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Diaz Caballero, J.

Publications and source records attributed to Diaz Caballero, J..

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amr.watch - monitoring antimicrobial resistance trends from global genomics data

BackgroundWhole genome sequencing (WGS) is increasingly supporting routine pathogen surveillance at local and national levels, providing comparable data that can inform on the emergence and spread of antimicrobial resistance (AMR) globally. However, the potential for shared WGS data to guide interventions around AMR remains under-exploited, in part due to challenges in collating and transforming the growing volumes of data into timely insights. We present an interactive platform, amr.watch (https://amr.watch), that enables interrogation of AMR trends from public WGS data on an ongoing basis to support research and policy. MethodsThe amr.watch platform incorporates, analyses and visualises high-quality WGS data from WHO-defined priority bacterial pathogens. Analytics are performed using community-standard methods with bespoke species-specific curation of AMR mechanisms. FindingsBy 31 March 2025, amr.watch included data from 620,700 pathogen genomes with geotemporal information, with highly variable representation of different species and geographic regions. By integrating WGS data with sampling information, amr.watch enables users to assess geotemporal trends among genotypic variants (e.g. sequence types) and AMR mechanisms, with implications for interventions including antimicrobial prescribing and drug and vaccine development. Interpretationamr.watch is an information platform for scientists and policy-makers delivering ongoing situational awareness of AMR trends from genomic data. As broad adoption of WGS continues, amr.watch is positioned to monitor both pathogen populations and our global efforts in genomic surveillance, guiding control strategies tailored to each pathogens characteristics. FundingUK National Institute for Health Research & Gates Foundation. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWhole genome sequencing (WGS) approaches enable us to track the spread of bacterial pathogens and antimicrobial resistance (AMR) with high resolution at local, national and global levels. To date, genomic studies assessing AMR dynamics have largely used retrospective data collected for specific research agendas. However, the growing volumes of publicly-shared WGS data, generated increasingly from routine surveillance, provide improved power to detect novel trends and guide interventions. Efforts to collate public bacterial genome data exist, such as AllTheBacteria, although these are aimed at the research community and do not facilitate data usage and interpretation by non-genomics experts, particularly those in public health. To our knowledge, no platforms exist for readily interrogating AMR dynamics from continuously updated public genome data across the array of different WHO priority bacterial pathogens. Of note, however, TyphiNet provides an interactive online dashboard for examining AMR trends exclusively from Salmonella Typhi using a periodically-updated data set from the Pathogenwatch platform. These findings are based on searching PubMed without language restrictions from Jan 1 2000 to December 31 2024, using terms related to "genomic surveillance" and "antimicrobial resistance". Added value of this studyWe have developed amr.watch which, to our knowledge, is the first platform that enables ongoing analysis and visualisation of AMR trends from public genome data across the spectrum of WHO priority bacterial pathogens via an accessible interface. Crucially, the platform incorporates processed genome data via a live always-on stream, enabling insights that are delayed only by the time to data deposition. We reviewed public genomes with available geotemporal information up until 31 March 2025, providing a contemporary global landscape of pathogen genome sequencing. While the number of genomes available annually grew over five-fold globally between 2010 and 2018, we also revealed the extent of differences in geographic representation, with 89.6% of genomes originating from high-income countries and 89 countries contributing no genome data from the priority pathogens. Implications of all the available evidenceRapid generation and sharing of global genome data enables us to more precisely track the spread and define the characteristics of contemporary circulating resistant pathogens. The amr.watch platform provides a solution for retrieving, curating and translating shared genomic data into relevant insights that are accessible and actionable by diverse stakeholders. It thereby forms a basis for monitoring progress in surveillance efforts, alerting on ongoing population changes and guiding enhanced precision for surveillance and interventional development. Additionally, our review of available bacterial genome data highlights the need for additional efforts to increase and sustain the implementation of genomic surveillance of AMR globally, and improve the timely sharing of WGS data and its associated metadata.

genomics↗

Polyclonal pathogen populations accelerate the evolution of antibiotic resistance in patients

Antibiotic resistance poses a global health threat, but the within-host drivers of resistance remain poorly understood. Pathogen populations are often assumed to be clonal within hosts, and resistance is thought to emerge due to selection for de novo variants. Here we show that pulmonary populations of the opportunistic pathogen P. aeruginosa are often polyclonal. Crucially, resistance evolves rapidly in patients colonized by polyclonal populations through selection for pre-existing resistant strains. In contrast, resistance evolves sporadically in patients colonized by monoclonal populations due to selection for novel resistance mutations. However, strong trade-offs between resistance and fitness occur in polyclonal populations that can drive the loss of resistant strains. In summary, we show that the within-host diversity of pathogen populations plays a key role in shaping the emergence of resistance in response to treatment. One sentence summaryAntibiotic resistance evolves quickly in patients colonized by polyclonal pathogen populations.

evolutionary biology↗

Rapid evolution drives the rise and fall of carbapenem resistance during an acute Pseudomonas aeruginosa infection

It is well established that antibiotic treatment selects for resistance in pathogenic bacteria. However, the evolutionary responses of pathogen populations to antibiotic treatment during infections remain poorly resolved, especially in acute infections. Here we map the evolutionary responses to treatment in high definition through genomic and phenotypic characterization of >100 isolates from a patient with P. aeruginosa pneumonia. Antibiotic therapy (meropenem, colistin) caused a rapid crash of the P. aeruginosa population in the lung, but this decline was followed by the spread of meropenem resistance mutations that restrict antibiotic uptake (oprD) or modify LPS biosynthesis (wbpM). Low fitness strains with high-level meropenem resistance (oprD) were then replaced by high fitness strains with anti-resistance mutations in the MexAB-OprM efflux pump, causing a rapid decline in resistance to both meropenem and a collateral loss of resistance to a broad spectrum of antibiotics. In contrast, we did not observe any evolutionary responses to antibiotic treatment in the intestinal population of P. aeruginosa. Carbapenem antibiotics are key to the treatment of infections caused by Gram negative pathogens, and our work highlights the ability of natural selection to drive both the rapid rise and fall of carbapenem resistance during acute infections.

evolutionary biology↗

A Genome-Wide Association Analysis Reveals a Role for Recombination in the Evolution of Antimicrobial Resistance in Burkholderia multivorans

Cystic fibrosis (CF) lung infections caused by members of the Burkholderia cepacia complex, such as Burkholderia multivorans, are associated with high rates of mortality and morbidity. We performed a population genomic study of 111 B. multivorans sputum isolates from a single CF patient through three stages of infection including the initial incident infection, deep sampling of a one-year period of chronic infection, and deep sampling of a post-transplant recolonization. We reconstructed the evolutionary history of the population and used a lineage-controlled genome-wide association study (GWAS) approach to identify genetic variants associated with antibiotic resistance. We found that the incident isolate was more susceptible to agents from three antimicrobial classes ({beta}-lactams, aminoglycosides, quinolones), while the chronic isolates diversified into distinct genetic lineages with reduced antimicrobial susceptibility to the same agents. The post-transplant reinfection isolates displayed genetic and phenotypic signatures that were distinct from sputum isolates from all CF lung specimens. There were numerous examples of parallel pathoadaptation, in which individual loci, or even the same codon, were independently mutated multiple times. This set of loci was enriched for functions associated with virulence and resistance. Our GWAS approach identified one variant in the ampD locus (which was independently mutated four times in our dataset) associated with resistance to {beta}-lactams, and two non-synonymous polymorphisms associated with resistance to both aminoglycosides and quinolones, affecting an araC family transcriptional regulator, which was independently mutated three times, and an outer member porin, which was independently mutated twice. We also performed recombination analysis and identified a minimum of 14 recombination events. Parallel pathoadaptive loci and polymorphisms associated with {beta}-lactam resistance were over-represented in these recombinogenic regions. This study illustrates the power of deep, longitudinal sampling coupled with evolutionary and lineage-corrected GWAS analyses to reveal how pathogens adapt to their hosts.

evolutionary biology↗