Transcriptome Analysis Provides Insights into the Markers of Resting and LPS-Activated Macrophages in Grass Carp (Ctenopharyngodon idella).
Yazhen HuXiaolei WeiZhiwei LiaoYu GaoXiaoling LiuJian-Guo SuGailing YuanPublished in: International journal of molecular sciences (2018)
Macrophages are very versatile immune cells, with the characteristics of a proinflammatory phenotype in response to pathogen-associated molecular patterns. However, the specific activation marker genes of macrophages have not been systematically investigated in teleosts. In this work, leukocytes (WBC) were isolated using the Percoll gradient method. Macrophages were enriched by the adherent culture of WBC, then stimulated with lipopolysaccharide (LPS). Macrophages were identified by morphological features, functional activity and authorized cytokine expression. Subsequently, we collected samples, constructed and sequenced transcriptomic libraries including WBC, resting macrophage (Mø) and activated macrophage (M(LPS)) groups. We gained a total of 20.36 Gb of clean data including 149.24 million reads with an average length of 146 bp. Transcriptome analysis showed 708 differential genes between WBC and Mø, 83 differentially expressed genes between Mø and M(LPS). Combined with RT-qPCR, we proposed that four novel cell surface marker genes (CD22-like, CD63, CD48 and CD276) and two chemokines (CXCL-like and CCL39.3) would be emerging potential marker genes of macrophage in grass carp. Furthermore, CD69, CD180, CD27, XCL32a.2 and CXCL8a genes can be used as marker genes to confirm whether macrophages are activated. Transcriptome profiling reveals novel molecules associated with macrophages in C. Idella, which may represent a potential target for macrophages activation.
Keyphrases
- genome wide
- bioinformatics analysis
- inflammatory response
- genome wide identification
- adipose tissue
- heart rate
- anti inflammatory
- genome wide analysis
- poor prognosis
- dna methylation
- cell surface
- single cell
- gene expression
- heart rate variability
- transcription factor
- artificial intelligence
- deep learning
- machine learning
- climate change
- electronic health record
- toll like receptor
- liver injury
- human health
- binding protein
- high speed
- lps induced
- drug induced