The honeybee （Apis mellifera） is a social insect with strong sensory capacity and diverse behavioral repertoire and is recognized as a good model organism for studying the neurobiological basis of learning and memor...The honeybee （Apis mellifera） is a social insect with strong sensory capacity and diverse behavioral repertoire and is recognized as a good model organism for studying the neurobiological basis of learning and memory. In this study, we analyzed the changes in microRNA （miRNA） and messenger RNA （mRNA） following maze-based visual learning using next-generation small RNA sequencing and Solexa/lllumina Digital Gene Expression tag profiling （DGE）. For small RNA sequencing, we obtained 13 367 770 and 13 132 655 clean tags from the maze and control groups, respectively. A total of 40 differentially expressed known miRNAs were detected between these two samples, and all of them were up-regulated in the maze group compared to the control group. For DGE, 5 681 320 and 5 939 855 clean tags were detected from the maze and control groups, respectively. There were a total of 388 differentially expressed genes between these two samples, with 45 genes up-regulated and 343 genes down-regulated in the maze group, compared to the control group. Additionally, the expression levels of 10 differentially expressed genes were confirmed by quantitative reverse transcription polymerase chain reaction （qRT-PCR） and the expression trends of eight of them were consistent with the DGE result, although the degree of change was lower in amplitude. The integrative analysis of miRNA and mRNA expression showed that, among the 40 differentially expressed known miRNAs and 388 differentially expressed genes, 60 pairs of miRNA/mRNA were identified as co-expressed in our present study. These results suggest that both miRNA and mRNA may play a pivotal role in the process of learning and memory in honeybees. Our sequencing data provide comprehensive miRNA and gene expression information for maze-based visual learning, which will facilitate understanding of the molecular mechanisms of honeybee learning and memory.展开更多
Storms are usually accompanied by a drop in tempera- ture, and an increase in wind and barometric pressure and rainfall, which have negative impacts on most activ- ities, survival and reproduction in insects （Gillot,...Storms are usually accompanied by a drop in tempera- ture, and an increase in wind and barometric pressure and rainfall, which have negative impacts on most activ- ities, survival and reproduction in insects （Gillot, 2005）. The majority of studies have mainly focused on how the flight activity of various flying insects such as honey- bees, bumble bees, horse flies and leafminers were directly influenced by intraday weather changes （Burnett ＆ Hays, 1974; Lundberg, 1980; Casas, 1989; Vicens ＆ Bosch, 2000）.展开更多
基金Acknowledgments This work was supported by the Earmarked Fund for the China Agricultural Research System （No. CARS- 45-KXJ12） and the National Natural Science Foundation of China （No. 31260524）. The deep sequencing and bio-information analysis work were carried out in the Beijing Genome Institute （http：//www.genomics.cn/ index.php）. We thank Hong Zhu for invaluable guidance and assistance in the maze experiments and dissection of samples, Dr. Aung Si and Dr. Andrew B. Barron for helpful suggestions that improved the manuscript, Fei Zhang and Zhen-Xiu Zeng for help with beekeeping, Xu Han and Shu-Yun Li for their help in maze experiments.
文摘The honeybee （Apis mellifera） is a social insect with strong sensory capacity and diverse behavioral repertoire and is recognized as a good model organism for studying the neurobiological basis of learning and memory. In this study, we analyzed the changes in microRNA （miRNA） and messenger RNA （mRNA） following maze-based visual learning using next-generation small RNA sequencing and Solexa/lllumina Digital Gene Expression tag profiling （DGE）. For small RNA sequencing, we obtained 13 367 770 and 13 132 655 clean tags from the maze and control groups, respectively. A total of 40 differentially expressed known miRNAs were detected between these two samples, and all of them were up-regulated in the maze group compared to the control group. For DGE, 5 681 320 and 5 939 855 clean tags were detected from the maze and control groups, respectively. There were a total of 388 differentially expressed genes between these two samples, with 45 genes up-regulated and 343 genes down-regulated in the maze group, compared to the control group. Additionally, the expression levels of 10 differentially expressed genes were confirmed by quantitative reverse transcription polymerase chain reaction （qRT-PCR） and the expression trends of eight of them were consistent with the DGE result, although the degree of change was lower in amplitude. The integrative analysis of miRNA and mRNA expression showed that, among the 40 differentially expressed known miRNAs and 388 differentially expressed genes, 60 pairs of miRNA/mRNA were identified as co-expressed in our present study. These results suggest that both miRNA and mRNA may play a pivotal role in the process of learning and memory in honeybees. Our sequencing data provide comprehensive miRNA and gene expression information for maze-based visual learning, which will facilitate understanding of the molecular mechanisms of honeybee learning and memory.
基金Acknowledgments This work was supported by the earmarked fund for China Agriculture Research System （No. CARS-45-KXJ 12） and the National Natural Science Foundation of China （No. 31360587）. We thank Prof. Dr. Zachary Y. Huang and Dr. Qiang Huang for revising the paper, and we thank Hao Liu and Hai-Yan Gan for helping with the experiment.
文摘Storms are usually accompanied by a drop in tempera- ture, and an increase in wind and barometric pressure and rainfall, which have negative impacts on most activ- ities, survival and reproduction in insects （Gillot, 2005）. The majority of studies have mainly focused on how the flight activity of various flying insects such as honey- bees, bumble bees, horse flies and leafminers were directly influenced by intraday weather changes （Burnett ＆ Hays, 1974; Lundberg, 1980; Casas, 1989; Vicens ＆ Bosch, 2000）.
