Mapping antibody class switch mechanisms and function

Lead Research Organisation: King's College London
Department Name: Randall Div of Cell and Molecular Biophy

Abstract

Antibodies are produced by a specialised immune cell (B cell) and act as important immune mediators, bridging between pathogens and effector cells to protect us from infection. Antibody molecules can also exist bound to the B cell surface where they act as receptor for detecting target molecules (antigens). Their versatility is immense, they are used to make diagnostics, research tools and therapeutics.

One part of the antibody (variable region) is responsible for binding to the antigen, the other end of the molecule is responsible for activating/mediating different functions in the immune system. Uniquely, the antibody variable region can evolve within the organism within a short timescale, in response to infection or vaccination, to improve its binding to the antigen. The constant region does not evolve, but it can be changed to one of 9 different classes or subclasses in order to change the function of the antibody in a genetic process known as Class Switch Recombination (CSR). The (sub)classes of antibody are arranged in the genome in this order: IgM-IgD-IgG3-IgG1-IgA1-IgG2-IgG4-IgE-IgA2; a B cell starts life with IgM and IgD and after activation switches to another (sub)class. Until very recently it was thought that CSR had no effect on the binding abilities of the Variable region.

Recent research in different areas (Ageing, Ebola, HIV infection, Cancer) indicates that we don't fully understand the processes that control which (sub)class will be used, the difference that CSR between subclasses makes to the outcome of an immune response or the exact molecular effects that CSR might have on the variable region binding properties. Therapeutic antibodies are a critical pharmaceutical resource, being the fastest growing class of pharmaceuticals, with thousands now in the development pipeline. Current products with regulatory approval/undergoing regulatory review are mostly IgG1 whilst none are IgA or IgE. As we understand more about the functions of these classes, we may find that the potential utility of antibodies can be increased, such as IgE in skin cancers or IgA in gut-related disorders.

In this programme we propose to harness the unique expertise of a team of bioinformaticians and immunologists to determine what factors, both outside the cell and inside the cell, control CSR. We will monitor how CSR progresses with time on a daily basis for a fortnight after challenge with the flu vaccine and we will use computer modelling of antibody structures to investigate how changing one side of the antibody molecule may affect the other. Each of these three main objectives will produce a range of results, some of which will help to understand the work in the other objectives, although would not be critical for their success. All parts of the programme require input from all the team to varying levels.

The methods we will use and develop are ground-breaking, in our preliminary data, we show pathways of class switching to different types of antibody in cell culture on a single cell basis. We will alter the conditions of these experiments, note the resulting changes and map the protein-protein and gene interactions to deduce what molecules are controlling CSR. These methods will be applicable in all cellular Bioscience disciplines and will transform cell biology research. The mapping of human CSR and the molecular modelling of antibody structure will also result in new tools for others to use, we have successfully done this before and have large global user groups in several areas. The interdisciplinary nature of our team means that we have insight into how to design tools that are user friendly and flexible, all data and tools will be made publicly available in a collective resource "BHive". We will also run our programme in such a way as to maximise the interdisciplinary familiarisation across all our teams and ensure our ECRs have a springboard into their future careers.

Technical Summary

Antibodies are a critical component of the immune system with multiple key functions. The huge range of possible specificities, coupled with the ability to change effector functions for the same specificity, makes them a unique adaptable resource - both in vivo in immune responses and in therapeutics. Effector functions are changed by switching to a different class, or subclass, of antibody in the process of Class Switch Recombination (CSR). Our understanding of CSR events and their consequences is inadequate for interpretation of recent key observations; eg failure of IgA1 responses in ageing, repertoire differences between IgG1 vs IgG2, change of CSR focus in Ebola survivors.
We have three main objectives 1) Determine endogenous and exogenous factors affecting CSR in vitro. Implementing novel methods to overcome difficulties inherent in current protocols, in an iterative process, combining B cell activation and gene silencing with single cell RNASeq. Our preliminary data shows that single cell RNASeq of ex vivo cultured B cells can distinguish different types of CSR and use pseudotime to monitor CSR progress. 2) Determine the dynamics of CSR in vivo after vaccination in different age groups. Using qualitative and quantitative repertoire analysis to track CSR at frequent (16 in 14 days) time points and model the dynamics of class switching. 3) Investigate the effect of allostery on antibody activity. Recent evidence indicates that not all antibody variable regions are resistant to allosteric effect of CSR, which has implications for the evolution/design of effective antibodies. We will measure potential allosteric effects in silico and verify outcomes in vitro.
This integrated program combines expertise in cellular/molecular immunology and bioinformatics/biophysics to achieve a better understanding of the control and relevance of class switching in the immune response and produce novel bio-analytical tools broadly applicable across many bioscience disciplines.

Planned Impact

The outputs of this collaboration will have extremely broad reach, informing the fields of Immunology (B cell development), vaccinology and immunotherapeutics. The methodological approaches and bioinformatical tools developed here will take advantage of cutting edge technologies in single cell analysis and will revolutionise biological gene silencing experiments in many other areas of biology.

Gene silencing in vitro is an immensely powerful tool, across all fields of biological sciences, to determine the function of intracellular molecules and determine their regulation and network interactions. A drawback has been that silencing methods are not 100% efficient, so any outcome of an experiment is an average of the results from cells with a varying degree of silencing. Engineered cell lines, expressing markers, can be used to monitor silencing, but there has been no widely applicable solution for research on primary cells. By integrating gene silencing with single cell transcriptomics we can determine the level of silencing per cell and correlate this with the level of the consequent effect. Thus, the combination of gene silencing and single cell technologies, with appropriate tools to facilitate analysis and interpretation, will be of immense benefit to many different researchers in a wide range of biological sciences as well as in immunology/host defence.

Antibody class switch recombination (CSR) is poorly understood in humans, yet evidence indicates that antibodies other than IgG1 play critical roles in protection against infectious disease and reducing our risk of autoimmunity and cancer. A full understanding of the human immune response to challenge, including dynamics of all B cell classes as well as serum Ig and cytokine measurement, is required if we are to design effective vaccines. It will also be important knowledge as the basis from which to design immune monitoring in clinical trials and will help us to understand the immune response in infectious diseases.

An antibody has two parts - Variable (V), with specificity for antigen, and Constant (C), encoding one of 9 classes of antibody. The prevailing paradigm is that the specificity of the V is unaffected by a change in C after class switching. Yet this may not always be the case, - changes in binding function have been seen between the same V on IgA2 versus IgG1, and different classes of antibody have different V repertoires. The latter could be due to historically different activation pathways (resulting in different C region use) selecting different V, or could be due to loss or gain of functionality upon CSR resulting in a change of representation by a particular V. Therapeutic antibodies are the fastest growing class of pharmaceuticals, with thousands now in the development pipeline. Of the products that have received regulatory approval, or are currently undergoing regulatory review, the majority are IgG1 and none are IgA or IgE. It is important to understand where V-C interactions might alter antibody function to optimise the pipeline and reduce development costs. As we understand more about the functions of different classes of antibody we may find that the potential utility of antibodies can be increased. For example, we recently showed that IgE is involved in the response to skin tumors in mice.

Stakeholders: The main beneficiaries of this basic science programme will be research academics in the life sciences and companies concerned with the use of antibodies in therapeutics/ diagnostics/ research applications. The ECRs on the programme and in the labs of the senior investigators will also increase and diversify their skills in this synergistic interdisciplinary environment. Information and tools from this program will lead to improvements in research and development which will eventually benefit public health in the way of improved vaccines and biological pharmaceuticals.

Publications

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Related Projects

Project Reference Relationship Related To Start End Award Value
BB/T002212/1 01/02/2020 31/10/2022 £2,823,420
BB/T002212/2 Transfer BB/T002212/1 01/11/2022 31/01/2026 £1,573,277
 
Description In this programme we propose to harness the unique expertise of a team of bioinformaticians and immunologists to determine what factors, both outside the cell and inside the cell, control Antibodies' function with time on a daily basis for a fortnight after challenge with the flu and COVID-19 vaccine. We aim at discovering changes in the antibodies' response related to age and immune response.
Main findings so far:
1. We have created a new bioinformatics method (sciCSR) which infers B cell maturation in single-cell RNA-sequencing data, and predicts the directionality of CSR (Ng et al. bioRxiv, doi:https://doi.org/10.1101/2023.02.02.526789 accepted in Nature Methods https://doi.org/10.1038/s41592-023-02060-1 ).

2. We have established a single-cell transcriptomic atlas of peripheral B cells in healthy individuals (Stewart, Ng et al. Front Immunol. 2021, doi: 10.3389/fimmu.2021.602539), which serves as a baseline reference to analyse B cell states and class-switching patterns in data obtained from B cells in response to in vitro stimulation (Objective 1) and immune challenges (SARS-CoV-2 vaccine, Objective 2).

3. We have collected and generated a multi-omic (single-cell RNA-seq, immunophenotyping, antibody repertoire) dataset to analyse B cell response to SARS-CoV-2 Moderna mRNA-1273 vaccine. We have published (Stewart, Sinclair, Ng et al. Front Immunol. 2022, doi: 10.3389/fimmu.2022.807104) analysis of antibody repertoire of SARS-CoV-2 hospitalised patients, together with a new bioinformatics pipeline (BrepPhylo) which analyses B cell lineages and extracts signals of class-switching from the lineages.
Exploitation Route The proposed project can inform new antibody-based therapies and tailored vaccine treatments.
Sectors Healthcare

Manufacturing

including Industrial Biotechology

 
Description We developed single-cell inference of CSR (sciCSR, pronounced as in "scissors"), a computational method that enables us to analyze single-cell RNA sequencing (scRNA-seq) data and extract CSR signals. sciCSR re-analyzes scRNA-seq alignments to distinguish sterile and productive transcripts (Fig. 1a). Because regions 5' to the constant region exons (denoted "5' C") are a hallmark of sterile transcripts1, a positive read count covering this region signifies sterile transcripts. Similarly, reads covering the antibody variable (V), diversity (D) and joining (J) segments imply productive transcripts. We reasoned that we could couple sciCSR with recent tools aimed at inferring cellular trajectories to resolve the temporal trajectory of CSR and the antibody response. We curated publicly available scRNA-seq datasets of B cells from vaccination studies (monitoring the development of antibody response over time) and genetically engineered systems, such as mice in which known CSR-related genes have been knocked down.
First Year Of Impact 2019
 
Description Parliamentary engagement during the pandemic
Geographic Reach National 
Policy Influence Type Implementation circular/rapid advice/letter to e.g. Ministry of Health
Impact Through the work I did with my BSI colleagues the BSI became known as a source of sound scientific advice during the pandemic. We have corresponded with the CMOs office in private as well as publicaly and the CMO has quoted our input on national briefings.
 
Description Establishing a network to catalyse collaboration for reducing immune ageing (CARINA: CAtalyst Reducing ImmuNe Ageing)
Amount £201,993 (GBP)
Funding ID BB/W018225/1 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start 02/2022 
End 02/2024
 
Description Molecular mapping of SARS-CoV2 and the host response with multiomics mass spectrometry to stratify disease outcomes
Amount £1,736,024 (GBP)
Funding ID BB/V011456/1 
Organisation Biotechnology and Biological Sciences Research Council (BBSRC) 
Sector Public
Country United Kingdom
Start 09/2020 
End 04/2023
 
Title NetworkMiner: Protocol for biological network analysis to identify novel genes and functional modules mediating class switch recombination in response to SARS-CoV-2 vaccination (part of the Macsmaf research project) 
Description We used Protein-Protein Interaction (PPI) network databases to identify novel genes involved in Class Switching Recombination (CSR), a fundamental process in the adaptive immune response. CSR permit immunoglobulin heavy-chain isotype switching, a key part of B-cell maturation in response to pathogen-specific antigens, which in this study, was from infection with SARS-CoV-2 virus or by induced immune challenge with an appropriate vaccine. From literature survey and analysis, we identified three suitable PPI network databases, STRING, Consensus Path DB (CPDB), and Humanbase (formerly: GIANT). In combination, these databases provided high overall interactome coverage, with each contributing a substantial proportion of unique proteins and interactions, thus highlighting distinct biological processes and network topologies. We analysed these PPI datasets to identify two types of network topology related to biological function using: 1) module detection to find highly interconnected cliques; 2) pathway detection to find extended chains of interacting proteins. Module detection aims to identify distinct sets of proteins that interact to mediate specific biological functions or processes within the cell. Challenges in identifying such modules include choice of suitable module detection algorithms, setting appropriate thresholds for pre-filtering network databases using the interaction confidence scores, and assessment of the reliability of the findings using the different sets of modules generated by each method. We applied the three top performing module detection algorithms from the 'The Dialogue on Reverse Engineering and Assessment (DREAM) Disease Module Identification Challenge'. Each algorithm is based on a distinct approach: Kernel detection (K1), Modularity optimization (M1), and Random search (R1). Pathways are associated with cellular response to external stimuli and involve a cascade of interactions that lead to this response. We identified pathways connecting the identified modules using the PathLinker algorithm. Our protocol applied each of the three module detection algorithms (M1, R1, and K1) to each of the three network databases (STRING, CPDB, and Humanbase), yielding nine sets of modules and networks. The advantages of this strategy are two-fold. First, we were able to detect a wide range of modules with different topologies, and potentially different functions; second, we could compare the nine datasets for consistency to assess the reliability of our findings. To focus this generic network protocol on CSR-specific modules, pathways and processes, we identified sets of 'seed genes' that were known from to be involved in CSR. These seed genes were identified through literature curation, domain-specific expertise, and previous experiments conducted by our MACSMAF collaborators. Modules that contained seed genes were assumed to be involved to with CSR to some extent, i.e., they comprised different proportions of seed genes to total genes in the module. Seed genes were split into two groups. The first group consisted of genes coding receptors and messenger proteins likely to be involved in the initiation of the CSR signaling cascade. The second group comprised effector genes mediating specific CSR process. The PathLinker algorithm was used to detect potential signaling pathways between genes and modules in these two seed groups. We tested whether our protocol captures genes involved in CSR using gene expression datasets from two experiments carried out by our collaborators. These experiments artificially induced CSR in B cells to identify differentially expressed genes, which we found to be significantly overrepresented in genes from our modules (P-values ~ < 10-150). This finding supports the validity of our approach, indicating that genes found in our protocol were more likely to be involved in CSR processes and therefore good candidates for further investigation. Candidate genes were ranked using a reliability score, calculated as the number of CSR modules containing each gene; higher ranking genes had better odd ratios, further supporting our approach. We then compared results from the two experiments through the lens of functional modules. We analyzed which modules were enriched in differentially expressed CSR genes in each of the two experiments, identifying modules activated in both cases, and modules unique to one experiment. To provide insight into the function of identified modules, we performed extensive functional enrichment analysis and ranking to determine the top functional terms for each module using terms from GO:BP and the Reactome and KEGG pathway databases. We utilized the hierarchical relationship of pathway terms to identify those which where over/underrepresented in each of the experiments and in CSR modules. From this this analysis we determined the functional roles of shared/unique functional modules in each of the experiments. 
Type Of Material Improvements to research infrastructure 
Year Produced 2024 
Provided To Others? No  
Impact We provide an annotated list of potential CSR genes, along with their rankings from the output of our protocol, providing a valuable resource for future study of CSR-related genes. From the functional enrichment analysis and ranking we provide extensive outputs, including the top ranked KEGG, Reactome and GO:BP function for each module. Together, these functional enrichments can aid in analysis and prioritization of the CSR candidate gene list by providing additional context of biological pathways and functions. 
 
Title SciCSR 
Description Tool for analysing single cell data from B cells to distinguish between sterile and productive antibody transcripts 
Type Of Material Improvements to research infrastructure 
Year Produced 2024 
Provided To Others? Yes  
Impact PAradigm shifting understanding of antibody class switch recombination 
 
Title HOIP-ensembles-Kausas-et-al 
Description Supplementary data for "Characterisation of HOIP RBR E3 ligase conformational dynamics using integrative modelling" 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
URL https://zenodo.org/record/6583368
 
Title HOIP-ensembles-Kausas-et-al 
Description Supplementary data for "Characterisation of HOIP RBR E3 ligase conformational dynamics using integrative modelling" 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
URL https://zenodo.org/record/7041795
 
Title HOIP-ensembles-Kausas-et-al 
Description Supplementary data for "Characterisation of HOIP RBR E3 ligase conformational dynamics using integrative modelling" 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
URL https://zenodo.org/record/6583322
 
Title HOIP-ensembles-Kausas-et-al 
Description Supplementary data for "Characterisation of HOIP RBR E3 ligase conformational dynamics using integrative modelling" 
Type Of Material Database/Collection of data 
Year Produced 2022 
Provided To Others? Yes  
URL https://zenodo.org/record/6583323
 
Title Predicted Class Switching Recombination Gene Network 
Description The dataset includes a gene network, consisting of 4739 genes predicted to be involved in antibody class switching recombination. The genes in the network are ranked based on the confidence level of the prediction. Furthermore, each gene is annotated based on its likely function using GO, KEGG, and Reactome annotations. Pathway genes along signalling pathways in the network are highlighted and ranked based on their predicted impact. A manuscript is in progress to describe the method used to generate the dataset and the outcomes of analysing the network. The dataset will be made available together with the manuscript. 
Type Of Material Database/Collection of data 
Year Produced 2024 
Provided To Others? No  
Impact The dataset provides ranked target genes from network modules. Our research has generated a novel list of genes predicted to be involved in antibody class switching recombination. These genes are currently being used by our collaborators to test their impact on class switching, enhancing our understanding of this fundamental process in the immune system. 
 
Title Supplementary datasets: sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data (Ng et al.) 
Description This repository contains data files from the manuscript Ng et al. "sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data". Directories Please untar the sciCSR-data-files.tar.gz archive. Folder "Simulated_data" 'simulated_IGHC_reads' folder: containing list of simulated data (FASTQ sequence files and aligned BAM files) to test the accuracy of commonly used RNA-seq aligners (STAR, HISAT2) to distinguish sterile and productive heavy-chain transcripts. The code to generate these data is in the repository https://github.com/Fraternalilab/sciCSR-analysis. 'simulated_transitions.RData': .RData file containing list of Seurat objects of simulated datasets of different number of cells, to test the robustness of sciCSR-inferred transitions across different dataset sizes. Folder "Seurat_objects" 'human_Bcells_atlas_IGHC_NMF_rank.rds': Nonnegative matrix factorization (NMF) results to derive isotype signatures from the human B cell atlas (see below). 'mouse_Bcells_atlas_IGHC_NMF_rank.rds': NMF results to derive isotype signatures from the mouse B cell atlas (see below) 'Human_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'human B cell atlas' (i.e. merging data from Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) and King et al (https://doi.org/10.1101/2020.04.28.054775)) 'mouse_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'mouse B cell atlas' (i.e. merging data from Mathew et al (https://doi.org/10.1016/j.celrep.2021.109286) and Luo et al (https://doi.org/10.1186/s13578-022-00795-6)) 'Stewart_HumanPeripheral_Bcells_IGHC.rds': Seurat object containing cells from the Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) peripheral blood B cell atlas. * 'King_HumanTonsil_Bcells_IGHC.rds': Seurat object containing cells from the King et al. (https://doi.org/10.1101/2020.04.28.054775) human tonsilar B cell atlas. 'Kim_Covid_Bcells_IGHC.rds': Seurat object containing cells from the Kim et al. (https://doi.org/10.1038/s41586-022-04527-1) time-course scRNA-seq data on human B cell response to SARS-CoV-2 vaccine. 'Gomez_AID_VDJ_IGHC.rds': Seurat object containing cells from the Gómez-Escolar et al. (https://doi.org/10.15252/embr.202255000) Aicda mouse knockout scRNA-seq data. 'Hong_IL23_Bcells_IGHC.rds': Seurat object containing cells from the Hong et al. (https://doi.org/10.4049/jimmunol.2000280) Il23 p19 mouse knockout scRNA-seq data. 'scIFNg.rds': Seurat object containing scRNA-seq data of time-course in vitro culture of B cells stimulated with interferon gamma generated in this work. 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
URL https://zenodo.org/record/8005705
 
Title Supplementary datasets: sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data (Ng et al.) 
Description This repository contains data files from the manuscript Ng et al. "sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data". Directories Please untar the sciCSR-data-files.tar.gz archive. Folder "Simulated_data" 'simulated_IGHC_reads' folder: containing list of simulated data (FASTQ sequence files and aligned BAM files) to test the accuracy of commonly used RNA-seq aligners (STAR, HISAT2) to distinguish sterile and productive heavy-chain transcripts. The code to generate these data is in the repository https://github.com/Fraternalilab/sciCSR-analysis. 'simulated_transitions.RData': .RData file containing list of Seurat objects of simulated datasets of different number of cells, to test the robustness of sciCSR-inferred transitions across different dataset sizes. Folder "Seurat_objects" 'human_Bcells_atlas_IGHC_NMF_rank.rds': Nonnegative matrix factorization (NMF) results to derive isotype signatures from the human B cell atlas (see below). 'mouse_Bcells_atlas_IGHC_NMF_rank.rds': NMF results to derive isotype signatures from the mouse B cell atlas (see below) 'Human_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'human B cell atlas' (i.e. merging data from Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) and King et al (https://doi.org/10.1101/2020.04.28.054775)) 'mouse_Bcells_atlas_IGHC.rds': Seurat object containing cells forming the 'mouse B cell atlas' (i.e. merging data from Mathew et al (https://doi.org/10.1016/j.celrep.2021.109286) and Luo et al (https://doi.org/10.1186/s13578-022-00795-6)) 'Stewart_HumanPeripheral_Bcells_IGHC.rds': Seurat object containing cells from the Stewart et al (https://doi.org/10.3389/fimmu.2021.602539) peripheral blood B cell atlas. * 'King_HumanTonsil_Bcells_IGHC.rds': Seurat object containing cells from the King et al. (https://doi.org/10.1101/2020.04.28.054775) human tonsilar B cell atlas. 'Kim_Covid_Bcells_IGHC.rds': Seurat object containing cells from the Kim et al. (https://doi.org/10.1038/s41586-022-04527-1) time-course scRNA-seq data on human B cell response to SARS-CoV-2 vaccine. 'Gomez_AID_VDJ_IGHC.rds': Seurat object containing cells from the Gómez-Escolar et al. (https://doi.org/10.15252/embr.202255000) Aicda mouse knockout scRNA-seq data. 'Hong_IL23_Bcells_IGHC.rds': Seurat object containing cells from the Hong et al. (https://doi.org/10.4049/jimmunol.2000280) Il23 p19 mouse knockout scRNA-seq data. 'scIFNg.rds': Seurat object containing scRNA-seq data of time-course in vitro culture of B cells stimulated with interferon gamma generated in this work. 
Type Of Material Database/Collection of data 
Year Produced 2023 
Provided To Others? Yes  
URL https://zenodo.org/record/8005706
 
Description ProtFunAI - Collababoration with Burkhard Rost Team 
Organisation Technical University of Munich
Country Germany 
Sector Academic/University 
PI Contribution Development of deep learning algorithms for protein function prediction, protein classification and analysis
Collaborator Contribution Training in deep learning protocols and protein language models. Contributions to project design. Novel protein language models to generate protein embeddings for protein function prediction and other protein based prediction tasks.
Impact Project has just started so no outputs yet
Start Year 2024
 
Description ROYAL FREE LONDON NHS FOUNDATION 
Organisation Royal Free Hospital
Department Department Immunology
Country United Kingdom 
Sector Hospitals 
PI Contribution Amir Gander Royal Free London NHS Foundation Trust Royal Free Hospital Collaboration SIB Vaccine study
Collaborator Contribution Collaboration on Vaccine study- recruitment and sample collection as part of the UKRI funded SLoLa award MACSMSAF
Impact Multidisciplinary: Virology, Immunology, Cpmputational Biology, Bcells Repertoire study, Data Analysis.
Start Year 2021
 
Description ROYAL FREE LONDON NHS FOUNDATION 
Organisation Royal Free Hospital
Country United Kingdom 
Sector Hospitals 
PI Contribution Amir Gander Royal Free London NHS Foundation Trust Royal Free Hospital Collaboration SIB Vaccine study
Collaborator Contribution Collaboration on Vaccine study- recruitment and sample collection as part of the UKRI funded SLoLa award MACSMSAF
Impact Multidisciplinary: Virology, Immunology, Cpmputational Biology, Bcells Repertoire study, Data Analysis.
Start Year 2021
 
Description Surrey COVID-19 Consortium 
Organisation University of Surrey
Department Department of Chemistry
Country United Kingdom 
Sector Academic/University 
PI Contribution We were PI, banked biological samples from COVID-19 patients throughout 2020 and 2021 and co-ordinated sample sharing between interested parties in this time, including companies developing rapid tests, academics looking for biomarkers and immunologists studying immune responses.
Collaborator Contribution Hospital clinical and research staff and students in chemistry dept contributed to sample collection from patients
Impact Samples were used by the UK Mass Spectrometry coalition (funded by BBSRC) Disciplines are immunology, emergency medicine, chemistry Samples were used by other immunologists in their research feeding into public health information (eg neutralisation ability of serum from earlier variants against omicron) Attracted funding from National Core Studies on rapid methods to detect cellular immunity
Start Year 2020
 
Description Communicating Immunology -award from British Society for Immunology 
Form Of Engagement Activity A broadcast e.g. TV/radio/film/podcast (other than news/press)
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact The COVID-19 pandemic is hopefully coming to an end through the approval of several different vaccines and the beginning of mass vaccination of the population. But how do these vaccines work? How do they affect our bodies? During viral invasion, our immune system uses several tools to fight infection. COVID-19 vaccines help our bodies develop immunity without us having to get the illness. In the short movies herewith planned, we will explore how two different vaccines - the Pfizer and AstraZeneca vaccine protect against SARS-CoV-2. We will also document the effects of vaccines on the antibody repertoires of vaccinated people.
We feel nevertheless that the large public is still unclear on how exactly the vaccines work and what are the differences in their development and functioning. We also feel that we should engage all ages and social layers of the population in communicating such information.
We propose therefore to produce two short films: a) one addressing how the Pfizer and AstraZeneca vaccines work against the disease to provide a better understanding of their mechanisms and potentially aid to address safety concerns (expected to be completed by the end of February 2021); b) the second documenting results from the analysis of the data on antibody repertoire and explaining differences observed in the vaccinated population studied. This second movie should be completed by the Autumn 2021 as it is dependent on data collection and analysis.
The target audience of the movies is the public, as young as 12 years old. This audience will be reached online through sharing of the movie via different social media platforms. We plan to post the movies on the web site established as a result of a Systems Immunology award from MRC: http://www.bcell.org.uk/mabra.html and we will use it in our engagement events planned in the context of the sLoLa MACSMAF award.
Year(s) Of Engagement Activity 2021
 
Description Interviews 
Form Of Engagement Activity A press release, press conference or response to a media enquiry/interview
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Media (as a channel to the public)
Results and Impact Various activities ranging from briefings to MP's, Lords, briefing notes, newspaper interviews, radio interviews, newsnight, evening news appearances on BBC ITV channels throughout the pandemic but especially around vaccine launches
Year(s) Of Engagement Activity 2020,2021,2022