Which phenotypic traits of resistance should be improved in cattle to control paratuberculosis dynamics in a dairy herd: a modelling approach.

Racem Ben Romdhane, Gaël Beaunée, Guillaume Camanes, Raphaël Guatteo, Christine Fourichon, Pauline Ezanno
Author Information
  1. Racem Ben Romdhane: BIOEPAR, INRA, ONIRIS, 44307, Nantes, France. racem.ben-romdhane@oniris-nantes.fr. ORCID
  2. Gaël Beaunée: MAIAGE, INRA, 78352, Jouy-en-Josas, France.
  3. Guillaume Camanes: BIOEPAR, INRA, ONIRIS, 44307, Nantes, France.
  4. Raphaël Guatteo: BIOEPAR, INRA, ONIRIS, 44307, Nantes, France.
  5. Christine Fourichon: BIOEPAR, INRA, ONIRIS, 44307, Nantes, France.
  6. Pauline Ezanno: BIOEPAR, INRA, ONIRIS, 44307, Nantes, France.

Abstract

Paratuberculosis is a worldwide disease causing production losses in dairy cattle herds. Variability of cattle response to exposure to Mycobacterium avium subsp. paratuberculosis (Map) has been highlighted. Such individual variability could influence Map spread at larger scale. Cattle resistance to paratuberculosis has been shown to be heritable, suggesting genetic selection could enhance disease control. Our objective was to identify which phenotypic traits characterising the individual course of infection influence Map spread in a dairy cattle herd. We used a stochastic mechanistic model. Resistance consisted in the ability to prevent infection and the ability to cope with infection. We assessed the effect of varying (alone and combined) fourteen phenotypic traits characterising the infection course. We calculated four model outputs 25 years after Map introduction in a naïve herd: cumulative incidence, infection persistence, and prevalence of infected and affected animals. A cluster analysis identified influential phenotypes of cattle resistance. An ANOVA quantified the contribution of traits to model output variance. Four phenotypic traits strongly influenced Map spread: the decay in susceptibility with age (the most effective), the quantity of Map shed in faeces by high shedders, the incubation period duration, and the required infectious dose. Interactions contributed up to 12% of output variance, highlighting the expected added-value of improving several traits simultaneously. Combinations of the four most influential traits decreased incidence to less than one newly infected animal per year in most scenarios. Future genetic selection should aim at improving simultaneously the most influential traits to reduce Map spread in cattle populations.

References

  1. Can J Vet Res. 2011 Apr;75(2):112-6 [PMID: 21731181]
  2. Vet Microbiol. 2008 Dec 10;132(3-4):274-82 [PMID: 18599225]
  3. J Clin Microbiol. 2011 Mar;49(3):893-901 [PMID: 21209171]
  4. Vet Clin North Am Food Anim Pract. 1996 Jul;12(2):383-415 [PMID: 8828112]
  5. Science. 2007 Nov 2;318(5851):812-4 [PMID: 17975068]
  6. Prev Vet Med. 2008 Mar 17;83(3-4):215-27 [PMID: 17868937]
  7. Am J Vet Res. 2001 Feb;62(2):270-4 [PMID: 11212038]
  8. Acta Vet Scand. 1982;23(3):325-35 [PMID: 7180779]
  9. Prev Vet Med. 2015 Oct 1;121(3-4):189-98 [PMID: 26321657]
  10. Vet J. 2009 Jan;179(1):60-9 [PMID: 17928247]
  11. J Dairy Sci. 2014 Jul;97(7):4562-7 [PMID: 24819128]
  12. PLoS One. 2014 Dec 04;9(12):e111704 [PMID: 25473852]
  13. Prev Vet Med. 2011 Jun 15;100(2):116-25 [PMID: 21549436]
  14. Comp Immunol Microbiol Infect Dis. 2011 May;34(3):197-208 [PMID: 21216466]
  15. Genet Mol Res. 2013 Jul 30;12(3):2702-11 [PMID: 23979895]
  16. Vet Res. 2015 Jun 19;46:68 [PMID: 26091904]
  17. J Dairy Sci. 2010 Oct;93(10):4455-70 [PMID: 20854979]
  18. Anim Genet. 2011 Apr;42(2):149-60 [PMID: 20618184]
  19. Ir Vet J. 2009 Sep 01;62(9):597-606 [PMID: 21851740]
  20. Vet Res. 2015 Jun 19;46:60 [PMID: 26092284]
  21. Vet Res. 2015 Sep 25;46:111 [PMID: 26407894]
  22. J Comp Pathol. 1962 Apr;72:113-7 [PMID: 14490277]
  23. Vet Clin North Am Food Anim Pract. 2011 Nov;27(3):559-71, vi [PMID: 22023834]
  24. J Theor Biol. 2016 Nov 7;408:105-17 [PMID: 27521525]
  25. J Dairy Sci. 2012 Oct;95(10):6145-51 [PMID: 22901469]
  26. Epidemiol Infect. 2012 Feb;140(2):231-46 [PMID: 21524342]
  27. Vet J. 2010 Apr;184(1):37-44 [PMID: 19246220]
  28. Anim Genet. 2015 Apr;46(2):122-32 [PMID: 25643727]
  29. J Dairy Sci. 2012 May;95(5):2734-9 [PMID: 22541503]
  30. Mamm Genome. 2010 Aug;21(7-8):419-25 [PMID: 20706723]
  31. Vet Microbiol. 2000 Dec 20;77(3-4):291-7 [PMID: 11118714]
  32. Anim Genet. 2009 Oct;40(5):655-62 [PMID: 19422364]
  33. Vet Res. 2011 Feb 15;42:36 [PMID: 21324117]
  34. Prev Vet Med. 2015 Dec 1;122(3):298-305 [PMID: 26520176]
  35. Aust Vet J. 2000 Jan;78(1):34-7 [PMID: 10736683]
  36. Vet Res. 2005 Sep-Dec;36(5-6):811-26 [PMID: 16120255]
  37. J Immune Based Ther Vaccines. 2011 Oct 31;9:8 [PMID: 22035107]
  38. Nat Rev Immunol. 2008 Nov;8(11):889-95 [PMID: 18927577]
  39. Anim Genet. 2011 Feb;42(1):28-38 [PMID: 20477805]
  40. PLoS One. 2014 Feb 11;9(2):e88380 [PMID: 24523889]
  41. Am J Vet Res. 1992 Apr;53(4):477-80 [PMID: 1586015]
  42. Vet J. 2008 May;176(2):129-45 [PMID: 17449304]
  43. Vet Microbiol. 2007 Jun 21;122(3-4):270-9 [PMID: 17317041]
  44. Vet Rec. 1947 Aug 16;59(31):397-401 [PMID: 20262432]
  45. Vet Microbiol. 2007 May 16;122(1-2):83-96 [PMID: 17289303]
  46. Vet Clin North Am Food Anim Pract. 1996 Jul;12(2):345-56 [PMID: 8828109]
  47. Vet Res. 2014 Jul 17;45:71 [PMID: 25224905]
  48. J Dairy Sci. 2011 Feb;94(2):992-7 [PMID: 21257067]
  49. Prev Vet Med. 2009 Jan 1;88(1):1-14 [PMID: 18817995]
  50. J Theor Biol. 2010 Jun 21;264(4):1190-201 [PMID: 20347851]
  51. J Dairy Sci. 2014 Mar;97(3):1762-73 [PMID: 24556012]
  52. Vet Res. 2015 Jun 19;46:69 [PMID: 26091672]

MeSH Term

Animals
Cattle
Dairying
Disease Resistance
Female
Models, Statistical
Mycobacterium avium subsp. paratuberculosis
Paratuberculosis
Phenotype
Virus Shedding

Word Cloud

Created with Highcharts 10.0.0traitsMapcattleinfectionphenotypicdairyparatuberculosisspreadresistancemodelinfluentialdiseaseindividualinfluencegeneticselectioncontrolcharacterisingcourseabilityfourherd:incidenceinfectedoutputvarianceimprovingsimultaneouslyParatuberculosisworldwidecausingproductionlossesherdsVariabilityresponseexposureMycobacteriumaviumsubsphighlightedvariabilitylargerscaleCattleshownheritablesuggestingenhanceobjectiveidentifyherdusedstochasticmechanisticResistanceconsistedpreventcopeassessedeffectvaryingalonecombinedfourteencalculatedoutputs25 yearsintroductionnaïvecumulativepersistenceprevalenceaffectedanimalsclusteranalysisidentifiedphenotypesANOVAquantifiedcontributionFourstronglyinfluencedspread:decaysusceptibilityageeffectivequantityshedfaeceshighsheddersincubationperioddurationrequiredinfectiousdoseInteractionscontributed12%highlightingexpectedadded-valueseveralCombinationsdecreasedlessonenewlyanimalperyearscenariosFutureaimreducepopulationsimproveddynamicsmodellingapproach

Similar Articles

Cited By