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Abstract Tuberculosis (TB) remains one of the leading infectious disease killers in the world. The ongoing development of novel anti-TB medications has yielded potent compounds that often target single sites with well-defined mechanisms of action. However, despite the identification of resistance-associated mutations through target deconvolution studies, comparing these findings with the diverse Mycobacterium tuberculosis populations observed in clinical settings is often challenging. To address this gap, we constructed an open-access database encompassing genetic variations from > 50,000 clinical isolates, spanning the entirety of the M. tuberculosis protein-encoding genome. This resource offers a valuable tool for investigating the prevalence of target-based resistance mutations in any drug target within clinical contexts. To demonstrate the practical application of this dataset in drug discovery, we focused on drug targets currently undergoing phase II clinical trials. By juxtaposing genetic variations of these targets with resistance mutations derived from laboratory-adapted strains, we identified multiple positions across three targets harbouring resistance-associated mutations already present in clinical isolates. Furthermore, our analysis revealed a discernible correlation between genetic diversity within each protein and their predicted essentiality. This meta-analysis, openly accessible via a dedicated dashboard, enables comprehensive exploration of genetic diversity pertaining to any drug target or resistance determinant in M. tuberculosis.

Abstract Background Anopheles melas is an understudied malaria vector with a potential role in malaria transmission on the Bijagós Archipelago of Guinea-Bissau. This study presents the first whole-genome sequencing and population genetic analysis for this species from the Bijagós. To our knowledge, this also represents the largest population genetic analysis using WGS data from non-pooled An. melas mosquitoes. Methods WGS was conducted for 30 individual An. melas collected during the peak malaria transmission season in 2019 from six different islands on the Bijagós Archipelago. Bioinformatics tools were used to investigate the population structure and prevalence of insecticide resistance markers in this mosquito population. Results Insecticide resistance mutations associated with pyrethroid resistance in Anopheles gambiae s.s. from the Bijagós were absent in the An. melas population, and no signatures of selective sweeps were identified in insecticide resistance-associated genes. Analysis of structural variants identified a large duplication encompassing the cytochrome-P450 gene cyp9k1. Phylogenetic analysis using publicly available mitochondrial genomes indicated that An. melas from the Bijagós split into two phylogenetic groups because of differentiation on the mitochondrial genome attributed to the cytochrome C oxidase subunits COX I and COX II and the NADH dehydrogenase subunits 1, 4, 4L and 5. Conclusions This study identified an absence of insecticide-resistant SNPs common to An. gambiae in the An. melas population, but did identify structural variation over insecticide resistance-associated genes. Furthermore, this study presents novel insights into the population structure of this malaria vector using WGS analysis. Additional studies are required to further understand the role of this vector in malaria transmission. Graphical abstract

ABSTRACT New and improved drugs are required for the treatment and ultimate eradication of malaria. The efficacy of front-line therapies is now threatened by emerging drug resistance; thus, new tools to support the development of drugs with a lower propensity for resistance are needed. Here, we describe the development of a RESistance Mapping And Profiling (ResMAP) platform for the identification of resistance-conferring mutations in Plasmodium drug targets. Proof-of-concept studies focused on interrogating the antimalarial drug target, Plasmodium falciparum lysyl tRNA synthetase (PfKRS). Saturation mutagenesis was used to construct a plasmid library encoding all conceivable mutations within a 20-residue span at the base of the PfKRS ATP-binding pocket. The superior transfection efficiency of Plasmodium knowlesi was exploited to generate a high coverage parasite library expressing PfKRS bearing all possible amino acid changes within this region of the enzyme. The selection of the library with PfKRS inhibitors, cladosporin and DDD01510706, successfully identified multiple resistance-conferring substitutions. Genetic validation of a subset of these mutations confirmed their direct role in resistance, with computational modeling used to dissect the structural basis of resistance. The application of ResMAP to inform the development of resistance-resilient antimalarials of the future is discussed.IMPORTANCEAn increase in treatment failures for malaria highlights an urgent need for new tools to understand and minimize the spread of drug resistance. We describe the development of a RESistance Mapping And Profiling (ResMAP) platform for the identification of resistance-conferring mutations in Plasmodium spp, the causative agent of malaria. Saturation mutagenesis was used to generate a mutation library containing all conceivable mutations for a region of the antimalarial-binding site of a promising drug target, Plasmodium falciparum lysyl tRNA synthetase (PfKRS). Screening of this high-coverage library with characterized PfKRS inhibitors revealed multiple resistance-conferring substitutions including several clinically relevant mutations. Genetic validation of these mutations confirmed resistance of up to 100-fold and computational modeling dissected their role in drug resistance. We discuss potential applications of this data including the potential to design compounds that can bypass the most serious resistance mutations and future resistance surveillance.

Abstract Tuberculosis (TB), caused by Mycobacterium tuberculosis, has a significant impact on global health worldwide. The development of multi-drug resistant strains that are resistant to the first-line drugs isoniazid and rifampicin threatens public health security. Rifampicin and isoniazid resistance are largely underpinned by mutations in rpoB and katG respectively and are associated with fitness costs. Compensatory mutations are considered to alleviate these fitness costs and have been observed in rpoC/rpoA (rifampicin) and oxyR’-ahpC (isoniazid). We developed a framework (CompMut-TB) to detect compensatory mutations from whole genome sequences from a large dataset comprised of 18,396 M . tuberculosis samples. We performed association analysis (Fisher’s exact tests) to identify pairs of mutations that are associated with drug-resistance, followed by mediation analysis to identify complementary or full mediators of drug-resistance. The analyses revealed several potential mutations in rpoC (N = 47), rpoA (N = 4), and oxyR’-ahpC (N = 7) that were considered either ‘highly likely’ or ‘likely’ to confer compensatory effects on drug-resistance, including mutations that have previously been reported and validated. Overall, we have developed the CompMut-TB framework which can assist with identifying compensatory mutations which is important for more precise genome-based profiling of drug-resistant TB strains and to further understanding of the evolutionary mechanisms that underpin drug-resistance.

Background Next-generation sequencing of Mycobacterium tuberculosis, the infectious agent causing tuberculosis, is improving the understanding of genomic diversity of circulating lineages and strain-types, and informing knowledge of drug resistance mutations. An increasingly popular approach to characterizing M. tuberculosis genomes (size: 4.4 Mbp) and variants (e.g., single nucleotide polymorphisms (SNPs)) involves the de novo assembly of sequence data. Methods We compared the performance of genome assembly tools (Unicycler, RagOut, and RagTag) on sequence data from nine drug resistant M. tuberculosis isolates (multi-drug (MDR) n = 1; pre-extensively-drug (pre-XDR) n = 8) generated using Illumina HiSeq, Oxford Nanopore Technology (ONT) PromethION, and PacBio platforms. Results Our investigation found that Unicycler-based assemblies had significantly higher genome completeness (~98.7%; p values = 0.01) compared to other assembler tools (RagOut = 98.6%, and RagTag = 98.6%). The genome assembly sizes (bp) across isolates and sequencers based on RagOut was significantly longer (p values < 0.001) (4,418,574 ± 8,824 bp) than Unicycler and RagTag assemblies (Unicycler = 4,377,642 ± 55,257 bp, and RagTag = 4,380,711 ± 51,164 bp). RagOut-based assemblies had the fewest contigs (~32) and the longest genome size (4,418,574 bp; vs. H37Rv reference size 4,411,532 bp) and therefore were chosen for downstream analysis. Pan-genome analysis of Illumina and PacBio hybrid assemblies revealed the greatest number of detected genes (4,639 genes; H37Rv reference contains 3,976 genes), while Illumina and ONT hybrid assemblies produced the highest number of SNPs. The number of genes from hybrid assemblies with ONT and PacBio long-reads (mean: 4,620 genes) was greater than short-read assembly alone (4,478 genes). All nine RagOut hybrid genome assemblies detected known mutations in genes associated with MDR-TB and pre-XDR-TB. Conclusions Unicycler software performed the best in terms of achieving contiguous genomes, whereas RagOut improved the quality of Unicycler’s genome assemblies by providing a longer genome size. Overall, our approach has demonstrated that short-read and long-read hybrid assembly can provide a more complete genome assembly than short-read assembly alone by detecting pan-genomes and more genes, including IS6110, and SNPs.

Abstract Background Insecticide resistance is reducing the efficacy of vector control interventions, consequently threatening efforts to control vector-borne diseases, including malaria. Investigating the prevalence of molecular markers of resistance is a useful tool for monitoring the spread of insecticide resistance in disease vectors. The Bijagós Archipelago (Bijagós) in Guinea-Bissau is a region of stable malaria transmission where insecticide-treated nets are the mainstay for malaria control. However, the prevalence of molecular markers of insecticide resistance in malaria vectors is not well understood. Methods A total of 214 Anopheles mosquitoes were analysed from 13 islands across the Bijagós. These mosquitoes were collected using CDC light traps in November 2019, during the peak malaria transmission season. High-throughput multiplex amplicon sequencing was used to investigate the prevalence of 17 different molecular markers associated with insecticide resistance in four genes: vgsc, rdl, ace1 and gste2. Results Of the 17 screened mutations, four were identified in mosquitoes from the Bijagós: vgsc L995F (12.2%), N1570Y (6.2%) and A1746S (0.7%) and rdl A269G (1.1%). This study is the first to report the L995F knock-down resistance (kdr)-west allele in Anopheles melas on the Archipelago. An additional eight non-synonymous single-nucleotide polymorphisms were identified across the four genes which have not been described previously. The prevalences of the vgsc L995F and N1570Y mutations were higher on Bubaque Island than on the other islands in this study; Bubaque is the most populous island in the archipelago, with the greatest population mobility and connection to continental Guinea-Bissau. Conclusions This study provides the first surveillance data for genetic markers present in malaria vectors from islands across the Bijagós Archipelago. Overall prevalence of insecticide resistance mutations was found to be low. However, the identification of the vgsc L995F and N1570Y mutations associated with pyrethroid resistance warrants further monitoring. This is particularly important as the mainstay of malaria control on the islands is the use of pyrethroid insecticide-treated nets. Graphical Abstract

Abstract Tuberculosis disease (TB), caused by Mycobacterium tuberculosis, is a major global public health problem, resulting in more than 1 million deaths each year. Drug resistance (DR), including multi-drug (MDR-TB), is making TB control difficult and accounts for 16% of new and 48% of previously treated cases. To further complicate treatment decision-making, many clinical studies have reported patients harbouring multiple distinct strains of M. tuberculosis across the main lineages (L1 to L4). The extent to which drug-resistant strains can be deconvoluted within mixed strain infection samples is understudied. Here, we analysed M. tuberculosis isolates with whole genome sequencing data (n = 50,723), which covered the main lineages (L1 9.1%, L2 27.6%, L3 11.8%, L4 48.3%), with genotypic resistance to isoniazid (HR-TB; n = 9546 (29.2%)), rifampicin (RR-TB; n = 7974 (24.4%)), and at least MDR-TB (n = 5385 (16.5%)). TB-Profiler software revealed 531 (1.0%) isolates with potential mixed sub-lineage infections, including some with DR mutations (RR-TB 21/531; HR-TB 59/531; at least MDR-TB 173/531). To assist with the deconvolution of such mixtures, we adopted and evaluated a statistical Gaussian Mixture model (GMM) approach. By simulating 240 artificial mixtures of different ratios from empirical data across L1 to L4, a GMM approach was able to accurately estimate the DR profile of each lineage, with a low error rate for the estimated mixing proportions (mean squared error 0.012) and high accuracy for the DR predictions (93.5%). Application of the GMM model to the clinical mixtures (n = 531), found that 33.3% (188/531) of samples consisted of DR and sensitive lineages, 20.2% (114/531) consisted of lineages with only DR mutations, and 40.6% (229/531) consisted of lineages with genotypic pan-susceptibility. Overall, our work demonstrates the utility of combined whole genome sequencing data and GMM statistical analysis approaches for providing insights into mono and mixed M. tuberculosis infections, thereby potentially assisting diagnosis, treatment decision-making, drug resistance and transmission mapping for infection control.

Abstract Bacterial strain-types in the Mycobacterium tuberculosis complex underlie tuberculosis disease, and have been associated with drug resistance, transmissibility, virulence, and host–pathogen interactions. Spoligotyping was developed as a molecular genotyping technique used to determine strain-types, though recent advances in whole genome sequencing (WGS) technology have led to their characterization using SNP-based sub-lineage nomenclature. Notwithstanding, spoligotyping remains an important tool and there is a need to study the congruence between spoligotyping-based and SNP-based sub-lineage assignation. To achieve this, an in silico spoligotype prediction method (“Spolpred2”) was developed and integrated into TB-Profiler. Lineage and spoligotype predictions were generated for > 28 k isolates and the overlap between strain-types was characterized. Major spoligotype families detected were Beijing (25.6%), T (18.6%), LAM (13.1%), CAS (9.4%), and EAI (8.3%), and these broadly followed known geographic distributions. Most spoligotypes were perfectly correlated with the main MTBC lineages (L1-L7, plus animal). Conversely, at lower levels of the sub-lineage system, the relationship breaks down, with only 65% of spoligotypes being perfectly associated with a sub-lineage at the second or subsequent levels of the hierarchy. Our work supports the use of spoligotyping (membrane or WGS-based) for low-resolution surveillance, and WGS or SNP-based systems for higher-resolution studies.

Abstract Sequence analysis of Plasmodium falciparum parasites is informative in ensuring sustained success of malaria control programmes. Whole-genome sequencing technologies provide insights into the epidemiology and genome-wide variation of P. falciparum populations and can characterise geographical as well as temporal changes. This is particularly important to monitor the emergence and spread of drug resistant P. falciparum parasites which is threatening malaria control programmes world-wide. Here, we provide a detailed characterisation of genome-wide genetic variation and drug resistance profiles in asymptomatic individuals in South-Western Mali, where malaria transmission is intense and seasonal, and case numbers have recently increased. Samples collected from Ouélessébougou, Mali (2019–2020; n = 87) were sequenced and placed in the context of older Malian (2007–2017; n = 876) and African-wide (n = 711) P. falciparum isolates. Our analysis revealed high multiclonality and low relatedness between isolates, in addition to increased frequencies of molecular markers for sulfadoxine-pyrimethamine and lumefantrine resistance, compared to older Malian isolates. Furthermore, 21 genes under selective pressure were identified, including a transmission-blocking vaccine candidate (pfCelTOS) and an erythrocyte invasion locus (pfdblmsp2). Overall, our work provides the most recent assessment of P. falciparum genetic diversity in Mali, a country with the second highest burden of malaria in West Africa, thereby informing malaria control activities.

BackgroundThe symptom profiles of acute SARS-CoV-2 infection and long-COVID in children and young people (CYP), risk factors, and associated healthcare needs, are poorly defined. The Schools Infection Survey 1 (SIS-1) was a nationwide study of SARS-CoV-2 infection in primary and secondary schools in England during the 2020/21 school year. The Covid-19 Mapping and Mitigation in Schools (CoMMinS) study was conducted in schools in the Bristol area over a similar period. Both studies conducted testing to identify current and previous SARS-CoV-2 infection, and recorded symptoms and school attendance. These research data have been linked to routine electronic health record (EHR) data.AimsTo better understand the short- and long-term consequences of SARS-CoV-2 infection, and their risk factors, in CYP.MethodsRetrospective cohort and nested case-control analyses will be conducted for SIS-1 and CoMMinS data linked to EHR data for the association between (1) acute symptomatic SARS-CoV-2 infection and risk factors; (2) SARS-CoV-2 infection and long-term effects on health: (a) persistent symptoms; (b) any new diagnosis; (c) a new prescription in primary care; (d) health service attendance; (e) a high rate of school absence.ResultsOur study will improve understanding of long-COVID in CYP by characterising the trajectory of long-COVID in CYP in terms of things like symptoms and diagnoses of conditions. The research will inform which groups of CYP are more likely to get acute- and long-term outcomes of SARS-CoV-2 infection, and patterns of related healthcare-seeking behaviour, relevant for healthcare service planning. Digested information will be produced for affected families, doctors, schools, and the public, as appropriate.ConclusionLinked SIS-1 and CoMMinS data represent a unique and rich resource for understanding the impact of SARS-CoV-2 infection on children's health, benefiting from enhanced SARS-CoV-2 testing and ability to assess a wide range of outcomes.

Sand fly transmitted Leishmania species are responsible for severe, wide ranging, visceral and cutaneous leishmaniases. Genetic exchange can occur among natural Leishmania populations and hybrids can now be produced experimentally, with limitations. Feeding Phlebotomus orientalis or Phlebotomus argentipes on two strains of Leishmania donovani yielded hybrid progeny, selected using double drug resistance and fluorescence markers. Fluorescence activated cell sorting of cultured clones derived from these hybrids indicated diploid progeny. Multilocus sequence typing of the clones showed hybridisation and nuclear heterozygosity, although with inheritance of single haplotypes in a kinetoplastid target. Comparative genomics showed diversity of clonal progeny between single chromosomes, and extraordinary heterozygosity across all 36 chromosomes. Diversity between progeny was seen for the HASPB antigen, which has been noted previously as having implications for design of a therapeutic vaccine. Genomic diversity seen among Leishmania strains and hybrid progeny is of great importance in understanding the epidemiology and control of leishmaniasis. As an outcome of this study we strongly recommend that wider biological archives of different Leishmania species from endemic regions should be established and made available for comparative genomics. However, in parallel, performance of genetic crosses and genomic comparisons should give fundamental insight into the specificity, diversity and limitations of candidate diagnostics, vaccines and drugs, for targeted control of leishmaniasis.

Abstract The microbiome plays a key role in the health of the human body. Interest often lies in finding features of the microbiome, alongside other covariates, which are associated with a phenotype of interest. One important property of microbiome data, which is often overlooked, is its compositionality as it can only provide information about the relative abundance of its constituting components. Typically, these proportions vary by several orders of magnitude in datasets of high dimensions. To address these challenges we develop a Bayesian hierarchical linear log-contrast model which is estimated by mean field Monte-Carlo co-ordinate ascent variational inference (CAVI-MC) and easily scales to high dimensional data. We use novel priors which account for the large differences in scale and constrained parameter space associated with the compositional covariates. A reversible jump Monte Carlo Markov chain guided by the data through univariate approximations of the variational posterior probability of inclusion, with proposal parameters informed by approximating variational densities via auxiliary parameters, is used to estimate intractable marginal expectations. We demonstrate that our proposed Bayesian method performs favourably against existing frequentist state of the art compositional data analysis methods. We then apply the CAVI-MC to the analysis of real data exploring the relationship of the gut microbiome to body mass index.

Abstract Following integrated malaria control interventions, malaria burden on the Bijagós Archipelago has significantly decreased. Understanding the genomic diversity of circulating Plasmodium falciparum malaria parasites can assist infection control, through identifying drug resistance mutations and characterising the complexity of population structure. This study presents the first whole genome sequence data for P. falciparum isolates from the Bijagós Archipelago. Amplified DNA from P. falciparum isolates sourced from dried blood spot samples of 15 asymptomatic malaria cases were sequenced. Using 1.3 million SNPs characterised across 795 African P. falciparum isolates, population structure analyses revealed that isolates from the archipelago cluster with samples from mainland West Africa and appear closely related to mainland populations; without forming a separate phylogenetic cluster. This study characterises SNPs associated with antimalarial drug resistance on the archipelago. We observed fixation of the PfDHFR mutations N51I and S108N, associated with resistance to sulphadoxine-pyrimethamine, and the continued presence of PfCRT K76T, associated with chloroquine resistance. These data have relevance for infection control and drug resistance surveillance; particularly considering expected increases in antimalarial drug use following updated WHO recommendations, and the recent implementation of seasonal malaria chemoprevention and mass drug administration in the region.

Background: Inference on pneumococcal transmission has mostly relied on longitudinal studies which are costly and resource intensive. Therefore, we conducted a pilot study to test the ability to infer who infected whom from cross-sectional pneumococcal sequences using phylogenetic inference. Methods: Five suspected transmission pairs, for which there was epidemiological evidence of who infected whom, were selected from a household study. For each pair, Streptococcus pneumoniae full genomes were sequenced from nasopharyngeal swabs collected on the same day. The within-host genetic diversity of the pneumococcal population was used to infer the transmission direction and then cross-validated with the direction suggested by the epidemiological records. Results: The pneumococcal genomes clustered into the five households from which the samples were taken. The proportion of concordantly inferred transmission direction generally increased with increasing minimum genome fragment size and single nucleotide polymorphisms. We observed a larger proportion of unique polymorphic sites in the source bacterial population compared to that of the recipient in four of the five pairs, as expected in the case of a transmission bottleneck. The only pair that did not exhibit this effect was also the pair that had consistent discordant transmission direction compared to the epidemiological records suggesting potential misdirection as a result of false-negative sampling. Conclusions: This pilot provided support for further studies to test if the direction of pneumococcal transmission can be reliably inferred from cross-sectional samples if sequenced with sufficient depth and fragment length.

Objectives To assess socioeconomic and geographical factors associated with COVID-19 vaccine uptake in pupils attending state-funded secondary schools in England.Design Cross-sectional observational study.Setting State-funded schools in England.Participants Pupils aged 12–17 years attending state-funded schools in England for the academic year 2021/2022.Outcome measures Demographic, socioeconomic and geographical factors associated with vaccination uptake. We linked individual-level data from the English Schools Census to the National Immunisation Management System to obtain COVID-19 vaccination status of 3.2 million adolescents. We used multivariable logistic regression to assess demographic, socioeconomic and geographical factors associated with vaccination.Results By 9 January 2022, 56.8% of adolescents aged 12–17 years old had received at least one dose, with uptake increasing from 48.7% in those aged 12 years old to 77.2% in those aged 17 years old. Among adolescents aged 12–15 years old, there were large variations in vaccine uptake by region and ethnic group. Pupils who spoke English as an additional language (38.2% vs 55.5%), with special educational needs (48.1% vs 53.5%), eligible for free school meals (35.9% vs 58.9%) and lived in more deprived areas (36.1% in most deprived vs 70.3% in least deprived) had lower vaccine uptake. Socioeconomic variables had greater impact on the odds of being vaccinated than geographical variables. School-level analysis found wide variation in vaccine uptake between schools even within the same region. Schools with higher proportions of pupils eligible for free school meals had lower vaccine uptake.Conclusions We found large differences in vaccine uptake by geographical region and ethnicity. Socioeconomic variables had a greater impact on the odds of being vaccinated than geographical variables. Further research is required to identify evidence-based interventions to improve vaccine uptake in adolescents.

Abstract The invasion and establishment of An. stephensi mosquitoes in the Horn of Africa represents a significant regional threat, which may jeopardise malaria control, particularly in urban areas which were formally free from disease transmission. Novel vector surveillance methods are urgently needed, both agnostic to mosquito larval morphology, and simple to implement at the sampling stage. Using new multiplex TaqMan assays, specifically targeting An. stephensi and Ae. aegypti, we validated the use of environmental DNA (eDNA) for simultaneous vector detection in shared artificial breeding sites. Study findings demonstrated that An. stephensi and Ae. aegypti eDNA deposited by as few as one second instar larva in 1L of water was detectable. Characterization of molecular insecticide resistance mechanisms, using novel amplicon-sequencing panels for both vector species, was possible from eDNA shed by as few as 16–32 s instar larvae in 50 ml of water. An. stephensi eDNA, derived from emergent pupae for 24 h, was remarkably stable, and still detectable ~ 2 weeks later. eDNA surveillance has the potential to be implemented in local endemic communities and at points of country entry, to monitor the spread of invasive vector species. Further studies are required to validate the feasibility of this technique under field conditions.

Few genetic loci have been associated with tuberculosis infection, possibly because of the influence of genetic variation in the pathogen. Here, the authors integrate human and Mycobacterium tuberculosis genetics to find genome-genome interactions associated with infection.

Klebsiella aerogenes, a nosocomial pathogen, is increasingly associated with extensive drug resistance and virulence profiles. It is responsible for high morbidity and mortality. This report describes the first successfully treated case of community-acquired urinary tract infection (UTI) caused by Klebsiella aerogenes in an elderly housewife with Type-2 diabetes (T2D) from Dhaka, Bangladesh. The patient was empirically treated with intravenous ceftriaxone (500 mg/8 h). However, she did not respond to the treatment. The urine culture and sensitivity tests, coupled with bacterial whole-genome sequencing (WGS) and analysis, revealed the bacteria to be K. aerogenes which was extensively drug-resistant but was susceptible to carbapenems and polymyxins. Based on these findings, meropenem (500 mg/8 h) was administered to the patient, who then responded to the treatment and recovered successfully without having a relapse. This case raises awareness of the importance of diagnosis of not-so-common etiological agents, correct identification of the pathogens, and targeted antibiotic therapy. In conclusion, correctly identifying etiological agents of UTI using WGS approaches that are otherwise difficult to diagnose could help improve the identification of infectious agents and improve the management of infectious diseases.

Abstract Surveillance of malaria vector species and the monitoring of insecticide resistance are essential to inform malaria control strategies and support the reduction of infections and disease. Genetic barcoding of mosquitoes is a useful tool to assist the high-throughput surveillance of insecticide resistance, discriminate between sibling species and to detect the presence of Plasmodium infections. In this study, we combined multiplex PCR, custom designed dual indexing, and Illumina next generation sequencing for high throughput single nucleotide polymorphism (SNP)-profiling of four species from the Anopheles (An.) gambiae complex (An. gambiae sensu stricto, An. coluzzii, An. arabiensis and An. melas). By amplifying and sequencing only 14 genetic fragments (500 bp each), we were able to simultaneously detect Plasmodium infection; insecticide resistance-conferring SNPs in ace1, gste2, vgsc and rdl genes; the partial sequences of nuclear ribosomal internal transcribed spacers (ITS1 and ITS2) and intergenic spacers (IGS), Short INterspersed Elements (SINE), as well as mitochondrial genes (cox1 and nd4) for species identification and genetic diversity. Using this amplicon sequencing approach with the four selected An. gambiae complex species, we identified a total of 15 non-synonymous mutations in the insecticide target genes, including previously described mutations associated with resistance and two new mutations (F1525L in vgsc and D148E in gste2). Overall, we present a reliable and cost-effective high-throughput panel for surveillance of An. gambiae complex mosquitoes in malaria endemic regions.

Abstract Klebsiella pneumoniae (Kp) bacteria are an increasing threat to public health and represent one of the most concerning pathogens involved in life-threatening infections and antimicrobial resistance (AMR). To understand the epidemiology of AMR of Kp in Portugal, we analysed whole genome sequencing, susceptibility testing and other meta data on 509 isolates collected nationwide from 16 hospitals and environmental settings between years 1980 and 2019. Predominant sequence types (STs) included ST15 (n = 161, 32%), ST147 (n = 36, 7%), ST14 (n = 26, 5%) or ST13 (n = 26, 5%), while 31% of isolates belonged to STs with fewer than 10 isolates. AMR testing revealed widespread resistance to aminoglycosides, fluoroquinolones, cephalosporins and carbapenems. The most common carbapenemase gene was bla KPC-3 . Whilst the distribution of AMR linked plasmids appears uncorrelated with ST, their frequency has changed over time. Before year 2010, the dominant plasmid group was associated with the extended spectrum beta-lactamase gene bla CTX-M-15 , but this group appears to have been displaced by another carrying the bla KPC-3 gene. Co-carriage of bla CTX-M and bla KPC-3 was uncommon. Our results from the largest genomics study of Kp in Portugal highlight the active transmission of strains with AMR genes and provide a baseline set of variants for future resistance monitoring and epidemiological studies.

Abstract Background SARS-CoV-2 virus sequencing has been applied to track the COVID-19 pandemic spread and assist the development of PCR-based diagnostics, serological assays, and vaccines. With sequencing becoming routine globally, bioinformatic tools are needed to assist in the robust processing of resulting genomic data. Results We developed a web-based bioinformatic pipeline (“COVID-Profiler”) that inputs raw or assembled sequencing data, displays raw alignments for quality control, annotates mutations found and performs phylogenetic analysis. The pipeline software can be applied to other (re-) emerging pathogens. Conclusions The webserver is available at http://genomics.lshtm.ac.uk/ . The source code is available at https://github.com/jodyphelan/covid-profiler .

PowerBacGWAS is a computational pipeline that uses existing genomic data to perform power calculations for bacterial genome-wide association studies.

Abstract Background Drug resistant Mycobacterium tuberculosis is complicating the effective treatment and control of tuberculosis disease (TB). With the adoption of whole genome sequencing as a diagnostic tool, machine learning approaches are being employed to predict M. tuberculosis resistance and identify underlying genetic mutations. However, machine learning approaches can overfit and fail to identify causal mutations if they are applied out of the box and not adapted to the disease-specific context. We introduce a machine learning approach that is customized to the TB setting, which extracts a library of genomic variants re-occurring across individual studies to improve genotypic profiling. Results We developed a customized decision tree approach, called Treesist-TB, that performs TB drug resistance prediction by extracting and evaluating genomic variants across multiple studies. The application of Treesist-TB to rifampicin (RIF), isoniazid (INH) and ethambutol (EMB) drugs, for which resistance mutations are known, demonstrated a level of predictive accuracy similar to the widely used TB-Profiler tool (Treesist-TB vs. TB-Profiler tool: RIF 97.5% vs. 97.6%; INH 96.8% vs. 96.5%; EMB 96.8% vs. 95.8%). Application of Treesist-TB to less understood second-line drugs of interest, ethionamide (ETH), cycloserine (CYS) and para-aminosalisylic acid (PAS), led to the identification of new variants (52, 6 and 11, respectively), with a high number absent from the TB-Profiler library (45, 4, and 6, respectively). Thereby, Treesist-TB had improved predictive sensitivity (Treesist-TB vs. TB-Profiler tool: PAS 64.3% vs. 38.8%; CYS 45.3% vs. 30.7%; ETH 72.1% vs. 71.1%). Conclusion Our work reinforces the utility of machine learning for drug resistance prediction, while highlighting the need to customize approaches to the disease-specific context. Through applying a modified decision learning approach (Treesist-TB) across a range of anti-TB drugs, we identified plausible resistance-encoding genomic variants with high predictive ability, whilst potentially overcoming the overfitting challenges that can affect standard machine learning applications.

Summary: Background: There remains uncertainty about the epidemiology of SARS-CoV-2 among school students and staff and the extent to which non-pharmaceutical-interventions reduce the risk of school settings. Methods: We conducted an open cohort study in a sample of 59 primary and 97 secondary schools in 15 English local authority areas that were implementing government guidance to schools open during the pandemic. We estimated SARS-CoV-2 infection prevalence among those attending school, antibody prevalence, and antibody negative to positive conversion rates in staff and students over the school year (November 2020–July 2021). Findings: 22,585 staff and students participated. SARS-CoV-2 infection prevalence among those attending school was highest during the first two rounds of testing in the autumn term, ranging from 0.7% (95% CI 0.2, 1.2) among primary staff in November 2020 to 1.6% (95% CI 0.9, 2.3) among secondary staff in December 2020. Antibody conversion rates were highest in the autumn term. Infection patterns were similar between staff and students, and between primary and secondary schools. The prevalence of nucleoprotein antibodies increased over the year and was lower among students than staff. SARS-CoV-2 infection prevalence in the North-West region was lower among secondary students attending school on normal school days than the regional estimate for secondary school-age children. Interpretation: SARS-CoV-2 infection prevalence in staff and students attending school varied with local community infection rates. Non-pharmaceutical interventions intended to prevent infected individuals attending school may have partially reduced the prevalence of infection among those on the school site. Funding: UK Department of Health and Social Care.

Abstract Background Multidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly and accurate resistance prediction tools are needed to enable public health and clinical practitioners to rapidly diagnose resistance and inform treatment regimens. Results We present Translational Genomics platform for Tuberculosis (GenTB), a free and open web-based application to predict antibiotic resistance from next-generation sequence data. The user can choose between two potential predictors, a Random Forest (RF) classifier and a Wide and Deep Neural Network (WDNN) to predict phenotypic resistance to 13 and 10 anti-tuberculosis drugs, respectively. We benchmark GenTB’s predictive performance along with leading TB resistance prediction tools (Mykrobe and TB-Profiler) using a ground truth dataset of 20,408 isolates with laboratory-based drug susceptibility data. All four tools reliably predicted resistance to first-line tuberculosis drugs but had varying performance for second-line drugs. The mean sensitivities for GenTB-RF and GenTB-WDNN across the nine shared drugs were 77.6% (95% CI 76.6–78.5%) and 75.4% (95% CI 74.5–76.4%), respectively, and marginally higher than the sensitivities of TB-Profiler at 74.4% (95% CI 73.4–75.3%) and Mykrobe at 71.9% (95% CI 70.9–72.9%). The higher sensitivities were at an expense of ≤ 1.5% lower specificity: Mykrobe 97.6% (95% CI 97.5–97.7%), TB-Profiler 96.9% (95% CI 96.7 to 97.0%), GenTB-WDNN 96.2% (95% CI 96.0 to 96.4%), and GenTB-RF 96.1% (95% CI 96.0 to 96.3%). Averaged across the four tools, genotypic resistance sensitivity was 11% and 9% lower for isoniazid and rifampicin respectively, on isolates sequenced at low depth (< 10× across 95% of the genome) emphasizing the need to quality control input sequence data before prediction. We discuss differences between tools in reporting results to the user including variants underlying the resistance calls and any novel or indeterminate variants Conclusions GenTB is an easy-to-use online tool to rapidly and accurately predict resistance to anti-tuberculosis drugs. GenTB can be accessed online at https://gentb.hms.harvard.edu , and the source code is available at https://github.com/farhat-lab/gentb-site .

Abstract Background Malaria, caused by Plasmodium parasites, is a major global public health problem. To assist an understanding of malaria pathogenesis, including drug resistance, there is a need for the timely detection of underlying genetic mutations and their spread. With the increasing use of whole-genome sequencing (WGS) of Plasmodium DNA, the potential of deep learning models to detect loci under recent positive selection, historically signals of drug resistance, was evaluated. Methods A deep learning-based approach (called “DeepSweep”) was developed, which can be trained on haplotypic images from genetic regions with known sweeps, to identify loci under positive selection. DeepSweep software is available from https://github.com/WDee/Deepsweep . Results Using simulated genomic data, DeepSweep could detect recent sweeps with high predictive accuracy (areas under ROC curve > 0.95). DeepSweep was applied to Plasmodium falciparum (n = 1125; genome size 23 Mbp) and Plasmodium vivax (n = 368; genome size 29 Mbp) WGS data, and the genes identified overlapped with two established extended haplotype homozygosity methods (within-population iHS, across-population Rsb) (~ 60–75% overlap of hits at P

The genetic diversity of Plasmodium vivax strains in South Asia isn’t well described. Here, the authors sequence P. vivax from returning UK travelers and establish South Asian isolates as subpopulation distinct from East African and South East Asian isolates.

Abstract Despite the advent of whole genome metagenomics, targeted approaches (such as 16S rRNA gene amplicon sequencing) continue to be valuable for determining the microbial composition of samples. Amplicon microbiome sequencing can be performed on clinical samples from a normally sterile site to determine the aetiology of an infection (usually single pathogen identification) or samples from more complex niches such as human mucosa or environmental samples where multiple microorganisms need to be identified. The methodologies are frequently applied to determine both presence of micro-organisms and their quantity or relative abundance. There are a number of technical steps required to perform microbial community profiling, many of which may have appreciable precision and bias that impacts final results. In order for these methods to be applied with the greatest accuracy, comparative studies across different laboratories are warranted. In this study we explored the impact of the bioinformatic approaches taken in different laboratories on microbiome assessment using 16S rRNA gene amplicon sequencing results. Data were generated from two mock microbial community samples which were amplified using primer sets spanning five different variable regions of 16S rRNA genes. The PCR-sequencing analysis included three technical repeats of the process to determine the repeatability of their methods. Thirteen laboratories participated in the study, and each analysed the same FASTQ files using their choice of pipeline. This study captured the methods used and the resulting sequence annotation and relative abundance output from bioinformatic analyses. Results were compared to digital PCR assessment of the absolute abundance of each target representing each organism in the mock microbial community samples and also to analyses of shotgun metagenome sequence data. This ring trial demonstrates that the choice of bioinformatic analysis pipeline alone can result in different estimations of the composition of the microbiome when using 16S rRNA gene amplicon sequencing data. The study observed differences in terms of both presence and abundance of organisms and provides a resource for ensuring reproducible pipeline development and application. The observed differences were especially prevalent when using custom databases and applying high stringency operational taxonomic unit (OTU) cut-off limits. In order to apply sequencing approaches with greater accuracy, the impact of different analytical steps needs to be clearly delineated and solutions devised to harmonise microbiome analysis results.

Abstract Klebsiella pneumoniae is an important nosocomial infectious agent with a high antimicrobial resistance (AMR) burden. The application of long read sequencing technologies is providing insights into bacterial chromosomal and putative extra-chromosomal genetic elements (PEGEs) associated with AMR, but also epigenetic DNA methylation, which is thought to play a role in cleavage of foreign DNA and expression regulation. Here, we apply the PacBio sequencing platform to eight Portuguese hospital isolates, including one carbapenemase producing isolate, to identify methylation motifs. The resulting assembled chromosomes were between 5.2 and 5.5Mbp in length, and twenty-six PEGEs were found. Four of our eight samples carry bla CTX-M-15, a dominant Extended Spectrum Beta Lactamase in Europe. We identified methylation motifs that control Restriction–Modification systems, including GATC of the DNA adenine methylase (Dam), which methylates N6-methyladenine (m6A) across all our K. pneumoniae assemblies. There was a consistent lack of methylation by Dam of the GATC motif downstream of two genes: fosA, a locus associated with low level fosfomycin resistance, and tnpB transposase on IncFIB(K) plasmids. Overall, we have constructed eight high quality reference genomes of K. pneumoniae, with insights into horizontal gene transfer and methylation m6A motifs.

Abstract Background Tuberculosis, caused by bacteria in the Mycobacterium tuberculosis complex (MTBC), is a major global public health burden. Strain-specific genomic diversity in the known lineages of MTBC is an important factor in pathogenesis that may affect virulence, transmissibility, host response and emergence of drug resistance. Fast and accurate tracking of MTBC strains is therefore crucial for infection control, and our previous work developed a 62-single nucleotide polymorphism (SNP) barcode to inform on the phylogenetic identity of 7 human lineages and 64 sub-lineages. Methods To update this barcode, we analysed whole genome sequencing data from 35,298 MTBC isolates (~ 1 million SNPs) covering 9 main lineages and 3 similar animal-related species (M. tuberculosis var. bovis, M. tuberculosis var. caprae and M. tuberculosis var. orygis). The data was partitioned into training (N = 17,903, 50.7%) and test (N = 17,395, 49.3%) sets and were analysed using an integrated phylogenetic tree and population differentiation (F ST) statistical approach. Results By constructing a phylogenetic tree on the training MTBC isolates, we characterised 90 lineages or sub-lineages or species, of which 30 are new, and identified 421 robust barcoding mutations, of which a minimal set of 90 was selected that included 20 markers from the 62-SNP barcode. The barcoding SNPs (90 and 421) discriminated perfectly the 86 MTBC isolate (sub-)lineages in the test set and could accurately reconstruct the clades across the combined 35k samples. Conclusions The validated 90 SNPs can be used for the rapid diagnosis and tracking of MTBC strains to assist public health surveillance and control. To facilitate this, the SNP markers have now been incorporated into the TB-Profiler informatics platform ( https://github.com/jodyphelan/TBProfiler ).

ABSTRACTThe Beijing genotype comprises a highly disseminated strain type that is frequently associated with multidrug resistant (MDR) tuberculosis (TB) and increased transmissibility but, countries such as Portugal and Guinea-Bissau fall outside the regions phylogeographically associated with this specific genotype. Nevertheless, recent data shows that this genotype might be gradually emerging in these two countries as an underlying cause of primary MDR-TB. Here, we describe the emergence of Mycobacterium tuberculosis Beijing strains associated with MDR-TB in Portugal and Guinea-Bissau demonstrating the presence of the well described superclusters 100-32 and 94-32 in Portugal and Guinea-Bissau, respectively. Genome-wide analysis and comparison with a global genomic dataset of M. tuberculosis Beijing strains, revealed the presence of two genomic clusters encompassing isolates from Portugal and Guinea-Bissau, GC1 (n = 121) and GC2 (n = 39), both of which bore SNP signatures compatible with the 100-32/B0/W148 and 94-32/Central Asia Outbreak clades, respectively. Moreover, GC2 encompasses a cross-border cluster between Portugal, Guinea-Bissau and Brazil thus supporting migration-associated introduction of MDR-TB and subsequent clonal expansion at the community-level. The comparison with global Beijing datasets demonstrates the global reach of the disease and its complex dissemination across multiple countries while in parallel there are clear microevolutionary trajectories towards extensively drug resistant TB.

Antimicrobials against bacterial, viral and parasitic pathogens have transformed human and animal health. Nevertheless, their widespread use (and misuse) has led to the emergence of antimicrobial resistance (AMR) which poses a potentially catastrophic threat to public health and animal husbandry. There are several routes, both intrinsic and acquired, by which AMR can develop. One major route is through non-synonymous single nucleotide polymorphisms (nsSNPs) in coding regions. Large scale genomic studies using high-throughput sequencing data have provided powerful new ways to rapidly detect and respond to such genetic mutations linked to AMR. However, these studies are limited in their mechanistic insight. Computational tools can rapidly and inexpensively evaluate the effect of mutations on protein function and evolution. Subsequent insights can then inform experimental studies, and direct existing or new computational methods. Here we review a range of sequence and structure-based computational tools, focussing on tools successfully used to investigate mutational effect on drug targets in clinically important pathogens, particularly Mycobacterium tuberculosis. Combining genomic results with the biophysical effects of mutations can help reveal the molecular basis and consequences of resistance development. Furthermore, we summarise how the application of such a mechanistic understanding of drug resistance can be applied to limit the impact of AMR.

Following a huge global effort, the first World Health Organization (WHO)-endorsed catalogue of 17,356 variants in the Mycobacterium tuberculosis complex along with their classification as associated with resistance (interim), not associated with resistance (interim) or uncertain significance was made public In June 2021. This marks a critical step towards the application of next generation sequencing (NGS) data for clinical care. Unfortunately, the variant format used makes it difficult to look up variants when NGS data is generated by other bioinformatics pipelines. Furthermore, the large number of variants of uncertain significance in the catalogue hamper its useability in clinical practice. We successfully converted 98.3% of variants from the WHO catalogue format to the standardized HGVS format. We also created TBProfiler version 4.4.0 to automate the calling of all variants located in the tier 1 and 2 candidate resistance genes along with their classification when listed in the WHO catalogue. Using a representative sample of 339 clinical isolates from South Africa containing 691 variants in a tier 1 or 2 gene, TBProfiler classified 105 (15%) variants as conferring resistance, 72 (10%) as not conferring resistance and 514 (74%) as unclassified, with an average of 29 unclassified variants per isolate. Using a second cohort of 56 clinical isolates from a TB outbreak in Spain containing 21 variants in the tier 1 and 2 genes, TBProfiler classified 13 (61.9%) as unclassified, 7 (33.3%) as not conferring resistance, and a single variant (4.8%) classified as conferring resistance. Continued global efforts using standardized methods for genotyping, phenotyping and bioinformatic analyses will be essential to ensure that knowledge on genomic variants translates into improved patient care.

Resistance to drugs used to treat tuberculosis disease (TB) continues to remain a public health burden, with missense point mutations in the underlying Mycobacterium tuberculosis bacteria described for nearly all anti-TB drugs. The post-genomics era along with advances in computational and structural biology provide opportunities to understand the interrelationships between the genetic basis and the structural consequences of M. tuberculosis mutations linked to drug resistance. Pyrazinamide (PZA) is a crucial first line antibiotic currently used in TB treatment regimens. The mutational promiscuity exhibited by the pncA gene (target for PZA) necessitates computational approaches to investigate the genetic and structural basis for PZA resistance development. We analysed 424 missense point mutations linked to PZA resistance derived from ∼35K M. tuberculosis clinical isolates sourced globally, which comprised the four main M. tuberculosis lineages (Lineage 1–4). Mutations were annotated to reflect their association with PZA resistance. Genomic measures (minor allele frequency and odds ratio), structural features (surface area, residue depth and hydrophobicity) and biophysical effects (change in stability and ligand affinity) of point mutations on pncA protein stability and ligand affinity were assessed. Missense point mutations within pncA were distributed throughout the gene, with the majority (>80%) of mutations with a destabilising effect on protomer stability and on ligand affinity. Active site residues involved in PZA binding were associated with multiple point mutations highlighting mutational diversity due to selection pressures at these functionally important sites. There were weak associations between genomic measures and biophysical effect of mutations. However, mutations associated with PZA resistance showed statistically significant differences between structural features (surface area and residue depth), but not hydrophobicity score for mutational sites. Most interestingly M. tuberculosis lineage 1 (ancient lineage) exhibited a distinct protein stability profile for mutations associated with PZA resistance, compared to modern lineages.

Abstract Global Mycobacterium tuberculosis population comprises 7 major lineages. The Beijing strains, particularly the ones classified as Modern groups, have been found worldwide, frequently associated with drug resistance, younger ages, outbreaks and appear to be expanding. Here, we report analysis of whole genome sequences of 1170 M. tuberculosis isolates together with their patient profiles. Our samples belonged to Lineage 1–4 (L1–L4) with those of L1 and L2 being equally dominant. Phylogenetic analysis revealed several new or rare sublineages. Differential associations between sublineages of M. tuberculosis and patient profiles, including ages, ethnicity, HIV (human immunodeficiency virus) infection and drug resistance were demonstrated. The Ancestral Beijing strains and some sublineages of L4 were associated with ethnic minorities while L1 was more common in Thais. L2.2.1.Ancestral 4 surprisingly had a mutation that is typical of the Modern Beijing sublineages and was common in Akha and Lahu tribes who have migrated from Southern China in the last century. This may indicate that the evolutionary transition from the Ancestral to Modern Beijing sublineages might be gradual and occur in Southern China, where the presence of multiple ethnic groups might have allowed for the circulations of various co-evolving sublineages which ultimately lead to the emergence of the Modern Beijing strains.

Abstract Background Continuing evolution of the Mycobacterium tuberculosis (Mtb) complex genomes associated with resistance to anti-tuberculosis drugs is threatening tuberculosis disease control efforts. Both multi- and extensively drug resistant Mtb (MDR and XDR, respectively) are increasing in prevalence, but the full set of Mtb genes involved are not known. There is a need for increased sensitivity of genome-wide approaches in order to elucidate the genetic basis of anti-microbial drug resistance and gain a more detailed understanding of Mtb genome evolution in a context of widespread antimicrobial therapy. Population structure within the Mtb complex, due to clonal expansion, lack of lateral gene transfer and low levels of recombination between lineages, may be reducing statistical power to detect drug resistance associated variants. Results To investigate the effect of lineage-specific effects on the identification of drug resistance associations, we applied genome-wide association study (GWAS) and convergence-based (PhyC) methods to multiple drug resistance phenotypes of a global dataset of Mtb lineages 2 and 4, using both lineage-wise and combined approaches. We identify both well-established drug resistance variants and novel associations; uniquely identifying associations for both lineage-specific and -combined GWAS analyses. We report 17 potential novel associations between antimicrobial resistance phenotypes and Mtb genomic variants. Conclusions For GWAS, both lineage-specific and -combined analyses are useful, whereas PhyC may perform better in contexts of greater diversity. Unique associations with XDR in lineage-specific analyses provide evidence of diverging evolutionary trajectories between lineages 2 and 4 in response to antimicrobial drug therapy.

Tuberculosis disease is a major global public health concern and the growing prevalence of drug-resistant Mycobacterium tuberculosis is making disease control more difficult. However, the increasing application of whole-genome sequencing as a diagnostic tool is leading to the profiling of drug resistance to inform clinical practice and treatment decision making. Computational approaches for identifying established and novel resistance-conferring mutations in genomic data include genome-wide association study (GWAS) methodologies, tests for convergent evolution and machine learning techniques. These methods may be confounded by extensive co-occurrent resistance, where statistical models for a drug include unrelated mutations known to be causing resistance to other drugs. Here, we introduce a novel 'cannibalistic' elimination algorithm ("Hungry, Hungry SNPos") that attempts to remove these co-occurrent resistant variants. Using an M. tuberculosis genomic dataset for the virulent Beijing strain-type (n = 3,574) with phenotypic resistance data across five drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, and streptomycin), we demonstrate that this new approach is considerably more robust than traditional methods and detects resistance-associated variants too rare to be likely picked up by correlation-based techniques like GWAS.

Background: Many health facilities in malaria endemic countries are dependent on Rapid diagnostic tests (RDTs) for diagnosis and some National Health Service (NHS) hospitals without expert microscopists rely on them for diagnosis out of hours. The emergence of P. falciparum lacking the gene encoding histidine-rich protein 2 and 3 (HRP2 and HRP3) and escaping RDT detection threatens progress in malaria control and elimination. Currently, confirmation of RDT negative due to the deletion of pfhrp2 and pfhrp3, which encodes a cross-reactive protein isoform, requires a series of PCR assays. These tests have different limits of detection and many laboratories have reported difficulty in confirming the absence of the deletions with certainty. Methods: We developed and validated a novel and rapid multiplex real time quantitative (qPCR) assay to detect pfhrp2, pfhrp3, confirmatory parasite and human reference genes simultaneously. We also applied the assay to detect pfhrp2 and pfhrp3 deletion in 462 field samples from different endemic countries and UK travellers. Results: The qPCR assay demonstrated diagnostic sensitivity of 100% (n = 19, 95% CI= (82.3%; 100%)) and diagnostic specificity of 100% (n = 31; 95% CI= (88.8%; 100%)) in detecting pfhrp2 and pfhrp3 deletions. In addition, the assay estimates P. falciparum parasite density and accurately detects pfhrp2 and pfhrp3 deletions masked in polyclonal infections. We report pfhrp2 and pfhrp3 deletions in parasite isolates from Kenya, Tanzania and in UK travellers. Interpretation: The new qPCR is easily scalable to routine surveillance studies in countries where P. falciparum parasites lacking pfhrp2 and pfhrp3 are a threat to malaria control.

Although Plasmodium vivax parasites are the predominant cause of malaria outside of sub-Saharan Africa, they not always prioritised by elimination programmes. P. vivax is resilient and poses challenges through its ability to re-emerge from dormancy in the human liver. With observed growing drug-resistance and the increasing reports of life-threatening infections, new tools to inform elimination efforts are needed. In order to halt transmission, we need to better understand the dynamics of transmission, the movement of parasites, and the reservoirs of infection in order to design targeted interventions. The use of molecular genetics and epidemiology for tracking and studying malaria parasite populations has been applied successfully in P. falciparum species and here we sought to develop a molecular genetic tool for P. vivax. By assembling the largest set of P. vivax whole genome sequences (n = 433) spanning 17 countries, and applying a machine learning approach, we created a 71 SNP barcode with high predictive ability to identify geographic origin (91.4%). Further, due to the inclusion of markers for within population variability, the barcode may also distinguish local transmission networks. By using P. vivax data from a low-transmission setting in Malaysia, we demonstrate the potential ability to infer outbreak events. By characterising the barcoding SNP genotypes in P. vivax DNA sourced from UK travellers (n = 132) to ten malaria endemic countries predominantly not used in the barcode construction, we correctly predicted the geographic region of infection origin. Overall, the 71 SNP barcode outperforms previously published genotyping methods and when rolled-out within new portable platforms, is likely to be an invaluable tool for informing targeted interventions towards elimination of this resilient human malaria.

Abstract Background The ongoing epidemic of multidrug-resistant tuberculosis (MDR-TB) in Georgia highlights the need for more effective control strategies. A new regimen to treat MDR-TB that includes pyrazinamide (PZA) is currently being evaluated and PZA resistance status will largely influence the success of current and future treatment strategies. PZA susceptibility testing was not routinely performed at the National Reference Laboratory (NRL) in Tbilisi between 2010 and September 2015. We here provide a first insight into the prevalence of PZA resistant TB in this region. Methods Phenotypic susceptibility to PZA was determined in a convenience collection of well-characterised TB patient isolates collected at the NRL in Tbilisi between 2012 and 2013. In addition, the pncA gene was sequenced and whole genome sequencing was performed on two isolates. Results Out of 57 isolates tested 33 (57.9%) showed phenotypic drug resistance to PZA and had a single pncA mutation. All of these 33 isolates were MDR-TB strains. pncA mutations were absent in all but one of the 24 PZA susceptible isolate. In total we found 18 polymorphisms in the pncA gene. From the two major MDR-TB clusters represented (94–32 and 100–32), 10 of 15, 67.0% and 13 of 14, 93.0% strains, respectively were PZA resistant. We also identified a member of the potentially highly transmissive clade A strain carrying the characteristic I6L substitution in PncA. Another strain with the same MLVA type as the clade A strain acquired a different mutation in pncA and was genetically more distantly related suggesting that different branches of this particular lineage have been introduced into this region. Conclusion In this high MDR-TB setting more than half of the tested MDR-TB isolates were resistant to PZA. As PZA is part of current and planned MDR-TB treatment regimens this is alarming and deserves the attention of health authorities. Based on our typing and sequence analysis results we conclude that PZA resistance is the result of primary transmission as well as acquisition within the patient and recommend prospective genotyping and PZA resistance testing in high MDR-TB settings. This is of utmost importance in order to preserve bacterial susceptibility to PZA to help protect (new) second line drugs in PZA containing regimens.

Background: Tuberculosis disease, caused by Mycobacterium tuberculosis, is a major public health problem. The emergence of M. tuberculosis strains resistant to existing treatments threatens to derail control efforts. Resistance is mainly conferred by mutations in genes coding for drug targets or converting enzymes, but our knowledge of these mutations is incomplete. Whole genome sequencing (WGS) is an increasingly common approach to rapidly characterize isolates and identify mutations predicting antimicrobial resistance and thereby providing a diagnostic tool to assist clinical decision making.Methods: We applied machine learning approaches to 16,688 M. tuberculosis isolates that have undergone WGS and laboratory drug-susceptibility testing (DST) across 14 antituberculosis drugs, with 22.5% of samples being multidrug resistant and 2.1% being extensively drug resistant. We used non-parametric classification-tree and gradient-boosted-tree models to predict drug resistance and uncover any associated novel putative mutations. We fitted separate models for each drug, with and without “co-occurrent resistance” markers known to be causing resistance to drugs other than the one of interest. Predictive performance was measured using sensitivity, specificity, and the area under the receiver operating characteristic curve, assuming DST results as the gold standard.Results: The predictive performance was highest for resistance to first-line drugs, amikacin, kanamycin, ciprofloxacin, moxifloxacin, and multidrug-resistant tuberculosis (area under the receiver operating characteristic curve above 96%), and lowest for third-line drugs such as D-cycloserine and Para-aminosalisylic acid (area under the curve below 85%). The inclusion of co-occurrent resistance markers led to improved performance for some drugs and superior results when compared to similar models in other large-scale studies, which had smaller sample sizes. Overall, the gradient-boosted-tree models performed better than the classification-tree models. The mutation-rank analysis detected no new single nucleotide polymorphisms linked to drug resistance. Discordance between DST and genotypically inferred resistance may be explained by DST errors, novel rare mutations, hetero-resistance, and nongenomic drivers such as efflux-pump upregulation.Conclusion: Our work demonstrates the utility of machine learning as a flexible approach to drug resistance prediction that is able to accommodate a much larger number of predictors and to summarize their predictive ability, thus assisting clinical decision making and single nucleotide polymorphism detection in an era of increasing WGS data generation.

The evolutionary epidemiology, resistome, virulome and mobilome of thirty-one multidrug resistant Klebsiella pneumoniae clinical isolates from the northern Vila Real region of Portugal were characterized using whole-genome sequencing and bioinformatic analysis. The genomic population structure was dominated by two main sequence types (STs): ST147 (n = 17; 54.8%) and ST15 (n = 6; 19.4%) comprising four distinct genomic clusters. Two main carbapenemase coding genes were detected (blaKPC-3 and blaOXA-48) along with additional extended-spectrum β-lactamase coding loci (blaCTX-M-15, blaSHV-12, blaSHV-27, and blaSHV-187). Moreover, whole genome sequencing enabled the identification of one Klebsiella variicola KPC-3 producer isolate previously misidentified as K. pneumoniae, which in addition to the blaKPC-3 carbapenemase gene, bore the chromosomal broad spectrum β-lactamase blaLEN-2 coding gene, oqxAB and fosA resistance loci. The blaKPC-3 genes were located in a Tn4401b transposon (K. variicolan = 1; K. pneumoniaen = 2) and Tn4401d isoform (K. pneumoniaen = 28). Overall, our work describes the first report of a blaKPC-3 producing K. variicola, as well as the detection of this species during infection control measures in surveillance cultures from infected patients. It also highlights the importance of additional control measures to overcome the clonal dissemination of carbapenemase producing clones.

BACKGROUND:Plasmodium knowlesi is the most common cause of malaria in Malaysian Borneo, with reporting limited to clinical cases presenting to health facilities and scarce data on the true extent of transmission. Serological estimations of transmission have been used with other malaria species to garner information about epidemiological patterns. However, there are a distinct lack of suitable serosurveillance tools for this neglected disease. METHODOLOGY/PRINCIPAL FINDINGS:Using in silico tools, we designed and expressed four novel P. knowlesi protein products to address the distinct lack of suitable serosurveillance tools: PkSERA3 antigens 1 and 2, PkSSP2/TRAP and PkTSERA2 antigen 1. Antibody prevalence to these antigens was determined by ELISA for three time-points post-treatment from a hospital-based clinical treatment trial in Sabah, East Malaysia (n = 97 individuals; 241 total samples for all time points). Higher responses were observed for the PkSERA3 antigen 2 (67%, 65/97) across all time-points (day 0: 36.9% 34/92; day 7: 63.8% 46/72; day 28: 58.4% 45/77) with significant differences between the clinical cases and controls (n = 55, mean plus 3 SD) (day 0 p

Zika virus infections can cause a range of neurologic disorders including congenital microcephaly. However, while Zika infections have been notified across all regions in Brazil, there has been an unusual number of congenital microcephaly case notifications concentrated in the Northeast of the country. To address this observation, we investigated epidemiological data (2014-2016) on arbovirus co-distribution, environmental and socio-economic factors for each region in Brazil. Data on arbovirus reported cases and microcephaly were collected from several Brazilian Ministry of Health databases for each Federal unit. These were complemented by environmental management, social economic and Aedes aegypti infestation index data, extracted from multiple databases. Spatial time "ecological" analysis on the number of arboviruses transmitted by Aedes mosquitoes in Brazil show that the distribution of dengue and Zika was widespread in the whole country, with higher incidence in the West-Central region. However, reported chikungunya cases were higher in the Northeast, the region also with the highest number of microcephaly cases registered. Social economic factors (human development index and poverty index) and environmental management (water supply/storage and solid waste management) pointed the Northeast as the less wealthy region. The Northeast is also the region with the highest risk of Aedes aegypti house infestation due to the man-made larval habitats. In summary, the results of our ecological analysis support the hypothesis that the unusual distribution of microcephaly might not be due to Zika infection alone and could be accentuated by poverty and previous or co-infection with other pathogens. Our study reinforces the link between poverty and the risk of disease and the need to understand the effect on pathogenesis of sequential exposure to arboviruses and co-viral infections. Comprehensive large-scale cohort studies are required to corroborate our findings. We recommend that the list of infectious diseases screened, particularly during pregnancy, be regularly updated to include and effectively differentiate all viruses from ongoing outbreaks.

Mycobacterium aurum (M. aurum) is an environmental mycobacteria that has previously been used in studies of anti-mycobacterial drugs due to its fast growth rate and low pathogenicity. The M. aurum genome has been sequenced and assembled into 46 contigs, with a total length of 6.02 Mb containing 5684 annotated protein-coding genes. A phylogenetic analysis using whole genome alignments positioned M. aurum close to Mycobacterium vaccae and Mycobacterium vanbaalenii, within a clade related to fast-growing mycobacteria. Large-scale genomic rearrangements were identified by comparing the M. aurum genome to those of Mycobacterium tuberculosis and Mycobacterium leprae. M. aurum orthologous genes implicated in resistance to anti-tuberculosis drugs in M. tuberculosis were observed. The sequence identity at the DNA level varied from 68.6% for pncA (pyrazinamide drug-related) to 96.2% for rrs (streptomycin, capreomycin). We observed two homologous genes encoding the catalase-peroxidase enzyme (katG) that is associated with resistance to isoniazid. Similarly, two emb B homologues were identified in the M. aurum genome. In addition to describing for the first time the genome of M. aurum , this work provides a resource to aid the use of M. aurum in studies to develop improved drugs for the pathogenic mycobacteria M. tuberculosis and M. leprae.

The Americas were the last continent colonized by humans carrying malaria parasites. Plasmodium falciparum from the New World shows very little genetic diversity and greater linkage disequilibrium, compared with its African counterparts, and is clearly subdivided into local, highly divergent populations. However, limited available data have revealed extensive genetic diversity in American populations of another major human malaria parasite, P. vivax.We used an improved sample preparation strategy and next-generation sequencing to characterize 9 high-quality P. vivax genome sequences from northwestern Brazil. These new data were compared with publicly available sequences from recently sampled clinical P. vivax isolates from Brazil (BRA, total n = 11 sequences), Peru (PER, n = 23), Colombia (COL, n = 31), and Mexico (MEX, n = 19).We found that New World populations of P. vivax are as diverse (nucleotide diversity π between 5.2 × 10-4 and 6.2 × 10-4) as P. vivax populations from Southeast Asia, where malaria transmission is substantially more intense. They display several non-synonymous nucleotide substitutions (some of them previously undescribed) in genes known or suspected to be involved in antimalarial drug resistance, such as dhfr, dhps, mdr1, mrp1, and mrp-2, but not in the chloroquine resistance transporter ortholog (crt-o) gene. Moreover, P. vivax in the Americas is much less geographically substructured than local P. falciparum populations, with relatively little between-population genome-wide differentiation (pairwise FST values ranging between 0.025 and 0.092). Finally, P. vivax populations show a rapid decline in linkage disequilibrium with increasing distance between pairs of polymorphic sites, consistent with very frequent outcrossing. We hypothesize that the high diversity of present-day P. vivax lineages in the Americas originated from successive migratory waves and subsequent admixture between parasite lineages from geographically diverse sites. Further genome-wide analyses are required to test the demographic scenario suggested by our data.

Background There is growing recognition that recent global increases in obesity are the product of a complex interplay between genetic and environmental factors. However, in gene-environment studies of obesity, ‘environment’ usually refers to individual behavioural factors that influence energy balance, whereas more upstream environmental factors are overlooked. We examined gene-environment interactions between genetic risk of obesity and two neighbourhood characteristics likely to be associated with obesity (proximity to takeaway/fast-food outlets and availability of physical activity facilities).Methods We used data from 335 046 adults aged 40–70 in the UK Biobank cohort to conduct a population-based cross-sectional study of interactions between neighbourhood characteristics and genetic risk of obesity, in relation to body mass index (BMI). Proximity to a fast-food outlet was defined as distance from home address to nearest takeaway/fast-food outlet, and availability of physical activity facilities as the number of formal physical activity facilities within 1 km of home address. Genetic risk of obesity was operationalised by weighted Genetic Risk Scores of 91 or 69 single nucleotide polymorphisms (SNP), and by six individual SNPs considered separately. Multivariable, mixed-effects models with product terms for the gene-environment interactions were estimated.Results After accounting for likely confounding, the association between proximity to takeaway/fast-food outlets and BMI was stronger among those at increased genetic risk of obesity, with evidence of an interaction with polygenic risk scores (p=0.018 and p=0.028 for 69-SNP and 91-SNP scores, respectively) and in particular with a SNP linked to MC4R (p=0.009), a gene known to regulate food intake. We found very little evidence of gene-environment interaction for the availability of physical activity facilities.Conclusions Individuals at an increased genetic risk of obesity may be more sensitive to exposure to the local fast-food environment. Ensuring that neighbourhood residential environments are designed to promote a healthy weight may be particularly important for those with greater genetic susceptibility to obesity.