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BackgroundKlebsiella pneumoniae is a critically important pathogen in the Philippines. Isolates are commonly resistant to at least two classes of antibiotics, yet mechanisms and spread of its resistance are not well studied.MethodsA retrospective sequencing survey was performed on carbapenem-, extended spectrum beta-lactam- and cephalosporin-resistant Klebsiella pneumoniae isolated at 20 antimicrobial resistance (AMR) surveillance sentinel sites from 2015-2017. We characterized 259 isolates using biochemical methods, antimicrobial susceptibility testing, and whole genome sequencing (WGS). Known AMR mechanisms were identified. Potential outbreaks were investigated by detecting clusters from epidemiologic, phenotypic and genome-derived data.ResultsPrevalent AMR mechanisms detected include blaCTX-M-15 (76.8%) and blaNDM-1 (37.5%). An epidemic IncFII(Yp) plasmid carrying blaNDM-1 was also detected in 46 isolates from 6 sentinel sites and 14 different sequence types (ST). This plasmid was also identified as the main vehicle of carbapenem resistance in 2 previously unrecognized local outbreaks of ST348 and ST283 at 2 different sentinel sites. A third local outbreak of ST397 was also identified but without the IncFII(Yp) plasmid. Isolates in each outbreak site showed identical STs, K- and O-loci, and similar resistance profiles and AMR genes. All outbreak isolates were collected from blood of children aged ConclusionWGS provided an in-depth understanding of the epidemiology of AMR in the Philippines, which was not possible with only phenotypic and epidemiologic data. The identification of three previously unrecognized Klebsiella outbreaks highlights the utility of WGS in outbreak detection, as well as its importance in public health and in implementing infection control programs.summaryWhole genome sequencing identified three distinct previously unrecognized local outbreaks in a retrospective study in the Philippines, along with an epidemic plasmid carrying antimicrobial resistance genes, highlighting its importance in antimicrobial resistance surveillance, outbreak detection and infection control.

Performing whole genome sequencing (WGS) for the surveillance of antimicrobial resistance offers the ability to determine not only the antimicrobials to which rates of resistance are increasing, but also the evolutionary mechanisms and transmission routes responsible for the increase at local, national, and global scales. To derive WGS-based outputs, a series of processes are required, beginning with sample and metadata collection, followed by nucleic acid extraction, library preparation, sequencing, and analysis. Throughout this pathway there are many data-related operations required (informatics) combined with more biologically focused procedures (bioinformatics). For a laboratory aiming to implement pathogen genomics, the informatics and bioinformatics activities can be a barrier to starting on the journey; for a laboratory that has already started, these activities may become overwhelming. Here we describe these data bottlenecks and how they have been addressed in laboratories in India, Colombia, Nigeria, and the Philippines, as part of the National Institute for Health Research Global Health Research Unit on Genomic Surveillance of Antimicrobial Resistance. The approaches taken include the use of reproducible data parsing pipelines and genome sequence analysis workflows, using technologies such as Data-flo, the Nextflow workflow manager, and containerization of software dependencies. By overcoming barriers to WGS implementation in countries where genome sampling for some species may be underrepresented, a body of evidence can be built to determine the concordance of antimicrobial sensitivity testing and genome-derived resistance, and novel high-risk clones and unknown mechanisms of resistance can be discovered.

Antimicrobial resistance (AMR) is considered a global threat, and novel drug discovery needs to be complemented with systematic and standardized epidemiological surveillance. Surveillance data are currently generated using phenotypic characterization. However, due to poor scalability, this approach does little for true epidemiological investigations. There is a strong case for whole-genome sequencing (WGS) to enhance the phenotypic data. To establish global AMR surveillance using WGS, we developed a laboratory implementation approach that we applied within the NIHR Global Health Research Unit (GHRU) on Genomic Surveillance of Antimicrobial Resistance. In this paper, we outline the laboratory implementation at 4 units: Colombia, India, Nigeria, and the Philippines. The journey to embedding WGS capacity was split into 4 phases: Assessment, Assembly, Optimization, and Reassessment. We show that on-boarding WGS capabilities can greatly enhance the real-time processing power within regional and national AMR surveillance initiatives, despite the high initial investment in laboratory infrastructure and maintenance. Countries looking to introduce WGS as a surveillance tool could begin by sequencing select Global Antimicrobial Resistance Surveillance System (GLASS) priority pathogens that can demonstrate the standardization and impact genome sequencing has in tackling AMR.

The administration and governance of grant funding across global health organizations presents enormous challenges. Meeting these challenges is crucial to ensuring that funds are used in the most effective way to improve health outcomes, in line with the United Nations’ Sustainable Development Goal 3, “Ensure healthy lives and promote well-being for all at all ages.” The Good Financial Grant Practice (GFGP) Standard (ARS 1651) is the world’s first and, currently, only international standard for the financial governance and management of grant funding. Through consensus building and global harmonization between both low- and middle-income and high-income country players, the GFGP Standard has achieved a leveling impact: GFGP applies equally to, and can be implemented by, all types of organization, regardless of location, size, or whether they predominantly give or receive funding.GFGP can be used as a tool for addressing some of the challenges of the current funding model. Here, we describe our experiences and lessons learned from implementing GFGP across 4 diverse research institutions in India, Nigeria, Colombia, and the Philippines as part of our National Institute for Health Research Global Health Research Unit on Genomic Surveillance of Antimicrobial Resistance.

In this Supplement, we detail outputs of the National Institute for Health Research Global Health Research Unit on Genomic Surveillance of Antimicrobial Resistance project, covering practical implementation of whole-genome sequencing across our consortium, which consists of laboratories in Colombia, India, Nigeria, and the Philippines.

Advanced genomics and sequencing technologies are increasingly becoming critical for global health applications such as pathogen and antimicrobial resistance (AMR) surveillance. Limited resources challenge capacity development in low- and middle-income countries (LMICs), with few countries having genomics facilities and adequately trained staff. Training research and public health experts who are directly involved in the establishment of such facilities offers an effective, but limited, solution to a growing need. Instead, training them to impart their knowledge and skills to others provides a sustainable model for scaling up the much needed capacity and capability for genomic sequencing and analysis locally with global impact. We designed and developed a Train-the-Trainer course integrating pedagogical aspects with genomic and bioinformatics activities. The course was delivered to 18 participants from 12 countries in Africa, Asia, and Latin America. A combination of teaching strategies culminating in a group project created a foundation for continued development at home institutions. Upon follow-up after 6 months, at least 40% of trainees had initiated training programs and collaborations to build capacity at local, national, and regional level. This work provides a framework for implementing a training and capacity building program for the application of genomics tools and resources in AMR surveillance.

Author(s): Jacob, Christopher G; Thuy-Nhien, Nguyen; Mayxay, Mayfong; Maude, Richard J; Quang, Huynh Hong; Hongvanthong, Bouasy; Vanisaveth, Viengxay; Ngo Duc, Thang; Rekol, Huy; van der Pluijm, Rob; von Seidlein, Lorenz; Fairhurst, Rick; Nosten, Francois; Hossain, Md Amir; Park, Naomi; Goodwin, Scott; Ringwald, Pascal; Chindavongsa, Keobouphaphone; Newton, Paul; Ashley, Elizabeth; Phalivong, Sonexay; Maude, Rapeephan; Leang, Rithea; Huch, Cheah; Dong, Le Thanh; Nguyen, Kim-Tuyen; Nhat, Tran Minh; Hien, Tran Tinh; Nguyen, Hoa; Zdrojewski, Nicole; Canavati, Sara; Sayeed, Abdullah Abu; Uddin, Didar; Buckee, Caroline; Fanello, Caterina I; Onyamboko, Marie; Peto, Thomas; Tripura, Rupam; Amaratunga, Chanaki; Myint Thu, Aung; Delmas, Gilles; Landier, Jordi; Parker, Daniel M; Chau, Nguyen Hoang; Lek, Dysoley; Suon, Seila; Callery, James; Jittamala, Podjanee; Hanboonkunupakarn, Borimas; Pukrittayakamee, Sasithon; Phyo, Aung Pyae; Smithuis, Frank; Lin, Khin; Thant, Myo; Hlaing, Tin Maung; Satpathi, Parthasarathi; Satpathi, Sanghamitra; Behera, Prativa K; Tripura, Amar; Baidya, Subrata; Valecha, Neena; Anvikar, Anupkumar R; Ul Islam, Akhter; Faiz, Abul; Kunasol, Chanon; Drury, Eleanor; Kekre, Mihir; Ali, Mozam; Love, Katie; Rajatileka, Shavanthi; Jeffreys, Anna E; Rowlands, Kate; Hubbart, Christina S; Dhorda, Mehul; Vongpromek, Ranitha; Kotanan, Namfon; Wongnak, Phrutsamon; Almagro Garcia, Jacob; Pearson, Richard D; Ariani, Cristina V; Chookajorn, Thanat; Malangone, Cinzia; Nguyen, T; Stalker, Jim; Jeffery, Ben | Abstract: BackgroundNational Malaria Control Programmes (NMCPs) currently make limited use of parasite genetic data. We have developed GenRe-Mekong, a platform for genetic surveillance of malaria in the Greater Mekong Subregion (GMS) that enables NMCPs to implement large-scale surveillance projects by integrating simple sample collection procedures in routine public health procedures.MethodsSamples from symptomatic patients are processed by SpotMalaria, a high-throughput system that produces a comprehensive set of genotypes comprising several drug resistance markers, species markers and a genomic barcode. GenRe-Mekong delivers Genetic Report Cards, a compendium of genotypes and phenotype predictions used to map prevalence of resistance to multiple drugs.ResultsGenRe-Mekong has worked with NMCPs and research projects in eight countries, processing 9623 samples from clinical cases. Monitoring resistance markers has been valuable for tracking the rapid spread of parasites resistant to the dihydroartemisinin-piperaquine combination therapy. In Vietnam and Laos, GenRe-Mekong data have provided novel knowledge about the spread of these resistant strains into previously unaffected provinces, informing decision-making by NMCPs.ConclusionsGenRe-Mekong provides detailed knowledge about drug resistance at a local level, and facilitates data sharing at a regional level, enabling cross-border resistance monitoring and providing the public health community with valuable insights. The project provides a rich open data resource to benefit the entire malaria community.FundingThe GenRe-Mekong project is funded by the Bill and Melinda Gates Foundation (OPP11188166, OPP1204268). Genotyping and sequencing were funded by the Wellcome Trust (098051, 206194, 203141, 090770, 204911, 106698/B/14/Z) and Medical Research Council (G0600718). A proportion of samples were collected with the support of the UK Department for International Development (201900, M006212), and Intramural Research Program of the National Institute of Allergy and Infectious Diseases.

BackgroundKlebsiella species, including the notable pathogen K. pneumoniae, are increasingly associated with antimicrobial resistance (AMR). Genome-based surveillance can inform interventions aimed at controlling AMR. However, its widespread implementation requires tools to streamline bioinformatic analyses and public health reporting.MethodsWe developed the web application Pathogenwatch, which implements analytics tailored to Klebsiella species for integration and visualization of genomic and epidemiological data. We populated Pathogenwatch with 16,537 public Klebsiella genomes to enable contextualization of user genomes. We demonstrated its features with 1,636 genomes from four low- and middle-income countries (LMICs) participating in the NIHR Global Health Research Unit (GHRU) on AMR.ResultsUsing Pathogenwatch, we found that GHRU genomes were dominated by a small number of epidemic drug-resistant clones of K. pneumoniae. However, differences in their distribution were observed (e.g. ST258/512 dominated in Colombia, ST231 in India, ST307 in Nigeria, ST147 in the Philippines). Phylogenetic analyses including public genomes for contextualization enabled retrospective monitoring of their spread. In particular, we identified hospital outbreaks, detected introductions from abroad, and uncovered clonal expansions associated with resistance and virulence genes. Assessment of loci encoding O-antigens and capsule in K. pneumoniae, which represent possible vaccine candidates, showed that three O-types (O1-O3) represented 88.9% of all genomes, whereas capsule types were much more diverse.ConclusionsPathogenwatch provides a free, accessible platform for real-time analysis of Klebsiella genomes to aid surveillance at local, national and global levels. We have improved representation of genomes from GHRU participant countries, further facilitating ongoing surveillance.40-word summaryPathogenwatch is a free web-application for analysis of Klebsiella genomes to aid surveillance at local, national and global levels. We improved the representation of genomes from middle-income countries through the Global Health Research Unit on AMR, further facilitating ongoing surveillance.FUNDINGThis work was supported by Official Development Assistance (ODA) funding from the National Institute of Health Research [grant number 16_136_111].This research was commissioned by the National Institute of Health Research using Official Development Assistance (ODA) funding. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health.CONFLICT OF INTERESTThe authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest.

The analysis of rich catalogues of genetic variation from population-based sequencing provides an opportunity to screen for functional effects. Here we report a rare variant in APOC3 (rs138326449-A, minor allele frequency ~0.25% (UK)) associated with plasma triglyceride (TG) levels (−1.43 s.d. (s.e.=0.27 per minor allele (P-value=8.0 × 10−8)) discovered in 3,202 individuals with low read-depth, whole-genome sequence. We replicate this in 12,831 participants from five additional samples of Northern and Southern European origin (−1.0 s.d. (s.e.=0.173), P-value=7.32 × 10−9). This is consistent with an effect between 0.5 and 1.5 mmol l−1 dependent on population. We show that a single predicted splice donor variant is responsible for association signals and is independent of known common variants. Analyses suggest an independent relationship between rs138326449 and high-density lipoprotein (HDL) levels. This represents one of the first examples of a rare, large effect variant identified from whole-genome sequencing at a population scale., Population-based genome sequencing provides an increasingly rich resource for the identification of low-frequency, large effect variants associated with clinically important phenotypes. Timpson et al. use UK10K data to identify a variant of the APOC3 gene strongly associated with plasma triglyceride levels.

Normal thyroid function is essential for health, but its genetic architecture remains poorly understood. Here, for the heritable thyroid traits thyrotropin (TSH) and free thyroxine (FT4), we analyse whole-genome sequence data from the UK10K project (N=2,287). Using additional whole-genome sequence and deeply imputed data sets, we report meta-analysis results for common variants (MAF≥1%) associated with TSH and FT4 (N=16,335). For TSH, we identify a novel variant in SYN2 (MAF=23.5%, P=6.15 × 10(-9)) and a new independent variant in PDE8B (MAF=10.4%, P=5.94 × 10(-14)). For FT4, we report a low-frequency variant near B4GALT6/SLC25A52 (MAF=3.2%, P=1.27 × 10(-9)) tagging a rare TTR variant (MAF=0.4%, P=2.14 × 10(-11)). All common variants explain ≥20% of the variance in TSH and FT4. Analysis of rare variants (MAF1%) using sequence kernel association testing reveals a novel association with FT4 in NRG1. Our results demonstrate that increased coverage in whole-genome sequence association studies identifies novel variants associated with thyroid function.

From 2010 to 2013, UK10K was Britain’s largest genomic sequencing consortium, awarded £10.5 million by the Wellcome Trust to investigate how low-frequency and rare genetic variants contribute to human disease (www.uk10k.org). This collaborative project brought together researchers working on obesity, autism, schizophrenia, and a number of rare conditions (familial hypercholesterolemia, thyroid disorders, learning disabilities, ciliopathies, congenital heart disease, coloboma, neuromuscular disorders, and severe insulin resistance) to generate whole genome and exome sequence data for almost 10,000 highly phenotyped individuals. The data generated by UK10K not only enabled the discovery of novel disease-causing genes by the consortium, but was also made available to the research community during the life of the project as a managed access data resource; providing access to data an order of magnitude deeper than was previously possible, and empowering future research into human genetics.

Recent advances in sequencing technology allow data on the human genome to be generated more quickly and in greater detail than ever before. Such detail includes findings that may be of significance to the health of the research participant involved. Although research studies generally do not feed back information on clinically significant findings (CSFs) to participants, this stance is increasingly being questioned. There may be difficulties and risks in feeding clinically significant information back to research participants, however, the UK10K consortium sought to address these by creating a detailed management pathway. This was not intended to create any obligation upon the researchers to feed back any CSFs they discovered. Instead, it provides a mechanism to ensure that any such findings can be passed on to the participant where appropriate. This paper describes this mechanism and the specific criteria, which must be fulfilled in order for a finding and participant to qualify for feedback. This mechanism could be used by future research consortia, and may also assist in the development of sound principles for dealing with CSFs. © 2014 Macmillan Publishers Limited All rights reserved. Wellcome Trust/WT091310 Wellcome Trust/WT096599/2/11/Z

This paper outlines the history behind open access principles and describes the development of a managed access data-sharing process for the UK10K Project, currently Britain's largest genomic sequencing consortium (2010 to 2013). Funded by the Wellcome Trust, the purpose of UK10K was two-fold: to investigate how low-frequency and rare genetic variants contribute to human disease, and to provide an enduring data resource for future research into human genetics. In this paper, we discuss the challenge of reconciling data-sharing principles with the practicalities of delivering a sequencing project of UK10K's scope and magnitude. We describe the development of a sustainable, easy-to-use managed access system that allowed rapid access to UK10K data, while protecting the interests of participants and data generators alike. Specifically, we focus in depth on the three key issues that emerge in the data pipeline: study recruitment, data release and data access.

Biologists currently face a daunting challenge when trying to discover which of the multitude of computational and data resources to use in analysing their results and developing their hypotheses. The basic task of identifying appropriate online resources in a research field is non-trivial and typically involves ad hoc Internet trawling, recommendations from colleagues or literature searching. This is then followed by the more complex task of establishing whether the resource is relevant, reliable, well curated, and maintained. If programmatic access is required, discovering whether this exists and how to utilize it is another challenge. As time is short, most researchers often end up using familiar resources, which are not always the best or most relevant, while the developers and funders of under-utilized but valuable resources essentially waste time and money. What is required is a solution that helps to maximize the usefulness of each resource to the overall community. At present, approaches are being developed to construct two types of registry. One type, ‘databases of databases’, deal with describing the contents and other metadata about databases. The other type, web service registries, deal with the explicit description of services available at particular sites (not always databases). We present the two areas separately, but ultimately we expect solutions to arrive that merge the two approaches.