Microbial Diversity and Open Questions about the Deep Tree of Life

In this perspective, we explore the transformative impact and inherent limitations of metagenomics and single-cell genomics on our understanding of microbial diversity and their integration into the Tree of Life. We delve into the key challenges associated with incorporating new microbial lineages into the Tree of Life through advanced phylogenomic approaches. Additionally, we shed light on enduring debates surrounding various aspects of the microbial Tree of Life, focusing on recent advances in some of its deepest nodes, such as the roots of bacteria, archaea, and eukaryotes. We also bring forth current limitations in genome recovery and phylogenomic methodology, as well as new avenues of research to uncover additional key microbial lineages and resolve the shape of the Tree of Life.

The ATCC genome portal: 3,938 authenticated microbial reference genomes

The ATCC Genome Portal (AGP, https://genomes.atcc.org/) is a database of authenticated genomes for bacteria, fungi, protists, and viruses held in ATCC's biorepository. It now includes 3,938 assemblies (253% increase) produced under ISO 9000 by ATCC. Here, we present new features and content added to the AGP for the research community.

The military gear microbiome: risk factors surrounding the warfighter

Combat extremity wounds are highly susceptible to contamination from surrounding environmental material. This bioburden could be partially transferred from materials in immediate proximity to the wound, including fragments of the uniform and gear. However, the assessment of the microbial bioburden present on military gear during operational conditions of deployment or training is relatively unexplored. Opportunistic pathogens that can survive on gear represent risk factors for infection following injury, especially following combat blasts, where fibers and other materials are embedded in wounded tissue. We utilized 16S rRNA sequencing to assess the microbiome composition of different military gear types (boot, trouser, coat, and canteen) from two operational environments (training in Hawai'i and deployed in Indonesia) across time (days 0 and 14). We found that microbiome diversity, stability, and composition were dependent on gear type, training location, and sampling timepoint. At day 14, species diversity was significantly higher in Hawai'i samples compared to Indonesia samples for boot, coat, and trouser swabs. In addition, we observed the presence of potential microbial risk factors, as opportunistic pathogenic species, such as Acinetobacter, Pseudomonas, and Staphylococcus, were found to be present in all sample types and in both study sites. These study outcomes will be used to guide the design of antimicrobial materials and uniforms and for infection control efforts following combat blasts and other injuries, thereby improving treatment guidance during military training and deployment.IMPORTANCECombat extremity wounds are vulnerable to contamination from environments of proximity to the warfighter, leading to potential detrimental outcomes such as infection and delayed wound healing. Therefore, microbial surveillance of such environments is necessary to aid the advancement of military safety and preparedness through clinical diagnostics, treatment protocols, and uniform material design.

The population-level impact of Enterococcus faecalis genetics on intestinal colonization and extraintestinal infection

IMPORTANCE

Enterococcus faecalis causes life-threatening invasive hospital- and community-associated infections that are usually associated with multidrug resistance globally. Although E. faecalis infections cause opportunistic infections typically associated with antibiotic use, immunocompromised immune status, and other factors, they also possess an arsenal of virulence factors crucial for their pathogenicity. Despite this, the relative contribution of these virulence factors and other genetic changes to the pathogenicity of E. faecalis strains remain poorly understood. Here, we investigated whether specific genomic changes in the genome of E. faecalis isolates influence its pathogenicity-infection of hospitalized and nonhospitalized individuals and the propensity to cause extraintestinal infection and intestinal colonization. Our findings indicate that E. faecalis genetics partially influence the infection of hospitalized and nonhospitalized individuals and the propensity to cause extraintestinal infection, possibly due to gut-to-bloodstream translocation, highlighting the potential substantial role of host and environmental factors, including gut microbiota, on the opportunistic pathogenic lifestyle of this bacterium.

Targeted metagenomic assessment reflects critical colonization in battlefield injuries

IMPORTANCE

Microbial contamination in combat wounds can lead to opportunistic infections and adverse outcomes. However, current microbiological detection has a limited ability to capture microbial functional genes. This work describes the application of targeted metagenomic sequencing to profile wound bioburden and capture relevant wound-associated signatures for clinical utility. Ultimately, the ability to detect such signatures will help guide clinical decisions regarding wound care and management and aid in the prediction of wound outcomes.

Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology

Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.

A Comprehensive Bioinformatics Resource Guide for Genome-Based Antimicrobial Resistance Studies

The use of high-throughput sequencing technologies and bioinformatic tools has greatly transformed microbial genome research. With the help of sophisticated computational tools, it has become easier to perform whole genome assembly, identify and compare different species based on their genomes, and predict the presence of genes responsible for proteins, antimicrobial resistance, and toxins. These bioinformatics resources are likely to continuously improve in quality, become more user-friendly to analyze the multiple genomic data, efficient in generating information and translating it into meaningful knowledge, and enhance our understanding of the genetic mechanism of AMR. In this manuscript, we provide an essential guide for selecting the popular resources for microbial research, such as genome assembly and annotation, antibiotic resistance gene profiling, identification of virulence factors, and drug interaction studies. In addition, we discuss the best practices in computer-oriented microbial genome research, emerging trends in microbial genomic data analysis, integration of multi-omics data, the appropriate use of machine-learning algorithms, and open-source bioinformatics resources for genome data analytics.

Uncharacterized and lineage-specific accessory genes within the Proteus mirabilis pan-genome landscape

Proteus mirabilis is a Gram-negative bacterium recognized for its unique swarming motility and urease activity. A previous proteomic report on four strains hypothesized that, unlike other Gram-negative bacteria, P. mirabilis may not exhibit significant intraspecies variation in gene content. However, there has not been a comprehensive analysis of large numbers of P. mirabilis genomes from various sources to support or refute this hypothesis. We performed comparative genomic analysis on 2,060 Proteus genomes. We sequenced the genomes of 893 isolates recovered from clinical specimens from three large US academic medical centers, combined with 1,006 genomes from NCBI Assembly and 161 genomes assembled from Illumina reads in the public domain. We used average nucleotide identity (ANI) to delineate species and subspecies, core genome phylogenetic analysis to identify clusters of highly related P. mirabilis genomes, and pan-genome annotation to identify genes of interest not present in the model P. mirabilis strain HI4320. Within our cohort, Proteus is composed of 10 named species and 5 uncharacterized genomospecies. P. mirabilis can be subdivided into three subspecies; subspecies 1 represented 96.7% (1,822/1,883) of all genomes. The P. mirabilis pan-genome includes 15,399 genes outside of HI4320, and 34.3% (5,282/15,399) of these genes have no putative assigned function. Subspecies 1 is composed of several highly related clonal groups. Prophages and gene clusters encoding putatively extracellular-facing proteins are associated with clonal groups. Uncharacterized genes not present in the model strain P. mirabilis HI4320 but with homology to known virulence-associated operons can be identified within the pan-genome. IMPORTANCE Gram-negative bacteria use a variety of extracellular facing factors to interact with eukaryotic hosts. Due to intraspecies genetic variability, these factors may not be present in the model strain for a given organism, potentially providing incomplete understanding of host-microbial interactions. In contrast to previous reports on P. mirabilis, but similar to other Gram-negative bacteria, P. mirabilis has a mosaic genome with a linkage between phylogenetic position and accessory genome content. P. mirabilis encodes a variety of genes that may impact host-microbe dynamics beyond what is represented in the model strain HI4320. The diverse, whole-genome characterized strain bank from this work can be used in conjunction with reverse genetic and infection models to better understand the impact of accessory genome content on bacterial physiology and pathogenesis of infection.

Identification of an Outbreak Cluster of Extensively Antibiotic-Resistant GC1 Acinetobacter baumannii Isolates in U.S. Military Treatment Facilities

An outbreak involving an extensively antibiotic-resistant Acinetobacter baumannii strain in three military treatment facilities was identified. Fifty-nine isolates recovered from 30 patients over a 4-year period were found among a large collection of isolates using core genome multilocus sequence typing (MLST). They differed by only 0 to 18 single nucleotide polymorphisms (SNPs) and carried the same resistance determinants except that the aphA6 gene was missing in 25 isolates. They represent a novel sublineage of GC1 lineage 1 that likely originated in Afghanistan. IMPORTANCE A. baumannii is recognized as one of the most important nosocomial pathogens, and carbapenem-resistant strains pose a particularly difficult treatment challenge. Outbreaks linked to this pathogen are reported worldwide, particularly during periods of societal upheaval, such as natural disasters and conflicts. Understanding how this organism enters and establishes itself within the hospital environment is key to interrupting transmission, but few genomic studies have examined these transmissions over a prolonged period. Though historical, this report provides an in-depth analysis of nosocomial transmission of this organism across continents and within and between different hospitals.

Whole Genome Sequencing of Avian Pathogenic Escherichia coli Causing Bacterial Chondronecrosis and Osteomyelitis in Australian Poultry

Bacterial chondronecrosis with osteomyelitis (BCO) impacts animal welfare and productivity in the poultry industry worldwide, yet it has an understudied pathogenesis. While Avian Pathogenic Escherichia coli (APEC) are known to be one of the main causes, there is a lack of whole genome sequence data, with only a few BCO-associated APEC (APEC_BCO) genomes available in public databases. In this study, we conducted an analysis of 205 APEC_BCO genome sequences to generate new baseline phylogenomic knowledge regarding the diversity of E. coli sequence types and the presence of virulence associated genes (VAGs). Our findings revealed the following: (i) APEC_BCO are phylogenetically and genotypically similar to APEC that cause colibacillosis (APEC_colibac), with globally disseminated APEC sequence types ST117, ST57, ST69, and ST95 being predominate; (ii) APEC_BCO are frequent carriers of ColV-like plasmids that carry a similar set of VAGs as those found in APEC_colibac. Additionally, we performed genomic comparisons, including a genome-wide association study, with a complementary collection of geotemporally-matched genomes of APEC from multiple cases of colibacillosis (APEC_colibac). Our genome-wide association study found no evidence of novel virulence loci unique to APEC_BCO. Overall, our data indicate that APEC_BCO and APEC_colibac are not distinct subpopulations of APEC. Our publication of these genomes substantially increases the available collection of APEC_BCO genomes and provides insights for the management and treatment strategies of lameness in poultry.

Culturable and unculturable potential heterotrophic microbiological threats to the oldest pyramids of the Memphis necropolis, Egypt

A large percentage of the world's tangible cultural heritage is made from stone; thus, it deteriorates due to physical, chemical, and/or biological factors. The current study explored the microbial community inhabiting two prehistoric sites with high cultural value in the Memphis necropolis of Egypt (Djoser and Lahun Pyramids) using amplicon-based metabarcoding and culture-dependent isolation methods. Samples were examined by epifluorescent microscopy for biological signs before environmental DNA extraction and in vitro cultivation. The metabarcoding analysis identified 644 bacterial species (452 genera) using the 16S rRNA and 204 fungal species (146 genera) using ITS. In comparison with the isolation approach, an additional 28 bacterial species (13 genera) and 34 fungal species (20 genera) were identified. A total of 19 bacterial and 16 fungal species were exclusively culture-dependent, while 92 bacterial and 122 fungal species were culture-independent. The most abundant stone-inhabiting bacteria in the current study were Blastococcus aggregatus, Blastococcus saxobsidens, and Blastococcus sp., among others. The most abundant rock-inhabiting fungi were Knufia karalitana and Pseudotaeniolina globosa, besides abundant unknown Sporormiaceae species. Based on previous reports, microorganisms associated with biodeterioration were detected on color-altered sites at both pyramids. These microorganisms are potentially dangerous as physical and chemical deterioration factors and require proper conservation plans from a microbiological perspective.

Clinical Diagnostics of Bacterial Infections and Their Resistance to Antibiotics-Current State and Whole Genome Sequencing Implementation Perspectives

Antimicrobial resistance (AMR), defined as the ability of microorganisms to withstand antimicrobial treatment, is responsible for millions of deaths annually. The rapid spread of AMR across continents warrants systematic changes in healthcare routines and protocols. One of the fundamental issues with AMR spread is the lack of rapid diagnostic tools for pathogen identification and AMR detection. Resistance profile identification often depends on pathogen culturing and thus may last up to several days. This contributes to the misuse of antibiotics for viral infection, the use of inappropriate antibiotics, the overuse of broad-spectrum antibiotics, or delayed infection treatment. Current DNA sequencing technologies offer the potential to develop rapid infection and AMR diagnostic tools that can provide information in a few hours rather than days. However, these techniques commonly require advanced bioinformatics knowledge and, at present, are not suited for routine lab use. In this review, we give an overview of the AMR burden on healthcare, describe current pathogen identification and AMR screening methods, and provide perspectives on how DNA sequencing may be used for rapid diagnostics. Additionally, we discuss the common steps used for DNA data analysis, currently available pipelines, and tools for analysis. Direct, culture-independent sequencing has the potential to complement current culture-based methods in routine clinical settings. However, there is a need for a minimum set of standards in terms of evaluating the results generated. Additionally, we discuss the use of machine learning algorithms regarding pathogen phenotype detection (resistance/susceptibility to an antibiotic).

Whole genome sequencing to study antimicrobial resistance and RTX virulence genes in equine Actinobacillus isolates

Actinobacillus equuli is mostly associated with disease in horses and is most widely known as the causative agent of sleepy foal disease. Even though existing phenotypic tools such as biochemical tests, 16S rRNA gene sequencing, and Matrix Assisted Laser Desorption Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS) can be used to identify members of the Actinobacillus genus, these methods struggle to differentiate between certain species and do not allow strain, virulence, and antimicrobial susceptibility typing. Hence, we performed in-depth analysis of 24 equine Actinobacillus isolates using phenotypic identification and susceptibility testing on the one hand, and long-read nanopore whole genome sequencing on the other hand. This allowed to address strain divergence down to the whole genome single nucleotide polymorphism (SNP) level. While lowest resolution was observed for 16S rRNA gene classification, a new multi-locus sequence typing (MLST) scheme allowed proper classification up to the species level. Nevertheless, a SNP-level analysis was required to distinguish A. equuli subspecies equuli and haemolyticus. Our data provided first WGS data on Actinobacillus genomospecies 1, Actinobacillus genomospecies 2, and A. arthritidis, which allowed the identification of a new Actinobacillus genomospecies 1 field isolate. Also, in-depth characterization of RTX virulence genes provided information on the distribution, completeness, and potential complementary nature of the RTX gene operons within the Actinobacillus genus. Even though overall low prevalence of acquired resistance was observed, two plasmids were identified conferring resistance to penicillin-ampicillin-amoxicillin and chloramphenicol in one A. equuli strain. In conclusion our data delivered new insights in the use of long-read WGS in high resolution identification, virulence gene typing, and antimicrobial resistance (AMR) of equine Actinobacillus species.

PhyloPlus: a Universal Tool for Phylogenetic Interrogation of Metagenomic Communities

Phylogeny is a powerful tool that can be incorporated into quantitative descriptions of community diversity, yet its use has been limited largely due to the difficulty in constructing phylogenies which incorporate the wide genomic diversity of microbial communities. Here, we describe the development of a web portal, PhyloPlus, which enables users to generate customized phylogenies that may be applied to any bacterial or archaeal communities. We demonstrate the power of phylogeny by comparing metrics that employ phylogeny with those that do not when applied to data sets from two metagenomic studies (fermented food, n = 58; human microbiome, n = 60). This example shows how inclusion of all bacterial species identified by taxonomic classifiers (Kraken2 and Kaiju) made the phylogeny perfectly congruent to the corresponding classification outputs. Our phylogeny-based approach also enabled the construction of more constrained null models which (i) shed light into community structure and (ii) minimize potential inflation of type I errors. Construction of such null models allowed for the observation of under-dispersion in 44 (75.86%) food samples, with the metacommunity defined as bacteria that were found in different food matrices. We also observed that closely related species with high abundance and uneven distribution across different sites could potentially exaggerate the dissimilarity between phylogenetically similar communities if they were measured using traditional species-based metrics (P_adj. = 0.003), whereas this effect was mitigated by incorporating phylogeny (P_adj. = 1). In summary, our tool can provide additional insights into microbial communities of interest and facilitate the use of phylogeny-based approaches in metagenomic analyses. IMPORTANCE There has been an explosion of interest in how microbial diversity affects human health, food safety, and environmental functions among many other processes. Accurately measuring the diversity and structure of those communities is central to understanding their effects. Here, we describe the development of a freely available online tool, PhyloPlus, which allows users to generate custom phylogenies that may be applied to any data set, thereby removing a major obstacle to the application of phylogeny to metagenomic data analysis. We demonstrate that the genetic relatedness of the organisms within those communities is a critical feature of their overall diversity, and that using a phylogeny which captures and quantifies this diversity allows for much more accurate descriptions while preventing misleading conclusions based on estimates that ignore evolutionary relationships.

Metaphor-A workflow for streamlined assembly and binning of metagenomes

Recent advances in bioinformatics and high-throughput sequencing have enabled the large-scale recovery of genomes from metagenomes. This has the potential to bring important insights as researchers can bypass cultivation and analyze genomes sourced directly from environmental samples. There are, however, technical challenges associated with this process, most notably the complexity of computational workflows required to process metagenomic data, which include dozens of bioinformatics software tools, each with their own set of customizable parameters that affect the final output of the workflow. At the core of these workflows are the processes of assembly-combining the short-input reads into longer, contiguous fragments (contigs)-and binning, clustering these contigs into individual genome bins. The limitations of assembly and binning algorithms also pose different challenges depending on the selected strategy to execute them. Both of these processes can be done for each sample separately or by pooling together multiple samples to leverage information from a combination of samples. Here we present Metaphor, a fully automated workflow for genome-resolved metagenomics (GRM). Metaphor differs from existing GRM workflows by offering flexible approaches for the assembly and binning of the input data and by combining multiple binning algorithms with a bin refinement step to achieve high-quality genome bins. Moreover, Metaphor generates reports to evaluate the performance of the workflow. We showcase the functionality of Metaphor on different synthetic datasets and the impact of available assembly and binning strategies on the final results.

Application Value and Research Progress of Human Microbiome in Sexual Assault Cases

In recent years, sexual assault cases have been on the rise, seriously infringing the legitimate rights and interests of women and children, causing widespread concern in society. DNA evidence has become the key evidence to prove the facts in sexual assault cases, but lack of DNA evidence or only DNA evidence in some sexual assault cases leads to unclear facts and insufficient evidence. With the emergence of high-throughput sequencing technology and the development of bioinformatics and artificial intelligence, new progress has been made in the study of human microbiome. Researchers have begun to use human microbiome for difficult sexual assault cases indentification. This paper reviews the characteristics of human microbiome, and its application value in the inferences of the body fluid stain origin, the sexual assault method, the crime time, etc. In addition, the challenges faced by the application of the human microbiome in practical case handling, the solutions and future development potential are analyzed and prospected.

A reverse-ecology framework to uncover the potential metabolic interplay among 'Candidatus Liberibacter' species, Citrus hosts and psyllid vector

'Candidatus Liberibacter' species have developed a dependency on essential nutrients and metabolites from the host cell, as a result of substantial genome reduction. Still, it is difficult to state which nutrients they acquire and whether or not they are metabolically reliant. We used a reverse-ecology model to investigate the potential metabolic interactions of 'Ca Liberibacter' species, Citrus, and the psyllid Diaphorina citri in the huanglongbing disease pyramid. Our findings show that hosts (citrus and psyllid) tend to support the nutritional needs of 'Ca. Liberibacter' species, implying that the pathogen's metabolism has become tightly linked to hosts, which may reflect in the parasite lifestyle of this important genus.

Genetic Diversity of Antimicrobial Resistance and Key Virulence Features in Two Extensively Drug-Resistant Acinetobacter baumannii Isolates

In recent decades, Acinetobacter baumannii emerged as a major infective menace in healthcare settings due to scarce therapeutic options to treat infections. Therefore, undertaking genome comparison analyses of multi-resistant A. baumannii strains could aid the identification of key bacterial determinants to develop innovative anti-virulence approaches. Following genome sequencing, we performed a molecular characterization of key genes and genomic comparison of two A. baumannii strains, #36 and #150, with selected reference genomes. Despite a different antibiotic resistance gene content, the analyzed strains showed a very similar antibiogram profile. Interestingly, the lack of some important virulence determinants (i.e., bap, ata and omp33–36) did not abrogate their adhesive abilities to abiotic and biotic surfaces, as reported before; indeed, strains retained these capacities, although to a different extent, suggesting the presence of distinct vicarious genes. Conversely, secretion systems, lipopolysaccharide (LPS), capsule and iron acquisition systems were highly similar to A. baumannii reference strains. Overall, our analyses increased our knowledge on A. baumannii genomic content and organization as well as the genomic events occurring in nosocomial isolates to better fit into changing healthcare environments.

Antimicrobial resistance determinants are associated with Staphylococcus aureus bacteraemia and adaptation to the healthcare environment: a bacterial genome-wide association study

Staphylococcus aureus is a major bacterial pathogen in humans, and a dominant cause of severe bloodstream infections. Globally, antimicrobial resistance (AMR) in S. aureus remains challenging. While human risk factors for infection have been defined, contradictory evidence exists for the role of bacterial genomic variation in S. aureus disease. To investigate the contribution of bacterial lineage and genomic variation to the development of bloodstream infection, we undertook a genome-wide association study comparing bacteria from 1017 individuals with bacteraemia to 984 adults with asymptomatic S. aureus nasal carriage. Within 984 carriage isolates, we also compared healthcare-associated (HA) carriage with community-associated (CA) carriage. All major global lineages were represented in both bacteraemia and carriage, with no evidence for different infection rates. However, kmers tagging trimethoprim resistance-conferring mutation F99Y in dfrB were significantly associated with bacteraemia-vs-carriage (P=10-8.9-10-9.3). Pooling variation within genes, bacteraemia-vs-carriage was associated with the presence of mecA (HMP=10-5.3) as well as the presence of SCCmec (HMP=10-4.4). Among S. aureus carriers, no lineages were associated with HA-vs-CA carriage. However, we found a novel signal of HA-vs-CA carriage in the foldase protein prsA, where kmers representing conserved sequence allele were associated with CA carriage (P=10-7.1-10-19.4), while in gyrA, a ciprofloxacin resistance-conferring mutation, L84S, was associated with HA carriage (P=10-7.2). In an extensive study of S. aureus bacteraemia and nasal carriage in the UK, we found strong evidence that all S. aureus lineages are equally capable of causing bloodstream infection, and of being carried in the healthcare environment. Genomic variation in the foldase protein prsA is a novel genomic marker of healthcare origin in S. aureus but was not associated with bacteraemia. AMR determinants were associated with both bacteraemia and healthcare-associated carriage, suggesting that AMR increases the propensity not only to survive in healthcare environments, but also to cause invasive disease.

Bacterial nomenclature in the era of genomics

The remarkable success of taxonomic discovery, powered by culturomics, genomics and metagenomics, creates a pressing need for new bacterial names while holding a mirror up to the slow pace of change in bacterial nomenclature. Here, I take a fresh look at bacterial nomenclature, exploring how we might create a system fit for the age of genomics, playing to the strengths of current practice while minimizing difficulties. Adoption of linguistic pragmatism-obeying the rules while treating recommendations as merely optional-will make it easier to create names derived from descriptions, from people or places or even arbitrarily. Simpler protologues and a relaxed approach to recommendations will also remove much of the need for expert linguistic quality control. Automated computer-based approaches will allow names to be created en masse before they are needed while also relieving microbiologists of the need for competence in Latin. The result will be a system that is accessible, inclusive and digital, while also fully capable of naming the unnamed millions of bacteria.

Metagenomic strategies identify diverse integron-integrase and antibiotic resistance genes in the Antarctic environment

The objective of this study is to identify and analyze integrons and antibiotic resistance genes (ARGs) in samples collected from diverse sites in terrestrial Antarctica. Integrons were studied using two independent methods. One involved the construction and analysis of intI gene amplicon libraries. In addition, we sequenced 17 metagenomes of microbial mats and soil by high-throughput sequencing and analyzed these data using the IntegronFinder program. As expected, the metagenomic analysis allowed for the identification of novel predicted intI integrases and gene cassettes (GCs), which mostly encode unknown functions. However, some intI genes are similar to sequences previously identified by amplicon library analysis in soil samples collected from non-Antarctic sites. ARGs were analyzed in the metagenomes using ABRIcate with CARD database and verified if these genes could be classified as GCs by IntegronFinder. We identified 53 ARGs in 15 metagenomes, but only four were classified as GCs, one in MTG12 metagenome (Continental Antarctica), encoding an aminoglycoside-modifying enzyme (AAC(6´)acetyltransferase) and the other three in CS1 metagenome (Maritime Antarctica). One of these genes encodes a class D β-lactamase (blaOXA-205) and the other two are located in the same contig. One is part of a gene encoding the first 76 amino acids of aminoglycoside adenyltransferase (aadA6), and the other is a qacG2 gene.

Genomic Sequence Analysis of Methicillin- and Carbapenem-Resistant Bacteria Isolated from Raw Sewage

Antibiotic resistance is one of the largest threats facing global health. Wastewater treatment plants are well-known hot spots for interaction between diverse bacteria, genetic exchange, and antibiotic resistance. Nonpathogenic bacteria theoretically act as reservoirs of antibiotic resistance subsequently transferring antibiotic resistance genes to pathogens, indicating that evolutionary processes occur outside clinical settings and may drive patterns of drug-resistant infections. We isolated and sequenced 100 bacterial strains from five wastewater treatment plants to analyze regional dynamics of antibiotic resistance in the California Central Valley. The results demonstrate the presence of a wide diversity of pathogenic and nonpathogenic bacteria, with an arithmetic mean of 5.1 resistance genes per isolate. Forty-three percent of resistance genes were located on plasmids, suggesting that large levels of gene transfer between bacteria that otherwise may not co-occur are facilitated by wastewater treatment. One of the strains detected was a Bacillus carrying pX01 and pX02 anthrax-like plasmids and multiple drug resistance genes. A correlation between resistance genes and taxonomy indicates that taxon-specific evolutionary studies may be useful in determining and predicting patterns of antibiotic resistance. Conversely, a lack of geographic correlation may indicate that landscape genetic studies to understand the spread of antibiotic resistance genes should be carried out at broader scales. This large data set provides insights into how pathogenic and nonpathogenic bacteria interact in wastewater environments and the resistance genes which may be horizontally transferred between them. This can help in determining the mechanisms leading to the increasing prevalence of drug-resistant infections observed in clinical settings. IMPORTANCE The reasons for the increasing prevalence of antibiotic-resistant infections are complex and associated with myriad clinical and environmental processes. Wastewater treatment plants operate as nexuses of bacterial interaction and are known hot spots for genetic exchange between bacteria, including antibiotic resistance genes. We isolated and sequenced 100 drug-resistant bacteria from five wastewater treatment plants in California's Central Valley, characterizing widespread gene sharing between pathogens and nonpathogens. We identified a novel, multiresistant Bacillus carrying anthrax-like plasmids. This empirical study supports the likelihood of evolutionary and population processes in the broader environment affecting the prevalence of clinical drug-resistant infections and identifies several taxa that may operate as reservoirs and vectors of antibiotic resistance genes.

Evidence for Selection in the Abundant Accessory Gene Content of a Prokaryote Pangenome

A pangenome is the complete set of genes (core and accessory) present in a phylogenetic clade. We hypothesize that a pangenome's accessory gene content is structured and maintained by selection. To test this hypothesis, we interrogated the genomes of 40 Pseudomonas species for statistically significant coincident (i.e., co-occurring/avoiding) gene patterns. We found that 86.7% of common accessory genes are involved in ≥1 coincident relationship. Further, genes that co-occur and/or avoid each other-but are not vertically inherited-are more likely to share functional categories, are more likely to be simultaneously transcribed, and are more likely to produce interacting proteins, than would be expected by chance. These results are not due to coincident genes being adjacent to one another on the chromosome. Together, these findings suggest that the accessory genome is structured into sets of genes that function together within a given strain. Given the similarity of the Pseudomonas pangenome with open pangenomes of other prokaryotic species, we speculate that these results are generalizable.

Achromobacter spp. genetic adaptation in cystic fibrosis

Achromobacter spp. are emerging pathogens in patients with cystic fibrosis (CF) and Achromobacter spp. caused infections are associated with more severe disease outcomes and high intrinsic antibiotic resistance. While conventional CF pathogens are studied extensively, little is known about the genetic determinants leading to antibiotic resistance and the genetic adaptation in Achromobacter spp. infections. Here, we analysed 101 Achromobacter spp. genomes from 51 patients with CF isolated during the course of up to 20 years of infection to identify within-host adaptation, mutational signatures and genetic variation associated with increased antibiotic resistance. We found that the same regulatory and inorganic ion transport genes were frequently mutated in persisting clone types within and between Achromobacter species, indicating convergent genetic adaptation. Genome-wide association study of six antibiotic resistance phenotypes revealed the enrichment of associated genes involved in inorganic ion transport, transcription gene enrichment in β-lactams, and energy production and translation gene enrichment in the trimethoprim/sulfonamide group. Overall, we provide insights into the pathogenomics of Achromobacter spp. infections in patients with CF airways. Since emerging pathogens are increasingly recognized as an important healthcare issue, our findings on evolution of antibiotic resistance and genetic adaptation can facilitate better understanding of disease progression and how mutational changes have implications for patients with CF.

Experimental-Evolution-Driven Identification of Arabidopsis Rhizosphere Competence Genes in Pseudomonas protegens

Beneficial plant root-associated microorganisms carry out a range of functions that are essential for plant performance. Establishment of a bacterium on plant roots, however, requires overcoming several challenges, including competition with neighboring microorganisms and host immunity. Forward and reverse genetics have led to the identification of mechanisms that are used by beneficial microorganisms to overcome these challenges, such as the production of iron-chelating compounds, the formation of strong biofilms, or the concealment of characteristic microbial molecular patterns that trigger the host immune system. However, how such mechanisms arose from an evolutionary perspective is much less understood. To study bacterial adaptation in the rhizosphere, we employed experimental evolution to track the physiological and genetic dynamics of root-dwelling Pseudomonas protegens in the Arabidopsis thaliana rhizosphere under axenic conditions. This simplified binary one plant/one bacterium system allows for the amplification of key adaptive mechanisms for bacterial rhizosphere colonization. We identified 35 mutations, including single-nucleotide polymorphisms, insertions, and deletions, distributed over 28 genes. We found that mutations in genes encoding global regulators and in genes for siderophore production, cell surface decoration, attachment, and motility accumulated in parallel, underlining the finding that bacterial adaptation to the rhizosphere follows multiple strategies. Notably, we observed that motility increased in parallel across multiple independent evolutionary lines. All together, these results underscore the strength of experimental evolution in identifying key genes, pathways, and processes for bacterial rhizosphere colonization and a methodology for the development of elite beneficial microorganisms with enhanced root-colonizing capacities that can support sustainable agriculture in the future. IMPORTANCE Beneficial root-associated microorganisms carry out many functions that are essential for plant performance. Establishment of a bacterium on plant roots, however, requires overcoming many challenges. Previously, diverse mechanisms that are used by beneficial microorganisms to overcome these challenges were identified. However, how such mechanisms have developed from an evolutionary perspective is much less understood. Here, we employed experimental evolution to track the evolutionary dynamics of a root-dwelling pseudomonad on the root of Arabidopsis. We found that mutations in global regulators, as well as in genes for siderophore production, cell surface decoration, attachment, and motility, accumulate in parallel, emphasizing these strategies for bacterial adaptation to the rhizosphere. We identified 35 mutations distributed over 28 genes. All together, our results demonstrate the power of experimental evolution in identifying key pathways for rhizosphere colonization and a methodology for the development of elite beneficial microorganisms that can support sustainable agriculture.

Key Technologies for Progressing Discovery of Microbiome-Based Medicines

A growing number of experimental and computational approaches are illuminating the “microbial dark matter” and uncovering the integral role of commensal microbes in human health. Through this work, it is now clear that the human microbiome presents great potential as a therapeutic target for a plethora of diseases, including inflammatory bowel disease, diabetes and obesity. The development of more efficacious and targeted treatments relies on identification of causal links between the microbiome and disease; with future progress dependent on effective links between state-of-the-art sequencing approaches, computational analyses and experimental assays. We argue determining causation is essential, which can be attained by generating hypotheses using multi-omic functional analyses and validating these hypotheses in complex, biologically relevant experimental models. In this review we discuss existing analysis and validation methods, and propose best-practice approaches required to enable the next phase of microbiome research.

Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3

Culture-independent analyses of microbial communities have progressed dramatically in the last decade, particularly due to advances in methods for biological profiling via shotgun metagenomics. Opportunities for improvement continue to accelerate, with greater access to multi-omics, microbial reference genomes, and strain-level diversity. To leverage these, we present bioBakery 3, a set of integrated, improved methods for taxonomic, strain-level, functional, and phylogenetic profiling of metagenomes newly developed to build on the largest set of reference sequences now available. Compared to current alternatives, MetaPhlAn 3 increases the accuracy of taxonomic profiling, and HUMAnN 3 improves that of functional potential and activity. These methods detected novel disease-microbiome links in applications to CRC (1262 metagenomes) and IBD (1635 metagenomes and 817 metatranscriptomes). Strain-level profiling of an additional 4077 metagenomes with StrainPhlAn 3 and PanPhlAn 3 unraveled the phylogenetic and functional structure of the common gut microbe Ruminococcus bromii, previously described by only 15 isolate genomes. With open-source implementations and cloud-deployable reproducible workflows, the bioBakery 3 platform can help researchers deepen the resolution, scale, and accuracy of multi-omic profiling for microbial community studies.

Genomic islands and the evolution of livestock-associated Staphylococcus aureus genomes

BACKGROUND

Genomic Islands (GIs) are commonly believed to be relics of horizontal transfer and associated with specific metabolic capacities, including virulence of the strain. Horizontal gene transfer (HGT) plays a vital role in the acquisition of GIs and the evolution and adaptation of bacterial genomes.

OBJECTIVE

The present study was designed to predict the GIs and role of HGT in evolution of livestock-associated Staphylococcus aureus (LA-SA).

METHODS

GIs were predicted with two methods namely, Ensemble algorithm for Genomic Island Detection (EGID) tool, and Seq word Sniffer script. Functional characterization of GI elements was performed with clustering of orthologs. The putative donor predictions of GIs was done with the aid of the pre_GI database.

RESULTS

The present study predicted a pan of 46 GIs across the LA-SA genomes. Functional characterization of GI sequences revealed few unique results like the presence of metabolic operons like leuABCD and folPK genes in GIs and showed the importance of GIs in the adaptation to the host niche. The developed framework for GI donor prediction results revealed Rickettsia and Mycoplasma as the major donors of GI elements.

CONCLUSIONS

The role of GIs during the evolutionary race of LA-SA could be concluded from the present study. Niche adaptation of LA-SA enhanced presumably due to these GIs. Future studies could focus on the evolutionary relationships between Rickettsia and Mycoplasma sp. with S. aureus and also the evolution of Leucine/Isoleucine mosaic operon (leuABCD).

Repeat-Driven Generation of Antigenic Diversity in a Major Human Pathogen, Trypanosoma cruzi

Trypanosoma cruzi, a zoonotic kinetoplastid protozoan parasite, is the causative agent of American trypanosomiasis (Chagas disease). Having a very plastic, repetitive and complex genome, the parasite displays a highly diverse repertoire of surface molecules, with pivotal roles in cell invasion, immune evasion and pathogenesis. Before 2016, the complexity of the genomic regions containing these genes impaired the assembly of a genome at chromosomal level, making it impossible to study the structure and function of the several thousand repetitive genes encoding the surface molecules of the parasite. We here describe the genome assembly of the Sylvio X10/1 genome sequence, which since 2016 has been used as a reference genome sequence for T. cruzi clade I (TcI), produced using high coverage PacBio single-molecule sequencing. It was used to analyze deep Illumina sequence data from 34 T. cruzi TcI isolates and clones from different geographic locations, sample sources and clinical outcomes. Resolution of the surface molecule gene distribution showed the unusual duality in the organization of the parasite genome, a synteny of the core genomic region with related protozoa flanked by unique and highly plastic multigene family clusters encoding surface antigens. The presence of abundant interspersed retrotransposons in these multigene family clusters suggests that these elements are involved in a recombination mechanism for the generation of antigenic variation and evasion of the host immune response on these TcI strains. The comparative genomic analysis of the cohort of TcI strains revealed multiple cases of such recombination events involving surface molecule genes and has provided new insights into T. cruzi population structure.

Population Genomic Analysis of Listeria monocytogenes From Food Reveals Substrate-Specific Genome Variation

Listeria monocytogenes is the major causative agent of the foodborne illness listeriosis. Listeriosis presents as flu-like symptoms in healthy individuals, and can be fatal for children, elderly, pregnant women, and immunocompromised individuals. Estimates suggest that L. monocytogenes results in ∼1,600 illnesses and ∼260 deaths annually in the United States. L. monocytogenes can survive and persist in a variety of harsh environments, including conditions encountered in production of fermented dairy products such as cheese. For instance, microbial growth is often limited in soft cheese fermentation because of harsh pH, water content, and salt concentrations. However, L. monocytogenes has caused a number of deadly listeriosis outbreaks through the contamination of cheese. The purpose of this study was to understand if genetically distinct populations of L. monocytogenes are associated with particular foods, including cheese and dairy. To address this goal, we analyzed the population genetic structure of 504 L. monocytogenes strains isolated from food with publicly available genome assemblies. We identified 10 genetically distinct populations spanning L. monocytogenes lineages 1, II, and III and serotypes 1/2a, 1/2b, 1/2c, 4b, and 4c. We observed an overrepresentation of isolates from specific populations with cheese (population 2), fruit/vegetable (population 2), seafood (populations 5, 8 and 9) and meat (population 10). We used the Large Scale Blast Score Ratio pipeline and Roary to identify genes unique to population 1 and population 2 in comparison with all other populations, and screened for the presence of antimicrobial resistance genes and virulence genes across all isolates. We identified > 40 genes that were present at high frequency in population 1 and population 2 and absent in most other isolates. Many of these genes encoded for transcription factors, and cell surface anchored proteins. Additionally, we found that the virulence genes aut and ami were entirely or partially deleted in population 2. These results indicate that some L. monocytogenes populations may exhibit associations with particular foods, including cheese, and that gene content may contribute to this pattern.

Insights from the reanalysis of high-throughput chemical genomics data for Escherichia coli K-12

Despite the demonstrated success of genome-wide genetic screens and chemical genomics studies at predicting functions for genes of unknown function or predicting new functions for well-characterized genes, their potential to provide insights into gene function has not been fully explored. We systematically reanalyzed a published high-throughput phenotypic dataset for the model Gram-negative bacterium Escherichia coli K-12. The availability of high-quality annotation sets allowed us to compare the power of different metrics for measuring phenotypic profile similarity to correctly infer gene function. We conclude that there is no single best method; the three metrics tested gave comparable results for most gene pairs. We also assessed how converting quantitative phenotypes to discrete, qualitative phenotypes affected the association between phenotype and function. Our results indicate that this approach may allow phenotypic data from different studies to be combined to produce a larger dataset that may reveal functional connections between genes not detected in individual studies.

BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters

BACKGROUND

Genome mining for biosynthetic gene clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools are hindered by a bottleneck caused by the expensive network-based approach used to group these BGCs into gene cluster families (GCFs).

RESULTS

Here, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes within 10 days on a typical 36-core CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a "query mode" that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration.

CONCLUSIONS

BiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-group/bigslice.

Distinct Bacterial Pathways Influence the Efficacy of Antibiotics against Mycobacterium tuberculosis

Understanding how Mycobacterium tuberculosis survives during antibiotic treatment is necessary to rationally devise more effective tuberculosis (TB) chemotherapy regimens. Using genome-wide mutant fitness profiling and the mouse model of TB, we identified genes that alter antibiotic efficacy specifically in the infection environment and associated several of these genes with natural genetic variants found in drug-resistant clinical isolates. These data suggest strategies for synergistic therapies that accelerate bacterial clearance, and they identify mechanisms of adaptation to drug exposure that could influence treatment outcome.

Effective tuberculosis treatment requires at least 6 months of combination therapy. Alterations in the physiological state of the bacterium during infection are thought to reduce drug efficacy and prolong the necessary treatment period, but the nature of these adaptations remain incompletely defined. To identify specific bacterial functions that limit drug effects during infection, we employed a comprehensive genetic screening approach to identify mutants with altered susceptibility to the first-line antibiotics in the mouse model. We identified many mutations that increase the rate of bacterial clearance, suggesting new strategies for accelerating therapy. In addition, the drug-specific effects of these mutations suggested that different antibiotics are limited by distinct factors. Rifampin efficacy is inferred to be limited by cellular permeability, whereas isoniazid is preferentially affected by replication rate. Many mutations that altered bacterial clearance in the mouse model did not have an obvious effect on drug susceptibility using in vitro assays, indicating that these chemical-genetic interactions tend to be specific to the in vivo environment. This observation suggested that a wide variety of natural genetic variants could influence drug efficacy in vivo without altering behavior in standard drug-susceptibility tests. Indeed, mutations in a number of the genes identified in our study are enriched in drug-resistant clinical isolates, identifying genetic variants that may influence treatment outcome. Together, these observations suggest new avenues for improving therapy, as well as the mechanisms of genetic adaptations that limit it.

IMPORTANCE Understanding how Mycobacterium tuberculosis survives during antibiotic treatment is necessary to rationally devise more effective tuberculosis (TB) chemotherapy regimens. Using genome-wide mutant fitness profiling and the mouse model of TB, we identified genes that alter antibiotic efficacy specifically in the infection environment and associated several of these genes with natural genetic variants found in drug-resistant clinical isolates. These data suggest strategies for synergistic therapies that accelerate bacterial clearance, and they identify mechanisms of adaptation to drug exposure that could influence treatment outcome.

Microbial genomic island discovery, visualization and analysis

Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions.

Reconciling the Potentially Irreconcilable? Genotypic and Phenotypic Amoxicillin-Clavulanate Resistance in Escherichia coli

Resistance to amoxicillin-clavulanate, a widely used beta-lactam/beta-lactamase inhibitor combination antibiotic, is rising globally, and yet susceptibility testing remains challenging. To test whether whole-genome sequencing (WGS) could provide a more reliable assessment of susceptibility than traditional methods, we predicted resistance from WGS for 976 Escherichia coli bloodstream infection isolates from Oxfordshire, United Kingdom, comparing against phenotypes from the BD Phoenix (calibrated against EUCAST guidelines).

Resistance to amoxicillin-clavulanate, a widely used beta-lactam/beta-lactamase inhibitor combination antibiotic, is rising globally, and yet susceptibility testing remains challenging. To test whether whole-genome sequencing (WGS) could provide a more reliable assessment of susceptibility than traditional methods, we predicted resistance from WGS for 976 Escherichia coli bloodstream infection isolates from Oxfordshire, United Kingdom, comparing against phenotypes from the BD Phoenix (calibrated against EUCAST guidelines). A total of 339/976 (35%) isolates were amoxicillin-clavulanate resistant. Predictions based solely on beta-lactamase presence/absence performed poorly (sensitivity, 23% [78/339]) but improved when genetic features associated with penicillinase hyperproduction (e.g., promoter mutations and copy number estimates) were considered (sensitivity, 82% [277/339]; P < 0.0001). Most discrepancies occurred in isolates with MICs within ±1 doubling dilution of the breakpoint. We investigated two potential causes: the phenotypic reference and the binary resistant/susceptible classification. We performed reference standard, replicated phenotyping in a random stratified subsample of 261/976 (27%) isolates using agar dilution, following both EUCAST and CLSI guidelines, which use different clavulanate concentrations. As well as disagreeing with each other, neither agar dilution phenotype aligned perfectly with genetic features. A random-effects model investigating associations between genetic features and MICs showed that some genetic features had small, variable and additive effects, resulting in variable resistance classification. Using model fixed-effects to predict MICs for the non-agar dilution isolates, predicted MICs were in essential agreement (±1 doubling dilution) with observed (BD Phoenix) MICs for 691/715 (97%) isolates. This suggests amoxicillin-clavulanate resistance in E. coli is quantitative, rather than qualitative, explaining the poorly reproducible binary (resistant/susceptible) phenotypes and suboptimal concordance between different phenotypic methods and with WGS-based predictions.

Microbial genomics amidst the Arctic crisis

The Arctic is warming - fast. Microbes in the Arctic play pivotal roles in feedbacks that magnify the impacts of Arctic change. Understanding the genome evolution, diversity and dynamics of Arctic microbes can provide insights relevant for both fundamental microbiology and interdisciplinary Arctic science. Within this synthesis, we highlight four key areas where genomic insights to the microbial dimensions of Arctic change are urgently required: the changing Arctic Ocean, greenhouse gas release from the thawing permafrost, 'biological darkening' of glacial surfaces, and human activities within the Arctic. Furthermore, we identify four principal challenges that provide opportunities for timely innovation in Arctic microbial genomics. These range from insufficient genomic data to develop unifying concepts or model organisms for Arctic microbiology to challenges in gaining authentic insights to the structure and function of low-biomass microbiota and integration of data on the causes and consequences of microbial feedbacks across scales. We contend that our insights to date on the genomics of Arctic microbes are limited in these key areas, and we identify priorities and new ways of working to help ensure microbial genomics is in the vanguard of the scientific response to the Arctic crisis.

A review of methods and databases for metagenomic classification and assembly

Microbiome research has grown rapidly over the past decade, with a proliferation of new methods that seek to make sense of large, complex data sets. Here, we survey two of the primary types of methods for analyzing microbiome data: read classification and metagenomic assembly, and we review some of the challenges facing these methods. All of the methods rely on public genome databases, and we also discuss the content of these databases and how their quality has a direct impact on our ability to interpret a microbiome sample.

Analyses of Livestock-Associated Staphylococcus aureus Pan-Genomes Suggest Virulence Is Not Primary Interest in Evolution of Its Genome

Staphylococcus aureus is not only part of normal flora but also an opportunistic pathogen relevant to microbial genomics, public health, and veterinary medicine. In addition to being a well-known human pathogen, S. aureus causes various infections in economically important livestock animals such as cows, sheep, goats, and pigs. There are very few studies that have examined the pan-genome of S. aureus or the host-specific strains' pan-genomes. We report on livestock-associated S. aureus' (LA-SA) pan-genome and suggest that virulence is not the primary interest in evolution of its genome. LA-SA' complete genomes were retrieved from the NCBI and pan-genome was constructed by high-speed Roary pipeline. The pan-genome size was 4637 clusters, whereas 42.46% of the pan-genome was associated with the core genome. We found 1268 genes were associated with the strain-unique genome, and the remaining 1432 cluster with the accessory genome. COG (clusters of orthologous group of proteins) analysis of the core genes revealed 34% of clusters related to metabolism responsible for amino acid and inorganic ion transport (COG categories E and P), followed by carbohydrate metabolism (category G). Virulent gene analysis revealed the core genes responsible for antiphagocytosis and iron uptake. The fluidity of pan-genome was calculated as 0.082 ± 0.025. Importantly, the positive selection analysis suggested a slower rate of evolution among the LA-SA genomes. We call for comparative microbial and pan-genome research between human and LA-SA that can help further understand the evolution of virulence and thus inform future microbial diagnostics and drug discovery.

Hiding in Plain Sight: The Globally Distributed Bacterial Candidate Phylum PAUC34f

Bacterial candidate phylum PAUC34f was originally discovered in marine sponges and is widely considered to be composed of sponge symbionts. Here, we report 21 single amplified genomes (SAGs) of PAUC34f from a variety of environments, including the dark ocean, lake sediments, and a terrestrial aquifer. The diverse origins of the SAGs and the results of metagenome fragment recruitment suggest that some PAUC34f lineages represent relatively abundant, free-living cells in environments other than sponge microbiomes, including the deep ocean. Both phylogenetic and biogeographic patterns, as well as genome content analyses suggest that PAUC34f associations with hosts evolved independently multiple times, while free-living lineages of PAUC34f are distinct and relatively abundant in a wide range of environments.

Comparative Metagenome-Assembled Genome Analysis of "Candidatus Lachnocurva vaginae", Formerly Known as Bacterial Vaginosis-Associated Bacterium-1 (BVAB1)

Bacterial vaginosis-associated bacterium 1 (BVAB1) is an as-yet uncultured bacterial species found in the human vagina that belongs to the family Lachnospiraceae within the order Clostridiales. As its name suggests, this bacterium is often associated with bacterial vaginosis (BV), a common vaginal disorder that has been shown to increase a woman's risk for HIV, Chlamydia trachomatis, and Neisseria gonorrhoeae infections as well as preterm birth. BVAB1 has been further associated with the persistence of BV following metronidazole treatment, increased vaginal inflammation, and adverse obstetrics outcomes. There is no available complete genome sequence of BVAB1, which has made it difficult to mechanistically understand its role in disease. We present here a circularized metagenome-assembled genome (cMAG) of BVAB1 as well as a comparative analysis including an additional six metagenome-assembled genomes (MAGs) of this species. These sequences were derived from cervicovaginal samples of seven separate women. The cMAG was obtained from a metagenome sequenced with long-read technology on a PacBio Sequel II instrument while the others were derived from metagenomes sequenced on the Illumina HiSeq platform. The cMAG is 1.649 Mb in size and encodes 1,578 genes. We propose to rename BVAB1 to "Candidatus Lachnocurva vaginae" based on phylogenetic analyses, and provide genomic and metabolomic evidence that this candidate species may metabolize D-lactate, produce trimethylamine (one of the chemicals responsible for BV-associated odor), and be motile. The cMAG and the six MAGs are valuable resources that will further contribute to our understanding of the heterogeneous etiology of bacterial vaginosis.

Optimization of sample preparation for culture-independent sequencing of Bordetella pertussis

Bordetella pertussis, the aetiological agent of whooping cough, is re-emerging globally despite widespread vaccination. B. pertussis is highly infectious and, prior to vaccination programmes, was the leading cause of infant mortality. The WHO estimated that over 600 000 deaths are prevented annually by pertussis vaccination, but B. pertussis infection was still responsible for over 63 000 deaths globally in 2013. The re-emergence of B. pertussis has been linked to strains with inactive or absent major virulence factors included in vaccines such as pertactin, pertussis toxin and filamentous haemagglutinin. Thus, the molecular surveillance of currently circulating strains is critical in understanding and controlling B. pertussis. Such information provides data on strains to inform control measures and the identification of future vaccine antigens. Current surveillance and typing methods for B. pertussis rely on the availability of clinical isolates. However, since the 1990s, the majority of pertussis cases have been diagnosed by PCR, where an isolate is not needed. The rapid decline in the availability of B. pertussis isolates impacts our ability to monitor this infection. The growing uptake of next-generation sequencing (NGS) has offered the opportunity for culture-independent genome sequencing and typing of this fastidious pathogen. Therefore, the objective of the study was to optimize respiratory sample preparation, independent of culture, in order to type B. pertussis using NGS. The study compared commercial depletion kits and specimen-processing methods using selective lysis detergents. The goal was to deplete human DNA, the major obstacle for sequencing a pathogen directly from a clinical sample. Samples spiked with a clinically relevant amount of B. pertussis were used to provide comparison between the different methods. Commercial depletion kits including the MolYsis, Qiagen Microbiome and NEBNext Kits were tested. Previously published methods, for Saponin and TritonX-100, were also trialled as a depletion. The ratio of B. pertussis to human DNA was determined by real-time PCR for ERV3 and IS481 (as markers of human and B. pertussis DNA, respectively), then samples were sequenced using the Illumina NextSeq 500 platform. The number of human and B. pertussis sequenced reads were then compared between treatments. The results showed that commercial kits reduced the human DNA present, but also reduced the concentration of target B. pertussis. However, selective lysis with Saponin treatment resulted in almost undetectable levels of human DNA, with minimal loss of target B. pertussis DNA. Sequencing read depth improved five-fold in reads to B. pertussis. Our investigation delivered a potential protocol that will enable the public health laboratory surveillance of B. pertussis in the era of culture-independent testing.

Host Range and Genetic Plasticity Explain the Coexistence of Integrative and Extrachromosomal Mobile Genetic Elements

Self-transmissible mobile genetic elements drive horizontal gene transfer between prokaryotes. Some of these elements integrate in the chromosome, whereas others replicate autonomously as plasmids. Recent works showed the existence of few differences, and occasional interconversion, between the two types of elements. Here, we enquired on why evolutionary processes have maintained the two types of mobile genetic elements by comparing integrative and conjugative elements (ICE) with extrachromosomal ones (conjugative plasmids) of the highly abundant MPFT conjugative type. We observed that plasmids encode more replicases, partition systems, and antibiotic resistance genes, whereas ICEs encode more integrases and metabolism-associated genes. ICEs and plasmids have similar average sizes, but plasmids are much more variable, have more DNA repeats, and exchange genes more frequently. On the other hand, we found that ICEs are more frequently transferred between distant taxa. We propose a model where the different genetic plasticity and amplitude of host range between elements explain the co-occurrence of integrative and extrachromosomal elements in microbial populations. In particular, the conversion from ICE to plasmid allows ICE to be more plastic, while the conversion from plasmid to ICE allows the expansion of the element's host range.

Whole-genome sequencing reveals resistome of highly drug-resistant retail meat and human Salmonella Dublin

Non-typhoidal Salmonella (NTS) are a significant source of foodborne illness worldwide, with disease symptoms most often presenting as self-limiting gastroenteritis; however, occasionally the infection spreads and becomes invasive, frequently requiring anti-microbial treatment. The cattle-adapted Dublin serovar of NTS has commonly been associated with invasive illness and anti-microbial resistance (AMR). Here, the enhanced resolution conferred by whole-genome sequencing was utilized to elucidate and compare the resistome and genetic relatedness of 14 multidrug-resistant (MDR) and one pan-susceptible S. Dublin, isolated primarily in Pennsylvania, from fresh retail meat (one isolate) and humans (14 isolates). Twelve different genetic AMR determinants, including both acquired and chromosomal, were identified. Furthermore, comparative plasmid analysis indicated that AMR was primarily conferred by a putative IncA/C2 plasmid. A single pan-susceptible S. Dublin isolate, collected from the same timeframe and geographical region as the MDR isolates, did not carry an IncA/C2 replicon sequence within its genome. Moreover, the pan-susceptible isolate was genetically distinct from its MDR counterparts, as it was separated by ≥267 single nucleotide polymorphisms (SNPs), whereas there was a ≤38 SNP distance between the MDR isolates. Collectively, this data set advances our understanding of the genetic basis of the highly drug-resistant nature of S. Dublin, a serovar with significant public health implications.

Benchmarking of long-read assemblers for prokaryote whole genome sequencing

Background: Data sets from long-read sequencing platforms (Oxford Nanopore Technologies and Pacific Biosciences) allow for most prokaryote genomes to be completely assembled - one contig per chromosome or plasmid. However, the high per-read error rate of long-read sequencing necessitates different approaches to assembly than those used for short-read sequencing. Multiple assembly tools (assemblers) exist, which use a variety of algorithms for long-read assembly. Methods: We used 500 simulated read sets and 120 real read sets to assess the performance of six long-read assemblers (Canu, Flye, Miniasm/Minipolish, Raven, Redbean and Shasta) across a wide variety of genomes and read parameters. Assemblies were assessed on their structural accuracy/completeness, sequence identity, contig circularisation and computational resources used. Results: Canu v1.9 produced moderately reliable assemblies but had the longest runtimes of all assemblers tested. Flye v2.6 was more reliable and did particularly well with plasmid assembly. Miniasm/Minipolish v0.3 was the only assembler which consistently produced clean contig circularisation. Raven v0.0.5 was the most reliable for chromosome assembly, though it did not perform well on small plasmids and had circularisation issues. Redbean v2.5 and Shasta v0.3.0 were computationally efficient but more likely to produce incomplete assemblies. Conclusions: Of the assemblers tested, Flye, Miniasm/Minipolish and Raven performed best overall. However, no single tool performed well on all metrics, highlighting the need for continued development on long-read assembly algorithms.

Benchmarking of long-read assemblers for prokaryote whole genome sequencing

Background: Data sets from long-read sequencing platforms (Oxford Nanopore Technologies and Pacific Biosciences) allow for most prokaryote genomes to be completely assembled - one contig per chromosome or plasmid. However, the high per-read error rate of long-read sequencing necessitates different approaches to assembly than those used for short-read sequencing. Multiple assembly tools (assemblers) exist, which use a variety of algorithms for long-read assembly. Methods: We used 500 simulated read sets and 120 real read sets to assess the performance of seven long-read assemblers (Canu, Flye, Miniasm/Minipolish, NECAT, Raven, Redbean and Shasta) across a wide variety of genomes and read parameters. Assemblies were assessed on their structural accuracy/completeness, sequence identity, contig circularisation and computational resources used. Results: Canu v1.9 produced moderately reliable assemblies but had the longest runtimes of all assemblers tested. Flye v2.7 was more reliable and did particularly well with plasmid assembly. Miniasm/Minipolish v0.3 and NECAT v20200119 were the most likely to produce clean contig circularisation. Raven v0.0.8 was the most reliable for chromosome assembly, though it did not perform well on small plasmids and had circularisation issues. Redbean v2.5 and Shasta v0.4.0 were computationally efficient but more likely to produce incomplete assemblies. Conclusions: Of the assemblers tested, Flye, Miniasm/Minipolish and Raven performed best overall. However, no single tool performed well on all metrics, highlighting the need for continued development on long-read assembly algorithms.

Benchmarking of long-read assemblers for prokaryote whole genome sequencing

Background: Data sets from long-read sequencing platforms (Oxford Nanopore Technologies and Pacific Biosciences) allow for most prokaryote genomes to be completely assembled - one contig per chromosome or plasmid. However, the high per-read error rate of long-read sequencing necessitates different approaches to assembly than those used for short-read sequencing. Multiple assembly tools (assemblers) exist, which use a variety of algorithms for long-read assembly. Methods: We used 500 simulated read sets and 120 real read sets to assess the performance of eight long-read assemblers (Canu, Flye, Miniasm/Minipolish, NECAT, NextDenovo/NextPolish, Raven, Redbean and Shasta) across a wide variety of genomes and read parameters. Assemblies were assessed on their structural accuracy/completeness, sequence identity, contig circularisation and computational resources used. Results: Canu v2.0 produced reliable assemblies and was good with plasmids, but it performed poorly with circularisation and had the longest runtimes of all assemblers tested. Flye v2.8 was also reliable and made the smallest sequence errors, though it used the most RAM. Miniasm/Minipolish v0.3/v0.1.3 was the most likely to produce clean contig circularisation. NECAT v20200119 was reliable and good at circularisation but tended to make larger sequence errors. NextDenovo/NextPolish v2.3.0/v1.2.4 was reliable with chromosome assembly but bad with plasmid assembly. Raven v1.1.10 was the most reliable for chromosome assembly, though it did not perform well on small plasmids and had circularisation issues. Redbean v2.5 and Shasta v0.5.1 were computationally efficient but more likely to produce incomplete assemblies. Conclusions: Of the assemblers tested, Flye, Miniasm/Minipolish and Raven performed best overall. However, no single tool performed well on all metrics, highlighting the need for continued development on long-read assembly algorithms.

Benchmarking of long-read assemblers for prokaryote whole genome sequencing

Background: Data sets from long-read sequencing platforms (Oxford Nanopore Technologies and Pacific Biosciences) allow for most prokaryote genomes to be completely assembled - one contig per chromosome or plasmid. However, the high per-read error rate of long-read sequencing necessitates different approaches to assembly than those used for short-read sequencing. Multiple assembly tools (assemblers) exist, which use a variety of algorithms for long-read assembly. Methods: We used 500 simulated read sets and 120 real read sets to assess the performance of eight long-read assemblers (Canu, Flye, Miniasm/Minipolish, NECAT, NextDenovo/NextPolish, Raven, Redbean and Shasta) across a wide variety of genomes and read parameters. Assemblies were assessed on their structural accuracy/completeness, sequence identity, contig circularisation and computational resources used. Results: Canu v2.1 produced reliable assemblies and was good with plasmids, but it performed poorly with circularisation and had the longest runtimes of all assemblers tested. Flye v2.8 was also reliable and made the smallest sequence errors, though it used the most RAM. Miniasm/Minipolish v0.3/v0.1.3 was the most likely to produce clean contig circularisation. NECAT v20200803 was reliable and good at circularisation but tended to make larger sequence errors. NextDenovo/NextPolish v2.3.1/v1.3.1 was reliable with chromosome assembly but bad with plasmid assembly. Raven v1.3.0 was reliable for chromosome assembly, though it did not perform well on small plasmids and had circularisation issues. Redbean v2.5 and Shasta v0.7.0 were computationally efficient but more likely to produce incomplete assemblies. Conclusions: Of the assemblers tested, Flye, Miniasm/Minipolish, NextDenovo/NextPolish and Raven performed best overall. However, no single tool performed well on all metrics, highlighting the need for continued development on long-read assembly algorithms.

PIRATE: A fast and scalable pangenomics toolbox for clustering diverged orthologues in bacteria

BACKGROUND

Cataloguing the distribution of genes within natural bacterial populations is essential for understanding evolutionary processes and the genetic basis of adaptation. Advances in whole genome sequencing technologies have led to a vast expansion in the amount of bacterial genomes deposited in public databases. There is a pressing need for software solutions which are able to cluster, catalogue and characterise genes, or other features, in increasingly large genomic datasets.

RESULTS

Here we present a pangenomics toolbox, PIRATE (Pangenome Iterative Refinement and Threshold Evaluation), which identifies and classifies orthologous gene families in bacterial pangenomes over a wide range of sequence similarity thresholds. PIRATE builds upon recent scalable software developments to allow for the rapid interrogation of thousands of isolates. PIRATE clusters genes (or other annotated features) over a wide range of amino acid or nucleotide identity thresholds and uses the clustering information to rapidly identify paralogous gene families and putative fission/fusion events. Furthermore, PIRATE orders the pangenome using a directed graph, provides a measure of allelic variation, and estimates sequence divergence for each gene family.

CONCLUSIONS

We demonstrate that PIRATE scales linearly with both number of samples and computation resources, allowing for analysis of large genomic datasets, and compares favorably to other popular tools. PIRATE provides a robust framework for analysing bacterial pangenomes, from largely clonal to panmictic species.