Molecular epidemiology of Group B streptococci in Lithuania identifies multi-drug resistant clones and sporadic ST1 serotypes Ia and Ib

Streptococcus agalactiae (Group B Streptococcus, GBS) is a leading cause of neonatal infections. Yet, detailed assessment of the genotypic and phenotypic factors associated with GBS carriage, mother-to-baby transmission, and GBS infection in neonates and adults is lacking. Understanding the distribution of GBS genotypes, including the predominance of different serotypes, antimicrobial resistance (AMR) genes, and virulence factors, is likely to help to prevent GBS diseases, as well as inform estimates of the efficacy of future GBS vaccines. To this end, we set out to characterise GBS isolates collected from pregnant and non-pregnant women in Kaunas region in Lithuania. Whole genome sequences of 42 GBS isolates were analysed to determine multi-locus sequence typing (MLST), the presence of acquired AMR and surface protein genes, and the phylogenetic relatedness of isolates. We identified serotypes Ia (42.9%, 18/42), III (33.3%, 14/42), V (21.4%, 9/42), and a single isolate of serotype Ib. Genomic analyses revealed high diversity among isolates, with 18 sequence types (STs) identified, including three novel STs. 85.7% (36/42) of isolates carried at least one AMR gene: tetM or tetO (35/42), ermB or lsaC (8/42) and ant6-Ia and aph3-III (2/42). This study represents the first genomic analysis of GBS isolated from women in Lithuania and contributes to an improved understanding of the global spread of GBS genotypes and phenotypes, laying the foundations for future GBS surveillance in Lithuania

Reconstructing and predicting the spatial evolution of carbapenemase-producing enterobacteriaceae outbreaks

IntroControlling the spread of infectious diseases requires correctly targeting preventative resources. When poorly deployed, these resources are misspent and have an inefficient impact. In a dynamic environment where sources of outbreaks are ever-changing, how to optimally deploy these resources is difficult to identify and can moreover change over time. In addressment, we outline and test network-driven framework that accounts for underlying patient mobility, and outbreak dynamics in hospitals to predict the temporal and spatial arrival of carbapenemase-producing Enterobacteriaceae (CPE) outbreaks.MethodsWe reconstructed CPE-outbreaks using a novel formulation based on transmission-dynamics, contact-interactions, and microbiology data. For each outbreak, we then examine their spatial evolution and, using background hospital population movement (entire patient population from Imperial College Healthcare NHS Trust between 2018-08-17 and 2022-02-03), we predict the arrival times of new CPE cases across wards.FindingsThe background mobility-network contained ward-transitions from 181,512 patients (178 wards). Based on the construction, the network comprises a single giant component and acts as a medium for disease transmission. For the results of a fitted regression predicting outbreak arrival, we included locational attributes, in addition to network distances. Overall, we found that effective distance (a graphbased path measure shown previously epidemiologically predictive) contained unique predictive power compared to edge weight. However, the effective distance could be complemented by information regarding patient demographics (Age and Sex) of patient transfers; their predictive power is suggestive of specific sub-population mobility as more important drivers of CPE.ConclusionWe investigated a network-driven framework showing the potential to anticipate arrival-times of hospital CPE outbreaks. In including additional information, we also showed how specific hospital population movements were key drivers of CPE. In furthering our results, we next plan to investigate additional diseases, validate our findings beyond our current dataset, and explore further locational attributes.</p

Evidence review and recommendations for the implementation of genomics for antimicrobial resistance surveillance: reports from an international expert group

Nearly a century after the beginning of the antibiotic era, which has been associated with unparalleled improvements in human health and reductions in mortality associated with infection, the dwindling pipeline for new antibiotic classes coupled with the inevitable spread of antimicrobial resistance (AMR) poses a major global challenge. Historically, surveillance of AMR bacteria typically relied on phenotypic analysis of isolates taken from infected individuals, which provides only a low-resolution view of the epidemiology behind an individual infection or wider outbreak. Recent years have seen increasing adoption of powerful new genomic technologies with the potential to revolutionise AMR surveillance by providing a high-resolution picture of the AMR profile of the bacteria causing infections and provide real-time actionable information for treating and preventing infection. However, many barriers remain to be overcome before genomic technologies can be adopted as a standard part of routine AMR surveillance around the world. Accordingly, the Surveillance and Epidemiology of Drug-resistant Infections Consortium (SEDRIC; www.sedric.org.uk) convened an expert working group on Genomics Surveillance for AMR to assess the benefits and challenges of using genomics for AMR surveillance. This overview, and the associated four workshop summaries detail these discussions and provide a series of recommendations from the working group that can help to realise the massive potential benefits for genomics in surveillance of AMR.KSB reports funding from the BBSRC and MRC and partial salary cover from Wellcome Trust and UKHSA over the course of this work. EJ had partial salary cover from Wellcome Trust over the course of this work. RAA reports funding unrelated to this study from Novo Nordisk, Roche, Novartis and UICC, and honoraria (unrelated to this study) from Merck&Co, Novartis, F. Hoffmann-La Roche Ltd. BE and INO report receiving funding from the UK Department of Health and Social Care: grant managed by the Fleming Fund and work performed under the auspices of the SEQAFRICA project. INO reports funding from the Bill and Melinda Gates Foundation, JPIAMR, Wellcome Trust, Grand Challenges Africa Award, UK MCR, royalties for Genetics: Genes, Genomes and Evolution (Oxford University Press), Divining Without Seeds and for Antimicrobial Resistance in Developing Countries (Springer), consulting fees from Wellcome Trust, honoraria for Harvard University seminars and Peter Wildy Lecture Award 2023. LYH reports funding from Pfizer Inc and honoraria from BioMerieux for lecture in 2022. DMM reports funding from BSAC. NEW reports funding from Nuclear Threat Initiative, MRC, Open Philantropy and Shionogi as well as consulting fees from Nuclear Threat Initiative. DMA reports funding from the NIHR. NAF reports funding from the BMGF, UKRI and NIHR. SJP is a member of the Scientific Advisory Board of Next Gen Diagnostics and was supported by Illumina to attend the ECCMID conference. All other authors declare no conflicts of interest

Evidence review and recommendations for the implementation of genomics for antimicrobial resistance surveillance: reports from an international expert group.

Nearly a century after the beginning of the antibiotic era, which has been associated with unparalleled improvements in human health and reductions in mortality associated with infection, the dwindling pipeline for new antibiotic classes coupled with the inevitable spread of antimicrobial resistance (AMR) poses a major global challenge. Historically, surveillance of bacteria with AMR typically relied on phenotypic analysis of isolates taken from infected individuals, which provides only a low-resolution view of the epidemiology behind an individual infection or wider outbreak. Recent years have seen increasing adoption of powerful new genomic technologies with the potential to revolutionise AMR surveillance by providing a high-resolution picture of the AMR profile of the bacteria causing infections and providing real-time actionable information for treating and preventing infection. However, many barriers remain to be overcome before genomic technologies can be adopted as a standard part of routine AMR surveillance around the world. Accordingly, the Surveillance and Epidemiology of Drug-resistant Infections Consortium convened an expert working group to assess the benefits and challenges of using genomics for AMR surveillance. In this Series, we detail these discussions and provide recommendations from the working group that can help to realise the massive potential benefits for genomics in surveillance of AMR