Introduction
Bombyx mori, an important economic insect species, has been domesticated for silk production for more than 5000 years (Xia et al., Reference Xia, Wang, Zhou, Li, Fan, Cheng, Cheng and Qin2009). Besides its economic benefits, it also serves as a lepidopteran model system (Cheng et al., Reference Cheng, Li, Chen, Wang, Mao, Li, Hu and Li2019). In sericulture, the domesticated silkworms are susceptible to diseases caused by numerous factors such as viruses, bacteria, and pests, leading to severe yield losses (Xu et al., Reference Xu, Qian, Sun, Liu, Lin, Li, Li, Zhang and Zhao2019a). Among those factors, the dipteran tachinid parasitoid is one of the most common causes of reducing sericultural income (Khanikor and Bora, Reference Khanikor and Bora2022). To date, no effective and environmental friendly strategies have been developed to control this pest. Therefore, breeding B. mori strains with high resistance to tachinid parasitoids or developing effective strategies for parasitoid control is essential in sericulture. To achieve this goal, some of the current researches have addressed how tachinid parasitoids infect and manipulate the host physiology and immunity, and how the host responds to infection (Makwana et al., Reference Makwana, Pradeep, Hungund, Ponnuvel and Trivedy2017, Reference Makwana, Hungund and Pradeep2023).
The dipteran Tachinidae are the second largest group of parasitoids in diversity and ecological importance, exceeded only by the parasitic Hymenoptera (Wang et al., Reference Wang, Wang, Chen-Nuo, Zufan, Zhang, Wang, Zhu, Shen, Shen and Tang2022). The female adults lay eggs on the host cuticle; the hatched larvae penetrate into the host haemocoel, and complete larval stage inside the host (Dindo and Nakamura, Reference Dindo and Nakamura2018). These tachinid parasitoids can attack larval Lepidoptera and thus are of great importance in biological control of lepidopteran pests (Nakamura et al., Reference Nakamura, Ichiki, Kainoh, Wajnberg and Colazza2013). However, few species are notorious parasites of silkworms. For example, as pests, Exorista sorbillans (Diptera, Tachinidae) and Blepharipa zebina (Diptera, Tachinidae) frequently parasitise 5th-instar silkworms, causing silkworm maggot disease (Xu et al., Reference Xu, Zhang, Gao, Wu, Qian, Li and Xu2019b). As natural enemies, they attack a variety of pest herbivores, mainly the order Lepidoptera, such as Hyphantria cunea (Lepidoptera: Lasiocampidae), Dendrolimus punctatus (Lepidoptera: Arctiidae) and Spodoptera frugiperda (Lepidoptera: Noctuidae) (Chai et al., Reference Chai, He, Jiang, Wu, Pan, Hu and Ruan2000; Sullivan and Ozman-Sullivan, Reference Sullivan and Ozman-Sullivan2012; Sharanabasappa et al., Reference Sharanabasappa, Kalleshwaraswamy, Poorani, Maruthi, Pavithra and Diraviam2019). Compared to the highly host specific hymenopteran parasitoids, the dipteran Tachinidae have wider host ranges (Stireman et al., Reference Stireman, O'Hara and Wood2006). Thus, for biological control of pest Lepidoptera or others such as hemipterans, the Tachinidae can be a complementary natural enemy resource in biocontrol programmes (Elkinton et al., Reference Elkinton, Boettner and Broadley2021). To increase their effectiveness as biological control agents, mass rearing of Tachinidae with artificial diets is necessary (Dindo and Grenier, Reference Dindo, Grenier, Morales-Ramos, Rojas and Shapiro-Ilan2014). Since the overall rearing efficiency of in vitro rearing methods of Tachinidae is actually low, improved diet formulations are desired (Dindo et al., Reference Dindo, Marchetti and Baronio2007, Reference Dindo, Rezaei and De Clercq2019, Reference Dindo, Modesto, Rossi, Di Vito, Burgio, Barbanti and Mattarelli2021). Generally, the tachinid parasitoid must manipulate host metabolism to meet its nutritional requirements. For example, E. sorbillans and Exorista japonica (Diptera: Tachinidae) can modulate host basal metabolism, hence, addressing the modulation mechanism will help determine the nutritional demands of these parasitoids, and further improve in vitro mass production of tachinid parasitoids for biocontrol (Xu et al., Reference Xu, Zhang, Gao, Wu, Qian, Li and Xu2019b; Dai et al., Reference Dai, Yang, Liu, Gu, Li, Li and Wei2022a).
Gene expression analyses are important for characterising gene function and the molecular mechanisms that regulate host-parasitoid interactions (Etebari et al., Reference Etebari, Palfreyman, Schlipalius, Nielsen, Glatz and Asgari2011). Reverse-transcription quantitative real-time polymerase chain reaction (RT-qPCR) is commonly used for quantifying gene expression. The accuracy of RT-qPCR data is highly dependent on the appropriate reference genes, also named housekeeping genes that are stably expressed in a species under different experimental conditions (Wang et al., Reference Wang, Chen, Zhang, Bai, Yan, Qin and Xia2018). Although the suitable reference genes have been studied in B. mori under diverse experimental conditions, those reference genes are not stably expressed in all conditions. For example, glyceraldehyde-3-phosphate dehydrogenase is the best reference gene in BmE cells under B. mori nucleopolyhedrovirus (BmNPV) infection (Guo et al., Reference Guo, Jiang and Xia2016). In contrast, in the fifth-instar B. mori infected with BmNPV, Tua1 exhibited the most stable expression (Nie et al., Reference Nie, Lü, Chen, Wang, Meng, Lu, Dong and Chen2017). Given this, reference genes are essential to be evaluated in B. mori under the parasitic stress of tachinid parasitoids.
In this study, we screened several reference genes in B. mori following E. sorbillans parasitism. The expression stability of 13 traditional used housekeeping genes in insects, actin A1 (A1), actin A3 (A3), α-tubulin (Tua1), β-tubulin (Tub1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), eukaryotic translation initiation factor 4A (Eif4a), 28S large subunit ribosomal RNA gene (28S), ribosomal protein L32 (RPL32), ribosomal protein L3 (RPL3), ribosomal protein L40 (RPL40), TATA-box-binding protein (TBP), elongation factor 1α (EF1a), and elongation factor 1γ (EF1g), was evaluated in different tissues of 5th-instar B. mori under the condition of parasitism using five commonly used analysing tools (ΔCT, geNorm, NormFinder, BestKeeper, and RefFinder). This work provides reliable reference genes for expanding further understanding of the associations between the dipteran parasitoid E. sorbillans and its lepidopteran host B. mori, which offers a basis for dealing with pest problems in sericulture or enhancing biocontrol activity with dipteran parasitoids.
Materials and methods
Insect rearing
The larvae of B. mori (Youshi No.1 strain) were reared with mulberry leaves under the conditions of 25 ± 2°C, 70% ± 5% of relative humidity, and 14 h: 10 h light/dark cycle. The laboratory colonies of adult E. sorbillans were fed with 20% honey solution and kept in insect cages. We used the 5th-instar silkworms as the host for rearing larval parasitoids.
B. mori parasitisation treatment
We used the silkworms on day 1 at 5th instar for the parasitisation treatment. The larval silkworms were exposed to mated female E. sorbillans adults. Once the female E. sorbillans exhibited a featured oviposition behaviour, the hosts were collected immediately to ensure that only one egg was laid on the body surface of each host. Both control and treatment groups were reared with sliced mulberry leaves in transparent plastic boxes. At 3 days after egg-laying, E. sorbillans larva successfully invaded the host, and a black-marked respiratory funnel on silkworm epidermis was observed (Dai et al., Reference Dai, Ye, Jiang, Feng, Zhu, Sun, Li, Wei and Li2022b). Thirty silkworms at 1 day after parasitism were randomly selected and divided into three biological replicates (ten individuals each). The silkworms were dissected on ice, the head, epidermis, prothoracic gland, silk gland, midgut, fat body, haemolymph, Malpighian tubules, ovary and testis specimens were collected for exploring the expression of reference and target genes in different tissues.
Total RNA extraction and cDNA synthesis
Total RNAs of all tissue samples were extracted using Trizol reagent (Takara, CN). Genomic DNA contamination was removed with DNase treatment. The RNA was quantified by NanoDrop-2000 spectrophotometer (Thermo Fisher Scientific, USA). The cDNA for each tissue sample was synthesised from 2 μg of total RNA. The M-MLV reverse transcriptase and an oligo (dT) primer (Takara) were used to complete first-strand cDNA synthesis (Hu et al., Reference Hu, Li, Xu, Ni, Wang, Tian, Li, Shen and Li2016).
Primer design and RT-qPCR analysis
The sequences of each reference gene were obtained from B. mori genome database (SilkDB3.0, https://silkdb.bioinfotoolkits.net/main/species-info/-1). The primers for RT-qPCR were designed using NCBI Primer-BLAST. The amplicon sequences were provided in Supplementary file 1. The quantification was performed using the Viia 7 real-time PCR system (Applied Biosystems, USA) in a reaction volume of 20 μl per sample with 2 μl of cDNA template, 6.8 μl of ddH2O, 0.4 μl of each specific primer (10 μM), 10 μl of TB Green Premix Taq and 0.4 μL of ROX Reference Dye (Takara). The amplification cycle was as follows: 95°C for 1 min, 45 cycles of 95°C for 5 s, 55°C for 10 s, and 72°C for 10 s. Relative standard curves were created for each primer pair with five serial dilutions of cDNA (1, 1/10, 1/100, 1/1000, and 1/10000). The amplification efficiencies (E) were calculated based on the equation: E = (10[−1/slope] − 1) × 100 (Radonić et al., Reference Radonić, Thulke, Mackay, Landt, Siegert and Nitsche2004). Three technical replicates were used for each cDNA sample.
Analysis of the gene expression stability
The geNorm (Vandesompele et al., Reference Vandesompele, Preter, Pattyn, Poppe, Roy, Paepe and Speleman2002), NormFinder (Andersen et al., Reference Andersen, Jensen and Orntoft2004), BestKeeper (Pfaffl et al., Reference Pfaffl, Tichopad, Prgomet and Neuvians2004), and ΔCt algorithms (Silver et al., Reference Silver, Best, Jiang and Thein2006) were applied to assess the expression stability of each candidate reference gene. To give a comprehensive ranking of these genes, RefFinder was used to integrate the analysis from the above four programs by a web-based tool (Xie et al., Reference Xie, Xiao, Chen, Xu and Zhang2012). The geNorm algorithm was used to determine the optimal number of reference genes for standardisation (Zhang et al., Reference Zhang, Zhang, Ren, Liu, Zhou and Yang2022). The pairwise variation values (V n/V n + 1) obtained from geNorm analyses used 0.15 as the default value, the value of V n/V n + 1 < 0.15 suggested that n is the suitable number of stable reference genes for data normalisation.
Normalisation analysis using the reference genes
Antimicrobial peptides (AMPs) are components of the inherent innate immune mechanism against alien pathogens (Radek and Gallo, Reference Radek and Gallo2007). B. mori gloverins are inducible antimicrobial proteins (Nesa et al., Reference Nesa, Sadat, Buccini, Kati, Mandal and Franco2020). To verify the expression stability of selected reference genes, the expression levels of gloverin 1 (NM_001043465), gloverin 2 (NM_001044218), and gloverin 3 (NM_001099842) in B. mori after parasitised by E. sorbillans were normalised with these genes. The primers for gloverin 1, gloverin 2, and gloverin 3 for RT-qPCR were adopted from the published study (Wu et al., Reference Wu, Zhang, He, Shuai, Chen and Ling2010). The sequences of primers were listed as follows: gloverin 1: F-(5′-3′:TCGCGATATTCACGACTTTG), R-(5′-3′:GCCTCCAGGCCCTAATACTC); gloverin 2: F-(5′-3′:GCACTTTGGGACAAAACGAT), R-(5′-3′:TGGCTTGTGCATTCTTGTTC); gloverin 3: F-(5′-3′:TTACGGCACCAGGGTCTTAG), R-(5′-3′:CCGGATCTCTGCTTGAAGAC), respectively. The expression of gloverin genes relative to reference genes was analysed using the 2−ΔCt method (Livak and Schmittgen, Reference Livak and Schmittgen2001).
Results
Specificity and efficiency of the primers
To identify the best reference genes in B. mori under the parasitic stress of E. sorbillans, 13 potential reference genes including A1, A3, Tua1, Tub1, GAPDH, Eif4a, 28S, RPL32, RPL3, RPL40, TBP, EF1a, and EF1g were selected. The detailed information on the candidate genes and primer pairs were listed in table 1. To confirm the primer specificity, PCR products with the expected size (90 ~ 150 bp) were obtained using 1.5% agarose gel electrophoresis (Supplementary fig. 1a). Consistently, the RT-qPCR melting curves showed that all genes produced a single peak, with no primer dimers or non-specific amplification (Supplementary fig. 1b). Furthermore, the amplification efficiency of these genes was 100.19% ~ 115.92%, and the linear correlation coefficient (R 2) was 0.978 ~ 0.9997 (table 1). Therefore, the primers for amplification of these candidate reference genes were specific and exhibited adequate efficiency.
Table 1. Primer amplification characteristics of the 13 candidate reference genes in B. mori
a Amplicon length.
b Real-time qPCR efficiency.
c Regression coefficient.
Expression of candidate reference genes
Gene expression analysis of each candidate reference gene in all tissues of B. mori showed that mean Ct values ranged from 16.7 to 25.7 (fig. 1). The RPL40 transcripts were the most abundant (mean Ct = 16.74), followed by RPL32 (mean Ct = 17.14) and 28S (mean Ct = 17.57). TBP had the lowest transcript expression (mean Ct = 25.68). Overall, the large variation in the expression level of these genes under different experimental conditions indicated that none of these genes could be stably expressed in all conditions.
Figure 1. Expression profiles of selected reference genes. The expression levels of candidate reference genes in all tissue samples are documented as mean cycle threshold (Ct) values. The bars indicate the standard deviation of the mean (SD).
Expression stability and optimal number of reference genes in different tissues of B. mori parasitised by E. sorbillans
Head
For the head samples, EF1g, GAPDH, and EF1a were suggested as the top three stable genes by ΔCt method and Normfinder. In addition, RPL40 and RPL3 were the most stable genes recommended by geNorm and BestKeeper, respectively. Tua1 was determined as the least stable gene using ΔCt method, geNorm, and Normfinder, whereas A1 was identified as the least stable gene by BestKeeper (table 2). The RefFinder analysis proposed the gene stability ranking order from the highest to the lowest as follows: EF1g > GAPDH > EF1a > RPL40 > Eif4a > RPL32 > RPL3 > Tub1 > Tua1 > A1 > A3 > TBP > 28S (fig. 2a). The geNorm analyses indicated that the value of V 2/3 was <0.15 (fig. 3). Thus, EF1g and GAPDH were sufficient for accurate normalisation in this tissue.
Table 2. Ranking order of candidate reference genes in different tissues of B. mori
Figure 2. Comprehensive stability ranking of candidate reference genes based on the Geomean method of RefFinder. A lower Geomean value indicates the more stable expression.
Figure 3. The optimal number of normalised reference genes determined by the geNorm analysis. The geNorm is used to obtain the pair-wise variation value (V n/n + 1). The value of V n/n+1 < 0.15 indicates that the number of reference genes for best normalisation is n.
Epidermis
For samples from epidermis, EF1a, Tub1, Eif4a, and A1 were identified as the most stable genes using ΔCt method, geNorm, NormFinder, and BestKeeper, respectively. Results of the four programs indicated that A3 was the least stable gene (table 2). RefFinder identified the ranking order from the most stable to the least stable as follows: TBP > EF1a > RPL40 > Eif4a > A1 > EF1g > Tub1 > 28S > RPL32 > RPL3 > GAPDH > Tua1 > A3 (fig. 2b). The geNorm analyses presented that the value of V 2/3 was <0.15, suggesting that TBP and EF1a could be used for accurate normalisation in epidermis (fig. 3).
Prothoracic gland
In prothoracic gland, ΔCt, geNorm, and BestKeeper all recommended EF1a as the most stable reference gene. However, NormFinder ranked RPL3 as the most stable one. Results of all four programs indicated that A3 was the least stable gene (table 2). The overall stability ranking determined by RefFinder from the most stable to the least stable as follows: EF1a > EF1g > GAPDH > A1 > RPL3 > TBP > Tub1 > Eif4a > RPL40 > Tua1 > RPL32 > 28S > A3 (fig. 2c). Analyses by geNorm revealed that the value of V 2/3 was below 0.15. Therefore, EF1a and EF1g were suitable for adequate normalisation in this tissue (fig. 3).
Silk gland
For samples from silk gland, RPL3, GAPDH, and TBP were regarded as the most stable genes by ΔCt method and NormFinder. Tua1, 28S, and Tub1 were recommended as the most stable genes by BestKeeper, while geNorm rated TBP, Eif4a, and RPL32. A1 was considered as the least stable gene by the four programs (table 2). RefFinder suggested the ranking order from the most stable to the least stable as follows: RPL3 > TBP > GAPDH > Eif4a > RPL32 > Tua1 > EF1g > 28S > RPL40 > EF1a > Tub1 > A3 > A1 (fig. 2d). The geNorm results showed that the value of V 2/3 was <0.15. Therefore, RPL3 and TBP were required for adequate normalisation in this tissue (fig. 3).
Midgut
The ΔCt and geNorm algorithms showed that EF1a was the most stable gene. BestKeeper ranked EF1g as the most stable gene while geNorm ranked Eif4a as the most stable gene. All algorithms showed that A3 was the least stable one (table 2). RefFinder rated the gene stability order (the most stable to the least stable) as follows: EF1a > Eif4a > EF1g > Tub1 > 28S > RPL40 > RPL32 > TBP > GAPDH > RPL3 > Tua1 > A1 > A3 (fig. 2e). The geNorm analyses determined that the value of V 2/3 was < 0.15, indicating that EF1a and Eif4a were suitable genes for adequate normalisation in midgut (fig. 3).
Fat body
The genes A1 and EF1a were identified as the most stable genes by ΔCt, NormFinder, and BestKeeper algorithms in fat body. The geNorm predicted Eif4a and RPL40 as the most stable genes. The four programs all identified A3 as the least stable gene in fat body (table 2). The RefFinder analysis suggested the order from the most stable to the least stable as follows: A1 > EF1a > Eif4a > RPL40 > EF1g > TBP > RPL32 > Tub1 > RPL3 > 28S > GAPDH > Tua1 > A3 (fig. 2f). Analyses by geNorm revealed that the value of V 2/3 was less than the proposed 0.15 cutoff. Therefore, two stable genes were suitable for accurate normalisation in fat body (fig. 3).
Haemolymph
In haemolymph, the stable genes recommended by the four programs were quite different. Specifically, RPL40, Tub1, and A1 were the top three stable genes predicted by ΔCt, while geNorm suggested A3, Eif4a, and EF1a as the most stable genes. NormFinder considered RPL40, GAPDH, and TBP as the most stable ones. A3, Eif4a, and Tub1 were the most stable genes as recommended by BestKeeper. All programs suggested that 28S was the least stable one (table 2). The RefFinder analysis predicted the gene stability from the highest to the lowest as follows: RPL40 > A3 > Tub1 > Eif4a > A1 > TBP > RPL3 > GAPDH > EF1a > RPL32 > EF1g > Tua1 > 28S (fig. 2g). The V 2/3 value was < 0.15, therefore, A3 and RPL40 were optimal for gene expression normalisation in haemolymph (fig. 3).
Malpighian tubule
For Malpighian tubules, GAPDH and EF1a were identified as the most stable genes using ΔCt, A3 and RPL40 were identified as the most stable genes using geNorm. EF1a and TBP were rated as the most stable genes by NormFinder, Eif4a and GAPDH were suggested as the most stable genes by BestKeeper. A1 was identified as the least stable gene by the four programs (table 2). Furthermore, the gene stability ranking order (highest to lowest) suggested by RefFinder was as follows: GAPDH > Eif4a > EF1a > RPL32 > A3 > TBP > RPL40 > RPL3 > EF1g > 28S > Tub1 > Tua1 > A1 (fig. 2h). In addition, the value of V2/3 was < 0.15 in Malpighian tubules, suggesting that two stable genes, GAPDH and Eif4a were the best for normalisation (fig. 3).
Ovary
The gene TBP was determined as the most stable gene in ovary by the programs ΔCt, and NormFinder. GeNorm and BestKeeper suggested Eif4a as the most stable gene. The four programs all indicated that the least stable gene was GAPDH (table 2). The RefFinder analysis showed the stability ranking order from the most stable to the least stable as follows: Eif4a > EF1g > TBP > Tub1 > RPL40 > RPL32 > RPL3 > A1 > A3 > 28S > Tua1 > EF1a > GAPDH (fig. 2i). The geNorm analyses showed that the value of V2/3 was below the proposed 0.15 cut-off, suggesting that two stable reference genes were required for normalisation of the target gene expression in ovary (fig. 3).
Testis
In testis samples of B. mori, TBP was recommended as the most stable gene by ΔCt, geNorm, and NormFinder, while 28S was rated as the most stable gene by BestKeeper. However, ΔCt, geNorm, and NormFinder identified RPL40 as the least stable gene in testis. BestKeeper identified RPL3 as the least stable one (table 2). Analyses by RefFinder showed the ranking order (the most stable to the least stable) as follows: TBP > EF1a > Eif4a > GAPDH > Tua1 > 28S > A1 > A3 > RPL32 > Tub1 > RPL40 > EF1g > RPL3 (fig. 2j). Moreover, the geNorm analyses showed that two of the stable reference genes were suitable for adequate normalisation of gene expression in testis (fig. 3).
All tissues
Across all tissue samples, the RefFinder integrative analysis showed the ranking order of gene expression stability from the most stable to the least stable as follows: TBP > Eif4a > EF1g > RPL32 > GAPDH > RPL3 > Tua1 > EF1a > RPL40 > Tub1 > A3 > 28S > A1 (fig. 2k). Overall, for all tissue types, two stable reference genes were essential for accurate gene expression normalisation (fig. 3).
Validation of selected reference genes
The immune responses in fat body, haemolymph and midgut of B. mori are activated after parasitism of tachinid parasitoids (Chen and Lu, Reference Chen and Lu2018; Xu et al., Reference Xu, Zhang, Gao, Wu, Qian, Li and Xu2019b), thus, the reliability of suggested reference genes was validated in these three tissues. Since the expression of AMP genes differed across tissues, the two most stable genes, two least stable genes and combination of the stable genes were selected to generate the expression profiles of three AMP genes, gloverin 1, gloverin 2, and gloverin 3 in fat body, haemolymph, and midgut of B. mori following E. sorbillans parasitism. Meanwhile, to determine the best combination of reference genes for normalisation, The RefFinder program was applied to validate the expression stability of AMPs through normalisation with the top four ranked genes in two-by-two combinations. In fat body of parasitised silkworms, when normalised with the two most stable reference genes A1 and EF1a, the expression levels of gloverin 1 were increased by 2.08- and 2.46-fold, respectively. When normalised with the combination of A1 and EF1a, gloverin 1 transcription was up-regulated by 2.18-fold. However, when normalised with the two least stable genes Tua1 and A3, the expression levels of gloverin 1 were up-regulated by 31.32- and 22.60-fold, respectively (fig. 4a). As revealed by RefFinder analysis, among all the reference gene combinations, gloverin 1 showed the most stable expression when normalisation with the combination of A1 and EF1a (fig. 4b). In haemolymph of parasitised silkworms, the expression levels of gloverin 2 were up-regulated by 5.72-, 6.69- and 6.84-fold when normalised with the best reference genes A3, RPL40, and their combination, respectively. In contrast, the expression of gloverin 2 was increased by 36.94- and 139.46-fold when normalised with the two least stable genes Tua1 and 28S, respectively (fig. 4c). The RefFinder analysis showed that gloverin 2 expression exhibited the highest stability when normalised with the combination of A3 and RPL40 (fig. 4d). In midgut, the expression levels of gloverin 3 were significantly up-regulated by 1.44-, 2.61-, and 1.59-fold when normalised with the two most stable reference genes EF1a, Eif4a, and their combination, respectively. By contrast, the expression of gloverin 3 was up-regulated by 13.79- and 1.58-fold when normalised with the least stable genes A3 and A1, respectively (fig. 4e). The expression stability of gloverin 3 was the highest when normalised with the combination of the two most stable genes EF1a and Eif4a (fig. 4f).
Figure 4. The expression profiles of AMP genes and stability ranking of reference gene combinations. (a, c, e) The expression profiles of gloverin 1, gloverin 2 and gloverin 3 normalised with different reference genes in B. mori. (b, d, f) Stability ranking of the four most stable genes in two-by-two combinations. The delta Ct values (target gene Ct value minus candidate reference Ct value) are directly used as input data in RefFinder analysis to generate the expression stability of AMP genes. The statistical significance is indicated by *(P < 0.05) or ** (P < 0.01). The results are shown as the mean ± SD from three biological replications (independent-sample t-test at 0.05 level).
Discussion
As the two largest groups of parasitic insects, hymenopteran parasitoids and dipteran Tachinidae exhibit differences in behaviour, physiology, morphology, and life history (Stireman, Reference Stireman2016). Both groups have evolved extraordinarily diverse strategies to ensure the successful parasitism and facilitate the development of the parasitoid larvae. Therefore, we propose that some aspects of host-parasitoid interactions should be different and intriguing between these two parasitoid groups. The system comprised of B. mori and its destructive pest E. sorbillans appears to provide an ideal scenario to study host-dipteran parasitoid interactions. To understand their interactions at the molecular level and determine the specific mechanisms of parasitism, the selection of suitable reference genes for gene expression normalisation in B. mori under the parasitic stress is imperative. In this study, we contributed with key information about the expression stability of reference genes in B. mori under the parasitic stress, an experimental condition previously unexplored.
The expression stability of reference genes in insects differed considerably between non-parasitic and parasitic conditions. For example, in different developmental stages and tissues of Chilo suppressalis (Lepidoptera: Pyralidae), Histone 3, ubiquitin-conjugating enzyme and EF1 were the most stable genes, actin and 18S were the least stable genes (Xu et al., Reference Xu, Lu, Cui and Du2017; He et al., Reference He, Lu and Du2021). By contrast, in C. suppressalis parasitised by the parasitoid Cotesia chilonis (Hymenoptera: Braconidae), Tub was ranked as the most stable reference gene, and RPS11 had the lowest expression stability (Li et al., Reference Li, Pan, Lu and Du2020). In different tissues of B. mori, RPL32 and GAPDH were considered as the most reliable reference genes (Teng et al., Reference Teng, Zhang, He, Yang and Li2012). Our study showed that TBP was the most stable gene in different tissues of B. mori after parasitism, RPL32 and GAPDH were ranked as the 4th and 5th most stable genes, respectively. Thus, the stability of reference genes cannot remain constant in insects after parasitism. Results of the current study provide further support for characterising the molecular mechanisms behind host manipulation by dipteran parasitoids.
It is questionable whether the suitable reference genes identified in a specific strain can be directly used in other strains of the same species for gene expression normalisation. In Drosophila melanogaster (Diptera: Drosophilidae), both RP49 and TBP showed the highest stability in different fly strains, while eIF-2α showed a distinct stability ranking order, demonstrating that the best ranked genes could be taken as good candidates in other strains of the same species (Matta et al., Reference Matta, Bitner-Mathé and Alves-Ferreira2011). Similarly, Tua and TBP showed the best stability in different strains of Drosophila suzukii (Diptera: Drosophilidae). These studies suggest that the most suitable reference genes identified in a single strain are also applicable in different strains under the same treatment conditions.
Most of the reference gene studies have demonstrated that two or more reference genes are required for adequate gene expression normalisation (Lü et al., Reference Lü, Yang, Zhang and Pan2018; Shakeel et al., Reference Shakeel, Rodriguez, Tahir and Jin2018). Using only one reference gene for normalisation may lead to inaccurate findings (Kozera and Rapacz, Reference Kozera and Rapacz2013). In this study, we concluded that two reference genes were required for accurate gene expression normalisation in different tissues of B. mori following parasitism. AMP genes showed the highest expression stability when normalisation with combination of the two most stable reference genes. Intriguingly, the expression changes of AMP genes were similar when normalised with a single stable gene and the combination of the two most stable genes. For example, in fat body of parasitised B. mori, when normalising with the two most stable genes A1, EF1a and their combination, the fold changes of gloverin 1 were similar (2.08-, 2.46-, and 2.18-fold, respectively). Therefore, in the case of E. sorbillans parasitism and this specific tissue, a single stable reference gene can be used for gene expression normalisation.
In conclusion, we have identified suitable reference gene sets in B. mori following parasitism by the tachinid parasitoid E. sorbillans. The best reference genes are recommended for different tissue types. These results would be helpful as a tool for further gene expression studies that explore the molecular interactions between the host and the dipteran parasitoid.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0007485323000536
Acknowledgement
The authors wish to thank the anonymous reviewers for their comments and kindness in improving the manuscript. This project was supported by the National Natural Science Foundation of China (Grant No. 32172795), the Science and Technology support Program of Suzhou (SNG2021025), the earmarked fund for CARS-18, and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
Author's contributions
J. W. and B. L. conceived the study. X. L., H. G., Q. X., and Z. J. performed the experiments. X. L. and J. W. wrote the manuscript. All authors read and approved the manuscript.
Competing interests
None.
