Mark W Watson Ph.D. B.Sc.

Autoantibodies targeting cytosolic 5′-nucleotidase 1A (cN1A) are found in several autoimmune diseases, including inclusion body myositis (IBM), Sjögren’s syndrome, and systemic lupus erythematosus. While they have diagnostic relevance for IBM, little is known about the autoreactive B cells that produce these antibodies. To address this, we developed a robust protocol for the expression and site-specific biotinylation of recombinant human cN1A in
Escherichia coli
. The resulting antigen is suitable for generating double-labelled fluorescent baits for the isolation and characterisation of cN1A-specific B cells by flow cytometry. Site-specific biotinylation was achieved using the AviTag and BirA ligase, preserving the protein’s structure and immunoreactivity. Western blot analysis confirmed that the biotinylated cN1A was recognised by both human and rabbit anti-cN1A antibodies. Compared to conventional chemical biotinylation, this strategy minimises structural alterations that may affect antigen recognition. This approach provides a reliable method for producing biotinylated antigens for use in immunological assays. While demonstrated here for cN1A, the protocol can be adapted for other autoantigens to support studies of antigen-specific B cells in autoimmune diseases.

Immune checkpoint therapy (ICT) causes durable tumour responses in a subgroup of patients, but it is not well known how T cell receptor beta (TCRβ) repertoire dynamics contribute to the therapeutic response. Using murine models that exclude variation in host genetics, environmental factors and tumour mutation burden, limiting variation between animals to naturally diverse TCRβ repertoires, we applied TCRseq, single cell RNAseq and flow cytometry to study TCRβ repertoire dynamics in ICT responders and non-responders. Increased oligoclonal expansion of TCRβ clonotypes was observed in responding tumours. Machine learning identified TCRβ CDR3 signatures unique to each tumour model, and signatures associated with ICT response at various timepoints before or during ICT. Clonally expanded CD8+ T cells in responding tumours post ICT displayed effector T cell gene signatures and phenotype. An early burst of clonal expansion during ICT is associated with response, and we report unique dynamics in TCRβ signatures associated with ICT response.

Cryptosporidium is a major cause of diarrhoeal disease and mortality in young children in resource-poor countries, for which no vaccines or adequate therapeutic options are available. Infection in humans is primarily caused by two species: C. hominis and C. parvum . Despite C. hominis being the dominant species infecting humans in most countries, very little is known about its growth characteristics and life cycle in vitro, given that the majority of our knowledge of the in vitro development of Cryptosporidium has been based on C. parvum . In the present study, the growth and development of two C. parvum isolates (subtypes Iowa-IIaA17G2R1 and IIaA18G3R1) and one C. hominis isolate (subtype IdA15G1) in HCT-8 cells were examined and compared at 24 h and 48 h using morphological data acquired with scanning electron microscopy. Our data indicated no significant differences in the proportion of meronts or merozoites between species or subtypes at either time-point. Sexual development was observed at the 48-h time-point across both species through observations of both microgamonts and macrogamonts, with a higher frequency of macrogamont observations in C. hominis (IdA15G1) cultures at 48-h post-infection compared to both C. parvum subtypes. This corresponded to differences in the proportion of trophozoites observed at the same time point. No differences in proportion of microgamonts were observed between the three subtypes, which were rarely observed across all cultures. In summary, our data indicate that asexual development of C. hominis is similar to that of C. parvum, while sexual development is accelerated in C. hominis. This study provides new insights into differences in the in vitro growth characteristics of C. hominis when compared to C. parvum , which will facilitate our understanding of the sexual development of both species.

Pre-existing pathogen-specific memory T cell responses can contribute to multiple adverse outcomes including autoimmunity and drug hypersensitivity. How the specificity of the T cell receptor (TCR) is subverted or seconded in many of these diseases remains unclear. Here, we apply abacavir hypersensitivity (AHS) as a model to address this question because the disease is linked to memory T cell responses and the HLA risk allele, HLA-B*57:01, and the initiating insult, abacavir, are known. To investigate the role of pathogen-specific TCR specificity in mediating AHS we performed a genome-wide screen for HLA-B*57:01 restricted T cell responses to Epstein-Barr virus (EBV), one of the most prevalent human pathogens. T cell epitope mapping revealed HLA-B*57:01 restricted responses to 17 EBV open reading frames and identified an epitope encoded by EBNA3C. Using these data, we cloned the dominant TCR for EBNA3C and a previously defined epitope within EBNA3B. TCR specificity to each epitope was confirmed, however, cloned TCRs did not cross-react with abacavir plus self-peptide. Nevertheless, abacavir inhibited TCR interactions with their cognate ligands, demonstrating that TCR specificity may be subverted by a drug molecule. These results provide an experimental road map for future studies addressing the heterologous immune responses of TCRs including T cell mediated adverse drug reactions.

Type B adverse drug reactions (ADRs) are iatrogenic immune-mediated syndromes with mechanistic etiologies that remain incompletely understood. Some of the most severe ADRs, including delayed drug hypersensitivity reactions, are T-cell mediated, restricted by specific human leukocyte antigen risk alleles and sometimes by public or oligoclonal T-cell receptors (TCRs), central to the immunopathogenesis of tissue-damaging response. However, the specific cellular signatures of effector, regulatory, and accessory immune populations that mediate disease, define reaction phenotype, and determine severity have not been defined. Recent development of single-cell platforms bringing together advances in genomics and immunology provides the tools to simultaneously examine the full transcriptome, TCRs, and surface protein markers of highly heterogeneous immune cell populations at the site of the pathological response at a single-cell level. However, the requirement for advanced bioinformatics expertise and computational hardware and software has often limited the ability of investigators with the understanding of diseases and biological models to exploit these new approaches. Here we describe the features and use of a state-of-the-art, fully integrated application for analysis and visualization of multiomic single-cell data called Visual Genomics Analysis Studio (VGAS). This unique user-friendly, Windows-based graphical user interface is specifically designed to enable investigators to interrogate their own data. While VGAS also includes tools for sequence alignment and identification of associations with host or organism genetic polymorphisms, in this review we focus on its application for analysis of single-cell TCR–RNA–Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE)-seq, enabling holistic cellular characterization by unbiased transcriptome and select surface proteome. Critically, VGAS does not require user-directed coding or access to high-performance computers, instead incorporating performance-optimized hidden code to provide application-based fast and intuitive tools for data analyses and production of high-resolution publication-ready graphics on standard specification laptops. Specifically, it allows analyses of comprehensive single-cell TCR sequencing (scTCR-seq) data, detailing (i) functional pairings of α–β heterodimer TCRs, (ii) one-click histograms to display entropy and gene rearrangements, and (iii) Circos and Sankey plots to visualize clonality and dominance. For unbiased single-cell RNA sequencing (scRNA-seq) analyses, users extract cell transcriptome signatures according to global structure via principal component analysis, t-distributed stochastic neighborhood embedding, or uniform manifold approximation and projection plots, with overlay of scTCR-seq enabling identification and selection of the immunodominant TCR-expressing populations. Further integration with similar sequence-based detection of surface protein markers using oligo-labeled antibodies (CITE-seq) provides comparative understanding of surface protein expression, with differential gene or protein analyses visualized using volcano plot or heatmap functions. These data can be compared to reference cell atlases or suitable controls to reveal discrete disease-specific subsets, from epithelial to tissue-resident memory T-cells, and activation status, from senescence through exhaustion, with more finite transcript expression displayed as violin and box plots. Importantly, guided tutorial videos are available, as are regular application updates based on the latest advances in bioinformatics and user feedback.

Immunotherapies have revolutionized cancer treatment. In particular, immune checkpoint therapy (ICT) leads to durable responses in some patients with some cancers. However, the majority of treated patients do not respond. Understanding immune mechanisms that underlie responsiveness to ICT will help identify predictive biomarkers of response and develop treatments to convert non-responding patients to responding ones. ICT primarily acts at the level of adaptive immunity. The specificity of adaptive immune cells, such as T and B cells, is determined by antigen-specific receptors. T cell repertoires can be comprehensively profiled by high-throughput sequencing at the bulk and single-cell level. T cell receptor (TCR) sequencing allows for sensitive tracking of dynamic changes in antigen-specific T cells at the clonal level, giving unprecedented insight into the mechanisms by which ICT alters T cell responses. Here, we review how the repertoire influences response to ICT and conversely how ICT affects repertoire diversity. We will also explore how changes to the repertoire in different anatomical locations can better correlate and perhaps predict treatment outcome. We discuss the advantages and limitations of current metrics used to characterize and represent TCR repertoire diversity. Discovery of predictive biomarkers could lie in novel analysis approaches, such as network analysis of amino acids similarities between TCR sequences. Single-cell sequencing is a breakthrough technology that can link phenotype with specificity, identifying T cell clones that are crucial for successful ICT. The field of immuno-sequencing is rapidly developing and cross-disciplinary efforts are required to maximize the analysis, application, and validation of sequencing data. Unravelling the dynamic behavior of the TCR repertoire during ICT will be highly valuable for tracking and understanding anti-tumor immunity, biomarker discovery, and ultimately for the development of novel strategies to improve patient outcomes.

Immune checkpoint therapy (ICT) results in durable responses in individuals with some cancers, but not all patients respond to treatment. ICT improves CD8+ cytotoxic T lymphocyte (CTL) function, but changes in tumor antigen-specific CTLs post-ICT that correlate with successful responses have not been well characterized. Here, we studied murine tumor models with dichotomous responses to ICT. We tracked tumor antigen-specific CTL frequencies and phenotype before and after ICT in responding and non-responding animals. Tumor antigen-specific CTLs increased within tumor and draining lymph nodes after ICT, and exhibited an effector memory-like phenotype, expressing IL-7R (CD127), KLRG1, T-bet, and granzyme B. Responding tumors exhibited higher infiltration of effector memory tumor antigen-specific CTLs, but lower frequencies of regulatory T cells compared to non-responders. Tumor antigen-specific CTLs persisted in responding animals and formed memory responses against tumor antigens. Our results suggest that increased effector memory tumor antigen-specific CTLs, in the presence of reduced immunosuppression within tumors is part of a successful ICT response. Temporal and nuanced analysis of T cell subsets provides a potential new source of immune based biomarkers for response to ICT.

Background: We aimed to assess the impact of genomic human leukocyte antigen (HLA)-I/II homozygosity on the survival benefit of patients with unresectable locally advanced, metastatic non-small lung cancer treated by single-agent programmed cell death protein-1/programmed death ligand 1 (PD1/PDL1) inhibitors. Methods: We collected blood from 170 patients with advanced lung cancer treated with immunotherapy at two major oncology centers in Western Australia. Genomic DNA was extracted from white blood cells and used for HLA-I/II high-resolution typing. HLA-I/II homozygosity was tested for association with survival outcomes. Univariable and multivariable Cox regression models were constructed to determine whether HLA homozygosity was an independent prognostic factor affecting Overall Survival (OS) and Progression Free Survival (PFS). We also investigated the association between individual HLA-A and -B supertypes with OS. Results: Homozygosity at HLA-I loci, but not HLA-II, was significantly associated with shorter OS (HR=2.17, 95% CI 1.13 to 4.17, p=0.02) in both univariable and multivariable analysis. The effect of HLA-I homozygosity in OS was particularly relevant for patients with tumors expressing PDL1 ≥50% (HR=3.93, 95% CI 1.30 to 11.85, p<0.001). The adverse effect of HLA-I homozygosity on PFS was only apparent after controlling for interactions between PDL1 status and HLA-I genotype (HR=2.21, 95% CI 1.04 to 4.70, p=0.038). The presence of HLA-A02 supertype was the only HLA-I supertype to be associated with improved OS (HR=0.56, 95% CI 0.34 to 0.93, p=0.023). Conclusion: Our results suggest that homozygosity at ≥1 HLA-I loci is associated with short OS and PFS in patients with advanced non-small cell lung cancer with PDL1 ≥50% treated with single-agent immunotherapy. Carriers of HLA-A02 supertype reported better survival outcomes in this cohort of patients.

Background Vancomycin is a prevalent cause of the severe hypersensitivity syndrome drug reaction with eosinophilia and systemic symptoms (DRESS) which leads to significant morbidity and mortality and commonly occurs in the setting of combination antibiotic therapy which impacts future treatment choices. Variations in human leukocyte antigen (HLA) class I in particular have been associated with serious T-cell mediated adverse drug reactions which has led to preventive screening strategies for some drugs. Objective To determine if variation in the HLA region is associated with vancomycin-induced DRESS. Methods Probable vancomycin DRESS cases were matched 1:2 with tolerant controls based on sex, race, and age using BioVU, Vanderbilt’s deidentified electronic health record database. Associations between DRESS and carriage of HLA class I and II alleles were assessed by conditional logistic regression. An extended sample set from BioVU was utilized to conduct a time-to-event analysis of those exposed to vancomycin with and without the identified HLA risk allele. Results Twenty-three individuals met inclusion criteria for vancomycin-associated DRESS. 19/23 (82.6%) cases carried HLA-A*32:01 compared to 0/46 (0%) of the matched vancomycin tolerant controls (p=1x10-8) and 6.3% of the BioVU population (n=54,249) (p=2x10-16). Time-to-event analysis of DRESS development during vancomycin treatment among the HLA-A*32:01 positive group indicated that 19.2% developed DRESS and did so within four weeks. Conclusions HLA-A*32:01 is strongly associated with vancomycin DRESS in a population of predominantly European ancestry. HLA-A*32:01 testing could improve antibiotic safety, help implicate vancomycin as the causal drug and preserve future treatment options with co-administered antibiotics.