Honey bees are both important pollinators and model insects due to their highly developed sociality and colony management. To better understand the molecular mechanisms underlying honey bee colony management, it is important to investigate the expression of genes putatively involved in colony physiology. Although quantitative real-time PCR (qRT-PCR) can be used to quantify the relative expression of target genes, internal reference genes (which are stably expressed across different conditions) must first be identified to ensure accurate normalisation of target genes. To identify reliable reference genes in honey bee (Apis mellifera) colonies, therefore, we evaluated seven candidate genes (ACT , EIF, EF1, RPN2, RPS5, RPS18 and GAPDH) in samples collected from three honey bee tissue types (head, thorax and abdomen) across all four seasons using three analysis programmes (NormFinder, BestKeeper and geNorm). Subsequently, we validated various normalisation methods using each of the seven reference genes and a combination of multiple genes by calculating the expression of catalase (CAT). Although the genes ranked as the most stable gene were slightly different on conditions and analysis methods, our results suggest that RPS5, RPS18 and GAPDH represent optimal honey bee reference genes for target gene normalisation in qRT-PCR analysis of various honey bee tissue samples collected across seasons. The Western honey bee, Apis mellifera L., plays an important role as a pollinator 1. In addition, the honey bee is considered to be a key model insect due to its relatively complex behaviours, including sociality, labour division and colony management 2. Previous studies have demonstrated that endocrine system status and gene expression are important factors for flexible honey bee colony management, which involves colonies seasonally regulating their labour division and population dynamics 3-5. In order to extend our understanding of the molecular mechanisms that underlie the regulation of honey bee colony physiology, information on the physiological functions of the genes putatively associated with colony management can be determined by analysing their expression profiles among different seasons and honey bee tissues 6,7. In quantitative real-time PCR (qRT-PCR), gene-specific mRNA (or cDNA) is quantified; this method has been used extensively because of its relative speed, sensitivity, replicability and accuracy 8,9. Therefore, qRT-PCR would be an ideal method for analysing the expression patterns of honey bee genes putatively involved in the plasticity of colony molecular physiology in samples collected across different seasons and tissues. However, because qRT-PCR results are highly sensitive to the initial amount of RNA content in the amplification reaction, the interpretation of target gene expression levels among various conditions would result in appreciable errors without the use of a reliable internal standard 7-10. Therefore, prior to analysing target gene expression levels among conditions, reference genes are requir...

Mosquitoes were collected biweekly from March to November for 3 years (2017)(2018)(2019) in three different habitats, including a migratory bird refuge, cowshed, and an urban area. Among a total of 22,783 female mosquitoes comprising six genera and 13 species collected for 3 years, Aedes vexans nipponii (56.08%) was the dominant species, followed by Culex pipiens complex (14.26%), Anopheles spp. (11.57%), Culex orientalis (6.90%), Aedes albopictus (4.66%), Armigeres subalbatus (4.06%), and Culex tritaeniorhynchus (1.41%); the remaining six species comprised only 1.1% of all mosquito species collected. In the three habitats, 70.42% of mosquitoes were collected in the migratory bird refuge, whereas 10.68% and 18.90% were investigated in the cowshed and urban areas, respectively. The dominant species were Ae. vexans nipponii in the migratory bird refuge, Anopheles spp. in the cowshed, and Cx. pipiens complex in the urban area. The seasonal distribution of mosquitoes showed that most were obtained between May and September, with bimodal peaks in the third week of June and third week of August. Meteorological analysis revealed a high correlation between temperature and mosquito abundance. In the flavivirus detection analysis, no virus-transmitting infectious diseases were amplified by PCR, but the Chaoyang virus was found in Ae. vexans nipponii. Considering that global warming is a major risk factor for the spread of vector-borne diseases and that mosquito has been a fatal vector of infectious disease, our study of surveillance and analysis of seasonal/environmental mosquito population dynamics are essential for public health.