Biology subjects

Eccles, D. A.

Publications and source records attributed to Eccles, D. A..

5 recordsLinked to original sources

bioRxiv2026

Engineering antigenic breadth against SARS-CoV-2 by pairing divergent RBDs within a single mRNA immunogen

Vaccines capable of eliciting broadly neutralising antibodies (bnAbs) are a major goal for pandemic preparedness. A persistent challenge across vaccine Wields is how to deliberately recruit the rare B cell clones that recognise conserved epitopes shared across diverse viral variants. BnAbs have been known to frequently emerge through extensive somatic hypermutation during afWinity maturation, here we describe an alternative, structure-driven mechanism for bnAb selection. We designed an mRNA vaccine in which two antigenically distinct SARS-CoV-2 variants (Omicron and Delta; O-{Delta}) receptor binding domains (RBDs) are physically fused on a single polypeptide. This design is predicted to favour B cell antigen receptors capable of engaging conserved epitopes on both RBDs with enhanced avidity. A matched non-divergent tandem RBD (Delta-Delta; {Delta}-{Delta}) served as a control. The divergent (O-{Delta}) immunogen was robustly expressed and retained high-afWinity ACE2 binding. In mice, immunisation elicited potent antibody responses and increased the frequency of antigen-speciWic cross-reactive B cells, recognising Delta, Omicron, and the 2002 pandemic strain SARS-CoV RBDs. Using multicolour RBD tetramers and single-cell B cell receptor sequencing, we show that breadth arises via two distinct pathways. The divergent vaccine preferentially enriches clonally distinct cross-reactive B cells (not present within non-cross-reactive B cell pools) with low levels of somatic hypermutation (SHM), consistent with selection of germline-biased precursors. In contrast, the matched control vaccine yields cross-reactivity primarily within existing clonal lineages (clonal overlap between cross-reactive and non-cross-reactive cells) and at higher mutational burdens, consistent with afWinity-maturation-driven acquisition of breadth. Together, these Windings demonstrate that antigen structure can bias B cell selection towards cross-reactive speciWicities without requiring extensive SHM. This work establishes a simple, modular antigen-design principle in which juxtaposing appropriately divergent antigens on a single scaffold promotes the enrichment of bnAb-prone B cells, providing a scalable strategy for vaccine development against rapidly evolving pathogens.

immunology↗

bioRxiv2025

A new genetically engineered transplant model of glioma recapitulates key phenotypes of low- and high-grade gliomas

Immune competent animal models are essential in preclinical glioma research. The ability to investigate tumor development with key tumor microenvironment components such as immune infiltration, stromal cells and extracellular matrix allows for the investigation of these complex tumors. However, the current range of syngeneic models of glioma possess intrinsic limitations which must be acknowledged when designing a preclinical study. To address this gap, we developed genetically engineered mouse cell line models (GEM-CLeMs) by introducing common glioma driver mutations into immortalized astrocytes. A high-grade glioma model was generated by combining Pten knockdown with RAS V12 overexpression, while a low-grade glioma model was produced through p53 knockdown with mutant IDH1R132H overexpression. The RAS/Pten GEM-CLeM tumors grew rapidly in vivo, displayed necrosis, multinucleated pleomorphisms, abundant vascularization, and showed strong enrichment for extracellular matrix remodelling and mesenchymal genes, features closely aligned with human glioblastoma. Importantly, the RAS/Pten GEM-CLeM tumors showed similar survival trends and immune infiltration patterns as a corresponding KrasG12D/PtencKO GEMM, but could be grown to larger sizes, facilitating better stromal and immune analyses. In contrast, the IDH1R132H/p53 GEM-CLeM formed slow-growing tumors with distinctive immune infiltration and vascular patterns, consistent with low-grade glioma phenotypes. Compared with the commonly used cell line GL261, the GEM-CLeM tumors had higher levels of stromal integration and immune suppression, making them a more faithful model of the glioma tumor microenvironment. This system enables rapid generation of transplantable glioma models with defined driver mutations in a low-mutational background, offering a flexible platform for dissecting glioma biology and evaluating immunotherapies. Importance of the studyWe have generated a set of customizable, modular Genetically Engineered Mouse Cell Line Models (GEM-CLeMs) of "high grade" and "low grade" glioma. They reliably form tumors when transplanted intracranially into immune-competent C57BL/6 mice, and they are cost- and time-effective at capturing the important characteristics of glioma, both mutant IDH1 low grade glioma and high grade glioblastoma. Critically these characteristics include the myeloid-rich immune suppressive tumor immune microenvironment, a key weakness of existing murine glioma cell lines like GL261. These GEM-CLeM models can be used in multiple ways. The cells are amenable to further manipulation, so therapeutic targets and drug mechanism of action can be assessed. The activity of candidate genes in tumour formation and phenotype can be determined. Most importantly the models can be used to develop effective immunotherapies, including strategies to target macrophage and myeloid cell immune suppression. Key pointsO_LICombinations of driver mutations were engineered into an immortalised mouse astrocyte. C_LIO_LIEngineered cells formed tumours on intracranial transplant into immune competent mice. C_LIO_LITumors had key histological and immune suppressive features of human glioma. C_LI

cancer biology↗

bioRxiv2021

The skin environment controls local dendritic cell differentiation and function through innate IL-13

The signals driving the adaptation of type-2 dendritic cells (DC2s) to diverse peripheral environments are not well understood. We show that the development of CD11blow migratory DC2s, a DC2 population unique to the dermis, requires STAT6- and KLF4-dependent IL-13 signaling, whereas DC2s in lung and small intestine are STAT6-independent. Dermal IL-13 is mostly derived from innate lymphoid cells expressing a resting ICOS+ KLRG1-ST2-phenotype. Analysis of public datasets indicates that human skin DC2s also express an IL-4/IL-13 gene signature compared to blood or spleen, suggesting a similar developmental pathway in mice and humans. In the absence of IL-13 signaling, dermal DC2s are stable in number but remain CD11bhi and show defective activation in response to allergen with diminished ability to support IL-4+ GATA3+ Th development, whereas anti-fungal IL-17+ ROR{gamma}t+ responses are increased. Thus, steady-state IL-13 fosters a non-inflammatory and pro-allergic environment in healthy skin via conditioning of local DC2s.

immunology↗

bioRxiv2019

A semi-automated technique for adenoma quantification in the ApcMin mouse using FeatureCounter

Colorectal cancer is a major contributor to death and disease worldwide. The ApcMin mouse is a widely used model of intestinal neoplasia, as it carries a mutation also found in human colorectal cancers. However, the method most commonly used to quantify tumour burden in these mice is manual adenoma counting, which is time consuming and poorly suited to standardization across different laboratories. We describe a method to produce suitable photographs of the small intestine, process them with an ImageJ macro, FeatureCounter, which automatically locates image features potentially corresponding to adenomas, and a machine learning pipeline to identify and quantify them. Compared to a manual method, the specificity (or True Negative Rate, TNR) and sensitivity (or True Positive Rate, TPR) of this method in detecting adenomas are similarly high at about 80% and 87%, respectively. Importantly, total adenoma area measures derived from the automatically-called tumours were just as capable of distinguishing high-burden from low-burden mice as those established manually. Overall, our strategy is quicker, helps control experimenter bias and yields a greater wealth of information about each tumour, thus providing a convenient route to getting consistent and reliable results from a study.

cancer biology↗

bioRxiv2017

Bootstrap Distillation: Non-parametric Internal Validation of GWAS Results by Subgroup Resampling

Genome-wide Association Studies are carried out on a large number of genetic variants in a large number of people, allowing the detection of small genetic effects that are associated with a trait. Natural variation of genotypes within populations means that any particular sample from the population may not represent the true genotype frequencies within that population. This may lead to the observation of marker-disease associations when no such association exists.\n\nA bootstrap population sub-sampling technique can reduce the influence of allele frequency variation in producing false-positive results for particular samplings of the population. In order to utilise bioinformatics in the service of a serious disease, this sub-sampling method has been applied to the Type 1 Diabetes dataset from the Wellcome Trust Case Control Consortium in order to evaluate its effectiveness.\n\nWhile previous literature on Type 1 Diabetes has identified some DNA variants that are associated with the disease, these variants are not informative for distinguishing between disease cases and controls using genetic information alone (AUC=0.7284). Population sub-sampling filtered out noise from genome-wide association data, and increased the chance of finding useful associative signals. Subsequent filtering based on marker linkage and testing of marker sets of different sizes produced a 5-SNP signature set of markers for Type 1 Diabetes. The group-specific markers used in this set, primarily from the HLA region on chromosome 6, are considerably more informative than previously known associated variants for predicting T1D phenotype from genetic data (AUC=0.8395). Given this predictive quality, the signature set may be useful alone as a screening test, and would be particularly useful in combination with other clinical cofactors for Type 1 Diabetes risk.

bioinformatics↗