Biology subjects

West, H.

Publications and source records attributed to West, H..

3 recordsLinked to original sources

bioRxiv2026

Systematic Infectome-Phenome Profiling Reveals Cryptococcal Infection-Associated Proteins Driving Immune System Remodeling and Immunization Potential

Mortality and morbidity associated with invasive fungal infections continue to rise, driven by the emergence of new pathogens, increasing antifungal resistance, and expanding immunocompromised populations. Despite this growing threat, fungal biology lacks scalable, unbiased strategies to link infection-responsive fungal proteins to functional outcomes that drive virulence and immune modulation. Here, we present an integrated infectome-phenome discovery platform that combines high resolution mass spectrometry-based proteomics with systematic phenotypic profiling to globally interrogate Cryptococcus neoformans-macrophage interactions. This approach reveals coordinated host immune suppression alongside complementary fungal virulence programs and enables the unbiased prioritization of infection-associated fungal proteins. Systematic phenome fingerprinting of candidate mutant strains resolves two functional classes of putative therapeutic relevance: antifungal and antivirulence, with in vitro characterizations corroborated with a murine model of cryptococcosis. Prioritization of a conserved, previously uncharacterized virulence-associated protein, CipC, uncovers altered extracellular vesicle composition and enhanced antigenic properties. Immunization with CipC-derived vesicles elicits a robust and diversified host immune response, implicating fungal extracellular vesicles in immune remodeling and host priming. Together, these findings establish a broadly applicable framework for systematically identifying and functionally characterizing fungal drivers of infection for therapeutic and immunological target discovery with relevance across diverse human fungal pathogens.

microbiology↗

bioRxiv2024

Reconstructing microvascular network skeletons from 3D images: what is the ground truth?

Structural changes to microvascular networks are increasingly highlighted as markers of pathogenesis in a wide range of disease, e.g. Alzheimers disease, vascular dementia and tumour growth. This has motivated the development of dedicated 3D imaging techniques, alongside the creation of computational modelling frameworks capable of using 3D reconstructed networks to simulate functional behaviours such as blood flow or transport processes. Extraction of 3D networks from imaging data broadly consists of two image processing steps: segmentation followed by skeletonisation. Much research effort has been devoted to segmentation field, and there are standard and widely-applied methodologies for creating and assessing gold standards or ground truths produced by manual annotation or automated algorithms. The Skeletonisation field, however, lacks widely applied, simple to compute metrics for the validation or optimisation of the numerous algorithms that exist to extract skeletons from binary images. This is particularly problematic as 3D imaging datasets increase in size and visual inspection becomes an insufficient validation approach. In this work, we first demonstrate the extent of the problem by applying 4 widely-used skeletonisation algorithms to 3 different imaging datasets. In doing so we show significant variability between reconstructed skeletons of the same segmented imaging dataset. Moreover, we show that such a structural variability propagates to simulated metrics such as blood flow. To mitigate this variability we introduce a new, fast and easy to compute super-metric that compares the volume, connectivity, medialness, correct bifurcation point identification and homology of the reconstructed skeletons to the original segmented data. We then show that such a metric can be used to select the best performing skeletonisation algorithm for a given dataset, as well as to optimize its parameters. Finally, we demonstrate that the super-metric can also be used to quickly identify how a particular skeletonisation algorithm could be improved, becoming a powerful tool in understanding the complex implication of small structural changes in a network.

bioengineering↗

bioRxiv2022

Antimicrobial resistance in dairy slurry tanks: a critical point for measurement and control

Waste from dairy production is one of the worlds largest sources of contamination from antimicrobial resistant bacteria (ARB) and genes (ARGs). However, studies to date do not provide necessary evidence to inform antimicrobial resistance (AMR) countermeasures. We undertook a detailed, interdisciplinary, longitudinal analysis of dairy slurry waste. The slurry contained a population of ARB and ARGs, with resistances to current, historical and never-used on-farm antibiotics; resistances were associated with Gram-negative and Gram-positive bacteria and mobile elements (ISEcp1, Tn916, Tn21-family transposons). Modelling and experimental work suggested that these populations are in dynamic equilibrium, with microbial death balanced by fresh input. Consequently, storing slurry without further waste input for at least 60 days was predicted to reduce ARB spread onto land, with >99% reduction in cephalosporin resistant Escherichia coli. The model also indicated that for farms with low antibiotic use, further reductions are unlikely to reduce AMR further. We conclude that the slurry tank is a critical point for prevalence and control of AMR, and that measures to limit the spread of AMR from dairy waste should combine responsible antibiotic use, including low total quantity, avoidance of human critical antibiotics, and choosing antibiotics with shorter half-lives, coupled with appropriate slurry storage.

microbiology↗