Biology subjects

Hutchings, E.

Publications and source records attributed to Hutchings, E..

3 recordsLinked to original sources

bioRxiv2026

A panel of genotypically and phenotypically characterised WHO priority Gram-negative bacteria to facilitate antimicrobial and diagnostic development

Background Antimicrobial-resistant Gram-negative bacteria pose a major threat to global health. In the WHO Bacterial Priority Pathogen List (BPPL) 2024, several species were classified as critical or high priority for research and development of new antimicrobial therapeutics and diagnostics. However, access to well-characterised and clinically relevant bacterial isolate panels remains an unmet need for development and validation. Methods We constructed five panels of Gram-negative bacterial isolates, cultured from samples originally referred to the UK Health Security Agency Antimicrobial Resistance and Healthcare-Associated Infections Reference Unit for analysis between 2014 and 2025. For each isolate, antimicrobial susceptibilities were determined against a range of clinically relevant antibiotics, including third-generation cephalosporins, carbapenems, colistin and other last-line antibiotics. WGS was performed on all isolates, and previously described resistance determinants were characterised. Results The five panels consist of 145 isolates and represent 81 sequence types across Escherichia coli (30 isolates), Klebsiella pneumoniae (30 isolates), carbapenem-resistant Acinetobacter baumannii (30 isolates), carbapenem-resistant Pseudomonas aeruginosa (30 isolates), and a mixed panel of other healthcare-associated Enterobacterales species (25 isolates). Panels are highly representative of globally relevant strains, including those currently in circulation and on the WHO BPPL 2024, and of resistance mechanisms of public health importance. Conclusion The panels provide a diverse, comprehensive set of multidrug-resistant Gram-negative bacterial isolates, representative of strains currently in circulation globally. Selected isolates carry a breadth of important resistance mechanisms and are highly relevant to the current epidemiological landscape. The isolates and associated metadata are available to industry, academia, and other laboratories for use in developing novel antimicrobial compounds and diagnostic assays.

microbiology↗

bioRxiv2026

MIND the gap: methodological considerations and guidance for structural MRI similarity network analysis with MIND

Structural similarity networks quantify the similarity of structural properties across cortical regions, providing a macroscopic window onto the organisation of cortical architecture. Morphometric inverse divergence (MIND) is a multivariate metric of similarity between cortical areas, based on the Kullback-Leibler (KL) divergence between areal distributions of multiple MRI features or morphometric variables locally measured at voxel or vertex resolution. MIND has demonstrated technical robustness and biological validity and is increasingly widely used as a measure of cortico-cortical similarity in clinical and developmental network neuroscience. Here we provide in-depth methodological background on KL divergence and MIND, highlighting possible sources of bias, critical user decision points in the design of a MIND processing pipeline, and recommendations for technical risk mitigation in using MIND as a metric of cortical similarity. We use simulated data and observational MRI datasets from adults (UK Biobank, N = 500 T1-weighted and diffusion scans) and neonates (Developing Human Connectome Project, N = 752 T2-weighted scans), to show how the estimator of KL divergence implemented in MIND is potentially influenced or biased by five properties of input MRI feature maps: (i) their smoothness; (ii) the proportion of identical values; (iii) analysis in native or common space and the choice of vertex mesh resolution; (iv) parcellation choice; and (v) covariance between input features. We offer principled and practical guidance for investigators wanting to specify and implement the MIND processing pipeline that is best suited to the constraints and opportunities of the MRI data available to them. These recommendations outline which pipeline steps should be used sparingly, such as vertex map smoothing; which should be used with informed caution, such as parcellation choice or vertex mesh resampling; and which could be newly implemented for more robust estimation of MIND, such as the use of principal component analysis to preprocess multivariate MRI features. To support further development of structural MRI similarity network analysis, and wider adoption of robust MIND methods, we also publish the code used to generate the results in this paper as an open resource.

neuroscience↗

bioRxiv2025

Cortical myelination networks reflect neuronal gene expression and track adolescent age in marmosets

Structural similarity provides a powerful framework for measuring coordinated macro- and microstructural variation across the cortex of a single brain. Similarity networks derived from myelin-sensitive MRI sequences undergo marked reorganisation during adolescence, linked to psychosocial outcomes in humans and rodents. However, the cellular mechanisms of myelination similarity and its development in non-human primate cortex remain unexplored. Here, we used myelin-sensitive T1w/T2w ratio images from a cross-sectional sample of 446 common marmosets (aged 0.62 to 12.75 years) to estimate cortical similarity networks in individual animals. Cortical areas with similar myeloarchitecture showed highly similar patterns of gene co-expression in glutamatergic neurons and PV+ and VIP+ interneurons, reflecting the activity dependence of myelination. A reliable age-based signal exists within network features, with coordinated developmental trajectories observed across the cortical hierarchy from primary to transmodal association cortices - a pattern that mirrors findings in human cortex. Taken together, marmosets demonstrate phylogenetically conserved patterns of myelination network development, potentially underpinned by key neuronal cell types that shape the functional specialisation of cortical areas.

neuroscience↗