The emergence of highly resistant and hypervirulent Klebsiella pneumoniae CC14 clone in a tertiary hospital over 8 years.

BACKGROUND: Klebsiella pneumoniae is a major bacterial and opportunistic human pathogen, increasingly recognized as a healthcare burden globally. The convergence of resistance and virulence in K. pneumoniae strains has led to the formation of hypervirulent and multidrug-resistant strains with dual risk, limiting treatment options. K. pneumoniae clones are known to emerge locally and spread globally. Therefore, an understanding of the dynamics and evolution of the emerging strains in hospitals is warranted to prevent future outbreaks. METHODS: In this study, we conducted an in-depth genomic analysis on a large-scale collection of 328 multidrug-resistant (MDR) K. pneumoniae strains recovered from 239 patients from a single major hospital in the western coastal city of Jeddah in Saudi Arabia from 2014 through 2022. We employed a broad range of phylogenetic and phylodynamic methods to understand the evolution of the predominant clones on epidemiological time scales, virulence and resistance determinants, and their dynamics. We also integrated the genomic data with detailed electronic health record (EHR) data for the patients to understand the clinical implications of the resistance and virulence of different strains. RESULTS: We discovered a diverse population underlying the infections, with most strains belonging to Clonal Complex 14 (CC14) exhibiting dominance. Specifically, we observed the emergence and continuous expansion of strains belonging to the dominant ST2096 in the CC14 clade across hospital wards in recent years. These strains acquired resistance mutations against colistin and extended spectrum ß-lactamase (ESBL) and carbapenemase genes, namely blaOXA-48 and blaOXA-232, located on three distinct plasmids, on epidemiological time scales. Strains of ST2096 exhibited a high virulence level with the presence of the siderophore aerobactin (iuc) locus situated on the same mosaic plasmid as the ESBL gene. Integration of ST2096 with EHR data confirmed the significant link between colonization by ST2096 and the diagnosis of sepsis and elevated in-hospital mortality (p-value < 0.05). CONCLUSIONS: Overall, these results demonstrate the clinical significance of ST2096 clones and illustrate the rapid evolution of an emerging hypervirulent and MDR K. pneumoniae in a clinical setting.

Reduced peptidoglycan synthesis capacity impairs growth of E. coli at high salt concentration.

Gram-negative bacteria have a thin peptidoglycan layer between the cytoplasmic and outer membranes protecting the cell from osmotic challenges. Hydrolases of this structure are needed to cleave bonds to allow the newly synthesized peptidoglycan strands to be inserted by synthases. These enzymes need to be tightly regulated and their activities coordinated to prevent cell lysis. To better understand this process in Escherichia coli, we probed the genetic interactions of mrcA (encodes PBP1A) and mrcB (encodes PBP1B) with genes encoding peptidoglycan amidases and endopeptidases in envelope stress conditions. Our extensive genetic interaction network analysis revealed relatively few combinations of hydrolase gene deletions with reduced fitness in the absence of PBP1A or PBP1B, showing that none of the amidases or endopeptidases is strictly required for the functioning of one of the class A PBPs. This illustrates the robustness of the peptidoglycan growth mechanism. However, we discovered that the fitness of ∆mrcB cells is significantly reduced under high salt stress and in vitro activity assays suggest that this phenotype is caused by a reduced peptidoglycan synthesis activity of PBP1A at high salt concentration.IMPORTANCEEscherichia coli and many other bacteria have a surprisingly high number of peptidoglycan hydrolases. These enzymes function in concert with synthases to facilitate the expansion of the peptidoglycan sacculus under a range of growth and stress conditions. The synthases PBP1A and PBP1B both contribute to peptidoglycan expansion during cell division and growth. Our genetic interaction analysis revealed that these two penicillin-binding proteins (PBPs) do not need specific amidases, endopeptidases, or lytic transglycosylases for function. We show that PBP1A and PBP1B do not work equally well when cells encounter high salt stress and demonstrate that PBP1A alone cannot provide sufficient PG synthesis activity under this condition. These results show how the two class A PBPs and peptidoglycan hydrolases govern cell envelope integrity in E. coli in response to environmental challenges and particularly highlight the importance of PBP1B in maintaining cell fitness under high salt conditions.

Unveiling the dynamics of antimicrobial utilization and resistance in a large hospital network over five years: Insights from health record data analysis.

Antimicrobial Resistance (AMR) presents a pressing public health challenge globally which has been compounded by the COVID-19 pandemic. Elucidation of the impact of the pandemic on AMR evolution using population-level data that integrates clinical, laboratory and prescription data remains lacking. Data was extracted from the centralized electronic platform which captures the health records of 60,551 patients with a confirmed infection across the network of public healthcare facilities in Dubai, United Arab Emirates. For all inpatients and outpatients diagnosed with bacterial infection between 01/01/2017 and 31/05/2022, structured and unstructured Electronic Health Record data, microbiological laboratory data including antibiogram, molecular typing and COVID-19 testing information as well as antibiotic prescribing data were extracted curated and linked. Various analytical methods, including time-series analysis, natural language processing (NLP) and unsupervised clustering algorithms, were employed to investigate the trends of antimicrobial usage and resistance over time, assess the impact of prescription practices on resistance rates, and explore the effects of COVID-19 on antimicrobial usage and resistance. Our findings identified a significant impact of COVID-19 on antimicrobial prescription practices, with short-term and long-lasting over-prescription of these drugs. Resistance to antimicrobials increased the odds ratio of all mortality to an average of 2.18 (95% CI: 1.87-2.49) for the most commonly prescribed antimicrobials. Moreover, the effects of antimicrobial prescription practices on resistance were observed within one week of initiation. Significant trends in antimicrobial resistance, exhibiting fluctuations for various drugs and organisms, with an overall increasing trend in resistance levels, particularly post-COVID-19 were identified. This study provides a population-level insight into the evolution of AMR in the context of COVID-19 pandemic. The findings emphasize the impact of COVID-19 on the AMR crisis, which remained evident even two years after the onset of the pandemic. This underscores the necessity for enhanced antimicrobial stewardship to address the evolution of AMR.

Plasmid permissiveness of wastewater microbiomes can be predicted from 16S rRNA sequences by machine learning.

MOTIVATION: Wastewater treatment plants (WWTPs) harbor a dense and diverse microbial community. They constantly receive antimicrobial residues and resistant strains, and therefore provide conditions for horizontal gene transfer (HGT) of antimicrobial resistance (AMR) determinants. This facilitates the transmission of clinically important genes between, e.g. enteric and environmental bacteria, and vice versa. Despite the clinical importance, tools for predicting HGT remain underdeveloped. RESULTS: In this study, we examined to which extent water cycle microbial community composition, as inferred by partial 16S rRNA gene sequences, can predict plasmid permissiveness, i.e. the ability of cells to receive a plasmid through conjugation, based on data from standardized filter mating assays using fluorescent bio-reporter plasmids. We leveraged a range of machine learning models for predicting the permissiveness for each taxon in the community, representing the range of hosts a plasmid is able to transfer to, for three broad host-range resistance IncP plasmids (pKJK5, pB10, and RP4). Our results indicate that the predicted permissiveness from the best performing model (random forest) showed a moderate-to-strong average correlation of 0.49 for pB10 [95% confidence interval (CI): 0.44-0.55], 0.43 for pKJK5 (0.95% CI: 0.41-0.49), and 0.53 for RP4 (0.95% CI: 0.48-0.57) with the experimental permissiveness in the unseen test dataset. Predictive phylogenetic signals occurred despite the broad host-range nature of these plasmids. Our results provide a framework that contributes to the assessment of the risk of AMR pollution in wastewater systems. AVAILABILITY AND IMPLEMENTATION: The predictive tool is available as an application at https://github.com/DaneshMoradigaravand/PlasmidPerm.

ChemGAPP: a tool for chemical genomics analysis and phenotypic profiling.

MOTIVATION: High-throughput chemical genomic screens produce informative datasets, providing valuable insights into unknown gene function on a genome-wide level. However, there is currently no comprehensive analytic package publicly available. We developed ChemGAPP to bridge this gap. ChemGAPP integrates various steps in a streamlined and user-friendly format, including rigorous quality control measures to curate screening data. RESULTS: ChemGAPP provides three sub-packages for different chemical-genomic screens: ChemGAPP Big for large-scale screens; ChemGAPP Small for small-scale screens; and ChemGAPP GI for genetic interaction screens. ChemGAPP Big, tested against the Escherichiacoli KEIO collection, revealed reliable fitness scores which displayed biologically relevant phenotypes. ChemGAPP Small demonstrated significant changes in phenotype in a small-scale screen. ChemGAPP GI was benchmarked against three sets of genes with known epistasis types and successfully reproduced each interaction type. AVAILABILITY AND IMPLEMENTATION: ChemGAPP is available at https://github.com/HannahMDoherty/ChemGAPP, as a standalone Python package as well as Streamlit applications.

The lipoprotein DolP affects cell separation in Escherichia coli, but not as an upstream regulator of NlpD.

Bacterial amidases are essential to split the shared envelope of adjunct daughter cells to allow cell separation. Their activity needs to be precisely controlled to prevent cell lysis. In Escherichia coli, amidase activity is controlled by three regulatory proteins NlpD, EnvC and ActS. However, recent studies linked the outer membrane lipoprotein DolP (formerly YraP) as a potential upstream regulator of NlpD. In this study we explored this link in further detail. To our surprise DolP did not modulate amidase activity in vitro and was unable to interact with NlpD in pull-down and MST (MicroScale Thermophoresis) assays. Next, we excluded the hypothesis that ΔdolP phenocopied ΔnlpD in a range of envelope stresses. However, morphological analysis of double deletion mutants of amidases (AmiA, AmiB AmiC) and amidase regulators with dolP revealed that ΔamiAΔdolP and ΔenvCΔdolP mutants display longer chain length compared to their parental strains indicating a role for DolP in cell division. Overall, we present evidence that DolP does not affect NlpD function in vitro, implying that DolP is not an upstream regulator of NlpD. However, DolP may impact daughter cell separation by interacting directly with AmiA or AmiC, or by a yet undiscovered mechanism.

Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes.

Escherichia coli is an important cause of bacterial infections worldwide, with multidrug-resistant strains incurring substantial costs on human lives. Besides therapeutic concentrations of antimicrobials in health care settings, the presence of subinhibitory antimicrobial residues in the environment and in clinics selects for antimicrobial resistance (AMR), but the underlying genetic repertoire is less well understood. Here, we used machine learning to predict the population doubling time and cell growth yield of 1,407 genetically diverse E. coli strains expanding under exposure to three subinhibitory concentrations of six classes of antimicrobials from single-nucleotide genetic variants, accessory gene variation, and the presence of known AMR genes. We predicted cell growth yields in the held-out test data with an average correlation (Spearman's ρ) of 0.63 (0.36 to 0.81 across concentrations) and cell doubling times with an average correlation of 0.59 (0.32 to 0.92 across concentrations), with moderate increases in sample size unlikely to improve predictions further. This finding points to the remaining missing heritability of growth under antimicrobial exposure being explained by effects that are too rare or weak to be captured unless sample size is dramatically increased, or by effects other than those conferred by the presence of individual single-nucleotide polymorphisms (SNPs) and genes. Predictions based on whole-genome information were generally superior to those based only on known AMR genes and were accurate for AMR resistance at therapeutic concentrations. We pinpointed genes and SNPs determining the predicted growth and thereby recapitulated many known AMR determinants. Finally, we estimated the effect sizes of resistance genes across the entire collection of strains, disclosing the growth effects for known resistance genes in each individual strain. Our results underscore the potential of predictive modeling of growth patterns from genomic data under subinhibitory concentrations of antimicrobials, although the remaining missing heritability poses a challenge for achieving the accuracy and precision required for clinical use. IMPORTANCE Predicting bacterial growth from genome sequences is important for a rapid characterization of strains in clinical diagnostics and to disclose candidate novel targets for anti-infective drugs. Previous studies have dissected the relationship between bacterial growth and genotype in mutant libraries for laboratory strains, yet no study so far has examined the predictive power of genome sequence in natural strains. In this study, we used a high-throughput phenotypic assay to measure the growth of a systematic collection of natural Escherichia coli strains and then employed machine learning models to predict bacterial growth from genomic data under nontherapeutic subinhibitory concentrations of antimicrobials that are common in nonclinical settings. We found a moderate to strong correlation between predicted and actual values for the different collected data sets. Moreover, we observed that the known resistance genes are still effective at sublethal concentrations, pointing to clinical implications of these concentrations.

Genomic Epidemiology and Evolution of Escherichia coli in Wild Animals in Mexico.

Escherichia coli is a common bacterial species in the gastrointestinal tracts of warm-blooded animals and humans. Pathogenicity and antimicrobial resistance in E. coli may emerge via host switching from animal reservoirs. Despite its potential clinical importance, knowledge of the population structure of commensal E. coli within wild hosts and the epidemiological links between E. coli in nonhuman hosts and E. coli in humans is still scarce. In this study, we analyzed the whole-genome sequencing data of a collection of 119 commensal E. coli strains recovered from the guts of 55 mammal and bird species in Mexico and Venezuela in the 1990s. We observed low concordance between the population structures of E. coli isolates colonizing wild animals and the phylogeny, taxonomy, and ecological and physiological attributes of the host species, with distantly related E. coli strains often colonizing the same or similar host species and distantly related host species often hosting closely related E. coli strains. We found no evidence for recent transmission of E. coli genomes from wild animals to either domesticated animals or humans. However, multiple livestock- and human-related virulence factor genes were present in E. coli of wild animals, including virulence factors characteristic of Shiga toxin-producing E. coli (STEC) and atypical enteropathogenic E. coli (aEPEC), where several isolates from wild hosts harbored the locus of enterocyte effacement (LEE) pathogenicity island. Moreover, E. coli isolates from wild animal hosts often harbored known antibiotic resistance determinants, including those against ciprofloxacin, aminoglycosides, tetracyclines, and beta-lactams, with some determinants present in multiple, distantly related E. coli lineages colonizing very different host animals. We conclude that genome pools of E. coli colonizing the guts of wild animals and humans share virulence and antibiotic resistance genes, underscoring the idea that wild animals could serve as reservoirs for E. coli pathogenicity in human and livestock infections.IMPORTANCEEscherichia coli is a clinically important bacterial species implicated in human- and livestock-associated infections worldwide. The bacterium is known to reside in the guts of humans, livestock, and wild animals. Although wild animals are recognized as potential reservoirs for pathogenic E. coli strains, the knowledge of the population structure of E. coli in wild hosts is still scarce. In this study, we used fine resolution of whole-genome sequencing to provide novel insights into the evolution of E. coli genomes from a small yet diverse collection of strains recovered within a broad range of wild animal species (including mammals and birds), the coevolution of E. coli strains with their hosts, and the genetics of pathogenicity of E. coli strains in wild hosts in Mexico. Our results provide evidence for the clinical importance of wild animals as reservoirs for pathogenic strains and highlight the need to include nonhuman hosts in the surveillance programs for E. coli infections.

Phylogenetically informative mutations in genes implicated in antibiotic resistance in Mycobacterium tuberculosis complex.

BACKGROUND: A comprehensive understanding of the pre-existing genetic variation in genes associated with antibiotic resistance in the Mycobacterium tuberculosis complex (MTBC) is needed to accurately interpret whole-genome sequencing data for genotypic drug susceptibility testing (DST). METHODS: We investigated mutations in 92 genes implicated in resistance to 21 anti-tuberculosis drugs using the genomes of 405 phylogenetically diverse MTBC strains. The role of phylogenetically informative mutations was assessed by routine phenotypic DST data for the first-line drugs isoniazid, rifampicin, ethambutol, and pyrazinamide from a separate collection of over 7000 clinical strains. Selected mutations/strains were further investigated by minimum inhibitory concentration (MIC) testing. RESULTS: Out of 547 phylogenetically informative mutations identified, 138 were classified as not correlating with resistance to first-line drugs. MIC testing did not reveal a discernible impact of a Rv1979c deletion shared by M. africanum lineage 5 strains on resistance to clofazimine. Finally, we found molecular evidence that some MTBC subgroups may be hyper-susceptible to bedaquiline and clofazimine by different loss-of-function mutations affecting a drug efflux pump subunit (MmpL5). CONCLUSIONS: Our findings underline that the genetic diversity in MTBC has to be studied more systematically to inform the design of clinical trials and to define sound epidemiologic cut-off values (ECOFFs) for new and repurposed anti-tuberculosis drugs. In that regard, our comprehensive variant catalogue provides a solid basis for the interpretation of mutations in genotypic as well as in phenotypic DST assays.

A One Health Study of the Genetic Relatedness of Klebsiella pneumoniae and Their Mobile Elements in the East of England.

BACKGROUND: Klebsiella pneumoniae is a human, animal, and environmental commensal and a leading cause of nosocomial infections, which are often caused by multiresistant strains. We evaluate putative sources of K. pneumoniae that are carried by and infect hospital patients. METHODS: We conducted a 6-month survey on 2 hematology wards at Addenbrooke's Hospital, Cambridge, United Kingdom, in 2015 to isolate K. pneumoniae from stool, blood, and the environment. We conducted cross-sectional surveys of K. pneumoniae from 29 livestock farms, 97 meat products, the hospital sewer, and 20 municipal wastewater treatment plants in the East of England between 2014 and 2015. Isolates were sequenced and their genomes compared. RESULTS: Klebsiella pneumoniae was isolated from stool of 17/149 (11%) patients and 18/922 swabs of their environment, together with 1 bloodstream infection during the study and 4 others over a 24-month period. Each patient carried 1 or more lineages that was unique to them, but 2 broad environmental contamination events and patient-environment transmission were identified. Klebsiella pneumoniae was isolated from cattle, poultry, hospital sewage, and 12/20 wastewater treatment plants. There was low genetic relatedness between isolates from patients/their hospital environment vs isolates from elsewhere. Identical genes encoding cephalosporin resistance were carried by isolates from humans/environment and elsewhere but were carried on different plasmids. CONCLUSION: We identified no patient-to-patient transmission and no evidence for livestock as a source of K. pneumoniae infecting humans. However, our findings reaffirm the importance of the hospital environment as a source of K. pneumoniae associated with serious human infection.

Detection of vancomycin-resistant Enterococcus faecium hospital-adapted lineages in municipal wastewater treatment plants indicates widespread distribution and release into the environment.

Vancomycin-resistant Enterococcus faecium (VREfm) is a leading cause of healthcare-associated infection. Reservoirs of VREfm are largely assumed to be nosocomial although there is a paucity of data on alternative sources. Here, we describe an integrated epidemiological and genomic analysis of E. faecium associated with bloodstream infection and isolated from wastewater. Treated and untreated wastewater from 20 municipal treatment plants in the East of England, United Kingdom was obtained and cultured to isolate E. faecium, ampicillin-resistant E. faecium (AREfm), and VREfm. VREfm was isolated from all 20 treatment plants and was released into the environment by 17/20 plants, the exceptions using terminal ultraviolet light disinfection. Median log10 counts of AREfm and VREfm in untreated wastewater from 10 plants in direct receipt of hospital sewage were significantly higher than 10 plants that were not. We sequenced and compared the genomes of 423 isolates from wastewater with 187 isolates associated with bloodstream infection at five hospitals in the East of England. Among 481 E. faecium isolates belonging to the hospital-adapted clade, we observed genetic intermixing between wastewater and bloodstream infection, with highly related isolates shared between a major teaching hospital in the East of England and 9/20 plants. We detected 28 antibiotic resistance genes in the hospital-adapted clade, of which 23 were represented in bloodstream, hospital sewage, and municipal wastewater isolates. We conclude that our findings are consistent with widespread distribution of hospital-adapted VREfm beyond acute healthcare settings with extensive release of VREfm into the environment in the East of England.

Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data.

The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The detection of antibiotic resistance is typically made by measuring a few known determinants previously identified from genome sequencing, and thus requires the prior knowledge of its biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average accuracy of 0.91 on held-out data (range 0.81-0.97). While the best models most frequently employed gene content, an average accuracy score of 0.79 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and significantly improved prediction only for two antibiotics, including ciprofloxacin. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data can be informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic.

Genomic survey of Clostridium difficile reservoirs in the East of England implicates environmental contamination of wastewater treatment plants by clinical lineages.

There is growing evidence that patients with Clostridiumdifficile-associated diarrhoea often acquire their infecting strain before hospital admission. Wastewater is known to be a potential source of surface water that is contaminated with C. difficile spores. Here, we describe a study that used genome sequencing to compare C. difficile isolated from multiple wastewater treatment plants across the East of England and from patients with clinical disease at a major hospital in the same region. We confirmed that C. difficile from 65 patients were highly diverse and that most cases were not linked to other active cases in the hospital. In total, 186 C. difficile isolates were isolated from effluent water obtained from 18 municipal treatment plants at the point of release into the environment. Whole genome comparisons of clinical and environmental isolates demonstrated highly related populations, and confirmed extensive release of toxigenic C. difficile into surface waters. An analysis based on multilocus sequence types (STs) identified 19 distinct STs in the clinical collection and 38 STs in the wastewater collection, with 13 of 44 STs common to both clinical and wastewater collections. Furthermore, we identified five pairs of highly similar isolates (≤2 SNPs different in the core genome) in clinical and wastewater collections. Strategies to control community acquisition should consider the need for bacterial control of treated wastewater.

What Is Resistance? Impact of Phenotypic versus Molecular Drug Resistance Testing on Therapy for Multi- and Extensively Drug-Resistant Tuberculosis.

Rapid and accurate drug susceptibility testing (DST) is essential for the treatment of multi- and extensively drug-resistant tuberculosis (M/XDR-TB). We compared the utility of genotypic DST assays with phenotypic DST (pDST) using Bactec 960 MGIT or Löwenstein-Jensen to construct M/XDR-TB treatment regimens for a cohort of 25 consecutive M/XDR-TB patients and 15 possible anti-TB drugs. Genotypic DST results from Cepheid GeneXpert MTB/RIF (Xpert) and line probe assays (LPAs; Hain GenoType MTBDRplus 2.0 and MTBDRsl 2.0) and whole-genome sequencing (WGS) were translated into individual algorithm-derived treatment regimens for each patient. We further analyzed if discrepancies between the various methods were due to flaws in the genotypic or phenotypic test using MIC results. Compared with pDST, the average agreement in the number of drugs prescribed in genotypic regimens ranged from just 49% (95% confidence interval [CI], 39 to 59%) for Xpert and 63% (95% CI, 56 to 70%) for LPAs to 93% (95% CI, 88 to 98%) for WGS. Only the WGS regimens did not contain any drugs to which pDST showed resistance. Importantly, MIC testing revealed that pDST likely underestimated the true rate of resistance for key drugs (rifampin, levofloxacin, moxifloxacin, and kanamycin) because critical concentrations (CCs) were too high. WGS can be used to rule in resistance even in M/XDR strains with complex resistance patterns, but pDST for some drugs is still needed to confirm susceptibility and construct the final regimens. Some CCs for pDST need to be reexamined to avoid systematic false-susceptible results in low-level resistant isolates.

Within-host evolution of Enterococcus faecium during longitudinal carriage and transition to bloodstream infection in immunocompromised patients.

BACKGROUND: Enterococcus faecium is a leading cause of hospital-acquired infection, particularly in the immunocompromised. Here, we use whole genome sequencing of E. faecium to study within-host evolution and the transition from gut carriage to invasive disease. METHODS: We isolated and sequenced 180 E. faecium from four immunocompromised patients who developed bloodstream infection during longitudinal surveillance of E. faecium in stool and their immediate environment. RESULTS: A phylogenetic tree based on single nucleotide polymorphisms (SNPs) in the core genome of the 180 isolates demonstrated several distinct clones. This was highly concordant with the population structure inferred by Bayesian methods, which contained four main BAPS (Bayesian Analysis of Population Structure) groups. The majority of isolates from each patient resided in a single group, but all four patients also carried minority populations in stool from multiple phylogenetic groups. Bloodstream isolates from each case belonged to a single BAPS group, which differed in all four patients. Analysis of 87 isolates (56 from blood) belonging to a single BAPS group that were cultured from the same patient over 54 days identified 30 SNPs in the core genome (nine intergenic, 13 non-synonymous, eight synonymous), and 250 accessory genes that were variably present. Comparison of these genetic variants in blood isolates versus those from stool or environment did not identify any variants associated with bloodstream infection. The substitution rate for these isolates was estimated to be 128 (95% confidence interval 79.82 181.77) mutations per genome per year, more than ten times higher than previous estimates for E. faecium. Within-patient variation in vancomycin resistance associated with vanA was common and could be explained by plasmid loss, or less often by transposon loss. CONCLUSIONS: These findings demonstrate the diversity of E. faecium carriage by individual patients and significant within-host diversity of E. faecium, but do not provide evidence for adaptive genetic variation associated with invasion.

Role of Alanine Racemase Mutations in Mycobacterium tuberculosis d-Cycloserine Resistance.

A screening of more than 1,500 drug-resistant strains of Mycobacterium tuberculosis revealed evolutionary patterns characteristic of positive selection for three alanine racemase (Alr) mutations. We investigated these mutations using molecular modeling, in vitro MIC testing, as well as direct measurements of enzymatic activity, which demonstrated that these mutations likely confer resistance to d-cycloserine.

Evolution of the Staphylococcus argenteus ST2250 Clone in Northeastern Thailand Is Linked with the Acquisition of Livestock-Associated Staphylococcal Genes.

Staphylococcus argenteus is a newly named species previously described as a divergent lineage of Staphylococcus aureus that has recently been shown to have a global distribution. Despite growing evidence of the clinical importance of this species, knowledge about its population epidemiology and genomic architecture is limited. We used whole-genome sequencing to evaluate and compare S. aureus (n = 251) and S. argenteus (n = 68) isolates from adults with staphylococcal sepsis at several hospitals in northeastern Thailand between 2006 and 2013. The majority (82%) of the S. argenteus isolates were of multilocus sequence type 2250 (ST2250). S. aureus was more diverse, although 43% of the isolates belonged to ST121. Bayesian analysis suggested an S. argenteus ST2250 substitution rate of 4.66 (95% confidence interval [CI], 3.12 to 6.38) mutations per genome per year, which was comparable to the S. aureus ST121 substitution rate of 4.07 (95% CI, 2.61 to 5.55). S. argenteus ST2250 emerged in Thailand an estimated 15 years ago, which contrasts with the S. aureus ST1, ST88, and ST121 clades that emerged around 100 to 150 years ago. Comparison of S. argenteus ST2250 genomes from Thailand and a global collection indicated a single introduction into Thailand, followed by transmission to local and more distant countries in Southeast Asia and further afield. S. argenteus and S. aureus shared around half of their core gene repertoire, indicating a high level of divergence and providing strong support for their classification as separate species. Several gene clusters were present in ST2250 isolates but absent from the other S. argenteus and S. aureus study isolates. These included multiple exotoxins and antibiotic resistance genes that have been linked previously with livestock-associated S. aureus, consistent with a livestock reservoir for S. argenteus These genes appeared to be associated with plasmids and mobile genetic elements and may have contributed to the biological success of ST2250.IMPORTANCE In this study, we used whole-genome sequencing to understand the genome evolution and population structure of a systematic collection of ST2250 S. argenteus isolates. A newly identified ancestral species of S. aureus, S. argenteus has become increasingly known as a clinically important species that has been reported recently across various countries. Our results indicate that S. argenteus has spread at a relatively rapid pace over the past 2 decades across northeastern Thailand and acquired multiple exotoxin and antibiotic resistance genes that have been linked previously with livestock-associated S. aureus Our findings highlight the clinical importance and potential pathogenicity of S. argenteus as a recently emerging pathogen.

Evolution and Epidemiology of Multidrug-Resistant Klebsiella pneumoniae in the United Kingdom and Ireland.

Klebsiella pneumoniae is a human commensal and opportunistic pathogen that has become a leading causative agent of hospital-based infections over the past few decades. The emergence and global expansion of hypervirulent and multidrug-resistant (MDR) clones of K. pneumoniae have been increasingly reported in community-acquired and nosocomial infections. Despite this, the population genomics and epidemiology of MDR K. pneumoniae at the national level are still poorly understood. To obtain insights into these, we analyzed a systematic large-scale collection of invasive MDR K. pneumoniae isolates from hospitals across the United Kingdom and Ireland. Using whole-genome phylogenetic analysis, we placed these in the context of previously sequenced K. pneumoniae populations from geographically diverse countries and identified their virulence and drug resistance determinants. Our results demonstrate that United Kingdom and Ireland MDR isolates are a highly diverse population drawn from across the global phylogenetic tree of K. pneumoniae and represent multiple recent international introductions that are mainly from Europe but in some cases from more distant countries. In addition, we identified novel genetic determinants underlying resistance to beta-lactams, gentamicin, ciprofloxacin, and tetracyclines, indicating that both increased virulence and resistance have emerged independently multiple times throughout the population. Our data show that MDR K. pneumoniae isolates in the United Kingdom and Ireland have multiple distinct origins and appear to be part of a globally circulating K. pneumoniae population.IMPORTANCEKlebsiella pneumoniae is a major human pathogen that has been implicated in infections in healthcare settings over the past few decades. Antimicrobial treatment of K. pneumoniae infections has become increasingly difficult as a consequence of the emergence and spread of strains that are resistant to multiple antimicrobials. To better understand the spread of resistant K. pneumoniae, we studied the genomes of a large-scale population of extensively antimicrobial-resistant K. pneumoniae in the United Kingdom and Ireland by utilizing the fine resolution that whole-genome sequencing of pathogen genomes provides. Our results indicate that the K. pneumoniae population is highly diverse and that, in some cases, resistant strains appear to have spread across the country over a few years. In addition, we found evidence that some strains have acquired antimicrobial resistance genes independently, presumably in response to antimicrobial treatment.