Novel insights into host-fungal pathogen interactions derived from live-cell imaging

Acknowledgments The authors acknowledge funding from the Wellcome Trust (080088, 086827, 075470 and 099215) including a Wellcome Trust Strategic Award for Medical Mycology and Fungal Immunology 097377 and FP7-2007–2013 grant agreement HEALTH-F2-2010-260338–ALLFUN to NARG.Peer reviewedPublisher PD

Imaging interactions between the immune and cardiovascular systems in vivo by multiphoton microscopy

Several recent studies in immunology have used multiphoton laser-scanning microscopy to visualise the induction of an immune response in real time in vivo. These experiments are illuminating the cellular and molecular interactions involved in the induction, maintenance and regulation of immune responses. Similar approaches are being applied in cardiovascular research where there is an increasing body of evidence to support a significant role for the adaptive immune system in vascular disease. As such, we have begun to dissect the role of T lymphocytes in atherosclerosis in real time in vivo. Here, we provide step-by-step guides to the various stages involved in visualising the migration of T cells within a lymph node and their infiltration into inflamed tissues such as atherosclerotic arteries. These methods provide an insight into the mechanisms involved in the activation and function of immune cells in vivo

Immunoinformatic identification of CD8+ T-cell epitopes

Antigen-specific T-cells play a crucial role in the adaptive immune response by providing a defence mechanism against pathogens and maintaining tolerance against self-antigens. This sparked interest in the development of epitope-based vaccines and immunotherapies that elicit antigen-specific T-cell responses. However, screening the antigens driving the response is currently labour-intensive, low-throughput and costly. Due to the limitations of experimental approaches, computational methods for predicting CD8+ T-cells have started to emerge. However, predicting the T-cell recognition potential of MHC-presented peptides has shown to be more challenging than predicting MHC ligands, and the full spectrum of features underlying peptide immunogenicity remains to be explored. Hence, this thesis presents a systems biology approach to study features of peptide immunogenicity and accurately predict CD8+ T-cell epitopes from HLA-I presented pathogenic or cancer peptides. The thesis begins with an immunoinformatic analysis of antigen-specific T-cell profiles in the contexts of autoinflammatory and infectious diseases. In autoinflammatory disease, the multi-modal single-cell sequencing of ulcerative colitis and checkpoint treatment-induced colitis revealed pathology-specific differential expressions of cytotoxic T-cells. The current technologies, however, were unable to identify the source antigen, emphasising the importance of predicting T-cell targets to better understand disease pathology. Moreover, in infectious diseases, CD8+ T-cell epitope prediction algorithms facilitated the understanding of disease heterogeneity and vaccine design during the COVID-19 pandemic, but many existing algorithms were found to be ill-suited for predicting epitopes from emerging pathogens. Therefore, a novel computational workflow was developed for an accurate and robust prediction of source antigens driving the cellular immune response. First, an unbiased evaluation of state-of-the-art algorithms revealed that they perform poorly on both cancer neoepitopes (e.g. glioblastoma) and pathogenic (e.g. SARS-CoV-2) epitopes. After investigating the reasons for low performance, TRAP, a deep learning workflow for context-specific prediction of CD8+ T-cell epitopes, was developed to effectively capture T-cell recognition motifs. The application of TRAP was demonstrated by using it to investigate the immune escape potential of all theoretical SARS-CoV-2 mutants. Thus, this thesis presents a novel computational platform for accurately predicting CD8+ T-cell epitopes to foster a better understanding of TCR:pMHC interaction and the development of effective clinical therapeutics

Supernatants derived from chemotherapy-treated cancer cell lines can modify angiogenesis

BACKGROUND: There is evidence that tumours produce substances such as cytokines and microvesicular bodies bearing bioactive molecules, which support the carcinogenic process. Furthermore, chemotherapy has also been shown to modify these exudates and in doing so, neutralise their tumourigenic influence. METHODS: In the current study, we have investigated the effect of chemotherapy agents on modifying the cytokine profile and microvesicular cargo of supernatants derived from cancer cell lines. In addition, we have explored the effect of these tumour-derived supernatants on angiogenesis, and how chemotherapy can alter the supernatants rendering them less pro-angiogenic. RESULTS: Herein, we show that supernatants contain a rich cocktail of cytokines, a number of which are potent modulators of angiogenesis. They also contain microvesicular bodies containing RNA transcripts that code for proteins involved in transcription, immune modulation and angiogenesis. These supernatants altered intracellular signalling molecules in endothelial cells and significantly enhanced their tubulogenic character; however, this was severely compromised when supernatants from tumours treated with chemotherapy was used instead. CONCLUSION: This study suggests tumour exudates and bioactive material from tumours can influence cellular functions, and that treatment with some chemotherapy can serve to negate these pro-tumourigenic processes

ANIMA: Association network integration for multiscale analysis [version 1; referees: 2 approved with reservations]

Contextual functional interpretation of -omics data derived from clinical samples is a classical and difficult problem in computational systems biology. The measurement of thousands of data points on single samples has become routine but relating ‘big data’ datasets to the complexities of human pathobiology is an area of ongoing research. Complicating this is the fact that many publically available datasets use bulk transcriptomics data from complex tissues like blood. The most prevalent analytic approaches derive molecular ‘signatures’ of disease states or apply modular analysis frameworks to the data. Here we describe ANIMA (association network integration for multiscale analysis), a network-based data integration method using clinical phenotype and microarray data as inputs. ANIMA is implemented in R and Neo4j and runs in Docker containers. In short, the build algorithm iterates over one or more transcriptomics datasets to generate a large, multipartite association network by executing multiple independent analytic steps (differential expression, deconvolution, modular analysis based on co-expression, pathway analysis) and integrating the results. Once the network is built, it can be queried directly using Cypher, or via custom functions that communicate with the graph database via language-specific APIs. We developed a web application using Shiny, which provides fully interactive, multiscale views of the data. Using our approach, we show that we can reconstruct multiple features of disease states at various scales of organization, from transcript abundance patterns of individual genes through co-expression patterns of groups of genes to patterns of cellular behaviour in whole blood samples, both in single experiments as well as in a meta-analysis of multiple datasets

Pre-existing T cell-mediated cross-reactivity to SARS-CoV-2 cannot solely be explained by prior exposure to endemic human coronaviruses

T-cell-mediated immunity to SARS-CoV-2-derived peptides in individuals unexposed to SARS-CoV-2 has been previously reported. This pre-existing immunity was suggested to largely derive from prior exposure to ‘common cold’ endemic human coronaviruses (HCoVs). To test this, we characterised the sequence homology of SARS-CoV-2-derived T-cell epitopes reported in the literature across the full proteome of the Coronaviridae family. 54.8% of these epitopes had no homology to any of the HCoVs. Further, the proportion of SARS-CoV-2-derived epitopes with any level of sequence homology to the proteins encoded by any of the coronaviruses tested is well-predicted by their alignment-free phylogenetic distance to SARS-CoV-2 (Pearson's r = −0.958). No coronavirus in our dataset showed a significant excess of T-cell epitope homology relative to the proportion of expected random matches, given their genetic similarity to SARS-CoV-2. Our findings suggest that prior exposure to human or animal-associated coronaviruses cannot completely explain the T-cell repertoire in unexposed individuals that recognise SARS-CoV-2 cross-reactive epitopes

Probing The Age Structure Within Population Of B-lymphocytes And Investigating The Impact Upon Their Life Long Maintenance

Lymphocytes are continuously generated and destroyed throughout life, yet it is not understood how these life/death decisions are made and how they influence immune function. We examined B cell turnover throughout life using a chimeric mouse system that involves the treatment of host mice with conditioning drug busulfan. This specifically depletes hematopoeitic stem cells, leaving mature lymphocyte compartments intact. Following reconstitution with congenically labelled bone marrow, the progeny of new hematopoietic stem cells can be tracked for more than a year. Using this approach, we were able to analyse the tonic reconstitution of B cell compartments. Interestingly, we found that the naive B cell compartment is fully replaced in a relatively short time window, regardless of host age, suggesting that the B cell compartment is both homeostatically homogeneous and highly reliant on de novo generation of B cells for its long-term maintenance. Further investigation revealed extremely dynamic behaviour within different compartments, established by their age structure. In order to further understand the cellular mechanisms responsible for these dynamics, we considered different models for B cell homeostasis using mathematical modelling. This analysis confirms that follicular mature B cells behave homogeneously. All cells are lost at a fixed rate throughout life with a short lifetime. However, this life expectancy increases with host age. Similarly, germinal center B cells behave homogeneously. Although these cells persist three times longer in lymph nodes than in the spleen, modelling suggests that an extensive part of the compartment is replaced every day. Furthermore, we found the marginal zone B cells to behave homogeneously and that their lifespan is independent of host age. Whereas cell-proliferation was not important for marginal zone B cell maintenance at steady state, analysis of reporter mouse models suggests proliferating cells to be precursors of the marginal zone B cell compartment

Processing, visualising and reconstructing network models from single-cell data.

New single-cell technologies readily permit gene expression profiling of thousands of cells at single-cell resolution. In this review, we will discuss methods for visualisation and interpretation of single-cell gene expression data, and the computational analysis needed to go from raw data to predictive executable models of gene regulatory network function. We will focus primarily on single-cell real-time quantitative PCR and RNA-sequencing data, but much of what we cover will also be relevant to other platforms, such as the mass cytometry technology for high-dimensional single-cell proteomics.S.W is supported by a Microsoft Research PhD Scholarship.This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/icb.2015.10