Introduction

Maconellicoccus hirsutus is a pestiferous insect species, worldwide recognised as an invasive pest, causing damage to hibiscus, mulberry, grapevines, cotton, okra, and other woody ornamental plants. Its population has surged recently, notably becoming a significant threat to grape cultivation1. A better understanding of its molecular adaptations and physiological traits could offer crucial insights for effective pest control strategies by employing advanced techniques such as sprayable dsRNA-mediated gene silencing. Evaluating gene expression studies stands out as a key approach toward achieving this goal. In contrast to other quantitative techniques, such as Northern blotting, insitu hybridization, and cDNA arrays, Real Time-quantification Polymerase Chain Reaction (RT-qPCR) frequently serves as a valuable tool for the accurate quantification of mRNA transcript levels of specific genes2. Due to its high sensitivity, speed, and precision, RT-qPCR has become the most reproducible analytical method for validating gene expression, making it a preferred approach for generating reliable and interpretable expression data3. To improve the credibility of RT-qPCR data interpretation, effective normalization is essential to remove background variations and biases that may influence transcript expression and provide false positives2. Several factors influence the RT-qPCR study’s validity, including RNA quality and quantity between biological samples and reverse transcription efficiency. The reference genes of RT-qPCR are ubiquitous and stable regardless of environmental conditions4. To be considered a suitable reference gene, a gene must satisfy three major criteria: first, it must have better amplification efficiency comparable to the target genes; second, it must have a reasonable expression level; and third, its expression must be constant across all experimental or environmental conditions. To get accurate measurements of gene expression, it is important to choose the right reference genes for each experiment. Many factors, including the type of organism, developmental stage, and the experimental conditions, influence reference gene expression2. It is essential to identify an experimental-specific reference gene for a given species to ensure accurate quantification of gene expression. A favorable reference gene should have a consistent expression with minimal variation under a variety of experimental circumstances. RNAi has gained significant importance in pest management, and dsRNA-based gene silencing has demonstrated effectiveness against target pests. To evaluate the effect of dsRNA against the target genes, it requires quantification of gene expression; the normalization entirely depends on the stability of the reference genes. A previous study in M. hirsutus assessed the stability of reference genes across different developmental stages under different agrochemical stress conditions5. However, the stable reference gene for experimental conditions, dsRNA-treatment, feeding status (starved & fed conditions), different food sources, and sex-specific conditions has not yet been investigated in M. hirsutus. Hence, the focus of the present study was to identify candidate reference genes to study the stability of six reference genes, namely 28S ribosomal RNA (28S rRNA), beta-tubulin (β-tubulin), alpha-tubulin (α-tubulin), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), Actin, and adenosine triphosphate synthase alpha subunit precursor (ATP51a), when subjected to targeted dsRNA, different food sources, Starvation, and sex-specific conditions by using four algorithms: geNorm, NormFinder, RefFinder, and BestKeeper. Our results provide a comprehensive list of optimal reference genes under the mentioned experimental conditions for the first time in M. hirsutus.

Materials and methods

Insect rearing and maintenance

The M. hirsutus culture was maintained at ICAR-NBAIR, Bengaluru, India (Latitude: 13.0270, Longitude: 77.5842), under controlled laboratory conditions. Dull green pumpkins, characterized by a waxy coating and prominent ridges, were selected as the rearing material due to their suitability for handling insects. Before inoculation, pumpkins were washed with running tap water to remove surface dust and air dried. These air-dried pumpkins were then placed inside rearing cages. To prevent overpopulation and ensure the continuous viability of the culture, mealybugs were periodically transferred to fresh pumpkins using a fine brush. The pure culture was maintained throughout the experimentation period.

In vitro synthesis and microinjection of double-stranded RNA (dsRNA)

The target dsRNA was synthesised by amplifying the target region of the FAR (Fatty acyl-CoA reductase) gene (Accession No. PQ849828), which is involved in the wax biosynthesis pathway, using the corresponding gene-specific primer pairs with the T7 promoter sequence (TAATACGACTCACTATAGGGAGA) appended to the 5’ end of both the primers. GUS (Beta-glucuronidase) gene used as a control was synthesised using plasmid DNA containing the GUS gene as a template, along with the T7 promoter incorporated primers at the 5’ end. Further, the target gene and GUS gene containing T7 promoter-amplified product were purified by using the Sure Extract PCR/Gel Extraction Kit (Genentix; cat: NP-36105) by following the manufacturers protocol. The PCR-purified product was examined for quality and quantity using 1.2% agarose gel electrophoresis and a nanodrop (Jenway 7415 nano-micro-volume spectrophotometer). Purified PCR products of GUS (360 ng) and FAR (455 ng) were used as templates in separate 20 μL in-vitro transcription reactions using the MEGAscript® Kit (Invitrogen™), following the manufacturer’s instructions. The reaction mix was incubated overnight at 37 °C and then treated with DNase for 1 h. Subsequently, the reaction mixture was then washed with 500 μL of 75% ethanol twice. The ethanol was air dried, and dsRNA was diluted in a 70 μL elution buffer. The quality of the dsRNA was assessed on a 1% agarose gel and quantified using a nanodrop (Jenway 7415 nano-micro-volume spectrophotometer)6.

Two- to three-day-old third instar nymphs of M. hirsutus were microinjected with 250 ng of target dsRNA (dsFAR) for the treatment group, while control insects were injected with 250 ng of control dsRNA (dsGUS). The dsRNAs were microinjected into the ventral thorax, specifically between the meso coxae and hind coxae, using a FemtoJet® 4x (Eppendorf) system, following the methodology described by Tong et al., 20227. Following microinjection, the treated nymphs were transferred to sliced potato sprouts. After 48 h, individuals from both the treatment and control groups were collected and RNA was isolated, using RNAiso Plus (TRIzol, Takara, Japan) by following the manufacturer’s protocol. The experiment was conducted in three independent replications for both the treatment and control groups.

Feeding on different food sources

Mealybug ovisacs (10 ovisacs) were collected in specimen tubes and kept for incubation. Upon emergence, the mealybugs were provided with three different food sources: pumpkin, hibiscus (leaves), and a 15% sucrose solution. For the sucrose treatment, 200 µL of a 15% sucrose solution was given to the mealybugs using the parafilm diet sandwich method8 (Fig. S1 and Video S1). This method typically involves enclosing the liquid diet between two layers of stretched Parafilm, which mimics a plant membrane and allows the mealybugs to feed by piercing the Parafilm with their stylets. Once the mealybugs had acclimatized to their respective diets and commenced active feeding, individuals were collected for total RNA isolation. This collection was conducted in triplicate for each feeding treatment after 24 h. Additionally, a group of mealybugs was subjected to 24 h of starvation to represent the starved feeding status, and RNA was similarly isolated from these individuals.

Sex-specific experimental conditions

To investigate sex-specific gene expression, five M. hirsutus females and thirty males of equivalent weight were pooled to constitute a single biological replicate for RNA isolation. This sampling strategy was replicated three times for each sex, ensuring three independent biological replicates for both females and males.

Total RNA isolation and cDNA synthesis

Total RNA was extracted from samples from different experimental conditions using RNAiso Plus (Takara, Japan) according to the manufacturer’s protocol. Further, the extracted total RNA was resuspended in 30 µL of nuclease-free water. Following, RNA integrity and quality were assessed using a Jenway 7415 nano-micro-volume spectrophotometer and 1.2% agarose gel electrophoresis. For cDNA synthesis, total RNA was reverse transcribed using the PrimeScript™ RT Reagent Kit by following manufacturers protocol. A good quality 1μg RNA sample was first treated with gDNA Eraser to remove genomic DNA contamination. The reaction mixture, containing 1 µL of gDNA Eraser, 2 µL of 5X gDNA Eraser Buffer, and nuclease-free water to a final volume of 10 µL, was incubated at 25 °C for 30 min. Following gDNA elimination, the reverse transcription reaction was prepared by adding 1 µL of RT Primer Mix, 1 µL of PrimeScript RT Enzyme Mix I, and 4 µL of nuclease-free water to the 10 µL reaction mixture (for a total volume of 20 µL) The reverse transcription reaction was carried out with the following thermal cycling conditions: 37 °C for 15 min, followed by 85 °C for 5 s, and then 40 °C for an infinite hold. The synthesized cDNA was stored at -20 °C until further use. The quality and quantity of the extracted RNA are tabulated in Table S1.

Selection of reference genes

From our in-house-built transcriptome, we have mined the six potential reference genes9 (Table S2). These consist of 28S rRNA (Accession No. PQ851060), β-tubulin (Accession No. PQ849832), GAPDH (Accession No. PQ858250), Actin (Accession No. PQ849830), ATP51a (Accession No. PQ849831), and α-tubulin (Accession No. PQ849829).

RT-qPCR-based expression profiling

RT-qPCR primers for the six reference genes were designed using the IDT PrimerQuest Tool™ and were synthesized by Eurofins, Bengaluru, India. RT-qPCR was performed using the CFX96 Touch Real-Time PCR system (Bio-Rad, USA) and TB Green™ Premix Ex Taq™ II (Takara Biotechnology, Japan). Expression studies were conducted in two technical replicates for each of three independent biological replicates. Each 45 µL reaction contained 2 µL of 1:10 diluted cDNA, 22.5 μL of TB Green™ Premix Ex Taq™ II, 1.5 μL of forward and reverse primers and 17.5 μL of RNase free water. The cycling conditions were initial denaturation at 95 °C for 3 min, followed by 35 cycles of 95 °C for 15 s (denaturation) and 56 °C for 30 s (annealing). Following amplification, a melting curve analysis from 65 °C to 95 °C was performed to confirm the consistency and specificity of each amplified product. The relative mRNA expression levels of target genes were normalized using the selected internal control genes, and the data were analyzed using the Pfaffl method10, which incorporates primer amplification efficiencies into the calculation of relative expression.

Statistical data analysis

The stability of reference genes was assessed using various algorithms, including geNorm11, NormFinder12, BestKeeper13, and RefFinder14. In geNorm, the normalization factor for gene expression was determined using the geometric mean of reference genes. Genes were given an M value (stability value, M < 1.5), and pairwise variation (Vn/Vn + 1) was used to find the right number of reference genes for correct normalization11. NormFinder evaluates the candidate genes based on the stability of their expression using a cutoff value of 0.1515. BestKeeper identifies potentially stable genes by analyzing the standard deviation of Cq values and computing the Pearson correlation coefficient (r) for each reference gene; genes with standard deviation values exceeding 1 are considered unstable, while those with SD values approaching 1.0 are considered the most stable13,16. RefFinder is an accessible online tool that assesses the reference gene using many computer tools, including geNorm, NormFinder, BestKeeper, and the comparative ΔCt approach. RefFinder computed extensive stability rankings based on the four programs and designated the optimal reference gene as an internal reference16. Relative quantification was determined using the following formula: Relative quantity = E (Cq min – Cq sample), where E = amplification efficiency of the primer set (% efficiency × 0.01) + 1, Cq min = minimum Cq value, and Cq sample = each corresponding Cq value. In geNorm and NormFinder experiments, relative quantified values were employed, while raw Cq values were utilised in the BestKeeper and RefFinder algorithms.

Results

Comparative expression levels of candidate reference genes

The amplification of the reference genes utilized in these studies using RT-qPCR had a sharp and single peak suggesting the specificity of the primers and amplification efficiency varied between 87.6% and 105.6% (Table S2 and Figure S2 & S3). In gene expression analysis, high or low expression of a reference gene could contribute to variability in data analysis; a moderately expressed reference gene is always preferable and will be stable for RT-qPCR gene expression normalization. In dsRNA-treated conditions, expression values varied between a mean Cq value of 10.07 and 24.21 for 28S and α-tubulin, respectively. Actin showed the highest Cq value (23.34), while 28S had the lowest (17.7). In sex-specific conditions, the lowest Cq was observed for 28S (13.00), and the highest for α-tubulin (24.26). Among different food sources, β-tubulin exhibited the highest Cq (27.95), whereas 28S had the lowest (10.1) (Figure S4).

Reference gene stability under dsRNA treatment

To determine suitable reference genes for specific experimental conditions, we analyzed approximately four sets of experimental conditions, including dsRNA treatment. For the dsRNA treatment, the FAR gene was knocked down, and the stability of potential reference genes was evaluated. The analysis was performed using four different algorithms: geNorm, NormFinder, BestKeeper, and an overall ranking was generated using the web-based algorithm, RefFinder. The geNorm M value is crucial for stability calculations; a lower M value indicates greater expression stability. Of the six reference genes, ATP51a and Actin were the most stable in the dsRNA-treated condition, followed by 28S, with M values of 0.25, 0.54, and 0.73, respectively. On the other hand, α-tubulin and β-tubulin were the least stable, with M values of 1.2 and 0.8. NormFinder analysis showed that GAPDH was the most stable gene in the dsRNA-treated condition, with a stability value of 0.08. It was followed by β-tubulin and 28S with stability values of 0.37 and 0.45. According to the BestKeeper algorithm, the reference genes GAPDH and β-tubulin were the most stable, with standard deviation values of 0.20 and 0.39, respectively. The RefFinder web-based algorithm suggested the following genes in order of preference based on their stability: GAPDH > β-tubulin > Actin > ATP51a > 28S > α-tubulin (Fig. 1A-D). Based on the rankings obtained from all four statistical algorithms, the comprehensive ranking concluded that GAPDH and β-tubulin are an ideal combination of reference genes for the normalization of target genes in dsRNA treatment (Table S3).

Fig. 1
Fig. 1
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Expression stability of the candidate reference gene under dsRNA treatment (A) geNorm analysis; (B) NormFinder analysis; (C) BestKeeper analysis; (D) RefFinder analysis.

Stability analysis of reference genes across different food sources

The most stable genes according to the geNorm algorithm were GAPDH and α-tubulin, each with an M value of 0.37, followed by ATP51a, with an M value of 0.85. 28S and β-tubulin were the least stable (1.6 and 3.0). NormFinder analysis revealed that the reference gene Actin exhibited the highest stability (stability value of 0.54), followed by GAPDH and ATP51a, both with stability values of 0.66. 28S was the least stable gene. Stability analysis using BestKeeper suggested that Actin has the highest stability (value of 0.65), while 28S demonstrated the least stability (4.5). The RefFinder tool ranked the reference genes in the following order of stability: GAPDH > α-tubulin > Actin > ATP51a > β-tubulin > 28S (Fig. 2A-D). Based on the rankings obtained from the four algorithms, GAPDH and Actin were identified as an ideal combination of reference genes for different feeding status (Table S3).

Fig. 2
Fig. 2
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Expression stability of the candidate reference gene under different food sources (A) geNorm analysis; (B) NormFinder analysis; (C) BestKeeper analysis; (D) RefFinder analysis.

Evaluation of the most stable reference genes between males and females

In different sexes, ATP51a and GAPDH were the most stable genes with an M value of 0.46, followed by α-tubulin (1.5) and β-tubulin (1.8). The least stable gene was Actin (2.5) in the geNorm analysis. However, the NormFinder analysis showed that GAPDH was the most stable of the candidate genes, with a score of 0.62. It was followed by ATP51a, α-tubulin, and β-tubulin, with scores of 0.71, 1.04, and 1.34, respectively. The BestKeeper analysis identified GAPDH as a stable reference gene with a stability value of 0.67, while Actin demonstrated the least stability with a gene stability value of 2.6. The stable gene ranking by RefFinder was GAPDH > ATP51a > α-tubulin > β-tubulin > 28S > Actin (Fig. 3A-D). The ranking from four algorithms revealed that GAPDH and ATP51a were an ideal combination for sex specific experimental conditions in the pink mealybug (Table S3).

Fig. 3
Fig. 3
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Expression stability of the candidate reference gene in different Sexes; (A) geNorm analysis; (B) NormFinder analysis; (C) BestKeeper analysis; (D) RefFinder analysis.

Reference gene stability analysis under sucrose feeding and starved conditions

In starved conditions, GAPDH and 28S displayed reliable expression in geNorm algorithms with an M value of 0.39, followed by α-tubulin (0.5), Actin (0.6), and ATP51a (0.8). The least stable gene was β-tubulin (0.8). In NormFinder, GAPDH, followed by α-tubulin, and ATP51a were shown to be the best reference genes with stability values of 0.34, 0.39 and 0.87, respectively. Whereas in BestKeeper, ATP51a had the highest stability value (0.45) and was followed by β-tubulin (0.46). The comprehensive stability score of the RefFinder revealed that the least stable genes were β-tubulin and α-tubulin, whereas the most stable reference genes were in the order of 28S > GAPDH > ATP51a > Actin (Fig. 4A-D). Under starved conditions, GAPDH, ATP51a, and 28S showed stable expression when stability rankings were obtained from different algorithms (Table S3).

Fig. 4
Fig. 4
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Expression stability of the candidate reference gene under Starvation and sucrose-fed condition (A) geNorm analysis; (B) NormFinder analysis; (C) BestKeeper analysis; (D) RefFinder analysis.

Selection of the most stable reference gene combinations by NormFinder

The use of two reference genes when combined yields greater accuracy compared to relying on a single reference gene. NormFinder selects the optimal reference genes from a candidate set and, in contrast to geNorm, incorporates information on sample groupings while assessing variance across these groups to determine the best combination of two reference genes for normalization12. In dsRNA treatment, the best combination of reference genes was GAPDH and β-tubulin, with a stability value of 0.202. In starved conditions, the best combination of reference genes suggested was GAPDH and α-tubulin, with a stability of 0.377. In the specific sex condition, ATP51a and α-tubulin were the ideal combinations for normalization, whereas, in the different food sources, α-tubulin and Actin were the best combinations for the gene expression normalization studies (Table S4).

Validation of candidate reference genes in dsRNA treated mealybugs

Validation of reference gene was performed under dsRNA treatment, where expression was studied with one stable gene, GAPDH, followed by two stable genes, GAPDH + β-tubulin, two least stable genes, 28S + α-tubulin, and two medium stable genes, ATP51a + Actin. When the expression of the target gene FAR was studied after knocking down with one stable reference gene, the gene exhibited down regulation with an expression of 0.82, but without any significant difference. However, when two stable genes were used as a reference, the target gene (FAR) showed significant downregulation with the expression of 0.572. In contrast, normalization with the two least stable genes showed the highest downregulation of the FAR gene (0.22). When two moderately stable genes were used, FAR gene expression after knocking down was 0.41-fold downregulated with a p-value threshold (p < 0.050) (Fig. S5).

This substantial downregulation with unstable reference genes strongly indicates misleading or artifactual results stemming from inadequate normalization. The instability of 28S and α-tubulin under dsRNA treatment likely distorted the true expression of FAR, leading to an exaggerated perception of downregulation. This underscores the critical importance of thoroughly validating the stability of reference genes across different experimental conditions to ensure accurate and reliable quantification of target gene expression, as unstable reference genes can significantly misrepresent experimental outcomes.

Validation of candidate reference genes for sex-specific conditions

Validation of reference genes was done for sex-specific conditions by taking a combination of stable genes and the least stable genes based on the comprehensive ranking obtained from different software analyses. Female specific isoforms of the fruitless (fru) and doublesex (dsx) genes were selected, and expression was studied with different combinations of reference genes: (i) two least stable genes (28S + Actin), (ii) one stable gene (GAPDH), and (iii) two most stable (GAPDH + ATP51a) (Fig. S6). When GAPDH was used as a single reference gene, substantial expression differences were identified for fru and dsx between females and males. These genes were considerably more expressed in females, with p-values of 0.0014 (fru) and 0.0004 (dsx). With the two most stable reference gene conditions (GAPDH + ATP51a), both fru and dsx were substantially expressed at higher levels in female mealybugs than in males, with p values of 0.0007 (fru) and 0.0008 (dsx), respectively. When the least stable gene combinations (28S + β-tubulin) were used, no substantial expression change was detected (Fig. S6).

Discussion

In the present study, we evaluated the expression stability of six candidate genes (28S, ATP51a, GAPDH, β-tubulin, α-tubulin, and Actin) under four different experimental conditions, including the dsRNA-treated condition, sex-specific condition, starved conditions, and insects feeding on different food sources.

In dsRNA-mediated functional genomics, the selection of reference genes is crucial to getting reliable results in RT-qPCR-based expression studies. To ensure the reliability of results, the RT-qPCR gene expression normalization study should consider reference genes with intermediate expression in comparison to genes with greater and lower expression6. All algorithms yielded consistent results, further validated under specific experimental conditions, confirming the reliability of our findings. Below, we discuss in detail about gene stability findings under each experimental condition. GAPDH is the key enzyme involved in glycolysis and plays a crucial role in growth and development. It is commonly used as a reference gene and is often referred to as a classical reference gene17. It is ranked as the fourth most widely utilised reference gene18. We observed consistency in GAPDH ranking in dsRNA-treated conditions, which aligns with the findings of Reddy et al.5, who also found GAPDH to be the most stable reference gene under varied experimental conditions. The GAPDH gene worked best for studies that looked at different stages of development and tissues in Bombyx mori, Spodoptera exigua, and Phthorimaea absoluta19,20,21. Liu et al.22 and Kariyanna, et al.23 reported that GAPDH, along with 28S, was the most suitable gene in different diets and pesticide exposures in Leptocybe invasa and Leucinodes orbonalis. In Apis mellifera, GAPDH showed stable expression in comparison to other reference genes24. On the other hand, researchers observed wide fluctuations in GAPDH expression in Phthorimaea operculella and Bactrocera dorsalis under various experimental conditions25,26. The reference gene β-tubulin was also found to be the most stable reference gene along with GAPDH in M. hirsutus, which is in agreement with reference gene studies conducted in Phenacoccus solenopsis, Paederus fuscipes, Ips sexdentatus, and Aquatica lei under varied biotic and abiotic stress conditions2,27,28,29. This aligns with our current finding that β-tubulin was stable under dsRNA treatment. However, the β-tubulin gene was one of the least stable reference genes in RNAi studies of Diabrotica virgifera virgifera and Hippodamia variegata in different developmental stages, tissue-specific conditions, and biotic and abiotic stresses14,30.

When mealybugs were fed on different food sources (pumpkin, hibiscus, and sucrose), GAPDH and Actin were found to be the most reliable genes among six candidate reference genes. Actin is a structural protein expressed in many cell types and is reported to be an ideal internal control and widely used as a reference gene in many insect species. Our study aligns with a previous study conducted in L. invasa22, where Actin was found to be the most stable under different dietary conditions. Similarly, GAPDH was observed to be expressed optimally in different developmental stages and under different diet conditions in S. litura 31. In Myzus persicae, Actin was observed as the most reliable gene under varied experimental conditions (photoperiod, temperature starvation, developmental stages, and insecticide exposure)32,33. Under nutritional conditions, Actin was reported as the optimal reference gene in the alligator weed flea beetle, Agasicles hygrophila34. The Actin gene was found to be the most stable in Diaphania caesalis, Colaphellus bowringi, and Trichoplusia ni35,36,37. Our study also provides evidence by validating Actin and GAPDH as robust reference genes in mealybugs under different dietary conditions.

In the present study, ATP51a and 28S were identified as the most stable reference genes in mealybugs under starved conditions. Although ATP51a is a rarely used reference gene in expression studies, it is stable across different developmental stages and under insecticide exposure in mealybugs5. However, ATP51a exhibited lower stability in Trichogramma chilonis38. In previous studies on insects, 28S has been reported as a stable reference gene under various experimental conditions22,23,39,40,41,42. For example, 28S was found to be a suitable candidate reference gene for developmental stages in Helicoverpa armigera43. However, Zhao, et al.37 observed that 28S was not suitable as a reference gene for RT-qPCR analysis in Antheraea pernyi, which supports our findings that 28S and Actin were the least stable genes when all experimental conditions were considered. This current study suggests that while 28S may be a suitable reference gene under specific conditions, such as starvation, it cannot be used for various experimental conditions. This highlights the significance of using condition-specific reference genes for the normalization of RT-qPCR studies.

Under sex-specific conditions, the genes GAPDH and ATP51a exhibited higher stability. This finding is supported by several studies conducted on diverse insect species. For instance, elongation factor 1-alpha (EF1A) and ribosomal protein 49 (RP49) were identified as the most stable under various biotic conditions (development, tissue, sex) in the monarch butterfly44. Similarly, GAPDH and cyclophilin A (CypA) demonstrated the highest stability in adult Hippodamia convergens of both sexes4. Further emphasizing this variability, studies on blister beetles (Mylabris cichorii) reported different suitable reference genes based on sex: ubiquitin-protein ligase E3A (UBE3A) and ribosomal proteins 22e (RPL22e) for females, and UBE3A, transcription initiation factor TFIID subunit 5 (TAF5), and RPL22e for males45. In C. bowringi, ribosomal protein 19 (RPL19) and EF1A exhibited equal stability35. The insect S. exigua also showed sex-specific differences, with superoxide dismutase (SOD) and ribosomal protein L17A (L17A) being stable in males, and elongation factor 2 (EF2) and SOD in females46.

The geNorm algorithm calculated the expression stability value (M value) and pairwise variation for all candidate genes. The Vn/n + 1 value determines the optimal number of reference genes for accurate normalization. A cut-off value of Vn/n + 1 < 0.15 indicates that an additional reference gene makes no significant contribution to the normalization. Unfortunately, none of the reference genes evaluated was below the < 0.15 general cut-off value. Therefore, to reduce experimental error, it is necessary to use multiple reference genes to analyse target gene expression. Similar results were reported in Dendroctonus valens, Ericerus pela, Chilo partellus, Coleomegilla maculata, and Paederus fuscipes2,28,47,48,49. Pairwise variation values were above the suggested threshold of 0.15, which indicates that an extra reference gene must be used, but as few research works indicate that using more than three reference genes to normalize data may result in inaccuracies. The current study’s comprehensive ranking obtained from a web-based tool (RefFinder) was used for further validation studies; a similar recommendation was presented by Zhang, et al.50.

The first condition considered for validation in the present study was knockdown of the FAR gene via RNAi (dsRNA treatment); the gene mainly participates in wax biosynthesis by regulating the production of cuticular hydrocarbons (CHCs) in mealybugs7. The knocking down of the FAR gene resulted in a 50% reduction of the transcript level of M. hirsutus when the expression was normalized with GAPDH and β-tubulin. Similar to the Tong, et al.7 study, where the transcript level of the FAR gene was reduced to 54.8% in P. solenopsis. In P. solenopsis among different reference genes, β-tubulin was reported as the most reliable51. After normalization with the combination of the two most stable genes (GAPDH + β-tubulin), moderately stable genes (ATP51a + Actin), and the least stable gene (28S + α-tubulin), the results obtained for the expression of the FAR gene varied drastically.

The second experimental condition used for validation was a sex-specific condition where sex-specific splicing of major transcripts of the dsx and fru genes were analyzed. These genes encode highly conserved transcription factors that control the genetically fixed sex and sexual dimorphism in most insects52. In D. melanogaster, dsx generates male- and female-specific isoforms, dsxM and dsxF. Both fru and dsx control morphological and behavioral sexual dimorphism in insects. Therefore, in this study, we selected female sex-specific isoforms that showed a significant increase in expression when normalized using different stable reference gene combinations. In Ips sexdentatus, ribosomal protein S3 (RPS3), β-tubulin, and translation elongation factor (EeF2) were the most stable reference genes in sex-specific conditions53. Whereas in our study, β-tubulin was among the least stable genes in the sex-specific condition. In D. melanogaster, GAPDH, β-Actin, and 18S rRNA exhibited stable expression in sex-biased conditions, which is consistent with the present study, where GAPDH exhibited stable expression54. In L. invasa and Hyphantria cunea, Actin was observed as the most stable reference gene and GAPDH as the least stable22,55, which was contrary to the present investigation.

Conclusion

We assessed the stability of six reference genes utilising four methods throughout all experimental conditions (dsRNA treatment, sex-specific conditions, starved and fed conditions, and different food sources). Through extensive assessment, GAPDH was found to be the most stable reference gene across all experimental settings. The reference genes GAPDH and β-tubulin exhibited stable and consistent expression following dsRNA treatment. GAPDH and Actin exhibited consistent expression across various dietary sources. GAPDH and ATP51a exhibited consistent expression under sex-specific conditions. GAPDH and ATP51a demonstrated consistent expression under both starved and feeding conditions. The identification of the most and least stable genes was further corroborated under sex-specific conditions. These findings provide a strong foundation for accurate gene expression studies under the specified experimental conditions and will be particularly valuable for future RNAi-based studies involving dsRNA in mealybugs and related pest species.