Biology subjects

Mead, G.

Publications and source records attributed to Mead, G..

3 recordsLinked to original sources

bioRxiv2026

Transient immune landscape remodelling shapes CD8 T-cell priming during infection

Immune responses to intracellular pathogens require the generation of a CD8 T-cell response comprised of clones with a broad range of T Cell Receptor (TCR) affinities for their cognate peptide. While high-affinity clones clear pathogens during initial infection, low-affinity T-cells are critical to protect against subsequent encounters with mutated pathogens. Low-affinity T-cells are recruited to an immune response despite known mechanisms that limit their expansion and priming. As such, the mechanism safeguarding the recruitment of low-affinity T-cell clones during infection remains unclear. Here, we show that at the start of an infection, the spatial immune architecture is transiently reorganised to initiate broad T-cell priming. In particular, Tregs become segregated from the initial priming site, resulting in a spatio-temporal window during which pathogen-specific T-cell priming is protected from Treg inhibition. Priming induces the expression of CXCR3 in a TCR affinity-dependent manner, which conditions the retention of activated T-cells in the priming region. Inability to express CXCR3 leads to T-cell relocation towards Treg-rich areas, where their expansion is limited by the lack of the costimulatory molecule CD70. Our study redefines the role of Treg during infection and provides a mechanism to explain how the priming and breadth of the CD8 T-cell response is protected during infection.

immunology↗

bioRxiv2024

IFNgamma-dependent remodelling of the myeloid landscape underlies control of IFNgamma-insensitive tumours

Loss of IFN{gamma}-sensitivity by tumours is thought to be a mechanism enabling evasion, as some cancers lacking IFN{gamma}-signalling demonstrate resistance to checkpoint immunotherapy. However, recent studies demonstrated that IFN{gamma}-resistant tumours are well-controlled and sensitized for immunotherapy. The underlying mechanism leading to enhanced immune responses in those patients is unknown. Using IFN{gamma}-insensitive melanoma tumours which were well-controlled by the endogenous anti-tumour response, we found that despite low basal MHC class I expression by tumours, CD8+ T cell infiltration was not hindered and, unexpectedly, their production of IFN{gamma} was still important for tumour control. Mechanistically, IFN{gamma} triggers pro-inflammatory remodelling of IFN{gamma}-insensitive tumours, affecting the differentiation of myeloid cells. Predominantly, immunosuppressive macrophages are inhibited, while inflammatory phenotypes of monocytes and mono-macs are preserved in IFN{gamma}-insensitive tumours. This is supported by a co-dependency between CD8+ T cells and monocyte/macrophages, as depletion of one resulted in loss of the other. Our work demonstrates an important mechanistic understanding of how IFN{gamma} resistance does not preclude failure of anti-tumour responses. Importantly, immune remodelling appears to be dominant in IFN{gamma}-sensitive and IFN{gamma}-insensitive mixed tumours, and is enriched in humans with tumours mutated in the IFN{gamma} pathway, suggesting this may be leveraged for therapy in the future.

immunology↗

bioRxiv2017

Bat Detective - Deep Learning Tools for Bat Acoustic Signal Detection

O_LIPassive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings.\nC_LIO_LIWe developed a convolutional neural network (CNN) based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats (BatDetect). Our deep learning algorithms (CNN FULL and CNN FAST) were trained on full-spectrum ultrasonic audio collected along road-transects across Romania and Bulgaria by citizen scientists as part of the iBats programme and labelled by users of www.batdetective.org. We compared the performance of our system to other algorithms and commercial systems on expert verified test datasets recorded from different sensors and countries. As an example application, we ran our detection pipeline on iBats monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system.\nC_LIO_LIHere, we show that both CNNFULL and CNNFAST deep learning algorithms have a higher detection performance (average precision, and recall) of search-phase echolocation calls with our test sets, when compared to other existing algorithms and commercial systems tested. Precision scores for commercial systems were reasonably good across all test datasets (>0.7), but this was at the expense of recall rates. In particular, our deep learning approaches were better at detecting calls in road-transect data, which contained more noisy recordings. Our comparison of CNNFULL and CNNFAST algorithms was favourable, although CNNFAST had a slightly poorer performance, displaying a trade-off between speed and accuracy. Our example monitoring application demonstrated that our open-source, fully automatic, BatDetect CNNFAST pipeline does as well or better compared to a commercial system with manual verification previously used to analyse monitoring data.\nC_LIO_LIWe show that it is possible to both accurately and automatically detect bat search-phase echolocation calls, particularly from noisy audio recordings. Our detection pipeline enables the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale, particularly when combined with automatic species identification. We release our system and datasets to encourage future progress and transparency.\nC_LI

ecology↗