Gene number determination and genetic polymorphism of the gamma delta T cell co-receptor WC1 genes
© Chen et al.; licensee BioMed Central Ltd. 2012
Received: 28 December 2011
Accepted: 3 October 2012
Published: 16 October 2012
WC1 co-receptors belong to the scavenger receptor cysteine-rich (SRCR) superfamily and are encoded by a multi-gene family. Expression of particular WC1 genes defines functional subpopulations of WC1+ γδ T cells. We have previously identified partial or complete genomic sequences for thirteen different WC1 genes through annotation of the bovine genome Btau_3.1 build. We also identified two WC1 cDNA sequences from other cattle that did not correspond to sequences in the Btau_3.1 build. Their absence in the Btau_3.1 build may have reflected gaps in the genome assembly or polymorphisms among animals. Since the response of γδ T cells to bacterial challenge is determined by WC1 gene expression, it was critical to understand whether individual cattle or breeds differ in the number of WC1 genes or display polymorphisms.
Real-time quantitative PCR using DNA from the animal whose genome was sequenced (“Dominette”) and sixteen other animals representing ten breeds of cattle, showed that the number of genes coding for WC1 co-receptors is thirteen. The complete coding sequences of those thirteen WC1 genes is presented, including the correction of an error in the WC1-2 gene due to mis-assembly in the Btau_3.1 build. All other cDNA sequences were found to agree with the previous annotation of complete or partial WC1 genes. PCR amplification and sequencing of the most variable N-terminal SRCR domain (domain 1 which has the SRCR “a” pattern) of each of the thirteen WC1 genes showed that the sequences are highly conserved among individuals and breeds. Of 160 sequences of domain 1 from three breeds of cattle, no additional sequences beyond the thirteen described WC1 genes were found. Analysis of the complete WC1 cDNA sequences indicated that the thirteen WC1 genes code for three distinct WC1 molecular forms.
The bovine WC1 multi-gene family is composed of thirteen genes coding for three structural forms whose sequences are highly conserved among individual cattle and breeds. The sequence diversity necessary for WC1 genes to function as a multi-genic pattern recognition receptor array is encoded in the genome, rather than generated by recombinatorial diversity or hypermutation.
Workshop cluster 1 (WC1) co-receptors belong to group B of the scavenger receptor cysteine-rich (SRCR) superfamily, as do CD163, CD5, CD6, and Spα, all of which are expressed in immune system cells . We have shown that WC1 is a member of the CD163 multigene family whose other members are CD163A, CD163b and CD163c-α . WC1 co-receptors are composed of up to eleven extracellular SRCR domains with interdomain homology, organized in the domain pattern of a-[b-c-d-e-d]-[b-c-d-e-d’] according to the nomenclature of Sarrias et al. . The greatest difference among WC1 genes occurs in the most distal SRCR domain (“a” pattern) with identities as low as 50%, contrasting with other SRCR domains which have identities of approximately 90% with like domains . WC1 and CD163c-α have the most similar extracellular SRCR domain organization [3, 4] and it has been proposed that the human and murine homologs of ruminant WC1 are CD163c-α (known as SCART1 and SCART2 and also expressed on γδ T cells in mice) [2, 5, 6]. We have shown that a multigenic array of WC1/CD163c-α homologues is conserved over evolutionary time including in the prototherian mammal duck-billed platypus and in the sauropsid chicken .
Based on reactivity with specific monoclonal antibodies (mAbs) using WC1-transfected cells, WC1 bearing γδ T cells (WC1+ γδ T cell) were defined as WC1.1+, WC1.2+, and WC1.3+ wherein the WC1.3+ population is a subpopulation of WC1.1+ cells . The WC1.1+ and WC1.2+ mAb-defined subpopulations are largely nonoverlapping and may be functionally distinct subsets of WC1+ γδ T cells since they have different cytokine production and cellular proliferation in response to stimulation [8, 9]. For example, ex vivo WC1.1+ γδ T cells, but not WC1.2+ γδ T cells, proliferate well to the γδ T cell antigens of Leptospira, and produce IFN-γ in response to either antigen or IL-12 [8, 9]. However, WC1.2+ γδ T cells respond to the rickettsiales bacteria Anaplasma . It was also notable that WC1.1+ cells decreased steadily with aging, while the WC1.2+ cells did not, suggesting their different functional roles . Since γδ TCR gene usage is not different between WC1.1+ and WC1.2+ γδ T cells , this may suggest that expression of particular WC1 family members directs the antigen-specific activation of γδ T cells.
Based on Southern blot analysis, it was predicted that there were over fifty WC1 (also known as T19) ovine genes [12, 13], and nineteen WC1 bovine genes . To better characterize the WC1 co-receptor family, we annotated the WC1 regions in the bovine genome Btau_3.1 assembly, identifying partial or complete sequences of thirteen WC1 genes distributed between two regions on chromosome 5 . The annotated number of WC1 genes is consistent with our previous study that identified thirteen different WC1 intracytoplasmic tail transcripts  but was fewer than the nineteen genes predicted by Southern blot analysis. In addition, we had also identified two additional Domain A transcript sequences, WC1-nd1 and WC1-nd2, derived from a different breed of cattle than that used for the genome sequencing . The missing genomic evidence for WC1-nd1 and WC1-nd2 in the genome of the animal “Dominette” could be due to gene number variation, polymorphisms among individual cattle or alternatively gaps in the assembled genome. Thus, the complexity of the WC1 multi-gene family remained unresolved including gene number and potential sequence polymorphisms; more recent assemblies have not ameliorated these problems.
Real-time quantitative PCR (Q-PCR) is highly sensitive and allows quantification of very small changes in sequence and rare transcripts [16, 17]. Real-time Q-PCR has evolved to increase the accuracy and efficiency of the nucleic acid quantification process, making Q-PCR a reliable and powerful tool . For example, Q-PCR has successfully quantified viral copy number and gene number in transgenic animals and measured oncogene amplification in tumor cells [19–23]. In relative quantification methods, the amount of target gene in a sample is presented relative to a calibrator which contains both target and reference genes at a constant ratio . In this study, we adapted it to determine the gene number of WC1 genes in bovine genomes.
Cattle of the Belted Galloway and Holstein breeds were 12–24 months of age. Blood was collected into heparin by venipuncture of the jugular vein. Peripheral blood mononuclear cells (PBMC) were isolated from blood via density gradient centrifugation over ficoll-hypaque (Ficoll-Paque, LKB-Pharmacia Biotechnology, Piscataway, NJ) using standard techniques and viable cell concentrations determined by trypan blue exclusion. PBMC were cultured at 2.5 × 106 cells/ml with Concanavalin A (ConA; 1.0 μg/ml; Sigma-Aldrich, St. Louis, MO) or leptospira antigen (, 0.5 μg/ml; sonicated whole cells of L. borgpetersenii serovar hardjo clone RZ33) in RPMI 1640 medium containing 10% heat-inactivated fetal bovine serum (HyClone, Logan, UT), 2 mM L-glutamine, 50 μM 2-mercaptoethanol and 50 μg/ml gentamicin at 37°C with 5% CO2 in air for six days. All animal use complied with federal guidelines and had Institutional Animal Care and Use Committee (IACUC) approvals.
Genomic DNA extraction and RNA isolation
Genomic DNA of seven cattle from two different breeds (5 Belted Galloway and 2 Holstein) was extracted from whole blood using FlexiGene DNA Kit (50) (Qiagen, Valencia, CA) according to the manufacturer’s protocol at the University of Massachusetts. To isolate RNA, pelleted ex vivo, ConA-activated, and Leptospira-activated PBMC, as well as sorted WC1.1+ γδ T cells, were resuspended in TRIzol (Invitrogen, Carlsbad, CA) and RNA was isolated according to the manufacturer’s protocol. Reverse transcription (RT) was performed using 1 μg of total RNA, oligo dT primers and AMV reverse transcriptase (AMV RT kit; Promega, Madison, WI). Genomic DNA and cDNA from the Herford Dominette, the animal used for the current bovine genome sequencing and annotation project [25, 26], were also obtained with total RNA isolated using a LeukoLOCK kit (Ambion, Austin TX) at USDA-ARS Fort Keogh, while genomic DNA from Red Angus, Angus, Charolais, Limousin, Brahman crossed with Angus, Gelbvieh, and Angus crossed with Hereford were obtained from semen or leukocytes using standard isolation methods at USDA-ARS Clay Center.
Real time quantitative-PCR
Real-time Q-PCR amplification and analysis were performed using a Stratagene Mx3005P instruments with software version 4.01 (Stratagene, La Jolla, CA). The Q-PCR assays were optimized in terms of Mg2+ concentration and the annealing temperature . Q-PCR amplification mixture (25ul) was prepared by using Sybr Premix Ex Taq (TAKARA, Pittsburgh, PA): 20 ng template DNA, 2-fold concentration of premix reagent including Takara Ex Taq™ HS and SYBR® Green I, 0.5ul ROX reference dye, and 1ul of forward and reverse primers (final concentration is 0.5uM for each). Real-time PCR amplification was conducted for 35 cycles, each cycle consisting of denaturation (95°C for 5 sec), annealing (55°C for 20 sec) with a single fluorescence measurement taken at the end of the annealing step, and extension (72°C for 20 sec). After amplification, melting-curve analysis was performed by raising the temperature to 95°C for 1 min, heating the sample at 55°C for 30 sec followed by 95°C for 30 sec. The ΔΔCT method was applied for gene number determination : relative amount of targets = (1 + E)− ΔΔCT, where ΔΔCT : ΔCT of the targets − ΔCT of the calibrators, ΔCT of the target: CT of the targets − CT of the reference, and ΔCT of the calibrators: CT of the calibrators − CT of the reference. In this case, the ‘targets’ were bovine WC1 domain 1, bovine IFNA, bovine IFNB, bovine IFNW while the ‘reference’ was bovine GAPD, and the ‘calibrators’ were bovine TRDJ1 and bovine IFNE. Real-time PCR products were analyzed on 1% or 1.2% TAE agarose gels, visualized using SYBR Safe (Invitrogen) and cloned into the pCR2.1 vector (Invitrogen) according to the manufacturer’s protocol for sequencing.
PCR amplification specific for Domain 1
Chromosomal location in Btau_3.1assembly and GenBank accession number of WC1 genes
GenBank accession number
PCR amplification for complete coding sequence
For amplifying the complete coding sequence of WC1 genes, 2 μl of pooled cDNA was used as a template and PCR reactions were conducted using the Elongase Amplification system (Invitrogen) with a final concentration of 1.5 mM Mg2+. Based on previous research, forward primers in the signal sequence (WC1atg-for 5′ATGGCTCTGGGCAGACACCTCTC) and reverse (WC1groups1,2-rev 5′TCAYGAGAAAGTCAYTGKGGATG) primers in the intracytoplasmic tail sequence were designed to amplify all known WC1 transcripts except WC1-11 which required the following primers: forward (WC1atg-for 5′ATGGCTCTGGGCAGACACCTCTC) and reverse (WC1group3rev 5′-CTACATGGTGCTAAGCTCCACATC) . Cycling parameters were 30 sec at 94°C, 30 sec at 55°C and 5 min 30 sec at 68°C for 35 cycles for all reactions. PCR products were analyzed on 1.2% TAE agarose gels, visualized using SYBR Safe (Invitrogen) and cloned into the pCR-XL vector (Invitrogen) for sequencing.
Sequencing was performed commercially (Genewiz) to verify amplicons. Nucleotide sequences were aligned and consensus sequences were created using Bioedit version 188.8.131.52 . GenBank accession numbers of annotated sequences used for comparisons in analyses are shown in Table 1 as annotated and/or reported in our previous research  except archetypal WC1.1 whose GenBank number is X63723. Multiple sequence alignments were performed using clustalw2 (http://www.ebi.ac.uk/Tools/clustalw2/index.html webcite; ) and the default parameters, but manually optimized when necessary, and were visualized using Bioedit . Phylogenetic analyses were performed using deduced amino acid sequences of WC1 domain 1 as indicated. Phylogenetic trees were created using Bayesian analysis in MrBayes3.2 . For Bayesian analysis, 2 runs with 3 cold chains and 1 heated chain each were done. An amino acid mixed model was used to approximate the posterior probabilities of trees. The 90-taxa SRCR domain 1 alignment was run with temperature settings of 0.2 for 830,000 generations. Trees were sampled every 100 generations and the burnin fraction was 0.25. The convergence diagnostic used was the average standard deviation of split frequencies, which were <0.05 (0.01) for the run. Phylograms were visualized using FigTree V1.3.1 (http://tree.bio.ed.ac.uk/software/figtree/).
Magnetic bead cell sorting
PBMC were stained for surface markers at 4°C for 20 min in PBS with 2 mM EDTA and 0.5% BSA. The anti-WC1 mAb BAG25A (VMRD, Pullman, WA) for WC1.1 epitopes was used for sorting. Cells were then incubated with goat anti-mouse IgM-conjugtated magnetic microbeads (Miltenyi Biotec, Auburn, CA, USA) at 4°C for 20 min. After washing twice, cells were applied to the column following the manufacturer’s instructions. The purity of collected fractions was assessed by flow cytometry and analyzed using FlowJo (Tree Star, Ashland, OR, USA).
The WC1 family is composed of thirteen genes
Due to gaps in the bovine genome Btau_3.1 assembly [3, 26], we were uncertain whether we had identified the total complement of WC1 genes present. Moreover, the possibility existed that gene number variation occurs among breeds of cattle or individuals within a breed. To address this we adapted Q-PCR to determine WC1 gene numbers in the Hereford Dominette, the reference/donor animal used for the Bovine Genome Sequencing and Annotation project , as well as in additional breeds of cattle.
Although considerable repetition of sequence occurs among repeating SRCR domains of WC1 molecules (i.e., b,c,d,e,d’), the most distal SRCR domain (domain 1 which has an “a” pattern ) of each known WC1 molecule is unique in terms of structure and sequence relative to all other WC1 domains  and coded for by a single exon. Thus, we reasoned that the number of SRCR domain 1 gene exons would be proportional to the WC1 gene number. As controls, bovine IFNA, bovine IFNB, and bovine IFNW genes were evaluated in our system since they are multigene families with known gene numbers . Bovine T cell receptor δ J1 gene (TRDJ1) and IFNE were both used as calibrators since they are present as single gene copies in the bovine genome [28, 34]. Bovine glyceraldehyde-3-phosphate dehydrogenase (GAPD) was used as a reference gene for DNA quality .
The results from the relative quantification and the calculated gene numbers are shown in Figure 2D, which were based on amplification efficiencies calculated as described above and the equation (relative amount of target = (1 + E)− ΔΔCT ) described previously . The results for sixteen animals of ten different breeds of cattle (Herford, Belted Galloway, Holstein, Red Angus, Angus, Charolais, Gelbvieh, Limousin, Brahman cross Angus, and Angus cross Hereford) showed a mean gene number of 13.01, 6.02, and 23.69 for bovine IFNA (13 expected), bovine IFNB (6 expected), and bovine IFNW (24 expected), respectively, which are consistent with results in previous studies [26, 34]. For bovine WC1 genes we obtained a mean gene number of 13.17 (Figure 2D). According to the obtained Q-PCR results, the number of WC1 genes for some tested cattle (one Holstein and one Red Angus) was less than thirteen. It is possible that those cattle have fewer than 13 WC1 genes, but statistical analysis indicated that the mean number of WC1 genes was thirteen without variation among all the tested individuals and breeds. Thus we conclude that the bovine genome contains thirteen WC1 genes and that this number is consistent among ten breeds of cattle.
Complete SRCR domain 1 sequences of the thirteen WC1 genes in the donor/reference animal Dominette
Available sequence information from the 13 WC1s in previous study a
Annotated genomic sequences from Dominetteb
RNA transcripts representing the expressed gene sequences obtained from other animals
Analysis of PCR products obtained showed that all thirteen known WC1 domain 1 sequences, including that for WC1-nd1 and WC1-nd2, were present in both Dominette’s genomic DNA and cDNA (Figure 3B). Thus, we conclude that WC1-nd1 and WC1-nd2 correspond to gaps in the assembled genome and reasoned that they might represent the missing WC1-2 and WC1-8 domain 1 sequences.
Generating templates to obtain complete coding sequences for all thirteen WC1 genes
The results also confirmed and extended our previous observations that most WC1 transcripts display alternative splicing of coding exons given that domain 1 sequence of all 13 WC1 genes could be amplified from the smaller (as well as the larger) bands .
Complete coding sequences for the annotated WC1-6, WC1-7 and WC1-12genes
cDNA evidence for transcription of WC1-6 , WC1-7 , WC1-12 , WC1-nd1 and WC1-nd2 a
WC1-nd2 is WC1-8
Using the templates generated above and the forward primer specific for WC1-nd2 domain 1 sequence (Figure 1), coding sequence for WC1-nd2 was obtained from the 4.4 kb amplified material. This sequence was compared to the partially annotated WC1-8 sequence and found to be identical. Thus, despite the fact that domain 1 and 2 sequence for WC1-8 was unavailable due to gaps in the genome (Table 2, ) we have re-classified WC1-nd2 as WC1-8 and henceforth will refer to it as such.
WC1-nd1 is WC1-2
WC1 genes in other breeds of cattle
The complexity of the WC1 multi-gene family in cattle has been resolved in this study: thirteen functional genes were found associated with ten different breeds of animals. The question of gene number variation among cattle was addressed by adapting Q-PCR for quantification. While the result was consistent with our previous WC1 gene annotation undertaken as part of the Bovine Genome Sequencing and Annotation Consortium , errors existed in the annotation due to incomplete or mis-assembly of the genome and those were corrected herein. The confirmation of thirteen WC1 genes corresponds reasonably well to the estimate derived by Southern blot, which suggested nineteen genes , and another study from our group suggesting thirteen genes based on the number of unique intracytoplasmic tail transcripts obtained . However, it is fewer than the fifty WC1 genes predicted for sheep by Southern blotting [12, 13]. Recently, we obtained evidence that sheep have twice the number of WC1 genes as cattle (Kim, Chen and Baldwin, unpublished data). Sequences of SRCR domain 1, the most divergent among the WC1 domains, showed that the domain 1 sequence for an individual gene is highly conserved among breeds, with zero to three amino acids differences found per gene. Despite these differences, phylograms confirmed that the evolutionary divergence between individual WC1 genes was still greater than the divergence among animals for a particular gene. This suggests that the array of WC1 genes has been conserved for diverse functions. Also, we now conclude that there are three distinct WC1 molecular forms based on variation in the number of extracellular domains and intracytoplasmic tail sequences including their signaling motifs (Figure 11). These differences in the molecular structure of members of this multi-gene family have implications regarding ligand binding capacity and its signaling outcomes, which would be consistent among animals.
The conservation of WC1 gene sequences among animals and the number of family members is similar to those characteristics of other pattern recognition receptor (PRR) families. It has been proposed that under natural selection pressure, closely related non-rearranging immunoreceptors found on lymphocytes and antigen-presenting cells diversify in response to multiple ligands, such as bacterial and viral pathogen-associated molecular patterns (PAMPs) [38, 39]. PRR’s that recognize PAMPs include Toll-like receptors (TLRs) and the functionally similar but structurally distinct NOD-like receptors (NLRs). Individual TLRs and NLRs specifically recognize individual PAMPs, but also act together to recognize diverse microorganisms, initiating a range of host defense mechanisms [40, 41]. The TLR family consists of 10 functional genes in humans , 12 in mice and ten in cattle  while NLRs  have 22 genes in humans and 34 in mice . Two other multi-gene families expressed on NK and γδ T cells are the C-type lectin-like Ly49 family [44–46], which is encoded by 15 functional genes in mice [47, 48] but only a single related gene in humans and cattle [49, 50], and the killer-IG-like receptor (KIR) family [50–52] which underwent rapid repeated gene duplication in humans and cattle and has 4-14 genes depending upon the individual [47, 50, 51, 53, 54]. The ligands for Ly49 and KIR are comprised of a large family [50, 55], including MHC class I-related molecules, that are rapidly evolving to evade the immune system. For example, infection of mice with murine cytomegalovirus (MCMV) caused the outgrowth of MCMV mutants which allowed the virus to escape recognition by the activating NK-cell receptor Ly49H .
Thus, we hypothesize that the WC1 family also expanded to keep pace with immune challenges from multiple pathogenic microorganisms and may be particularly important to γδ T cells given that the TCR γ gene usage of WC1+ cells is restricted . Evidence to support this comes from our and other’s studies showing that the expression of particular WC1 molecules defines subpopulations of bovine WC1+ γδ T cells that differ in their response to pathogens [9, 37] and irradiated/stressed autologous monocytes . In addition, shRNA-mediated selective reduction of WC1 expression by γδ T cells decreases γδ T cell response to Leptospira, supporting the hypothesis that WC1 proteins function as PRRs . Moreover, some members of the SRCR superfamily have been shown to bind PAMPs via interactions with one or multiple SRCR domains. That is, the group B SRCR molecules CRP-ductin, Spα and CD6 specifically bind to the bacterial products lipoteichoic acid (LTA) and lipopolysaccharide (LPS) [57–59] and DMBT1 binds to selected bacteria through a RVEVLxxxxW motif in most of its SRCR domains . Recently, we have localized Leptospira-binding activity to five of the eleven individual SRCR domains of specific WC1 molecules (Hsu and Telfer, unpublished data).
With regard to correcting errors in the previous assembly and annotation, here we found that WC1-nd1 and WC1-nd2, the two WC1 transcripts that did not correspond to sequences in the Btau_3.1 genome assembly in our previous study , are indeed present in the genome and are transcribed by Dominette. The inability to identify corresponding genomic sequence for WC1-nd2 resulted from gaps in the assembly. That is, WC1-8 was a partial sequence with no WC1 domain 1 sequence available, but we show here that it corresponds to WC1-nd2 by analyzing the complete transcript sequence. The second major error was regarding a gene previously annotated as WC1-2, which was found to be a concatamer of mis-assembled SRCR domains corresponding to domains of WC1-1, WC1-13, and WC1-nd1. Using genomic DNA and cDNA from Dominette, we found that the most membrane-proximal SRCR domain, the transmembrane region and the intracytoplasmic tail sequences of the previously annotated WC1-2 corresponded to our unplaced to WC1-nd1 sequence. Thus, WC1-nd1 has been assigned as WC1-2, completing the panel of thirteen complete coding sequences for WC1 genes. In an attempt to further confirm our conclusions, we searched for WC1 sequences in the more recently released assemblies Btau_4.0 and UMD3 but found them to be less informative. WC1 coreceptors are unique to T cells of “γδ T cells high” species  including cattle  but not found for “γδ T cells low” species, such as human or mice . Thus, the gaps regarding the WC1 coding region in assemblies Btau_3.1, Btau_4.0 and UMD3 may be a consequence of the absence in the human genome which was used for scaffolding the bovine genome.
Differences in the intracytoplasmic tails likely play an important role in signal transduction. Type II WC1 molecules have a “long tail” molecular form, with fifteen or more amino acids encoded by an additional (5th) exon  (Figure 11). Type III contains a very long intracytoplasmic domain resulting from a 6th exon coding for amino acids inserted into the sequence coded for by the middle exon (the 5th exon) of Type II WC1 genes  (Figure 11). Short and long tails are also found with other immunoreceptor families: KIR and NKG2D. Activating KIRs have short cytoplasmic tails with ITAMs that pair with DAP12/KARAP; inhibitory KIRs possess long cytoplasmic tails with ITIM motifs . NKG2D long form tails associate with DAP10 , while the short form  associates with DAP10 or DAP12. The adaptor determines the outcome of signaling following ligand binding [63, 64]. A signaling role for the most common WC1 tail sequence, which is the shortest, is shown by the requirement for phosphorylation of the second tyrosine for transmission of signaling through the TCR . It is notable that three gene products, WC1-4, WC1-7, and WC1-9, all have highly similar extracellular domains, possibly recognizing the same ligands, but the intracytoplasmic tails of WC1-4 and WC1-7 are archetypal (or short) while the tail of WC1-9 is longer as illustrated in Figure 11. This may indicate that cells bearing WC1-4 or WC1-7 vs. WC1-9 have different functional outcomes even if they bind the same ligands consistent with the paired receptor hypothesis for KIR molecules . The signaling role for the other intracytoplasmic sequences of WC1 molecules is under investigation.
Using Q-PCR to quantitate gene number, we showed that the WC1 immunoreceptor family comprises thirteen genes in the bovine genome, without variation in number among ten cattle breeds tested. Moreover, conservation of sequences for the thirteen WC1 genes existed among breeds. We found that all thirteen WC1 molecules fit into the three distinct molecular forms we previously described. While it has already been shown that functionally distinct subpopulations of bovine WC1+ γδ T cells can be defined by the expression of particular WC1 molecules, future studies need to address the significant questions of the signaling potential of each type of WC1 molecule in γδ T cell responses and the identification of ligand-binding domains in the various WC1 molecules. WC1 co-receptors on γδ T cells may be a type of PRRs on nonconventional T cells that participate with the TCR for maximal cell activation. Understanding the mechanism of activation of nonconventional γδ T cells that serve to bridge between innate and adaptive immune response might be exploited for efficacious vaccine design to improve human and domesticated animal health.
This work was conducted as part of the Bovine Genome Annotation Sequencing Consortium and supported by CSREES USDA-NRI grant #2006-01691, 2005-01812 and CSREES-USDA Massachusetts Agricultural Experiment Station under project No.MA0209204, and by Agriculture and Food Research Initiative Competitive Grant no. 2011-67015-30736 from the National Institute of Food and Agriculture USDA and NIH (R01 HD070056-01) program titled Dual Purpose with Dual Benefit: Research in Biomedicine and Agriculture using Agriculturally Important Domestic Species. Mention of trade names or commercial products is solely for the purpose of providing information and does not imply recommendation, endorsement or exclusion of other suitable products by the U.S. Department of Agriculture.
- Sarrias MR, Gronlund J, Padilla O, Madsen J, Holmskov U, Lozano F: The Scavenger Receptor Cysteine-Rich (SRCR) domain: an ancient and highly conserved protein module of the innate immune system. Crit Rev Immunol. 2004, 24 (1): 1-37.View ArticlePubMedGoogle Scholar
- Herzig CT, Waters RW, Baldwin CL, Telfer JC: Evolution of the CD163 family and its relationship to the bovine gamma delta T cell co-receptor WC1. BMC Evol Biol. 2010, 10: 181-PubMed CentralView ArticlePubMedGoogle Scholar
- Herzig CT, Baldwin CL: Genomic organization and classification of the bovine WC1 genes and expression by peripheral blood gamma delta T cells. BMC Genomics. 2009, 10: 191-PubMed CentralView ArticlePubMedGoogle Scholar
- Wang F, Herzig C, Ozer D, Baldwin CL, Telfer JC: Tyrosine phosphorylation of scavenger receptor cysteine-rich WC1 is required for the WC1-mediated potentiation of TCR-induced T-cell proliferation. Eur J Immunol. 2009, 39 (1): 254-266.View ArticlePubMedGoogle Scholar
- Holm D, Fink DR, Gronlund J, Hansen S, Holmskov U: Cloning and characterization of SCART1, a novel scavenger receptor cysteine-rich type I transmembrane molecule. Mol Immunol. 2009, 46 (8–9): 1663-1672.View ArticlePubMedGoogle Scholar
- Kisielow J, Kopf M, Karjalainen K: SCART scavenger receptors identify a novel subset of adult gamma delta T cells. J Immunol. 2008, 181 (3): 1710-1716.View ArticlePubMedGoogle Scholar
- Wijngaard PL, MacHugh ND, Metzelaar MJ, Romberg S, Bensaid A, Pepin L, Davis WC, Clevers HC: Members of the novel WC1 gene family are differentially expressed on subsets of bovine CD4-CD8- gamma delta T lymphocytes. J Immunol. 1994, 152 (7): 3476-3482.PubMedGoogle Scholar
- Rogers AN, VanBuren DG, Hedblom E, Tilahun ME, Telfer JC, Baldwin CL: Function of ruminant gamma delta T cells is defined by WC1.1 or WC1.2 isoform expression. Vet Immunol Immunopathol. 2005, 108 (1–2): 211-217.View ArticlePubMedGoogle Scholar
- Rogers AN, Vanburen DG, Hedblom EE, Tilahun ME, Telfer JC, Baldwin CL: Gamma delta T cell function varies with the expressed WC1 coreceptor. J Immunol. 2005, 174 (6): 3386-3393.View ArticlePubMedGoogle Scholar
- Lahmers KK, Hedges JF, Jutila MA, Deng M, Abrahamsen MS, Brown WC: Comparative gene expression by WC1+ gamma delta and CD4+ alpha beta T lymphocytes, which respond to Anaplasma marginale, demonstrates higher expression of chemokines and other myeloid cell-associated genes by WC1+ gamma delta T cells. J Leukoc Biol. 2006, 80 (4): 939-952.View ArticlePubMedGoogle Scholar
- Blumerman SL, Herzig CT, Rogers AN, Telfer JC, Baldwin CL: Differential TCR gene usage between WC1- and WC1+ ruminant gamma delta T cell subpopulations including those responding to bacterial antigen. Immunogenetics. 2006, 58 (8): 680-692.View ArticlePubMedGoogle Scholar
- O’Keeffe MA, Metcalfe SA, Glew MD, Bowden T, McInnes S, Kimpton WG, Cahill RN, Hein WR, Walker ID: Lymph node homing cells biologically enriched for gamma delta T cells express multiple genes from the T19 repertoire. Int Immunol. 1994, 6 (11): 1687-1697.View ArticlePubMedGoogle Scholar
- Walker ID, Glew MD, O’Keeffe MA, Metcalfe SA, Clevers HC, Wijngaard PL, Adams TE, Hein WR: A novel multi-gene family of sheep gamma delta T cells. Immunology. 1994, 83 (4): 517-523.PubMed CentralPubMedGoogle Scholar
- Wijngaard PL, Metzelaar MJ, MacHugh ND, Morrison WI, Clevers HC: Molecular characterization of the WC1 antigen expressed specifically on bovine CD4-CD8- gamma delta T lymphocytes. J Immunol. 1992, 149 (10): 3273-3277.PubMedGoogle Scholar
- Rogers AN, Vanburen DG, Zou B, Lahmers KK, Herzig CT, Brown WC, Telfer JC, Baldwin CL: Characterization of WC1 co-receptors on functionally distinct subpopulations of ruminant gamma delta T cells. Cell Immunol. 2006, 239 (2): 151-161.View ArticlePubMedGoogle Scholar
- Heid CA, Stevens J, Livak KJ, Williams PM: Real time quantitative PCR. Genome Res. 1996, 6 (10): 986-994.View ArticlePubMedGoogle Scholar
- Higuchi R, Fockler C, Dollinger G, Watson R: Kinetic PCR analysis: real-time monitoring of DNA amplification reactions. Biotechnology (N Y). 1993, 11 (9): 1026-1030.View ArticleGoogle Scholar
- Pfaffl MW: A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001, 29 (9): e45-PubMed CentralView ArticlePubMedGoogle Scholar
- Hilscher C, Vahrson W, Dittmer DP: Faster quantitative real-time PCR protocols may lose sensitivity and show increased variability. Nucleic Acids Res. 2005, 33 (21): e182-PubMed CentralView ArticlePubMedGoogle Scholar
- Larionov A, Krause A, Miller W: A standard curve based method for relative real time PCR data processing. BMC Bioinforma. 2005, 6: 62-View ArticleGoogle Scholar
- Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001, 25 (4): 402-408.View ArticlePubMedGoogle Scholar
- Ferreira ID, Rosario VE, Cravo PV: Real-time quantitative PCR with SYBR Green I detection for estimating copy numbers of nine drug resistance candidate genes in Plasmodium falciparum. Malar J. 2006, 5: 1-PubMed CentralView ArticlePubMedGoogle Scholar
- Bieche I, Olivi M, Champeme MH, Vidaud D, Lidereau R, Vidaud M: Novel approach to quantitative polymerase chain reaction using real-time detection: application to the detection of gene amplification in breast cancer. Int J Cancer. 1998, 78 (5): 661-666.View ArticlePubMedGoogle Scholar
- Klein D: Quantification using real-time PCR technology: applications and limitations. Trends Mol Med. 2002, 8 (6): 257-260.View ArticlePubMedGoogle Scholar
- Liu GE, Van Tassel CP, Sonstegard TS, Li RW, Alexander LJ, Keele JW, Matukumalli LK, Smith TP, Gasbarre LC: Detection of germline and somatic copy number variations in cattle. Dev Biol (Basel). 2008, 132: 231-237.Google Scholar
- Elsik CG, Tellam RL, Worley KC, Gibbs RA, Muzny DM, Weinstock GM, Adelson DL, Eichler EE, Elnitski L, Guigo R, et al: The genome sequence of taurine cattle: a window to ruminant biology and evolution. Science. 2009, 324 (5926): 522-528.PubMed CentralView ArticlePubMedGoogle Scholar
- Goossens K, Van Poucke M, Van Soom A, Vandesompele J, Van Zeveren A, Peelman LJ: Selection of reference genes for quantitative real-time PCR in bovine preimplantation embryos. BMC Dev Biol. 2005, 5: 27-PubMed CentralView ArticlePubMedGoogle Scholar
- Herzig CT, Lefranc MP, Baldwin CL: Annotation and classification of the bovine T cell receptor delta genes. BMC Genomics. 2010, 11: 100-PubMed CentralView ArticlePubMedGoogle Scholar
- Wang F, Herzig CT, Chen C, Hsu H, Baldwin CL, Telfer JC: Scavenger receptor WC1 contributes to the gamma delta T cell response to Leptospira. Mol Immunol. 2011, 48 (6–7): 801-809.View ArticlePubMedGoogle Scholar
- Yu Y, Lee C, Kim J, Hwang S: Group-specific primer and probe sets to detect methanogenic communities using quantitative real-time polymerase chain reaction. Biotechnol Bioeng. 2005, 89 (6): 670-679.View ArticlePubMedGoogle Scholar
- Hall TA: BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp Ser. 1999, 41: 95-98.Google Scholar
- Chenna R, Sugawara H, Koike T, Lopez R, Gibson TJ, Higgins DG, Thompson JD: Multiple sequence alignment with the Clustal series of programs. Nucleic Acids Res. 2003, 31 (13): 3497-3500.PubMed CentralView ArticlePubMedGoogle Scholar
- Ronquist F, Huelsenbeck JP: MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics. 2003, 19 (12): 1572-1574.View ArticlePubMedGoogle Scholar
- Walker AM, Roberts RM: Characterization of the bovine type I IFN locus: rearrangements, expansions, and novel subfamilies. BMC Genomics. 2009, 10: 187-PubMed CentralView ArticlePubMedGoogle Scholar
- Ririe KM, Rasmussen RP, Wittwer CT: Product differentiation by analysis of DNA melting curves during the polymerase chain reaction. Anal Biochem. 1997, 245 (2): 154-160.View ArticlePubMedGoogle Scholar
- Nystrom K, Biller M, Grahn A, Lindh M, Larson G, Olofsson S: Real time PCR for monitoring regulation of host gene expression in herpes simplex virus type 1-infected human diploid cells. J Virol Methods. 2004, 118 (2): 83-94.View ArticlePubMedGoogle Scholar
- Lahmers KK, Norimine J, Abrahamsen MS, Palmer GH, Brown WC: The CD4+ T cell immunodominant Anaplasma marginale major surface protein 2 stimulates gamma delta T cell clones that express unique T cell receptors. J Leukoc Biol. 2005, 77 (2): 199-208.View ArticlePubMedGoogle Scholar
- Leulier F, Lemaitre B: Toll-like receptors–taking an evolutionary approach. Nat Rev Genet. 2008, 9 (3): 165-178.View ArticlePubMedGoogle Scholar
- Chen G, Shaw MH, Kim YG, Nunez G: NOD-like receptors: role in innate immunity and inflammatory disease. Annu Rev Pathol. 2009, 4: 365-398.View ArticlePubMedGoogle Scholar
- Medzhitov R, Janeway C: The Toll receptor family and microbial recognition. Trends Microbiol. 2000, 8 (10): 452-456.View ArticlePubMedGoogle Scholar
- Aderem A: Role of Toll-like receptors in inflammatory response in macrophages. Crit Care Med. 2001, 29 (7 Suppl): S16-S18.View ArticlePubMedGoogle Scholar
- Poulain-Godefroy O, Le Bacquer O, Plancq P, Lecoeur C, Pattou F, Fruhbeck G, Froguel P: Inflammatory role of Toll-like receptors in human and murine adipose tissue. Mediators Inflamm. 2010, 2010: 823486-PubMed CentralView ArticlePubMedGoogle Scholar
- Villasenor-Cardoso MI, Ortega E: Polymorphisms of innate immunity receptors in infection by parasites. Parasite Immunol. 2011, 33 (12): 643-653.View ArticlePubMedGoogle Scholar
- Hara T, Nishimura H, Hasegawa Y, Yoshikai Y: Thymus-dependent modulation of Ly49 inhibitory receptor expression on NK1.1+gamma/delta T cells. Immunology. 2001, 102 (1): 24-30.PubMed CentralView ArticlePubMedGoogle Scholar
- Held W, Roland J, Raulet DH: Allelic exclusion of Ly49-family genes encoding class I MHC-specific receptors on NK cells. Nature. 1995, 376 (6538): 355-358.View ArticlePubMedGoogle Scholar
- Ortaldo JR, Winkler-Pickett R, Mason AT, Mason LH: The Ly-49 family: regulation of cytotoxicity and cytokine production in murine CD3+ cells. J Immunol. 1998, 160 (3): 1158-1165.PubMedGoogle Scholar
- Hao L, Nei M: Genomic organization and evolutionary analysis of Ly49 genes encoding the rodent natural killer cell receptors: rapid evolution by repeated gene duplication. Immunogenetics. 2004, 56 (5): 343-354.View ArticlePubMedGoogle Scholar
- Wilhelm BT, Mager DL: Rapid expansion of the Ly49 gene cluster in rat. Genomics. 2004, 84 (1): 218-221.View ArticlePubMedGoogle Scholar
- Westgaard IH, Berg SF, Orstavik S, Fossum S, Dissen E: Identification of a human member of the Ly-49 multigene family. Eur J Immunol. 1998, 28 (6): 1839-1846.View ArticlePubMedGoogle Scholar
- McQueen KL, Wilhelm BT, Harden KD, Mager DL: Evolution of NK receptors: a single Ly49 and multiple KIR genes in the cow. Eur J Immunol. 2002, 32 (3): 810-817.View ArticlePubMedGoogle Scholar
- Martin AM, Freitas EM, Witt CS, Christiansen FT: The genomic organization and evolution of the natural killer immunoglobulin-like receptor (KIR) gene cluster. Immunogenetics. 2000, 51 (4–5): 268-280.View ArticlePubMedGoogle Scholar
- Wilson MJ, Torkar M, Haude A, Milne S, Jones T, Sheer D, Beck S, Trowsdale J: Plasticity in the organization and sequences of human KIR/ILT gene families. Proc Natl Acad Sci U S A. 2000, 97 (9): 4778-4783.PubMed CentralView ArticlePubMedGoogle Scholar
- Guethlein LA, Flodin LR, Adams EJ, Parham P: NK cell receptors of the orangutan (Pongo pygmaeus): a pivotal species for tracking the coevolution of killer cell Ig-like receptors with MHC-C. J Immunol. 2002, 169 (1): 220-229.View ArticlePubMedGoogle Scholar
- Hershberger KL, Shyam R, Miura A, Letvin NL: Diversity of the killer cell Ig-like receptors of rhesus monkeys. J Immunol. 2001, 166 (7): 4380-4390.View ArticlePubMedGoogle Scholar
- Gagnier L, Wilhelm BT, Mager DL: Ly49 genes in non-rodent mammals. Immunogenetics. 2003, 55 (2): 109-115.PubMedGoogle Scholar
- French AR, Pingel JT, Wagner M, Bubic I, Yang L, Kim S, Koszinowski U, Jonjic S, Yokoyama WM: Escape of mutant double-stranded DNA virus from innate immune control. Immunity. 2004, 20 (6): 747-756.View ArticlePubMedGoogle Scholar
- Madsen J, Tornoe I, Nielsen O, Lausen M, Krebs I, Mollenhauer J, Kollender G, Poustka A, Skjodt K, Holmskov U: CRP-ductin, the mouse homologue of gp-340/deleted in malignant brain tumors 1 (DMBT1), binds gram-positive and gram-negative bacteria and interacts with lung surfactant protein D. Eur J Immunol. 2003, 33 (8): 2327-2336.View ArticlePubMedGoogle Scholar
- Sarrias MR, Rosello S, Sanchez-Barbero F, Sierra JM, Vila J, Yelamos J, Vives J, Casals C, Lozano F: A role for human Sp alpha as a pattern recognition receptor. J Biol Chem. 2005, 280 (42): 35391-35398.View ArticlePubMedGoogle Scholar
- Sarrias MR, Farnos M, Mota R, Sanchez-Barbero F, Ibanez A, Gimferrer I, Vera J, Fenutria R, Casals C, Yelamos J, et al: CD6 binds to pathogen-associated molecular patterns and protects from LPS-induced septic shock. Proc Natl Acad Sci U S A. 2007, 104 (28): 11724-11729.PubMed CentralView ArticlePubMedGoogle Scholar
- Bikker FJ, Ligtenberg AJ, End C, Renner M, Blaich S, Lyer S, Wittig R, van’t Hof W, Veerman EC, Nazmi K, et al: Bacteria binding by DMBT1/SAG/gp-340 is confined to the VEVLXXXXW motif in its scavenger receptor cysteine-rich domains. J Biol Chem. 2004, 279 (46): 47699-47703.View ArticlePubMedGoogle Scholar
- Adams EJ, Strop P, Shin S, Chien YH, Garcia KC: An autonomous CDR3delta is sufficient for recognition of the nonclassical MHC class I molecules T10 and T22 by gammadelta T cells. Nat Immunol. 2008, 9 (7): 777-784.PubMed CentralView ArticlePubMedGoogle Scholar
- Parham P: Killer cell immunoglobulin-like receptor diversity: balancing signals in the natural killer cell response. Immunol Lett. 2004, 92 (1–2): 11-13.View ArticlePubMedGoogle Scholar
- Raulet DH: Roles of the NKG2D immunoreceptor and its ligands. Nat Rev Immunol. 2003, 3 (10): 781-790.View ArticlePubMedGoogle Scholar
- Eagle RA, Trowsdale J: Promiscuity and the single receptor: NKG2D. Nat Rev Immunol. 2007, 7 (9): 737-744.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.