School of Mathematics and Statistics - Research Publications
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ItemVariable selection in microbiome compositional data analysis(Oxford University Press, 2020-06-01)Though variable selection is one of the most relevant tasks in microbiome analysis, e.g. for the identification of microbial signatures, many studies still rely on methods that ignore the compositional nature of microbiome data. The applicability of compositional data analysis methods has been hampered by the availability of software and the difficulty in interpreting their results. This work is focused on three methods for variable selection that acknowledge the compositional structure of microbiome data: selbal, a forward selection approach for the identification of compositional balances, and clr-lasso and coda-lasso, two penalized regression models for compositional data analysis. This study highlights the link between these methods and brings out some limitations of the centered log-ratio transformation for variable selection. In particular, the fact that it is not subcompositionally consistent makes the microbial signatures obtained from clr-lasso not readily transferable. Coda-lasso is computationally efficient and suitable when the focus is the identification of the most associated microbial taxa. Selbal stands out when the goal is to obtain a parsimonious model with optimal prediction performance, but it is computationally greedy. We provide a reproducible vignette for the application of these methods that will enable researchers to fully leverage their potential in microbiome studies.
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ItemModel-based joint visualization of multiple compositional omics datasets(Oxford University Press, 2020-09-01)The integration of multiple omics datasets measured on the same samples is a challenging task: data come from heterogeneous sources and vary in signal quality. In addition, some omics data are inherently compositional, e.g. sequence count data. Most integrative methods are limited in their ability to handle covariates, missing values, compositional structure and heteroscedasticity. In this article we introduce a flexible model-based approach to data integration to address these current limitations: COMBI. We combine concepts, such as compositional biplots and log-ratio link functions with latent variable models, and propose an attractive visualization through multiplots to improve interpretation. Using real data examples and simulations, we illustrate and compare our method with other data integration techniques. Our algorithm is available in the R-package combi.
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ItemAltered Repertoire Diversity and Disease-Associated Clonal Expansions Revealed by T Cell Receptor Immunosequencing in Ankylosing Spondylitis Patients(WILEY, 2020-08)Objective Ankylosing spondylitis (AS) is a common spondyloarthropathy primarily affecting the axial skeleton and strongly associated with HLA–B*27 carriage. Genetic evidence implicates both autoinflammatory processes and autoimmunity against an HLA–B*27–restricted autoantigen in immunopathology. In addition to articular symptoms, up to 70% of AS patients present with concurrent bowel inflammation, suggesting that adverse interactions between a genetically primed host immune system and the gut microbiome contribute to the disease. Accordingly, this study aimed to characterize adaptive immune responses to antigenic stimuli in AS. Methods The peripheral CD4 and CD8 T cell receptor (TCR) repertoire was profiled in AS patients (n = 47) and HLA–B*27–matched healthy controls (n = 38). Repertoire diversity was estimated using the Normalized Shannon Diversity Entropy (NSDE) index, and univariate and multivariate statistical analyses were performed to characterize AS‐associated clonal signatures. Furthermore, T cell proliferation and cytokine production in response to immunogenic antigen exposure were investigated in vitro in peripheral blood mononuclear cells from AS patients (n = 19) and HLA–B*27–matched healthy controls (n = 14). Results Based on the NSDE measure of sample diversity across CD4 and CD8 T cell repertoires, AS patients showed increased TCR diversity compared to healthy controls (for CD4 T cells, P = 7.8 × 10−6; for CD8 T cells, P = 9.3 × 10−4), which was attributed to a significant reduction in the magnitude of peripheral T cell expansions globally. Upon in vitro stimulation, fewer T cells from AS patients than from healthy controls expressed interferon‐γ (for CD8 T cells, P = 0.03) and tumor necrosis factor (for CD4 T cells, P = 0.01; for CD8 T cells, P = 0.002). In addition, the CD8 TCR signature was altered in HLA–B*27+ AS patients compared to healthy controls, with significantly expanded Epstein‐Barr virus–specific clonotypes (P = 0.03) and cytomegalovirus‐specific clonotypes (P = 0.02). HLA–B*27+ AS patients also showed an increased incidence of “public” CD8 TCRs, representing identical clonotypes emerging in response to common antigen encounters, including homologous clonotypes matching those previously isolated from individuals with bacterial‐induced reactive arthritis. Conclusion The dynamics of peripheral T cell responses in AS patients are altered, suggesting that differential antigen exposure and disrupted adaptive immunity are underlying features of the disease.
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ItemMulti-Omic Data Integration Allows Baseline Immune Signatures to Predict Hepatitis B Vaccine Response in a Small Cohort(FRONTIERS MEDIA SA, 2020-11-30)BACKGROUND: Vaccination remains one of the most effective means of reducing the burden of infectious diseases globally. Improving our understanding of the molecular basis for effective vaccine response is of paramount importance if we are to ensure the success of future vaccine development efforts. METHODS: We applied cutting edge multi-omics approaches to extensively characterize temporal molecular responses following vaccination with hepatitis B virus (HBV) vaccine. Data were integrated across cellular, epigenomic, transcriptomic, proteomic, and fecal microbiome profiles, and correlated to final HBV antibody titres. RESULTS: Using both an unsupervised molecular-interaction network integration method (NetworkAnalyst) and a data-driven integration approach (DIABLO), we uncovered baseline molecular patterns and pathways associated with more effective vaccine responses to HBV. Biological associations were unravelled, with signalling pathways such as JAK-STAT and interleukin signalling, Toll-like receptor cascades, interferon signalling, and Th17 cell differentiation emerging as important pre-vaccination modulators of response. CONCLUSION: This study provides further evidence that baseline cellular and molecular characteristics of an individual's immune system influence vaccine responses, and highlights the utility of integrating information across many parallel molecular datasets.
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ItemA simple, scalable approach to building a cross-platform transcriptome atlas(PUBLIC LIBRARY SCIENCE, 2020-09)Gene expression atlases have transformed our understanding of the development, composition and function of human tissues. New technologies promise improved cellular or molecular resolution, and have led to the identification of new cell types, or better defined cell states. But as new technologies emerge, information derived on old platforms becomes obsolete. We demonstrate that it is possible to combine a large number of different profiling experiments summarised from dozens of laboratories and representing hundreds of donors, to create an integrated molecular map of human tissue. As an example, we combine 850 samples from 38 platforms to build an integrated atlas of human blood cells. We achieve robust and unbiased cell type clustering using a variance partitioning method, selecting genes with low platform bias relative to biological variation. Other than an initial rescaling, no other transformation to the primary data is applied through batch correction or renormalisation. Additional data, including single-cell datasets, can be projected for comparison, classification and annotation. The resulting atlas provides a multi-scaled approach to visualise and analyse the relationships between sets of genes and blood cell lineages, including the maturation and activation of leukocytes in vivo and in vitro. In allowing for data integration across hundreds of studies, we address a key reproduciblity challenge which is faced by any new technology. This allows us to draw on the deep phenotypes and functional annotations that accompany traditional profiling methods, and provide important context to the high cellular resolution of single cell profiling. Here, we have implemented the blood atlas in the open access Stemformatics.org platform, drawing on its extensive collection of curated transcriptome data. The method is simple, scalable and amenable for rapid deployment in other biological systems or computational workflows.
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ItemMultiple interaction nodes define the postreplication repair response to UV-induced DNA damage that is defective in melanomas and correlated with UV signature mutation load(WILEY, 2020-01)Ultraviolet radiation-induced DNA mutations are a primary environmental driver of melanoma. The reason for this very high level of unrepaired DNA lesions leading to these mutations is still poorly understood. The primary DNA repair mechanism for UV-induced lesions, that is, the nucleotide excision repair pathway, appears intact in most melanomas. We have previously reported a postreplication repair mechanism that is commonly defective in melanoma cell lines. Here we have used a genome-wide approach to identify the components of this postreplication repair mechanism. We have used differential transcript polysome loading to identify transcripts that are associated with UV response, and then functionally assessed these to identify novel components of this repair and cell cycle checkpoint network. We have identified multiple interaction nodes, including global genomic nucleotide excision repair and homologous recombination repair, and previously unexpected MASTL pathway, as components of the response. Finally, we have used bioinformatics to assess the contribution of dysregulated expression of these pathways to the UV signature mutation load of a large melanoma cohort. We show that dysregulation of the pathway, especially the DNA damage repair components, are significant contributors to UV mutation load, and that dysregulation of the MASTL pathway appears to be a significant contributor to high UV signature mutation load.
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ItemEpistatic interactions between killer immunoglobulin-like receptors and human leukocyte antigen ligands are associated with ankylosing spondylitis(PUBLIC LIBRARY SCIENCE, 2020-08)The killer immunoglobulin-like receptors (KIRs), found predominantly on the surface of natural killer (NK) cells and some T-cells, are a collection of highly polymorphic activating and inhibitory receptors with variable specificity for class I human leukocyte antigen (HLA) ligands. Fifteen KIR genes are inherited in haplotypes of diverse gene content across the human population, and the repertoire of independently inherited KIR and HLA alleles is known to alter risk for immune-mediated and infectious disease by shifting the threshold of lymphocyte activation. We have conducted the largest disease-association study of KIR-HLA epistasis to date, enabled by the imputation of KIR gene and HLA allele dosages from genotype data for 12,214 healthy controls and 8,107 individuals with the HLA-B*27-associated immune-mediated arthritis, ankylosing spondylitis (AS). We identified epistatic interactions between KIR genes and their ligands (at both HLA subtype and allele resolution) that increase risk of disease, replicating analyses in a semi-independent cohort of 3,497 cases and 14,844 controls. We further confirmed that the strong AS-association with a pathogenic variant in the endoplasmic reticulum aminopeptidase gene ERAP1, known to alter the HLA-B*27 presented peptidome, is not modified by carriage of the canonical HLA-B receptor KIR3DL1/S1. Overall, our data suggests that AS risk is modified by the complement of KIRs and HLA ligands inherited, beyond the influence of HLA-B*27 alone, which collectively alter the proinflammatory capacity of KIR-expressing lymphocytes to contribute to disease immunopathogenesis.