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Prada, J. M.

Publications and source records attributed to Prada, J. M..

2 recordsLinked to original sources

Genomic screening of antimicrobial resistance markers in UK and US Campylobacter isolates highlights stability of resistance over an 18 year period

Campylobacter jejuni and Campylobacter coli are important bacterial causes of human foodborne illness. Despite several years of reduced antibiotics usage in livestock production in the UK and US, high prevalence of antimicrobial resistance (AMR) persists in Campylobacter. Both countries have instigated genome sequencing-based surveillance programs for Campylobacter, and here we have identified AMR genes in 32,256 C. jejuni and 8,776 C. coli publicly available genome sequences to compare the prevalence and trends of AMR in Campylobacter isolated in the UK and US between 2001-2018. AMR markers were detected in 68% of C. coli and 53% of C. jejuni, with 15% of C. coli being multi-drug resistant (MDR) compared to only 2% of C. jejuni. The prevalence of aminoglycoside, macrolide, quinolone and tetracycline resistance remained fairly stable from 2001-2018 in both C. jejuni and C. coli, but statistically significant differences were observed between the UK and US. There was a statistically significant higher prevalence of aminoglycoside and tetracycline resistance for US C. coli and C. jejuni, and macrolide resistance for US C. coli. In contrast, UK C. coli and C. jejuni showed a significantly higher prevalence of quinolone resistance. Specific MLST clonal complexes (e.g. ST-353/464) showed >95% quinolone resistance. This large-scale comparison of AMR prevalence has shown that the prevalence of AMR remains stable for Campylobacter in the UK and the US. This suggests that antimicrobial stewardship and restricted antibiotic usage may help contain further expansion of AMR prevalence in Campylobacter, but are unlikely to reduce it in the short term.

microbiology↗

PANINI: Pangenome Neighbor Identification for Bacterial Populations

The standard workhorse for genomic analysis of the evolution of bacterial populations is phylogenetic modelling of mutations in the core genome. However, in the current era of population genomics, a notable amount of information about evolutionary and transmission processes in diverse populations can be lost unless the accessory genome is also taken into consideration. Here we introduce PANINI, a computationally scalable method for identifying the neighbours for each isolate in a data set using unsupervised machine learning with stochastic neighbour embedding. PANINI is browser-based and integrates with the Microreact platform for rapid online visualisation and exploration of both core and accessory genome evolutionary signals together with relevant epidemiological, geographic, temporal and other metadata. Several case studies with single-and multi-clone pneumococcal populations are presented to demonstrate ability to identify biologically important signals from gene content data. PANINI is available at http://panini.wgsa.net/ and code at http://gitlab.com/cgps/panini

microbiology↗