Literature DB >> 24136738

The integrative analysis of microRNA and mRNA expression in Apis mellifera following maze-based visual pattern learning.

Qiu-Hong Qin1, Zi-Long Wang, Liu-Qing Tian, Hai-Yan Gan, Shao-Wu Zhang, Zhi-Jiang Zeng.   

Abstract

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.
© 2013 Institute of Zoology, Chinese Academy of Sciences.

Entities:  

Keywords:  Apis mellifera; DGE; Y-maze; differential expression; learning and memory; microRNA

Mesh:

Substances:

Year:  2014        PMID: 24136738     DOI: 10.1111/1744-7917.12065

Source DB:  PubMed          Journal:  Insect Sci        ISSN: 1672-9609            Impact factor:   3.262


  12 in total

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3.  Identification and comparative analysis of differentially expressed miRNAs in leaves of two wheat (Triticum aestivum L.) genotypes during dehydration stress.

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4.  Age-dependent transcriptional and epigenomic responses to light exposure in the honey bee brain.

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5.  Large-scale transcriptome changes in the process of long-term visual memory formation in the bumblebee, Bombus terrestris.

Authors:  Li Li; Songkun Su; Clint J Perry; Maurice R Elphick; Lars Chittka; Eirik Søvik
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6.  miR-12 and miR-124 contribute to defined early phases of long-lasting and transient memory.

Authors:  Julia Michely; Susanne Kraft; Uli Müller
Journal:  Sci Rep       Date:  2017-08-11       Impact factor: 4.379

7.  Genome-wide DNA methylation changes associated with olfactory learning and memory in Apis mellifera.

Authors:  You Li; Li-Zhen Zhang; Yao Yi; Wan-Wan Hu; Ya-Hui Guo; Zhi-Jiang Zeng; Zachary-Y Huang; Zi-Long Wang
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8.  Comparative Identification of MicroRNAs in Apis cerana cerana Workers' Midguts in Responseto Nosema ceranae Invasion.

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Journal:  Insects       Date:  2019-08-21       Impact factor: 2.769

9.  Dnmts and Tet target memory-associated genes after appetitive olfactory training in honey bees.

Authors:  Stephanie D Biergans; C Giovanni Galizia; Judith Reinhard; Charles Claudianos
Journal:  Sci Rep       Date:  2015-11-04       Impact factor: 4.379

10.  High throughput sequencing of small RNAs reveals dynamic microRNAs expression of lipid metabolism during Camellia oleifera and C. meiocarpa seed natural drying.

Authors:  Jin-Ling Feng; Zhi-Jian Yang; Shi-Pin Chen; Yousry A El-Kassaby; Hui Chen
Journal:  BMC Genomics       Date:  2017-07-20       Impact factor: 3.969

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