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Article

Polymorphisms within the Toll-Like Receptor (TLR)-2, -4, and -6 Genes in Cattle

1
Dipartimento di Produzioni Animali, Università della Tuscia, Viterbo, Italy
2
Parco Tecnologico Padano, Polo Universitario, Lodi, Italy
3
Departamento de Producción Animal, Facultad de Veterinaria, Universidad Complutense, Madrid, Spain
*
Author to whom correspondence should be addressed.
Diversity 2009, 1(1), 7-18; https://doi.org/10.3390/d1010007
Submission received: 4 May 2009 / Accepted: 17 July 2009 / Published: 30 July 2009
(This article belongs to the Special Issue Biodiversity Feature Papers)

Abstract

:
In mammals, members of the TLR gene family play a primary role in the recognition of pathogen-associated molecular patterns from bacteria, viruses, protozoa and fungi. Recently, cattle TLR genes have been mapped to chromosomes using a radiation hybrid panel. Nucleotide sequences of bovine TLR2, TLR4 and TLR6 genes were screened to identify novel SNPs that can be used in studies of cattle resistance to diseases. In total, 8 SNPs were identified and were submitted to the NCBI dbSNP database. The frequencies of the SNPs were assessed in 16 different bovine European cattle breeds and a phylogenetic analysis carried out to describe the relationships between the breeds. Even if from our analysis the SNPs do not appear located in loci under selection, a deviation of three SNPs from Hardy Weinberg equilibrium was observed, and we hypothesize that some of the polymorphisms may be fixated since many generations. The described variations in immune function related genes will contribute to research on disease response in cattle. In fact, the SNPs can be used in association studies between polymorphisms and cattle resistance to diseases.

1. Introduction

The immune system in mammals consists of innate and adaptive immune responses. Adaptive immunity is mediated by antigen specific T and B cells responses, and is observed only in vertebrates. Innate immunity, however, is conserved between invertebrates and vertebrates [1]. Toll-like Receptors (TLRs) play an important role in the recognition of components of pathogens and subsequent activation of the innate immune response, which then leads to development of adaptive immune responses [2,3]. The TLRs are an ancient gene group which is found both in invertebrates and vertebrates; related genes are found also in plants [4]. In mammals, members of the TLR gene family play a primary role in the recognition of pathogen-associated molecular patterns (PAMPs) in proteins from bacteria, viruses, protozoa and fungi [5,6]. Mammalian TLRs derive their name from the Drosophila Toll protein, with which they share sequence similarity. The drosophila Toll protein was shown to be involved in dorsal-ventral pattern formation in fly embryos and was also implicated as a key component of host immunity against fungal infection [7,8,9].
The TLRs consist of a large extracellular domain responsible for PAMP binding, a transmembrane domain and an intracellular Toll/interleukin-1 receptor (TIR) domain which binds molecules and initiates cellular immune responses [10]. The extracellular domains are composed of about 20 leucine-rich repeats (LRRs) motifs of 20–30 amino acids (AA) and form a solenoid shape with the potential to bind the TLR specific PAMP [11].
Ten TLRs, which recognize molecular patterns from all major classes of pathogens, have been identified in mammals, eleven in mice [12,13]. TLRs operate with diverse variety of ligands ranging from hydrophilic nucleic acid to LPS, furthermore the heterodimerization expands the ligand spectrum [14]. TLR2 and TLR4 recognize bacterial cell components, and are critical in the immune response against Gram positive and negative bacteria [15]. TLR6 in association with TLR2 recognizes a wide variety of bacterial cell wall components including lipopolysaccharides, teichoic acid and lipoproteins [16,17] and induce NFkB signalling pathway [18].
Recently, all 10 TLR genes have been mapped in cattle using a radiation hybrid panel: TLR2 and TLR4 have been previously mapped to the proximal end of Bos taurus chromosome (BTA) 17 and the distal end of BTA 8, respectively [19]. TLR6, TLR1 and TLR10 cluster on BTA 6 [20], as observed on human chromosome 4; this organization is most likely the result of gene duplication [21].
Several studies have shown that mutations in the TLR may reduce the ability of the protein to recognise PAMP and hence interfere with innate immune activation. Describing genetic variation in these loci in relation to resistance against specific diseases in livestock may be useful in guiding genetic selection for disease resistance. Single nucleotide polymorphisms (SNPs) within TLR genes in humans seem to be associated with susceptibility to infection by specific diseases [22]. Among cattle genes TLR1, TLR5 and TLR10, 98 polymorphisms have been identified, 14 of which are non synonymous SNPs positioned in domains considered to be functionally significant [23]. Eighty three polymorphisms have been also identified for bovine TLR2 and TLR6 [24]. The initiation of the innate response to bovine respiratory syncytial virus (BRSV) requires the interaction of the viral F protein with TLR4, which leads to activation of NFkB via the Myd88-dependent pathway [25]. A recent study showed an association between TLR mutations and increased susceptibility to MAP (Mycobacterium avium paratubercolosis) infection in cattle, exactly two missense mutations in TLR4 (LRR domain) were associated with MAP infection [26].
In this study, we screened nucleotide sequences of bovine TLR2, TLR4 and TLR6 genes to identify SNPs that can be used in disease resistance studies in cattle. Eight new SNPs were identified and their frequency assessed in 16 different European cattle breeds.

Materials and Methods 

Samples:

A total of 951 individuals belonging to the following European breeds were analysed: Jersey (50), South Devon (43), Aberdeen Angus (45) and Highlands (48), from Great Britain; Holstein (60), Danish Red (59) and Simmental (30), from Denmark; Asturiana de los Valles (66), Casina (66), Avilena (65) and Pirenaica (73), from Spain; Piemontese (67), Marchigiana (36) and Maremmana (91), from Italy; Limousin (72) and Charolais (80), from France. Genomic DNA was isolated from blood using conventional methods and concentration and quality were evaluated by agarose gel electrophoresis.

Polymerase Chain Reaction (PCR) Conditions:

PCR primers for TLR2, 4 and 6 were designed using PolyPrimers [27] from the sequences available in Genebank (TLR2: AY634629, TLR4: DQ839567, TLR6: AJ618974) to amplify genomic fragments of approximately 1 kb (Table 1, Figure S1) covering most of the gene sequence. Each polymerase chain reaction (PCR) was performed in a total volume of 30 μL containing 30 ng of genomic DNA, 1.6 pMol of each primer (Sigma-Aldrich), 200 μM dNTPs, 1X PCR buffer and 0.2 units of Taq DNA polymerase (Promega) on a PCR Express cycler (Hybaid), using the annealing temperatures reported in Table 1. A 5 minutes denaturation step was followed by 14 cycles of denaturation at 94 °C (30 sec), annealing starting from T.A. + 7 °C and decreasing 0.5 °C per cycle (45 sec) and extension at 72 °C (40 sec), then by 20 cycles of denaturation at 94 °C (30 sec), annealing at T.A. (45 sec) and extension at 72 °C (40 sec); the final extension step was carried out at 72 °C for 5 minutes.

Sequence analysis:

PCR products were purified through ExoSap-IT (USB Corporation) to remove residual primers and dNTPs and used as templates for forward and reverse sequencing reactions. Sequencing was performed by means of a ceq 8,800 sequencer using DTCS QuickStart Kit and purifying with Agencourt CleanSEQ 96 (Beckman Coulter), according to manufacturer instructions. To identify SNPs, sequences of at least one individual each of six different breeds (Maremmana, Charolais, Jersey, Holstein, Pirenaica and Piemontese) were analysed and aligned with Bioedit software [28]. The putative SNPs identified by sequencing were confirmed and allele frequencies estimated by genotyping 951 individuals. SNP genotyping was performed by Kbiosciences using the patented technology KASPar (www.Kbioscience.com).

Data analysis:

Allelic frequencies, Gene Diversity, Heterozygosity and PIC were calculated using Powermarker software [29]. Genotypes were analysed using Fdist2 software to verify whether any of the loci were under selection [30]. Hardy-Weinberg equilibrium and Nei genetic distances [31] between populations pairs were calculated using Powermarker. The Neighborjoining algorithm was used to calculate the phylogeny relationship which was visualised using Treeview [32].
Table 1. Sequence of Forward (Fw) and Reverse (Rw) primers, annealing temperature (T.A.), amplicon size and amplicon position relatively to Genbank sequences.
Table 1. Sequence of Forward (Fw) and Reverse (Rw) primers, annealing temperature (T.A.), amplicon size and amplicon position relatively to Genbank sequences.
LocusSequence (5’→3’)T.A.
(°C)
Amplicon size (bp)Amplicon positionGenbank Accession #
TLR2 Fw: CTGTCCAACAATGAGATCACCT
Rw: AATTCTGTCCAAACTCAGTGCT
49735311-1045AY634629
TLR2Fw: GTTCAGGTCCCTTTATGTCTTG
Rw: ATGGGTACAGTCATCAAACTCT
47509493-1003AY634629
TLR2Fw: ACTACCGCTGTGACTCTCCCTC
Rw: GACCACCACCAGACCAAGACT
557111818-2530AY634629
TLR2Fw: CTCCCTTTCTGAATGCCACA
Rw: AAAGTATTGGAGCTTCAGCA
477541876-2631AY634629
TLR4Fw: GTGTGGAGACCTAGATGACTGG
Rw: GTACGCTATCCGGAATTGTTCA
507057938-8644DQ839567
TLR4Fw: CTACCAAGCCTTCAGTATCTAG
Rw: GGCATGTCCTCCATATCTAAAG
477418880-9623DQ839567
TLR4Fw: TCAGGAACGCCACTTGTCAGCT
Rw: TGAACACGCCCTGCATCCATCT
557109635-10346DQ839567
TLR6Fw: AAAGAATCTCCCATCAGAAGCT
Rw: GAAGGATACAACTTAGGTGCAA
46515228-745AJ618974
TLR6Fw: CTGCCCATCTGTAAGGAATTTG
Rw: GATAAGTGTCTCCAATCTAGCT
47739624-1382AJ618974
TLR6Fw: TTGGAAACACTGGATGTTAGCT
Rw: ACTGGAGAGTTCTTTGGAGTTC
497101428-2138AJ618974
TLR6Fw: CTGCCTGGGTGAAGAATGAATT
Rw: TGTAGTTGCACTTCCGGGCT
507152173-2888AJ618974

2. Results and Discussion

To discover SNPs in the three TLR genes, 12 PCR fragments were amplified and sequenced, five for TLR2, three for TLR4 and four for TLR6. One of the TLR2 primer pairs was soon discharged because of BLASTing problems. We then choose nine of 12 fragments, giving better results in terms of amplification and sequencing. In total eight SNPs were identified, three in TLR2, three in TLR4 and two in TLR6 [33] and were deposited in NCBI dbSNP (the ss# identities are listed in Table 2). These three genes are very important because they could be involved in immune response against various bovine diseases. In fact, TLR2 and TLR6 are critical in the immune response against Gram positive bacteria, TLR4 against Gram negative bacteria and virus. The polymorphisms in TLRs may reduce the ability of the protein to recognise ligands.
Table 2. Characterization of the detected SNPs.*: also described by Seabury and Womack [24].
Table 2. Characterization of the detected SNPs.*: also described by Seabury and Womack [24].
SNPsPosition in GeneBank sequence1aa changePosition in the protein2SNP ID number
TLR2_591G>A591non - coding ss107911951
TLR2_738A>G738non - coding ss107911952
TLR2_767G>A*767non - coding ss107911953
TLR4_254G>A254non - coding ss107911954
TLR4_1678C>T1678Synonymous (Ser)552: LRR domainss107911955
TLR4_2043T>C2043non - coding ss107911956
TLR6_855G>A*855Asp/Asn214ss107911957
TLR6_2315T>C*2315Synonymous (Phe)315: TIR domainss107911958
1 Nucleotide positions are numbered relatively to the first base of the sequence in GeneBank.2 Aminoacid position are numbered according to protein sequence in GeneBank (TLR4: NP776623, TLR6: NP001001159).
The allele frequencies are reported in Table 3 and major allele frequencies ranged from 0.557 (locus TLR2_767) to 0.969 (locus TLR2_738). Except for the latter, in all SNPs the frequency of the minor allele is greater than 5%. Observed heterozygosity (Ho) and Expected heterozygosity (He) of the loci determined from SNP frequencies ranged from 0.051 to 0.466 and from 0.060 to 0.493, respectively. Polymorphism Information Content (PIC) ranged from 0.058 to 0.372 (Table 3).
Eight of the breeds analysed were polymorphic at all the SNPs (Holstein, Asturiana de los Valles, Casina, Avilena, Pirenaica, Piemontese, Charolais ) and five SNPs were polymorphic in all the breeds (TLR2_767, TLR4_254, TLR4_1678, TLR6_855, TLR6_2315). Both SNPs identified in TLR6 gene were polymorphic in all the breeds.
Table 3. Frequencies of the major allele (M.A.F.), expected heterozygosity (He), observed heterozygosity (Ho), Polymorphism Information Content (PIC) of the 8 characterized SNPs.
Table 3. Frequencies of the major allele (M.A.F.), expected heterozygosity (He), observed heterozygosity (Ho), Polymorphism Information Content (PIC) of the 8 characterized SNPs.
SNPM.A.F.HeHoPIC
TLR2_5910.8660..330.2050.206
TLR2_7380.9690.0600.0510.058
TLR2_7670.5570.4930.4610.372
TLR4_2540.5950.4820.4660.366
TLR4_16780.6550.4520.4210.350
TLR4_20430.8430.2640.2340.230
TLR6_8550.6080.4770.4440.363
TLR6_23150.6880.4290.4250.337
Some breeds were fixed at a number of SNPs, particularly TLR2_738 is fixed in seven breeds (Highlands, Jersey, Limousine, Marchigiana, Maremmana, Simmenthal and South Devon), as shown in Table 4. TLR2_591 and TLR4_2043 are fixed in one breed (South Devon and Highlands, respectively). Interestingly, the SNP TLR2_738 is fixed in three breeds from Great Britain: Highlands, Jersey and South Devon. Indeed, the only two breeds with two fixed alleles are South Devon and Highlands. Moreover, its rare allele frequency is lower than 0.05 in seven other breeds (Aberdeen Angus, Asturiana de los Valles, Avilena, Charolais, Danish Red, Piemontese and Pirenaica), being higher than 0.05 in Holstein and Casina breeds only (Table 4). This suggests an involvement of the gene in some important roles which prevents its polymorphism.
Table 4. Allelic frequencies in the 16 European cattle breeds.
Table 4. Allelic frequencies in the 16 European cattle breeds.
SNPTLR2_591TLR2_738TLR2_767TLR4_254TLR4_1678TLR4_2043TLR6_855TLR6_2315
ALLELEAGAGAGAGCTCTAGCT
A. Angus0.0110.9890.0120.9880.6930.3070.8370.1630.6830.3170.9890.0110.5810.4190.3490.651
A. Valles0.0860.9140.0160.9840.5480.4520.5810.4190.6350.3650.8980.1020.3730.6270.2340.766
Avilena0.2500.7500.0310.9690.3170.6830.7120.2880.5330.4670.9130.0870.4370.5630.3530.647
Casina0.3270.6730.1520.8480.4320.5680.5380.4620.6490.3510.8480.1520.3020.6980.1960.804
Charolais0.1140.8860.0440.9560.4870.5130.5450.4550.6710.3290.8400.1600.3530.6470.3360.664
Danish Red0.0350.9650.0170.9830.5950.4050.6320.3680.5820.4180.7960.2040.5000.5000.3240.676
Highlands0.0110.989010.9320.0680.4150.5850.5640.436100.2600.7400.2390.761
Holstein0.1470.8530.1540.8460.7590.2410.4170.5830.7400.2600.8550.1450.3660.6340.3640.636
Jersey0.1700.830010.3720.6280.7070.2930.8880.1120.4130.5870.7760.2240.5820.418
Limousine0.2710.729010.4790.5210.5830.4170.6160.3840.8190.1810.3400.6600.3260.674
Marchigiana0.0300.970010.5290.4710.8940.1060.3380.6620.8330.1670.5570.4430.5590.441
Maremmana0.0930.907010.5170.4830.5720.4280.7420.2580.8840.1160.3300.6700.3310.669
Piemontese0.2150.7850.0380.9620.4050.5950.6750.3250.5380.4620.8170.1830.5000.5000.2810.719
Pirenaica0.1320.8680.0140.9860.6500.3500.4180.5820.6900.3100.9630.0370.2150.7850.1740.826
Simmenthal0.0960.904010.7170.2830.5220.4780.5910.4090.8750.1250.5190.4810.3950.605
S. Devon01010.8380.1630.6380.3630.9070.0930.6430.3570.1160.8840.0850.915
Selection can leave, in the genes under its influence, a set of signatures that can be analyzed to identify genes or chromosomal regions which are likely targets of positive selection. We used Fst statistic to assess if the variation of SNP allele frequencies among populations leads to signatures of selection. For each locus, the allele frequencies are used to compute Fst values conditional on heterozygosity and to calculate P-values for each locus. This method provides evidence for divergent selection by looking for outliers with Fst values higher than expected, controlling for heterozygosity. The analysis performed using FDist2 software to identify outlier loci revealed that none of the SNPs lied outside the 95% confidence limits assumed for conditional joint distribution of Fst vs. mean heterozygosity. Analysis was performed by bootstrapping 200,000 replications on real data using a coalescent model (Figure 1).
Figure 1. Upper ( Diversity 01 00007 i001) and lower ( Diversity 01 00007 i002) confidence limits of 95% quantiles; median (-) of 200,000 replications of expected Fst and heterozygosity using the coalescent model.
Figure 1. Upper ( Diversity 01 00007 i001) and lower ( Diversity 01 00007 i002) confidence limits of 95% quantiles; median (-) of 200,000 replications of expected Fst and heterozygosity using the coalescent model.
Diversity 01 00007 g001
None of the identified SNPs is located in loci under selection according to the model of Beaumont and Nichols [30]. Anyway, significant deviations from Hardy-Weinberg equilibrium over all populations (p-value < 0.01) were observed in three SNPs at two loci: TLR2_591, TLR2_738 and TLR4_2043 (Table 5). We hypothesize that polymorphisms are fixed in the analysed breeds since many generations, and that the coalescent model employed is not powerful enough to identify selection events happened too far in the past.
Distance based phylogenetic analysis was used to describe the relationships between breeds regarding the investigated TLRs. Table 6 presents the Nei genetic distances relating the 16 breeds studied. The lowest distance values are observed between Charolais and Asturiana de los Valles (0.002), while the highest distance is observed between Highlands and Jersey (0.117). Furthermore, the Jersey breed results very distant from all the other breeds of Great Britain, confirming the results obtained by AFLP and suggesting isolation within the Jersey island as the major cause of distinctiveness [34]. Indeed, Nei distances show that the highest genetic diversity is observed in the geographically isolated breeds: it is suggestive (Figure 2) that the breeds of Great Britain (Aberdeen Angus, Highlands, South Devon and Jersey), using the analysed polymorphisms, are distributed accordingly to their geographic provenience.
Table 5. Hardy-Weinberg equilibrium Test.
Table 5. Hardy-Weinberg equilibrium Test.
LocusHw Test
TLR2_5910.0007
TLR2_7380.0011
TLR2_7670.0542
TLR4_2540.2693
TLR4_16780.0345
TLR4_20430.0012
TLR6_8550.0303
TLR6_23150.7460
Table 6. Nei genetic distances. GB: Great Britain; ES: Spain; FR: France; DK: Denmark; IT: Italy.
Table 6. Nei genetic distances. GB: Great Britain; ES: Spain; FR: France; DK: Denmark; IT: Italy.
Geographic
location
of the breed
Aberdeen AngusAsturiana de los Valles AvilenaCasinaCharolaisDanish RedHighlandsHolsteinJerseyLimousinMarchigianaMaremmanaPiemontesePirenaicaSimmenthalSouth Devon
Aberdeen AngusGB0
Asturiana de los Valles ES0.0160
AvilenaES0.0270.0100
CasinaES0.0440.0120.0110
CharolaisFR0.0230.0020.0080.0090
Danish RedDK0.0140.0040.0150.0220.0050
HighlandsGB0.0290.0270.0570.0580.0370.0340
HolsteinDK0.0320.0130.0270.0140.0090.0150.0330
JerseyGB0.0610.0480.0440.0580.0390.0360.1170.0560
LimousinFR0.0320.0070.0070.0120.0060.0120.0450.0200.0380
MarchigianaIT0.0200.0260.0230.0520.0280.0150.0600.0500.0480.0290
MaremmanaIT0.0190.0030.0130.0180.0040.0070.0300.0160.0400.0050.0280
PiemonteseIT0.0260.0070.0030.0090.0060.0080.0550.0200.0350.0060.0220.0110
PirenaicaES0.0290.0060.0230.0160.0100.0180.0180.0130.0760.0140.0520.0090.0210
SimmenthalDK0.0160.0070.0190.0270.0090.0050.0230.0140.0410.0100.0210.0070.0140.0150
South DevonGB0.0530.0350.0730.0600.0400.0370.0460.0440.0820.0490.0760.0330.0600.0350.0440
Figure 2. Phylogenetic relationship among the 16 breeds studied. The genetic distances were calculated from allelic frequencies by using Nei distances. The reconstruction was done with UPGMA (Sneath and Sokal, 1973).
Figure 2. Phylogenetic relationship among the 16 breeds studied. The genetic distances were calculated from allelic frequencies by using Nei distances. The reconstruction was done with UPGMA (Sneath and Sokal, 1973).
Diversity 01 00007 g002
0.01 (meaning 0.01 nucleotide substitutions per site)

3. Conclusions

We could identify eight SNPs in genes of great interest in cattle management by screening the nucleotide sequences of bovine TLR2, TLR4, and TLR6 genes. These variations in immune function related genes will contribute to research on disease response in cattle. In fact, the newly identified SNPs can be used in association studies between polymorphisms and cattle resistance to diseases.
The SNPs characterization was performed by analysing a conspicuous number of individuals from 16 European breeds, and the main statistics were calculated. Even if from our analysis the SNPs do not appear located in loci under selection, a deviation of three SNPs from Hardy Weinberg equilibrium was observed. We hypothesize that some of the polymorphisms were fixated many generations ago within breed and the coalescent model could not be powerful enough to reveal selection events so far in the past. It would be interesting to apply a more powerful model to confirm the absence of selection in the SNPs and their suitableness as neutral markers.

Acknowledgements

The authors wish to thank Paolo Ciorba and Gabriella Porcai for technical assistance, the Eu-GeMQual project QLRT-1999-30147 for providing some of the data and Flora Jeane Dause for language revision. The research was partially carried out by funds of Regione Lazio PRAL 2003, “Valorizzazione e sviluppo dell'allevamento bovino di razze locali nel Lazio attraverso la caratterizzazione genetica per la resistenza alle malattie, per la longevità e per la qualità della carne”. JLW thanks the Cariplo Foundation for financial support.

Electronic Supplementary Information

Figure S1. PCR amplified fragments: 1. TLR6_1; 2. TLR6_2; 3. TLR6_3; 4. TLR6_4; 5. TLR4_1; 6. TLR4_2; 7. TLR4_3; 8. TLR2_5; 9. TLR2_4; 10. TLR2_3; 11. TLR2_2.
Figure S1. PCR amplified fragments: 1. TLR6_1; 2. TLR6_2; 3. TLR6_3; 4. TLR6_4; 5. TLR4_1; 6. TLR4_2; 7. TLR4_3; 8. TLR2_5; 9. TLR2_4; 10. TLR2_3; 11. TLR2_2.
Diversity 01 00007 g003

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MDPI and ACS Style

Mariotti, M.; Williams, J.L.; Dunner, S.; Valentini, A.; Pariset, L. Polymorphisms within the Toll-Like Receptor (TLR)-2, -4, and -6 Genes in Cattle. Diversity 2009, 1, 7-18. https://doi.org/10.3390/d1010007

AMA Style

Mariotti M, Williams JL, Dunner S, Valentini A, Pariset L. Polymorphisms within the Toll-Like Receptor (TLR)-2, -4, and -6 Genes in Cattle. Diversity. 2009; 1(1):7-18. https://doi.org/10.3390/d1010007

Chicago/Turabian Style

Mariotti, Marco, John L. Williams, Susana Dunner, Alessio Valentini, and Lorraine Pariset. 2009. "Polymorphisms within the Toll-Like Receptor (TLR)-2, -4, and -6 Genes in Cattle" Diversity 1, no. 1: 7-18. https://doi.org/10.3390/d1010007

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