Fish IRF3 up-regulates the transcriptional level of IRF1, IRF2, IRF3 and IRF7 in CIK cells
FISH & SHELLFISH IMMUNOLOGY
Authors: Xu, Xiaowen; Lai, Qinan; Gu, Meihui; Liu, Dan; Hou, Qunhao; Liu, Xiancheng; Mi, Yichuan; Sun, Zhicheng; Wang, Haizhou; Lin, Gang; Hu, Chengyu
Abstract
Interferon Regulatory Factors (IRFs) belong to a family of transcription factor involved in transcriptional regulation of type I IFN and IFN-stimulated genes (ISG) in cells. In the present study, an IRF3 full-length cDNA (termed CiIRF3, JX999055) and its promoter sequence were cloned by homology cloning strategy and genome walking from grass carp (Ctenopharyngodon idella). The full-length cDNA sequence of CiIRF3 is comprised of a 5'UTR (195 bp), a 3'UTR (269 bp) and a largest open reading frame (ORF) of 1377 bp encoding a polypeptide of 458 amino acids. CiIRF3 has a conservative DNA-binding domain (DBD) at N-terminal and a relatively conserved interferon regulatory factors association domain (IAD). Phylogenetic tree analysis indicated that CiIRF3 gathers together with other IRF-3 from higher vertebrates in the same branch. The promoter sequence of CURF3 (596 bp) consists of three IRF-E, a C/EBP beta, a NF-kappa B and a TATA-BOX. CHRF3 was constitutively expressed at low level in different grass carp tissues but was rapidly up-regulated with Poly I:C stimulation. To study the molecular mechanism of CiIRF3 regulating the transcription of IRFs, CiIRF3 was expressed in Escherichia coli BL21 and purified by affinity chromatography with the Ni-NTA His-Bind Resin. Gel mobility shift assays revealed the affinity of CiIRF3 protein with promoters of CiIRF1, CURF2, CiIRF3 and CiIRF7 respectively. Then, CIK cells were co-transfected with pcDNA3.1-CiIRF3, pGL3-promotor (pGL3-CiIRF1, pGL3-CiIRF2, pGL3-CURF3, pGL3-CiIRF7) and luciferase reporter vector respectively. The cotransfection experiment showed that CiIRF3 increased the promoter activity of CiIRF1, CiIRF2, CiIRF3 and CiIRF7. Furthermore, overexpression of CURF3 in CIK cells also up-regulated the expressions of CiIRF1, CiIRF2, CiIRF3 and CiIRF7. So, CiIRF3 can improve the transcriptional level of CiIRF1, CURF2, CiIRF3 and CiIRF7. (C) 2015 Elsevier Ltd. All rights reserved.
Genome-wide characterization of copy number variations in the host genome in genetic resistance to Marek's disease using next generation sequencing
BMC GENETICS
Authors: Bai, Hao; He, Yanghua; Ding, Yi; Chu, Qin; Lian, Ling; Heifetz, Eliyahu M.; Yang, Ning; Cheng, Hans H.; Zhang, Huanmin; Chen, Jilan; Song, Jiuzhou
Abstract
Background Marek's disease (MD) is a highly neoplastic disease primarily affecting chickens, and remains as a chronic infectious disease that threatens the poultry industry. Copy number variation (CNV) has been examined in many species and is recognized as a major source of genetic variation that directly contributes to phenotypic variation such as resistance to infectious diseases. Two highly inbred chicken lines, 6(3)(MD-resistant) and 7(2)(MD-susceptible), as well as their F(1)generation and six recombinant congenic strains (RCSs) with varied susceptibility to MD, are considered as ideal models to identify the complex mechanisms of genetic and molecular resistance to MD. Results In the present study, to unravel the potential genetic mechanisms underlying resistance to MD, we performed a genome-wide CNV detection using next generation sequencing on the inbred chicken lines with the assistance of CNVnator. As a result, a total of 1649 CNV regions (CNVRs) were successfully identified after merging all the nine datasets, of which 90 CNVRs were overlapped across all the chicken lines. Within these shared regions, 1360 harbored genes were identified. In addition, 55 and 44 CNVRs with 62 and 57 harbored genes were specifically identified in line 6(3)and 7(2), respectively. Bioinformatics analysis showed that the nearby genes were significantly enriched in 36 GO terms and 6 KEGG pathways including JAK/STAT signaling pathway. Ten CNVRs (nine deletions and one duplication) involved in 10 disease-related genes were selected for validation by using quantitative real-time PCR (qPCR), all of which were successfully confirmed. Finally, qPCR was also used to validate two deletion events in line 7(2)that were definitely normal in line 6(3). One high-confidence gene,IRF2was identified as the most promising candidate gene underlying resistance and susceptibility to MD in view of its function and overlaps with data from previous study. Conclusions Our findings provide valuable insights for understanding the genetic mechanism of resistance to MD and the identified gene and pathway could be considered as the subject of further functional characterization.