Distinct genealogies for plasmids and chromosome

Phylogenomics of Globally Spread Clonal Groups 14 and 15 of Klebsiella pneumoniae

Klebsiella pneumoniae sequence type 14 (ST14) and ST15 caused outbreaks of CTX-M-15 and/or carbapenemase producers worldwide, but their phylogeny and global dynamics remain unclear. We clarified the evolution of K. pneumoniae clonal group 14 (CG14) and CG15 by analyzing the capsular locus (KL), resistome, virulome, and plasmidome of public genomes (n = 481) and de novo sequences (n = 9) representing main sublineages circulating in Portugal. CG14 and CG15 evolved independently within 6 main subclades defined according to the KL and the accessory genome. The CG14 (n = 65) clade was structured in two large monophyletic subclades, CG14-I (KL2, 86%) and CG14-II (KL16, 14%), whose emergences were dated to 1932 and 1911, respectively. Genes encoding extended-spectrum β-lactamase (ESBL), AmpC, and/or carbapenemases were mostly observed in CG14-I (71% versus 22%). CG15 clade (n = 170) was segregated into subclades CG15-IA (KL19/KL106, 9%), CG15-IB (variable KL types, 6%), CG15-IIA (KL24, 43%) and CG15-IIB (KL112, 37%). Most CG15 genomes carried specific GyrA and ParC mutations and emerged from a common ancestor in 1989. CTX-M-15 was especially prevalent in CG15 (68% CG15 versus 38% CG14) and in CG15-IIB (92%). Plasmidome analysis revealed 27 predominant plasmid groups (PG), including particularly pervasive and recombinant F-type (n = 10), Col (n = 10), and new plasmid types. While blaCTX-M-15 was acquired multiple times by a high diversity of F-type mosaic plasmids, other antibiotic resistance genes (ARGs) were dispersed by IncL (blaOXA-48) or IncC (blaCMY/TEM-24) plasmids. We first demonstrate an independent evolutionary trajectory for CG15 and CG14 and how the acquisition of specific KL, quinolone-resistance determining region (QRDR) mutations (CG15), and ARGs in highly recombinant plasmids could have shaped the expansion and diversification of particular subclades (CG14-I and CG15-IIA/IIB). IMPORTANCE Klebsiella pneumoniae represents a major threat in the burden of antibiotic resistance (ABR). Available studies to explain the origin, the diversity, and the evolution of certain ABR K. pneumoniae populations have mainly been focused on a few clonal groups (CGs) using phylogenetic analysis of the core genome, the accessory genome being overlooked. Here, we provide unique insights into the phylogenetic evolution of CG14 and CG15, two poorly characterized CGs which have contributed to the global dissemination of genes responsible for resistance to first-line antibiotics such as β-lactams. Our results point out an independent evolution of these two CGs and highlight the existence of different subclades structured by the capsular type and the accessory genome. Moreover, the contribution of a turbulent flux of plasmids (especially multireplicon F type and Col) and adaptive traits (antibiotic resistance and metal tolerance genes) to the pangenome reflect the exposure and adaptation of K. pneumoniae under different selective pressures.

Microbial Genomics: Innovative Targets and Mechanisms

Multidrug resistance (MDR) has become an increasing threat to global health because bacteria can develop resistance to antibiotics over time. Scientists worldwide are searching for new approaches that go beyond traditional antibiotic discovery and development pipelines. Advances in genomics, however, opened up an unexplored therapeutic opportunity for the discovery of new antibacterial agents. Genomic approaches have been used to discover several novel antibiotics that target critical processes for bacterial growth and survival, including histidine kinases (HKs), LpxC, FabI, peptide deformylase (PDF), and aminoacyl-tRNA synthetases (AaRS). In this review, we will discuss the use of microbial genomics in the search for innovative and promising drug targets as well as the mechanisms of action for novel antimicrobial agents. We will also discuss future directions on how the utilization of the microbial genomics approach could improve the odds of antibiotic development having a more successful outcome.

EnteroBase: hierarchical clustering of 100 000s of bacterial genomes into species/subspecies and populations

The definition of bacterial species is traditionally a taxonomic issue while bacterial populations are identified by population genetics. These assignments are species specific, and depend on the practitioner. Legacy multilocus sequence typing is commonly used to identify sequence types (STs) and clusters (ST Complexes). However, these approaches are not adequate for the millions of genomic sequences from bacterial pathogens that have been generated since 2012. EnteroBase (http://enterobase.warwick.ac.uk) automatically clusters core genome MLST allelic profiles into hierarchical clusters (HierCC) after assembling annotated draft genomes from short-read sequences. HierCC clusters span core sequence diversity from the species level down to individual transmission chains. Here we evaluate HierCC's ability to correctly assign 100 000s of genomes to the species/subspecies and population levels for Salmonella, Escherichia, Clostridoides, Yersinia, Vibrio and Streptococcus. HierCC assignments were more consistent with maximum-likelihood super-trees of core SNPs or presence/absence of accessory genes than classical taxonomic assignments or 95% ANI. However, neither HierCC nor ANI were uniformly consistent with classical taxonomy of Streptococcus. HierCC was also consistent with legacy eBGs/ST Complexes in Salmonella or Escherichia and with O serogroups in Salmonella. Thus, EnteroBase HierCC supports the automated identification of and assignment to species/subspecies and populations for multiple genera. This article is part of a discussion meeting issue 'Genomic population structures of microbial pathogens'.

ProkEvo: an automated, reproducible, and scalable framework for high-throughput bacterial population genomics analyses

Whole Genome Sequence (WGS) data from bacterial species is used for a variety of applications ranging from basic microbiological research, diagnostics, and epidemiological surveillance. The availability of WGS data from hundreds of thousands of individual isolates of individual microbial species poses a tremendous opportunity for discovery and hypothesis-generating research into ecology and evolution of these microorganisms. Flexibility, scalability, and user-friendliness of existing pipelines for population-scale inquiry, however, limit applications of systematic, population-scale approaches. Here, we present ProkEvo, an automated, scalable, reproducible, and open-source framework for bacterial population genomics analyses using WGS data. ProkEvo was specifically developed to achieve the following goals: (1) Automation and scaling of complex combinations of computational analyses for many thousands of bacterial genomes from inputs of raw Illumina paired-end sequence reads; (2) Use of workflow management systems (WMS) such as Pegasus WMS to ensure reproducibility, scalability, modularity, fault-tolerance, and robust file management throughout the process; (3) Use of high-performance and high-throughput computational platforms; (4) Generation of hierarchical-based population structure analysis based on combinations of multi-locus and Bayesian statistical approaches for classification for ecological and epidemiological inquiries; (5) Association of antimicrobial resistance (AMR) genes, putative virulence factors, and plasmids from curated databases with the hierarchically-related genotypic classifications; and (6) Production of pan-genome annotations and data compilation that can be utilized for downstream analysis such as identification of population-specific genomic signatures. The scalability of ProkEvo was measured with two datasets comprising significantly different numbers of input genomes (one with ~2,400 genomes, and the second with ~23,000 genomes). Depending on the dataset and the computational platform used, the running time of ProkEvo varied from ~3-26 days. ProkEvo can be used with virtually any bacterial species, and the Pegasus WMS uniquely facilitates addition or removal of programs from the workflow or modification of options within them. To demonstrate versatility of the ProkEvo platform, we performed a hierarchical-based population structure analyses from available genomes of three distinct pathogenic bacterial species as individual case studies. The specific case studies illustrate how hierarchical analyses of population structures, genotype frequencies, and distribution of specific gene functions can be integrated into an analysis. Collectively, our study shows that ProkEvo presents a practical viable option for scalable, automated analyses of bacterial populations with direct applications for basic microbiology research, clinical microbiological diagnostics, and epidemiological surveillance.

Microbial Genomics as a Catalyst for Targeted Antivirulence Therapeutics

Virulence arresting drugs (VAD) are an expanding class of antimicrobial treatment that act to “disarm” rather than kill bacteria. Despite an increasing number of VAD being registered for clinical use, uptake is hampered by the lack of methods that can identify patients who are most likely to benefit from these new agents. The application of pathogen genomics can facilitate the rational utilization of advanced therapeutics for infectious diseases. The development of genomic assessment of VAD targets is essential to support the early stages of VAD diffusion into infectious disease management. Genomic identification and characterization of VAD targets in clinical isolates can augment antimicrobial stewardship and pharmacovigilance. Personalized genomics guided use of VAD will provide crucial policy guidance to regulating agencies, assist hospitals to optimize the use of these expensive medicines and create market opportunities for biotech companies and diagnostic laboratories.

A publicly accessible database for Clostridioides difficile genome sequences supports tracing of transmission chains and epidemics

Clostridioides difficile is the primary infectious cause of antibiotic-associated diarrhea. Local transmissions and international outbreaks of this pathogen have been previously elucidated by bacterial whole-genome sequencing, but comparative genomic analyses at the global scale were hampered by the lack of specific bioinformatic tools. Here we introduce a publicly accessible database within EnteroBase (http://enterobase.warwick.ac.uk) that automatically retrieves and assembles C. difficile short-reads from the public domain, and calls alleles for core-genome multilocus sequence typing (cgMLST). We demonstrate that comparable levels of resolution and precision are attained by EnteroBase cgMLST and single-nucleotide polymorphism analysis. EnteroBase currently contains 18 254 quality-controlled C. difficile genomes, which have been assigned to hierarchical sets of single-linkage clusters by cgMLST distances. This hierarchical clustering is used to identify and name populations of C. difficile at all epidemiological levels, from recent transmission chains through to epidemic and endemic strains. Moreover, it puts newly collected isolates into phylogenetic and epidemiological context by identifying related strains among all previously published genome data. For example, HC2 clusters (i.e. chains of genomes with pairwise distances of up to two cgMLST alleles) were statistically associated with specific hospitals (P<10-4) or single wards (P=0.01) within hospitals, indicating they represented local transmission clusters. We also detected several HC2 clusters spanning more than one hospital that by retrospective epidemiological analysis were confirmed to be associated with inter-hospital patient transfers. In contrast, clustering at level HC150 correlated with k-mer-based classification and was largely compatible with PCR ribotyping, thus enabling comparisons to earlier surveillance data. EnteroBase enables contextual interpretation of a growing collection of assembled, quality-controlled C. difficile genome sequences and their associated metadata. Hierarchical clustering rapidly identifies database entries that are related at multiple levels of genetic distance, facilitating communication among researchers, clinicians and public-health officials who are combatting disease caused by C. difficile.

The EnteroBase user's guide, with case studies on Salmonella transmissions, Yersinia pestis phylogeny, and Escherichia core genomic diversity

EnteroBase is an integrated software environment that supports the identification of global population structures within several bacterial genera that include pathogens. Here, we provide an overview of how EnteroBase works, what it can do, and its future prospects. EnteroBase has currently assembled more than 300,000 genomes from Illumina short reads from Salmonella, Escherichia, Yersinia, Clostridioides, Helicobacter, Vibrio, and Moraxella and genotyped those assemblies by core genome multilocus sequence typing (cgMLST). Hierarchical clustering of cgMLST sequence types allows mapping a new bacterial strain to predefined population structures at multiple levels of resolution within a few hours after uploading its short reads. Case Study 1 illustrates this process for local transmissions of Salmonella enterica serovar Agama between neighboring social groups of badgers and humans. EnteroBase also supports single nucleotide polymorphism (SNP) calls from both genomic assemblies and after extraction from metagenomic sequences, as illustrated by Case Study 2 which summarizes the microevolution of Yersinia pestis over the last 5000 years of pandemic plague. EnteroBase can also provide a global overview of the genomic diversity within an entire genus, as illustrated by Case Study 3, which presents a novel, global overview of the population structure of all of the species, subspecies, and clades within Escherichia.

A genomic overview of the population structure of Salmonella

For many decades, Salmonella enterica has been subdivided by serological properties into serovars or further subdivided for epidemiological tracing by a variety of diagnostic tests with higher resolution. Recently, it has been proposed that so-called eBurst groups (eBGs) based on the alleles of seven housekeeping genes (legacy multilocus sequence typing [MLST]) corresponded to natural populations and could replace serotyping. However, this approach lacks the resolution needed for epidemiological tracing and the existence of natural populations had not been independently validated by independent criteria. Here, we describe EnteroBase, a web-based platform that assembles draft genomes from Illumina short reads in the public domain or that are uploaded by users. EnteroBase implements legacy MLST as well as ribosomal gene MLST (rMLST), core genome MLST (cgMLST), and whole genome MLST (wgMLST) and currently contains over 100,000 assembled genomes from Salmonella. It also provides graphical tools for visual interrogation of these genotypes and those based on core single nucleotide polymorphisms (SNPs). eBGs based on legacy MLST are largely consistent with eBGs based on rMLST, thus demonstrating that these correspond to natural populations. rMLST also facilitated the selection of representative genotypes for SNP analyses of the entire breadth of diversity within Salmonella. In contrast, cgMLST provides the resolution needed for epidemiological investigations. These observations show that genomic genotyping, with the assistance of EnteroBase, can be applied at all levels of diversity within the Salmonella genus.

Urinary tract infections attributed to diverse ExPEC strains in food animals: evidence and data gaps

Between 70 and 95% of urinary tract infections (UTI) are caused by strains of Escherichia coli. These strains, often termed Extraintestinal Pathogenic E. coli (ExPEC), possess specific virulence traits allowing them to colonize more inhospitable environments, such as the urogenital tract. Some ExPEC isolates from humans have similar virulence factor profiles to ExPEC isolates from animals, and because of the potential for these strains to cause UTI in people, these infections have been referred to as foodborne UTI, or FUTI. Finding similarities in ExPEC in animals and humans is not necessarily proof of transmission, particularly a unidirectional pathway from animals to humans; similarities in virulence factor profiles should be expected given the specific bacterial requirements for colonizing physiological compartments with similar characteristics in all animals. Many of the most important strains of human ExPEC globally, such as ST131, are highly virulent and clonal implying routes of transmission other than food. Documenting routes of transmission is particularly difficult due to the wide range of potential ExPEC sources, including the human intestinal tract, and non-human reservoirs such as food animals and retail meat products, sewage and other environmental sources, and companion animals. The significant environmental reservoir of ExPEC, including strains such as ST131, could potentially explain much more completely the global dissemination of virulent ExPEC clones and the rapid dissemination of new strains within the community. Taken in its totality, the link between ExPEC in animals and UTI in humans might exist, but studies conducted to date do not enable an estimation of the relative importance of this route of transmission. To reduce the burden of illness associated with ExPEC, the scientific community needs to push forward with ecologically-based, scientifically-sound study designs that can address the plethora of ways in which E. coli can spread.