Improvement of Disease Resistance in Livestock: Application of Immunogenomics and CRISPR/Cas9 Technology
Abstract
Simple Summary
Abstract
1. Introduction
2. Disease Resistance: The Phenotype
3. Advances of Immunogenomics
3.1. Sequencing Technology
3.2. Bioinformatics Tools
4. Applications of Immunogenomics in Livestock Disease Management
5. Advances of Genome Editing Technology
6. Applications of Genome Editing in Livestock Disease Management
6.1. Porcine Reproductive and Respiratory Syndrome (PRRS) in Pigs
6.2. African Swine Fever Resistance in Pig
6.3. Tuberculosis Resistance in Cattle
6.4. Enzootic Pneumonia Resistance in Cattle
7. Ethics, Regulations, and Social Acceptance of Genome-Edited Livestock Products
8. Potentials and Prospects of CRISPR/Cas9 Technology in Livestock Production
9. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Bioinformatics Tools/Databases | Potential Implications | References |
---|---|---|
‘Bowtie’, ‘msa’ | Sequence-read alignment, | https://cran.r-project.org/ |
R/Bioconductor, limma, DESeq2 | Differential gene expression analyses | https://cran.r-project.org/ |
GSEA-Gene Set Enrichment Analysis | Gene set enrichment analysis | [41] https://www.gsea-msigdb.org/gsea/index.jsp |
DAVID | Gene ontology and pathway analysis | [42] https://david.ncifcrf.gov/ |
KEEG-Kyoto Encyclopedia of Genes and Genomes | Gene ontology and pathway analysis | https://www.genome.jp/kegg/ |
InnateDB | Database for gene ontology, pathway analysis and prediction interactome | [43] https://www.innatedb.com/ |
REACTOME | Database for gene ontology and pathway analysis | [44] https://reactome.org/ |
QTLdb | Database of quantitative trait loci of animals | [39] https://www.animalgenome.org/cgi-bin/QTLdb/index |
BovineMine | Annotation and functions of gene | [45] http://128.206.116.13:8080/bovinemine/begin.do |
bioDBnet-Biological database network | Interconnected access to many types of biological databases, conversion of gene or protein identifies | [46] https://biodbnet-abcc.ncifcrf.gov/ |
STRING | Functional protein association network analysis and visualization | [47] https://string-db.org/ |
NetworkAnalyst | Co-regulatory gene or protein network analysis and visualization | [48] https://www.networkanalyst.ca/ |
WGCNA | Weighted gene co-expression network analysis | [49] https://cran.r-project.org/ |
Cytoscape | Creation and visualization gene network | [50] https://cytoscape.org/ |
Species | Disease | Targets of Genome Modification | Reference |
---|---|---|---|
Goat | Mastitis | Lysozyme (human) | [88] |
Cattle | Mastitis | Lysostaphin (Staphylococcus simulans) | [84] |
Enzootic pneumonia | Cluster of differentiation 18 (CD18) | [89] | |
Tuberculosis | The natural resistance to infection with intracellular pathogens 1 (NRAMP1) gene | [90] | |
Pigs | African swine fever | RELA | [86] |
PRRS | Histone deacetylase HDAC6 | [91] | |
Cluster of differentiation 163 (CD163) | [92,93] |
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Islam, M.A.; Rony, S.A.; Rahman, M.B.; Cinar, M.U.; Villena, J.; Uddin, M.J.; Kitazawa, H. Improvement of Disease Resistance in Livestock: Application of Immunogenomics and CRISPR/Cas9 Technology. Animals 2020, 10, 2236. https://doi.org/10.3390/ani10122236
Islam MA, Rony SA, Rahman MB, Cinar MU, Villena J, Uddin MJ, Kitazawa H. Improvement of Disease Resistance in Livestock: Application of Immunogenomics and CRISPR/Cas9 Technology. Animals. 2020; 10(12):2236. https://doi.org/10.3390/ani10122236
Chicago/Turabian StyleIslam, Md. Aminul, Sharmin Aqter Rony, Mohammad Bozlur Rahman, Mehmet Ulas Cinar, Julio Villena, Muhammad Jasim Uddin, and Haruki Kitazawa. 2020. "Improvement of Disease Resistance in Livestock: Application of Immunogenomics and CRISPR/Cas9 Technology" Animals 10, no. 12: 2236. https://doi.org/10.3390/ani10122236
APA StyleIslam, M. A., Rony, S. A., Rahman, M. B., Cinar, M. U., Villena, J., Uddin, M. J., & Kitazawa, H. (2020). Improvement of Disease Resistance in Livestock: Application of Immunogenomics and CRISPR/Cas9 Technology. Animals, 10(12), 2236. https://doi.org/10.3390/ani10122236