Lloyd D'Orsogna

Objectives: Despite the availability of vaccines, influenza viruses still cause substantial morbidity and mortality. This highlights the need for improved vaccines and novel therapeutics. CD8 ⁺ T cells recognise conserved viral epitopes and can provide broad protection, making them attractive targets for next‐generation vaccine design. A key step in this process is to identify T cell epitopes across influenza viruses. Here, we characterised three homologous influenza Polymerase Basic 1 (PB1)‐derived peptides from influenza viruses (PB1‐A ₃₁₀ , PB1‐B ₃₁₀ and PB1‐C ₃₁₂ , respectively) presented by the common, but understudied, HLA‐B*18:01 molecule. Methods: We used a thermal melt assay and X‐ray crystallography to assess the stability and structural features of each peptide. CD8 ⁺ T‐cell responses were evaluated using intracellular cytokine staining with HLA‐B*18:01 ⁺ samples. Results: The structures of HLA‐B*18:01 presenting each peptide were solved, including the first structure of HLA‐B*18:01 presenting a Gammainfluenzavirus (formerly influenza C virus) derived peptide. PB1‐C ₃₁₂ exhibited a higher thermal stability and a different conformation compared to PB1‐A ₃₁₀ and PB1‐B ₃₁₀ . CD8 ⁺ T cell responses varied among samples, with PB1‐A ₃₁₀ eliciting the strongest activation. Minimal T cell cross‐reactivity was observed and was mostly between PB1‐A ₃₁₀ and PB1‐B ₃₁₀ likely reflecting shared structural features. Conclusion: These findings further expand knowledge on peptide presentation by the common HLA‐B*18:01 molecule, and the limitations on CD8 ⁺ T cell cross‐recognition of this PB1‐derived epitope across influenza lineages. This highlights the importance of more research on cross‐lineage T cell cross‐reactivity that could inform the design of broadly protective T cell‐based vaccines and therapeutics.

Vaccination against SARS-CoV-2 has been the most effective tool in mitigating the COVID-19 pandemic. However, some individuals experience side effects that cause distress and interfere with daily activities, which can limit vaccine uptake, with important public health implications. Here, we considered the impact of HLA variation on the propensity for mild side effects with COVID-19 vaccination. We examined variation in HLA-A, -B, -C, -DRB1, and -DQB1 for association with self-reported side effects in a large cohort of U.S. Euro-ancestry vaccinated individuals (N = 50,535) and confirmed results in an independent replication cohort (N = 4,575). We found that HLA-A*03:01 was significantly associated with systemic side effects (OR = 1.36, CI = 1.31-1.41, p = 6.79×10-57) and fewer breakthrough infections, and that this phenomenon is specific to the COVID-19 vaccine. Surprisingly, we observed limited activation of CD8+ T cells in HLA-A*03:01+ samples to the Spike-derived peptides, excluding them as a likely source of the reported vaccine side effects. Rather, examination of immune cell subsets, prior and after vaccination, points to a central role for monocytes in the production of IL-6 and IL-8, which significantly correlates with the reported severity of side effects in HLA-A*03:01+ donors. Meanwhile, the large, mostly naïve, and low-affinity population of Spike-specific CD8+ T cells likely contribute to an inflammatory milieu in HLA-A*03:01 carriers through weak binding to antigen presenting cells. This work sheds light on the mechanisms underlying HLA-mediated COVID-19 vaccine reactogenicity and associated reduction in infections, providing important new insights that may support efforts to optimize vaccine efficacy and promote broader public involvement in vaccination programs.

Third-generation sequencing (TGS), also known as long-read sequencing, has become a promising tool in clinical and research laboratories because it delivers high-resolution results with unmatched throughput. Specialised immunohematology laboratories currently employ sequencing-based methods to characterise rare ABO blood group phenotypes that cannot be identified through serology and genotyping methods. However, routine clinical application of these methods remains elusive due to the absence of validated laboratory protocols and bioinformatics tools. In this study, we have developed and validated a TGS-based workflow for comprehensive determination of the clinically relevant ABO phenotypes from DNA isolated from buccal swabs or whole blood. The region spanning exons 2 to 7 of the ABO gene were amplified and sequenced on MinION 10.4.1 flow cells. Predicted ABO phenotypes were initially determined based on single-nucleotide variants at gDNA261 (rs8176719), gDNA796 (rs8176746), and gDNA803 (rs8176747). However, certain O subtypes lacked the distinguishing deletion (rs8176719) and instead exhibited variations in exon 7 at gDNA802 (rs41302905) and gDNA805, caused by gDNA804 (rs782782485), which differentiate them from A alleles sharing the same nucleotides at gDNA261, gDNA796, and gDNA803. These additional variants were added to the analysis pipeline to identify the additional subtypes. DNA sequence data were sufficient to distinguish between the four clinically relevant ABO blood group phenotypes based on five polymorphic positions. While high sequencing coverage allowed for higher resolution genetic analysis, as few as 20 reads are sufficient for determining the ABO genotype and predicted phenotype of an individual. Typing results generated by this pipeline showed remarkable concordance with both serological results and molecular typing results by an independent laboratory, indicating its accuracy and reliability. This study demonstrates a comprehensive characterisation of clinically relevant ABO blood genotypes and predicted phenotypes using TGS methods. The approach provided a scalable and precise method for routine ABO blood group screening and aided in the development of pioneering bioinformatics tools suitable for clinical and research application.

HLA‐compatibility remains an important triage test for deceased donor kidney allocation. Low–intermediate resolution donor HLA‐typing is typically available at allocation, but its accuracy in assigning pre‐transplant donor‐specific anti‐HLA antibody (DSA) and HLA mismatches compared to 2‐field high‐resolution typing is poorly characterised. Consecutive deceased donor/recipient pairs from a single centre between 2016 and 2020 were included. Majority of donor typing at HLA‐ABDRB1 loci were performed at low–intermediate resolution, with 2‐field high‐resolution NGS typing across extended loci performed by NGS‐technique post‐transplantation. We compared the two typing methods for (1) accuracy of pre‐transplant DSA assignment; (2) misassignment of HLA‐antigen/allele mismatches and performance of each model for acute rejection and (3) proportion of recipients who developed de novo DSA (dnDSA) when matched at antigen but mismatched at allele level. Of 179 deceased donor/recipient pairs, 157 donors had low–intermediate resolution typing and 22 with high‐resolution ONT typing. Sixty‐two recipients (35%) had potential pre‐transplant DSAs, with incorrect assignment of allele‐specific Class I and II actual DSAs in 31% and 53% of cases, respectively. NGS typing identified 59 (33%) additional HLA‐DRB1 allele mismatches. ONT typing accurately assigned pre‐transplant DSAs and allele mismatches in all cases. Seven (4%) recipients with antigen/allele level discordance developed dnDSAs, majority HLA‐DQ antibodies. Two‐field high‐resolution donor HLA typing may provide a more accurate transplant immunological risk assessment and identify those at risk of developing dnDSA to matched HLA antigen.

The advent of third‐generation sequencing (TGS) represents a significant shift in the field of genetic sequencing, enabling single‐molecule sequencing to overcome limitations of short‐read NGS platforms. Several studies have assessed the utilisation of TGS in HLA genotyping, though many of these studies have described the high error rate as an obstacle to achieving a robust and highly accurate HLA typing assay. In 2021, Oxford Nanopore Technologies (ONT) released the high‐accuracy sequencing Kit 14 and the MinION flow cell model R10.4.1, which were reported to achieve sequencing accuracies up to 99%. The aim of this study was to validate this novel high‐accuracy sequencing kit for HLA genotyping coupled with a full‐gene HLA PCR assay. Comparison with historical data obtained using legacy flow cell models such as R9.4, R10.3 and R10.4 was also done to assess progressive improvement in sequencing performance with each sequential release. The workflow was validated based on data throughput, sequence quality and accuracy, and HLA genotyping resolution. An initial validation was performed using an internal reference panel of 42 samples representing common HLA allele groups, followed by an analysis of data obtained from 111 sequencing batch runs since the implementation, to assess assay performance and define quality control metrics to assess inter‐run variability and monitor quality. Furthermore, challenges arising from MinION flow cell stability and use, and assessment of barcode contamination are discussed. The findings of this study highlight advantages of ONT sequencing kit 14/R10.4.1 for HLA genotyping and the implementation considerations for the routine diagnostic HLA laboratory.

The primary goal of the HLA‐DPA1 ~ promoter ~ HLA‐DPB1 haplotype component of the 18th IHIWS was to characterise the extended haplotypes within the HLA‐DP region and survey the extent of genetic diversity in this region across human populations. In this report, we analysed single‐nucleotide polymorphisms (SNPs) in 255 subjects from 6 different cohorts. The results from the HLA‐DP haplotype component have validated findings from the initial pilot study. SNPs in this region were inherited in strong linkage, particularly HLA‐DPA1, SNP‐linked promoter haplotypes and motifs in exon 2 of HLA‐DPB1. We reported 17 SNP‐linked haplotypes in the promoter region. Together with HLA‐DPA1 and HLA‐DPB1 alleles, they formed 74 distinct extended HLA‐DP haplotypes in 438 sequences. We also observed the presence of region‐specific alleles and promoter haplotypes. Our approach involved phasing extended SNPs including promoter SNPs, HLA‐DPA1 and HLA‐DPB1 alleles, in a 22 kb region, GRCh38/hg38 (chr6:33,064,111‐33,086,679), followed by clustering of these SNPs as one extended haplotype. This hierarchical clustering revealed four major clades, suggesting that haplotypes within each clade may have diverged from a common ancestral haplotype and undergone similar evolutionary processes. The correlation between HLA‐DPA1 and the promoter region raises questions about the role of HLA‐DPA1 antigen in the heterodimer. This finding requires validation on a larger sample size specifically designed for anthropological analysis. Nevertheless, the results from this study highlight the clinical potential of selecting better‐matched donors for patients awaiting haematopoietic stem cell transplants from genetically overlapping groups that share common ancestral haplotypes.

The CDC crossmatch test is being phased out in solid organ donor allocation, and standard luminex single antigen bead assays do not differentiate complement activating function of HLA antibodies. The current study investigated the LIFECODES C3d-binding assay to determine if it could accurately predict actual T and B cell CDC results in a cohort of highly sensitised patients. Nineteen serum samples from different highly sensitised solid organ patients were crossmatched against cells from 62 unique donors, with 174 total T and B cell crossmatches performed. The sera also underwent SAB assay using OLI and LC platforms, and C3d-binding assay. Complement activating ability of each unique HLA antibody specificity detected using SAB was assigned based on the actual CDC results, which was then used to determine the accuracy of the C3d-binding assay. The C3d-binding assay was found to be highly accurate, with sensitivity of 95%, specificity 89% and negative predictive value 97% for class I DSA and the T cell CDC crossmatch results. Furthermore, we found 100% accuracy for prediction of the complement activating function of HLA-C antibodies. Negative predictive value of above 90% was also found for HLA class II DSA. C3d-binding proved more accurate than virtual crossmatch alone to predict CDC results. This study confirms that the C3d-binding assay predicts actual CDC crossmatch results accurately. In particular, the high negative predictive value of the C3d-binding assay may be extremely useful to define HLA antibodies that do not activate complement in highly sensitised recipients.

Kidney donor allocation can occasionally be difficult in Australia given a small population spread over vast distances. Therefore, between 2017 and 2019 our service allowed transplantation from deceased donors into local (same-state) preemptive recipients, only if no well-matched dialysis-dependent transplant waitlist recipient was available. Transplantation using this novel allocation pathway was associated with good clinical and immunological outcomes.

Genetic information provides insights into the exome, genome, epigenetics and structural organisation of the organism. Given the enormous amount of genetic information, scientists are able to perform mammoth tasks to improve the standard of health care such as determining genetic influences on outcome of allogeneic transplantation. Cloud based computing has increasingly become a key choice for many scientists, engineers and institutions as it offers on-demand network access and users can conveniently rent rather than buy all required computing resources. With the positive advancements of cloud computing and nanopore sequencing data output, we were motivated to develop an automated and scalable analysis pipeline utilizing cloud infrastructure in Microsoft Azure to accelerate HLA genotyping service and improve the efficiency of the workflow at lower cost. In this study, we describe (i) the selection process for suitable virtual machine sizes for computing resources to balance between the best performance versus cost effectiveness; (ii) the building of Docker containers to include all tools in the cloud computational environment; (iii) the comparison of HLA genotype concordance between the in-house manual method and the automated cloud-based pipeline to assess data accuracy. In conclusion, the Microsoft Azure cloud based data analysis pipeline was shown to meet all the key imperatives for performance, cost, usability, simplicity and accuracy. Importantly, the pipeline allows for the on-going maintenance and testing of version changes before implementation. This pipeline is suitable for the data analysis from MinION sequencing platform and could be adopted for other data analysis application processes.