47 research outputs found
Impact of SARS-CoV-2 Alpha variant (B.1.1.7) on prisons, England
No full textObjectives: prisons are high-risk settings for infectious disease outbreaks because of their highly dynamic and crowded nature. During late 2020, prisons in England observed a surge in COVID-19 infection. This study describes the emergence of the Alpha variant in prisons during this period.Methods: Alpha and non-Alpha variant COVID-19 cases were identified in prisoners in England using address-matched laboratory notifications and genomic information from COG-UK.Results: of 14,094 COVID-19-positive prisoner cases between 1 October 2020 and 28 March 2021, 11.5% (n = 1621) had sequencing results. Of these, 1082 (66.7%) were identified as the Alpha variant. Twenty-nine (2.7%) Alpha cases required hospitalisation compared with only five (1.0%; P = 0.02) non-Alpha cases. A total of 14 outbreaks were identified with the median attack rate higher for Alpha (17.9%, interquartile range [IQR] 3.2%-32.2%; P = 0.11) than non-Alpha outbreaks (3.5%, IQR 2.0%-10.2%).Conclusion: higher attack rates and increased likelihood of hospitalisations were observed for Alpha cases compared with non-Alpha. This suggests a key contribution to the rise in cases, hospitalisations and outbreaks in prisons in the second wave. With prisons prone to COVID-19 outbreaks and the potential to act as reservoirs for variants of concern, sequencing of prison-associated cases alongside whole-institution vaccination should be prioritised.</p
Migration of <i>Escherichia coli</i> and <i>Klebsiella pneumoniae</i> Carbapenemase (KPC)-Producing <i>Enterobacter cloacae</i> through Wastewater Pipework and Establishment in Hospital Sink Waste Traps in a Laboratory Model System
No full textSink waste traps and drains are a reservoir for multi-drug resistant Gram-negative bacteria in the hospital environment. It has been suggested that these bacteria can migrate through hospital plumbing. Hospital waste traps were installed in a laboratory model system where sinks were connected through a common wastewater pipe. Enterobacterales populations were monitored using selective culture, MALDI-TOF identification and antibiotic resistance profiling before and after a wastewater backflow event. When transfer between sinks was suspected, isolates were compared using whole-genome sequencing. Immediately after the wastewater backflow, two KPC-producing Enterobacter cloacae were recovered from a waste trap in which Carbapenemase-producing Enterobacterales (CPE) had not been detected previously. The isolates belonged to ST501 and ST31 and were genetically indistinguishable to those colonising sinks elsewhere in the system. Following inter-sink transfer, KPC-producing E. cloacae ST501 successfully integrated into the microbiome of the recipient sink and was detected in the waste trap water at least five months after the backflow event. Seven weeks and three months after the backflow, other inter-sink transfers involving Escherichia coli ST5295 and KPC-producing E. cloacae ST501 were also observed
Phylogenomic early warning signals for SARS-CoV-2 epidemic waves
No full textBackground:
Epidemic waves of coronavirus disease 2019 (COVID-19) infections have often been associated with the emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Rapid detection of growing genomic variants can therefore serve as a predictor of future waves, enabling timely implementation of countermeasures such as non-pharmaceutical interventions (social distancing), additional vaccination (booster campaigns), or healthcare capacity adjustments. The large amount of SARS-CoV-2 genomic sequence data produced during the pandemic has provided a unique opportunity to explore the utility of these data for generating early warning signals (EWS).
Methods:
We developed an analytical pipeline (Transmission Fitness Polymorphism Scanner – designated in an R package mrc-ide/tfpscanner) for systematically exploring all clades within a SARS-CoV-2 virus phylogeny to detect variants showing unusually high growth rates. We investigated the use of these cluster growth rates as the basis for a variety of statistical time series to use as leading indicators for the epidemic waves in the UK during the pandemic between August 2020 and March 2022. We also compared the performance of these phylogeny-derived leading indicators with a range of non-phylogeny-derived leading indicators. Our experiments simulated data generation and real-time analysis.
Findings:
Using phylogenomic analysis, we identified leading indicators that would have generated EWS ahead of significant increases in COVID-19 hospitalisations in the UK between August 2020 and March 2022. Our results also show that EWS lead time is sensitive to the threshold set for the number of false positive (FP) EWS. It is often possible to generate longer EWS lead times if more FP EWS are tolerated. On the basis of maximising lead time and minimising the number of FP EWS, the best performing leading indicators that we identified, amongst a set of 1.4 million, were the maximum logistic growth rate (LGR) amongst clusters of the dominant Pango lineage and the mean simple LGR across a broader set of clusters. In the case of the former, the time between the EWS and wave inflection points (a conservative measure of wave start dates) for the seven waves ranged between a 20-day lead time and a 7-day lag, with a mean lead time of 5.4 days. The maximum number of FP EWS generated prior to a true positive (TP) EWS was two and this only occurred for two of the seven waves in the period. The mean simple LGR amongst a broader set of clusters also performed well in terms of lead time but with slightly more FP EWS.
Interpretation:
As a result of the significant surveillance effort during the pandemic, early detection of SARS-CoV-2 variants of concern Alpha, Delta, and Omicron provided some of the first examples where timely detection and characterisation of pathogen variants has been used to tailor public health response. The success of our method in generating early warning signals based on phylogenomic analysis for SARS-CoV-2 in the UK may make it a worthwhile addition to existing surveillance strategies. In addition, the method may be translatable to other countries and/or regions, and to other pathogens with large-scale and rapid genomic surveillance.
Funding:
This research was funded in whole, or in part, by the Wellcome Trust (220885_Z_20_Z). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. KOD, OB, VBF and EMV acknowledge funding from the MRC Centre for Global Infectious Disease Analysis (reference MR/X020258/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 programme supported by the European Union. RMC acknowledges funding from the Wellcome Trust Collaborators Award (206298/Z/17/Z)
Selection and characterization of mutational resistance to aztreonam/avibactam in β-lactamase-producing Enterobacterales
No full textBackground: Aztreonam/avibactam is being developed for its broad activity against carbapenemase-producing Enterobacterales, including those with metallo-β-lactamases (MBLs). Its potential to select resistance in target pathogens was explored. Findings are compared with previous data for ceftazidime/avibactam and ceftaroline/avibactam. Methods: Single-step mutants were sought from 52 Enterobacterales with AmpC, ESBL, KPC, MBL and OXA-48-like enzymes. Mutation frequencies were calculated. MICs were determined by CLSI agar dilution. Genomes were sequenced using Illumina methodology. Results: Irrespective of β-lactamase type and of whether avibactam was used at 1 or 4 mg/L, mutants could rarely be obtained at >4× the starting MIC, and most MIC rises were correspondingly small. Putative resistance (MIC >8 + 4 mg/L) associated with changes to β-lactamases was seen only for mutants of AmpC, where it was associated with Asn346Tyr and Tyr150Cys substitutions. Asn346Tyr led to broad resistance to avibactam combinations; Tyr150Cys significantly affected only aztreonam/avibactam. MIC rises up to 4 + 4 mg/L were seen for producers of mutant KPC-2 or -3 enzymes, and were associated with Trp105Arg, Ser106Pro and Ser109Pro substitutions, which all reduced the MICs of other β-lactams. For producers of other β-lactamase types, we largely found mutants with lesions in baeRS or envZ, putatively affecting drug accumulation. Single mutants had lesions in ampD, affecting AmpC expression or ftsI, encoding PBP3. Conclusions: The risk of mutational resistance to aztreonam/avibactam appears smaller than for ceftazidime/avibactam, where Asp179Tyr arises readily in KPC enzymes, conferring frank resistance. Asn346 substitutions in AmpC enzymes may remain a risk, having been repeatedly selected with multiple avibactam combinations in vitro
NDM-1 carbapenemase resistance gene vehicles emergent on distinct plasmid backbones from the IncL/M family
No full textObjectives: To assess the genetic contexts surrounding blaNDM-1 genes carried on IncM plasmids harboured by six carbapenemase-producing Enterobacterales (CPE) isolates referred to the UK Health Security Agency's Antimicrobial Resistance and Healthcare Associated Infections (AMRHAI) Reference Unit.
Methods: Between 2014 and 2018, the AMRHAI Reference Unit undertook WGS of CPE isolates using Illumina NGS. Nanopore sequencing was used for selected isolates and publicly available plasmid references were downloaded. Analysis of incRNA, which encodes the antisense RNA regulating plasmidic repA gene expression, was performed and bioinformatics tools were used to analyse whole plasmid sequences.
Results: Of 894 NDM-positive isolates of Enterobacterales, 44 NDM-1-positive isolates of five different species (Citrobacter spp., Enterobacter cloacae, Escherichia coli, Klebsiella pneumoniae and Klebsiella oxytoca) encoded the IncRNA locus of IncM2 plasmids. Long-read sequencing of six diverse isolates revealed related IncM2, NDM-1-encoding plasmids. Plasmid 'backbone' areas were conserved and contrasted with highly variable resistance regions. Sub-groupings of IncM2 plasmids encoding blaNDM-1 were detected; one sub-group occurred in five different health regions of England in every year. The diversity of NDM-1-encoding resistance gene integrons and transposons and their insertions sites in the plasmids indicated that NDM-1 has been acquired repeatedly by IncM2 variants.
Conclusions: The use of sequencing helped inform: (i) a wide geographical distribution of isolates encoding NDM-1 on emergent IncM2 plasmids; (ii) variant plasmids have acquired NDM-1 separately; and (iii) dynamic arrangements and evolution of the resistance elements in this plasmid group. The geographical and temporal distribution of IncM2 plasmids that encode NDM-1 highlights them as a public health threat that requires ongoing monitoring
A Broad-Host-Range Plasmid Outbreak: Dynamics of IncL/M Plasmids Transferring Carbapenemase Genes
No full textOHEJP Project: ARDI
Phylogenomic early warning signals for SARS-CoV-2 epidemic waves
No full textSummary Background Epidemic waves of coronavirus disease 2019 (COVID-19) infections have often been associated with the emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Rapid detection of growing genomic variants can therefore serve as a predictor of future waves, enabling timely implementation of countermeasures such as non-pharmaceutical interventions (social distancing), additional vaccination (booster campaigns), or healthcare capacity adjustments. The large amount of SARS-CoV-2 genomic sequence data produced during the pandemic has provided a unique opportunity to explore the utility of these data for generating early warning signals (EWS). Methods We developed an analytical pipeline (Transmission Fitness Polymorphism Scanner – designated in an R package mrc-ide/tfpscanner) for systematically exploring all clades within a SARS-CoV-2 virus phylogeny to detect variants showing unusually high growth rates. We investigated the use of these cluster growth rates as the basis for a variety of statistical time series to use as leading indicators for the epidemic waves in the UK during the pandemic between August 2020 and March 2022. We also compared the performance of these phylogeny-derived leading indicators with a range of non-phylogeny-derived leading indicators. Our experiments simulated data generation and real-time analysis. Findings Using phylogenomic analysis, we identified leading indicators that would have generated EWS ahead of significant increases in COVID-19 hospitalisations in the UK between August 2020 and March 2022. Our results also show that EWS lead time is sensitive to the threshold set for the number of false positive (FP) EWS. It is often possible to generate longer EWS lead times if more FP EWS are tolerated. On the basis of maximising lead time and minimising the number of FP EWS, the best performing leading indicators that we identified, amongst a set of 1.4 million, were the maximum logistic growth rate (LGR) amongst clusters of the dominant Pango lineage and the mean simple LGR across a broader set of clusters. In the case of the former, the time between the EWS and wave inflection points (a conservative measure of wave start dates) for the seven waves ranged between a 20-day lead time and a 7-day lag, with a mean lead time of 5.4 days. The maximum number of FP EWS generated prior to a true positive (TP) EWS was two and this only occurred for two of the seven waves in the period. The mean simple LGR amongst a broader set of clusters also performed well in terms of lead time but with slightly more FP EWS. Interpretation As a result of the significant surveillance effort during the pandemic, early detection of SARS-CoV-2 variants of concern Alpha, Delta, and Omicron provided some of the first examples where timely detection and characterisation of pathogen variants has been used to tailor public health response. The success of our method in generating early warning signals based on phylogenomic analysis for SARS-CoV-2 in the UK may make it a worthwhile addition to existing surveillance strategies. In addition, the method may be translatable to other countries and/or regions, and to other pathogens with large-scale and rapid genomic surveillance. <br/
Escherichia coli from six European countries reveals differences in profile and distribution of critical antimicrobial resistance determinants within One Health compartments, 2013 to 2020
No full textContribución de autores:
HK performed the phylogenetic analysis and the transmission cluster detection. MB assembled all raw data and performed all analysis for ResFinder to detect AMR genes. JNG performed plasmid analysis, including constructing a novel tool. MFA conceived the project; MFA, JNG, MB, and HK wrote the initial manuscript, with MFA providing oversight and direction. HK, MB, JNG, ICR, MA, ND, NE, JDS, JAH, MG, CS, TN, KTV, AB, MS, SSM, SBJ, ME, BGZ, RLR, PG and MFA contributed to the overall project design, editing, and reviewing of the manuscript.Background:
Antimicrobial resistance (AMR) is a global threat. Monitoring using an integrated One Health approach is essential to detect changes in AMR occurrence.
Aim:
We aimed to detect AMR genes in pathogenic and commensal Escherichia coli collected 2013–2020 within monitoring programmes and research from food animals, food (fresh retail raw meat) and humans in six European countries, to compare vertical and horizontal transmission.
Methods:
We whole genome sequenced (WGS) 3,745 E. coli isolates, detected AMR genes using ResFinder and performed phylogenetic analysis to determine isolate relatedness and transmission. A BLASTn-based bioinformatic method compared draft IncI1 genomes to conserved plasmid references from Europe.
Results:
Resistance genes to medically important antimicrobials (MIA) such as extended-spectrum cephalosporins (ESC) were widespread but predicted resistance to MIAs authorised for human use (carbapenem, tigecycline) was detected only in two human and three cattle isolates. Phylogenetic analysis clustered E. coli according to phylogroups; commensal animal isolates showed greater diversity than those from human patients. Only 18 vertical animal-food and human-animal transmission events of E. coli clones were detected. However, IncI1 plasmids from different sources and/or countries carrying resistance to ESCs were conserved and widely distributed, although these variants were rarely detected in human pathogens.
Conclusion:
Using WGS we demonstrated AMR is driven vertically and horizontally. Human clinical isolates were more closely related, but their IncI1 plasmids were more diverse, while animal or food isolates were less similar with more conserved IncI1 plasmids. These differences likely arose from variations in selective pressure, influencing AMR evolution and transmission.European CommissionDutch Ministry for Agriculture, Nature and Food Quality (Países Bajos)Bundesinstitut für Risikobewertung (Alemania)Veterinary Medicines Directorate (Reino Unido)University of Surrey (Reino Unido)Higher Education Funding Council (Reino Unido)Norwegian Veterinary Institute (Noruega)Depto. de Sanidad AnimalCentro de Vigilancia Sanitaria Veterinaria (VISAVET)Fac. de VeterinariaTRUEpu
In vitro and in vivo vein assessment of a novel vein visualizing device to improve first-time peripheral venous access
No full textObjective: Inserting needles into veins is fundamental to medical care with up to 90% of inpatients requiring a peripheral intravenous catheter/cannula (PIVC) during their stay. Yet 40%–50% of PIVC insertions fail on the first attempt. Here, we present an easy-to-use novel vein visualizing ultrasound prototype device and data from in vitro and in vivo performance. Methods: Locational accuracy was determined through phantom simulated forearm veins, across variations of vein diameter (3–5 mm), depth (10–20 mm), and velocity (10–100 mm/s). Usability studies were conducted on nine clinicians to establish effectiveness and ease of use of the proposed prototype assisted cannulation workflow. Sensitivity of the prototype was demonstrated by scanning 80 forearm veins across 40 healthy volunteers. Results: Our prototype's locational accuracy in simulated forearm veins is 0.21 mm ± 1.71 mm (s.d.) (97.7% agreement to the ground truth, p < .001). Usability studies found that 100% of users were able to handle the prototype in a sterile manner with minimal assistance. The sensitivity was excellent at finding veins (94%). In comparison, sensitivity of vein finding using landmark technique with torniquet (visible 46% and palpable 74%) were far inferior. Conclusion: Initial performance verification and validation studies presented suggest that the proposed ultrasound visualization method can simply and reliably help clinicians detect well-perfused veins at depth and visualize in the coronal view onboard the probe in alignment with the transducers. With improved ergonomics, the device has the potential to be an easy to use device for clinicians performing vascular access
A phylogenetics and variant calling pipeline to support SARS-CoV-2 genomic epidemiology in the UK
No full textIn response to the escalating SARS-CoV-2 pandemic, in March 2020 the COVID-19 Genomics UK (COG-UK) consortium was established to enable national-scale genomic surveillance in the United Kingdom. By the end of 2020, 49% of all SARS-CoV-2 genome sequences globally had been generated as part of the COG-UK programme and to date this system has generated more than 3 million SARS-CoV-2 genomes. Rapidly and reliably analysing this unprecedented number of genomes was an enormous challenge. To fulfil this need and to inform public health decision making, we developed a centralised pipeline that performs quality control, alignment and variant calling, and provides the global phylogenetic context of sequences. We present this pipeline and describe how we tailored it as the pandemic progressed to scale with the increasing amounts of data and to provide the most relevant analyses on a daily basis
