bioRxivGenomicsPreprint
Identification of Essential Temperature-Stressed Genes From Apis mellifera Hypopharyngeal Glands Transcriptomes Under Variable Temperatures
Abdulkadir Yusif MaigoroiD, Jeong‐Hyeon LeeiD, Sujin LeeiD, Hyung Wook KwoniD
Abstract
Temperature is one of the essential abiotic factors required for honey bee survival and pollination. It affects honey bee physiology, behavior, and expression of related genes. Also, considered one of the major factors contributing to colony collapse disease (CCD). In this research, RNA-seq analysis was performed using hypopharyngeal glands (HGs) tissue at low (18 {degrees}C), high (25 {degrees}C), and regular (22 {degrees}C) temperatures. Differentially expressed genes (DEGs) were identified after comparing the three groups with one another based on temperature differences using DESeq analysis. 5196 common DEGs (cDEGs) were identified among the groups. They are highly enriched in RNA processing and RNA metabolism process while the KEGG pathway enrichment analysis showed that the cDEGs are enriched in longevity regulating pathway, MAPK signaling pathway-fly, and Glycerophospholipid metabolism. Further, 360 temperature-stressed genes identified are highly enriched in translation, oxidative activity, and ribonucleoprotein complex. The enriched KEGG pathway includes ribosome, oxidative phosphorylation, fatty acid metabolism, and citrate cycle (TCA cycle). All the top ten (10) hub genes among the 360 temperature-stressed genes are found up-regulated. In addition, heat-shock protein 90 (HSP90) known as the stressed response gene, and Gr10, the amino acid response gene were up-regulated and down-regulated respectively in the temperature-stressed group. Low expression of Gr10 under temperature-stress can affect nursing behavior and bee development. Ultimately, these findings will help in identifying honeybee-temperature survival mechanisms under varying temperature effects.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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