Abstract:The major histocompatibility complex (MHC) contains many genes that play key roles in initiating and regulating immune responses. This includes the polymorphic MHCI and MHCII genes that present epitopes to CD8+ and CD4+ T‐cells, respectively. Consequently, the characterisation of the repertoire of MHC genes is an important component of improving our understanding of the genetic variation that determines the outcomes of immune responses. In cattle, MHC (BoLA) research has predominantly focused on Holstein‐Fries… Show more
“…In this study we conducted parallel analyses of the Holstein‐Friesian cohort, however comparison with the Zambian cohort is confounded by the fact that the former was composed of animals from a single breed and from a single farm. A more equitable comparison is with the cohort of Brazilian animals published recently 15 which was composed of 555 individuals from 4 breeds (Gyr, Guzerat, Nelore and Girlando) from 7 different farms, so of a more similar structure and complexity to the Zambian cohort; however only the MHCI and DRB was sequenced. A total of 165 different combined MHCI‐DR haplotypes were identified in the Brazilian cohort (29.7 haplotypes/100 animals)—the equivalent figure for the Zambian cohort was 457 MHCI‐DR haplotypes (72.6 haplotypes/100 animals) and the difference in complexity can be visualised by comparing Figure 6B (left side of figure) with Supplementary Data 9 in Vasoya et al (2021) 15 .…”
Section: Discussionmentioning
confidence: 99%
“…A more equitable comparison is with the cohort of Brazilian animals published recently 15 which was composed of 555 individuals from 4 breeds (Gyr, Guzerat, Nelore and Girlando) from 7 different farms, so of a more similar structure and complexity to the Zambian cohort; however only the MHCI and DRB was sequenced. A total of 165 different combined MHCI‐DR haplotypes were identified in the Brazilian cohort (29.7 haplotypes/100 animals)—the equivalent figure for the Zambian cohort was 457 MHCI‐DR haplotypes (72.6 haplotypes/100 animals) and the difference in complexity can be visualised by comparing Figure 6B (left side of figure) with Supplementary Data 9 in Vasoya et al (2021) 15 . A number of factors may contribute to the disparity between the Brazilian and Zambian cohorts, but a major factor is likely to be the relatively small founder populations of the Bos indicus breeds in Brazil and continuing genetic selection as a consequence of selective breeding 36 .…”
Section: Discussionmentioning
confidence: 99%
“…To provide a consistent system for the nomenclature of MHCII haplotypes (defined as a unique combination of DRB3, DQA and DQB alleles) we adopted the following approach; (i) pairs of DQA/DQB alleles were assigned a name composed of two numbers, with the first number representing the expression of a unique combination of DQA and DQB allelic groups and the second representing variants that contain a different allele of one or both of the DQA/DQB alleles (e.g., DQ30. MiSeq protocol as described previously 15 ). Bioinformatic analysis of the DQA and DQB sequencing data generated was performed using a pipeline based on that previously developed for the DRB3 data, which included filters to remove artefact reads that (i) had ambiguous base-calls (N), (ii) were >9 bp different from the anticipated sequence size, (iii) were identified as PCR chimaeras and (iv) contained potential PCR/sequencing errors that led to the generation of 1 bp variants.…”
Section: Nomenclature Of Novel Mhc Sequences and Haplotypesmentioning
confidence: 99%
“…In recent studies, we have exploited the opportunities afforded by high-throughput sequencing technologies to study the BoLA-I and BoLA-DRB repertoires of large cattle cohorts from the UK, Brazil, Kenya and Cameroon. 14,15 In this study, we expand on our previous work by developing an equivalent NGS approach to the analysis of BoLA-DQA and -DQB. This was used in conjunction with the previously described BoLA-I and -DRB typing to characterise the MHC haplotypes of a cohort of indigenous cattle from Zambia.…”
The classical MHC class I and class II molecules play key roles in determining the antigenic-specificity of CD8+ and CD4+ T-cell responses-as such characterisation of the repertoire of MHCI and MHCII allelic diversity is fundamental to our ability to understand, and potentially, exploit how genetic diversity influences the outcome of immune responses. Cattle remain one of the most economically livestock species, with particular importance to many small-holder farmers in low-and-middle income countries (LMICs). However, our knowledge of MHC (BoLA) diversity in the indigenous breeds that form the mainstay of cattle populations in many LMICs remains very limited. In this study we develop a MiSeq-based platform to enable the rapid analysis of BoLA-DQA and BoLA-DQB, and combine this with similar platforms to analyse BoLA-I and BoLA-DRB repertoires, to study a large cohort of cattle ($800 animals) representing the 3 major indigenous breeds (Angoni, Barotse, Tonga) in Zambia. The data presented confirms the capacity of this high-throughput and high-resolution approach to provide a full characterisation of the MHCI-MHCII genotypes of cattle for which little previous MHC sequence data has been obtained. The cattle in Zambia were found to express a diverse range of MHCI, MHCII and extended MHCI-MHCII haplotypes. The combined MHCI-MHCII genotyping now possible opens new opportunities to rapidly expand our knowledge of MHC diversity Isaac Silwamba and Deepali Vasoya contributed equally to this study.
“…In this study we conducted parallel analyses of the Holstein‐Friesian cohort, however comparison with the Zambian cohort is confounded by the fact that the former was composed of animals from a single breed and from a single farm. A more equitable comparison is with the cohort of Brazilian animals published recently 15 which was composed of 555 individuals from 4 breeds (Gyr, Guzerat, Nelore and Girlando) from 7 different farms, so of a more similar structure and complexity to the Zambian cohort; however only the MHCI and DRB was sequenced. A total of 165 different combined MHCI‐DR haplotypes were identified in the Brazilian cohort (29.7 haplotypes/100 animals)—the equivalent figure for the Zambian cohort was 457 MHCI‐DR haplotypes (72.6 haplotypes/100 animals) and the difference in complexity can be visualised by comparing Figure 6B (left side of figure) with Supplementary Data 9 in Vasoya et al (2021) 15 .…”
Section: Discussionmentioning
confidence: 99%
“…A more equitable comparison is with the cohort of Brazilian animals published recently 15 which was composed of 555 individuals from 4 breeds (Gyr, Guzerat, Nelore and Girlando) from 7 different farms, so of a more similar structure and complexity to the Zambian cohort; however only the MHCI and DRB was sequenced. A total of 165 different combined MHCI‐DR haplotypes were identified in the Brazilian cohort (29.7 haplotypes/100 animals)—the equivalent figure for the Zambian cohort was 457 MHCI‐DR haplotypes (72.6 haplotypes/100 animals) and the difference in complexity can be visualised by comparing Figure 6B (left side of figure) with Supplementary Data 9 in Vasoya et al (2021) 15 . A number of factors may contribute to the disparity between the Brazilian and Zambian cohorts, but a major factor is likely to be the relatively small founder populations of the Bos indicus breeds in Brazil and continuing genetic selection as a consequence of selective breeding 36 .…”
Section: Discussionmentioning
confidence: 99%
“…To provide a consistent system for the nomenclature of MHCII haplotypes (defined as a unique combination of DRB3, DQA and DQB alleles) we adopted the following approach; (i) pairs of DQA/DQB alleles were assigned a name composed of two numbers, with the first number representing the expression of a unique combination of DQA and DQB allelic groups and the second representing variants that contain a different allele of one or both of the DQA/DQB alleles (e.g., DQ30. MiSeq protocol as described previously 15 ). Bioinformatic analysis of the DQA and DQB sequencing data generated was performed using a pipeline based on that previously developed for the DRB3 data, which included filters to remove artefact reads that (i) had ambiguous base-calls (N), (ii) were >9 bp different from the anticipated sequence size, (iii) were identified as PCR chimaeras and (iv) contained potential PCR/sequencing errors that led to the generation of 1 bp variants.…”
Section: Nomenclature Of Novel Mhc Sequences and Haplotypesmentioning
confidence: 99%
“…In recent studies, we have exploited the opportunities afforded by high-throughput sequencing technologies to study the BoLA-I and BoLA-DRB repertoires of large cattle cohorts from the UK, Brazil, Kenya and Cameroon. 14,15 In this study, we expand on our previous work by developing an equivalent NGS approach to the analysis of BoLA-DQA and -DQB. This was used in conjunction with the previously described BoLA-I and -DRB typing to characterise the MHC haplotypes of a cohort of indigenous cattle from Zambia.…”
The classical MHC class I and class II molecules play key roles in determining the antigenic-specificity of CD8+ and CD4+ T-cell responses-as such characterisation of the repertoire of MHCI and MHCII allelic diversity is fundamental to our ability to understand, and potentially, exploit how genetic diversity influences the outcome of immune responses. Cattle remain one of the most economically livestock species, with particular importance to many small-holder farmers in low-and-middle income countries (LMICs). However, our knowledge of MHC (BoLA) diversity in the indigenous breeds that form the mainstay of cattle populations in many LMICs remains very limited. In this study we develop a MiSeq-based platform to enable the rapid analysis of BoLA-DQA and BoLA-DQB, and combine this with similar platforms to analyse BoLA-I and BoLA-DRB repertoires, to study a large cohort of cattle ($800 animals) representing the 3 major indigenous breeds (Angoni, Barotse, Tonga) in Zambia. The data presented confirms the capacity of this high-throughput and high-resolution approach to provide a full characterisation of the MHCI-MHCII genotypes of cattle for which little previous MHC sequence data has been obtained. The cattle in Zambia were found to express a diverse range of MHCI, MHCII and extended MHCI-MHCII haplotypes. The combined MHCI-MHCII genotyping now possible opens new opportunities to rapidly expand our knowledge of MHC diversity Isaac Silwamba and Deepali Vasoya contributed equally to this study.
“…3 shows the distribution of the percent pairwise sequence identities when the class I MHC alleles of the African buffalo were compared to alleles from different African cattle breeds grouped in terms of exons. Sequences derived from the following European Bos taurus class I MHC haplotypes were also included in the analysis: A10, A11, A12 (w12B), A13, A14, A15, A15v, A19, A20 (v2), A31, BF1, H5 (New5), HP1.1, HP1.2, HP1.3, HP1.51.1, HP1.52.1, HP1.53.1, HP1.54.1, HP1.12.4, unHP1.74.1, unHP1.20.3 (Vasoya et al 2016 , 2021 ). Fig.…”
African buffalo (Syncerus caffer) have been distinct from the Auroch lineage leading to domestic cattle for 5 million years, and are reservoirs of multiple pathogens, that affect introduced domestic cattle. To date, there has been no analysis of the class I MHC locus in African buffalo. We present the first data on African buffalo class I MHC, which demonstrates that gene and predicted protein coding sequences are approximately 86–87% similar to that of African domestic cattle in the peptide binding region. The study also shows concordance in the distribution of codons with elevated posterior probabilities of positive selection in the buffalo class I MHC and known antigen binding sites in cattle. Overall, the diversity in buffalo class I sequences appears greater than that in cattle, perhaps related to a more complex pathogen challenge environment in Africa. However, application of NetMHCpan suggested broad clustering of peptide binding specificities between buffalo and cattle. Furthermore, in the case of at least 20 alleles, critical peptide-binding residues appear to be conserved with those of cattle, including at secondary anchor residues. Alleles with six different length transmembrane regions were detected. This preliminary analysis suggests that like cattle, but unlike most other mammals, African buffalo appears to exhibit configuration (haplotype) variation in which the loci are expressed in distinct combinations.
Immunogenomic loci remain poorly understood because of their genetic complexity and size. Here, we report the de novo assembly of a cattle genome and provide a detailed annotation of the immunogenomic loci. The assembled genome contains 143 contigs (N50 ~ 74.0 Mb). In contrast to the current reference genome (ARS-UCD1.2), 156 gaps are closed and 467 scaffolds are located in our assembly. Importantly, the immunogenomic regions, including three immunoglobulin (IG) loci, four T-cell receptor (TR) loci, and the major histocompatibility complex (MHC) locus, are seamlessly assembled and precisely annotated. With the characterization of 258 IG genes and 657 TR genes distributed across seven genomic loci, we present a detailed depiction of immune gene diversity in cattle. Moreover, the MHC gene structures are integrally revealed with properly phased haplotypes. Together, our work describes a more complete cattle genome, and provides a comprehensive view of its complex immune-genome.
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