Biology subjects

Bradford, J.

Publications and source records attributed to Bradford, J..

9 recordsLinked to original sources

bioRxiv2026

Macrophages use sub-micron actin ridges in phagocytosis of encapsulated fungal pathogens

Ingestion and killing of pathogens by phagocytic cells is a key aspect of animal immunity. Cryptococcus neoformans is a major pathogen of AIDS patients that has a polysaccharide capsule that is critical for virulence. Phagocytic function is necessary for normal immunity to cryptococcal infection, and we sought to determine how Cryptococcus capsule prevents phagocytosis. In doing so, we discovered a new sub-micron actin structure, the ridge, that is present on macrophages, neutrophils and amoebae. Macrophages preferentially bind cryptococci via ridges and suppression of ridges is sufficient to prevent phagocytosis. Ridges are present on macrophages in vivo and are lost in interleukin 10 stimulated macrophages. The discovery and functional description of macrophage surface ridges fills a critical gap in our understanding of how phagocytes ingest encapsulated fungal pathogens, a crucial step in treating infection.

cell biology↗

bioRxiv2026

Mutant KRAS dosage contributes to heterogeneity in lung cancer therapeutic response

Oncogenic KRAS mutations promote tumorigenesis by constitutive activation of multiple, well-characterised signalling pathways. However, there is significant heterogeneity across mutant KRAS tumours in terms of mutation present, mutant allele abundance and downstream signalling strength. It is unclear whether these variations can impact responses to specific therapies. Here, we demonstrate that [~]20% of lung adenocarcinomas (LUAD) show an increase in mutant KRAS dosage (KRASmutant allele fraction > KRASwild-type). Furthermore, we show that KRAS mutant dosage can directly influence clinical outcome and therapeutic susceptibilities in lung cancer. Our findings show that mutant KRAS copy gains specifically affect platinum lung cancer response, promoting resistance to this standard-of-care therapy. Importantly, increases in KRAS mutant dosage are also associated with an increased vulnerability to pS6K inhibition, due to the unique metabolic rewiring of these cells. Together, we show that mutant KRAS dosage contributes to the phenotypic heterogeneity of mutant KRAS NSCLC and that assessment of mutant KRAS content or signalling strength can help optimise treatments strategies for these patients.

cancer biology↗

bioRxiv2025

CRISPR-DA: Adapting CRISPR gRNA design for detection assays

CRISPR-Cas systems offer a viable alternative to traditional detection and diagnosis methods. However, their effectiveness relies heavily on the selection of appropriate guide RNA sequences. Existing gRNA design tools were primarily developed for gene editing and are not always directly applicable to CRISPR-based detection assays. In particular, alignmentbased methods are still used to estimate gRNA specificity, even though they can miss a substantial portion of off-target sites. In this work, we introduce CRISPR-DA, a CRISPR gRNA design tool for detection assays. We show that it provides a better assessment of gRNA specificity than BLAST, which detected only 33.27 % and 0.43 % of cross-species off-targets in two datasets. Additionally, CRISPR-DA ran two and six times faster than BLAST on these datasets, respectively. Our method incorporates advances from gene-editing guide RNA design tools, including uncertainty-informed guide RNA design, to improve the selection of guides with high on-target activity. CRISPR-DA is available at https://github.com/bmds-lab/CRISPR-DA.

bioinformatics↗

bioRxiv2024

Crackling Cloud: an event-driven, cloud-based CRISPR-Cas9 guide RNA design tool

Gene editing has been revolutionised by the CRISPR-Cas9 technology. The versatility and ease-of-use of the technology far exceeds its predecessors, however, the selection of a high-quality guide RNA (gRNA) is critical to directing it to a target site. Selecting gRNA calls upon high-performance algorithms that evaluate nuclease activity at on-target and off-target sites. While there are a suite of programs available, many struggle to analyse the largest genomes, or their predictive accuracy is low. We have previously published a program, named Crackling that is amongst the fastest and most accurate tools available, however, it requires an end-user to have access to a traditional high-performance computing environment. Here, we present an adaptation of Crackling, named Crackling Cloud, that takes advantage of modern serverless cloud technologies that are widely available to anyone, and do not consume resources and incur costs when sitting idle, but can scale to use large volumes of resources when analyses require that. Crackling Cloud is provided as a templated solution using technologies of Amazon Web Services, and is available for free on GitHub under the terms of the BSD 3-clause licence: https://github.com/bmds-lab/Crackling-AWS

bioinformatics↗

bioRxiv2024

Leveraging uncertainty quantification to optimise CRISPR guide RNA selection

CRISPR-based genome editing relies on guide RNA sequences to target specific regions of interest. A large number of methods have been developed to predict how efficient different guides are at inducing indels. As more experimental data becomes available, methods based on machine learning have become more prominent. Here, we explore whether quantifying the uncertainty around these predictions can be used to design better guide selection strategies. We demonstrate how using a deep ensemble approach achieves better performance than utilising a single model. This approach can also provide uncertainty quantification. This allows to design, for the first time, strategies that consider uncertainty in guide RNA selection. These strategies achieve precision over 91% and can identify suitable guides for more than 93% of genes in the mouse genome. Our deep ensemble model is available at https://github.com/bmdslab/CRISPR_DeepEnsemble.

bioinformatics↗

bioRxiv2024

Fast and scalable off-target assessment for CRISPR guide RNAs using partial matches

The design of CRISPR-Cas9 guide RNAs is not trivial. In particular, it is crucial to evaluate the risk of unintended, off-target modifications, but this is computationally expensive. To avoid a brute-force approach where each guide RNA is compared against every possible CRISPR target site in the genome, we previously introduced Crackling, a guide RNA design tool that relies on exact matches over 4bp subsequences to approximate a neighbourhood and accelerate off-target scoring by greatly reducing the search space. While this was faster than other existing tools, it still generates large neighbourhoods. Here, we aim to further reduce the search space by requiring more, now non-contiguous, exact matches. The new implementation, called Crackling++, is benchmarked against our initial approach and other off-target evaluation tools. We show that it provides the fastest way to assess candidate guide RNAs. By using memorymapped files, it also scales to the largest genomes. Crackling++ is available at https://github.com/bmds-lab/CracklingPlusPlus under the Berkeley Software Distribution (BSD) 3-Clause license.

bioinformatics↗

bioRxiv2020

Reproductive history determines ErbB2 locus amplification, WNT signalling and tumour phenotype in a murine breast cancer model

Understanding the mechanisms underlying tumour heterogeneity is key to development of treatments that can target specific tumour subtypes. We have previously targeted CRE recombinase-dependent conditional deletion of the tumour suppressor genes Brca1, Brca2, p53 and/or Pten to basal or luminal ER- cells of the mouse mammary epithelium. We demonstrated that both the cell-of-origin and the tumour-initiating genetic lesions co-operate to influence mammary tumour phenotype. Here, we use a CRE-activated HER2 orthologue to specifically target HER2/ERBB2 oncogenic activity to basal or luminal ER- mammary epithelial cells and carry out a detailed analysis of the tumours which develop. We find that in contrast to our previous studies, basal epithelial cells are refractory to transformation by the activated NeuKI allele, with mammary epithelial tumour formation largely confined to luminal ER- cells. Histologically, the majority of tumours that developed were classified as either adenocarcinomas of no special type or metaplastic adenosquamous tumours. Remarkably, the former were more strongly associated with virgin animals and were typically characterised by amplification of the NeuNT/ErbB2 locus and activation of non-canonical WNT signalling. In contrast, tumours characterised by squamous metaplasia were associated with animals that had been through at least one pregnancy and typically had lower levels of NeuNT/ErbB2 locus amplification but had activated canonical WNT signalling. Squamous changes in these tumours were associated with activation of the Epidermal Differentiation Cluster. Thus, in this model of HER2 breast cancer, cell-of-origin, reproductive history, NeuNT/ErbB2 locus amplification, and the activation of specific branches of the WNT signalling pathway all interact to drive inter-tumour heterogeneity.

cancer biology↗

bioRxiv2020

Faster and better CRISPR guide RNA design with the Crackling method

The design of CRISPR-Cas9 guide RNAs is not trivial, and is a computationally demanding task. Design tools need to identify target sequences that will maximise the likelihood of obtaining the desired cut, whilst minimising off-target risk. There is a need for a tool that can meet both objectives while remaining practical to use on large genomes. Here, we present Crackling, a new method that is more suitable for meeting these objectives. We test its performance on 12 genomes and on data from validation studies. Crackling maximises guide efficiency by combining multiple scoring approaches. On experimental data, the guides it selects are better than those selected by others. It also incorporates Inverted Signature Slice Lists (ISSL) for faster off-target scoring. ISSL provides a gain of an order of magnitude in speed, while preserving the same level of accuracy. Overall, this makes Crackling a faster and better method to design guide RNAs at scale. Crackling is available at https://github.com/bmds-lab/Crackling under the Berkeley Software Distribution (BSD) 3-Clause license.

bioinformatics↗

bioRxiv2018

A benchmark of computational CRISPR-Cas9 guide design methods

The popularity of CRISPR-based gene editing has resulted in an abundance of tools to design CRISPR-Cas9 guides. This is also driven by the fact that designing highly specific and efficient guides is a crucial, but not trivial, task in using CRISPR for gene editing. Here, we thoroughly analyse the performance of 17 design tools. They are evaluated based on runtime performance, compute requirements, and guides generated. To achieve this, we implemented a method for auditing system resources while a given tool executes, and tested each tool on datasets of increasing size, derived from the mouse genome. We found that only five tools had a computational performance that would allow them to analyse an entire genome in a reasonable time, and without exhausting computing resources. There was wide variation in the guides identified, with some tools reporting every possible guide while others filtered for predicted efficiency. Some tools also failed to exclude guides that would target multiple positions in the genome. We also considered a collection of over a thousand guides for which experimental data is available. For the tools that attempt to filter based on efficiency, 65% to 85% of the guides they reported were experimentally found to be efficient, but with limited overlap in the sets produced by different tools. Our results show that CRISPR-Cas9 guide design tools need further work in order to achieve rapid whole-genome analysis and that improvements in guide design will likely require combining multiple approaches.

bioinformatics↗