Metabolic and transcriptome responses of RNAi-mediated AMPKα knockdown in Tribolium castaneum

doi:10.21203/rs.3.rs-24958/v3

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Metabolic and transcriptome responses of RNAi-mediated AMPKα knockdown in Tribolium castaneum

https://doi.org/10.21203/rs.3.rs-24958/v3

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published 23 Sep, 2020

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Background The AMP-activated protein kinase (AMPK) is an intracellular fuel sensor for lipid and glucose metabolism. In addition to the short-term regulation of metabolic enzymes by phosphorylation, AMPK may also exert long-term effects on the transcription of downstream genes through the regulation of transcription factors and coactivators. In this study, RNA interference (RNAi) was conducted to investigate the effects of knockdown of TcAMPKα on lipid and carbohydrate metabolism in the red flour beetle, Tribolium castaneum, and the transcriptome profiles of dsTcAMPKα-injected and dsEGFP-injected beetles under normal conditions were compared by RNA-sequencing.

Results RNAi-mediated suppression of TcAMPKα increased whole-body triglyceride (TG) level and the ratio between glucose and trehalose, as was confirmed by in vivo treatment with the AMPK-activating compound, 5-Aminoimidazole-4-carboxamide1-β-D-ribofuranoside (AICAR). A total of 1184 differentially expressed genes (DEGs) were identified between dsTcAMPKα-injected and dsEGFP-injected beetles. These include genes involved in lipid and carbohydrate metabolism as well as insulin/insulin-like growth factor signaling (IIS). Real-time quantitative polymerase chain reaction analysis confirmed the differential expression of selected genes. Interestingly, metabolism-related transcription factors such as sterol regulatory element-binding protein 1 (SREBP1) and carbohydrate response element-binding protein (ChREBP) were also significantly upregulated in dsTcAMPKα-injected beetles.

Conclusions AMPK plays a critical role in the regulation of beetle metabolism. The findings of DEGs involved in lipid and carbohydrate metabolism provide valuable insight into the role of AMPK signaling in the transcriptional regulation of insect metabolism.

Epigenetics & Genomics

Tribolium castaneum

AMPK

RNA interference

transcriptome

lipid metabolism

carbohydrate metabolism IIS pathway

The survival of all organisms depends on the maintenance of energy homeostasis. AMP-activated protein kinase (AMPK) is a cellular energy sensor conserved across all eukaryotic species (Garcia and Shaw 2017). As a serine/threonine protein kinase complex, AMPK consists of a catalytic subunit α and two regulatory subunits, β and γ, and is activated in response to energy stress by sensing increases in ADP/ATP and AMP/ATP ratios, which leads to the activation of ATP-generating catabolic pathways including glycolysis and fatty acid oxidation and the inhibition of ATP-consuming anabolic pathways such as gluconeogenesis, fatty acid and protein synthesis (Hardie 2007). While nucleotide-dependent phosphorylation of Thr172 in the α subunit by liver kinase B1 (LKB1) is the principal event required for full activation of AMPK in mammalian cells (Hawley et al. 2003; Woods et al. 2003), several studies have revealed the nucleotide-independent regulation of AMPK via the phosphorylation of Thr172 by calcium/calmodulin-dependent kinase kinase 2 in mammals (CAMKK2) (Hawley et al. 2005; Hurley et al. 2005; Woods et al. 2005).

Lipids and carbohydrates are major sources for energy storage and supply in cells. Under aerobic conditions, most energy is derived from fatty acids oxidation and the rest of energy is obtained from glucose oxidation. In contrast, glycolysis plays an important role in ATP production under anoxic condition (Sambandam and Lopaschuk 2003). Activation of AMPK occurs in response to stress circumstances such as starvation, exercise, hypoxia and ischemia, heat shock, and oxidative stress (Hardie et al. 1994; Wilson et al. 1996; Rasmussen and Winder 1997; Choi et al. 2001; Fisher et al. 2002; Platara et al. 2006; Rider et al. 2011). It is well known that AMPK can regulate lipid and carbohydrate homeostasis via direct phosphorylation of multiple downstream effectors. Acetyl-CoA carboxylase (ACC), the first rate-limiting enzyme in fatty acid synthesis, glycerol-3-phosphate acyltransferases (GPAT), the rate-limiting enzyme in triglyceride (TG) synthesis, and 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR), the rate-limiting enzyme of cholesterol biosynthesis pathway, can be inhibited by AMPK through phosphorylation in rats (Carling et al. 1987; Winder and Hardie 1996; Muoio et al. 1999). Subsequently, AMPK was found to promote lipid absorption and release by directly phosphorylating lipases like hormone-sensitive lipase (HSL) and adipocyte-triglyceride lipase (ATGL) in mice and Caenorhabditis elegans (Watt et al. 2006; Narbonne and Roy 2009; Ahmadian et al. 2011). On the other hand, AMPK also stimulates glycolysis via the phosphorylation and activation of 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) and 6-phosphofructo-2-kinase (PFK2) in human tissues (Marsin et al. 2000; Marsin et al. 2002). Additionally, AMPK activation was also shown to increase whole-body insulin sensitivity by phosphorylation of insulin receptor substrate-1 (IRS-1) in mouse (Jakobsen et al. 2001) and insulin receptor (InR) in rodent muscle (Chopra et al. 2012). Given the functional attributes of AMPK in lipid and carbohydrate metabolism, AMPK is considered as an important therapeutic target for treating metabolic diseases including obesity and type 2 diabetes (Musi and Goodyear 2002).

Although the role of AMPK in the regulation of cell metabolism is well studied in mammals, related research is still limited in insects. Notably, AMPK can regulate energy balance via modulation of transcriptional expression of metabolic enzymes in the long term, however, its downstream transcriptional pathways remains largely elusive (Cantó and Auwerx 2010). Recently, we reported the transcriptional and post-translational activation of TcAMPKα by oxidative, heat and cold stresses in the red flour beetle, Tribolium castaneum (Jiang et al. 2019). In this study, RNAi was employed to determine the roles of TcAMPKα in lipid and carbohydrate metabolism. Comparison, annotation and classification of DEGs between dsTcAMPKα treatment and control groups were also conducted by high-throughput transcriptome sequencing to identify metabolism-related genes modulated by AMPK in T. castaneum.

Effects of TcAMPKα suppression on TG, glucose and trehalose levels

RNAi was conducted to determine the effects of TcAMPKα knockdown on TG, glucose and trehalose levels. The injection of 20-day-old larvae with dsTcAMPKα reduced transcription levels by 95.50% ± 1.86% (ANOVA, df_{2, 4}, F = 97.506, P value = 0.0027) on the sixth day after injection. TG measurement showed that the TG level in dsTcAMPKα group (9.03 ± 0.76 mmol/mgprot) was significantly increased by 53.49% ± 8.61% (ANOVA, df_{1, 4}, F= 7.917, P value = 0.04813) when compared with the dsEGFP group (5.96 ± 0.78 mmol/mgprot) (Figure 1A).

Similarly, increased glucose level by 62.34% ± 11.61% (ANOVA, df_{1, 4}, F = 8.196, P value = 0.04580) was observed in the beetles injected with dsTcAMPKα (25.85 ± 6.12 μmol/g) when compared to the control beetles (8.32 ± 0.08 μmol/g) (Figure 1B). However, the trehalose level in dsTcAMPKα group (3.73 ± 0.10 mg/g) was significantly reduced by 8.56% ± 3.01% (ANOVA, df_{1, 4}, F = 9.357, P value = 0.0377) than that in dsEGFP group (4.08 ± 0.05 mg/g) (Figure 1C). These data suggested that RNAi of TcAMPKα increased TG production and the ratio between glucose and trehalose (Figure 2A).

Effects of AICAR treatment on TG, glucose and trehalose levels

To confirm the RNAi results, the 20-day-old larvae were treated with an activator of AMPK 5-Aminoimidazole-4-carboxamide1-β-D-ribofuranoside (AICAR), and the TG, glucose and trehalose levels were measured. The results showed that the levels of TG and glucose in AICAR group were significantly decreased by 34.60% ± 5.74% (ANOVA, df_{1, 4}, F = 10.770, P value = 0.03045) and 41.89% ± 2.27% (ANOVA, df_{1, 4}, F = 93.320, P value = 0.0006), respectively, compared with injection buffer (IB) group, whereas the trehalose level increased by 17.07% ± 4.02% (ANOVA, df_{1, 4}, F = 8.910, P value = 0.0405) in beetles treated with AICAR. These data suggested that activation of TcAMPK decreased TG production and the ratio between glucose and trehalose (Figure 1 D-F; Figure 2B).

Transcriptome sequence and reads mapping

The dsTcAMPKα and dsEGFP groups were analyzed by RNA-Seq (three independent biological replicates of each treatment). A mean of 23,570,938 clean reads were generated among six independent libraries (T01-T06) (Table 1). Evaluation of clean data quality showed that the GC counts ranged from 42% to 45% and Q30 ratios were >93%, indicating a high level of data quality. The alignment of clean reads to the reference genome database of T. castaneum showed that 83.50% and 77.15% reads of the dsEGFP and dsTcAMPKα groups were aligned on average, respectively (Table 2).

Correlation analysis was conducted with Pearson’s Correlation Coefficient (R value) to evaluate the biological repeatability based on expression values of each library (Schulze et al. 2012). The results showed that the Pearson’s Correlation Coefficient between the three control samples (T01-T03) was 0.81, and that of dsTcAMPKα treatment samples (T04-T06) was 0.94 (Figure 3A). Box plot analysis revealed that the three samples in each group had similar expression distributions of reads, while the control and treatment groups had significantly different expression distributions (Figure 3B).

Further annotation of expressed unigenes revealed that a total of 14,095 unigenes out of 31,944 unique sequences were annotated and classified into at least one database of Non-redundant (Nr), EuKaryotic Orthologous Groups (KOG), Clusters of Orthologous Groups of proteins (COG), Kyoto Encyclopedia of Genes and Genomes (KEGG), Protein family (Pfam), Gene Ontology (GO) and Swiss-Prot databases (Figure S1, Figure S2 and Table S2).

Changes in gene expression profiles

To identify the effects of knock-down of TcAMPKα on global unigene expression patterns of T. castaneum, DEGs between dsTcAMPKα and dsEGFP groups were identified based on their Fragments Per Kilobase of transcript sequence per Million base pairs sequenced (FPKM) values. 1184 DEGs were obtained including 349 upregulated and 835 downregulated unigenes (Figure 4A, Figure 4B and Table S3). The log₂-fold variation range of DEGs was between -6.07 and 3.75 (Fold change from -67.18 to 13.45, P value from 1.27×10^-53 to 0.04978).

DEGs were classified by searching against GO and KOG databases. GO term enrichments was used to further analyze physiological changes associated with DEGs in T. castaneum. GO enrichments revealed that the DEGs involved in cellular component category were enriched in cell part, cell, membrane and organelle (Figure S3). In molecular function category, most of the DEGs were enriched in binding and catalytic activity such as Retrovirus-related Pol polyprotein and ATPase inhibitor, while metabolic and cellular process were the most enriched subcategories in biological process (Figure S3).

KOG database was used to annotate DEGs with specific physiological functions (Figure 5, Table S4). Although the “General function prediction only” was the most abundant group among all subcategories in KOG database, a large number of DEGs were annotated into the metabolism classifications such as “Lipid transport and metabolism”, “Carbohydrate transport and metabolism”, “Amino acid transport and metabolism”, “Energy production and conversion” and “Secondary metabolites biosynthesis, transport and catabolism” (Table 3). Furthermore, most DEGs involved in protein translation were downregulated such as some ribosomal proteins (Table 3). Similarly, among the 42 DEGs in the post-translational modification class, 35 DEGs were down-regulated, including 3 heat shock proteins (Hsps), whereas the phosphatidylinositol 4,5-bisphosphate 3-kinase (PIK3) and InR2 involved in the signal transduction mechanisms were upregulated.

Expression of genes involved in lipid metabolism, carbohydrate metabolism and insulin signaling

Besides the analysis of the entire gene set, we specifically checked for up- or downregulation of lipid and carbohydrate metabolism genes in our set of DEGs. The results showed that the insect adipose triacylglycerol lipase homologue, brummer (Accession no. TC011935), which is responsible for the first step of TG hydrolysis, was significantly downregulated with the expression log₂-fold change of -2.20 (Fold change: 0.22; ANOVA, df_{1, 4}, F = 5.152, P value = 1.60×10^-05), whereas two fatty acid synthetase genes (FAS1-2) (Accession no. TC015337 and TC015340) (Fold change: 2.03 and 3.22, ANOVA, df_{1, 4}, F = 9.182 and 157.306, P value = 0.03878 and 0.00023) involved in fatty acid biosynthetic pathways, and the transcription factor ChREBP (Accession no. TC010471) (Fold change: 2.27, ANOVA, df_{1, 4}, F = 96.147, P value = 0.00061), a key regulator of glucose and lipid metabolism and fat storage (Uyeda et al. 2002), were upregulated with the expression log₂-fold change from 1.02 to 1.76 (Fold change from 2.03 to 3.39) (Table 4). Knock-down of TcAMPKα also caused upregulation of genes involved in IIS pathway, including PI3K, IRS1 and InR2 (Accession no. TC011996, TC034013 and TC010784) (Fold change: 2.22, 2.19 and 2.46; ANOVA, df_{1, 4}, F =86.102, 14.636 and 34.155, P value = 0.00075, 0.01869 and 3.37×10^-08). To confirm the reliability of the DEG data, the expression levels of these DEGs were determined using RT-qPCR (Figure 6 and Table 4). Gene expression levels validated by RT-qPCR showed the high consistency with transcriptome sequencing.

To be as inclusive as possible, less strict screening criteria (Fold change |log₂(treatment/control) | > 0.5) were applied, and additional lipid and carbohydrate metabolism-related genes with the expression changed in transcriptome were selected for further verification using RT-qPCR. The results showed that five genes involved in fatty acid and triglyceride biosynthetic pathways including three FAS (FAS3-5) (Accession no. TC011522, TC015400 and TC000238) (Fold change: 1.73, 1.55 and 1.50; ANOVA, df_{1, 4}, F = 1.717, 0.785 and 2.781, P value = 0.26021, 0.42567 and 0.17070), one ACC (Accession no. TC015612) (Fold change: 1.77; ANOVA, df_{1, 4}, F = 2.539, P value = 0.18632), one glycerol-3-phosphate acyltransferases (GPAT) (Accession no. TC004512) (Fold change: 1.59; ANOVA, df_{1, 4}, F = 8.092, P value = 0.04665), four trehalase (TRE) genes responsible for trehalose hydrolysis (Accession no. TC006698, LOC659620, TC004791 and TC006697) (Fold change: 1.86, 1.48, 1.60 and 1.86; ANOVA, df_{1, 4}, F = 9.491, 0.699, 25.660 and 9.491, P value = 0.03690, 0.45001, 0.00715 and 0.03690), two genes involved in gluconeogenesis including pyruvate carboxylase (PC) (Accession no. TC032730) (Fold change: 1.73; ANOVA, df_{1, 4}, F = 19.919, P value = 0.01113) and phosphoenolpyruvate carboxykinase (PEPCK) (Accession no. TC009072) (Fold change: 1.23; ANOVA, df_{1, 4}, F = 1.491, P value = 0.28918) were significantly upregulated (Table 4). Interestingly, SREBP1(Accession no. TC007163) (Fold change: 1.93; ANOVA, df_{1, 4}, F = 9.490, P value = 0.03691), the master regulator of lipid homeostasis, and SREBP cleavage-activating protein (SCAP) (Accession no. TC013456) (Fold change: 1.75; ANOVA, df_{1, 4}, F = 10.483, P value = 0.03174), a central regulator of lipogenesis that controls the activity of SREBP (Shao and Espenshade 2014) were also significantly upregulated (Table 4).

In insects, the energy for growth and development is mainly derived from the storage and utilization of lipids and carbohydrates in specific tissues such as fat body, midgut and oenocytes (Martins et al. 2011). TG is the master form of lipids stored in fat body and plays an important role in energy storage and release (Warnakula et al. 2011). Meanwhile, glucose (monosaccharide) and trehalose (disaccharide) provide energy through glycolysis (Reyes-DelaTorre et al. 2012). Given that AMPK activates ATP-generating pathways and inhibits energy-consuming processes under conditions of low energy status (Towler and Hardie 2007), the attenuation of AMPK signaling in insects may disrupt the energy balance in vivo. In this study, we found that the TG and glucose levels in dsTcAMPKα-injected T. castaneum were significantly increased compared with dsEGFP-injected insects, while a decreased trehalose content was observed in dsTcAMPKα-injected insects. These results were further confirmed by in vivo AICAR treatment, which resulted in the decreased TG and glucose levels and increased trehalose content. Similarly, AICAR-induced AMPK activation resulted in significantly decreased TG level in lean and obese rodents, in vivo (Bergeron et al. 2001). In human HepG2 cells, the kinase-inactive AMPKα increased lipid content and prevent the metformin from decreasing lipid accumulation (Zang et al. 2004). However, lower TG levels were observed in D. melanogaster with reduced AMPK function during fed conditions (Johnson et al. 2010). On the other hand, while activation of AMPK triggered a reduction in glucose levels in vivo in mice (Yamauchi et al. 2002), and increased hepatic glucose production was observed in AMPKα2 knockout mice (Andreelli et al. 2006), knockdown of hypothalamic AMPK activity in male Sprague-Dawley rats led to a significant suppression of glucose production (Yang et al. 2010). These results indicate the complexity of the role of AMPK in the regulation of metabolic processes.

As an energy sensor that regulates cellular metabolism, AMPK not only has acute effects on metabolic enzymes by direct phosphorylation, but also shows long term action to change the transcriptional levels of metabolic proteins and enzymes. For example, activation of AMPK in liver and adipocytes can downregulate FAS activity and inhibit lipogenesis (Madsen et al. 2015). Activation of AMPK by 4‐Hydroxyderricin and xanthoangelol downregulates GPAT in 3T3‐L1 cells (Zhang et al. 2013), an enzyme necessary for triacylglycerol synthesis (Wendel et al. 2009). Similarly, in C. elegans, AAK-2 can inhibit fat synthesis under stress condition by downregulating lipid synthesis-related genes such as Δ9 fatty acid desaturases which can produce monounsaturated fatty acids to constitute TGs (Shin et al. 2011; Watts 2009). To investigate the downstream transcriptional pathways of AMPK in T. castaneum, the transcriptomes of dsTcAMPKα-injected and dsEGFP-injected larvae under normal conditions were compared. Global gene expression profiles of dsTcAMPKα group were distinct from dsEGFP group with 1184 DEGs. Of particular interest is the upregulation of genes involved in fatty acid and TG biosynthetic pathways, such as FAS, ACC and GPAT, whereas the relative expression level of brummer involved in TG hydrolysis (Grönke et al. 2005) was dramatically downregulated. We also observed the upregulation of several carbohydrate metabolism-related genes, such as TRE, and two key enzymes in gluconeogenesis (de novo synthesis of glucose), PC and PEPCK (Jitrapakdee et al. 2008; Hanson and Reshef 1997), in the dsTcAMPKα-injected insects. The transcriptional changes of genes encoding metabolism enzymes might contribute to the increased TG and glucose levels and decreased trehalose content in dsEGFP-injected beetles.

AMPK activation has been reported to cause a reduction in transcriptional activity of several metabolism-related transcription factors. As a key transcription factor that regulates cellular lipogenesis in liver, skeletal muscle and adipose tissue, insulin-activated SREBP1 acts in synergy with glucose-senstive ChREBP, which mediates the response to dietary carbohydrates and is essential for regulating lipogenic gene expression (Postic and Ortega-Prieto 2019). AMPK-inhibited SREBP1 can block expression of some gluconeogenic and lipogenic genes, whereas SREBP1 overexpression can increase their transcription (Shimomura et al. 1999; Foretz et al. 1999; Kohjima et al. 2008). Activation of AMPK by metformin or an adenosine analogue suppresses the expression of SREBP1. In metformin-treated rats, hepatic expression of SREBP1 mRNAs and protein is reduced (Zhou et al. 2001). A recent study indicated that AMPK interacts with and directly phosphorylates SREBP1, suppresses SREBP1 cleavage and nuclear translocation, and represses SREBP1 target gene Fas expression in hepatocytes in response to metformin treatment, leading to reduced lipogenesis (Li et al. 2011b). On the other hand, AMPK also phosphorylates Ser568 and reduce DNA binding capacity and promote nuclear exclusion of murine ChREBP (Kawaguchi et al. 2001; Kawaguchi et al. 2002). Interestingly, the promotion of PEPCK expression is associated with increased expression of SREBP-1 and ChREBP in high free fatty acid (HFFA)-treated hepatocytes (Kuo et al. 2012), and a recent study also revealed the role of ChREBP in gluconeogenesis (Tang et al. 2016). In this study, SREBP1, ChREBP and SCAP were significantly upregulated in dsTcAMPKα-injected insects, which in turn might modulated the expression of genes involved in lipid and carbohydrate metabolism.

IIS pathway was involved in the regulation of glucose and lipid metabolism (Saltie and Kahn 2001). In addition to the regulation of lipid synthesis, studies reveal expanding roles for SREBP1 in controlling pathways for insulin resistance (Li et al. 2011b), in which the pathological process involves a series of cascades, including defective activation of IRS and PI3K (Kerouz et al. 1997; Huang et al. 2005; Taniguchi et al. 2005). Overexpression of SREBP1 decreased Irs-1 mRNA levels in a dose-dependent manner, and SREBP1 knockdown led to an upregulation of IRS-1 levels (Li et al. 2011b). Further luciferase reporter assay confirmed that Irs-1 promoter activity was repressed by SREBP1 overexpression (Li et al. 2011b). However, in liver with Nonalcoholic fatty liver disease (NAFLD), IRS-1 expression was enhanced and correlated positively with SREBP1 expression. In contrast, IRS-2 expression decreased by 50% and was not correlated with SREBP1 (Kohjima et al. 2008). In sebocytes, insulin-like growth factor-1 (IGF-1) induces SREBP-1 expressions at both mRNA and protein levels in a PI3K-dependent manner, accompanied by an increase in the transcription of SREBP-1 target genes such as FAS (Smith et al. 2008). Additionally, insulin-stimulated endogenous ChREBP expression was also observed in HepG2 and primary hamster hepatocytes (Sirek et al. 2009). In this study, we observed the upregulation of IIS-related DEGs like IRS1, InR2 and PI3K in dsTcAMPKα-injected beetles, which might result in the upregulation of SREBP1 and ChREBP. Interestingly, it has been reported that knockdown in expression of insulin like peptide 2 (ILP2) caused a decrease in TRE mRNA levels in T. castaneum (Xu et al. 2013), suggesting the positive control of TRE transcription by IIS signaling. Further study is needed to clarify the mechanism of upregulation of IIS signaling in dsTcAMPKα-injected insects.

This study confirmed that AMPK has an important role in the regulation of beetle metabolism. Specifically, our study showed that knockdown of AMPK causes alteration in expression levels of genes involved in lipid and carbohydrate metabolism as well as IIS signaling. Such investigations will help us understand the function of AMPK in transcriptional regulation of insect metabolism.

Experimental insects

The Georgia-1 (GA-1) strain of T. castaneum was reared at 30 °C and 50% relative humidity in 5% yeasted flour under standard conditions as described previously (Haliscak and Beeman 1983; Li et al. 2011a).

Double-strand RNA synthesis and injection

Gene specific primers (Table S1) with T7 promoter were designed to synthesize the dsRNAs targeting nucleotides 844-1285 (442 bp) of the ORF region of the TcAMPKα using TranscriptAid™ T7 High Yield Transcription Kit (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer's instructions. The synthesized dsRNAs were diluted in diethyl pyrocarbonate (DEPC)-treated water with a concentration of 2 μg/μL, and about 200 ng of dsRNA in 200 nL IB was injected into 20-day-old larvae using a Nanoliter 2010 injector system (WPI, Sarasota, FL, USA) under a stereomicroscope. A total of 10 unsexed insect larvae were collected as a sample for RNA extraction at six- days post-injection. The dsEGFP-injected larvae (CK group) were set as controls in all injection experiments. Three biologically independent replicates were carried out with at least 100 insects (≥ 200mg) in each replicate.

Triglyceride (TG) measurement

Total TG levels were determined using the liquid TG (GPO-PAP) method (Annoni et al. 1982). Briefly, each replicate with 10 injected larvae were homogenized in 270 μL of PBS (0.1 mol/L PH 7.4) and centrifuged at 2,500 rpm for 10 min. The supernatant was collected, and the TG level was analyzed by using TG Assay Kit (catalogue no. A110-1, Jiancheng Bioengineering Institute, Nanjing, China) according to the manufacturer's instructions. Three independent biological replicates and four technical replicates were performed for every treatment.

Glucose and trehalose measurement

Total glucose levels were measured using glucose content assay kit (catalogue no. BC2500, Solarbio Science & Technology, Beijing, China) according to the manufacturer's instructions. Briefly, 10 larvae on day 6 after injection of dsTcAMPKα or dsEGFP were weighed and homogenized in 0.3 mL double distilled water. The supernatant was collected after centrifugation at 8,000 g for 10 minutes and used to quantify the level of glucose at 505 nm with Multiskan FC microplate reader (Thermo Fisher Scientific, Waltham, MA, USA). Three independent biological replicates and 4 technical replicates were performed for every treatment.

Total trehalose level was measured using Trehalose content detection kit (catalogue no. A149-1-1, Jiancheng Bioengineering Institute, Nanjing, China) according to the manufacturer's instructions. Six days after injection of dsTcAMPKα or dsEGFP, 10 larvae were homogenized in 0.3 mL extraction solution and incubated at room temperature for 45 minutes. The supernatant was collected after centrifugation at 8,000 g for 10 minutes, and used to quantify the level of trehalose at 620 nm with Multiskan FC microplate reader (Thermo Fisher Scientific, Waltham, MA, USA). Three independent biological replicates and four technical replicates were performed for every treatment.

AICAR treatment

To investigate the effects of AMPK activation on lipids and carbohydrates metabolism, 20-day-old larvae were injected with 200 nL of 1.6 mg/mL AICAR or IB as control. AMPK could be activated immediately by AICAR treatment among a couple of hours (Matsumoto et al. 2011). The total amounts of TG, glucose and glucose were determined at one- hour post-injection.

RNA extraction, library construction and sequencing

Total RNA was isolated from 10 larvae of dsTcAMPKα or dsEGFP group on the sixth day after injection using TRIzol Reagent (Invitrogen, USA), and digested by RNase-free DNase I (Takara, Dalian, China) to remove genomic DNA contaminants. To ensure the quality of the samples for transcriptome sequencing, concentration and integrity of RNA samples were checked using a Nanodrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE) and an Agilent Bioanalyzer 2100 (Agilent, Palo Alto, CA), respectively. The qualified RNA samples were used for mRNA preparation and cDNA library construction. Three independent biological replicates were performed for every treatment. cDNA libraries were sequenced as described by Meng et al. (2019).

Sequence annotation

To ensure the accuracy of sequence alignment, raw reads were cleaned by removing adapter and primer sequences, reads with ambiguous nucleotides larger than 5% and low-quality reads. Clean reads were aligned to the reference genome of T. castaneum (Reference genome version: GCF_000002335.3_Tcas5.2_genomic.fna; Reference genome site: ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/002/335/GCF_000002335.3_Tcas5.2/GCF_000002335.3_Tcas5.2_genomic.fna.gz) (Herndon et al. 2020) using HISAT2 (CCB, Johns Hopkins University, USA). Subsequently, the transcripts were subjected to blast against GO, KOG and KEGG databases. GO is an international standard classification system of gene function (Figure S1 and Table S2). KOG database is based on the phylogenetic relationship of bacteria, algae and eukaryotes (Table S2). The KEGG database provides a powerful tool to discover the pathways in which genes are involved (Figure S2). In addition, Nr, COG, Pfam, GO and Swiss-Prot databases were also used to annotate unigenes. The E-value threshold was set to 10^-5.

Differentially expressed genes analysis

Transcript abundances were measured by FPKM (Trapnell et al. 2010). Differential expression analysis of dsTcAMPKα and dsEGFP groups were performed using DESeq2 software (Anders and Huber 2010). Unigene with False Discovery Rate (FDR) values < 0.05 and fold change |log₂(treatment/control) | > 1 were set as the threshold for DEGs. GO and KOG database annotations were further analyzed to understand the functions of DEGs in T. castaneum.

Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR)

To validate the DEGs from the RNA-sequencing, RT-qPCR reactions were performed on the Bio-Rad CFX 96 Real-time PCR system using TB Green™ Premix Ex Taq™ (Takara, Dalian, China) and gene specific primers (Table S1). The stably expressed gene encoding ribosomal protein S3 (rps3, GenBank: CB335975) was used as a reference gene (Arakane et al. 2008). PCR conditions were set as an initial incubation of 95℃ for 30s, 40 cycles of 95°C for 5s and 60°C for 30s, and a final melting curve analysis was performed. The mRNA levels were normalized to reference gene with the 2^-ΔΔCT method (Livak and Schmittgen 2001). The means and standard errors (mean ± SE) for each time point were obtained from the average of at least three biologically independent sample sets.

Statistical analysis

Statistical analysis was performed using Graphpad Prism 6 (GraphPad Software Inc., San Diego, USA) by one-way analysis of variance, followed by Tukey’s Honestly Significant Difference test. All data are presented as the mean ± SE.

ACC: acetyl-CoA carboxylase

AICAR: 5-Aminoimidazole-4-carboxamide1-β-D-ribofuranoside

ATGL: adipocyte-triglyceride lipase

AMPK: AMP-activated protein kinase

CAMKK2: calcium/calmodulin-dependent kinase kinase 2

CHREBP: carbohydrate response element-binding protein

COG: Clusters of Orthologous Groups of proteins

DEGs: differentially expressed genes

DEPC: diethyl pyrocarbonate

FAS: fatty acid synthetase

FDR: False Discovery Rate

FPKM: Fragments Per Kilobase of transcript sequence per Million base pairs sequenced

GO: Gene Ontology

GPAT: glycerol-3-phosphate acyltransferases

HMGR: 3-hydroxy-3-methylglutaryl-CoA reductase

HSL: hormone-sensitive lipase

Hsps: heat shock proteins

IB: injection buffer

IIS: insulin/insulin-like growth factor signaling

ILP2: insulin like peptide 2

InR: insulin receptor

IRS-1: insulin receptor substrate-1

KEGG: Kyoto Encyclopedia of Genes and Genomes

KOG: EuKaryotic Orthologous Groups

LKB1: liver kinase B1

NAFLD: Nonalcoholic fatty liver disease

Nr: Non-redundant

PC: pyruvate carboxylase

PEPCK: phosphoenolpyruvate carboxykinase

Pfam: Protein family

PFK2: 6-phosphofructo-2-kinase

PFKFB3: 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3

PIK3: phosphatidylinositol 4,5-bisphosphate 3-kinase

RNAi: RNA interference

SCAP: SREBP cleavage-activating protein

SREBP1: sterol regulatory element-binding protein 1

TG: triglyceride

TRE: trehalase

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and material

All data generated or analyzed during this study are included in this article and its supplementary information files.

We have uploaded the raw data of our transcriptome sequencing to GEO database. The GEO accession number is GSE155634.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the National Natural Science Foundation of China under grant nos. 31871974 and 31572000.

The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Authors’ Contributions

JW conceived and designed research. HJ, NZ and CJ conducted experiments. XM, YZ and KQ analyzed the data. HJ and JW wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Not applicable.

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Table 1 Summary of the transcriptome sequencing data from the controls and dsTcAMPKα treated samples
Samples	ID	Clean Read Number	Clean Base Number	GC (%)	Q30 (%)
EGFP1	T01	21,746,430	6,523,929,000	42.79	93.39
EGFP2	T02	20,376,206	6,112,861,800	42.86	93.94
EGFP3	T03	26,557,829	7,967,348,700	43.85	92.88
dsTcAMPKα1	T04	24,192,194	7,257,658,200	44.91	93.25
dsTcAMPKα2	T05	23,215,801	6,964,740,300	45.01	92.85
dsTcAMPKα3	T06	25,337,170	7,601,151,000	45.54	92.75


Table 2 Summary of average read numbers based on the RNA-sequencing data
	dsTcAMPKα	dsEGFP
Total alignments	38,451,597	39,074,583
Reads aligned	37,340,338	37,891,202
Unique alignments	36,870,045	37,564,766
Not aligned	11,156,438	7,895,775

Table 3. DEGs involved in different functional categories of KOG database

Categories	Up-regulated DEGs		Down-regulated DEGs
Categories	NO.	Partial gene description	NO.	Partial gene description
Signal transduction mechanisms	26	tyrosine-protein phosphatase; phorbol ester/diacylglycerol-binding protein; lachesin； arf-GAP; citron Rho-interacting kinase; cyclic nucleotide-gated cation channel; E3 ubiquitin; sortilin; lipid transfer protein	23	neurogenic locus protein; extensin; SNF1A/AMP-activated protein kinase; cGMP-dependent phosphodiesterase; troponin C; sortilin; atrial natriuretic; sensory neuron membrane protein; regulator complex protein; tetra phosphatase
Translation, ribosomal structure and biogenesis	1	eukaryotic translation initiation factor	42	ribosomal protein; exosome complex component; ribonuclease; H/ACA ribonucleoprotein; eukaryotic translation initiation factor
Posttranslational modification, protein turnover, chaperones	7	papilin; heat shock protein 68a; pregnancy zone protein; fucosyltransferase; brain tumor protein; E3 ubiquitin;	35	mannosyltransferase; sulfotransferase; GILT-like protein; heat shock protein 23; ubiquitin; suppressor protein; collagenase; glutathione S-transferase; protein transport protein; NEDD8
Amino acid transport and metabolism	4	hydroxylase; protease; transporter; glucose dehydrogenase	30	proteinase; Carboxypeptidase; glucose dehydrogenase; trypsin
Lipid transport and metabolism	5	nose resistant to fluoxetine protein; fatty acyl-CoA reductase; apolipophorins; ATP-binding cassette; Fatty acid synthetase	17	dehydrogenase/reductase; NADPH; alpha-tocopherol; acyl-CoA-binding protein; nose resistant to fluoxetine protein; fatty acyl-CoA reductase; desaturase; monooxygenase
Energy production and conversion	1	titin isoform X4	23	cytochrome b-c1 complex; stunted; ATP synthase; V-type proton ATPase; NADH dehydrogenase; cytochrome c oxidase; cytochrome b5; acylphosphatase;
Secondary metabolites biosynthesis, transport and catabolism	5	fatty acyl-CoA reductase; cytochrome P450s; multidrug resistance-associated protein lethal	12	dehydrogenase/reductase; NADPH; laccase; fatty acyl-CoA reductase; cytochrome P450s
Carbohydrate transport and metabolism	1	mucin	6	amylase; chitinase; lactoylglutathione lyase; glycolipid transfer protein; myrosinase; peritrophic matrix protein

Table 4. DEGs encoding metabolism related proteins and transcription factors/ co-activators from T. castaneum responding to dsTcAMPKα treatment.

Unigene name	padj	Description (blast)	Length (ORF bp)	Log₂Ratio Transcriptome	Log₂Ratio qRT-PCR
Lipid metabolism
FAS1	0.038775298	fatty acid synthase	12981	1.02	0.37
FAS2	0.056982009	fatty acid synthase	6522	1.76	0.71
FAS3	0.260213539	fatty acid synthase	7152	0.79	1.05
FAS4	0.425672932	fatty acid synthase	6630	0.63	1.16
FAS5	0.170696071	fatty acid synthase	6450	0.58	1.53
ACC	0.186316184	acetyl-CoA carboxylase	7005	0.83	0.10
GPAT3	0.046646995	glycerol-3-phosphate acyltransferases	1440	0.67	1.51
Brummer	1.60E-05	triacylglycerol lipase	1635	-2.20	-0.37
Carbohydrate metabolism
TRE1-1	0.036902117	Trehalase1-1	1662	0.89	1.46
TRE1-3	0.450009964	Trehalase1-3	>507	0.56	0.58
TRE1-4	0.007152475	Trehalase1-4	1812	0.68	0.59
TRE2	0.036902117	Trehalase2	1647	0.89	1.61
Insulin signaling pathway
IRS1	0.018687502	insulin receptor substrate	2760	1.13	0.63
InR2	0.004419643	insulin-like receptor	4185	1.09	0.23
PI3K	3.37E-08	phosphatidylinositol 4,5-bisphosphate 3-kinase	3186	1.02	0.63
Transcription factor and co-activator
SCAP	0.031742692	sterol regulatory element binding protein cleavage-activating protein	3783	0.81	1.12
SREBP1	0.036908768	sterol regulatory element binding protein 1	3078	0.94	1.74
ChREBP	0.000606393	carbohydrate response-element-binding protein	510	1.18	1.94

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Metabolic and transcriptome responses of RNAi-mediated AMPKα knockdown in Tribolium castaneum

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