Chancellery Research - Research Publications
Permanent URI for this collection
Search Results
1 - 10 of 61
-
ItemSuppression of MR1 by human cytomegalovirus inhibits MAIT cell activation(FRONTIERS MEDIA SA, 2023-02-10)INTRODUCTION: The antigen presentation molecule MHC class I related protein-1 (MR1) is best characterized by its ability to present bacterially derived metabolites of vitamin B2 biosynthesis to mucosal-associated invariant T-cells (MAIT cells). METHODS: Through in vitro human cytomegalovirus (HCMV) infection in the presence of MR1 ligand we investigate the modulation of MR1 expression. Using coimmunoprecipitation, mass spectrometry, expression by recombinant adenovirus and HCMV deletion mutants we investigate HCMV gpUS9 and its family members as potential regulators of MR1 expression. The functional consequences of MR1 modulation by HCMV infection are explored in coculture activation assays with either Jurkat cells engineered to express the MAIT cell TCR or primary MAIT cells. MR1 dependence in these activation assays is established by addition of MR1 neutralizing antibody and CRISPR/Cas-9 mediated MR1 knockout. RESULTS: Here we demonstrate that HCMV infection efficiently suppresses MR1 surface expression and reduces total MR1 protein levels. Expression of the viral glycoprotein gpUS9 in isolation could reduce both cell surface and total MR1 levels, with analysis of a specific US9 HCMV deletion mutant suggesting that the virus can target MR1 using multiple mechanisms. Functional assays with primary MAIT cells demonstrated the ability of HCMV infection to inhibit bacterially driven, MR1-dependent activation using both neutralizing antibodies and engineered MR1 knockout cells. DISCUSSION: This study identifies a strategy encoded by HCMV to disrupt the MR1:MAIT cell axis. This immune axis is less well characterized in the context of viral infection. HCMV encodes hundreds of proteins, some of which regulate the expression of antigen presentation molecules. However the ability of this virus to regulate the MR1:MAIT TCR axis has not been studied in detail.
-
ItemRIPK3 controls MAIT cell accumulation during development but not during infection(SPRINGERNATURE, 2023-02-11)Cell death mechanisms in T lymphocytes vary according to their developmental stage, cell subset and activation status. The cell death control mechanisms of mucosal-associated invariant T (MAIT) cells, a specialized T cell population, are largely unknown. Here we report that MAIT cells express key necroptotic machinery; receptor-interacting protein kinase 3 (RIPK3) and mixed lineage kinase domain-like (MLKL) protein, in abundance. Despite this, we discovered that the loss of RIPK3, but not necroptotic effector MLKL or apoptotic caspase-8, specifically increased MAIT cell abundance at steady-state in the thymus, spleen, liver and lungs, in a cell-intrinsic manner. In contrast, over the course of infection with Francisella tularensis, RIPK3 deficiency did not impact the magnitude of the expansion nor contraction of MAIT cell pools. These findings suggest that, distinct from conventional T cells, the accumulation of MAIT cells is restrained by RIPK3 signalling, likely prior to thymic egress, in a manner independent of canonical apoptotic and necroptotic cell death pathways.
-
ItemQuantitative affinity measurement of small molecule ligand binding to major histocompatibility complex class-I-related protein 1 MR1(ELSEVIER, 2022-12-01)The Major Histocompatibility Complex class I-related protein 1 (MR1) presents small molecule metabolites, drugs, and drug-like molecules that are recognized by MR1-reactive T cells. While we have an understanding of how antigens bind to MR1 and upregulate MR1 cell surface expression, a quantitative, cell-free, assessment of MR1 ligand-binding affinity was lacking. Here, we developed a fluorescence polarization-based assay in which fluorescent MR1 ligand was loaded into MR1 protein in vitro and competitively displaced by candidate ligands over a range of concentrations. Using this assay, ligand affinity for MR1 could be differentiated as strong (IC50 < 1 μM), moderate (1 μM < IC50 < 100 μM), and weak (IC50 > 100 μM). We demonstrated a clear correlation between ligand-binding affinity for MR1, the presence of a covalent bond between MR1 and ligand, and the number of salt bridge and hydrogen bonds formed between MR1 and ligand. Using this newly developed fluorescence polarization-based assay to screen for candidate ligands, we identified the dietary molecules vanillin and ethylvanillin as weak bona fide MR1 ligands. Both upregulated MR1 on the surface of C1R.MR1 cells and the crystal structure of a MAIT cell T cell receptor-MR1-ethylvanillin complex revealed that ethylvanillin formed a Schiff base with K43 of MR1 and was buried within the A'-pocket. Collectively, we developed and validated a method to quantitate the binding affinities of ligands for MR1 that will enable an efficient and rapid screening of candidate MR1 ligands.
-
ItemPropagation, detection and correction of errors using the sequence database network(OXFORD UNIV PRESS, 2022-10-20)Nucleotide and protein sequences stored in public databases are the cornerstone of many bioinformatics analyses. The records containing these sequences are prone to a wide range of errors, including incorrect functional annotation, sequence contamination and taxonomic misclassification. One source of information that can help to detect errors are the strong interdependency between records. Novel sequences in one database draw their annotations from existing records, may generate new records in multiple other locations and will have varying degrees of similarity with existing records across a range of attributes. A network perspective of these relationships between sequence records, within and across databases, offers new opportunities to detect-or even correct-erroneous entries and more broadly to make inferences about record quality. Here, we describe this novel perspective of sequence database records as a rich network, which we call the sequence database network, and illustrate the opportunities this perspective offers for quantification of database quality and detection of spurious entries. We provide an overview of the relevant databases and describe how the interdependencies between sequence records across these databases can be exploited by network analyses. We review the process of sequence annotation and provide a classification of sources of error, highlighting propagation as a major source. We illustrate the value of a network perspective through three case studies that use network analysis to detect errors, and explore the quality and quantity of critical relationships that would inform such network analyses. This systematic description of a network perspective of sequence database records provides a novel direction to combat the proliferation of errors within these critical bioinformatics resources.
-
ItemDestabilizing Racial Discourses in Casual Talk-in-interaction(Oxford University Press, 2022)Racialized descriptions are a constant practice in our societies and a fundamental aspect of racial discourses. This paper uses conversation analytic tools within a Foucauldian perspective on discourse to investigate how discourses of race are (re)produced, and consequently navigated, in talk-in-interaction among speakers of Chinese. Four instances of racialized person description, taken from a larger corpus of 16 hours of casual conversation among Chinese migrants in Melbourne and their acquaintances, are explored in detail. The analysis identifies two interactional sequences, joking and accounting sequences, which allow participants to resist racialized descriptions while still orienting to the interactional preference for sociality in casual conversation. The paper argues that casual and friendly interaction may provide empirical evidence for how discourses of race are destabilized at the level of talk-in-interaction.
-
ItemUS Copyright Termination Notices 1977-2020: Introducing New Datasets(WILEY, 2022-03-01)Copyright termination laws in the United States allow creators to end their copyright assignments and licences after various time periods and regain their rights. These laws are designed to protect authors and their heirs by giving them a second opportunity to profit from their works, where they might have assigned them initially for relatively little. Similar laws are in force and being recommended for implementation around the world. However, there is little data on how these laws are being used. Such data is vital because it provides insights into the pros and cons of different systems. We fill this gap by providing the first large-scale study of copyright termination notice records from the U.S. Copyright Office. Utilising data scraping and manipulation techniques in the Python programming language, we have created two brand new datasets for scholars, copyright experts, creators, publishers, and other industry stakeholders to examine. In our accompanying paper, we document some preliminary trends from the data and how it might be used for further analysis.
-
ItemMetaGenePipe: An Automated, Portable Pipeline for Contig-based Functional and Taxonomic Analysis(The Open Journal, 2023)MetaGenePipe (MGP) is an efficient, flexible, portable, and scalable metagenomics pipeline that uses performant bioinformatics software suites and genomic databases to create an accurate taxonomic and functional characterization of the prokaryotic fraction of sequenced microbiomes. Written in the Workflow Definition Language (WDL), MGP produces output that can be explored and interpreted directly, or can be used for downstream analysis. MGP is a pipeline-development best practice tool that uses Singularity for containerization and includes a setup script that downloads the necessary databases for setup. The source code for MGP is freely available and distributed under the Apache 2.0 license.
-
ItemNo Preview AvailableCommunity construals of CSR for happiness: a mixed-method study using natural language(Emerald, 2022-11-08)Purpose: Drawing upon a contractarian lens of corporate social responsibility (CSR), this study aims to explore community construals of happiness and evaluates conceptual boundaries of CSR for happiness. Design/methodology/approach: Using a mixed-methods design, natural language processing and thematic analysis techniques were used to analyse large volumes of textual survey data collected from over 1,000 research participants through an online survey. Findings: Results indicated that lay construals of happiness were primarily defined in terms of socioeconomic conditions and psychoemotional experiences. In explicating the boundary conditions, community perceptions regarding the extent of businesses’ social responsibilities for happiness were evidenced in five themes: that businesses have a responsibility not to harm happiness, a responsibility to enable conditions for happiness to occur, a responsibility to exercise awareness of happiness implications in decision-making, a responsibility for happiness that is limited by strategic purpose and resource capability and a responsibility for happiness that is limited by stakeholder proximity. Originality/value: This study contributes to the theoretical and empirical foundation of CSR for happiness while simultaneously developing and applying a novel approach for processing and analysing large volumes of qualitative survey-based data.
-
ItemNo Preview AvailableA mystery, and viewless / Even when present:” Exhibiting Music at International Exhibitions in Nineteenth-Century Britain(DocMus Research Publications, 2022)
-
ItemA forest fuel dryness forecasting system that integrates an automated fuel sensor network, gridded weather, landscape attributes and machine learning models(Imprensa da Universidade de Coimbra, 2022)Accurate and timely forecasting of forest fuel moisture is critical for decision making in the context of bushfire risk and prescribed burning. The moisture content in forest fuels is a driver of ignition probability and contributes to the success of fuel hazard reduction burns. Forecasting capacity is extremely limited because traditional modelling approaches have not kept pace with rapid technological developments of field sensors, weather forecasting and data-driven modelling approaches. This research aims to develop and test a 7-day-ahead forecasting system for forest fuel dryness that integrates an automated fuel sensor network, gridded weather, landscape attributes and machine learning models. The integrated system was established across a diverse range of 30 sites in south-eastern Australia. Fuel moisture was measured hourly using 10-hour automated fuel sticks. A subset of long-term sites (5 years of data) was used to evaluate the relative performance of a selection of machine learning (Light Gradient Boosting Machine (LightGBM) and Recurrent Neural Network (RNN) based Long-Short Term Memory (LSTM)), statistical (VARMAX) and process-based models. The best performing models were evaluated at all 30 sites where data availability was more limited, demonstrating the models' performance in a real-world scenario on operational sites prone to data limitations. The models were driven by daily 7-day continent-scale gridded weather forecasts, in-situ fuel moisture observation and site variables. The model performance was evaluated based on the capacity to successfully predict minimum daily fuel dryness within the burnable range for fuel reduction (11 – 16%) and bushfire risk (