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Berkley, J. A.

Publications and source records attributed to Berkley, J. A..

2 recordsLinked to original sources

Biological landscape of acute illness in children in sub-Saharan Africa and South Asia

Childhood illnesses including pneumonia, diarrhoea and malaria are leading causes of hospitalisation and mortality in resource-limited settings. However, we lack understanding of whether systemic responses to such diverse clinical syndromes are shared or specific, how they are impacted by malnutrition and how they differ from well children. We performed multi-omic profiling of plasma proteins, and serum metabolites and lipids in acutely ill hospitalised and well children in sub-Saharan Africa and South Asia. Using network-based clustering and mixed-effects modelling, we identified common and syndrome-specific omics responses to acute illness. We found that malnutrition often modifies host responses to disease. Although the internal structure of individual omics modules was largely preserved between ill and well children, the interactions between these preserved modules were markedly reorganised during acute illness. Compared to well children, biological systems in hospitalised children were more interconnected, exhibiting denser cross-omics interactions. These findings reveal widespread multisystem mobilisation during paediatric acute illness, offer deeper mechanistic insights and highlight candidate pathways for therapeutic intervention in high-burden settings.

systems biology↗

Metrics for Public Health Perspective Surveillance of Bacterial Antibiotic Resistance in Low- and Middle-Income Countries

Antimicrobial resistance (AMR) is a global health threat, especially in low-/middle-income countries (LMICs), where there is limited surveillance to inform empiric antibiotic treatment guidelines. Enterobacterales are amongst the most important causes of drug-resistant bacterial infections. We developed a novel AMR surveillance approach for Enterobacterales by profiling pooled human faecal metagenomes from three sites (n=563 individuals; Cambodia, Kenya, UK) to derive a taxonomy-adjusted AMR metric ("resistance potential") which could be used to predict the aggregate percentage of resistant invasive Enterobacterales infections within each setting. Samples were sequenced (Illumina); taxonomic and resistance gene profiling was performed using ResPipe. Data on organisms causing bacteraemia and meningitis and antibiotic susceptibility test results from 2010-2017 were collated for each site. Bayesian generalised linear models with a binomial likelihood were fitted to determine the capacity of the resistance potential to predict AMR in Enterobacterales infections in each setting. The most informative model accurately predicted the numbers of resistant infections in the target populations for 14/14 of antibiotics in the UK, 12/12 in Kenya, and 9/12 in Cambodia. Intermittent metagenomics of pooled human samples could represent a powerful pragmatic and economical approach for determining and monitoring AMR in clinical infections, especially in resource-limited settings.

genomics↗