Mucus piRNAs profiles of Vibrio harveyi ‐infected Cynoglossus semilaevis : A hint for fish disease monitoring

2021 ◽  
Author(s):  
Na Zhao ◽  
Qiuxia Deng ◽  
Chunhua Zhu ◽  
Bo Zhang
1992 ◽  
Vol 91 ◽  
pp. 173-192 ◽  
Author(s):  
AD Vethaak ◽  
D Bucke ◽  
T Lang ◽  
PW Wester ◽  
J Jol ◽  
...  

Aquaculture ◽  
2019 ◽  
Vol 503 ◽  
pp. 430-435 ◽  
Author(s):  
Yangzhen Li ◽  
Lei Wang ◽  
Yingming Yang ◽  
Xue Li ◽  
Huan Dai ◽  
...  

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Hao Xu ◽  
Xiwen Xu ◽  
Xihong Li ◽  
Lei Wang ◽  
Jiayu Cheng ◽  
...  

Abstract Vibrio harveyi is a major bacterial pathogen that causes fatal vibriosis in Chinese tongue sole (Cynoglossus semilaevis), resulting in massive mortality in the farming industry. However, the molecular mechanisms of C. semilaevis response to V. harveyi infection are poorly understood. Here, we performed transcriptomic analysis of C. semilaevis, comparing resistant and susceptible families in response to V. harveyi challenge (CsRC and CsSC) and control conditions (CsRU and CsSU). RNA libraries were constructed using 12 RNA samples isolated from three biological replicates of the four groups. We performed transcriptome sequencing on an Illumina HiSeq platform, and generating a total of 1,095 million paired-end reads, with the number of clean reads per library ranging from 75.27 M to 99.97 M. Through pairwise comparisons among the four groups, we identified 713 genes exhibiting significant differences at the transcript level. Furthermore, the expression levels were validated by real-time qPCR. Our results provide a valuable resource and new insights into the immune response to V. harveyi infection.


2020 ◽  
Author(s):  
Sheng Lu ◽  
Qian Zhou ◽  
Yadong Chen ◽  
Yang Liu ◽  
Yangzhen Li ◽  
...  

Abstract Background: In recent years, the disease outbreak caused by Vibrio harveyi upset the booming development of the Chinese tongue sole (Cynoglossus semilaevis) farming industry. Genomic selection (GS) is a powerful method to improve the traits of interest, which has been proved in livestock and some fishes. Besides, the single nucleotide polymorphism (SNP) array is an efficient genotyping platform that can be used for genetic studies. To improve V. harveyi resistance in C. semilaevis, we firstly constructed a reference group of 1,572 individuals and investigated accuracies of four genomic methods (genomic best linear unbiased prediction (GBLUP), weighted GBLUP, BayesB, and BayesC) at predicting the genomic estimated breeding value (GEBV) using five-fold cross-validation and SNPs varying from 0.5 k to 500 k. Then, an SNP array was developed using the Affymetrix Axiom technology, and its accuracy in genotyping was evaluated by comparing SNPs generated by the array and by the re-sequencing technology. Finally, we selected 44 candidates as the parents of 23 families of C. semilaevis to evaluate the feasibility of the SNP array for GS.Results: all genomic methods outperformed the pedigree-based BLUP (ABLUP) when at least 50 k SNPs used for prediction, of which GBLUP resulted in better estimation than ABLUP when more than 1 k SNPs used. A 38 k SNP array, “Solechip No.1”, was developed with an average of 10.5 kb inter-spacing between two adjacent SNPs. The SNPs generated by the array and by the re-sequencing reached an average consistency of 94.8 %, of which 79.3 % of loci had a more than 90 % of the consistency. The survival rates of these 23 offspring families had a correlation of 0.706 with the family GEBVs (mid-parental GEBVs), and the average survival rate of the top five families in GEBVs (79.1 %) is higher than the bottom five families (58.1 %).Conclusion: GS is an efficient method to improve the V. harveyi resistance in C. semilaevis, and the SNP array “Solechip No.1” is a convenient and reliable tool for the Chinses tongue sole selective breeding practice.


2017 ◽  
Vol 24 (1) ◽  
pp. 22 ◽  
Author(s):  
Huan DAI ◽  
Yang LIU ◽  
Wenwen WANG ◽  
Zhanfei WEI ◽  
Jin GAO ◽  
...  

2020 ◽  
Vol 128 ◽  
pp. 268-276
Author(s):  
Na Zhao ◽  
Bo Zhang ◽  
Zihui Xu ◽  
Lei Jia ◽  
Ming Li ◽  
...  

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