRNA

Following a previous study (Lian et al., 2022a), siRNA for the ZcVgR gene was synthesised by Tsingke Biotech (Shanghai, China) with a positive strand of 5' CGAUGUCGAGGAUGUGUUATT 3' and an antisense strand of 5' UAACACAUCCUCGACAUCGTT 3'.

2.3. Temperature treatment and injection of siRNA

A 20 µM siRNA solution was prepared by dissolving 2.5 nmol of dry siRNA powder in 125 µL of Tris-EDTA solution. Using an artificial climate box, newly emerged adult Z. cucurbitae were treated at 25°C and 45°C for 1 h. At the end of the treatment, they were further reared at room temperature (25°C ± 1°C), and when they were 5 d old, the junction of the second and third abdominal segments of the abdominal backplane of the female adult was selected as the injection site, and 1.25 µg (4.5 µL) of siRNA per female was injected using an IM-11-2 pneumatic microinjector (NARISHIGE, Japan), and a control check (CK) was set. At the end of the injection, the females with siRNA and CK were placed in small cages, and the same number of males were paired with females, placed in adult diets, water and thin slices of pumpkin used for oviposition, and kept at room temperature (25°C ± 1°C). After 72 h of injection, 30 females were collected separately from siRNA and CK and divided into two portions of 15 females each (one for transcriptome and one for proteome). 5 females were established as 1 replicate, with each group containing 3 replicates, and then snap frozen in liquid nitrogen and then placed in an ultra-low temperature refrigerator at -80°C for storage and reserve.

2.4. Transcriptome sequencing after silencing the ZcVgR gene

2.4.1. RNA extraction and sequencing

Total RNA was extracted using a Trizol kit (Ambion/Invitrogen, USA), and the integrity and total amount of RNA was accurately determined using a bioanalyzer (2100, Agilent, USA). PolyA-tail mRNA was obtained by Oligo(dT) magnetic bead enrichment, and mRNA fragments were randomly interrupted with divalent cations in Fragmentation Buffer (NEB, USA) (Parkhomchuk et al., 2009). A library was established using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA). Initial quantification was then performed using a Fluorometer (Qubit2.0, Invitrogen, USA) and the libraries were diluted to 1.5 ng/uL. The insert size of the libraries was then checked using a bioanalyzer (2100, Agilent, USA) and the insert size was as expected. After the insert size was as expected, qRT-PCR was performed to accurately quantify the effective concentration of the library (the effective concentration of the library was greater than 2nM) to ensure the quality of the library. The samples were then sequenced on the Illumina NovaSeq 6000 sequencing platform (Illumine, USA).

2.4.2. Data processing

The image data of sequenced fragments measured by high-throughput sequencers were converted to reads using CASAVA base recognition, and then the raw data were filtered to remove reads with adapters, N (N means that the base information could not be determined) and low-quality reads (the number of bases with Qphred < = 20 accounted for more than 50% of the total read length). Q20, Q30 and GC content were calculated for clean data. The Z. cucurbitae genome (ID ASM80634v2) was obtained as a reference genome by accessing NCBI (https://www.ncbi.nlm.nih.gov/), and clean reads were quickly and accurately compared to the reference genome using HISAT2 (2.0.5) (Kim et al., 2015) to obtain the read localisation information on the reference genome (Mortazavi et al., 2008).

2.4.3. Quantification of gene expression levels

The number of reads covered by each gene from the start to the end of the range was counted using featureCounts (subread version 1.5.0-p3), with reference to the positional information of the gene pairs on the reference genome. Reads with a pair quality value of less than 10, reads on non-composite pairs, and reads compared to more than one region of the genome were filtered out. (Liao et al., 2014). The FPKM of each gene was calculated based on the length of the gene, and the number of reads mapped to that gene was determined.

2.4.4. Analysis of Differential genes

After gene expression quantification, raw readcount were normalised, corrected for sequencing depth, and a statistical model was used to calculate the probability of hypothesis testing (p value), which was then corrected for multiple hypothesis testing to obtain the FDR value (false discovery rate, of which padj is a common form) (Anders and Huber, 2010). Using |log2(FoldChange)| >= 1 and padj < = 0.05 as screening criteria, differential expression analyses between the two comparison combinations were performed using DESeq2 (1.20.0) software (Love et al., 2014).

2.4.5. Analysis of Differential gene enrichment

Gene Ontology (GO) (Ashburner et al., 2000) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa and Goto, 2000) of differentially expressed genes were performed using clusterProfiler (3.8.1) software. GO is a comprehensive database describing gene functions, which can be divided into three parts: biological process (BP), cellular component (CC) and molecular function (MF), while KEGG is a comprehensive database integrating information on genome, chemical and system functions. For both GO function enrichment and KEGG pathway enrichment, padj < 0.05 was used as the threshold for significant enrichment.

2.5. Proteome after silencing the ZcVgR gene

2.5.1. Protein extraction and quality control

Total protein was extracted using PASP protein lysate (100 mM ammonium bicarbonate, 8 M urea, pH = 8), DTT, IAM, acetone and proteolysis solution (8 M urea, 100 mM TEAB, pH = 8.5), and then the BSA standard protein solution was prepared using the Bradford Protein Quantification Kit (Beyotime, China) according to the instructions. The absorbance of the standard protein solution was used to draw the standard curve and calculate the protein concentration of the samples to be tested, and the protein purity was determined by SDS-PAGE gel electrophoresis.

2.5.2. TMT mark

The peptide mixture was labelled using TMT® Mass Tagging Kits and Reagents (Thermo Fisher Chemica, USA) according to the manufacturer's instructions, followed by mobile phases A (2% acetonitrile, 98% water, ammonia to pH = 10) and B (98% acetonitrile, 2% water). The fractions were separated on an L-3000 HPLC system using a Waters BEH C18 column (4.6 × 250 mm, 5 µm) (Ross et al., 2004).

2.5.3. LC-MS/MS data acquisition

Mobile phases A (100% water, 0.1% formic acid) and B (80% acetonitrile, 0.1% formic acid) were prepared and chromatograms were separated using an EASY-nLCTM 1200 nanoscale UHPLC system with a home-made pre-column (4.5 cm×75 µm, 3 µm) and a home-made analytical column (25 cm × 150 µm, 1.9 µm). An Orbitrap Exploris 480 mass spectrometer (Thermo Fisher Chemica, USA) and a Nanospray Flex™ (ESI) ion source was used to generate raw mass spectrometry detection data (.raw) (Wu et al., 2012).

2.5.4. Protein identification

The library search software Proteome Discoverer 2.4 (PD2.4, Thermo Fisher Chemica) was used to search the resulting spectra from each run separately. To improve the quality of the analysis results, the PD2.4 software further filtered the search results: Peptide Spectrum Matches (PSMs) with a confidence level of 99% or higher were considered credible PSMs, proteins containing at least one unique peptide were considered credible proteins, only credible spectral peptides and proteins were retained, and FDR validation was performed to remove peptides and proteins with an FDR greater than 1%.

2.5.5. Protein quantification and analysis of differential proteins

Proteome Discoverer first determines the relative quantitation value of each PSM in each sample based on the peak area of the original downlinked spectrum, then corrects the relative quantitation value of the unique peptide based on the quantitation information of all PSM contained in the identified unique peptide, and then corrects the relative quantitation value of each protein based on the quantitation information of all unique peptides contained in each protein.

The ratio of the mean of the quantitative values of all biological replicates of each protein in the comparison sample pairs was taken as the Fold Change (FC) of the difference. A T-test was performed on the relative quantitative values of each protein in the two comparative sample pairs, and the corresponding P value was calculated, When FC ≥ 1.5 and P value ≤ 0.05, the up-regulated expression protein was screened, and when FC ≤ 0.67 and P value ≤ 0.05, the downregulated expression protein was screened.

2.5.6. Analysis of differential proteins enrichment

Differential proteins were analysed for GO functional annotation and KEGG enrichment using interproscan software, both with P value ≤ 0.05 as the threshold for significant enrichment.

2.6. Transcriptome and proteome association analysis

Comparative correlation studies of the transcriptome and proteome can reveal the post-transcriptional regulatory state of gene expression, and correlation analysis of transcriptome and proteome data is performed through analyses such as expression profile regulation statistics and expression profile correlation (Wang et al., 2013; Zeng et al., 2013).

3.1. Transcriptome sequencing after silencing the ZcVgR gene

3.1.1. Summary of transcriptome sequencing data

Sequenced on the Illumina NovaSeq 6000 platform (Illumina, USA), the number of reads obtained for each treatment is shown in Table 1. The clean reads were then compared to the Z. cucurbitae genome, 25°C- siRNA obtained 33009994 (77.26%), 33383781 (78.92%), 38429125 (77.36%) reads, 25°C- CK obtained 42356023 (90.19%), 37587832 (84.41%), 32676853 (75.33%) reads, 45°C- siRNA obtained 32773661 (75.53%), 41186037 (87.81%), 30280609 (71.22%) reads, and 45°C- CK obtained 37803599 (85.43%), 40385695 (90.64%), 38368594 (86.77%) reads.

Table 1 Summary of the sequencing metrics of transcriptome

Samples	Repeats	Total raw reads	Total clean reads	Q20 (%)	Q30 (%)	GC (%)	Total map (Total mapped ratio)
25°C- siRNA	1	42728176	42728176	97.78	93.53	40.26	33009994(77.26%)
	2	43212556	42302242	97.87	93.81	40.78	33383781(78.92%)
	3	50506270	49678102	97.96	93.99	39.94	38429125(77.36%)
25°C- CK	1	48054180	46963052	96.25	89.98	41.39	42356023(90.19%)
	2	46125726	44531204	96.46	90.42	41.75	37587832(84.41%)
	3	44308522	43376432	97.72	93.41	40.19	32676853(75.33%)
45°C- siRNA	1	44381518	43392714	97.25	93.41	40.1	32773661(75.53%)
	2	47494150	46905948	98.07	94.04	42.02	41186037(87.81%)
	3	43255216	42515658	96.95	91.66	39.28	30280609(71.22%)
45°C- CK	1	45438266	44252034	96.21	89.95	41.45	37803599(85.43%)
	2	45483036	44556024	97.42	93.16	41.61	40385695(90.64%)
	3	45529354	44219650	95.93	89.33	41.08	38368594(86.77%)

3.1.2. Analysis of gene expression levels

A violin plot of the gene expression distribution showed that the total amount of gene expression data across samples was basically the same, and the overall expression level of genes was high (Fig. 1). The correlation heatmap between samples showed that the correlation between replicates of each sample was high and the similarity of expression patterns between replicates was high (Fig. 2).

The abscissa is the repeated number of the sample, and the ordinate is log2 (FPKM+1).

The horizontal and vertical coordinates are the squares of the correlation coefficients of each sample.

3.1.3. Analysis of Differential genes

After quantitative analysis of the gene expression levels of the samples, the differentially expressed genes were screened by Padj value <= 0.05 and │log2FC│ >= 1. The results showed that 702 differentially expressed genes were obtained by silencing the ZcVgR gene in the 25°C treatment, of which 57 genes were up-regulated and 645 genes were downregulated (Fig. 3A); 1633 differentially expressed genes were obtained by silencing the ZcVgR gene in the 45°C treatment, of which 774 genes were up-regulated and 859 genes were downregulated (Fig. 3B). After silencing the ZcVgR gene, the ZcVgR gene was significantly downregulated at the transcriptional level, and the expression of the ZcVgR gene of siRNA was highly significantly lower than that of CK in the 25°C treatment (Fig. 4A), and the expression of the ZcVgR gene of siRNA was also significantly lower than that of CK in the 45°C treatment (Fig. 4B).

3.1.4. Differential genes associated with reproduction and lifespan

Silencing the ZcVgR gene in the 25°C treatment, reproduction-related genes such as doublesex (dsx), juvenile hormone epoxide hydrolase (JHEH), octopamine receptor and troponin C (TnC) were significantly downregulated at the transcriptional level; insulin receptor (IR) and catalase genes were the main differentially expressed genes related to lifespan, both of which were downregulated differentially expressed genes (Table 2). More differentially expressed genes were obtained by silencing the ZcVgR gene in the 45°C treatment. The differentially expressed genes related to reproduction were mainly Vg, ecdysone-induced protein 74EF (Eip74EF), Krueppel homolog 1 (Kr-h1) and OTU-related genes, except for ZcVg3 and Eip74EF-related genes which were significantly upregulated, all other genes were downregulated differential genes; lifespan-related genes mainly included heat shock protein (HSP), IR and G protein-coupled receptor (GPCR) genes, among which HSP70, HSP27 and IR genes were downregulated differential genes, and HSP23-like and GPCR Mth2-like were upregulated differential genes (Table 3). VgR is a specific binding receptor for Vg (Tufail and Takeda, 2005), and silencing the ZcVgR gene in the 45°C treatment resulted in significant downregulation of the vitellogenin-1 (ZcVg1) gene and significant upregulation of the ZcVg3 gene, suggesting that ZcVg3 may be the major binding ligand for ZcVgR. Short-term high temperature treatment resulted in the up-regulation of the HSP70 gene in Z. cucurbitae as a means of reducing or avoiding the adverse effects of high temperature stress (Tan et al., 2023). Silencing the ZcVgR gene in the 45°C treatment resulted in a significant downregulation of the HSP70 gene, suggesting that silencing the ZcVgR gene indirectly reduces the heat resistance of female Z. cucurbita.

Table 2 The information of differential genes related to reproduction and lifespan generated by silencing the ZcVgR gene in the 25°C treatment

Gene name	Padj value	Result	Gene description	Gene family
LOC105217947	0.011896449	Down	protein doublesex%2C transcript variant X5	DM
LOC105221309	0.007912653	Down	juvenile hormone epoxide hydrolase 1	EHN
LOC114804176	0.038879653	Down	octopamine receptor beta-2R	7tm_1
LOC105216072	0.013288639	Down	troponin C%2C transcript variant X1	-
LOC105215909	0.036206178	Down	insulin receptor	-
LOC105217045	0.040395994	Down	catalase	Catalase

Table 3 The information of differential genes related to reproduction and lifespan generated by silencing the ZcVgR gene in the 45°C treatment

Gene name	Padj value	Result	Gene description	Gene family
LOC105213778	0.002325837	Down	vitellogenin-1	Lipase
LOC105213843	0.032862325	Up	vitellogenin-1-like	Lipase
Yp3_0	1.34E-13	Up	vitellogenin-3	Lipase
LOC105217947	0.011896449	Down	protein doublesex%2C transcript variant X5	DM
LOC105211529	0.000229115	Up	ecdysone-induced protein 74EF%2C transcript variant X2	-
Kr-h1_3	0.005627728	Down	Krueppel homolog 1%2C transcript variant X2	-
LOC105210596	0.000132832	Down	OTU domain-containing protein 4%2C transcript variant X1	OTU
LOC105215719	9.07E-12	Down	maternal effect protein oskar	-
LOC105216072	7.19E-08	Down	troponin C%2C transcript variant X1	-
LOC105209524	1.05E-08	Down	heat shock 70 kDa protein cognate 2	HSP70
LOC105220844	7.19E-08	Up	heat shock protein 23-like	-
Hsp27_0	2.68E-05	Down	heat shock protein 27	-
LOC105215909	1.25E-24	Down	insulin receptor	-
LOC105210455	8.12E-08	Up	G-protein coupled receptor Mth2-like%2C transcript variant X2	-

3.1.5. GO enrichment analysis of differential genes

GO enrichment analysis of the differentially expressed genes was then performed using Padj value < 0.05 as the threshold for significance enrichment, and filtered to show the top 30 GO terms, or all terms if there were less than 30. The results showed that the upregulated differentially genes obtained by silencing the ZcVgR gene in the 25°C treatment included 10 terms belonging to BP, 3 terms belonging to CC, and 10 terms belonging to MF, among which proteolysis, serine-type endopeptidase activity, serine-type peptidase activity, and serine hydrolase activity had the most upregulated differential genes with 3 genes (Fig. 5A); 10 term of the downregulated differential genes belonged to BP, 10 term belonged to CC, 10 term belonged to MF, with peptidase activity, acting on L-amino, and peptidase activity possessed the most downregulated differential genes with 72 genes (Fig. 5B). The upregulated differential genes obtained by silencing the ZcVgR gene in the 45°C treatment each had 10 terms belonging to BP, CC, and MF, of which peptidase activity had the most upregulated differential genes with 60 genes, followed by peptidase activity, acting on L-amino and proteolysis, with 59 upregulated differential genes (Fig. 5C); the downregulated differential genes each had 10 terms belonging to BP, CC and MF, with oxidoreductase activity having the most downregulated differential genes with 64 genes (Fig. 5D). In summary, silencing the ZcVgR gene resulted in upregulated differential genes mainly enriched in terms related to protein and amino acid hydrolysis, and downregulated differential genes mainly enriched in terms related to peptidase and oxidoreductase activity. GO function enrichment analysis of reproduction-related differentially expressed genes revealed that several differentially expressed genes, including the ZcVgR gene, were enriched in the calcium ion binding term (GO:0005509), which belongs to MF. Silencing the ZcVgR gene in the 25°C treatment resulted in the enrichment of 4 downregulated differentially expressed genes in the calcium ion binding term, including LOC105209324, LOC105211247, Egfl8_0, and LOC105217203 genes, mainly cadherin and EGF_CA family related genes; silencing the ZcVgR gene in the 45°C treatment resulted in 6 downregulated differential genes enriched in the calcium-binding term, including LOC105209324, LOC105211247, Egfl8_0, Srl_2 and LOC105215719 genes, mainly Cadherin and EGF_CA family related genes and TnC related genes.

3.1.6. KEGG pathway enrichment analysis of differential genes

Padj value < 0.05 was used as the threshold for significant enrichment. The differentially expressed genes were analysed by KEGG biochemical pathway analysis, and the 20 most significant KEGG pathways were selected to plot bubble plots, and if there were less than 20, all pathways were plotted. 2 of the upregulated differential genes obtained by silencing the ZcVgR gene in the 25°C treatment was enriched in the Toll and Imd signalling pathway (dme04624) and 1 in the Amino sugar and nucleotide sugar metabolism pathway (dme00520) (Fig. 6A); 16 of the downregulated differential genes were enriched in the Peroxisome pathway (dme04146), 13 in the Fatty acid metabolism pathway (dme01212), and 12 in the Carbon metabolism pathway (dme01200) (Fig. 6B). 14 of the upregulated differential genes obtained by silencing the ZcVgR gene in the 45°C treatment was enriched in the Toll and Imd signalling pathway (dme04624), and 10 in the Lysosome pathway (dme04142) (Fig. 6C); 15 of the downregulated differential genes were enriched in the Carbon metabolism pathway (dme01200), 11 in the Fatty acid metabolism pathway (dme01212), and 10 in the Oxidative phosphorylation pathway (dme00190) (Fig. 6D). The upregulated differential genes were mainly enriched in immune-related pathways, and the downregulated differential genes were mainly enriched in energy metabolism-related pathways.

3.2. Proteomic analysis after silencing the ZcVgR gene

3.2.1. Summary of Protein identification

The proteome after silencing the ZcVgR gene was analysed using the TMT quantitative proteomics method, and the number of peptides and proteins identified is shown in Table 4. 321710 validated profiles were obtained, with 18623 proteins and 257600 peptides identified, and the total number of proteins that could be quantified was 16517. The annotated sequences were compared with the genes in the COG classification (Fig. 7), in which general function prediction contained the highest number of sequences with 990, followed by post-translational modifications, protein transporters and chaperones with 737.

Table 4 Identification statistics of proteins

Total spectra	Matched spectrum	Peptide	Identified protein	ALL
524293	321710	257600	18623	16517

The repeatability Coefficient of Variance (CV) was analysed for each sample, and the cumulative plots of CV values of all proteins in the four samples were plotted and the results showed that the curves were all rapidly increasing, and that all four samples had good repeatability (Fig. 8).

The abscissa is the annotated functional classification, and the ordinate is the number of proteins annotated to the corresponding function.

3.2.2. Analysis of Differential protein

The quantified proteins were subjected to differential protein analysis, and upregulated differential proteins were screened by P value <= 0.05 and FC >= 1.5, and downregulated differential proteins were screened by P value <= 0.05 and FC <= 0.67. The results showed that 49 differential proteins were obtained by silencing the ZcVgR gene in the 25°C treatment, of which 23 were upregulated differential proteins and 26 were downregulated differential proteins (Fig. 9A). Silencing the ZcVgR gene in the 45°C treatment resulted in 1374 differential proteins, of which 600 were upregulated differential proteins and 774 were downregulated differential proteins (Fig. 9B). Silencing the ZcVgR gene in the 25°C (Fig. 10A) and 45°C (Fig. 10B) treatments, the ZcVgR gene was not significantly different from CK at the protein level. After silencing the ZcVgR gene, the ZcVgR gene was significantly downregulated at the transcriptional level, but there was no significant difference at the protein level, which may be due to the complex post-transcriptional processing mechanism and different proteins with different half-lives, leading to inconsistent expression of ZcVgR gene at the protein and mRNA levels (Luo et al., 2019).

3.2.3. Differential proteins associated with reproduction and lifespan

The number of reproductive-related differential proteins obtained by silencing the ZcVgR genes in the 25°C treatment was low, with only 1 upregulated differential protein (calcium-binding protein E63-1 isoform X3) and 1 downregulated differential protein (F-box only protein 32) (Table 5); silencing the ZcVgR gene in the 45°C treatment resulted in more reproductive and lifespan-related differential proteins, and the reproductive-related differential proteins were mainly Vg, OTU and TnC-related proteins, and all of them were upregulated differential proteins except TnC-related proteins which were significantly downregulated, and the lifespan-related proteins were mainly HSP and GPCR-related proteins, and all of them were upregulated differential proteins (Table 6). Among them, the expression trends of ZcVg3, TnC, HSP23-like and GPCR Mth2-like genes were consistent at both transcriptional and protein levels, and silencing the ZcVgR gene resulted in the significant upregulation of the ZcVg3 gene at both transcriptional and protein levels, further suggesting that ZcVg3 may be the main binding ligand of ZcVgR. The TnC gene was involved in the regulation of reproduction, and its downregulation suppressed egg-laying in female Z. cucurbitae; the GPCR Mth2-like gene was significantly upregulated at both the transcriptional and protein levels, and shortened the lifespan of female Z. cucurbitae. Studies have shown that the HSP23 gene is important for diapause in insects (Rinehart and Denlinger, 2000), so the HSP23-like genes may have similar regulatory functions affecting reproduction and lifespan in female Z. cucurbitae.

Table 5 The information of differential protein related to reproduction and lifespan generated by silencing the ZcVgR gene in the 25°C treatment

Protein	Pvalue	Result	Protein description	Gene
XP_028902016.1	0.013621843	Down	calcium-binding protein E63-1 isoform X3	LOC105221050
XP_011188571.1	0.006832998	Up	F-box only protein 32	LOC105216012

Table 6 The information of differential protein related to reproduction and lifespan generated by silencing the ZcVgR gene in the 45°C treatment

Protein	Pvalue	Result	Protein description	Gene
XP_011185138.1	0.006279213	Up	vitellogenin-3	Yp3_0
XP_028897205.1	0.036400419	Up	vitellogenin-1-like	LOC105213843
XP_028897575.1	0.000704419	Up	octopamine receptor beta-2R	LOC105214370
XP_011179957.1	2.46E-05	Up	OTU domain-containing protein 4 isoform X3	LOC105210596
XP_011188650.1	0.001277116	Down	troponin C isoform X1	LOC105216072
XP_011195675.1	0.0214973	Up	heat shock protein 23-like	LOC105220844
XP_011179738.1	0.028081399	Up	G-protein coupled receptor Mth2-like	LOC105210455

3.2.4. GO enrichment analysis of differential proteins

GO enrichment analysis was performed on the differential proteins, and the top 20 GO terms were screened for all three categories, and all terms were shown if there were less than 20. The results showed that among the upregulated differential proteins obtained by silencing the ZcVgR gene in the 25°C treatment, 3 terms belonged to BP, 1 term belonged to CC, and 1 term belonged to MF, of which the extracellular region had the most upregulated differential proteins with 2 proteins (Fig. 11A); among the downregulated differential proteins, 2 terms belonged to BP, no term belonged to CC, and 2 terms belonged to MF, of which sensory perception of taste, transcription corepressor activity, and calcium ion binding possessed the most downregulated differential proteins, all with 2 proteins (Fig. 11B). Among the upregulated differential proteins obtained by silencing the ZcVgR gene in the 45°C treatment, 20 terms belonged to BP, 2 terms belonged to CC, and 15 terms belonged to MF, of which localisation possessed the most upregulated differential proteins with 44 proteins (Fig. 11C); among the downregulated differential proteins, 20 terms belonged to BP, 9 terms belonged to CC, and 17 terms belonged to MF, of which membrane possessed the most downregulated differential proteins with 89 proteins (Fig. 11D).

3.2.5. KEGG pathway enrichment analysis of differential proteins

KEGG enrichment analysis was performed on the differential proteins, and the 20 most significant KEGG pathways were selected to be plotted in bubble maps, and if there were less than 20, all pathways were plotted. 1 upregulated differential protein obtained by silencing the ZcVgR gene in the 25°C treatment was enriched in the Galactose metabolism pathway (map00052) and 1 in the Starch and sucrose metabolism pathway (map00500) (Fig. 12A); 1 of the downregulated differential protein was enriched in the Phototransduction- fly pathway (map04745) (Fig. 12B). 13 of the upregulated differential proteins obtained by silencing the ZcVgR gene in the 45°C treatment was enriched in the Phagosome pathway (map04145) (Fig. 12C); 15 of the downregulated differential proteins were enriched in the Autophagy - animal pathway (map04140) (Fig. 12D). The differential proteins were mainly enriched in immune-related pathways and energy metabolism-related pathways.

3.3. Transcriptome and proteome correlation analysis

Correlation analysis of the transcriptome and proteome after silencing the ZcVgR gene in the 25°C treatment showed that 5 genes and proteins were differentially expressed at both the transcript and protein levels (Fig. 13A), and 168 genes and proteins were differentially expressed at both the transcript and protein levels in the 45°C treatment (Fig. 13B). The Pearson correlation test showed that the correlation between the proteome and the transcriptome was low, and the correlation between mRNAs with the same expression trend and their corresponding proteins was low, with a correlation of 0.04 in the 25°C treatment (Fig. 14A) and a correlation of 0.079 in the 45°C treatment (Fig. 14B), which may be due to translational regulation mechanisms and other reasons.

In recent years, transcriptome sequencing and proteomic analysis have been widely used in pest management. By determining the gene expression patterns of pests in specific states, it helps to excavate the important functional genes for insect growth and development, and provides a theoretical basis for integrated pest management (Chen et al., 2021). In this study, silencing the ZcVgR gene resulted in the downregulation of dsx, JHEH, octopamine receptor beta-2R, TnC, ZcVg1, Kr-h1, and OTU domain-containing protein related genes, and the upregulation of ZcVg3 and Eip74EF gene. Silencing the Bddsx gene in female Bactrocera dorsalis significantly inhibited ovarian development and egg laying in females (Chen et al., 2008). JH is one of the most important hormones regulating insect growth and development, metamorphosis, and reproduction (Zhang et al., 2022), and JHEH is the main JH degrading enzyme (Tusun et al., 2017), Kr-h1 is a key transcription factor that regulates the developmental process of insect metamorphosis and a marker gene for JH signalling pathway function (Wu et al., 2021), thus the JHEH and Kr-h1 genes are important for reproduction of female Z. cucurbitae. In Drosophila melanogaster strains lacking the octopamine receptor beta-2R gene, female oviposition is significantly inhibited (Li et al., 2015). The TnC gene, on the other hand, is mainly involved in muscle contraction, which is important for the normal functioning of the insect reproductive system (Heifetz and Wolfner, 2004), and the use of dsRNA to silence the TnC gene in female Bactrocera oleae, significantly reduced the number of eggs (Gregoriou et al., 2021). Vg and Eip74EF promote oocyte maturation in D. melanogaster (Roy et al., 2022; Schonbaum et al., 2000), and silencing the ZcVgR gene resulted in downregulation of the ZcVg1 gene, and upregulation of the ZcVg3 and Eip74EF genes, suggesting that the ZcVg1, ZcVg3, and Eip74EF genes have an important effect on female reproduction in Z. cucurbitae. In addition, silencing the ZcVgR gene resulted in the downregulation of IR, Hsp27, Hsp70, and catalase genes; HSP is a common protein used by insects to resist adversity, and overexpression of both Hsp70 and Hsp27 genes prolonged the lifespan of D. melanogaster (Seong et al., 2001), whereas the absence of either Hsp70 or Hsp27 genes resulted in a mutant with a shortened lifespan (Morrow et al., 2004), and the overexpression of catalase and IR genes significantly extended the lifespan of D. melanogaster (Orr et al., 1994; Ismail et al., 2015). Downregulation of the ZcVgR gene induced downregulation of IR, Hsp27, Hsp70, and catalase genes, resulting in a shortened lifespan of female Z. cucurbitae. Compared to the transcriptional level, the number of differential proteins related to reproduction and lifespan was low, with ZcVg3, Vg1-like, HSP23-like and GPCR Mth2-like genes significantly up-regulated and TnC genes significantly downregulated at both transcriptional and protein levels. The HSP23 gene is mainly associated with diapause of insects and is upregulated during diapause of pupa overwintering in Sarcophaga crassipalpis (Rinehart et al., 2007), the GPCR Mth2 gene plays a role in drug resistance in Musca domestica (Ma et al., 2020), and the Vg1-like gene is involved in nucleic acid excision during the developmental repair process in Danio rerio (Shen et al., 2017). The Vg1-like, HSP23-like, and GPCR Mth2-like genes of female Z. cucurbitae may have similar functions, but further studies are needed. In conclusion, these genes are directly or indirectly involved in the regulating of reproduction and lifespan of female Z. cucurbitae, and may be potential targets for the control of Z. cucurbitae.

KEGG enrichment analysis can provide valuable insights into biological processes and functions in insects by placing genes in metabolic pathways to understand their functions (Kanehisa et al., 2016). After silencing the ZcVgR gene in this study, KEGG enrichment analysis of differentially expressed genes showed that the upregulated differentially expressed genes were mainly enriched in the Toll and Imd signalling pathway, and the downregulated differentially expressed genes were mainly enriched in the Fatty acid metabolism and Carbon metabolism pathways. The Toll and Imd signaling pathway are an important signalling pathway of the insect immune system, which is responsible for responding to the invasion of pathogens such as bacteria, fungi, and parasites and triggering non-specific defence responses (Shrestha and Kim, 2010). When exogenous dsRNAs or siRNAs are introduced into insects, many defence-related genes (drgs) are induced and activated, resulting in an insect immune response (Yu et al., 2013). In this study, the injection of siRNA to silence the expression of the ZcVgR gene may have induced the insect immune response, which was manifested by the massive upregulation of genes related to the Toll and Imd signaling pathway. Fatty acids not only provide energy for insects, but also meet the needs of embryonic and larval development during reproduction (Chen et al., 2023). The fatty acid metabolism pathway includes several components such as fatty acid synthesis (FAS), β-oxidation, and membrane phospholipid metabolism, and several of these genes are closely related to insect reproduction. Deletion of the FAS gene reduces fecundity in Nilaparvata lugens (Li et al., 2016), and silencing the expression of the acetyl-coenzyme A carboxylase (ACC) gene reduces oviposition and fecundity of Aedes aegypti (Alabaster et al., 2011). The Carbon metabolism pathway involves complex processes such as gluconeogenesis, aerobic respiration, and anaerobic fermentation, which not only provide energy and maintain physiological functions, but also affect the function of the female reproductive system (Min et al., 2006). Sugar metabolic pathways in the adult stage are distinct from those in the larval stage, with adults accumulating more energy and other important metabolites for sexual maturation and oviposition (Bulgarella et al., 2015). Increased glucose concentration also promotes egg laying during insect oviposition (Fraga et al., 2013). Silencing the ZcVgR gene resulted in a decrease in the reproductive capacity of female Z. cucurbitae, leading to the downregulated differential genes mainly focused on two pathways closely related to reproduction (Fatty acid metabolism and Carbon metabolism). After silencing the ZcVgR gene, the KEGG enrichment of differential proteins was largely consistent with the transcription levels, which were mainly enriched in energy metabolism (Galactose metabolism and Phototransduction - fly) and immune-related (Phagosome and Autophagy - animal) pathways (Duan et al. 2017; Strand, 2008; Mulakkal et al. 2014).

In this study, the transcriptome and proteome of female Z. cucurbitae were measured after silencing the ZcVgR gene, and the results showed that the expression of the ZcVgR gene was significantly downregulated at the transcriptional level, while the expression at the protein level was not significantly different from that of CK, and the correlation between the transcriptome and the proteome was lower, which may be attributed to a number of reasons, first of all, the transition from RNA to protein and then to phenotype is a complex and delicate process, and after the formation of mRNA, regulatory activities such as post-transcriptional regulation and epigenetic modification and post-translational regulation will occur, and the mRNA level is somewhat independent of the final protein concentration (Greenbaum et al., 2003); and then, the detection time of mRNA and protein is inconsistent, and the mRNA is already degraded when the protein reaches the peak, or the protein has already been degraded when the mRNA reaches the peak (Lan et al., 2012). Secondly, natural and artificial external noise in mRNA and protein expression experiments is also one of the reasons for the inconsistency of transcriptome and proteome results (Greenbaum et al., 2002); the ZcVgR of Cucurbita pepo is structurally very similar to the D. melanogaster vitellogenin receptor but the ligands recognised by the two are completely unrelated (Yang, 2020), so it is speculated that the ZcVgR of female Z. cucurbitae may have the ability to recognise multiple ligands. Silencing the ZcVgR gene in female Z. cucurbitae resulted in a significant downregulation of the ZcVgR gene at the transcriptional level, while the upstream targets of the other pathways activated the synthesis of ZcVgR, resulting in no significant difference at the protein level. The exact cause and mechanism require further investigation.

Acknowledgements and Funding

The research was supported by the Project of Sanya Yazhou Bay Science and Technology City (SCKJ-JYRC-2023-14), Open Project of Key Laboratory of Tropical Fruit Tree Biology of Hainan Province (HAAS2023PT0206) and Hainan Major Science and Technology Project (ZDKJ201901).

Author contributions

YYL, SYY, SHP, AQW, JJJ, BW, JYC, XJF, QXW, LJZ, XCJ, XF Y, JJL, WW, LZ, MXM and SHZ participated in the study design and analysis the manuscript.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Conflict of interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Alabaster A, Isoe J, Zhou G, Lee A, Murphy A, Day WA, Miesfeld RL (2011) Deficiencies in acetyl-CoA carboxylase and fatty acid synthase 1 differentially affect eggshell formation and blood meal digestion in Aedes aegypti. Insect Biochem Mol Biol, 41: 946-955. https://doi.10.1016/j.ibmb.2011.09.004.
Anders S, & Huber W (2010) Differential expression analysis for sequence count data. Nat Precedings, 1-1. https://doi.10.1038/npre.2010.4282.1.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. Nat Genet, 25: 25-29. https://doi.10.1038/75556.
Barah P, Bones AM (2015) Multidimensional approaches for studying plant defence against insects: from ecology to omics and synthetic biology. J Exp Bot, 66: 479-493. https://doi.10.1093/jxb/eru489.
Bulgarella M, Trewick SA, Godfrey AJR, Sinclair BJ, Morgan-Richards M (2015) Elevational variation in adult body size and growth rate but not in metabolic rate in the tree weta Hemideina crassidens. J Insect Physiol, 75: 30-38. https://doi.10.1016/j.jinsphys.2015.02.012.
Chen J, Guo Y, Huang S, Zhan H, Zhang M, Wang J, Shu Y (2021) Integration of transcriptome and proteome reveals molecular mechanisms underlying stress responses of the cutworm, Spodoptera litura, exposed to different levels of lead (Pb). Chemosphere, 283: 131205. https://doi.10.1016/j.chemosphere.2021.131205.
Chen J, Wu YC, Chen JK, Zhu XJ, Merkler D, Liao CH, Han Q (2023) Elongases of Long-Chain Fatty Acids ELO2 and ELO9 are involved in cuticle formation and function in fecundity in the yellow fever mosquito, Aedes aegypti. Insects, 14: 189. https://doi.10.3390/insects14020189.
Chen SL, Dai SM, Lu KH, Chang C (2008) Female-specific doublesex dsRNA interrupts yolk protein gene expression and reproductive ability in oriental fruit fly, Bactrocera dorsalis (Hendel). Insect Biochem Mol Biol, 38: 155-165. https://doi.10.1016/j.ibmb.2007.10.003.
Du Y, Ma CS, Zhao QH, Ma G, Yang HP (2007) Effects of heat stress on physiological and biochemical mechanisms of insects: A literature review. Acta Ecol Sin, 4: 1565–1572.
Duan Y, Gong ZJ, Wu RH, Miao J, Jiang YL, Li T, Wu XB, Wu YQ (2017) Transcriptome analysis of molecular mechanisms responsible for light-stress response in Mythimna separata (Walker). Sci Rep, 7: 1-11. https://doi.10.1038/srep45188.
Fraga A, Ribeiro L, Lobato M, Santos V, Silva JR, Gomes H, da Cunha Moraes JL, de Souza Menezes J, de Oliveir CJL, Campos E, da Fonseca RN (2013) Glycogen and glucose metabolism are essential for early embryonic development of the red flour beetle Tribolium castaneum. PLoS One, 8: e65125. https://doi.10.1371/journal.pone.0065125.
Greenbaum D, Colangelo C, Williams K, Gerstein, M (2003) Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol, 4: 1-8. https://doi.10.1186/gb-2003-4-9-117.
Greenbaum D, Jansen R, Gerstein M (2002) Analysis of mRNA expression and protein abundance data: an approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts. Bioinformatics, 18: 585-596. https://doi.10.1093/bioinformatics/18.4.585.
Gregoriou ME, Reczko M, Kakani EG, Tsoumani KT, Mathiopoulos KD (2021) Decoding the reproductive system of the olive fruit fly, Bactrocera oleae. Genes, 12: 355. https://doi.3390/genes12030355.
Hadapad AB, Shettigar SK, Hire RS (2019) Bacterial communities in the gut of wild and mass-reared Zeugodacus cucurbitae and Bactrocera dorsalis revealed by metagenomic sequencing. BMC Microbiol, 19: 1-11. https://doi.10.1186/s12866-019-1647-8.
Heifetz Y, Wolfner MF (2004) Mating, seminal fluid components, and sperm cause changes in vesicle release in the Drosophila female reproductive tract. PNAS, 101: 6261-6266. https://doi.10.1073/pnas.0401337101.
Ismail MZBH, Hodges MD, Boylan M, Achall R, Shirras A, Broughton SJ (2015) The Drosophila insulin receptor independently modulates lifespan and locomotor senescence. PLoS One, 10: e0125312. https://doi.10.1371/journal.pone.0125312.
Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 28: 27-30. https://doi.10.1093/nar/28.1.27.
Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M (2016) KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res, 44: D457-D462. https://doi.10.1093/nar/gkv1070.
Kim D. Paggi JM, Park C, Bennett C, Salzberg SL (2019) Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol, 37: 907-915. https://doi.10.1038/s41587-019-0201-4.
Lan P, Li W, Schmidt W (2012) Complementary proteome and transcriptome profiling in phosphate-deficient Arabidopsis roots reveals multiple levels of gene regulation. Mol Cell Proteomics, 11: 1156-1166. https://doi.10.1074/mcp.M112.020461.
Li L, Jiang YP, Li ZY, You LL, Wu YY, Xu B, Ge LQ, Stanley D, Song QS, Wu JC (2016) Jinggangmycin increases fecundity of the brown planthopper, Nilaparvata lugens (Stal) via fatty acid synthase gene expression. J Proteomics, 130: 140-149. https://doi.10.1016/j.jprot.2015.09.022.
Li Y, Fink C, El‐Kholy S, Roeder T (2015) The octopamine receptor octß2R is essential for ovulation and fertilization in the fruit fly Drosophila melanogaster. Arch Insect Biochem Physiol, 88: 168-178. https://doi.10.1002/arch.21211.
Lian Y, Peng S, Jia J, Li J, Wang A, Yang S, Zheng R, Yang X, Zhou S (2022a) Function of Vitellogenin receptor gene in reproductive regulation of Zeugodacus cucurbitae (Coquillett) after short-term high-temperature treatment. Front Physiol, 2126. https://doi.10.3389/fphys.2022.995004.
Lian Y, Wang A, Yang H, Li J, Peng S, Zhou S (2022b) Dynamic expression analysis of Vitelloin‐related genes in Zeugodacus cucurbitae (Coquillett) driven by short‐term high‐temperature stress. Arch Insect Biochem Physiol, 109: e21865. https://doi.10.1002/arch.21865.
Liao Y, Smyth GK, Shi W (2014) FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 3: 923-930. https://doi.10.1093/bioinformatics/btt656.
Lin J, Yu XQ, Wang Q, Tao X, Li J, Zhang S, Xia X, You M (2020) Immune responses to Bacillus thuringiensis in the midgut of the diamondback moth, Plutella xylostella. Dev Comp Immunol, 107: 103661. https://doi.10.1016/j.dci.2020.103661.
Liu Y, Gu S, Zhang Y, Guo Y, Wang G (2012) Candidate olfaction genes identified within the Helicoverpa armigera antennal transcriptome. PLoS One, 7: e48260. https://doi.10.1371/journal.pone.0048260.
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 15: 1-21. https://doi.10.1186/s13059-014-0550-8.
Luo G, Zhao L, Xu X, Qin Y, Huang L, Su Y, Zheng W, Yan Q (2019) Integrated dual RNA-seq and dual iTRAQ of infected tissue reveals the functions of a diguanylate cyclase gene of Pseudomonas plecoglossicida in host-pathogen interactions with Epinephelus coioides. Fish Shellfish Immunol, 95: 481-490. https://doi.10.1016/j.fsi.2019.11.008.
Ma CS, Ma G, Pincebourde S (2021) Survive a warming climate: Insect responses to extreme high temperatures. Annu Rev Entomol, 66: 163–184. https://doi.10.1146/annurev-ento-041520-074454.
Ma G, Ma CS (2016) The impacts of extreme high temperature on insect populations under climate change: A review. Sci Sin Vitae, 46: 556–564.
Ma Z, Zhang Y, You C, Zeng X, Gao X (2020) The role of G protein‐coupled receptor‐related genes in cytochrome P450‐mediated resistance of the house fly, Musca domestica (Diptera: Muscidae), to imidacloprid. Insect Mol Biol, 29: 92-103. https://doi.10.1111/imb.12615.
Min KJ, Hogan MF, Tatar M, O’Brien DM (2006) Resource allocation to reproduction and soma in Drosophila: a stable isotope analysis of carbon from dietary sugar. J Insect Physiol, 52: 763-770. https://doi.10.1016/j.jinsphys.2006.04.004.
Mkiga AM, Mwatawala MW (2015) Developmental biology of Zeugodacus cucurbitae (Diptera: Tephritidae) in three cucurbitaceous hosts at different temperature regimes. J Insect Sci, 15: 160. https://doi.10.1093/jisesa/iev141.
Morrow G, Battistini S, Zhang P, Tanguay RM (2004) Decreased lifespan in the absence of expression of the mitochondrial small heat shock protein Hsp22 in Drosophila. J Biol Chem, 279: 43382-43385. https://doi.10.1074/jbc.C400357200.
Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods, 5: 621-628. https://doi.10.1038/nmeth.1226.
Mulakkal NC, Nagy P, Takats S, Tusco R, Juhász G, Nezis IP (2014) Autophagy in Drosophila: from historical studies to current knowledge. BioMed Res Int, 2014. https://doi.10.1155/2014/273473.
Orr WC, Sohal RS (1994) Extension of life-span by overexpression of superoxide dismutase and catalase in Drosophila melanogaster. Science, 263: 1128-1130. https://doi.10.1126/science.8108730.
Puri S, Singh S, Sohal SK (2022) Inhibitory effect of chrysin on growth, development and oviposition behaviour of melon fruit fly, Zeugodacus cucurbitae (Coquillett) (Diptera: Tephritidae). Phytoparasitica, 50: 151-162. https://doi. 10.1007/s12600-021-00940-w.
Rinehart JP, Li A, Yocum GD, Robich RM, Hayward SA, Denlinger DL (2007) Up-regulation of heat shock proteins is essential for cold survival during insect diapause. PNAS, 104: 11130-11137. https://doi.10.1073/pnas.0703538104.
Rinehart JP, Denlinger DL (2000) Heat‐shock protein 90 is down‐regulated during pupal diapause in the flesh fly, Sarcophaga crassipalpis, but remains responsive to thermal stress. Insect Mol Biol, 9: 641-645. https://doi.10.1046/j.1365-2583.2000.00230.x.
Ross PL, Huang YN, Marchese JN, Williamson B, Parker K, Hattan S, Khainovski N, Pillai S, Dey S, Daniels S, Purkayastha S, Juhasz P, Martin P, Michael S, Bartlet-Jones M, He F, Jacobson A, Pappin DJ (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics, 3: 1154-1169. https://doi.10.1074/mcp.M400129-MCP200.
Roy S, Saha TT, Ha J, Banerjee R, Aksoy E, Kulkarni A, Raikhel AS (2022) Direct and indirect gene repression by the ecdysone cascade during mosquito reproductive cycle. PNAS, 119: e2116787119. https://doi.10.1073/pnas.2116787119.
Schonbaum CP, Perrino JJ, Mahowald AP (2000) Regulation of the vitellogenin receptor during Drosophila melanogaster oogenesis. Mol Biol Cell, 11: 511-521. https://doi.10.1091/mbc.11.2.511.
Seong KH, Ogashiwa T, Matsuo T, Fuyama Y, Aigaki T (2001) Application of the gene search system to screen for longevity genes in Drosophila. Biogerontology, 2: 209-217. https://doi.10.1023/A:1011517325711.
Shen YC, Hsu T, Ling LB, You WC, Liu CW (2017) Identification of low-molecular-weight vitellogenin 1 (Vg1)-like proteins as nucleotide excision repair (NER) factors in developing zebrafish (Danio rerio) using a transcription-based DNA repair assay. Fish Physiol Biochem., 43: 663-676. https://doi.10.1007/s10695-016-0321-4.
Shrestha S, Kim Y (2010) Activation of immune-associated phospholipase A2 is functionally linked to Toll/Imd signal pathways in the red flour beetle, Tribolium castaneum. Dev Comp Immunol, 34: 530-537. https://doi.10.1016/j.dci.2009.12.013.
Strand MR (2008) The insect cellular immune response. Insect Sci, 15: 1-14. https://doi.10.1111/j.1744-7917.2008.00183.x.
Tan SY, Feng K, Zhai XD, Wang JJ, Wei D (2023) Expression and function analysis of two heat shock protein 70 genes (Hsp70s) involved in different high-temperature stresses in Zeugodacus cucurbitae (Diptera: Tephritidae). Entomol Gen, 481-490. https://doi.10.1127/entomologia/2023/1845.
Tufail M, Takeda M (2005) Molecular cloning, characterization and regulation of the cockroach vitellogenin receptor during oogenesis. Insect Mol Biol, 14: 389-401. https://doi.10.1111/j.1365-2583.2005.00570.x.
Tufail M, Takeda M (2009) Insect vitellogenin/lipophorin receptors: molecular structures, role in oogenesis, and regulatory mechanisms. J Insect Physiol, 55: 88-104. https://doi.10.1016/j.jinsphys.2008.11.007.
Tusun A, Li M, Liang X, Yang T, Yang B, Wang G (2017) Juvenile hormone epoxide hydrolase: A promising target for hemipteran pest management. Sci Rep, 7: 789. https://doi.10.1038/s41598-017-00907-0.
Wang J, Mei H, Zheng C, Qian H, Cui C, Fu Y, Su J, Liu Z, Yu Z, He J (2013) The metabolic regulation of sporulation and parasporal crystal formation in Bacillus thuringiensis revealed by transcriptomics and proteomics. Mol Cell Proteomics, 12: 1363-1376. https://doi.10.1074/mcp.M112.023986.
Wang Z, Fang B, Chen J, Zhang X, Luo Z, Huang L, Chen X, Li Y (2010) De novo assembly and characterization of root transcriptome using Illumina paired-end sequencing and development of cSSR markers in sweetpotato (Ipomoea batatas). BMC Genom, 11: 1-14. https://doi.10.1186/1471-2164-11-726.
Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomic. Nat Rev Genet, 10: 57-63. https://doi.10.1038/nrg2484.
Wilkins MR, Pasquali C, Appel RD, Ou K, Golaz O, Sanchez JC, Yan JX, Gooley AA, Hughes G, Humphery-Smith I, Williams KL, Hochstrasser DF (1996) From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and arnino acid analysis. Bio/Technology, 14: 61-65. https://doi.10.1038/nbt0196-61.
Wu J, Xie X, Liu Y, He J, Benitez R, Buckanovich RJ, Lubman DM (2012) Identification and confirmation of differentially expressed fucosylated glycoproteins in the serum of ovarian cancer patients using a lectin array and LC–MS/MS. J Proteome Res, 11: 4541-4552. https://doi.10.1021/pr300330z.
Wu Z, Yang L, Li H, Zhou S (2021) Krüppel-homolog 1 exerts anti-metamorphic and vitellogenic functions in insects via phosphorylation-mediated recruitment of specific cofactors. BMC Biol, 19: 1-14. https://doi.10.1186/s12915-021-01157-3.
Yang SS, Tian LH, Li X, Bai SF (2020) Advances in Parasitic Wasp Vitellogenins and Vitellogenin Receptors. Entomology of central China, 16: 44-55.
Yu N, Christiaens O, Liu J, Niu J, Cappelle K, Caccia S, Huvenne H, Smagghe G (2013) Delivery of dsRNA for RNAi in insects: an overview and future directions. Insect Sci, 20: 4-14. https://doi.10.1111/j.1744-7917.2012.01534.x.
Zeng B, Lian Y, Jia J, Liu Y, Wang A, Yang H, Li J, Yang S, Peng S, Zhou S (2022) Multigenerational effects of short-term high temperature on the development and reproduction of the Zeugodacus cucurbitae (Coquillett, 1899). Agriculture, 12: 954. https://doi.10.3390/agriculture12070954.
Zeng B, Zhu W, Fu Y, Zhou S (2019) Response Mechanism of oviposition and relevant protein expression of Bactrocera cucurbitae (Coquillet) to short-term high-temperature conditions. Neotrop Entomol, 48: 197-206. https://doi.10.1007/s13744-018-0638-z.
Zeng J, Liu Y, Liu W, Liu X, Liu F, Huang P, Zhu P, Chen J, Shi M, Guo F, Cheng P, Zeng J, Liao Y, Gong J, Zhang H, Wang D, Guo A, Xiong X (2013) Integration of transcriptome, proteome and metabolism data reveals the alkaloids biosynthesis in Macleaya cordata and Macleaya microcarpa. PLoS One, 8: e53409. https://doi.10.1371/journal.pone.0053409.
Zhang L, Elias JE (2017) Relative protein quantification using tandem mass tag mass spectrometry. Proteomics: Methods and Protocols, 155: 185-198. https://doi.10.1007/978-1-4939-6747-6_14.
Zhang X, Xie J (2019) Analysis of proteins associated with quality deterioration of grouper fillets based on TMT quantitative proteomics during refrigerated storage. Molecules, 24: 2641. https://doi.10.3390/molecules24142641.
Zhang X, Li S, Liu S (2022) Juvenile hormone studies in Drosophila melanogaster. Front Physiol, 12: 785320. https://doi.10.3389/fphys.2021.785320.
Zhang X, Yang S, Zhang X, Wang S, Zhang J, Xi J (2020) TMT-Based quantitative proteomic profiling of overwintering Lissorhoptrus oryzophilus. Front Physiol, 10: 1623. https://doi.10.3389/fphys.2019.01623.
Zhou SH (2016) Studies on the responses of Bactrocera Cucurbitae to high temperature stress and its molecular basis. PhD Thesis, Hainan University, Hainan, China.

No competing interests reported.

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The ZcVgR gene is involved in the regulation of reproduction and lifespan in female Zeugodacus cucurbitae (Coquillett) mediated by short-term high temperature

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Insect sources and feeding

2.2. Synthesis and design of siRNA

2.3. Temperature treatment and injection of siRNA

2.4. Transcriptome sequencing after silencing the ZcVgR gene

2.4.1. RNA extraction and sequencing

2.4.2. Data processing

2.4.3. Quantification of gene expression levels

2.4.4. Analysis of Differential genes

2.4.5. Analysis of Differential gene enrichment

2.5. Proteome after silencing the ZcVgR gene

2.5.1. Protein extraction and quality control

2.5.2. TMT mark

2.5.3. LC-MS/MS data acquisition

2.5.4. Protein identification

2.5.5. Protein quantification and analysis of differential proteins

2.5.6. Analysis of differential proteins enrichment

2.6. Transcriptome and proteome association analysis

3. Results

4. Discussion

Declarations

References

Additional Declarations

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