Biology subjects

Sanchez-Buso, L.

Publications and source records attributed to Sanchez-Buso, L..

16 recordsLinked to original sources

bioRxiv2026

AMRgen: an R package for antimicrobial resistance genotype-phenotype analysis

Microbial whole-genome sequence data is now generated at scale, including to support antimicrobial resistance (AMR) surveillance and understand resistance mechanisms, yet analytical infrastructure for systematically linking AMR genotypes to measured phenotypes remains fragmented. Here we present AMRgen, an R package to support systematic AMR genotype-phenotype analysis. AMRgen imports and harmonises genotypic data from common bioinformatics tools, alongside phenotypic data from automated antimicrobial susceptibility testing instruments and public repositories. It supports common analyses linking data to reference distributions, modelling associations, quantifying concordance, and producing publication-ready visualisations including UpSet plots that jointly display genotypic marker combination frequencies and associated phenotypic distributions. We demonstrate AMRgens utility using publicly available surveillance data for World Health Organization priority AMR pathogens, Neisseria gonorrhoeae, Klebsiella pneumoniae, Escherichia coli and Salmonella enterica. AMRgen, available free and open-source at https://AMRgen.org, provides a reproducible end-to-end foundation for genotype-phenotype research in AMR genomics, clinical microbiology, and public health surveillance.

microbiology↗

bioRxiv2023

pyngoST: fast, simultaneous and accurate multiple sequence typing of Neisseria gonorrhoeae genome collections

Extensive gonococcal surveillance has been performed using molecular typing at global, regional, national and local levels. The three main genotyping schemes for this pathogen, Multi-Locus Sequence Typing (MLST), Neisseria gonorrhoeae Multi-Antigen Sequence Typing (NG-MAST) and N. gonorrhoeae Sequence Typing for Antimicrobial Resistance (NG-STAR), allow inter-laboratory and inter-study comparability and reproducibility and provide an approximation to the gonococcal population structure. With high-throughput whole-genome sequencing (WGS), we obtain a substantially higher and more accurate discrimination between strains, i.e., compared to previous molecular typing schemes where isolates with the same sequence type often have a different genomic background. However, WGS remains unavailable or not affordable in many laboratories, accordingly, bioinformatic tools that allow the integration of data among laboratories with and without access to WGS is imperative for a joint effort to increase our understanding of global pathogen threats. Here, we present pyngoST, a command-line Python tool for a fast, simultaneous and accurate sequence typing of the WHO priority pathogen N. gonorrhoeae, from WGS assemblies. pyngoST integrates MLST, NG-MAST and NG-STAR, and can also designate NG-STAR clonal complexes and NG-MAST genogroups, facilitating multiple sequence typing from large WGS assembly collections. Exact matches for existing alleles and STs are reported, but also closest matches of new alleles and STs. The implementation of a fast multi-pattern searching algorithm allows pyngoST to be rapid and report results on 500 WGS assemblies in under 1 minute. The mapping of typing results on a core genome tree of 2,375 gonococcal genomes revealed that NG-STAR is the scheme that best represents the population structure of this pathogen, emphasizing the role of antimicrobial use and antimicrobial resistance (AMR) as a driver of gonococcal evolution. IMPACT STATEMENTMolecular typing has been key for N. gonorrhoeae epidemiological and AMR surveillance, and WGS has revolutionized this typing. The most frequently used molecular typing schemes include MLST, NG-MAST and NG-STAR, and modifications of those. These schemes can be extracted from WGS assemblies for comparability and reproducibility of results with laboratories that do not have access to WGS technologies. pyngoST is a unique command-line Python tool that integrates all these common typing schemes under the same framework and performs rapid simultaneous user-defined multiple typing of large number of gonococcal genomes through a fast multi-pattern searching algorithm. Typing results on 2,375 gonococcal genomes revealed that NG-STAR best represents the genomic population structure of N. gonorrhoeae, highlighting the importance of antimicrobial use and AMR on the evolution of this pathogen. DATA SUMMARYO_LIpyngoST is written in Python 3 and is available from Github under the GPL-3.0 License (https://github.com/leosanbu/pyngoST). C_LIO_LIThe script can be installed via the Python pip package. C_LIO_LIGenome assemblies used in this study are from the Euro-GASP 2018 WGS survey and are available from Pathogenwatch: https://pathogen.watch/collection/eurogasp2018 (1,2). Pairwise single nucleotide polymorphism (SNP) distances among isolates from this dataset are also available from the same link. C_LIO_LIResults from running pyngoST on the Euro-GASP 2018 WGS dataset can be explored from Microreact: https://microreact.org/project/wYpBzCs9A6Uf7HEMA6zmmY-eurogasp2018-pyngost. C_LI

bioinformatics↗

bioRxiv2020

A global resource for genomic predictions of antimicrobial resistance and surveillance of Salmonella Typhi at Pathogenwatch

BackgroundMicrobial whole-genome sequencing (WGS) is now increasingly used to inform public health investigations of infectious disease. This approach has transformed our understanding of the global population structure of Salmonella enterica serovar Typhi (S. Typhi), the causative agent of typhoid fever. WGS has been particularly informative for understanding the global spread of multi-drug resistant (MDR) typhoid. As WGS capacity becomes more decentralised, there is a growing opportunity for collaboration and sharing of surveillance data within and between countries to inform disease control policies. This requires freely available, community driven tools that reduce the barriers to access genomic data for public health surveillance and that deliver genomic data on a global scale. MethodsHere we present the Pathogenwatch (https://pathogen.watch/styphi) scheme for S. Typhi, a web application enabling the rapid identification of genomic markers of antimicrobial resistance (AMR) and contextualization with public genomic data to identify high-risk clones at a population level. Data are delivered in single genome reports or in collections of genomes combined with geographic and other data using trees, maps and tables. ResultsWe show that the clustering of S. Typhi genomes in Pathogenwatch is comparable to established bioinformatics methods, and that genomic predictions of AMR are largely concordant with phenotypic drug susceptibility data. We demonstrate the public health utility of Pathogenwatch with examples selected from over 4,300 public genomes available in the application. ConclusionsPathogenwatch democratises genomic epidemiology of S. Typhi by providing an intuitive entry point for the analysis of WGS and linked epidemiological data, enabling international public health monitoring of the emergence and spread of high risk clones.

genomics↗

bioRxiv2020

A community-driven resource for genomic surveillance of Neisseria gonorrhoeae at Pathogenwatch

BackgroundAntimicrobial resistant (AMR) Neisseria gonorrhoeae is an urgent threat to public health, as strains resistant to at least one of the two last line antibiotics used in empiric therapy of gonorrhoea, ceftriaxone and azithromycin, have spread internationally. Whole genome sequencing (WGS) data can be used to identify new AMR clones, transmission networks and inform the development of point-of-care tests for antimicrobial susceptibility, novel antimicrobials and vaccines. Community driven tools that provide an easy access to and analysis of genomic and epidemiological data is the way forward for public health surveillance. MethodsHere we present a public health focussed scheme for genomic epidemiology of N. gonorrhoeae at Pathogenwatch (https://pathogen.watch/ngonorrhoeae). An international advisory group of experts in epidemiology, public health, genetics and genomics of N. gonorrhoeae was convened to inform on the utility of current and future analytics in the platform. We implement backwards compatibility with MLST, NG-MAST and NG-STAR typing schemes as well as an exhaustive library of genetic AMR determinants linked to a genotypic prediction of resistance to eight antibiotics. A collection of over 12,000 N. gonorrhoeae genome sequences from public archives has been quality-checked, assembled and made public together with available metadata for contextualization. ResultsAMR prediction from genome data revealed specificity values over 99% for azithromycin, ciprofloxacin and ceftriaxone and sensitivity values around 99% for benzylpenicillin and tetracycline. A case study using the Pathogenwatch collection of N. gonorrhoeae public genomes showed the global expansion of an azithromycin resistant lineage carrying a mosaic mtr over at least the last 10 years, emphasizing the power of Pathogenwatch to explore and evaluate genomic epidemiology questions of public health concern. ConclusionsThe N. gonorrhoeae scheme in Pathogenwatch provides customized bioinformatic pipelines guided by expert opinion that can be adapted to public health agencies and departments with little expertise in bioinformatics and lower resourced settings with internet connection but limited computational infrastructure. The advisory group will assess and identify ongoing public health needs in the field of gonorrhoea, particularly regarding gonococcal AMR, in order to further enhance utility with modified or new analytic methods.

genomics↗

bioRxiv2020

Phylogenomics of Mycobacterium africanum reveals a new lineage and a complex evolutionary history

Human tuberculosis is caused by members of the Mycobacterium tuberculosis Complex (MTBC). The MTBC comprises several human-adapted lineages known as M. tuberculosis sensu stricto as well as two lineages (L5 and L6) traditionally referred to as M. africanum. Strains of L5 and L6 are largely limited to West Africa for reasons unknown, and little is known on their genomic diversity, phylogeography and evolution. Here, we analyzed the genomes of 365 L5 and 326 L6 strains, plus five related genomes that had not been classified into any of the known MTBC lineages, isolated from patients from 21 African countries. Our population genomic and phylogeographical analyses show that the unclassified genomes belonged to a new group that we propose to name MTBC Lineage 9 (L9). While the most likely ancestral distribution of L9 was predicted to be East Africa, the most likely ancestral distribution for both L5 and L6 was the Eastern part of West Africa. Moreover, we found important differences between L5 and L6 strains with respect to their phylogeographical substructure, genetic diversity and association with drug resistance. In conclusion, our study sheds new light onto the genomic diversity and evolutionary history of M. africanum, and highlights the need to consider the particularities of each MTBC lineage for understanding the ecology and epidemiology of tuberculosis in Africa and globally.

genomics↗

bioRxiv2020

Increased power from bacterial genome-wide association conditional on known effects identifies Neisseria gonorrhoeae macrolide resistance mutations in the 50S ribosomal protein L4

The emergence of resistance to azithromycin complicates treatment of N. gonorrhoeae, the etiologic agent of gonorrhea. Population genomic analyses of clinical isolates have demonstrated that some azithromycin resistance remains unexplained after accounting for the contributions of known resistance mutations in the 23S rRNA and the MtrCDE efflux pump. Bacterial genome-wide association studies (GWAS) offer a promising approach for identifying novel resistance genes but must adequately address the challenge of controlling for genetic confounders while maintaining power to detect variants with lower effect sizes. Compared to a standard univariate GWAS, conducting GWAS conditioned on known resistance mutations with high effect sizes substantially reduced the number of variants that reached genome-wide significance and identified a G70D mutation in the 50S ribosomal protein L4 (encoded by the gene rplD) as significantly associated with increased azithromycin minimum inhibitory concentrations ({beta} = 1.03, 95% CI [0.76, 1.30]). The role and prevalence of these rplD mutations in conferring macrolide resistance in N. gonorrhoeae had been unclear. Here, we experimentally confirmed our GWAS results, identified other resistance-associated mutations in RplD, and showed that in total these RplD binding site mutations are prevalent (present in 5.42% of 4850 isolates) and geographically and temporally widespread (identified in 21/65 countries across two decades). Overall, our findings demonstrate the utility of conditional associations for improving the performance of microbial GWAS and advance our understanding of the genetic basis of macrolide resistance in a prevalent multidrug-resistant pathogen.

microbiology↗

bioRxiv2020

Increased antibiotic susceptibility in Neisseria gonorrhoeae through adaptation to the cervical environment

Neisseria gonorrhoeae is an urgent public health threat due to rapidly increasing incidence and antibiotic resistance. In contrast with the trend of increasing resistance, clinical isolates that have reverted to susceptibility regularly appear, prompting questions about which pressures compete with antibiotics to shape gonococcal evolution. Here, we used genome-wide association on the largest collection of N. gonorrhoeae isolates to date (n=4852) to identify loss-of-function (LOF) mutations in the efflux pump mtrCDE operon as a mechanism of increased antibiotic susceptibility and demonstrate that these mutations are overrepresented in cervical isolates relative to urethral isolates (odds ratio (OR) = 3.74, 95% CI [1.98-6.70]). In support of a model in which pump expression incurs a fitness cost in this niche, cervical isolates were also enriched relative to urethral isolates in LOF mutations in the mtrCDE activator mtrA (OR = 8.60, 95% CI [4.96-14.57]) and in farA, a subunit of the FarAB efflux pump (OR = 6.25, 95% CI [3.90-9.83]). In total, approximately 2 in 5 cervical isolates (42.6%) contained a LOF mutation in either the efflux pump components mtrC or farA or the activator mtrA. Our findings extend beyond N. gonorrhoeae to other Neisseria: mtrC LOF mutations are rare (<1%) in the primarily nasopharyngeal-colonizing N. meningitidis in a collection of 14,798 genomes but enriched in a heterosexual urethritis-associated lineage (8.6%, p = 9.90x10-5), indicating that efflux pump downregulation contributes broadly to the adaptation of pathogenic Neisseria to the female urogenital tract. Overall, our findings highlight the impact of integrating microbial population genomics with host metadata and demonstrate how host environmental pressures can lead to increased antibiotic susceptibility.

microbiology↗

bioRxiv2019

Genetic variation regulates the activation and specificity of Restriction-Modification systems in Neisseria gonorrhoeae

Restriction-Modification systems (RMS) are one of the main mechanisms of defence against foreign DNA invasion and can have an important role in the regulation of gene expression. The obligate human pathogen Neisseria gonorrhoeae carries one of the highest loads of RMS in its genome; between 13 to 15 of the three main types. Previous work has described their organization in the reference genome FA1090 and has experimentally inferred the associated methylated motifs. Here, we studied the structure of RMS and target methylated motifs in 25 gonococcal strains sequenced with Single Molecule Real-Time (SMRT) technology, which provides data on DNA modification. The results showed a variable picture of active RMS in different strains, with phase variation switching the activity of Type III RMS, and both the activity and specificity of a Type I RMS. Interestingly, the Dam methylase was found in place of the NgoAXI endonuclease in two of the strains, despite being previously thought to be absent in the gonococcus. We also identified the real methylation target of NgoAX as 5-GCAGA-3, different from that previously described. Results from this work give further insights into the diversity and dynamics of RMS and methylation patterns in N. gonorrhoeae.

microbiology↗

bioRxiv2019

Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data

Prediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning methods has been proposed as a promising platform for the development of sequence-based diagnostics. However, there has been no systematic evaluation of factors that may influence performance of such models, how they might apply to and vary across clinical populations, and what the implications might be in the clinical setting. Here, we performed a meta-analysis of seven large Neisseria gonorrhoeae datasets, as well as Klebsiella pneumoniae and Acinetobacter baumannii datasets, with whole genome sequence data and antibiotic susceptibility phenotypes using set covering machine classification, random forest classification, and random forest regression models to predict resistance phenotypes from genotype. We demonstrate how model performance varies by drug, dataset, resistance metric, and species, reflecting the complexities of generating clinically relevant conclusions from machine learning-derived models. Our findings underscore the importance of incorporating relevant biological and epidemiological knowledge into model design and assessment and suggest that doing so can inform tailored modeling for individual drugs, pathogens, and clinical populations. We further suggest that continued comprehensive sampling and incorporation of up-to-date whole genome sequence data, resistance phenotypes, and treatment outcome data into model training will be crucial to the clinical utility and sustainability of machine learning-based molecular diagnostics.\n\nAuthor SummaryMachine learning-based prediction of antibiotic resistance from bacterial genome sequences represents a promising tool to rapidly determine the antibiotic susceptibility profile of clinical isolates and reduce the morbidity and mortality resulting from inappropriate and ineffective treatment. However, while there has been much focus on demonstrating the diagnostic potential of these modeling approaches, there has been little assessment of potential caveats and prerequisites associated with implementing predictive models of drug resistance in the clinical setting. Our results highlight significant biological and technical challenges facing the application of machine learning-based prediction of antibiotic resistance as a diagnostic tool. By outlining specific factors affecting model performance, our findings provide a framework for future work on modeling drug resistance and underscore the necessity of continued comprehensive sampling and reporting of treatment outcome data for building reliable and sustainable diagnostics.

microbiology↗

bioRxiv2019

Genome-wide epistasis and co-selection study using mutual information

Discovery of polymorphisms under co-selective pressure or epistasis has received considerable recent attention in population genomics. Both statistical modeling of the population level co-variation of alleles across the chromosome and model-free testing of dependencies between pairs of polymorphisms have been shown to successfully uncover patterns of selection in bacterial populations. Here we introduce a model-free method, SpydrPick, whose computational efficiency enables analysis at the scale of pan-genomes of many bacteria. SpydrPick incorporates an efficient correction for population structure, which is demonstrated to maintain a very low rate of false positive findings among those SNP pairs highlighted to deviate significantly from the null hypothesis of neutral co-evolution in simulated data. We also introduce a new type of visualization of the results similar to the Manhattan plots used in genome-wide association studies, which enables rapid exploration of the identified signals of co-evolution. Application of the method to large population genomic data sets of two major human pathogens, Streptococcus pneumoniae and Neisseria meningitidis, revealed both previously identified and novel putative targets of co-selection related to virulence and antibiotic resistance, highlighting the potential of this approach to drive molecular discoveries, even in the absence of phenotypic data.

genomics↗

bioRxiv2018

Molecular epidemiology and whole genome sequencing analysis of clinical Mycobacterium bovis from Ghana

BackgroundBovine tuberculosis (bTB) caused by Mycobacterium bovis is a re-emerging problem in both livestock and humans. The association of some M. bovis strains with hyper-virulence, MDR-TB and disseminated disease makes it imperative to understand the biology of the pathogen.\n\nMethodsMycobacterium bovis (15) among 1755 M. tuberculosis complex (MTBC) isolated between 2012 and 2014 were characterized and analyzed for associated patient demography and other risk factors. Five of the M. bovis were whole-genome sequenced and comparatively analyzed against a global collection of published M. bovis genomes.\n\nResultsMycobacterium bovis was isolated from 3/560(0.5%) females and 12/1195(1.0%) males with pulmonary TB. The average age of M. bovis infected cases was 46.8 years (7-72years). TB patients from the Northern region of Ghana (1.9%;4/212) had a higher rate of infection with M. bovis (OR=2.7,p=0.0968) compared to those from the Greater Accra region (0.7%;11/1543). Among TB patients with available HIV status, the odds of isolating M. bovis from HIV patients (2/119) was 3.3 higher relative to non-HIV patients (4/774). Direct contact with livestock or their unpasteurized products was significantly associated with bTB (p<0.0001,OR=124.4,95% CI=30.1-508.3). Two (13.3%) of the M. bovis isolates were INH resistant due to the S315T mutation in katG whereas one (6.7%) was RIF resistant with Q432P and I1491S mutations in rpoB. M. bovis from Ghana resolved as mono-phyletic branch among mostly M. bovis from Africa irrespective of the host and were closest to the root of the global M. bovis phylogeny. M. bovis-specific amino acid mutations were detected among MTBC core genes such as mce1A, mmpL1, pks6, phoT, pstB, glgP and Rv2955c. Additional mutations P6T in chaA, G187E in mgtC, T35A in Rv1979c, S387A in narK1, L400F in fas and A563T in eccA1 were restricted to the 5 clinical M. bovis from Ghana.\n\nConclusionOur data indicate potential zoonotic transmission of bTB in Ghana and hence calls for intensified public education on bTB, especially among risk groups.

microbiology↗

bioRxiv2018

Oxford Nanopore MinION genome sequencer: performance characteristics, optimised analysis workflow, phylogenetic analysis and prediction of antimicrobial resistance in Neisseria gonorrhoeae

Antimicrobial resistant (AMR) Neisseria gonorrhoeae strains are common and compromise gonorrhoea treatment internationally. Rapid identification and characterisation of AMR gonococcal strains could ensure appropriate and even personalised treatment, and support identification and investigation of gonorrhoea outbreaks in nearly real-time. Whole-genome sequencing is ideal for investigation of the emergence and dissemination of AMR determinants that predict AMR in the gonococcal population and spread of AMR strains in the human population. The novel, rapid and revolutionary long-read sequencer MinION is a small hand-held device that can generate bacterial genomes within one day. However, the accuracy of MinION reads has been suboptimal for many objectives and the MinION has not been evaluated for gonococci. In this first MinION study for gonococci, we show that MinION-derived sequences analysed with existing open-access, web-based sequence analysis tools are not sufficiently accurate to identify key gonococcal AMR determinants. Nevertheless, using an in house-developed CLC Genomics Workbench, we show that ONT-derived sequences can be used for accurate prediction of decreased susceptibility or resistance to recommended therapeutic antimicrobials. We also show that the ONT-derived sequences can be useful for rapid phylogenomic-based molecular epidemiological investigations, and, in hybrid assemblies with Illumina sequences, for producing contiguous assemblies and finished reference genomes.

genomics↗

bioRxiv2018

Antimicrobial exposure in sexual networks drives divergent evolution in modern gonococci

The sexually transmitted pathogen Neisseria gonorrhoeae is regarded as being on the way to becoming an untreatable superbug. Despite its clinical importance, little is known about its emergence and evolution, and how this corresponds with the introduction of antimicrobials. We present a genome-based phylogeographic analysis of 419 gonococcal isolates from across the globe. Results indicate that modern gonococci originated in Europe or Africa as late as the 16thcentury and subsequently disseminated globally. We provide evidence that the modern gonococcal population has been shaped by antimicrobial treatment of sexually transmitted and other infections, leading to the emergence of two major lineages with different evolutionary strategies. The well-described multi-resistant lineage is associated with high rates of homologous recombination and infection in high-risk sexual networks where antimicrobial treatment is frequent. A second, multi-susceptible lineage associated with heterosexual networks, where asymptomatic infection is more common, was also identified, with potential implications for infection control.

genomics↗

bioRxiv2018

Genomic determinants of sympatric speciation of the Mycobacterium tuberculosis complex across evolutionary timescales.

BACKGROUNDModels on how bacterial lineages differentiate increase our understanding on early bacterial speciation events and about the genetic loci involved. Here, we analyze the population genomics events leading to the emergence of the tuberculosis pathogen.\n\nRESULTSThe emergence is characterized by a combination of recombination events involving core pathogenesis functions and purifying selection on early diverging loci. We identify the phoR gene, the sensor kinase of a two-component system involved in virulence, as a key functional player subject to pervasive positive selection after the divergence of the MTBC from its ancestor. Previous evidence showed that phoR mutations played a central role in the adaptation of the pathogen to different host species. Now we show that phoR have been under selection during the early spread of human tuberculosis, during later expansions and in on-going transmission events.\n\nCONCLUSIONSOur results show that linking pathogen evolution across evolutionary and epidemiological timescales point to past and present virulence determinants.

evolutionary biology↗

bioRxiv2017

Comparative genomics of Mycobacterium africanum Lineage 5 and Lineage 6 from Ghana suggests different ecological niches

Mycobacterium africanum (Maf) causes up to half of human tuberculosis in West Africa, but little is known on this pathogen. We compared the genomes of 253 Maf clinical isolates from Ghana, including both L5 and L6. We found that the genomic diversity of L6 was higher than in L5, and the selection pressures differed between both groups. Regulatory proteins appeared to evolve neutrally in L5 but under purifying selection in L6. Conversely, human T cell epitopes were under purifying selection in L5, but under positive selection in L6. Although only 10% of the T cell epitopes were variable, mutations were mostly lineage-specific. Our findings indicate that Maf L5 and L6 are genomically distinct, possibly reflecting different ecological niches.

genomics↗

bioRxiv2017

ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads

Antimicrobial resistance (AMR) is one of the major threats to human and animal health worldwide, yet few high-throughput tools exist to analyse and predict the resistance of a bacterial isolate from sequencing data. Here we present a new tool, ARIBA, that identifies AMR-associated genes and single nucleotide polymorphisms directly from short reads, and generates detailed and customisable output. The accuracy and advantages of ARIBA over other tools are demonstrated on three datasets from Gram-positive and Gram-negative bacteria, with ARIBA outperforming existing methods. ARIBA is available at https://github.com/sanger-pathogens/ariba.

bioinformatics↗