Processing math: 100%\nSkip to main contentSkip to article\nBrought to you by:Iowa State University Library\nMy account\nOutline\nCited by (16)\nFigures (3)\nTables (5)\nComparative Immunology, Microbiology and Infectious Diseases\nVolume 35, Issue 6, December 2012, Pages 523-532\nGenetic variability in swine leukocyte antigen class II and Toll-like receptors affects immune responses to vaccination for bacterial infections in pigs\nAuthor links open overlay panel, , , , , , , ,\nhttps://doi.org/10.1016/j.cimid.2012.05.003Get rights and content\nAbstract\nThe genes encoding swine leukocyte antigen (SLA) and Toll-like receptor (TLR) are highly polymorphic in pig populations, and likely have influences on infection and the effects of vaccination.\n\nWe explored the associations of different genotypes of SLA class II and of the genes TLR1, TLR4, TLR5, and TLR6 with antibody responses after vaccination against Erysipelothrix rhusiopathiae (ER) and Actinobacillus pleuropneumoniae (APP) serotypes 1, 2, and 5 in 191 Duroc pigs maintained under specific pathogen-free conditions.\n\nWe demonstrated close relationships between SLA class II and ER antibody response and between TLR genes other than TLR4 and APP antibody responses.\n\nPigs with specific haplotypes in SLA class II or TLR5 showed decreased antibody response to ER vaccination or increased responses to APP2 and APP5 vaccination, respectively.\n\nIt might be possible to breed for responsiveness to vaccination and to implement new vaccine development strategies unaffected by genetic backgrounds of pigs.\n\nKeywords\nVaccine\nGenetic background\nErysipelothrix rhusiopathiae\nActinobacillus pleuropneumoniae\nPig\n1.\n\nIntroduction\nIn the modern swine industry, intensive husbandry that invites deterioration of the feeding environment and an increase in stress increases susceptibility to respiratory and intestinal infections [1].\n\nAntibiotics are used to suppress the infection rates and to cure affected animals.\n\nHowever, because of prohibition of the prophylactic use of antibiotics in many countries and consumer antipathy toward drug residues, a decrease in antibiotic use is desirable.\n\nAlthough the use of vaccination to prevent infection is beneficial as an alternative to antibiotic use, there are large differences in the efficacy of vaccination among individuals, even with the use of the same vaccine.\n\nThis difference is caused not only by environmental factors but also by genomic diversity among vaccinated individuals [2], [3], especially in terms of the genes coding for major histocompatibility complex (MHC) and Toll-like receptors (TLRs).\n\nMHC molecules present antigens to T cells.\n\nThese molecules are classified into class I and II groups, which are responsible for antigen presentation to CD8+ and CD4+ T cells, respectively [4].\n\nAntigenic peptides derived from bacteria are presented mainly on MHC class II molecules by dendritic cells (DCs) and other antigen-presenting cells and are recognized by CD4+ T cells; therefore, vaccination against bacterial infection exerts its effect mainly through class II, rather than class I, molecules [5].\n\nIn contrast, TLRs play a central role in the innate immune system that recognizes the patterns of specific molecules derived from microorganisms, such as lipopolysaccharides, lipopeptides, and nucleic acids [6].\n\nTLRs are critical proteins that link innate and acquired immunity, and TLR ligands are used as vaccine adjuvants to improve the efficiency of vaccination [7], [8].\n\nInflammatory stimuli mediated principally by TLRs and other pattern-recognition receptors promote the accumulation of MHC class II molecules along with antigenic peptides on the surfaces of immature DCs, resulting in maturation of DCs and effective presentation of the antigens to T-cells [9], [10].\n\nThe associations between polymorphisms in MHC class II genes and antibody responses after vaccination have been studied in many animals, including humans and domestic animals such as sheep, chickens, dogs, and cattle [11], [12], [13], [14], [15], [16], [17].\n\nIn contrast, similar studies in TLR genes have so far been limited to humans [18], [19], [20], [21], and there has been limited work on the effects of genetic polymorphisms on antibody production after vaccination in pigs [22].\n\nIn pigs, MHC is designated “swine leukocyte antigen” (SLA) and is highly polymorphic, as in other vertebrates [23], [24].\n\nThe latest nomenclature update report defined 125 alleles of SLA class I genes and 164 alleles of SLA class II genes [24].\n\nFurthermore, new alleles and even new loci observed in certain haplotypes have been reported since this update [25], showing the complexity of this region.\n\nSimilarly, we have reported the existence of many single nucleotide polymorphisms (SNPs) in porcine TLR1, TLR2, TLR4, TLR5, and TLR6 genes, which are predicted to be involved in the recognition of bacteria [26].\n\nThese findings suggest that there are differences among individual pigs in the ability to recognize pathogen-derived molecules.\n\nHere, we explored the influences of haplotypes of SLA class II and porcine TLR genes on the efficacy of antibody responses after vaccination against two pathogens that have long been problematic, namely Erysipelothrix rhusiopathiae (ER) and Actinobacillus pleuropneumoniae (APP), and we demonstrated differences in vaccination response among genotypes.\n\nER is the main causative agent of swine erysipelas, and infections with this organism in humans have become a problem in farms and slaughterhouses [27].\n\nAPP, which is classified into 15 serotypes on the basis of capsule structure, causes porcine pleuropneumonia; serotypes 1 (APP1), 2 (APP2), and 5 (APP5) are common in Japan [28], [29], [30], [31].\n\nOur results show that it may be possible to produce effective vaccines against bacterial infections and to breed pigs on the basis of responsiveness to vaccination.\n\n2.\n\nMaterials and methods\n2.1.\n\nPigs\nExperimental pigs were produced from 2005 to 2006 by mating of Duroc breeding stock consisting of 28 dams and 11 sires and were maintained under specific pathogen-free (SPF) conditions at the Shizuoka Swine and Poultry Experiment Center, Japan.\n\nWe used 206 progeny reared under the same SPF facility for vaccination and blood collection.\n\nAll experiments were approved by the Center under the bylaws of Shizuoka Prefecture.\n\n2.2.\n\nVaccination and antibody titration\nAt 35 and 60 days after birth, progeny were inoculated with a 1 mL of combined vaccine of inactivated ER and inactivated APP1, 2, and 5 (Pigwin-EA; Kyoto Biken Laboratories, Uji, Japan) in accordance with the manufacturer's instructions.\n\nThe vaccine consisted of antigens of ER (Kyoto strain) extracted with the bacterial cells by NaOH [32] and cell-free antigens of APP serotypes 1 (strain Y-1), 2 (strain G-4), and 5 (strain E-3) prepared from culture media [33] supplemented with anhydrous mannitol oleic ester as an adjuvant.\n\nBlood samples were collected just before the first vaccination and 3 weeks after the second vaccination.\n\nSera were separated from clots by centrifugation and frozen at −30 °C before use.\n\nTo measure antibody titers, 1:100 and 1:40 diluted sera (for ER and APPs, respectively) and five twofold serial dilutions of each of these were assessed for absorbance by using an ELISA developed by the vaccine manufacturer, using antigens identical to the microbes included in the vaccines [32], [33].\n\nThe frequency of the dilution showing an absorbance of 0.5 was estimated by interpolation of the actual measured absorbance values for the total series of six dilutions.\n\nThe frequency of the dilution showing an absorbance of 0.5 after the second vaccination was subtracted from that before the first vaccination, and the resulting value was considered to represent the relative antibody response.\n\n2.3.\n\nGenotyping\nGenomic DNA was extracted from the clots of blood samples from the 39 parents and 206 progeny by using a Gentra Puregene Blood Kit combined with Clotspin Baskets (Qiagen, Hilden, Germany).\n\nSamples from all of the parents and from the 191 progeny for which antibody titration and DNA extraction were successfully completed underwent the subsequent analyses.\n\nExisting haplotypes in the SLA class II region and TLR genes were determined in the parental generation.\n\nFor the SLA class II region, as genetic markers we used two microsatellite loci (SLAMS095 and SLAMS047), which were located at the two ends of the region and which we had developed and proved useful for determining SLA haplotypes [34], [35], as well as the genes DRB1 and DQB1, which were located inside the region (Table 1 and Fig.\n\n1).\n\nThe microsatellite markers were genotyped as previously described [34].\n\nExisting alleles in DRB1 and DQB1 were investigated by using the polymerase chain reaction (PCR)-sequence-based typing (PCR-SBT) method with primer pairs reported previously [36], [37] and additional primer pairs for DRB1 consisting of a forward primer 5′-GTCCACGCAGCGCATTTCTT-3′ [37] and reverse primers newly designed in this study as follows: 5′-TGTAGTTGTGTCTGCA-3′, 5′-GGTAGTTGTGTCTGCA-3′, or 5′-AAATCCTGTAGTTGTGTATGC-3′.\n\nAlleles in each animal were estimated by sequencing the PCR amplicons cloned by TOPO TA Cloning Kit (Invitrogen, Carlsbad, CA, USA).\n\nThe genotypes of DQB1 were determined by PCR-SBT with primers designed in exon 2 (Table 1).\n\nBecause of the difficulty in determining the genotypes of DRB1 by PCR-SBT, PCR primers to amplify the three alleles, DRB1*0201, DRB1*0403, and DRB1*0501, were specifically designed in exon 2 (Table 1), and the genotypes were determined with or without amplification by electrophoretic detection.\n\nHaplotypes of the SLA class II region, as indicated by the combination of four polymorphic loci, were estimated by expectation–maximization algorithm [38] using the Arlequin 3.5 software [39] (Table 2).\n\nTable 1.\n\nPrimers used for genotyping in this study.\n\nSite a Method Forward (5′–3′) Reverse (5′–3′) Size of amplicon (bp) Reference SLA class II SLAMS095 Microsatellite TCATGGCTGTGACCTTGGTA CCTGCTCCTTGCTCTGTAGG 227, 229, 231 Tanaka et al.\n\n[34] SLAMS047 Microsatellite AAAAGAGGCAATGAGGTCCA AGTGGGATGTTTTGGTCCAG 211, 215, 223 Tanaka et al.\n\n[34] DRB1 (exon 2) b *0201 -specific PCR CGCATTTCTTGCACCTGT CGTTATAGTATTGCCTCTCCAA 87 – *0403 -specific PCR TCTTGTTTCTGCGGAAGGCT CACGAGGAAGGTCTCTAAGATCTC 260 – *0501 -specific PCR TTCTTGCACCTGGTGAAACA GTATGCAGTACGTGTCCACT 223 – DQB1 (exon 2) PCR-SBT CAACGGAACGCAGCGGGTGC AGGGTCGTGCCTTCCTCTATCTGGT 259 – TLR1 648, 798 PCR-SBT TTTCGTGCCACCCTATGCTG AATGGGCTAATTTGGGATGG 727 Shinkai et al.\n\n[26] 1499 PCR-SBT TGGACCGGAAGTTTATTAAG AAATGAAGGCATGGAACTGG 664 Shinkai et al.\n\n[26] TLR4 960, 962 PCR-SBT CATCAAGTGCCCCTACACAA AAAATGCCACGGAAGACAAC 806 Shinkai et al.\n\n[26] TLR5 360 PCR-SBT GGACTCTCCCAAGCCCTAAG GATCAATGGCCTTCAAGGAA 610 Shinkai et al.\n\n[26] 1065 PCR-SBT TCTGGGTTTGGCTTCCATAA TCAGATGGCGAAAGACTCCT 778 Shinkai et al.\n\n[26] TLR6 663 PCR-SBT GCAGCTGACGGTTTTGAGA TTCCACATGGTTGAGCGTAA 553 Shinkai et al.\n\n[26] 1916, 1954 PCR-SBT CTGGCATTGGCTCTTACCAT TAAGTTCGCTGTGCCATGAG 494 Shinkai et al.\n\n[26]\nPCT-SBT, polymerase chain reaction-sequence-based typing.\n\nTable 2.\n\nSLA class II haplotypes observed in the vaccinated Duroc population.\n\nHaplotype a Microsatellite marker b Gene a Frequency c (%) ( n = 382) Empty Cell SLAMS095 SLAMS047 DRB1 DQB1 Empty Cell Hp-0.2 231 215 0201 0201 50.26 Hp-0.13 229 223 0403 0303 20.68 Hp-0.5 227 211 0501 0201 29.06\nFor TLR genes, the entire coding regions were sequenced in the 39 parents, as previously described [26], and the haplotypes of TLR4 and TLR5 and of the genomic region including TLR1 and TLR6 (TLR1–TLR6), as indicated by the existing SNPs, were estimated by using Arlequin 3.5 software (Table 3).\n\nAll of the PCR amplifications mentioned above were conducted with AmpliTaq Gold polymerase (Applied Biosystems, Foster City, CA) with 40 cycles, each consisting of 94 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s, after incubation at 94 °C for 9 min.\n\nSequencing and genotyping using microsatellite markers were conducted with an ABI 3730XL Genetic Analyzer (Applied Biosystems).\n\nThe combinations of haplotypes in the progeny generation were determined by genotyping the four polymorphic loci in the SLA class II region and representative SNPs in the TLR genes (Table 1, Table 2, Table 3).\n\nConsistency of all of the genotyping data was confirmed in mating pairs and their offspring.\n\nTable 3.\n\nTLR haplotypes observed in the vaccinated Duroc population.\n\nHaplotype Nucleotide positions in CDS of each TLR gene a Frequency c (%) ( n = 382) TLR1 Empty Cell TLR6 Empty Cell TLR1–TLR6 53 d 489 648 b 798 b 1499 b , d 1789 2305 d 228 266 d 663 b 1061 d 1284 1354 d 1376 d 1438 d 1502 d 1698 1916 b , d 1954 b a T C C T T C C A G G T A G A C T C A G 31.14 b – – – C – – – G A – C G A G G C T C – 19.37 c – – – C – – – G A T C G A G G C T C – 19.90 d – – – C C – – G A – C G A G G C T C – 19.90 e – – – C C – – G A – C G A G G C T C A 1.05 f C T T – – T T G A T C G A G G C T C – 8.64 TLR4 318 417 960 b 962 b , d 2397 a C G G G G 85.34 b A A A A A 14.66 TLR5 360 b 834 d 1065 b 1246 d 1269 1278 1398 2346 2391 2394 a T T T A G C C G T A 76.17 b – G C T A T T – – – 9.69 c C – – – – – – C C G 11.52 d C G C T A T T – – – 2.62\nCDS, coding sequence.\n\n2.4.\n\nStatistical analysis\nAnalysis of variance (ANOVA) for factors affecting the antibody responses, as well as estimation of the effects of genotype on antibody response, was performed by using a generalized linear model with the glm package of the R program [40].\n\nThe models were expressed as:where Yijklm is the antibody response of pig with the ith sex, the jth SLA class II, the kth TLR1–TLR6, the lth TLR4, and the mth TLR5, μ is the intercept, sexi is the effect of the ith sex (1 = male; 2 = castrated male; 3 = female), class IIj, TLR1–TLR6k, TLR4l, and TLR5m are the effects of the genotypes of the jth SLA class II, kth TLR1–TLR6, lth TLR4, and mth TLR5, respectively, and eijklm is the random error term.\n\nFor factors demonstrated by ANOVA to have an effect on antibody response, differences in the estimates of effects among genotypes were analyzed by using a general linear hypothesis test with the glht package in the R program.\n\nTajima's D statistics to test for selective neutrality were calculated using Arlequin 3.5 software, as previously described [41], [42].\n\n3.\n\nResults\n3.1.\n\nGenotyping of SLA class II and TLR genes in vaccinated Duroc population\nAmong previously developed markers, we selected two microsatellite markers, SLAMS095 and SLAMS047, located at the two ends of the class II region (Fig.\n\n1) [34].\n\nGenotyping confirmed the existence of three alleles at each marker locus in the vaccinated Duroc population (Table 2).\n\nIn addition, we found three alleles at the DRB1 locus and two at the DQB1 locus; these alleles had been defined by the SLA Nomenclature Committee of the International Society for Animal Genetics [23].\n\nReconstruction of haplotypes from these four polymorphic loci demonstrated the existence of three haplotypes, Hp-0.2, Hp-0.13, and Hp-0.5 [24], within the population; their frequencies were about 50%, 20%, and 30%, respectively, in the progeny generation (Table 2).\n\nWe had previously reported a number of SNPs in TLR1, TLR2, TLR4, TLR5, and TLR6 in various pig populations [26].\n\nTo examine SNPs within these genes in the vaccinated Duroc population, we sequenced the genomic DNA of all of the pigs in the parent generation.\n\nG1954A in TLR6 was the only newly discovered SNP; all of the other SNPs detected in TLR1, TLR4, TLR5, and TLR6 had been found in our previous study [26] (Table 3).\n\nNo SNP was found in TLR2 in our vaccinated Duroc population (data not shown).\n\nReconstructions of the haplotypes from these SNP data revealed two haplotypes in TLR4 and four in TLR5.\n\nBecause TLR1 and TLR6 were located adjacent to each other on the chromosome 8 [43], we estimated their haplotypes as a linked gene region (TLR1–TLR6); we demonstrated the existence of six haplotypes (Table 3).\n\nCombinations of haplotypes in the progeny generation were genotyped by using representative SNPs of the haplotypes; the frequencies of the haplotypes are shown in Table 3.\n\nTajima's D statistic [41] calculated for the coding sequences of TLR genes were as follows: TLR1 (D = 0.972; P = 0.844), TLR4 (D = 1.124; P = 0.875), TLR5 (D = 0.880; P = 0.832), and TLR6 (D = 3.566; P = 1.000).\n\n3.2.\n\nAntibody titrations in response to vaccination against E. rhusiopathiae and A. pleuropneumoniae serotypes 1, 2, and 5\nIn the 191 progeny for which genotyping was successfully completed, we obtained the titer values for antibodies specific to the antigens of ER and of APP serotypes 1, 2, and 5.\n\nThe antibody titers against ER before vaccination were not raised in all pigs (Fig.\n\n2A), whereas those against APPs were in some cases elevated (Fig.\n\n2B–D).\n\nThe values 3 weeks after the second vaccination showed that the vaccine had a marked effect in inducing the production of antibodies to ER and APPs (Fig.\n\n2).\n\nWe therefore considered the values obtained by subtracting the frequencies of the dilutions showing an absorbance of 0.5 before vaccination from those with the same absorbance after the second vaccination to indicate the traits of the antibody response to vaccination.\n\nTwo pigs that had abnormal trait values, as detected by the Smirnov–Grubbs test (P < 0.05), and 17 pigs that had rare genotypes (SLA class II Hp-0.13/0.13, 5; TLR1–TLR6a/e, 2; TLR1–TLR6b/e, 1; TLR1–TLR6d/e, 1; TLR1–TLR6d/f, 3; TLR5b/b, 1; TLR5b/c, 3; TLR5c/c, 1) were eliminated; the data from the remaining 172 progeny were analyzed (Table 4).\n\nTable 4.\n\nProfiles of samples used for data analysis.\n\nGroup Sample ( n = 172) Relative antibody response a (mean ± S.D.)\n\nEmpty Cell Empty Cell ER APP1 APP2 APP5 Sex Male 76 4.53 ± 0.84 2.44 ± 1.13 2.98 ± 1.20 2.25 ± 0.96 Castrated male 17 4.69 ± 0.74 3.16 ± 1.05 2.77 ± 1.12 1.46 ± 1.44 Female 79 4.49 ± 0.84 2.43 ± 1.15 2.57 ± 1.33 1.80 ± 1.24 SLA class II Hp-0.2/0.2 42 4.69 ± 0.68 2.27 ± 1.17 2.87 ± 1.27 2.02 ± 1.37 Hp-0.2/0.13 42 4.88 ± 0.80 2.77 ± 1.01 3.18 ± 1.12 2.27 ± 1.03 Hp-0.2/0.5 53 4.23 ± 0.75 2.32 ± 1.27 2.54 ± 1.28 1.72 ± 1.05 Hp-0.13/0.5 24 4.42 ± 0.91 2.70 ± 1.09 2.57 ± 1.36 2.10 ± 1.26 Hp-0.5/0.5 11 4.20 ± 1.12 2.89 ± 0.79 2.37 ± 1.18 1.54 ± 1.03 TLR1–TLR6 a/a 23 4.44 ± 0.86 3.18 ± 1.18 2.49 ± 0.99 1.42 ± 1.42 a/b 16 4.05 ± 0.95 2.87 ± 0.89 3.53 ± 1.02 2.18 ± 0.83 a/c 25 4.72 ± 0.86 2.73 ± 1.20 3.07 ± 1.17 2.55 ± 1.05 a/d 20 4.10 ± 0.69 1.85 ± 1.03 2.53 ± 1.17 1.90 ± 0.90 a/f 8 4.63 ± 0.51 2.29 ± 1.14 2.72 ± 1.12 0.83 ± 0.91 b/b 5 4.64 ± 0.56 2.68 ± 1.24 2.89 ± 1.52 2.56 ± 0.43 b/c 17 5.08 ± 0.86 2.29 ± 1.19 2.15 ± 0.87 2.07 ± 1.29 b/d 10 4.44 ± 0.92 2.63 ± 0.73 2.74 ± 1.64 1.92 ± 0.59 b/f 11 4.50 ± 0.40 1.73 ± 1.29 2.21 ± 1.17 1.55 ± 1.58 c/d 22 4.54 ± 0.79 2.67 ± 0.89 2.84 ± 1.62 2.38 ± 1.09 c/f 7 4.62 ± 0.88 1.89 ± 1.35 2.81 ± 1.44 1.10 ± 1.08 d/d 8 4.85 ± 0.83 2.38 ± 1.01 3.55 ± 1.23 2.28 ± 0.82 TLR4 a/a 117 4.54 ± 0.83 2.45 ± 1.15 2.75 ± 1.31 1.92 ± 1.13 a/b 55 4.49 ± 0.85 2.63 ± 1.15 2.81 ± 1.17 2.07 ± 1.26 TLR5 a/a 99 4.55 ± 0.91 2.46 ± 1.13 2.67 ± 1.23 1.86 ± 1.22 a/b 29 4.55 ± 0.56 2.77 ± 1.18 3.29 ± 0.95 2.22 ± 1.02 a/c 34 4.47 ± 0.89 2.28 ± 1.06 2.39 ± 1.44 1.73 ± 0.95 a/d 10 4.39 ± 0.37 3.02 ± 1.39 3.56 ± 1.02 3.11 ± 1.14\nER, Erysipelothrix rhusiopathiae; APP, Actinobacillus pleuropneumoniae.\n\n3.3.\n\nEffects of SLA class II and TLR genes on antibody responses to vaccination\nWe used ANOVA to analyze the effects of sex and genetic factors of SLA class II and TLR genes on antibody response (Table 5).\n\nSex influenced the response to vaccination in the case of APP1 (P < 0.05) and APP5 (P < 0.01).\n\nWe found relationships between SLA class II and ER (P < 0.001) and between TLR1–TLR6 and ER (P < 0.05), TLR1–TLR6 and APP1 (P < 0.01), TLR1–TLR6 and APP5 (P < 0.01), TLR5 and APP2 (P < 0.01), and TLR5 and APP5 (P < 0.001).\n\nNo significant effect of TLR4 on vaccine response was detected.\n\nIn a general linear hypothesis test, we then identified significant differences between estimates of the effects of genotypes on antibody response (Fig.\n\n3).\n\nThe genotype SLA class II Hp-0.2/0.13 had significantly greater effects on enhancement of antibody response to ER vaccination than SLA class II Hp-0.2/0.5 (P < 0.001) or SLA class II Hp-0.5/0.5 (P < 0.05) (Fig.\n\n3A).\n\nPigs of the genotype TLR5a/d had a significantly greater response to APP5 vaccination than TLR5a/a (P < 0.001), TLR5a/b (P < 0.01), or TLR5a/c (P < 0.001) (Fig.\n\n3F); there was a similar response to APP2 vaccination, although a significant difference was detected only compared with TLR5a/c (P < 0.01) (Fig.\n\n3D).\n\nIn the case of the genotypes TLR1–TLR6, pigs with TLR1–TLR6a/d showed a significantly weaker response to ER vaccination than did those with TLR1–TLR6b/c (P < 0.001) (Fig.\n\n3B), as well as a weaker response to APP1 vaccination than pigs with the genotype TLR1–TLR6a/a (P < 0.01) or TLR1–TLR6a/b (P < 0.05) (Fig.\n\n3C).\n\nPigs with the genotype TLR1–TLR6a/f had significantly lower antibody responses to APP5 vaccination than pigs with many other genotypes (TLR1–TLR6a/b, P < 0.01; TLR1–TLR6a/c, P < 0.001; TLR1–TLR6b/b, P < 0.01; TLR1–TLR6b/c, P < 0.05; TLR1–TLR6b/d, P < 0.05; TLR1–TLR6c/d, P < 0.001) (Fig.\n\n3E).\n\nAmong these pairs of TLR1–TLR6 genotypes showing significant differences in antibody response, no pairs were commonly observed among ER, APP1, and APP5 vaccinations (Fig.\n\n3B, C, and E).\n\nTable 5.\n\nSummary statistics of ANOVA for factors affecting antibody response after vaccination.\n\nFactor df ER APP1 APP2 APP5 Empty Cell Empty Cell F P F P F P F P Sex 2 0.489 0.614 3.569 0.031 2.306 0.103 5.918 0.003 SLA class II 4 5.362 <0.001 2.272 0.064 2.229 0.069 1.669 0.160 TLR1–TLR6 11 2.127 0.022 2.499 0.007 1.785 0.061 2.466 0.007 TLR4 1 0.439 0.508 1.409 0.237 0.262 0.610 1.711 0.193 TLR5 3 0.071 0.975 2.164 0.095 4.097 0.008 7.569 <0.001\ndf, degrees of freedom; ER, Erysipelothrix rhusiopathiae; APP, Actinobacillus pleuropneumoniae.\n\n4.\n\nDiscussion\nWe demonstrated here the association of different genotypes of SLA class II and porcine TLR genes with the variability of antibody responses to vaccination against ER and APPs.\n\nAlthough the modern swine industry suffers great economic losses from bacterial infections, genetic improvement in disease resistance has never been achieved, mainly because of the difficulty in estimating such traits, unlike the case with traits for meat production.\n\nEvaluation of pig vaccines has shown that there is a broad variety of efficacy of antibody responses among individuals [3]; however, the relationship between such differences and the genetic background of each pig has not been extensively investigated.\n\nThis report clearly demonstrated an association between genetic variation in a number of critical immune-system genes and antibody responses after vaccination in pigs.\n\nOur results may provide insight into new strategies for pig breeding and vaccine development.\n\nThe haplotype of the SLA class II region was extremely highly correlated with the response to ER vaccination.\n\nPigs with the Hp-0.5 haplotype of SLA class II tended to show low antibody responses to ER vaccination.\n\nThis haplotype has specifically the DRB1*0501 allele; DRB1 molecules may play an important role in the presentation of ER antigen to T-cells, thus influencing vaccine efficacy.\n\nER is a Gram-positive bacterium with a capsular structure outside the bacterial body and no flagellum [27].\n\nAlthough antibody responses to ER vaccination also involved the genomic region containing TLR1 and TLR6, no association of these responses with TLR4 or TLR5 was detected.\n\nThese results could be explained by previous findings that TLR4 generally recognizes the cell-wall components of Gram-negative bacteria [44] and TLR5 recognizes bacterial flagellin [45], whereas TLR1 and TLR2 cooperatively mediate immune responses to biofilms, which are similar structure to capsules [46].\n\nAfter vaccination, TLR5 was strongly correlated with antibody response to APP2 or APP5 but not APP1 or ER.\n\nAPPs are Gram-negative flagellated bacteria [47].\n\nO-polysaccharide and flagellin (also known as the O and H antigens, respectively) are the two major antigens of Gram-negative bacteria and are highly polymorphic in Escherichia coli [48].\n\nThe difference between the association of porcine TLR5 with APP2 or APP5 and that of porcine TLR5 with APP1 might be caused by the diverse antigenicity of the H antigens.\n\nPigs of the TLR5a/d genotype showed highly elevated antibody responses against APP2 and APP5 vaccinations, unlike genotypes with the TLR5a allele combined with TLR5a, TLR5b, or TLR5c, indicating that the TLR5d allele had the effect of increasing the antigen response.\n\nAlthough the TLR5d allele has two amino acid polymorphisms, H278Q (T834G SNP) and T416S (A1246T SNP), compared with the TLR5a allele, our previous study demonstrated that mutant expression vectors carrying each of these SNPs do not alter the degree of recognition of flagellin purified from Salmonella enterica serovar Typhimurium [49].\n\nThe synonymous polymorphism of T360C is the only SNP that discriminates between the TLR5d and TLR5b alleles.\n\nConsidering the difference between the TLR5a/d and TLR5a/b genotypes in terms of effect on antibody responses, the promoter region of the TLR5 gene or polymorphisms in other genes adjacent to TLR5 may be the source of the differences in responsiveness to APP vaccinations.\n\nThe genomic regions containing TLR1 and TLR6 were associated with responses to vaccination with ER, APP1, and APP5.\n\nPigs with the TLR1–TLR6a/f genotype had poor responses to APP5 vaccination, suggesting that the TLR1–TLR6f haplotype had low reactivity to the vaccine.\n\nUnlike in TLR6, in TLR1 the f haplotype had nucleotides that differed markedly from those of haplotypes a to e. Therefore, the weak response of the TLR1–TLR6f haplotype to APP5 vaccination may be attributable to polymorphisms in the TLR1 gene.\n\nPigs with the TLR1–TLR6a/d genotype had significantly weaker responses than those with TLR1–TLR6b/c to ER vaccination, as well as significantly weaker responses than those with TLR1–TLR6a/b to APP1 vaccination.\n\nHowever, there were no significant differences in response between pigs with TLR1–TLR6a/d and those with TLR1–TLR6b/c in the case of APP1 vaccination or TLR1–TLR6a/b in the case of ER vaccination.\n\nConsidering these differences between vaccination responses and the non-existence of the same combinations of TLR1–TLR6 genotypes with significant differences in responses among ER, APP1, and APP5 vaccinations, the diversity of TLR1 and TLR6 may have been maintained in our Duroc population.\n\nThe value of Tajima's D statistic for TLR6 was extremely high compared with those for other TLR genes, suggesting the diversity of TLR6 gene tends to be maintained in this population because of deviation toward balancing selection rather than purifying selection.\n\nIn contrast, we found no polymorphism in TLR2 among our vaccinated Duroc population.\n\nTLR2-deficient mice develop more severe periodontitis than wild-type mice after infection with Actinobacillus actinomycetemcomitans; this condition is characterized by significantly higher bone loss and inflammatory cell migration to periodontal tissues [50].\n\nTherefore, we think that TLR2 is important in recognition of APPs in pigs.\n\nUnlike with TLR1 and TLR6, in the case of TLR2, rigidity of the nucleotide sequence may be required for pathogen recognition.\n\nIn addition, we detected no association of TLR4 with vaccine responses.\n\nThe number of alleles of TLR4 was lower than those of other TLR genes, suggesting the importance of low variability of TLR4.\n\nWe used a combined vaccine including antigens derived from ER and three serotypes of APP in this study.\n\nAntigenic competition may cause decline of response to antigen(s) in combined vaccine in some cases, such as an inactivated polio vaccine in human and foot rot vaccine in sheep [51], [52].\n\nIn contrast, combination of antigens does not cause any competition in another case [53].\n\nIn this study, antigens derived from ER and APP were combined in a single dose of the vaccine.\n\nHowever, the vaccination program was unified throughout the experiment; therefore, possibility that antigenic competition caused variance of immune response among animals observed in this study can be ruled out.\n\nOf course, the combination of vaccines inoculated concurrently may affect an extent of influence of genetic background on response to antigens.\n\nVaccination program should be also considered to design more potent vaccines, in addition to influence on responses to vaccines by genetic background.\n\nIn conclusion, we demonstrated the effects of different genotypes of SLA class II and TLRs on antibody responses to vaccination with ER and APPs in pigs.\n\nThese results show the possibility of reducing costs in the swine industry by suppressing growth retardation and death from infection through the selection of pigs with effective antibody responses to vaccinations.\n\nDevelopment of effective vaccines may also be possible through the selection of adjuvants that are not influenced by genetic diversity among pigs.\n\nConflict of interest\nThe authors declare that there is no conflict of interest in this study.\n\nAcknowledgements\nWe are grateful to Drs.\n\nJoan K. Lunny (Beltsville Agricultural Research Center, United States Department of Agriculture), Yasuyuki Mori, and Yoshihiro Muneta (National Institute of Animal Health, Japan) for their valuable advice and comments on porcine infectious diseases and vaccination.\n\nWe are indebted to Drs.\n\nTakashi Kitajima and <PRESIDIO_ANONYMIZED_PERSON> (Kyoto Biken Laboratories) for antibody titration of pig sera.\n\nThis study was supported by a Grant-in-Aid from the Japan Racing Association and the Animal Genome Research Project of the Ministry of Agriculture, Forestry and Fisheries of Japan.\n\n----------\nAs a professional microbiologist, create a concise and comprehensive Chinese scientific report on Erysipelothrix Spp. research based on the provided text, whether it's an article, post, conversation, or passage. Follow these guidelines:\n\n 1. Produce a detailed and in-depth report that remains clear and concise.\n 2. Focus on main ideas and essential information, removing any extraneous language.\n 3. Strictly rely on the provided text without adding external information.\n 4. Format the report in paragraphs for easy understanding.\n 5. Conclude with “[End of Notes, Message #X],” where “X” represents the total number of messages I’ve sent. Increment the message counter with each new message.\n 6. Use English for technical terms, industry terminology, proper nouns, and species names. After you finish, add the following words: {{{ I_finished_papa!!! }}} to the end of you answer