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As a commercially important species, the Chinese mitten crab ( Eriocheir sinensis ) has been cultured for a long time in China. Agonistic behavior often causes limb disability and requires much energy, which is harmful to the growth and survival of crabs. In this paper, we divided crabs into a control group (control, no treatment) and an experimental group (fight, agonistic behavior after 1 h) and then collected the thoracic ganglia (TG) to extract RNA. Subsequently, we first used a deep sequencing approach to examine the transcripts of microRNAs (miRNAs) and messenger RNAs (mRNAs) in  E. sinensis  displaying agonistic behavior. According to the results, we found 29 significant differentially expressed miRNAs (DEMs) and 116 significant differentially expressed unigenes (DEGs). The DEMs esi-miR-199a-5p, esi-let-7d, esi-miR-200a, and esi-miR-200b might participate in the regulation of agonistic behavior by mediating neuroregulation and energy metabolism. Focusing on the transcripts of the mRNAs, the renin–angiotensin system (RAS) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway might be involved in the regulation of agonistic behavior through glucose metabolism as this pathway was significantly enriched with DEGs. Besides, an integrated analysis of the miRNA and mRNA profiles revealed that the retinoid X receptor (RXR) was also involved in visual signal transduction, which was important for agonistic behavior. In addition, four vital agonistic behavior-related metabolic pathways, including the cAMP signaling, MAPK, protein digestion and absorption, and fatty acid metabolism pathways, were significantly enriched with the predicted target unigenes. In conclusion, the findings of this study might provide important insight enhancing our understanding of the underlying molecular mechanisms of agonistic behavior in  E. sinensis .

frontiers in genetics

Identification and Integrated Analysis of MicroRNA and mRNA Expression Profiles During Agonistic Behavior in Chinese Mitten Crab (Eriocheir sinensis) Using a Deep Sequencing Approach