doi: 10.15389/agrobiology.2018.2.235eng

UDC 636.08:636.018:577.12/.17

 

MOLECULAR MARKERS IN IMMUNE RESPONSE MANIFESTATIONS
(review)

A.F. Yakovlev

All-Russian Research Institute for Farm Animal Genetics and Breeding — branch of L.K. Ernst Federal Science Center for Animal Husbandry,Federal Agency of Scientific Organizations,55А, Moskovskoe sh., pos. Tyarlevo, St. Petersburg—Pushkin, 196625 Russia, e-mail afyakov@mail.ru
(✉ corresponding author)

ORCID:
Yakovlev A.F. orcid.org/0000-0002-7503-8033

Received June 23, 2016

 

The immune system is genetically controlled and responsible for population heterogeneity on the immune response. The aim of the article is to analyze molecular mechanisms of cell-mediated and antibody-mediated immune response and molecular markers for genomic selection. Genotypic differences between individuals in terms of tolerance/susceptibility to infectious diseases are characteristic of animal populations (S.C. Bishop et al., 2014). Data massive indicates multiple SNPs associated with high and low immune responses of animals, providing the possibility of calculating the coefficients of genomic breeding values for this attribute. There is a need to assess the dispersion of indirect genetic effects that help to open up new possibilities for the control of infectious diseases through selection. However, it should be noted that the quantitative genetic analysis based on individual animal disease status covers only part of the total genetic variation that affects the dynamics of infectious diseases in populations. Estimation of gene expression patterns in a particular immune response is considered as the most valuable (V.V. Firstova et al., 2010). Study of the major histocompatibility complex (MHC-B) 209296 bp region in birds with high-density SNP chips allowed the authors to determine 45 key genes which affect MHC-B diversity through recombination. The findings extend the understanding of the contribution of recombination to the diversity of MHC-B haplotypes, including the ability to identify hot spots and recombination estimation of recombination frequency (J.E. Fulton et al., 2016). The causative mutations related to the basic genetic variability of innate and adaptive immune responses in chickens are mapped (A. Slawinska et al., 2013). Search for causal mutations responsible for genetic variation in the immune response can be used as an approach to diagnostic tests. E.g., SNP associated with susceptibility to tuberculosis are detected (M.L. Bermingham et al., 2014). Immune response falls into a category of complex and quantitative traits that are under control of multiple genes with a noticeable influence of the environment. Obviously, some genes of common universal action may participate in innate and adaptive immunity. We can assume that such immunity has predominantly additive mode of inheritance (M. Siwek et al., 2015). Breeding for diseases resistance is greatly difficult because of low heritability and lack of estimates for comprehensive genetic assessment of the disease resistance variability. Growing genomic evaluations of the animals has created a basis for the use of molecular markers in breeding to increase animal resistance to diseases. Studies of the genome and the overall adaptive immune response associations in different species of farm animals provide an important starting point for the implementation of such plans. Identification of potential biological pathways and genes associated with immune response can assist in advancing the understanding of the important processes in animal resistance or susceptibility to diseases.

Keywords: immune response, antibodies, genome, single nucleotide polymorphism, SNP, disease, resistance, selection, quantitative traits, receptors, animals, heritability, associations, mutations.

 

Full article (Rus)

Full article (Eng)

 

REFERENCES

  1. Breeding for disease resistance in farm animals. S.C. Bishop, R.F.E. Axford, F.W. Nicholas, J.B. Owen (eds.). CABI, Wallingford, 2010 CrossRef
  2. Bishop S.C., Woolliams J.A. Genomics and disease resistance studies in livestock. Livest. Sci., 2014, 146: 190-198 CrossRef
  3. Yakovlev A.F., Smaragdov M.G. Zootekhniya, 2011, 5: 2-4 (in Russ.).
  4. Anche M.T., Bijma P., De Jong M.C. Genetic analysis of infectious diseases: estimating gene effects for susceptibility and infectivity. Genet. Sel. Evol., 2015, 47: 85 CrossRef
  5. Lipschutz-Powell D., Woolliams J.A., Bijma P., Doeschl-Wilson A.B. Indirect genetic effects and the spread of infectious disease: are we capturing the full heritable variation underlying disease prevalence? PLoS ONE, 2012, 7(6): e39551 CrossRef
  6. Lipschutz-Powell D., Woolliams J.A., Doeschl-Wilson A.B. A unifying theory for genetic epidemiological analysis of binary disease data. Genet. Sel. Evol., 2014, 46: 15 CrossRef
  7. Meuwissen T.H.E., Hayes B.J., Goddard M.E. Prediction of total genetic value using genome-wide dense marker maps. Genetics, 2001, 157: 1819-1829.
  8. Fulton J.E., McCarron A.M., Lund A.R., Pinegar K.N., Wolc A., Chazara O., Bed’Hom B., Berres M., Miller M.M. A high-density SNP panel reveals extensive diversity, frequent recombination and multiple recombination hotspots within the chicken major histocompatibility complex B region between BG2 and CD1A1. Genet. Sel. Evol., 2016, 48: 1 CrossRef
  9. Siwek M., Slawinska A., Rydzanicz M., Wesoly J., Fraszczak M., Suchocki T., Skiba J., Skiba K., Szyda J. Identification of candidate genes and mutations in QTL regions for immune responses in chicken. Anim. Genet., 2015, 46(3): 247-254 CrossRef
  10. Slawinska A., Siwek M. Meta and combined QTL analysis of different experiments on immune traits in chickens. J. Appl. Genet., 2013, 54: 483-487 CrossRef
  11. Gray K.A., Cassady J.P., Huang Y., Maltecca C. Effectiveness of genomic prediction on milk flow traits in dairy cattle. Genet. Sel. Evol., 2012,44: 24 CrossRef
  12. Yakovlev A.F. Genetika i razvedenie zhivotnykh, 2014, 2: 3-6 (in Russ.).
  13. Lu X., Liu J., Fu W., Zhou J., Luo Y., Ding X., Liu Y., Zhang Q. Genome-wide association study for cytokines and immunoglobulin G in swine. PLoS ONE, 2013 CrossRef
  14. Huang H., Deng H., Yang Y., Tang Z., Yang S., Mu Y., Cui W., Yuan J., Wu Z., Li K. Molecular characterization and association analysis of porcine PANE1 gene. Mol. Biol. Rep., 2010, 37(5): 2571-2577 CrossRef
  15. Yudin N.S., Aitnazarov R.B., Knyazev S.P., Bekenev V.A., Podoba Yu.V., Berdibaeva A.B., Voevoda M.I. Vavilovskii zhurnal genetiki i selektsii, 2014, 18(2): 258-262 (in Russ.).
  16. Lee Y.M., Alam M., Choi B.H., Kim K.S., Kim J.J. Whole genome association study to detect single nucleotide polymorphisms for blood components (immunity) in a cross between Korean native pig and Yorkshire. Asian Austral. J. Anim., 2012, 25(12): 1674-1680 CrossRef
  17. Lim H.T., Lee J.B., Jung E.J., Ko M.S., Lee J.H., Jeon J.T. QTL analysis of white blood cell, platelet and red blood cell-related traits in an F2 intercross between Landrace and Korean native pigs. Anim Genet., 2011, 42(6): 621-626 CrossRef
  18. Lu X., Liu J.F., Gong Y.F., Wang Z.P., Liu Y., Zhang Q., Mapping quantitative trait loci for T lymphocyte subpopulations in peripheral blood in swine. BMC Genet., 2011, 12: 79-88 CrossRef
  19. Boddicker N.J., Bjorkquist A., Rowland R.R., Lunney J.K., Reecy J.M., Dekkers J.C. Genome-wide association and genomic prediction for host response to porcine reproductive and respiratory syndrome virus infection. Genet. Sel. Evol., 2014, 46(1): 18 CrossRef
  20. Boddicker N.J., Waide E.H., Rowland R.R.R., Lunney J.K., Garrick D.J., Reecy J.M., Dekkers J.C.M. Evidence for a major QTL associated with host response to porcine reproductive and respiratory syndrome virus challenge. J. Anim. Sci., 2012, 90: 1733-1746 CrossRef
  21. Bermingham M.L., Bishop S.C., Woolliams J.A., Pong-Wong R., Allen A.R., Mc Bride S.H. Genome-wide association study identifies novel loci associated with resistance to bovine tuberculosis. Heredity, 2014, 112: 543-551 CrossRef
  22. Bishop S.C., Doeschl-Wilson A.B., Woolliams J.A. Uses and implications of field disease data for livestock genomic and genetics studies. Frontiers in Genetics, 2012, 3: 114 CrossRef
  23. Kirkpatrick B.W., Shi X., Shook G.E., Collins M.T. Whole-genome association analysis of susceptibility to paratuberculosis in Holstein cattle. Anim. Genet., 2011, 42: 149-160 CrossRef
  24. Bermingham M.L., Bishop S.C., Woolliams J.A., Pong-Wong R., Allen A.R., McBride S.H. Genome-wide association study identifies novel loci associated with resistance to bovine tuberculosis. Heredity, 2014, 112: 543-553 CrossRef
  25. Kirkpatrick B.W., Shi X., Shook G.E., Collins M.T. Whole-genome association analysis of susceptibility to paratuberculosis in Holstein cattle. Anim. Genet., 2011, 42: 149-160 CrossRef
  26. Lipschutz-Powell D., Woolliams J.A., Bijma P., Doeschl-Wilson A.B. Indirect genetic effects and the spread of infectious disease: are we capturing the full heritable variation underlying disease prevalence PLoS ONE, 2012, 7(6): e39551 CrossRef
  27. Proshin S.N., Kosyakova G.P., YAkovlev A.F. Immunotsitokhimicheskie markery pro-liferatsii limfotsitov krovi pri leikoze korov. Voprosy normativno-pravovogo regulirovaniya v veterinarii, 2015, 2: 90-93
  28. Bishop S. Opportunities for incorporating genetic elements into the management of farm animal diseases: policy issues. In: Commission on Genetic Resources for Food and Agriculture. M. de Jong, D. Gray (eds.). Rome, FAO, 2002: 36-39.
  29. Lee C.-R., Cho I.H., Jeong B.C., Lee S.H. Strategies to minimize antibiotic resistance. Int. J. Environ. Res. Public Health, 2013, 10(9): 4274-4305 CrossRef
  30. Wagter L., Mallard B.A. Method of identifying high immune response animals. University of Guelph assignee. US20020051789A1. US Application. US Pat., inventors. 2007, No. 7.
  31. Thompson-Crispi K.A., Sewleam A., Miglior F., Mallard B A. Genetic parameters of adaptive immune response traits in Canadian Holsteins. J. Dairy Sci., 2012, 95: 401-409 (doi:  10.3168/jds.2011-4452).
  32. Thompson-Crispi K.A., Sargolzaei M., Ventura R., Abo-Ismail M., Miglior F., Schenkel F., Mallard B.A. A genome-wide association study of immune response traits in Canadian Holstein cattle. BMC Genomics, 2014, 15: 559-568 CrossRef
  33. Martin C.E., Paibomesai M.A., Emam S.M., Gallienne J., Hine B.C., Thompson-Crispi K.A., Mallard B.A. Cytokine profiles from blood mononuclear cells of dairy cows classified with divergent immune response phenotypes. J. Dairy Sci., 2016, 99(3): 2364-2371 CrossRef
  34. Fleming K., Thompson-Crispi K.A., Hodgins D.C., Miglior F., Corredig M., Mallard B.A. Variation of total immunoglobulin G and β-lactoglobulin concentrations in colostrum and milk from Canadian Holsteins classified as high, average, or low immune responders. J. Dairy Sci., 2016, 99(3): 2358-2363 CrossRef
  35. Firstova V.V., Pavlov V.M., Gorbatov A.A., Kombarova T.I., Karaulov A.V., Dyat-lov I.A. Immunologiya, 2014, 3: 147-150 (in Russ.).
  36. Firstova V.V., Bakhteeva I.V., Titareva G.M., Zyrina E.V., Ivanov S.A., Kiseleva N.V., Kopylov P.Kh., Anisimov A.P., Dyatlov I.A. Problemy osobo opasnykh infektsii, 2010, 1(103): 56-59 (in Russ.).
  37. Firstova V.V., Bakhteeva I.V., Titareva G.M., Zyrina E.V., Ivanov S.A., Anisimov A.P. Meditsinskaya immunologiya, 2009, 11(4-5): 336-337 (in Russ.).
  38. Shchukovskaya T.N., Smol'kova E.A., Shmel'kova T.P., Klyueva S.N., Bugorkova S.A. Epidemiologiya i vaktsinoprofilaktika, http://elibrary.ru/pic/1pix.gif2011, 6(61): 78-83 (in Russ.).
  39. Ezdakova I.Yu. Veterinarnaya meditsina, 2007, 1: 11-12 (in Russ.).
  40. Ezdakova I.Yu. Identifikatsiya i kharakteristika biologicheskikh svoistv belkov supersemeistva immunoglobulinov zhivotnykh. Avtoreferat doktorskoi dissertatsii [Identification and characterization of animal proteins of immunoglobulin superfamilies. DSci. Thesis]. Moscow, 2010 (in Russ.).
  41. Clop A., Huisman A., van As P., Sharaf A., Derdak S., Sanchez A. Identification of genetic variation in the swine toll-like receptors and development of a porcine TLR genotyping array. Genet. Sel. Evol., 2016, 48: 28 CrossRef
  42. Shinkai H., Arakawa A., Tanaka-Matsuda M., Ide-Okumura H., Terada K., Chikyu M. Genetic variability in swine leukocyte antigen class II and Toll-like receptors affects immune responses to vaccination for bacterial infections in pigs. Comp. Immunol. Microbiol. Infect. Dis., 2012, 35: 523-532 CrossRef
  43. Uenishi H., Shinkai H., Morozumi T., Muneta Y., Jozaki K., Kojima-Shibata C., Suzuki E. Polymorphisms in pattern recognition receptors and their relationship to infectious disease susceptibility in pigs. BMC Proceedings, 2011, 5(Suppl. 4): S27 CrossRef 
  44. Yang X., Murani E., Ponsuksili S., Wimmers K. Association of TLR5 sequence variants and mRNA level with cytokine transcription in pigs. Immunogenetics, 2013, 65: 125-132 CrossRef
  45. Shinkai H., Suzuki R., Akiba M., Okumura N., Uenishi H. Porcine Toll-like receptors: recognition of Salmonella enterica serovar Choleraesuis and influence of polymorphisms. Mol. Immunol., 2011, 48(9-10): 1114-1120 CrossRef
  46. Yang X.Q., Murani E., Ponsuksili S., Wimmers K. Association of TLR4 polymorphism with cytokine expression level and pulmonary lesion score in pigs. Mol. Biol. Rep., 2012, 39(6): 7003-7009 CrossRef 
  47. Kich J.D., Uthe J.J., Benavides M.V., Cantão M.E., Zanella R., Tuggle C.K. TLR4 single nucleotide polymorphisms (SNPs) associated with Salmonella shedding in pigs. J. Appl. Genet., 2014, 55(2): 267-271 CrossRef
  48. Sereda A.D., Kazakova A.S., Imatdinov A.R., Kolbasov D.V. Humoral and cell immune mechanisms under African swine fever (review). Agricultural Biology, 2015, 50(6): 709-718 CrossRef
  49. Obukhovskaya O.V., Stegnii B.T. Aktual'nye voprosy veterinarnoi biologii, 2015, 3: 27-31 (in Russ.).
  50. Miller M.M., Goto R.M., Taylor R.L., Zoorob R., Auffray C., Briles R.W. Assignment of Rfp-Y to the chicken major histocompatibility complex/NOR microchromosome and evidence for high-frequency recombination associated with the nucleolar organizer region. PNAS USA, 1996, 93(9): 3958-3962 CrossRef
  51. Hofmann A., Plachy J., Hunt L., Kaufman J., Hala K. V-src oncogene-specific carboxy-terminal peptide is immunoprotective against Rous sarcoma growth in chickens with MHC class I allele B-F12. Vaccine, 2003, 21(32): 4694-4699 CrossRef
  52. Goto R.M., Wang Y., Taylor R.L., Wakenell P.S., Hosomichi K., Shiina T. BG1 has a major role in MHC-linked resistance to malignant lymphoma in the chicken. PNAS USA, 2009, 106(39): 16740-16745 CrossRef 
  53. Miller M.M., Taylor J.R. Brief review of the chicken major histocompatibility complex — the genes, their distribution on chromosome 16 and their contributions to disease resistance. Poultry Sci., 2016, 95(2): 375-392 CrossRef 
  54. Briles W.E., Bumstead N., Ewert D.L., Gilmour D.G., Gogusev J., Hála K. Nomenclature for chicken major histocompatibility (B) complex. Immunogenetics, 1982, 15(5): 441-447 CrossRef
  55. Hosomichi K., Miller M.M., Goto R.M., Wang Y., Suzuki S., Kulski J.K. Contribution of mutation, recombination, and gene conversion to chicken MHC-B haplotype diversity. J. Immunol., 2008, 18: 3393-3399 CrossRef
  56. Nerren J.R., He H., Genovese K., Kogut M.H. Expression of the avian-specific toll-like receptor 15 in chicken heterophils is mediated by Gram-negative and Gram-positive bacteria, but not TLR agonists. Vet. Immunol. Immunop., 2010, 136(1-2): 151-156 CrossRef
  57. de Koning D.-J., Carlborg O., Haley C.S. The genetic dissection of immune response using gene-expression studies and genome mapping. Vet. Immunol. Immunop., 2005, 105(3-4): 343-352 CrossRef
  58. Lunney J. Understanding genetic disease resistance. U.S. National Hog Farmer. April 26, 2011. Available http://www.nationalhogfarmer.com/health-diseases/understanding-genetic-disease-resistance-0415. Accessed April 11, 2018.
  59. Glazanova T.V., Rozanova O.E., Bubnova L.N. Meditsinskaya immunologiya, 2014, 16(5): 409-416 (in Russ.).
  60. Baldueva I.A., Novik A.V., Karitskii A.P., Kuleva S.A., Nekhaeva T.L., Danilova A.B., Protsenko S.A., Semenova A.I., Komarov Yu.I., Pipia N.P., Slavyanskaya T.A., Avdonkina N.A., Sal'nikova S.V., Belyaev A.M., Sepiashvili R.I. Allergologiya i immunologiya, 2015, 16(4): 354-358 (in Russ.).
  61. Mougiakakos D., Choudhury A., Lladser A., Kiessling R., Johansson C.C. Regulatory T cells in cancer. Adv. Cancer Res., 2010, 107(57): 117-122 CrossRef
  62. Kakita N., Kanto T., Itose I., Kuroda S., Inoue M., Matsubara T., Higashitani K., Miyazaki M., Sakakibara M., Hiramatsu N., Takehara T., Kasahara A., Hayashi N. Comparative analyses of regulatory T cell subsets in patients with hepatocellular carcinoma: a crucial role of CD25-FoxP3-T cells. Int. J. Cancer, 2012, 131(11): 2573-2583 CrossRef
  63. Kudryavtsev I.V., Borisov A.G., Krobinets I.I., Savchenko A.A., Serebryakova M.K. Meditsinskaya immunologiya, 2015, 17(6): 525-538 CrossRef (in Russ.).

 

back