Transcriptome differences in the hypopharyngeal gland between Western Honeybees (Apis mellifera) and Eastern Honeybees (Apis cerana)
- Hao Liu†1,
- Zi-Long Wang†1,
- Liu-Qing Tian1,
- Qiu-Hong Qin1,
- Xiao-Bo Wu1,
- Wei-Yu Yan1 and
- Zhi-Jiang Zeng1Email author
© Liu et al.; licensee BioMed Central Ltd. 2014
Received: 14 April 2014
Accepted: 26 August 2014
Published: 30 August 2014
Apis mellifera and Apis cerana are two sibling species of Apidae. Apis cerana is adept at collecting sporadic nectar in mountain and forest region and exhibits stiffer hardiness and acarid resistance as a result of natural selection, whereas Apis mellifera has the advantage of producing royal jelly. To identify differentially expressed genes (DEGs) that affect the development of hypopharyngeal gland (HG) and/or the secretion of royal jelly between these two honeybee species, we performed a digital gene expression (DGE) analysis of the HGs of these two species at three developmental stages (newly emerged worker, nurse and forager).
Twelve DGE-tag libraries were constructed and sequenced using the total RNA extracted from the HGs of newly emerged workers, nurses, and foragers of Apis mellifera and Apis cerana. Finally, a total of 1482 genes in Apis mellifera and 1313 in Apis cerana were found to exhibit an expression difference among the three developmental stages. A total of 1417 DEGs were identified between these two species. Of these, 623, 1072, and 462 genes showed an expression difference at the newly emerged worker, nurse, and forager stages, respectively. The nurse stage exhibited the highest number of DEGs between these two species and most of these were found to be up-regulated in Apis mellifera. These results suggest that the higher yield of royal jelly in Apis mellifera may be due to the higher expression level of these DEGs.
In this study, we investigated the DEGs between the HGs of two sibling honeybee species (Apis mellifera and Apis cerana). Our results indicated that the gene expression difference was associated with the difference in the royal jelly yield between these two species. These results provide an important clue for clarifying the mechanisms underlying hypopharyngeal gland development and the production of royal jelly.
The hypopharyngeal gland (HG), which is a pair of glands located in the head of worker bees, is composed of clusters of acini, which deliver secretions (royal jelly) into a collecting duct that runs to the mouthparts. The main function of the HG is to produce and secrete the protein components of royal jelly, which is fed to the queen and larvae. The secretory activity and function of HGs are age-dependent . In newly emerged workers, the HGs are small and not fully developed. After that, the secretory activity of HGs could reach a peak within 6–12 days, and their main function at this stage is to synthesize and secrete royal jelly to feed larvae. The HGs then gradually degrade during the forager stage, and their protein secretion changes to the secretion of enzymes for brewing honey [2–4]. In addition, the HG has been reported to display flexible secretory activity in response to the needs of the feeding brood .
During the transition from newly emerged workers to foragers, the HGs show a marked change not only in size but also in protein synthesis. Some proteins, including alpha-glucosidase [2, 6], amylase and glucose oxidase , have been reported to display an age-dependent expression pattern in the HGs of workers. Ohashi identified a 64-kDa protein (RJP57-1) that is expressed specifically in the HGs of nurse bees and a 56-kDa protein that is expressed in the HGs of nurse bees and forager bees . Santos et al. identified the protein complement of the HGs of Africanized nurse bees (Apis mellifera L.) and found that almost all of them were related to the MRJP family and associated with the metabolism of carbohydrates and energy . Using proteomics method, Feng et al. analyzed the protein profile of six developmental stages of the Apis mellifera HGs and identified many proteins, including MRJPs and proteins involved in cytoskeleton, antioxidant activity, developmental regulation, and carbohydrate, lipid and protein metabolism . Moreover, Li et al. analyzed the protein expression difference in hypopharyngeal gland development between Italian and royal jelly-producing workers (Apis mellifera L.) through proteomics . Their results demonstrated that a high royal jelly-producing honeybee strain significantly up-regulates a large group of proteins involved in metabolism of carbohydrates, nucleotides, amino acids, and fatty acids, proteins involved in protein biosynthesis, energy production, development, antioxidation, and protein folding, and transporter and cytoskeleton proteins. Recently, Liu et al. analyzed the gene expression difference between five developmental time points of HGs in Apis mellifera and identified many DEGs .
Apis mellifera and Apis cerana, as representative honeybee species of the East and West, are two important honeybee species that are widely bred and studied. Recent studies on these two species have revealed that both geographical isolation and long-term evolutionary divergence are responsible for their differences in key biological characteristics, such as shape, individual development, and living habit . Apis cerana is adept at collecting sporadic nectar in the mountain or forest region and exhibits stiffer hardiness and acarid resistance as a result of natural selection. Apis mellifera has the advantage of yielding royal jelly which is one of the main differences between these two honeybee species . A previous study indicated that the mean length and the acini number of the Apis mellifera HGs were significantly greater than those of the Apis cerana HGs, and the royal jelly yielding ability of Apis mellifera was more than ten-fold higher than that of Apis cerana. Fang et al. compared the protein profiles of royal jelly produced by Apis mellifera ligustica and Apis cerana cerana using proteomic approaches and identified that royal jelly proteins (MRJPs), peroxiredoxin 2540, and glutathione S-transferase S1 were differentially expressed . However, no studies on the transcript and/or protein differences between the HGs of Apis mellifera and Apis cerana have been reported. Detecting the gene expression difference in HGs between these two sibling species is important for understanding the mechanism of high royal jelly production.
The completion of the honeybee (Apis mellifera L.) genome sequencing  and the development of high-throughput sequencing methods provide the possibility for us to investigate the genome-wide gene expression profile. The aim of this study was to use DGE-tag analysis to identify genes specifically expressed in HGs that were associated with a significant difference in the production of royal jelly between Apis mellifera and Apis cerana. Through DGE sequencing and rigorous screening, we identified 1417 DEGs between Apis mellifera and Apis cerana. Our study provides valuable data for clarifying the molecular mechanism of HG development and a high yield of royal jelly in honeybees.
Results and discussion
DGE library sequencing
Statistics of DGE sequencing at the three developmental stages
Distinct clean tag
All tag mapping to gene
Unambiguous tag mapping to gene
Mapping to genome
All tag-mapped genes
Unambiguous tag-mapped genes
To determine whether the depth of deep sequencing is sufficient, we performed a sequencing saturation analysis (Additional file 4: Figure S4). When the sequencing amount of the twelve DGE libraries reached a value close to 2 M, the number of detected genes reached a value near the limit, suggesting saturation of the sequencing depth.
DEGs between different developmental stages of the hypopharyngeal gland in Apis mellifera
We compared our results with those from previous proteomics studies performed by Feng et al., in which 27 proteins were identified as differentially expressed between day 1 to day 20 in Apis mellifera HGs. Of these, 12 showed some expression difference in our study among the three developmental stages of HGs, and most of them showed a similar expression pattern to that reported by Feng et al. This result to some extent confirmed the reliability of our experimental results.
Because the transition from newly emerged worker bees to nurse bees is the critical period for royal jelly production, we paid more attention to the DEGs between these two stages. We found the alpha-amylase (NM_001011598.1) and alpha-glucosidase (NM_001011608.1), which have been repeatedly reported to be expressed specifically in the HGs of foragers and have been speculated to be related to the processing of nectar into honey [2, 3, 6], were significantly up-regulated in nurses compared with the newly emerged workers and continued to be up-regulated in foragers, which is consistent with the proteomics results reported by Li et al.. These two enzymes are involved in the digestion of carbohydrates. Alpha-amylase is though to be needed to hydrolyze starch into glucose . Alpha-glucosidase can catalyze polysaccharide digestion and function in the final steps of starch digestion . The up-regulation of these two genes at the nurse stage may be related to some other function with the exception of brewing honey.
Although MRJPs are the major protein components of the royal jelly, we only found one MRJP member, namely MRJP7 (NM_001014429.1) expressed at its highest level at the nurse stage among the three stages. Moreover, MRJP1 (NM_001011579.1) and MRJP4 (NM_001011610.1) exhibited strong expression in the newly emerged workers. However, these two genes showed no statistically significant difference between the newly emerged workers and nurses, although their TPM values in nurses were higher than those found in the newly emerged workers. Feng et al. also found that MRJP1, 2 and 3 could be detected in the HGs of workers on day 1 . These results suggested that the HGs of workers already have secretory activity before the nurse stage.
The GO enrichment analysis of the DEGs between newly emerged workers and nurses showed that 21 items were significantly enriched (P < 0.05) (Additional file 7: Table S2). The KEGG pathway enrichment analysis of the DEGs between these two stages indicated that “Ribosome”, “Protein processing in endoplasmic reticulum”, and “Protein export” (Additional file 8: Table S3), which are related to protein synthesis or secretion, were significantly enriched items (Qvalue < 0.05).
In the comparison between nurses and foragers, most of the DEGs are down-regulated in foragers. The GO enrichment analysis revealed that eight items, including “macromolecular complex”, “ribonucleoprotein complex”, “intracellular”, “intracellular part”, “structural molecule activity”, “metal cluster binding”, “metabolic process”, and “gene expression”, were significantly enriched (P < 0.05) (Additional file 7: Table S2). The KEGG pathway enrichment analysis revealed that “Ribosome”, “Metabolic pathways”, “Oxidative phosphorylation”, “Parkinson’s disease”, “Fatty acid metabolism”, and “Protein processing in endoplasmic reticulum” were significantly enriched items (Qvalue < 0.05) (Additional file 8: Table S3).
DEGs between different developmental stages of the hypopharyngeal gland in Apis cerana
In Apis cerana, 7486 genes were detected to be transcribed at the three stages (Additional file 5: Figure S5). A total of 1313 genes showed an expression difference at least between two stages. Of them, 1209, 103, and 331 genes showed an expression difference in the comparisons of newly emerged worker vs. nurse, nurse vs. forager and newly emerged worker vs. forager (Figure 1, Additional file 9: Table S4), respectively. A total of 254, 4, and 32 genes showed their highest expression at the newly emerged worker, nurse, and forager stages, respectively.
Similar to the findings found in Apis mellifera, the 1313 DEGs overall showed a higher expression in the newly emerged workers and decreased expression at the nurse stage. However, unlike the findings found in Apis mellifera, most of these DEGs were slightly up-regulated at the forager stage compared with the nurse stage (Figure 2). The expression pattern of the 1313 DEGs in Apis cerana and the 1482 DEGs in Apis mellifera indicated that even though the HGs exhibit their highest activity for royal jelly secretion at the nurse stage, but no peak of a large amount of up-regulated genes was appeared in nurse. This expression pattern is in fact in accordance with the physiological activity of the HGs and can be reasonably explained. At the newly emerged worker stage, the HGs are in a phase of rapid growth, and a large number of genes are expressed at a higher level to promote their development. At the nurse stage, however, although the size and secretory activity of the HGs reach their maximum, the resources of the HG cells are mainly used for the synthesis of royal jelly; therefore, those genes related to royal jelly protein synthesis and secretion are highly expressed, whereas the expression level of the other genes are decreased. At the forager stage, the HGs of honeybees begin to shrink and their secretion activity is decreased, which leads to the expression level of most of the genes in HGs remaining at a relatively lower level or exhibiting a further declined.
Of the DEGs found between newly emerged workers and nurses of Apis cerana, we found several major royal jelly protein genes, including MRJP1 (NM_001011579.1), MRJP5 (NM_001011599.1), MRJP6 (NM_001011622.1), and MRJP7 (NM_001014429.1), were significantly up-regulated at the nurse stage, which is consistent with their function in the HG. Alpha-glucosidase (NM_001011608.1) and alpha-amylase (NM_001011598.1) were also up-regulated in nurses.
The GO enrichment analysis of the DEGs between newly emerged workers and nurses showed that 53 items were significantly enriched (P < 0.05) (Additional file 10: Table S5). The KEGG pathway enrichment analysis indicated that 19 items, including the above-mentioned three items found between newly emerged workers and nurses in Apis mellifera, were significantly enriched (Qvalue < 0.05) (Additional file 11: Table S6). These results are consistent with the physiological changes of honeybee HGs during this period.
Between the nurse and forager stages, however, only one GO item namely “protein tyrosine/serine/threonine phosphatase activity”, was significantly enriched (Qvalue < 0.05) (Additional file 10: Table S5), and no KEGG items were significantly enriched (Qvalue < 0.05).
Gene expression difference in the hypopharyngeal gland between Apis mellifera and Apis cerana
The GO enrichment analysis of all of the 1417 DEGs showed that “cytoplasmic part”, “cytoplasm”, “macromolecular complex”, “ribonucleoprotein complex”, “mitochondrion”, “mitochondrial part”, “structural molecule activity”, “metabolic process”, and “organic substance metabolic process” are dominant (P < 0.05) (Additional file 13: Table S8). The KEGG pathway enrichment analysis indicated that “Ribosome”, “Metabolic pathways”, “Oxidative phosphorylation”, “Parkinson’s disease”, “Fatty acid metabolism”, “Valine, leucine and isoleucine degradation”, and “Protein processing in endoplasmic reticulum” were significantly enriched (Qvalue < 0.05) (Additional file 14: Table S9).
TOR, insulin/IGF and TGF pathway genes
Because the size of the Apis mellifera HG is larger than that of Apis cerana, we speculated that some genes related to cell growth and differentiation may contribute to this difference; thus, more attention was paid to genes in related signaling pathways, such as the TOR, insulin/IGF and TGF pathways. Among the 1417 DEGs, we found that two TOR pathway genes, namely 3-phosphoinositide-dependent protein kinase 1 (PDK1) (XM_394208.4) and eukaryotic translation initiation factor 4E (XM_624287.3), and two insulin/IGF pathways genes, namely IGF-II mRNA-binding protein (XM_393878.4) and cell growth-regulating nucleolar protein-like (XM_623800.3), were significantly expressed higher in Apis mellifera than in Apis cerana at the newly emerged worker and nurse stages (Additional file 12: Table S7). The TOR and insulin/IGF signaling pathways have been identified as two main pathways that control cell growth through studies in model organisms . The TOR pathway acts as a nutrient sensor in multicellular organisms and regulates growth in response to nutrients, and the insulin/IGF pathway is involved in coordinating cellular growth in response to endocrine signals and plays a key role in regulating growth in invertebrates and vertebrates . The insulin and TOR pathways form a signaling network that integrates information about nutrient availability and an intrinsic developmental program. In addition, the TGF-beta receptor 1 genes (XM_003251608.1) were also expressed at higher levels in Apis mellifera at the newly emerged worker and nurse stages. The TGF-β signaling pathway has been implicated as an important regulator of almost all major cell behaviors, including proliferation, differentiation, cell death, and motility . The higher expression of these genes in Apis mellifera may suggest that the up-regulation of these genes can promote the development of HGs, which to some extent leads to the higher yield of royal jelly.
This study provides the first report of some DEGs in the hypopharyngeal gland between Apis mellifera and Apis cerana at the newly emerged worker, nurse and forager stages. Our results confirmed that many DEGs may play an important role in the development of HGs and the secretion of royal jelly. All of the information obtained in our study contributes to further research on the specifically expressed genes in HG at the molecular level.
The honeybee species Apis mellifera ligustica and Apis cerana cerana were used in this experiment. They were bred in the Honeybee Research Institute, Jiangxi Agricultural University, China (28.46 °N, 115.49 °E). Worker bees from these two species were gathered at the three developmental stages (newly emerged worker, nurse and forager). The foragers could be easily identified by the pollen loads on their hind legs. The nurses were caught at the time when they entered the cells and were nursing the larvae. For each developmental stage, two independent biological replicates were collected. Finally, a total of 720 workers were sampled randomly. All of the samples were collected alive, immediately flash frozen in liquid nitrogen, and then stored at −80°C until further processing. The HGs were dissected under a binocular stereo microscope. The detailed dissection steps are as follows: First the labrum was gripped with curved forceps to fix the head, and the skull of the head was then exscinded with a razor blade. After removing the shell on the cranial cavity using forceps, we instilled a few drops of normal saline (137 mmol/L NaCl, 2.7 mmol/L KCl, 10 mmol/L Na2HPO4, and 2 mmol/L KH2PO4) to ensure that the HGs dissociate from the brain tissue and then selected the HGs and cut them off from the mouthpiece. Finally, the HGs were rinsed with DEPC-treated water and promptly frozen with liquid nitrogen for Illumina sequencing analysis of the DGE. During dissection, the room temperature was maintained at 16°C, and the normal saline and DEPC-treated water were kept on ice. To prevent the degradation of mRNA, the sampled honeybee heads were preserved in dry ice before dissection and the HGs were dissected out within 4 min. The HGs from 60 worker bees were pooled as a sample to create the tag library.
Digital gene expression library preparation and sequencing
The total RNA was extracted using the SV Total RNA isolation System (Promega, USA) and then subjected to quality inspection. The tag-seq libraries were then constructed using the Illumina Gene Expression Sample Prep Kit according to the manufacturer’s instruction. Briefly, mRNA was purified from 6 μg of total RNA with oligo (dT) magnetic beads and then synthesized into double-stranded DNA (cDNA) by reverse transcription. The cDNA was digested with Nla III which could recognize CATG site and the Illumina adaptor 1 was ligated to the sticky 5′ end of the digested bead-bound 3′ cDNA fragments. The junction of Illumina adaptor 1 and the CATG site is the recognition site of Mme I, which is a type of endonuclease with separated recognition sites and digestion sites and cuts 17 bp downstream of the CATG site, producing tags containing adaptor 1. Then, Illumina adaptor 2 was ligated to the 3′ ends of the tags, obtaining tags with different adaptors on both ends. The cDNA tags containing adaptors 1 and 2 were enriched with 15-cycle PCR amplification with the sequencing primers and then purified by 6% PAGE gel electrophoresis. The single-stranded molecules were bound to the Illumina sequencing chip and sequenced using Illumina HiSeq™ 2000. The sequencing-received raw image data were transformed by base calling into sequence data and stored in FASTQ format. Each tunnel generated millions of raw reads with a sequencing length of 49 bp. The raw sequences were filtered into clean tags by the process, which included the removal of the adaptor sequence, empty tags, low-quality tags, tags with only one copy number and tags that were too long or too short, leaving tags 21 bp in length, which were named clean tags. The clean reads of Apis mellifera and Apis cerana were submitted to the NCBI Sequence Read Archive database under the accession numbers SRP033111 and SRP033303, respectively.
Tag mapping and statistical analysis
Before mapping tag to reference sequences, two tag libraries containing all of the possible CATG + 17 nt tag sequences were created as reference tag databases using all of the available mRNA sequences and genome sequences of A. mellifera downloaded from the GenBank database (ftp://ftp.ncbi.nih.gov/genomes/Apis_mellifera). Because A. mellifera and A. cerana are two closely related species, we used mRNA and genome sequences of A. mellifera as the reference sequences of A. cerana. All of the clean tags were then mapped to the reference tag database with only one nucleotide mismatch being allowed, and unambiguous tags were annotated. The number of unambiguous clean tags for each gene was calculated and normalized to TPM (number of transcripts per million clean tags). Those tags that cannot be mapped to any gene in the tag database of mRNA sequences were continuing mapped to the tag database of the reference genome sequence.
Identification of differentially expressed genes
To identify the differentially expressed genes (DEGs) among the sample libraries, we applied a rigorous statistical algorithm based on the protocol from Tarazona and García-Alcalde . The NOISeq method for the analysis of the “noise” distribution from the actual data could be better adapted to the size of the dataset and more effective for controlling the false discovery rate (FDR). Briefly, let Xgij be the expression of gene g in condition i (i = 1, 2) and replicate j. The log fold change and the difference are used to measure the expression level change between the two conditions. To determine the probability of differential expression, the algorithm creates a so-called “noise” distribution by pooling all of the replicates’ the empirical cumulative distribution function F (Mn, Dn) values within the same condition. The random variables describing the noise distribution can be regarded as F (|M*|, |D*|). A gene g is considered to be differentially expressed when the corresponding values for |Mg| and |Dg| are likely to be higher than that due to noise (|M*|and D* values). The probability can be written as P1 (|M*| < |Mg|, |D*| < |Dg|) and the probability of not being differentially expressed between the two conditions can be easily derived as P0 = 1-P1. The higher this probability, the higher the expression changes between conditions. We used a probability threshold of Q = 0.8, which is equivalent to an odds of 4:1 (P1/P0), which means that the gene is four-fold more likely to be differentially expressed than non-differentially expressed. The genes with a Q value ≥ 0.8 and an absolute value of log2 Ratio ≥ 1 were considered to be significantly expressed genes.
where N is the number of all genes with a GO annotation, n is the number of differentially expressed genes in N. M is the number of all genes that are annotated to the certain GO terms, and m is the number of differentially expressed genes in M.
The KEGG pathway enrichment analysis identified significantly enriched metabolic pathways or signal transduction pathways in the DEGs compared with the whole-genome background. The formula used is the same as that used in GO analysis.
The cluster analysis of gene expression patterns was performed using the cluster 3.0 software and the “Java Treeview” software. For each gene, the TPM mean value of the two biologically replicates at each stage were used for cluster analysis.
We thank Dr. Gui-Sheng Wu for helpful suggestions that improved the manuscript. This work was supported by the earmarked fund for China agriculture research system (No.CARS-45-KXJ12), the 555 talents project of GanPo of JiangXi province and the Natural Science Foundation of Jiangxi Province (No. 20114BAB214001). The deep sequencing and bio-information analysis work were carried out in the Beijing Genome Institute-Shenzhen (http://www.genomics.cn/index).
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