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Methodologies for source attribution (SA) of foodborne illnesses comprise a rapidly expanding suite of techniques for estimating the most important source or sources of human infection. Recently, the increasing availability of whole genome sequencing (WGS) data for a wide range of bacterial strains has led to the development of novel SA methods. These techniques utilize the unique features of bacterial genomes adapted to different host types and hence offer increased resolution of the outputs. Comparative studies of different SA techniques reliant on WGS data are currently lacking. Here, we critically assessed and compared the outputs of three SA methods: a supervised classification random forest machine learning algorithm (RandomForest), an Accessory genes-Based Source Attribution method (AB_SA), and a Bayesian frequency matching method (Bayesian). Each technique was applied to the WGS data of a panel of 902 reservoir host and human monophasic and biphasic Salmonella enterica subsp. enterica serovar Typhimurium isolates sampled in the British Isles (BI) and Denmark from 2012 to 2016. Additionally, for RandomForest and Bayesian, we explored whether utilization of accessory genome features as model inputs improved attribution accuracy of these methods over using the core genome derived features only. Results indicated that this was the case for RandomForest, but for Bayesian the overall attribution estimates varied little regardless of the inclusion or not of the accessory genome features. All three methods attributed the vast majority of human isolates to the Pigs primary source class, which was expected given the known high relative prevalence rates in pigs, and hence routes of infection into the human population, of monophasic and biphasic S. Typhimurium in the BI and Denmark. The accuracy of AB_SA was lower than of RandomForest when attributing the primary source classes to the 120 animal test set isolates with known primary sources. A major advantage of both AB_SA and Bayesian was a much faster execution time as compared to RandomForest. Overall, the SA method comparison presented in this study describes the strengths and weaknesses of each of the three methods applied to attributing potential monophasic and biphasic S. Typhimurium animal sources to human infections that could be valuable when deciding which SA methodology would be the most applicable to foodborne disease outbreak scenarios involving monophasic and biphasic S. Typhimurium. [ABSTRACT FROM AUTHOR]

The main objective of this study was to investigate the antimicrobial activity of three putative antagonist bacterial strains of Bacillus spp. against Xanthomonas vasicola pv. vasculorum (Xvv) and their potential to control bacterial leaf streak (BLS) of corn. Additionally, the study included investigations on the genome of one of these antagonist bacteria, such as genome sequencing and mining of genes involved in biofilm formation, swarming motility, and synthesis of secondary metabolites. The growth of Xvv was inhibited by both cell suspensions and cell-free supernatants of the bacterial strains LAFUEL 01, LAFUEL 02, and LAFUEL 03 in agar diffusion tests. All three antagonist strains significantly reduced the severity of BLS in the 3rd and 4th leaves of corn plants that were artificially inoculated at the V3 growth stage under greenhouse conditions. The 16S rRNA sequencing confirmed that the antagonistic bacterial strains belong to the genus Bacillus, with LAFUEL 03 having approximately 97% similarity to B. velezensis. B. velezensis LAFUEL 03 harbors genes related to the biosynthesis of secondary metabolites, biofilm formation/regulation, and swarming motility that enhances its potential for controlling BLS in corn and suggests a promising candidate for the development of a commercial biocontrol agent. [ABSTRACT FROM AUTHOR]

Legionella pneumophila is ubiquitous and sporadically infects humans causing Legionnaire's disease (LD). Globally, reported cases of LD have risen fourfold from 2000 to 2014. In 2016, Sydney, Australia was the epicenter of an outbreak caused by L. pneumophila serogroup 1 (Lpsg1). Whole-genome sequencing was instrumental in identifying the causal clone which was found in multiple locations across the city. This study examined the epidemiology of Lpsg1 in an urban environment, assessed typing schemes to classify resident clones, and investigated the association between local climate variables and LD outbreaks. Of 223 local Lpsg1 isolates, we identified dominant clones with one clone isolated from patients in high frequency during outbreak investigations. The core genome multi-locus sequence typing scheme was the most reliable in identifying this Lpsg1 clone. While an increase in humidity and rainfall was found to coincide with a rise in LD cases, the incidence of the major L. pneumophila outbreak clone did not link to weather phenomena. These findings demonstrated the role of high-resolution typing and weather context assessment in determining source attribution for LD outbreaks in urban settings, particularly when clinical isolates remain scarce. [ABSTRACT FROM AUTHOR]

Abstract Background Oxford Nanopore provides high throughput sequencing platforms able to reconstruct complete bacterial genomes with 99.95% accuracy. However, even small levels of error can obscure the phylogenetic relationships between closely related isolates. Polishing tools have been developed to correct these errors, but it is uncertain if they obtain the accuracy needed for the high-resolution source tracking of foodborne illness outbreaks. Results We tested 132 combinations of assembly and short- and long-read polishing tools to assess their accuracy for reconstructing the genome sequences of 15 highly similar Salmonella enterica serovar Newport isolates from a 2020 onion outbreak. While long-read polishing alone improved accuracy, near perfect accuracy (99.9999% accuracy or ~ 5 nucleotide errors across the 4.8 Mbp genome, excluding low confidence regions) was only obtained by pipelines that combined both long- and short-read polishing tools. Notably, medaka was a more accurate and efficient long-read polisher than Racon. Among short-read polishers, NextPolish showed the highest accuracy, but Pilon, Polypolish, and POLCA performed similarly. Among the 5 best performing pipelines, polishing with medaka followed by NextPolish was the most common combination. Importantly, the order of polishing tools mattered i.e., using less accurate tools after more accurate ones introduced errors. Indels in homopolymers and repetitive regions, where the short reads could not be uniquely mapped, remained the most challenging errors to correct. Conclusions Short reads are still needed to correct errors in nanopore sequenced assemblies to obtain the accuracy required for source tracking investigations. Our granular assessment of the performance of the polishing pipelines allowed us to suggest best practices for tool users and areas for improvement for tool developers.

Structural color is an optical phenomenon resulting from light interacting with nanostructured materials. Although structural color (SC) is widespread in the tree of life, the underlying genetics and genomics are not well understood. Here, we collected and sequenced a set of 87 structurally colored bacterial isolates and 30 related strains lacking SC. Optical analysis of colonies indicated that diverse bacteria from at least two different phyla (Bacteroidetes and Proteobacteria) can create two-dimensional packing of cells capable of producing SC. A pan-genome-wide association approach was used to identify genes associated with SC. The biosynthesis of uroporphyrin and pterins, as well as carbohydrate utilization and metabolism, was found to be involved. Using this information, we constructed a classifier to predict SC directly from bacterial genome sequences and validated it by cultivating and scoring 100 strains that were not part of the training set. We predicted that SCr is widely distributed within gram-negative bacteria. Analysis of over 13,000 assembled metagenomes suggested that SC is nearly absent from most habitats associated with multicellular organisms except macroalgae and is abundant in marine waters and surface/air interfaces. This work provides a large-scale ecogenomics view of SC in bacteria and identifies microbial pathways and evolutionary relationships that underlie this optical phenomenon. [ABSTRACT FROM AUTHOR]

Background: Oxford Nanopore provides high throughput sequencing platforms able to reconstruct complete bacterial genomes with 99.95% accuracy. However, even small levels of error can obscure the phylogenetic relationships between closely related isolates. Polishing tools have been developed to correct these errors, but it is uncertain if they obtain the accuracy needed for the high-resolution source tracking of foodborne illness outbreaks. Results: We tested 132 combinations of assembly and short- and long-read polishing tools to assess their accuracy for reconstructing the genome sequences of 15 highly similar Salmonella enterica serovar Newport isolates from a 2020 onion outbreak. While long-read polishing alone improved accuracy, near perfect accuracy (99.9999% accuracy or ~ 5 nucleotide errors across the 4.8 Mbp genome, excluding low confidence regions) was only obtained by pipelines that combined both long- and short-read polishing tools. Notably, medaka was a more accurate and efficient long-read polisher than Racon. Among short-read polishers, NextPolish showed the highest accuracy, but Pilon, Polypolish, and POLCA performed similarly. Among the 5 best performing pipelines, polishing with medaka followed by NextPolish was the most common combination. Importantly, the order of polishing tools mattered i.e., using less accurate tools after more accurate ones introduced errors. Indels in homopolymers and repetitive regions, where the short reads could not be uniquely mapped, remained the most challenging errors to correct. Conclusions: Short reads are still needed to correct errors in nanopore sequenced assemblies to obtain the accuracy required for source tracking investigations. Our granular assessment of the performance of the polishing pipelines allowed us to suggest best practices for tool users and areas for improvement for tool developers. [ABSTRACT FROM AUTHOR]

ABSTRACT Staphylococcus aureus (S. aureus) is a clinically significant opportunistic pathogen, which can colonize multiple body sites in healthy individuals and cause various life-threatening diseases in both children and adults worldwide. The genetic backgrounds of S. aureus that cause infection versus asymptomatic carriage vary widely, but the potential genetic elements (k-mers) associated with S. aureus infection remain unknown, which leads to difficulties in differentiating infection isolates from harmless colonizers. Here, we address the disease-associated k-mers by using a comprehensive genome-wide association study (GWAS) to compare the genetic variation of S. aureus isolates from clinical infection sites (272 isolates) with nasal carriage (240 isolates). This study uncovers consensus evidence that certain k-mers are overrepresented in infection isolates compared with carriage isolates, indicating the presence of specific genetic elements associated with S. aureus infection. Moreover, the random forest (RF) model achieved a classification accuracy of 77% for predicting disease status (infection vs carriage), with 68% accuracy for a single highest-ranked k-mer, providing a simple target for identifying high-risk genotypes. Our findings suggest that the disease-causing S. aureus is a pathogenic subpopulation harboring unique genomic variation that promotes invasion and infection, providing novel targets for clinical interventions.IMPORTANCEDefining the disease-causing isolates is the first step toward disease control. However, the disease-associated genetic elements of Staphylococcus aureus remain unknown, which leads to difficulties in differentiating infection isolates from harmless carriage isolates. Our comprehensive genome-wide association study (GWAS) found consensus evidence that certain genetic elements are overrepresented among infection isolates than carriage isolates, suggesting that the enrichment of disease-associated elements may promote infection. Notably, a single k-mer predictor achieved a high classification accuracy, which forms the basis for early diagnostics and interventions.

Source attribution has traditionally involved combining epidemiological data with different pathogen characterisation methods, including 7-gene multi locus sequence typing (MLST) or serotyping, however, these approaches have limited resolution. In contrast, whole genome sequencing data provide an overview of the whole genome that can be used by attribution algorithms. Here, we applied a random forest (RF) algorithm to predict the primary sources of human clinical Salmonella Typhimurium (S. Typhimurium) and monophasic variants (monophasic S. Typhimurium) isolates. To this end, we utilised single nucleotide polymorphism diversity in the core genome MLST alleles obtained from 1,061 laboratory-confirmed human and animal S. Typhimurium and monophasic S. Typhimurium isolates as inputs into a RF model. The algorithm was used for supervised learning to classify 399 animal S. Typhimurium and monophasic S. Typhimurium isolates into one of eight distinct primary source classes comprising common livestock and pet animal species: cattle, pigs, sheep, other mammals (pets: mostly dogs and horses), broilers, layers, turkeys, and game birds (pheasants, quail, and pigeons). When applied to the training set animal isolates, model accuracy was 0.929 and kappa 0.905, whereas for the test set animal isolates, for which the primary source class information was withheld from the model, the accuracy was 0.779 and kappa 0.700. Subsequently, the model was applied to assign 662 human clinical cases to the eight primary source classes. In the dataset, 60/399 (15.0%) of the animal and 141/662 (21.3%) of the human isolates were associated with a known outbreak of S. Typhimurium definitive type (DT) 104. All but two of the 141 DT104 outbreak linked human isolates were correctly attributed by the model to the primary source classes identified as the origin of the DT104 outbreak. A model that was run without the clonal DT104 animal isolates produced largely congruent outputs (training set accuracy 0.989 and kappa 0.985; test set accuracy 0.781 and kappa 0.663). Overall, our results show that RF offers considerable promise as a suitable methodology for epidemiological tracking and source attribution for foodborne pathogens. [ABSTRACT FROM AUTHOR]

Fundamental to effective Legionnaires’ disease outbreak control is the ability to rapidly identify the environmental source(s) of the causative agent, Legionella pneumophila. Genomics has revolutionized pathogen surveillance, but L. pneumophila has a complex ecology and population structure that can limit source inference based on standard core genome phylogenetics. Here, we present a powerful machine learning approach that assigns the geographical source of Legionnaires’ disease outbreaks more accurately than current core genome comparisons. Models were developed upon 534 L. pneumophila genome sequences, including 149 genomes linked to 20 previously reported Legionnaires’ disease outbreaks through detailed case investigations. Our classification models were developed in a cross-validation framework using only environmental L. pneumophila genomes. Assignments of clinical isolate geographic origins demonstrated high predictive sensitivity and specificity of the models, with no false positives or false negatives for 13 out of 20 outbreak groups, despite the presence of within-outbreak polyclonal population structure. Analysis of the same 534-genome panel with a conventional phylogenomic tree and a core genome multi-locus sequence type allelic distance-based classification approach revealed that our machine learning method had the highest overall classification performance—agreement with epidemiological information. Our multivariate statistical learning approach maximizes the use of genomic variation data and is thus well-suited for supporting Legionnaires’ disease outbreak investigations. IMPORTANCE Identifying the sources of Legionnaires’ disease outbreaks is crucial for effective control. Current genomic methods, while useful, often fall short due to the complex ecology and population structure of Legionella pneumophila, the causative agent. Our study introduces a high-performing machine learning approach for more accurate geographical source attribution of Legionnaires’ disease outbreaks. Developed using cross-validation on environmental L. pneumophila genomes, our models demonstrate excellent predictive sensitivity and specificity. Importantly, this new approach outperforms traditional methods like phylogenomic trees and core genome multi-locus sequence typing, proving more efficient at leveraging genomic variation data to infer outbreak sources. Our machine learning algorithms, harnessing both core and accessory genomic variation, offer significant promise in public health settings. By enabling rapid and precise source identification in Legionnaires’ disease outbreaks, such approaches have the potential to expedite intervention efforts and curtail disease transmission. [ABSTRACT FROM AUTHOR]

The main objective of this study was to investigate the antimicrobial activity of three putative antagonist bacterial strains of Bacillus spp. against Xanthomonas vasicola pv. vasculorum (Xvv) and their potential to control bacterial leaf streak (BLS) of corn. Additionally, the study included investigations on the genome of one of these antagonist bacteria, such as genome sequencing and mining of genes involved in biofilm formation, swarming motility, and synthesis of secondary metabolites. The growth of Xvv was inhibited by both cell suspensions and cell-free supernatants of the bacterial strains LAFUEL 01, LAFUEL 02, and LAFUEL 03 in agar diffusion tests. All three antagonist strains significantly reduced the severity of BLS in the 3rd and 4th leaves of corn plants that were artificially inoculated at the V3 growth stage under greenhouse conditions. The 16S rRNA sequencing confirmed that the antagonistic bacterial strains belong to the genus Bacillus, with LAFUEL 03 having approximately 97% similarity to B. velezensis. B. velezensis LAFUEL 03 harbors genes related to the biosynthesis of secondary metabolites, biofilm formation/regulation, and swarming motility that enhances its potential for controlling BLS in corn and suggests a promising candidate for the development of a commercial biocontrol agent.

BackgroundSince the prolonged use of insecticidal proteins has led to toxin resistance, it is important to search for novel insecticidal protein genes (IPGs) that are effective in controlling resistant insect populations. IPGs are usually encoded in the genomes of entomopathogenic bacteria, especially in large plasmids in strains of the ubiquitous soil bacteria, Bacillus thuringiensis (Bt). Since there are often multiple similar IPGs encoded by such plasmids, their assemblies are typically fragmented and many IPGs are scattered through multiple contigs. As a result, existing gene prediction tools (that analyze individual contigs) typically predict partial rather than complete IPGs, making it difficult to conduct downstream IPG engineering efforts in agricultural genomics.MethodsAlthough it is difficult to assemble IPGs in a single contig, the structure of the genome assembly graph often provides clues on how to combine multiple contigs into segments encoding a single IPG.ResultsWe describe ORFograph, a pipeline for predicting IPGs in assembly graphs, benchmark it on (meta)genomic datasets, and discover nearly a hundred novel IPGs. This work shows that graph-aware gene prediction tools enable the discovery of greater diversity of IPGs from (meta)genomes.ConclusionsWe demonstrated that analysis of the assembly graphs reveals novel candidate IPGs. ORFograph identified both already known genes "hidden" in assembly graphs and potential novel IPGs that evaded existing tools for IPG identification. As ORFograph is fast, one could imagine a pipeline that processes many (meta)genomic assembly graphs to identify even more novel IPGs for phenotypic testing than would previously be inaccessible by traditional gene-finding methods. While here we demonstrated the results of ORFograph only for IPGs, the proposed approach can be generalized to any class of genes. Video abstract.

ABSTRACTFunctional genomics techniques, such as transposon insertion sequencing and RNA-sequencing, are key to studying relative differences in bacterial mutant fitness or gene expression under selective conditions. However, certain stress conditions, mutations, or antibiotics can directly interfere with DNA synthesis, resulting in systematic changes in local DNA copy numbers along the chromosome. This can lead to artifacts in sequencing-based functional genomics data when comparing antibiotic treatment to an unstressed control. Further, relative differences in gene-wise read counts may result from alterations in chromosomal replication dynamics, rather than selection or direct gene regulation. We term this artifact “chromosomal location bias” and implement a principled statistical approach to correct it by calculating local normalization factors along the chromosome. These normalization factors are then directly incorporated into statistical analyses using standard RNA-sequencing analysis methods without modifying the read counts themselves, preserving important information about the mean-variance relationship in the data. We illustrate the utility of this approach by generating and analyzing a ciprofloxacin-treated transposon insertion sequencing data set in Escherichia coli as a case study. We show that ciprofloxacin treatment generates chromosomal location bias in the resulting data, and we further demonstrate that failing to correct for this bias leads to false predictions of mutant drug sensitivity as measured by minimum inhibitory concentrations. We have developed an R package and user-friendly graphical Shiny application, ChromoCorrect, that detects and corrects for chromosomal bias in read count data, enabling the application of functional genomics technologies to the study of antibiotic stress.IMPORTANCEAltered gene dosage due to changes in DNA replication has been observed under a variety of stresses with a variety of experimental techniques. However, the implications of changes in gene dosage for sequencing-based functional genomics assays are rarely considered. We present a statistically principled approach to correcting for the effect of changes in gene dosage, enabling testing for differences in the fitness effects or regulation of individual genes in the presence of confounding differences in DNA copy number. We show that failing to correct for these effects can lead to incorrect predictions of resistance phenotype when applying functional genomics assays to investigate antibiotic stress, and we provide a user-friendly application to detect and correct for changes in DNA copy number.

Source attribution has traditionally involved combining epidemiological data with different pathogen characterisation methods, including 7-gene multi locus sequence typing (MLST) or serotyping, however, these approaches have limited resolution. In contrast, whole genome sequencing data provide an overview of the whole genome that can be used by attribution algorithms. Here, we applied a random forest (RF) algorithm to predict the primary sources of human clinical Salmonella Typhimurium (S. Typhimurium) and monophasic variants (monophasic S. Typhimurium) isolates. To this end, we utilised single nucleotide polymorphism diversity in the core genome MLST alleles obtained from 1,061 laboratory-confirmed human and animal S. Typhimurium and monophasic S. Typhimurium isolates as inputs into a RF model. The algorithm was used for supervised learning to classify 399 animal S. Typhimurium and monophasic S. Typhimurium isolates into one of eight distinct primary source classes comprising common livestock and pet animal species: cattle, pigs, sheep, other mammals (pets: mostly dogs and horses), broilers, layers, turkeys, and game birds (pheasants, quail, and pigeons). When applied to the training set animal isolates, model accuracy was 0.929 and kappa 0.905, whereas for the test set animal isolates, for which the primary source class information was withheld from the model, the accuracy was 0.779 and kappa 0.700. Subsequently, the model was applied to assign 662 human clinical cases to the eight primary source classes. In the dataset, 60/399 (15.0%) of the animal and 141/662 (21.3%) of the human isolates were associated with a known outbreak of S. Typhimurium definitive type (DT) 104. All but two of the 141 DT104 outbreak linked human isolates were correctly attributed by the model to the primary source classes identified as the origin of the DT104 outbreak. A model that was run without the clonal DT104 animal isolates produced largely congruent outputs (training set accuracy 0.989 and kappa 0.985; test set accuracy 0.781 and kappa 0.663). Overall, our results show that RF offers considerable promise as a suitable methodology for epidemiological tracking and source attribution for foodborne pathogens.

Shiga toxin-producing Escherichia coli (STEC) O157:H7 strains with the T allele in the translocated intimin receptor polymorphism (tir) 255 A > T gene associate with human disease more than strains with an A allele; however, the allele is not thought to be the direct cause of this difference. We sequenced a diverse set of STEC O157:H7 strains (26% A allele, 74% T allele) to identify linked differences that might underlie disease association. The average chromosome and pO157 plasmid size and gene content were significantly greater within the tir 255 A allele strains. Eighteen coding sequences were unique to tir 255 A allele chromosomes, and three were unique to tir 255 T allele chromosomes. There also were nonpO157 plasmids that were unique to each tir 255 allele variant. The overall average number of prophages did not differ between tir 255 allele strains; however, there were different types between the strains. Genomic and mobile element variation linked to the tir 255 polymorphism may account for the increased frequency of the T allele isolates in human disease. [ABSTRACT FROM AUTHOR]

Carbapenem is one of the few available drugs to treat multidrug-resistance Gram-negative bacteria infections. Recently, the plasmid-mediated spread of the carbapenem resistance gene blaNDM poses a significant threat to public health, requiring global monitoring and surveillance. Here, we used both short-read (n = 2461) and long-read (n = 546) sequencing data to characterize the global distribution of blaNDM. We analyzed the replicon type of blaNDM-positive plasmids and found that the dominant plasmid type was different in diverse geographical locations. Although blaNDM gene has been transferred across diverse countries, its genetic backgrounds are highly conserved, and the mobile genetic element ISAba125, IS5, and IS26 may play an important role in the mobilization of blaNDM. A significant association was observed between host origin and gene presence/deletion variation on IncX3 plasmid, which may be a key factor in the bacterial adaption to diverse hosts. In this study, we analyzed the diversity, distribution and transmission of blaNDM-positive plasmids from a global perspective, and emphasize the importance of plasmid analysis for understanding the evolution and adaptation of blaNDM-positive plasmids and their co-evolution with bacterial genomes (resistome). [ABSTRACT FROM AUTHOR]

Purpose: Traditional epidemiological investigations of healthcare-associated Clostridioides difficile infection (HA-CDI) are often insufficient. This study aimed to evaluate a procedure that includes secondary isolation and genomic typing of single toxigenic colonies using core genome multilocus sequence typing (cgMLST) for the investigation of C. difficile transmission. Methods: We analyzed retrospectively all toxigenic C. difficile-positive stool samples stored at the Lausanne University Hospital over 6 consecutive months. All isolates were initially typed and classified using a modified double-locus sequence typing (DLST) method. Genome comparison of isolates with the same DLST and clustering were subsequently performed using cgMLST. The electronic administrative records of patients with CDI were investigated for spatiotemporal epidemiological links supporting hospital transmission. A comparative descriptive analysis between genomic and epidemiological data was then performed. Results: From January to June 2021, 86 C. difficile isolates were recovered from thawed samples of 71 patients. Thirteen different DLST types were shared by > 1 patient, and 13 were observed in single patients. A genomic cluster was defined as a set of isolates from different patients with ≤ 3 locus differences, determined by cgMLST. Seven genomic clusters were identified, among which plausible epidemiological links were identified in only 4/7 clusters. Conclusion: Among clusters determined by cgMLST analysis, roughly 40% included unexplained HA-CDI acquisitions, which may be explained by unidentified epidemiological links, asymptomatic colonization, and/or shared common community reservoirs. The use of DLST, followed by whole genome sequencing analysis, is a promising and cost-effective stepwise approach for the investigation of CDI transmission in the hospital setting. [ABSTRACT FROM AUTHOR]

Hospital bloodstream infection (BSI) caused by methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of morbidity and mortality and is frequently related to invasive procedures and medically complex patients. An important feature of MRSA is the clonal structure of its population. Specific MRSA clones may differ in their pathogenic, epidemiological, and antimicrobial resistance profiles. Whole-genome sequencing is currently the most robust and discriminatory technique for tracking hypervirulent/well-adapted MRSA clones. However, it remains an expensive and time-consuming technique that requires specialized personnel. In this work, we describe a pangenome protocol, based on binary matrix (1,0) of open reading frames (ORFs), that can be used to quickly find diagnostic, apomorphic sequence mutations that can serve as biomarkers. We use this technique to create a diagnostic screen for MRSA isolates circulating in the Rio de Janeiro metropolitan area, the RdJ clone, which is prevalent in BSI. The method described here has 100% specificity and sensitivity, eliminating the need to use genomic sequencing for clonal identification. The protocol used is relatively simple and all the steps, formulas and commands used are described in this work, such that this strategy can also be used to identify other MRSA clones and even clones from other bacterial species. [ABSTRACT FROM AUTHOR]

Shiga toxin-producing Escherichia coli (STEC) O157:H7 strains with the T allele in the translocated intimin receptor polymorphism (tir) 255 A > T gene associate with human disease more than strains with an A allele; however, the allele is not thought to be the direct cause of this difference. We sequenced a diverse set of STEC O157:H7 strains (26% A allele, 74% T allele) to identify linked differences that might underlie disease association. The average chromosome and pO157 plasmid size and gene content were significantly greater within the tir 255 A allele strains. Eighteen coding sequences were unique to tir 255 A allele chromosomes, and three were unique to tir 255 T allele chromosomes. There also were non-pO157 plasmids that were unique to each tir 255 allele variant. The overall average number of prophages did not differ between tir 255 allele strains; however, there were different types between the strains. Genomic and mobile element variation linked to the tir 255 polymorphism may account for the increased frequency of the T allele isolates in human disease.

Hospital bloodstream infection (BSI) caused by methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of morbidity and mortality and is frequently related to invasive procedures and medically complex patients. An important feature of MRSA is the clonal structure of its population. Specific MRSA clones may differ in their pathogenic, epidemiological, and antimicrobial resistance profiles. Whole-genome sequencing is currently the most robust and discriminatory technique for tracking hypervirulent/well-adapted MRSA clones. However, it remains an expensive and time-consuming technique that requires specialized personnel. In this work, we describe a pangenome protocol, based on binary matrix (1,0) of open reading frames (ORFs), that can be used to quickly find diagnostic, apomorphic sequence mutations that can serve as biomarkers. We use this technique to create a diagnostic screen for MRSA isolates circulating in the Rio de Janeiro metropolitan area, the RdJ clone, which is prevalent in BSI. The method described here has 100% specificity and sensitivity, eliminating the need to use genomic sequencing for clonal identification. The protocol used is relatively simple and all the steps, formulas and commands used are described in this work, such that this strategy can also be used to identify other MRSA clones and even clones from other bacterial species.

Inversions, also sometimes called reversals, are a major contributor to variation among bacterial genomes, with studies suggesting that those involving small numbers of regions are more likely than larger inversions. Deletions may arise in bacterial genomes through the same biological mechanism as inversions, and hence a model that incorporates both is desirable. However, while inversion distances between genomes have been well studied, there has yet to be a model which accounts for the combination of both deletions and inversions. To account for both of these operations, we introduce an algebraic model that utilises partial permutations. This leads to an algorithm for calculating the minimum distance to the most recent common ancestor of two bacterial genomes evolving by inversions (of adjacent regions) and deletions. The algebraic model makes the existing short inversion models more complete and realistic by including deletions, and also introduces new algebraic tools into evolutionary distance problems. [ABSTRACT FROM AUTHOR]

Background: Advancements in DNA sequencing technology have transformed the field of bacterial genomics, allowing for faster and more cost effective chromosome level assemblies compared to a decade ago. However, transforming raw reads into a complete genome model is a significant computational challenge due to the varying quality and quantity of data obtained from different sequencing instruments, as well as intrinsic characteristics of the genome and desired analyses. To address this issue, we have developed a set of container-based pipelines using Nextflow, offering both common workflows for inexperienced users and high levels of customization for experienced ones. Their processing strategies are adaptable based on the sequencing data type, and their modularity enables the incorporation of new components to address the community’s evolving needs. Methods: These pipelines consist of three parts: quality control, de novo genome assembly, and bacterial genome annotation. In particular, the genome annotation pipeline provides a comprehensive overview of the genome, including standard gene prediction and functional inference, as well as predictions relevant to clinical applications such as virulence and resistance gene annotation, secondary metabolite detection, prophage and plasmid prediction, and more. Results: The annotation results are presented in reports, genome browsers, and a web-based application that enables users to explore and interact with the genome annotation results. Conclusions: Overall, our user-friendly pipelines offer a seamless integration of computational tools to facilitate routine bacterial genomics research. The effectiveness of these is illustrated by examining the sequencing data of a clinical sample of Klebsiella pneumoniae.

Background: Advancements in DNA sequencing technology have transformed the field of bacterial genomics, allowing for faster and more cost effective chromosome level assemblies compared to a decade ago. However, transforming raw reads into a complete genome model is a significant computational challenge due to the varying quality and quantity of data obtained from different sequencing instruments, as well as intrinsic characteristics of the genome and desired analyses. To address this issue, we have developed a set of container-based pipelines using Nextflow, offering both common workflows for inexperienced users and high levels of customization for experienced ones. Their processing strategies are adaptable based on the sequencing data type, and their modularity enables the incorporation of new components to address the community’s evolving needs. Methods: These pipelines consist of three parts: quality control, de novo genome assembly, and bacterial genome annotation. In particular, the genome annotation pipeline provides a comprehensive overview of the genome, including standard gene prediction and functional inference, as well as predictions relevant to clinical applications such as virulence and resistance gene annotation, secondary metabolite detection, prophage and plasmid prediction, and more. Results: The annotation results are presented in reports, genome browsers, and a web-based application that enables users to explore and interact with the genome annotation results. Conclusions: Overall, our user-friendly pipelines offer a seamless integration of computational tools to facilitate routine bacterial genomics research. The effectiveness of these is illustrated by examining the sequencing data of a clinical sample of Klebsiella pneumoniae.

ABSTRACT Despite the widespread implementation of pneumococcal vaccines, hypervirulent Streptococcus pneumoniae serotype 19A is endemic worldwide. It is still unclear whether specific genetic elements contribute to complex pathogenicity of serotype 19A isolates. We performed a large-scale pan-genome-wide association study (pan-GWAS) of 1,292 serotype 19A isolates sampled from patients with invasive disease and asymptomatic carriers. To address the underlying disease-associated genotypes, a comprehensive analysis using three methods (Scoary, a linear mixed model, and random forest) was performed to compare disease and carriage isolates to identify genes consistently associated with disease phenotype. By using three pan-GWAS methods, we found consensus on statistically significant associations between genotypes and disease phenotypes (disease or carriage), with a subset of 30 consistently significant disease-associated genes. The results of functional annotation revealed that these disease-associated genes had diverse predicted functions, including those that participated in mobile genetic elements, antibiotic resistance, virulence, and cellular metabolism. Our findings suggest the multifactorial pathogenicity nature of this hypervirulent serotype and provide important evidence for the design of novel protein-based vaccines to prevent and control pneumococcal disease. IMPORTANCE It is important to understand the genetic and pathogenic characteristics of S. pneumoniae serotype 19A, which may provide important information for the prevention and treatment of pneumococcal disease. This global large-sample pan-GWAS study has identified a subset of 30 consistently significant disease-associated genes that are involved in mobile genetic elements, antibiotic resistance, virulence, and cellular metabolism. These findings suggest the multifactorial pathogenicity nature of hypervirulent S. pneumoniae serotype 19A isolates and provide implications for the design of novel protein-based vaccines.

ABSTRACT The world’s population is increasing at a rate not seen in the past. Agriculture, providing food for this increasing population, is reaching its boundaries of space and natural resources. In addition, changing legislation and increased ecological awareness are forcing agriculture to reduce its environmental impact. This entails the replacement of agrochemicals with nature-based solutions. In this regard, the search for effective biocontrol agents that protect crops from pathogens is in the spotlight. In this study, we have investigated the biocontrol activity of endophytic bacteria isolated from the medicinal plant Alkanna tinctoria Tausch. To do so, an extensive collection of bacterial strains was initially genome sequenced and in silico screened for features related to plant stimulation and biocontrol. Based on this information, a selection of bacteria was tested in vitro for antifungal activity using direct antagonism in a plate assay and in planta with a detached-leaf assay. Bacterial strains were tested individually and in combinations to assess the best-performing treatments. The results revealed that many bacteria could produce metabolites that efficiently inhibit the proliferation of several fungi, especially Fusarium graminearum. Among these, Pseudomonas sp. strain R-71838 showed a strong antifungal effect, in both dual-culture and in planta assays, making it the most promising candidate for biocontrol application. Using microbes from medicinal plants, this study highlights the opportunities of using genomic information to speed up the screening of a taxonomically diverse set of bacteria with biocontrol properties. IMPORTANCE Phytopathogenic fungi are a major threat to global food production. The most common management practice to prevent plant infections involves the intensive use of fungicides. However, with the growing awareness of the ecological and human impacts of chemicals, there is a need for alternative strategies, such as the use of bacterial biocontrol agents. Limitations in the design of bacterial biocontrol included the need for labor-intensive and time-consuming experiments to test a wide diversity of strains and the lack of reproducibility of their activity against pathogens. Here, we show that genomic information is an effective tool to select bacteria of interest quickly. Also, we highlight that the strain Pseudomonas sp. R-71838 produced a reproducible antifungal effect both in vitro and in planta. These findings build a foundation for designing a biocontrol strategy based on Pseudomonas sp. R-71838.

Aims Assess bacterial community changes over time in soybean (Glycine max) crop fields following cover crop (CC) and no-till (NT) implementation under natural abiotic stressors. Method and results Soil bacterial community composition was obtained by amplifying, sequencing, and analysing the V4 region of the 16S rRNA gene. Generalized linear mixed models were used to assess the effects of tillage, CC, and time on bacterial community response. The most abundant phyla present were Acidobacteria, Actinobacteria, Bacteroidetes, and Verrucomicrobia. Bacterial diversity increased in periods with abundant water. Reduced tillage (RT) increased overall bacterial diversity, but NT with a CC was not significantly different than RT treatments under drought conditions. CCs shifted abundances of Firmicutes and Bacteroidetes depending on abiotic conditions. Conclusions In the Lower Mississippi Alluvial Valley (LMAV), USA, NT practices lower diversity and influence long-term community changes while cover crops enact a seasonal response to environmental conditions. NT and RT management affect soil bacterial communities differently than found in other regions of the country. [ABSTRACT FROM AUTHOR]

ABSTRACT Streptococcus pyogenes is a leading cause of human morbidity and mortality, especially in resource-limited settings. The development of a vaccine against S. pyogenes is a global health priority to reduce the burden of postinfection rheumatic heart disease. To support this, molecular characterization of circulating S. pyogenes isolates is needed. We performed whole-genome analyses of S. pyogenes isolates from skin and soft tissue infections in Sukuta, The Gambia, a low-income country (LIC) in West Africa where there is a high burden of such infections. To act as a comparator to these LIC isolates, skin infection isolates from Sheffield, United Kingdom (a high-income country [HIC]), were also sequenced. The LIC isolates from The Gambia were genetically more diverse (46 emm types in 107 isolates) than the HIC isolates from Sheffield (23 emm types in 142 isolates), with only 7 overlapping emm types. Other molecular markers were shared, including a high prevalence of the skin infection-associated emm pattern D and the variable fibronectin-collagen-T antigen (FCT) types FCT-3 and FCT-4. Fewer of the Gambian LIC isolates carried prophage-associated superantigens (64%) and DNases (26%) than did the Sheffield HIC isolates (99% and 95%, respectively). We also identified streptococcin genes unique to 36% of the Gambian LIC isolates and a higher prevalence (48%) of glucuronic acid utilization pathway genes in the Gambian LIC isolates than in the Sheffield HIC isolates (26%). Comparison to a wider collection of HIC and LIC isolate genomes supported our findings of differing emm diversity and prevalence of bacterial factors. Our study provides insight into the genetics of LIC isolates and how they compare to HIC isolates. IMPORTANCE The global burden of rheumatic heart disease (RHD) has triggered a World Health Organization response to drive forward development of a vaccine against the causative human pathogen Streptococcus pyogenes. This burden stems primarily from low- and middle-income settings where there are high levels of S. pyogenes skin and soft tissue infections, which can lead to RHD. Our study provides much needed whole-genome-based molecular characterization of isolates causing skin infections in Sukuta, The Gambia, a low-income country (LIC) in West Africa where infection and RHD rates are high. Although we identified a greater level of diversity in these LIC isolates than in isolates from Sheffield, United Kingdom (a high-income country), there were some shared features. There were also some features that differed by geographical region, warranting further investigation into their contribution to infection. Our study has also contributed data essential for the development of a vaccine that would target geographically relevant strains.

Global antimicrobial resistance is a health crisis that can change the face of modern medicine. Exploring diverse natural habitats for bacterially-derived novel antimicrobial compounds has historically been a successful strategy. The deep-sea presents an exciting opportunity for the cultivation of taxonomically novel organisms and exploring potentially chemically novel spaces. In this study, the draft genomes of 12 bacteria previously isolated from the deep-sea sponges Phenomena carpenteri and Hertwigia sp. are investigated for the diversity of specialized secondary metabolites. In addition, early data support the production of antibacterial inhibitory substances produced from a number of these strains, including activity against clinically relevant pathogens Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus. Draft whole-genomes are presented of 12 deep-sea isolates, which include four potentially novel strains: Psychrobacter sp. PP-21, Streptomyces sp. DK15, Dietzia sp. PP-33, and Micrococcus sp. M4NT. Across the 12 draft genomes, 138 biosynthetic gene clusters were detected, of which over half displayed less than 50% similarity to known BGCs, suggesting that these genomes present an exciting opportunity to elucidate novel secondary metabolites. Exploring bacterial isolates belonging to the phylum Actinomycetota, Pseudomonadota, and Bacillota from understudied deep-sea sponges provided opportunities to search for new chemical diversity of interest to those working in antibiotic discovery. [ABSTRACT FROM AUTHOR]

The large-scale global use of plastics has led to one of the greatest environmental issues of the 21st century. The incredible durability of these polymers, whilst beneficial for a wide range of purposes, makes them hard to break down. True recycling of plastics is difficult and expensive, leading to accumulation in the environment as waste. Recently, a new field of research has developed, aiming to use natural biological processes to solve this man-made problem. Incredibly, some microorganisms are able to produce enzymes with the capacity to chemically break down plastic polymers into their monomeric building blocks. At an industrial scale, this process could allow for a circular recycling economy, whereby plastics are broken down, then built back up into novel consumer plastics. As well as providing a solution for the removal of plastics from the environment, this would also eliminate the need for the creation of virgin plastics. Analytical techniques, such as those allowing quantification of depolymerisation activity and enzyme characterization, have underpinned this field and created a strong foundation for this nascent inter-disciplinary field. Recent advances in cutting-edge 'omics approaches such as DNA and RNA sequencing, combined with machine learning strategies, provide in-depth analysis of genomic systems involved in degradation. In particular, this can provide understanding of the specific protein sequence of the enzymes involved in the process, as well as insights into the functional and mechanistic role of the enzymes within these microorganisms, allowing for potential high-throughput discovery and subsequent exploitation of novel depolymerases. Together, these cross-disciplinary analytical techniques offer a complete pipeline for the identification, validation, and upscaling of potential enzymatic solutions for industrial deployment. In this review, we provide a summary of the research within the field to date, the analytical techniques most commonly applied for enzyme discovery and industrial upscaling, and provide recommendations for a standardised approach to allow research conducted in this field to be benchmarked to ensure focus is on the discovery and characterisation of industrially relevant enzymes. [ABSTRACT FROM AUTHOR]

Methicillin-resistant Staphylococcus aureus (MRSA), a major human pathogen, uses the prophage-encoded tarP gene as an important immune evasion factor. TarP glycosylates wall teichoic acid (WTA) polymers, major S. aureus surface antigens, to impair WTA immunogenicity and impede host defence. However, tarP phages appear to be restricted to only a few MRSA clonal lineages, including clonal complexes (CC) 5 and 398, for unknown reasons. We demonstrate here that tarP-encoding prophages can be mobilized to lysogenize other S. aureus strains. However, transfer is largely restricted to closely related clones. Most of the non-transducible clones encode tarM, which generates a WTA glycosylation pattern distinct from that mediated by TarP. However, tarM does not interfere with infection by tarP phages. Clonal complex-specific Type I restriction-modification systems were the major reasons for resistance to tarP phage infection. Nevertheless, tarP phages were found also in unrelated S. aureus clones indicating that tarP has the potential to spread to distant clonal lineages and contribute to the evolution of new MRSA clones. [ABSTRACT FROM AUTHOR]

Recent years have seen a remarkable increase in the practicality of sequencing whole genomes from large numbers of bacterial isolates. The availability of this data has huge potential to deliver new insights into the evolution and epidemiology of bacterial pathogens, but the scalability of the analytical methodology has been lagging behind that of the sequencing technology. Here we present a step-by-step approach for such large-scale genomic epidemiology analyses, from bacterial genomes to epidemiological interpretations. A central component of this approach is the dated phylo- geny, which is a phylogenetic tree with branch lengths measured in units of time. The construction of dated phylogenies from bacterial genomic data needs to account for the disruptive effect of recombination on phyloge- netic relationships, and we describe how this can be achieved. Dated phylogenies can then be used to perform fine-scale or large-scale epidemio- logical analyses, depending on the proportion of cases for which genomes are available. A key feature of this approach is computational scalability and in particular the ability to process hundreds or thousands of genomes within a matter of hours. This is a clear advantage of the step-by-step approach described here. We discuss other advantages and disadvantages of the approach, as well as potential improvements and avenues for future research. This article is part of a discussion meeting issue ‘Genomic population structures of microbial pathogens’. [ABSTRACT FROM AUTHOR]

In the face of the unrelenting global burden of tuberculosis (TB), antibiotics remain our most effective tools to save lives and control the spread of Mycobacterium tuberculosis (Mtb). However, we confront a dual challenge in our use of antibiotics: simplifying and shortening the TB drug regimen while also limiting the emergence and propagation of antibiotic resistance. This task is now more feasible due to the increasing availability of bacterial genomic data at or near the point of care. These resources create an opportunity to envision how integration of bacterial genetic determinants of antibiotic response into treatment algorithms might transform TB care. Historically, Mtb drug resistance studies focused on mutations in genes encoding antibiotic targets and the resulting increases in the minimal inhibitory concentrations (MICs) above a breakpoint value. But recent progress in elucidating the effects of functional genetic diversity in Mtb has revealed various genetic loci that are associated with drug phenotypes such as low-level MIC increases and tolerance which predict the development of resistance and treatment failure. As a result, we are now poised to advance precision medicine approaches in TB treatment. By incorporating information regarding Mtb genetic characteristics into the development of drug regimens, clinical care which tailors antibiotic treatment to maximize the likelihood of success has come into reach. [ABSTRACT FROM AUTHOR]

Bacterial adaptation to stressful environments often produces evolutionary constraints whereby increases in resistance are associated with reduced fitness in a different environment. The exploitation of this resistance-cost trade-off has been proposed as the basis of rational antimicrobial treatment strategies designed to limit the evolution of drug resistance in bacterial pathogens. Recent theoretical, laboratory, and clinical studies have shown that fluctuating selection can maintain drug efficacy and even restore drug susceptibility, but can also increase the rate of adaptation and promote cross-resistance to other antibiotics. In this paper, we combine mathematical modeling, experimental evolution, and whole-genome sequencing to follow evolutionary trajectories towards β -lactam resistance under fluctuating selective conditions. Our experimental model system consists of eight populations of Escherichia coli K12 evolving in parallel to a serial dilution protocol designed to dynamically control the strength of selection for resistance. We implemented adaptive ramps with mild and strong selection, resulting in evolved populations with similar levels of resistance, but with different evolutionary dynamics and diverging genotypic profiles. We found that mutations that emerged under strong selection are unstable in the absence of selection, in contrast to resistance mutations previously selected in the mild selection regime that were stably maintained in drug-free environments and positively selected for when antibiotics were reintroduced. Altogether, our population dynamics model and the phenotypic and genomic analysis of the evolved populations show that the rate of resistance adaptation is contingent upon the strength of selection, but also on evolutionary constraints imposed by prior drug exposures. [ABSTRACT FROM AUTHOR]

Abstract While long-read sequencing allows for the complete assembly of bacterial genomes, long-read assemblies contain a variety of errors. Here, we present Trycycler, a tool which produces a consensus assembly from multiple input assemblies of the same genome. Benchmarking showed that Trycycler assemblies contained fewer errors than assemblies constructed with a single tool. Post-assembly polishing further reduced errors and Trycycler+polishing assemblies were the most accurate genomes in our study. As Trycycler requires manual intervention, its output is not deterministic. However, we demonstrated that multiple users converge on similar assemblies that are consistently more accurate than those produced by automated assembly tools.

Shuxiu Liu,1,2 Hao Xu,2 Xiaobing Guo,1 Shuang Li,3 Qian Wang,1,2 Yuan Li,4 Ruishan Liu,1,2 Jianjun Gou1 1Department of Laboratory Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, People’s Republic of China; 2State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, People’s Republic of China; 3Department of Obstetrics and Gynecology, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, 450052, People’s Republic of China; 4Department of Nuclear Medicine, The First Affiliated Hospital of ZhengZhou University, Zhengzhou, 450052, People’s Republic of ChinaCorrespondence: Jianjun Gou Tel +86 371 6627 8237Fax +86 371 6691 3569Email jianjung@zzu.edu.cnPurpose: To investigate the genomic and plasmid characteristics of a newly discovered Pseudomonas stutzeri strain with a blaVIM-2-carrying plasmid and novel integron In 1998 isolated from a cerebrospinal fluid specimen in a teaching hospital.Methods: Species identification was performed by MALDI-TOF MS, and blaVIM-2 was identified by PCR and Sanger sequencing. Whole-genome sequencing analysis was conducted using the Illumina NovaSeq 6000 and Oxford Nanopore platforms. Integron detection was performed using INTEGRALL. The phylogenetic tree was constructed by using kSNP3.0. Plasmid characteristics were assessed by S1-pulsed-field gel electrophoresis (S1-PFGE), Southern blotting, conjugation experiments, and whole-genome sequencing analysis. Comparative genomics analysis of the plasmid and genetic context of blaVIM-2 were conducted by using BLAST Ring Image Generator (BRIG) and Easyfig 2.3, respectively.Results: ZDHY95, an MDR strain of P. stutzeri harboring blaVIM-2, was identified. It was sensitive only to amikacin and was resistant to carbapenems, β-lactams, aztreonam, fluoroquinolones, and aminoglycosides. Joint S1-PFGE, Southern blot, conjugation assay, and whole-genome sequencing experiments confirmed that the blaVIM-2 gene was located within class I integron In 1722 of the plasmid and that the surrounding genetic environment was 5ʹCS-aacA4ʹ-30-blaVIM-2-aacA4ʹ-3ʹCS. The novel class I integron In 1998 was detected on the chromosome of P. stutzeri ZDHY95, and the gene cassette array was 5ʹCS-aacA3-aadA13-cmlA8-blaOXA-246-arr3-dfrA27-3ʹCS. Phylogenetic analysis showed that antimicrobial resistance gene-carrying P. stutzeri isolates were divided into two clusters, mainly containing isolates from the USA and Pakistan.Conclusion: A novel blaVIM-2-carrying conjugative plasmid, pZDHY95-VIM-2, was reported for the first time in P. stutzeri, elucidating the genetic environment and transfer mechanism. The gene structure of the novel class I integron In 1998 was also clarified. We explored the phylogenetic relationship of P. stutzeri with drug resistance genes and suggested that Pseudomonas with metallo-β-lactamases (MBLs) in the hospital environment may cause infection in patients with long-term intubation or after interventional surgery.Keywords: Pseudomonas stutzeri, blaVIM-2, In 1998, In 1722, Tn 5563, whole-genome sequencing, bacterial genomics, antibiotic resistance

Methicillin-resistant Staphylococcus aureus (MRSA), a major human pathogen, uses the prophage-encoded tarP gene as an important immune evasion factor. TarP glycosylates wall teichoic acid (WTA) polymers, major S. aureus surface antigens, to impair WTA immunogenicity and impede host defence. However, tarP phages appear to be restricted to only a few MRSA clonal lineages, including clonal complexes (CC) 5 and 398, for unknown reasons. We demonstrate here that tarP-encoding prophages can be mobilized to lysogenize other S. aureus strains. However, transfer is largely restricted to closely related clones. Most of the non-transducible clones encode tarM, which generates a WTA glycosylation pattern distinct from that mediated by TarP. However, tarM does not interfere with infection by tarP phages. Clonal complex-specific Type I restriction-modification systems were the major reasons for resistance to tarP phage infection. Nevertheless, tarP phages were found also in unrelated S. aureus clones indicating that tarP has the potential to spread to distant clonal lineages and contribute to the evolution of new MRSA clones.

In the face of the unrelenting global burden of tuberculosis (TB), antibiotics remain our most effective tools to save lives and control the spread of Mycobacterium tuberculosis (Mtb). However, we confront a dual challenge in our use of antibiotics: simplifying and shortening the TB drug regimen while also limiting the emergence and propagation of antibiotic resistance. This task is now more feasible due to the increasing availability of bacterial genomic data at or near the point of care. These resources create an opportunity to envision how integration of bacterial genetic determinants of antibiotic response into treatment algorithms might transform TB care. Historically, Mtb drug resistance studies focused on mutations in genes encoding antibiotic targets and the resulting increases in the minimal inhibitory concentrations (MICs) above a breakpoint value. But recent progress in elucidating the effects of functional genetic diversity in Mtb has revealed various genetic loci that are associated with drug phenotypes such as low-level MIC increases and tolerance which predict the development of resistance and treatment failure. As a result, we are now poised to advance precision medicine approaches in TB treatment. By incorporating information regarding Mtb genetic characteristics into the development of drug regimens, clinical care which tailors antibiotic treatment to maximize the likelihood of success has come into reach.

Abstract Background Since the prolonged use of insecticidal proteins has led to toxin resistance, it is important to search for novel insecticidal protein genes (IPGs) that are effective in controlling resistant insect populations. IPGs are usually encoded in the genomes of entomopathogenic bacteria, especially in large plasmids in strains of the ubiquitous soil bacteria, Bacillus thuringiensis (Bt). Since there are often multiple similar IPGs encoded by such plasmids, their assemblies are typically fragmented and many IPGs are scattered through multiple contigs. As a result, existing gene prediction tools (that analyze individual contigs) typically predict partial rather than complete IPGs, making it difficult to conduct downstream IPG engineering efforts in agricultural genomics. Methods Although it is difficult to assemble IPGs in a single contig, the structure of the genome assembly graph often provides clues on how to combine multiple contigs into segments encoding a single IPG. Results We describe ORFograph, a pipeline for predicting IPGs in assembly graphs, benchmark it on (meta)genomic datasets, and discover nearly a hundred novel IPGs. This work shows that graph-aware gene prediction tools enable the discovery of greater diversity of IPGs from (meta)genomes. Conclusions We demonstrated that analysis of the assembly graphs reveals novel candidate IPGs. ORFograph identified both already known genes “hidden” in assembly graphs and potential novel IPGs that evaded existing tools for IPG identification. As ORFograph is fast, one could imagine a pipeline that processes many (meta)genomic assembly graphs to identify even more novel IPGs for phenotypic testing than would previously be inaccessible by traditional gene-finding methods. While here we demonstrated the results of ORFograph only for IPGs, the proposed approach can be generalized to any class of genes. Video abstract

In early 2021, the 10-valent Pneumococcal conjugate vaccine (PCV10) was replaced with 13-valent (PCV13) by the federal directorate of immunization (FDI), Pakistan. We assessed the impact of a higher valent vaccine, PCV13, on the serotype distribution of nasopharyngeal carriage in rural Pakistan. Children <2 years were randomly selected from two rural union councils of Matiari, Sindh in Pakistan between September–October,2022. Clinical, sociodemographic and vaccination histories were recorded. Nasopharyngeal swabs were collected and processed at Infectious Disease Research Laboratory, Aga Khan University, Karachi. Whole genome sequencing was performed on the culture positive isolates. Of the 200 children enrolled, pneumococcus was detected in 140(70 %) isolates. Majority of age-eligible children (60.1 %,110/183) received 3 PCV13 doses. PCV10 carriage declined from 13.2 %(78/590) in 2017/18 to 7.2 % (10/140) in 2022, additional PCV13 serotypes (3, 6A/6C and 19A) decreased from 18.5 %(109/590) to 11.4 %(16/140) while non-PCV13 serotypes increased from 68.3 %(403/590) to 81.4 %(114/140). There were 88.5 %(n = 124), 80.7 %(n = 113), 55.0 %(n = 77), and 46.0 %(n = 65) isolates predicted to be resistant to cotrimoxazole, penicillin(meningitis cut-off), tetracycline, and erythromycin respectively. Replacing PCV10 with PCV13 rapidly decreased prevalence of PCV13 carriage among vaccinated children in Matiari, Pakistan. Vaccine-driven selection pressure may have been responsible for the increase of non-PCV13 serotypes. • Children <2 had nasopharyngeal swabs whole-genome sequenced. • PCV13 switch reduced PCV10 serotypes from 13.2 % to 7.2 % by 2022. • Non-PCV13 serotypes rose from 68.3 % in 2017/18 to 81.4 % in 2022. • High resistance for cotrimoxazole(88.5 %), penicillin(80.7 %), tetracycline(55.0 %). [ABSTRACT FROM AUTHOR]

Bacterial genome organization is primarily driven by chromosomal replication from a single origin of replication. However, chromosomal rearrangements, which can disrupt such organization, are inevitable in nature. Long DNA repeats are major players mediating rearrangements, large and small, via homologous recombination. Since changes to genome organization affect bacterial fitness—and more so in fast-growing than slow-growing bacteria—and are under selection, it is reasonable to expect that genomic positioning of long DNA repeats is also under selection. To test this, we identified identical DNA repeats of at least 100 base pairs across ∼6,000 bacterial genomes and compared their distribution in fast- and slow-growing bacteria. We found that long identical DNA repeats are distributed in a non-random manner across bacterial genomes. Their distribution differs in the overall number, orientation, and proximity to the origin of replication, between fast- and slow-growing bacteria. We show that their positioning—which might arise from a combination of the processes that produce repeats and selection on rearrangements that recombination between repeat elements might cause—permits less disruption to the replication-dependent genome organization of bacteria compared with random suggesting it as a major constraint to positioning of long DNA repeats. [ABSTRACT FROM AUTHOR]

The Salmonella Syst-OMICS consortium is sequencing 4,500 Salmonella genomes and building an analysis pipeline for the study of Salmonella genome evolution, antibiotic resistance and virulence genes. Metadata, including phenotypic as well as genomic data, for isolates of the collection are provided through the Salmonella Foodborne Syst-OMICS database (SalFoS), at https://salfos.ibis.ulaval.ca/. Here, we present our strategy and the analysis of the first 3,377 genomes. Our data will be used to draw potential links between strains found in fresh produce, humans, animals and the environment. The ultimate goals are to understand how Salmonella evolves over time, improve the accuracy of diagnostic methods, develop control methods in the field, and identify prognostic markers for evidence-based decisions in epidemiology and surveillance.