Antibiotic resistance determination using Enterococcus faecium whole-genome sequences: a diagnostic accuracy study using genotypic and phenotypic data.

BACKGROUND: DNA sequencing could become an alternative to in vitro antibiotic susceptibility testing (AST) methods for determining antibiotic resistance by detecting genetic determinants associated with decreased antibiotic susceptibility. Here, we aimed to assess and improve the accuracy of antibiotic resistance determination from Enterococcus faecium genomes for diagnosis and surveillance purposes. METHODS: In this retrospective diagnostic accuracy study, we first conducted a literature search in PubMed on Jan 14, 2021, to compile a catalogue of genes and mutations predictive of antibiotic resistance in E faecium. We then evaluated the diagnostic accuracy of this database to determine susceptibility to 12 different, clinically relevant antibiotics using a diverse population of 4382 E faecium isolates with available whole-genome sequences and in vitro culture-based AST phenotypes. Isolates were obtained from various sources in 11 countries worldwide between 2000 and 2018. We included isolates tested with broth microdilution, Vitek 2, and disc diffusion, and antibiotics with at least 50 susceptible and 50 resistant isolates. Phenotypic resistance was derived from raw minimum inhibitory concentrations and measured inhibition diameters, and harmonised primarily using the breakpoints set by the European Committee on Antimicrobial Susceptibility Testing. A bioinformatics pipeline was developed to process raw sequencing reads, identify antibiotic resistance genetic determinants, and report genotypic resistance. We used our curated database, as well as ResFinder, AMRFinderPlus, and LRE-Finder, to assess the accuracy of genotypic predictions against phenotypic resistance. FINDINGS: We curated a catalogue of 228 genetic markers involved in resistance to 12 antibiotics in E faecium. Very accurate genotypic predictions were obtained for ampicillin (sensitivity 99·7% [95% CI 99·5-99·9] and specificity 97·9% [95·8-99·0]), ciprofloxacin (98·0% [96·4-98·9] and 98·8% [95·9-99·7]), vancomycin (98·8% [98·3-99·2] and 98·8% [98·0-99·3]), and linezolid resistance (after re-testing false negatives: 100·0% [90·8-100·0] and 98·3% [97·8-98·7]). High sensitivity was obtained for tetracycline (99·5% [99·1-99·7]), teicoplanin (98·9% [98·4-99·3]), and high-level resistance to aminoglycosides (97·7% [96·6-98·4] for streptomycin and 96·8% [95·8-97·5] for gentamicin), although at lower specificity (60-90%). Sensitivity was expectedly low for daptomycin (73·6% [65·1-80·6]) and tigecycline (38·3% [27·1-51·0]), for which the genetic basis of resistance is not fully characterised. Compared with other antibiotic resistance databases and bioinformatic tools, our curated database was similarly accurate at detecting resistance to ciprofloxacin and linezolid and high-level resistance to streptomycin and gentamicin, but had better sensitivity for detecting resistance to ampicillin, tigecycline, daptomycin, and quinupristin-dalfopristin, and better specificity for ampicillin, vancomycin, teicoplanin, and tetracycline resistance. In a validation dataset of 382 isolates, similar or improved diagnostic accuracies were also achieved. INTERPRETATION: To our knowledge, this work represents the largest published evaluation to date of the accuracy of antibiotic susceptibility predictions from E faecium genomes. The results and resources will facilitate the adoption of whole-genome sequencing as a tool for the diagnosis and surveillance of antimicrobial resistance in E faecium. A complete characterisation of the genetic basis of resistance to last-line antibiotics, and the mechanisms mediating antibiotic resistance silencing, are needed to close the remaining sensitivity and specificity gaps in genotypic predictions. FUNDING: Wellcome Trust, UK Department of Health, British Society for Antimicrobial Chemotherapy, Academy of Medical Sciences and the Health Foundation, Medical Research Council Newton Fund, Vietnamese Ministry of Science and Technology, and European Society of Clinical Microbiology and Infectious Disease.

Patterns of within-host genetic diversity in SARS-CoV-2

Monitoring the spread of SARS-CoV-2 and reconstructing transmission chains has become a major public health focus for many governments around the world. The modest mutation rate and rapid transmission of SARS-CoV-2 prevents the reconstruction of transmission chains from consensus genome sequences, but within-host genetic diversity could theoretically help identify close contacts. Here we describe the patterns of within-host diversity in 1,181 SARS-CoV-2 samples sequenced to high depth in duplicate. 95% of samples show within-host mutations at detectable allele frequencies. Analyses of the mutational spectra revealed strong strand asymmetries suggestive of damage or RNA editing of the plus strand, rather than replication errors, dominating the accumulation of mutations during the SARS-CoV-2 pandemic. Within and between host diversity show strong purifying selection, particularly against nonsense mutations. Recurrent within-host mutations, many of which coincide with known phylogenetic homoplasies, display a spectrum and patterns of purifying selection more suggestive of mutational hotspots than recombination or convergent evolution. While allele frequencies suggest that most samples result from infection by a single lineage, we identify multiple putative examples of co-infection. Integrating these results into an epidemiological inference framework, we find that while sharing of within-host variants between samples could help the reconstruction of transmission chains, mutational hotspots and rare cases of superinfection can confound these analyses.

A prospective study of risk factors associated with seroprevalence of SARS-CoV-2 antibodies in healthcare workers at a large UK teaching hospital

BackgroundThe COVID-19 pandemic continues to grow at an unprecedented rate. Healthcare workers (HCWs) are at higher risk of SARS-CoV-2 infection than the general population but risk factors for HCW infection are not well described. MethodsWe conducted a prospective sero-epidemiological study of HCWs at a UK teaching hospital using a SARS-CoV-2 immunoassay. Risk factors for seropositivity were analysed using multivariate logistic regression. Findings410/5,698 (7{middle dot}2%) staff tested positive for SARS-CoV-2 antibodies. Seroprevalence was higher in those working in designated COVID-19 areas compared with other areas (9{middle dot}47% versus 6{middle dot}16%) Healthcare assistants (aOR 2{middle dot}06 [95%CI 1{middle dot}14-3{middle dot}71]; p=0{middle dot}016) and domestic and portering staff (aOR 3{middle dot}45 [95% CI 1{middle dot}07-11{middle dot}42]; p=0{middle dot}039) had significantly higher seroprevalence than other staff groups after adjusting for age, sex, ethnicity and COVID-19 working location. Staff working in acute medicine and medical sub-specialities were also at higher risk (aOR 2{middle dot}07 [95% CI 1{middle dot}31-3{middle dot}25]; p<0{middle dot}002). Staff from Black, Asian and minority ethnic (BAME) backgrounds had an aOR of 1{middle dot}65 (95% CI 1{middle dot}32 - 2{middle dot}07; p<0{middle dot}001) compared to white staff; this increased risk was independent of COVID-19 area working. The only symptoms significantly associated with seropositivity in a multivariable model were loss of sense of taste or smell, fever and myalgia; 31% of staff testing positive reported no prior symptoms. InterpretationRisk of SARS-CoV-2 infection amongst HCWs is heterogeneous and influenced by COVID-19 working location, role, age and ethnicity. Increased risk amongst BAME staff cannot be accounted for solely by occupational factors. FundingWellcome Trust, Addenbrookes Charitable Trust, National Institute for Health Research, Academy of Medical Sciences, the Health Foundation and the NIHR Cambridge Biomedical Research Centre. Research in context Evidence before this studySpecific risk factors for SARS-CoV-2 infection in healthcare workers (HCWs) are not well defined. Additionally, it is not clear how population level risk factors influence occupational risk in defined demographic groups. Only by identifying these factors can we mitigate and reduce the risk of occupational SARS-CoV-2 infection. We performed a review of the evidence for HCW-specific risk factors for SARS-CoV-2 infection. We searched PubMed with the terms "SARS-CoV-2" OR "COVID-19" AND "Healthcare worker" OR "Healthcare Personnel" AND "Risk factor" to identify any studies published in any language between December 2019 and September 2020. The search identified 266 studies and included a meta-analysis and two observational studies assessing HCW cohort seroprevalence data. Seroprevalence and risk factors for HCW infections varied between studies, with contradictory findings. In the two serological studies, one identified a significant increased risk of seroprevalence in those working with COVID-19 patients (Eyre et al 2020), as well as associations with job role and department. The other study (Dimcheff et al 2020) found no significant association between seropositivity and any identified demographic or occupational factor. A meta-analysis of HCW (Gomez-Ochoa et al 2020) assessed >230,000 participants as a pooled analysis, including diagnoses by both RT-PCR and seropositivity for SARS-CoV-2 antibodies and found great heterogeneity in study design and reported contradictory findings. Of note, they report a seropositivity rate of 7% across all studies reporting SARS-CoV-2 antibodies in HCWs. Nurses were the most frequently affected healthcare personnel and staff working in non-emergency inpatient settings were the most frequently affected group. Our search found no prospective studies systematically evaluating HCW specific risk factors based entirely on seroprevalence data. Added value of this studyOur prospective cohort study of almost 6,000 HCWs at a large UK teaching hospital strengthens previous findings from UK-based cohorts in identifying an increased risk of SARS-CoV-2 exposure amongst HCWs. Specifically, factors associated with SARS-CoV-2 exposure include caring for confirmed COVID-19 cases and identifying as being within specific ethnic groups (BAME staff). We further delineated the risk amongst BAME staff and demonstrate that occupational factors alone do not account for all of the increased risk amongst this group. We demonstrate for the first time that healthcare assistants represent a key at-risk occupational group, and challenge previous findings of significantly higher risk amongst nursing staff. Seroprevalence in staff not working in areas with confirmed COVID-19 patients was only marginally higher than that of the general population within the same geographical region. This observation could suggest the increased risk amongst HCWs arises through occupational exposure to confirmed cases and could account for the overall higher seroprevalence in HCWs, rather than purely the presence of staff in healthcare facilities. Over 30% of seropositive staff had not reported symptoms consistent with COVID-19, and in those who did report symptoms, differentiating COVID-19 from other causes based on symptom data alone was unreliable. Implications of all the available evidenceInternational efforts to reduce the risk of SARS-CoV-2 infection amongst HCWs need to be prioritised. The risk of SARS-CoV-2 infection amongst HCWs is heterogenous but also follows demonstrable patterns. Potential mechanisms to reduce the risk for staff working in areas with confirmed COVID-19 patients include improved training in hand hygiene and personal protective equipment (PPE), better access to high quality PPE, and frequent asymptomatic testing. Wider asymptomatic testing in healthcare facilities has the potential to reduce spread of SARS-CoV-2 within hospitals, thereby reducing patient and staff risk and limiting spread between hospitals and into the wider community. The increased risk of COVID-19 amongst BAME staff cannot be explained by purely occupational factors; however, the increased risk amongst minority ethnic groups identified here was stark and necessitates further evaluation.

A2B-COVID: A method for evaluating potential SARS-CoV-2 transmission events

Identifying linked cases of infection is a key part of the public health response to viral infectious disease. Viral genome sequence data is of great value in this task, but requires careful analysis, and may need to be complemented by additional types of data. The Covid-19 pandemic has highlighted the urgent need for analytical methods which bring together sources of data to inform epidemiological investigations. We here describe A2B-COVID, an approach for the rapid identification of linked cases of coronavirus infection. Our method combines knowledge about infection dynamics, data describing the movements of individuals, and novel approaches to genome sequence data to assess whether or not cases of infection are consistent or inconsistent with linkage via transmission. We apply our method to analyse and compare data collected from two wards at Cambridge University Hospitals, showing qualitatively different patterns of linkage between cases on designated Covid-19 and non-Covid-19 wards. Our method is suitable for the rapid analysis of data from clinical or other potential outbreak settings.

COVID-19 infection dynamics in care homes in the East of England: a retrospective genomic epidemiology study

COVID-19 poses a major challenge to care homes, as SARS-CoV-2 is readily transmitted and causes disproportionately severe disease in older people. Here, 1,167 residents from 337 care homes were identified from a dataset of 6,600 COVID-19 cases from the East of England. Older age and being a care home resident were associated with increased mortality. SARS-CoV-2 genomes were available for 700 residents from 292 care homes. By integrating genomic and temporal data, 409 viral clusters within the 292 homes were identified, indicating two different patterns - outbreaks among care home residents and independent introductions with limited onward transmission. Approximately 70% of residents in the genomic analysis were admitted to hospital during the study, providing extensive opportunities for transmission between care homes and hospitals. Limiting viral transmission within care homes should be a key target for infection control to reduce COVID-19 mortality in this population. Impact statementSARS-CoV-2 can spread efficiently within care homes causing COVID-19 outbreaks among residents, who are at increased risk of severe disease, emphasising the importance of stringent infection control in this population.

Development and implementation of a customised rapid syndromic diagnostic test for severe pneumonia

BackgroundMicrobial cultures for the diagnosis of pneumonia take several days to return a result, and are frequently negative, compromising antimicrobial stewardship. The objective of this study was to establish the performance of a syndromic molecular diagnostic approach, using a custom TaqMan array card (TAC) covering 52 respiratory pathogens, and assess its impact on antimicrobial prescribing. MethodsThe TAC was validated against a retrospective multi-centre cohort of broncho-alveolar lavage samples. The TAC was assessed prospectively in patients undergoing investigation for suspected pneumonia, with a comparator cohort formed of patients investigated when the TAC laboratory team were unavailable. Co-primary outcomes were sensitivity compared to conventional microbiology and, for the prospective study, time to result. Metagenomic sequencing was performed to validate findings in prospective samples. Antibiotic free days (AFD) were compared between the study cohort and comparator group. Results128 stored samples were tested, with sensitivity of 97% (95% CI 88-100%). Prospectively 95 patients were tested by TAC, with 71 forming the comparator group. TAC returned results 51 hours (IQR 41-69 hours) faster than culture and with sensitivity of 92% (95% CI 83-98%) compared to conventional microbiology. 94% of organisms identified by sequencing were detected by TAC. There was a significant difference in the distribution of AFDs with more AFDs in the TAC group (p=0.02). TAC group were more likely to experience antimicrobial de-escalation (OR 2.9 (95%1.5-5.5). ConclusionsImplementation of a syndromic molecular diagnostic approach to pneumonia led to faster results, with high sensitivity and impact on antibiotic prescribing. Trial registrationThe prospective study was registered with clinicaltrials.gov NCT03996330

Screening of healthcare workers for SARS-CoV-2 highlights the role of asymptomatic carriage in COVID-19 transmission

Significant differences exist in the availability of healthcare worker (HCW) SARS-CoV-2 testing between countries, and existing programmes focus on screening symptomatic rather than asymptomatic staff. Over a 3-week period (April 2020), 1,032 asymptomatic HCWs were screened for SARS-CoV-2 in a large UK teaching hospital. Symptomatic staff and symptomatic household contacts were additionally tested. Real-time RT-PCR was used to detect viral RNA from a throat+nose self-swab. 3% of HCWs in the asymptomatic screening group tested positive for SARS-CoV-2. 17/30 (57%) were truly asymptomatic/pauci-symptomatic. 12/30 (40%) had experienced symptoms compatible with coronavirus disease 2019 (COVID-19) >7 days prior to testing, most self-isolating, returning well. Clusters of HCW infection were discovered on two independent wards. Viral genome sequencing showed that the majority of HCWs had the dominant lineage B{middle dot}1. Our data demonstrates the utility of comprehensive screening of HCWs with minimal or no symptoms. This approach will be critical for protecting patients and hospital staff. Appendix: The CITIID-NIHR COVID-19 BioResource CollaborationO_ST_ABSPrincipal InvestigatorsC_ST_ABSStephen Baker, John Bradley, Gordon Dougan, Ian Goodfellow, Ravi Gupta, Paul J. Lehner, Paul A. Lyons, Nicholas J. Matheson, Kenneth G.C. Smith, M. Estee Torok, Mark Toshner, Michael P. Weekes Infectious Diseases DepartmentNicholas K. Jones, Lucy Rivett, Matthew Routledge, Dominic Sparkes, Ben Warne SARS-CoV-2 testing teamJosefin Bartholdson Scott, Claire Cormie, Sally Forrest, Harmeet Gill, Iain Kean, Mailis Maes, Joana Pereira-Dias, Nicola Reynolds, Sushmita Sridhar, Michelle Wantoch, Jamie Young COG-UK Cambridge Sequencing TeamSarah Caddy, Laura Caller, Theresa Feltwell, Grant Hall, William Hamilton, Myra Hosmillo, Charlotte Houldcroft, Aminu Jahun, Fahad Khokhar, Luke Meredith, Anna Yakovleva NIHR BioResourceHelen Butcher, Daniela Caputo, Debra Clapham-Riley, Helen Dolling, Anita Furlong, Barbara Graves, Emma Le Gresley, Nathalie Kingston, Sofia Papadia, Hannah Stark, Kathleen E. Stirrups, Jennifer Webster Research nursesJoanna Calder, Julie Harris, Sarah Hewitt, Jane Kennet, Anne Meadows, Rebecca Rastall, Criona O,Brien, Jo Price, Cherry Publico, Jane Rowlands, Valentina Ruffolo, Hugo Tordesillas NIHR Cambridge Clinical Research FacilityKaren Brookes, Laura Canna, Isabel Cruz, Katie Dempsey, Anne Elmer, Naidine Escoffery, Stewart Fuller, Heather Jones, Carla Ribeiro, Caroline Saunders, Angela Wright Cambridge Cancer Trial CentreRutendo Nyagumbo, Anne Roberts Clinical Research Network EasternAshlea Bucke, Simone Hargreaves, Danielle Johnson, Aileen Narcorda, Debbie Read, Christian Sparke, Lucy Warboys Administrative staff, CUHKirsty Lagadu, Lenette Mactavous CUH NHS Foundation TrustTim Gould, Tim Raine, Ashley Shaw Cambridge Cancer Trials CentreClaire Mather, Nicola Ramenatte, Anne-Laure Vallier Legal/EthicsMary Kasanicki CUH Improvement and Transformation TeamPenelope-Jane Eames, Chris McNicholas, Lisa Thake Clinical Microbiology & Public Health Laboratory (PHE): Neil Bartholomew, Nick Brown, Martin Curran, Surendra Parmar, Hongyi Zhang Occupational HealthAilsa Bowring, Mark Ferris, Geraldine Martell, Natalie Quinnell, Giles Wright, Jo Wright Health and SafetyHelen Murphy Department of Medicine Sample LogisticsBenjamin J. Dunmore, Ekaterina Legchenko, Stefan Graf, Christopher Huang, Josh Hodgson, Kelvin Hunter, Jennifer Martin, Federica Mescia, Ciara ODonnell, Linda Pointon, Joy Shih, Rachel Sutcliffe, Tobias Tilly, Zhen Tong, Carmen Treacy, Jennifer Wood Department of Medicine Sample Processing and Acquisition: Laura Bergamaschi, Ariana Betancourt, Georgie Bowyer, Aloka De Sa, Maddie Epping, Andrew Hinch, Oisin Huhn, Isobel Jarvis, Daniel Lewis, Joe Marsden, Simon McCallum, Francescsa Nice, Ommar Omarjee, Marianne Perera, Nika Romashova, Mateusz Strezlecki, Natalia Savoinykh Yarkoni, Lori Turner Epic team/other computing supportBarrie Bailey, Afzal Chaudhry, Rachel Doughton, Chris Workman Statistics/modellingRichard J. Samworth, Caroline Trotter

Rapid implementation of real-time SARS-CoV-2 sequencing to investigate healthcare-associated COVID-19 infections

BackgroundThe burden and impact of healthcare-associated COVID-19 infections is unknown. We aimed to examine the utility of rapid sequencing of SARS-CoV-2 combined with detailed epidemiological analysis to investigate healthcare-associated COVID-19 infections and to inform infection control measures. MethodsWe set up rapid viral sequencing of SARS-CoV-2 from PCR-positive diagnostic samples using nanopore sequencing, enabling sample-to-sequence in less than 24 hours. We established a rapid review and reporting system with integration of genomic and epidemiological data to investigate suspected cases of healthcare-associated COVID-19. ResultsBetween 13 March and 24 April 2020 we collected clinical data and samples from 5191 COVID-19 patients in the East of England. We sequenced 1000 samples, producing 747 complete viral genomes. We conducted combined epidemiological and genomic analysis of 299 patients at our hospital and identified 26 genomic clusters involving 114 patients. 66 cases (57.9%) had a strong epidemiological link and 15 cases (13.2%) had a plausible epidemiological link. These results were fed back to clinical, infection control and hospital management teams, resulting in infection control interventions and informing patient safety reporting. ConclusionsWe established real-time genomic surveillance of SARS-CoV-2 in a UK hospital and demonstrated the benefit of combined genomic and epidemiological analysis for the investigation of healthcare-associated COVID-19 infections. This approach enabled us to detect cryptic transmission events and identify opportunities to target infection control interventions to reduce further healthcare-associated infections.

Evolution of mobile genetic element composition in an epidemic methicillin-resistant Staphylococcus aureus: temporal changes correlated with frequent loss and gain events.

BACKGROUND: Horizontal transfer of mobile genetic elements (MGEs) that carry virulence and antimicrobial resistance genes mediates the evolution of methicillin-resistant Staphylococcus aureus, and the emergence of new MRSA clones. Most MRSA lineages show an association with specific MGEs and the evolution of MGE composition following clonal expansion has not been widely studied. RESULTS: We investigated the genomes of 1193 S. aureus bloodstream isolates, 1169 of which were MRSA, collected in the UK and the Republic of Ireland between 2001 and 2010. The majority of isolates belonged to clonal complex (CC)22 (n = 923), which contained diverse MGEs including elements that were found in other MRSA lineages. Several MGEs showed variable distribution across the CC22 phylogeny, including two antimicrobial resistance plasmids (pWBG751-like and SAP078A-like, carrying erythromycin and heavy metal resistance genes, respectively), a pathogenicity island carrying the enterotoxin C gene and two phage types Sa1int and Sa6int. Multiple gains and losses of these five MGEs were identified in the CC22 phylogeny using ancestral state reconstruction. Analysis of the temporal distribution of the five MGEs between 2001 and 2010 revealed an unexpected reduction in prevalence of the two plasmids and the pathogenicity island, and an increase in the two phage types. This occurred across the lineage and was not correlated with changes in the relative prevalence of CC22, or of any sub-lineages within in. CONCLUSIONS: Ancestral state reconstruction coupled with temporal trend analysis demonstrated that epidemic MRSA CC22 has an evolving MGE composition, and indicates that this important MRSA lineage has continued to adapt to changing selective pressure since its emergence.