Lisa E. Creary

Deciphering the fine nucleotide diversity of full HLA class I and class II genes in a well‐documented population from sub‐Saharan Africa

With the aim to understand how next‐generation sequencing (NGS) improves both our assessment of genetic variation within populations and our knowledge on HLA molecular evolution, we sequenced and analysed 8 HLA loci in a well‐documented population from sub‐Saharan Africa (Mandenka). The results of full‐gene NGS‐MiSeq sequencing compared with those obtained by traditional typing techniques or limited sequencing strategies showed that segregating sites located outside exon 2 are crucial to describe not only class I but also class II population diversity. A comprehensive analysis of exons 2, 3, 4 and 5 nucleotide diversity at the 8 HLA loci revealed remarkable differences among these gene regions, notably a greater variation concentrated in the antigen recognition sites of class I exons 3 and some class II exons 2, likely associated with their peptide‐presentation function, a lower diversity of HLA‐C exon 3, possibly related to its role as a KIR ligand, and a peculiar molecular diversity of HLA‐A exon 2, revealing demographic signals. Based on full‐length HLA sequences, we also propose that the most frequent DRB1 allele in the studied population, DRB1*13:04, emerged from an allelic conversion involving 3 potential alleles as donors and DRB1*11:02:01 as recipient. Finally, our analysis revealed a high occurrence of the DRB1*13:04‐DQA1*05:05:01‐DQB1*03:19 haplotype, possibly resulting from a selective sweep due to protection to Onchorcerca volvulus, a prevalent pathogen in West Africa. This study unveils highly relevant information on the molecular evolution of HLA genes in relation to their immune function, calling for similar analyses in other populations living in contrasting environments.

HLA-mismatched unrelated donor transplantation using TLI-ATG conditioning has a low risk of GVHD and potent antitumor activity

Many patients lack a fully HLA-matched donor for hematopoietic cell transplantation (HCT), and HLA mismatch is typically associated with inferior outcomes. Total lymphoid irradiation and antithymocyte globulin (TLI-ATG) is a nonmyeloablative conditioning regimen that is protective against graft-versus-host disease (GVHD), and we hypothesized that the protective effect would extend beyond HLA-matched donors. We report outcomes for all consecutively transplanted patients at Stanford University from December 2001 through May 2015 who received TLI-ATG conditioning and HCTs from 8 to 9 out of 10 HLA-mismatched unrelated donors (MMUDs, N = 72) compared with 10 out of 10 HLA-matched unrelated donors (MUDs, N = 193). The median age of the patients was 60 years with a median follow-up of 2 years, and there was a similar distribution of lymphoid and myeloid malignancies in both cohorts. There were no significant differences between MMUD and MUD cohorts in overall survival (46% vs 46% at 5 years, P = .86), disease-free survival (38% vs 28% at 5 years, P = .25), nonrelapse mortality (17% vs 12% at 2 years, P = .34), acute GVHD grades III-IV (6% vs 3% at day +100, P = .61), or chronic GVHD (39% vs 35% at 5 years, P = .49). There was a trend toward less relapse in the MMUD cohort (45% vs 60% at 5 years, hazard ratio: 0.71, P = .094), which was significant for patients with lymphoid malignancies (29% vs 57% at 5 years, hazard ratio: 0.55, P = .044). Achieving full donor chimerism was strongly associated with lower relapse rates. TLI-ATG conditioning may overcome the traditionally poorer outcome associated with HLA-mismatched donors and may be particularly well suited for patients with lymphoid malignancies who lack HLA-matched donors.

Deconstruction of HLA-DRB1*04:01:01 and HLA-DRB1*15:01:01 class II haplotypes using next-generation sequencing in European-Americans with multiple sclerosis

Background: The association between HLA-DRB1*15:01 with multiple sclerosis (MS) susceptibility is well established, but the contribution of the tightly associated HLA-DRB5*01:01 allele has not yet been completely ascertained. Similarly, the effects of HLA-DRB1*04:01 alleles and haplotypes, defined at the full-gene resolution level with MS risk remains to be elucidated. Objectives: To characterize the molecular architecture of class II HLA-DR15 and HLA-DR4 haplotypes associated with MS. Methods: Next-generation sequencing was used to determine HLA-DQB1, HLA-DQA1, and HLA-DRB1/4/5 alleles in 1403 unrelated European-American patients and 1425 healthy unrelated controls. Effect sizes of HLA alleles and haplotypes on MS risk were measured by odds ratio (OR) with 95% confidence intervals. Results: HLA-DRB1*15:01:01:01SG (OR = 3.20, p < 2.2E–16), HLA-DRB5*01:01:01 (OR = 2.96, p < 2.2E–16), and HLA-DRB5*01:01:01v1_STR1 (OR = 8.18, p = 4.3E–05) alleles all occurred at significantly higher frequencies in MS patients compared to controls. The most significant predis-posing haplotypes were HLA-DQB1*06:02:01~HLA-DQA1*01:02:01:01SG~HLA-DRB1*15:01:01:01SG~HLA-DRB5*01:01:01 and HLA-DQB1*06:02:01~HLA-DQA1*01:02:01:01SG~HLA-DRB1*15:01:01:01SG~HLA-DRB5*01:01:01v1_STR1 (OR = 3.19, p < 2.2E–16; OR = 9.30, p = 9.7E–05, respectively). Analyses of the HLA-DRB1*04 cohort in the absence of HLA-DRB1*15:01 haplotypes revealed that the HLA-DQB1*03:01:01:01~HLA-DQA1*03:03:01:01~HLA-DRB1*04:01:01:01SG~HLA-DRB4*01:03:01:01 haplotype was protective (OR = 0.64, p = 0.028), whereas the HLA-DQB1*03:02:01~HLA-DQA1*03:01:01~HLA-DRB1*04:01:01:01SG~HLA-DRB4*01:03:01:01 haplotype was associated with MS susceptibility (OR = 1.66, p = 4.9E–03). Conclusion: HLA-DR15 haplotypes, including genomic variants of HLA-DRB5, and HLA-DR4 haplotypes affect MS risk.

Allelic resolution NGS HLA typing of Class I and Class II loci and haplotypes in Cape Town, South Africa

The development of next-generation sequencing (NGS) methods for HLA genotyping has already had an impact on the scope and precision of HLA research. In this study, allelic resolution HLA typing was obtained for 402 individuals from Cape Town, South Africa. The data were produced by high-throughput NGS sequencing as part of a study of T-cell responses to Mycobacterium tuberculosis in collaboration with the University of Cape Town and Stanford University. All samples were genotyped for 11 HLA loci, namely HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, -DRB1, -DRB3, -DRB4, and -DRB5. NGS HLA typing of samples from Cape Town inhabitants revealed a unique cohort, including unusual haplotypes, and 22 novel alleles not previously reported in the IPD-IMGT/HLA Database. Eight novel alleles were in Class I loci and 14 were in Class II. There were 62 different alleles of HLA-A, 72 of HLA-B, and 47 of HLA-C. Alleles A∗23:17, A∗43:01, A∗29:11, A∗68:27:01, A∗01:23, B∗14:01:01, B∗15:10:01, B∗39:10:01, B∗45:07, B∗82:02:01 and C∗08:04:01 were notably more frequent in Cape Town compared to other populations reported in the literature. Class II loci had 21 different alleles of DPA1, 46 of DPB1, 27 of DQA1, 26 of DQB1, 41 of DRB1, 5 of DRB3, 4 of DRB4 and 6 of DRB5. The Cape Town cohort exhibited high degrees of HLA diversity and relatively high heterozygosity at most loci. Genetic distances between Cape Town and five other sub-Saharan African populations were also calculated and compared to European Americans.

Mandenka from Senegal: Next Generation Sequencing typings reveal very high frequencies of particular HLA class II alleles and haplotypes

A total of 72 unrelated Mandenka individuals from Eastern Senegal, Niokholo region, were typed using Next Generation Sequencing (library preparation with the Holotype HLA X2 and MIA FORA NGS HLA Typing kits, sequencing with Illumina MiSeq, and bio-informatic processing with HLA Twin v1.1.1 (Omixon) and MIA FORA NGS software) and yielded reliable genotypes for 8 HLA loci, namely A, B, C, DRB1, DQA1, DQB1, DPA1 and DPB1. We used the recommendations of the HLA-net consortium (http://hlanet.eu) to characterise the population under study (Figure 1A) and the HLA-net GENE[RATE] tools to compute 1and 2-locus frequencies and the main population genetics statistics (summarised in Figure 1B,C). The Mandenka population is considered at HardyWeinberg equilibrium, as the statistical tests indicate nonsignificant P-values (after correction for multiple tests) at all HLA loci except HLA-DPA1 but the adjusted P-value for DPA1 (.044) is very close to the 5% threshold and not significant at the 1% level with a sample size substantially smaller at this locus. Class I and class II loci differ regarding their most frequent alleles, the frequencies of which are all below 20% for the former (from 17% for A*23:01:01 to 18% for B*35:01:01 and C*04:01:01) and above 20% for the latter (from 22% for DPB1*17:01 to 50% for DQA1*05:01:01). This explains the contrasted heterozygosities observed at class I (above 90%) and class II (between 72% and 88%). As expected based on the IPD-IMGT/HLA data, DPA1 is the less diversified locus in the Mandenka, with only 10 third field level alleles detected (and 5 second field level alleles representing the functional diversity) and the 3 most common alleles representing as much as 80% of the total allele frequencies. Actually, the 3 HLA class I loci reject the assumption of neutrality towards an excess of heterozygotes, whereas none of the class II loci do. This suggests different types of selection acting on class I and class II loci in this population: class I loci would mainly evolve under balancing selection favouring heterozygous individuals; regarding class II loci, as a purely neutral evolution is unlikely due (like class I) to their immune function, our results merely suggest a combination of both balancing (heterozygous advantage, leading to a higher diversity) and positive (due to some specific pathogen resistance leading to a lower diversity) selection resulting in apparently neutral distributions. However, we do not exclude pathogen-driven selection at class I loci either, as the most common HLA-B allele found in the Mandenka, B*35:01:01, has been identified as a resistance allele against Plasmodium falciparum (the most prevalent malaria pathogen in West Africa) in a Ghanaian population and could have thus reached its high frequency in the Mandenka through positive selection. Received: 8 December 2017 Accepted: 19 December 2017