Identification of Selective Signatures Associated with Resistance to Johne’s Disease (JD) in Goat Breeds

پژوهشهای علوم دامی ایران

Article 11, Volume 15, Issue 3 - Serial Number 55, September 2023, Pages 463-473   

Document Type: Research Articles

DOI: 10.22067/ijasr.2023.80444.1120

Authors

Hossein Mohammadi^{* 1}; Mohammad Shamsollahi²

¹Department of Animal Sciences, Faculty of Agriculture and Natural Resources, Arak University, Arak, Iran.

²Department of Animal Sciences, Faculty of Agriculture, University of Ilam. Ilam, Iran

Abstract

 
Introduction: Paratuberculosis, or Johne’s disease, is a chronic, granulomatous, gastrointestinal tract disease of goat and other ruminants caused by the bacterium Mycobacterium avium ssp. paratuberculosis (MAP). The clinical signs of disease in goat are pipestream diarrhea, weight loss, and edema due to hypoproteinemia caused by protein-losing enteropathy. Knowledge concerning genetics of susceptibility to MAP infection can contribute to disease control programs by facilitating genetic selection for a less susceptible population to reduce incidence of infection in the future. The opportunity for genetic improvement in susceptibility to infection is evidenced by estimates of heritability of MAP infection ranging from 0.03 to 0.28 (Kirkpatrick and Lett, 2018). Domestication and selection has significantly changed the behavioral and phenotypic traits in modern domestic animals. The selection of animals by humans left detectable signatures on the genome of modern goat. The identification of these signals can help us to improve the genetic characteristics of economically important traits in goat. Over the last decade, interest in detection of genes or genomic regions that are targeted by selection has been growing. Identifying signatures of selection can provide valuable insights about the genes or genomic regions that are or have been under selection pressure, which in turn leads to a better understanding of genotype-phenotype relationships. The aim of this study was to identify the selection signatures using the unbiased Theta method associated with resistance to Johne’s disease in two Italian goat breeds.
Materials and Methods: The work described here is a case–control association study using the Illumina Caprine SNP50 BeadChip to unravel the genes involved in susceptibility of goats to Johne’s disease. Goats in herds with a high occurrence of Johne's disease were classified as healthy or infected based on the level of serum antibodies against MAP, and 331 animals were selected for the study. For the Siriana breed 174 samples (87 cases and 87 controls) were selected from 14 herds and for the Jonica breed 157 samples (77 cases and 80 controls) were selected from 10 herds. Cases were defined as animals serologically positive for MAP by ELISA with a sample to positive ratio (S/P) higher than 0.7 and MAP negative animals had a S/P lower than 0.6. Positive animals were tested twice with the ID Screen Paratuberculosis confirmation test. The 331 samples were genotyped using the Illumina GoatSNP50 BeadChip. SNP missing 5% of data, with MAF of <1% and Hardy–Weinberg equilibrium p-values <10⁻⁶ were removed. The genotyping efficiency for samples was also verified, and samples with more than 5% missing data were removed. Grouping was done to infer selection signatures based on F_ST statistic. Bioinformatics inquiries were conducted employing the Ensembl database (Cunningham et al., 2022), specifically for caprine genes (assembly ARS1). The aim was to pinpoint potential candidate genes that have either been previously reported in, or are situated within the genomic regions encompassing the peak of absolute extreme FST values. In this context, regions corresponding to the top and bottom 0.01% of acquired positive and negative FST scores were earmarked as areas undergoing selection. The identification of genes was executed through the application of a 250 Kb window both upstream and downstream of each core SNP.
Results and Discussion: By applying a threshold at the 99.90 percentile of the obtained Theta (θ) values, a total of 13 distinct genomic regions were identified in the Jonica breed. These regions were situated across chromosomes 1, 5, 7 (in 2 regions), 8, 9 (in 2 regions), 11, 16, 17, 18, and 20 (in 2 regions). Similarly, in the Siriana breed, genomic regions were pinpointed on chromosomes 3, 5 (in 2 regions), 10, 12, 16, 17, 18, 23, 24, and 29. Further exploration through bioinformatics tools brought to light the overlap of these genomic regions with genes associated with the immune system, disease resistance, bacterial infection resilience, response to oxidative stress, and tumor suppression. The study population size is relatively modest, predominantly due to the intricacy of procuring a substantial volume of blood samples from goats within commercial herds that have been diagnosed with JD and are poised for culling. It's worth noting that JD diagnosis and culling procedures are not infallible preventive measures. The gradual progression of the disease often leads to late-stage diagnosis, allowing subclinical goats to intermittently excrete MAP in the environment. As the infection and disease progress, the fecal shedding of MAP increases and contributes to its horizontal transmission. In combination with genetic improvement (innate protection), vaccination (acquired protection) will support eradicating this incurable disease.
Conclusions: To conclude, the findings of this study hold potential significance as they offer valuable insights for identifying genomic regions and subsequently, the genes that influence Johne's disease in goats. Nonetheless, additional research endeavors are essential to enhance and validate these outcomes. Utilizing a more extensive sample size, incorporating whole-genome sequencing, and implementing high-density genotyping are imperative steps to further refine and strengthen these findings.

Keywords

Immune system; Johne’s disease; Theta statistic; Signal selection

References

1. Abbasi Moshaii, B. (2018). Genomic scan for selection signatures and identification of some candidate regions associated with mastitis in German Holstein cattle. Ph.D. Thesis of Animal Breeding and Genetics, Sari Agricultural Sciences and Natural Resources University, Iran. 128.
2. Abioja, M. O., Logunleko, M. O., Majekodunmi, B. C., Adekunle, E. O., Shittu, O. O., Odeyemi, A. J., Nwosu, E. U., Oke, O. E., Iyasere, O. S., Abiona, J. A., & Williams, T. J. (2022). Roles of candidate genes in the adaptation of goats to heat stress: A review. Small Ruminant Research, 2,106878. https://doi.org/10.1016/j.smallrumres.2022.106878.
3. Akey, J. M., Zhang, G., Zhang, K., Jin, L., & Shriver, M. D. (2002). Interrogating a high-density SNP map for signatures of natural selection. Genome Research, 12(12), 1805-1814. doi: 10.1101/gr.631202
4. Asadollahpour Nanaei, H., Kharrati-Koopaee, H., & Esmailizadeh, A. (2022). Genetic diversity and signatures of selection for heat tolerance and immune response in Iranian native chickens. BMC Genomics, 23(1),224. doi: 10.1186/s12864-022-08434-7.
5. Azizpour, N., Khelatabadi, A., Moradi, M., Mohammadi, H. (2020). Genome-wide association study based on gene-set enrichment analysis associated with milk yield in Holstein cattle. Journal of Animal Science Research, 30(1), 79-92. (In Persian). doi: 10.22034/as.2020.11009.
6. Chen, Q., Wang, Z., Sun, J., Huang, Y., Hanif, Q., Liao, Y., & Lei, C. (2020). Identification of genomic characteristics and selective signals in a Du'an Goat Flock. Animals (Basel), 10(6),994. doi: 10.3390/ani10060994.
7. Cui, X., Chang, Z., Dang, T., Meng, J., Wang, P., Wu, J., & Chai, J. (2022). TNF upregulates peptidoglycan recognition protein 1 in esophageal cancer cells to clear the path to its signaling: Making the "enemy" a friend. Archives of Biochemistry and Biophysics, 722,109192. doi: 10.1016/j.abb.2022.109192.
8. Duarte, I. N. H., Bessa, A. F. O., Rola, L. D., Genuíno, M. V. H., Rocha, I. M., Marcondes, C. R., Regitano, L. C. A., Munari, D. P., Berry, D. P., & Buzanskas, M. E. (2022). Cross-population selection signatures in Canchim composite beef cattle. PLoS One, 17(4),e0264279. doi: 10.1371/journal.pone.0264279.
9. Gao, Y., Jiang, J., Yang, S., Cao, J., Han, B., Wang, Y., Zhang, Y., Yu, Y., Zhang, S., & Zhang Q. (2018). Genome-wide association study of Mycobacterium avium subspecies Paratuberculosis infection in Chinese Holstein. BMC Genomics, 19(1),972. doi: 10.1186/s12864-018-5385-3.
10. Ghoreishifar, S. M., Eriksson, S., Johansson, A. M., Khansefid, M., Moghaddaszadeh-Ahrabi, S., Parna, N., Davoudi, P., & Javanmard, A. (2020). Signatures of selection reveal candidate genes involved in economic traits and cold acclimation in five Swedish cattle breeds. Genetic Selection Evolution, 52(1),52. doi: 10.1186/s12711-020-00571-5.
11. Javan Nikkhah, M. (2019). Identification of selective signatures associated with resistance to bovine leukosis (BLV) in Iranian Holstein cows. D. Thesis of Animal Breeding and Genetics, Tehran University, Iran. 139.
12. Khaltabadi Farahani, A. H., mohammadi, H., & moradi, H. (2020). Gene set enrichment analysis using genome-wide association study to identify genes and pathways associated with litter size in various sheep breeds. Animal Production, 22(3), 325-335. (In Persian). doi: 10.22059/jap.2020.292715.623468.
13. Kim, H., Song, K. D., Kim, H. J., Park, W., Kim, J., & Lee, T. (2015). Exploring the genetic signature of body size in Yucatan miniature pig. PLoS one, 10,4e0121732. doi: 10.1371/journal.pone.0121732.
14. Kuenstner, J. T., Naser, S., Chamberlin, W., Borody, T., Graham, D. Y., McNees, A., Hermon-Taylor, J., Hermon-Taylor, A., Dow, C. T., & Kuenstner, L. (2017). The Consensus from the Mycobacterium avium paratuberculosis (MAP) Conference. Frontiers Public Health, 5,208. doi: 10.3389/fpubh.2017.00208.
15. Kijas, J. W., Lenstra, J. A., Hayes, B., Boitard, S., Porto Neto, L. R., San Cristobal, M., Servin, B., McCulloch, R., Whan, V., McEwan, J., & Dalrymple, B. (2012). International sheep genomics consortium members. Genome-wide analysis of the world's sheep breeds reveals high levels of historic mixture and strong recent selection. PLoS Biological, 10(2),e1001258. doi: 10.1371/journal.pbio.1001258.
16. McRae, K. M., McEwan, J. C., Dodds, K. G., & Gemmell, N. J. (2014). Signatures of selection in sheep bred for resistance or susceptibility to gastrointestinal nematodes. BMC Genomics, 15(1),637. doi: 10.1186/1471-2164-15-637.
17. Maiorano, A. M., Cardoso, D. F., Carvalheiro, R., Júnior, G. A. F., de Albuquerque, L. G., & de Oliveira, H. N. (2022). Signatures of selection in Nelore cattle revealed by whole-genome sequencing data. Genomics, 114(2),110304. doi: 10.1016/j.ygeno.2022.110304.
18. Minozzi, G., De Iorio, M. G., Palazzo, F., Gandini, G., Biffani, S., Paolillo, G., Ciani, E., Di Marco Lo Presti, V., Stella, A., & Williams, J. L. (2023). Genome-wide association study for antibody response to Mycobacterium avium paratubeculosis in goats. Animal Genetics, 54(1),78-81. doi: 10.1111/age.13271.
19. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., Maller, J., Sklar, P., de Bakker, P. I., Daly, M. J., & Sham, P. C. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81(3),559-75. doi: 10.1086/519795.
20. Ramey, H., Decker, J., McKay, S., Rolf, M., Schnabel, R., & Taylor, J. (2013). Detection of selective sweeps in cattle using genome-wide SNP data. BMC Genomics, 14,382. doi: 10.1186/1471-2164-14-382.
21. Rostamzadeh Mahdabi, E., Esmailizadeh, A., Ayatollahi Mehrgardi, A., & Asadi Fozi, M. (2021). A genome-wide scan to identify signatures of selection in two Iranian indigenous chicken ecotypes. Genetic Selection Evolution, 53(1),72. doi: 10.1186/s12711-021-00664-9.
22. Saravanan, K. A., Panigrahi, M., Kumar, H., Parida, S., Bhushan, B., Gaur, G. K., Dutt, T., Mishra, B. P., & Singh, R. K. (2021). Genomic scans for selection signatures revealed candidate genes for adaptation and production traits in a variety of cattle breeds. Genomics, 113(3),955-963. doi: 10.1016/j.ygeno.2021.02.009.
23. Sweeney, R. W., Collins, M. T., Koets, A. P., McGuirk, S. M., & Roussel, A. J. (2012). Paratuberculosis (Johne's disease) in cattle and other susceptible species. Journal of Veterinary International Medicine, (6),1239-50. doi: 10.1111/j.1939-1676.2012.01019.x.
24. Wang, H. L., Li, Z. X., Wang, L. J., He, H., Yang, J., Chen, L., & Liu, X. L. (2013). Polymorphism in PGLYRP-1 gene by PCR-RFLP and its association with somatic cell score in Chinese Holstein. Research Veterinary Science, 95(2),508-14. doi: 10.1016/j.rvsc.2013.06.005.
25. Weir, B. S., & Cockerham, C. C. (1984). Estimating F‐statistics for the analysis of population structure. Evolution, 38(6),1358-1370. doi: 10.1111/j.1558-5646.1984.tb05657.x
26. Wright, S. (1965). The interpretation of population structure by F-statistics with special regard to systems of mating. Evolution, 1,395-420.
27. Zhu, C., Fan, H., Yuan, Z., Hu, S., Zhang, L., Wei, C., Zhang, Q., Zhao, F., & Du, L. (2015). Detection of selection signatures on the X chromosome in three sheep breeds. International Journal of Molecular Sciences, 16,20360-20374. doi: 10.3390/ijms160920360.