Comparative Genomic Analysis of Bacterial Data in BV-BRC: An Example Exploring Antimicrobial Resistance.

As genomic and related data continue to expand, research biologists are often hampered by the computational hurdles required to analyze their data. The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Centers (BRC) to assist researchers with their analysis of genome sequence and other omics-related data. Recently, the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD), and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs merged to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) at https://www.bv-brc.org/ . The combined BV-BRC leverages the functionality of the original resources for bacterial and viral research communities with a unified data model, enhanced web-based visualization and analysis tools, and bioinformatics services. Here we demonstrate how antimicrobial resistance data can be analyzed in the new resource.

Isolation of Brucella inopinata from a White's tree frog (Litoria caerulea): pose exotic frogs a potential risk to human health?

Introduction: Cold-blooded hosts, particularly exotic frogs, have become a newly recognized reservoir for atypical Brucella species and strains worldwide, but their pathogenicity to humans remains largely unknown. Here we report the isolation and molecular characterization of a B. inopinata strain (FO700662) cultured from clinical samples taken from a captive diseased White's Tree Frog (Litoria caerulea) in Switzerland. The isolation of B. inopinata from a frog along with other reports of human infection by atypical Brucella raises the question of whether atypical Brucella could pose a risk to human health and deserves further attention. Methods: The investigations included histopathological analysis of the frog, bacterial culture and in-depth molecular characterization of strain FO700662 based on genome sequencing data. Results and Discussion: Originally identified as Ochrobactrum based on its rapid growth and biochemical profile, strain FO700622 was positive for the Brucella- specific markers bcsp31 and IS711. It showed the specific banding pattern of B. inopinata in conventional Bruce-ladder multiplex PCR and also had identical 16S rRNA and recA gene sequences as B. inopinata. Subsequent genome sequencing followed by core genome-based MLST (cgMLST) analysis using 2704 targets (74% of the total chromosome) revealed only 173 allelic differences compared to the type strain of B. inopinata BO1T, while previously considered the closest related strain BO2 differed in 2046 alleles. The overall average nucleotide identity (ANI) between the type strain BO1T and FO700622 was 99,89%, confirming that both strains were almost identical. In silico MLST-21 and MLVA-16 also identified strain FO700662 as B. inopinata. The nucleotide and amino acid-based phylogenetic reconstruction and comparative genome analysis again placed the isolate together with B. inopinata with 100% support. In conclusion, our data unequivocally classified strain FO700622, isolated from an exotic frog, as belonging to B. inopinata.

An automated system for interrogating the evolution of microbial endosymbiosis.

Inter-kingdom endosymbiotic interactions between bacteria and eukaryotic cells are critical to human health and disease. However, the molecular mechanisms that drive the emergence of endosymbiosis remain obscure. Here, we describe the development of a microfluidic system, named SEER (S̲ystem for the E̲volution of E̲ndosymbiotic R̲elationships), that automates the evolutionary selection of bacteria with enhanced intracellular survival and persistence within host cells, hallmarks of endosymbiosis. Using this system, we show that a laboratory strain of Escherichia coli that initially possessed limited abilities to survive within host cells, when subjected to SEER selection, rapidly evolved to display a 55-fold enhancement in intracellular survival. Notably, molecular dissection of the evolved strains revealed that a single-point mutation in a flexible loop of CpxR, a gene regulator that controls bacterial stress responses, substantially contributed to this intracellular survival. Taken together, these results establish SEER as the first microfluidic system for investigating the evolution of endosymbiosis, show the importance of CpxR in endosymbiosis, and set the stage for evolving bespoke inter-kingdom endosymbiotic systems with novel or emergent properties.

Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR.

The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Center (BRC) program to assist researchers with analyzing the growing body of genome sequence and other omics-related data. In this report, we describe the merger of the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD) and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) https://www.bv-brc.org/. The combined BV-BRC leverages the functionality of the bacterial and viral resources to provide a unified data model, enhanced web-based visualization and analysis tools, bioinformatics services, and a powerful suite of command line tools that benefit the bacterial and viral research communities.

Phenotype-Based Threat Assessment.

Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pathogen identification. However, relying solely on genetic information to identify emerging or new pathogens is fundamentally constrained, especially if novel virulence factors exist. In addition, even WGSs with ML pipelines are unable to discern phenotypes associated with cryptic genetic loci linked to virulence. Here, we set out to determine if ML using phenotypic hallmarks of pathogenesis could assess potential pathogenic threat without using any sequence-based analysis. This approach successfully classified potential pathogenetic threat associated with previously machine-observed and unobserved bacteria with 99% and 85% accuracy, respectively. This work establishes a phenotype-based pipeline for potential pathogenic threat assessment, which we term PathEngine, and offers strategies for the identification of bacterial pathogens.

A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes.

Antimicrobial resistance (AMR) is a major global health threat that affects millions of people each year. Funding agencies worldwide and the global research community have expended considerable capital and effort tracking the evolution and spread of AMR by isolating and sequencing bacterial strains and performing antimicrobial susceptibility testing (AST). For the last several years, we have been capturing these efforts by curating data from the literature and data resources and building a set of assembled bacterial genome sequences that are paired with laboratory-derived AST data. This collection currently contains AST data for over 67 000 genomes encompassing approximately 40 genera and over 100 species. In this paper, we describe the characteristics of this collection, highlighting areas where sampling is comparatively deep or shallow, and showing areas where attention is needed from the research community to improve sampling and tracking efforts. In addition to using the data to track the evolution and spread of AMR, it also serves as a useful starting point for building machine learning models for predicting AMR phenotypes. We demonstrate this by describing two machine learning models that are built from the entire dataset to show where the predictive power is comparatively high or low. This AMR metadata collection is freely available and maintained on the Bacterial and Viral Bioinformatics Center (BV-BRC) FTP site ftp://ftp.bvbrc.org/RELEASE_NOTES/PATRIC_genomes_AMR.txt.

Identification and Characterization of Mycobacterial Species Using Whole-Genome Sequences.

Whole-genome sequencing (WGS) has shown immense value in enabling identification and characterization of bacterial taxa. This is particularly true for mycobacteria, where culture-based characterization becomes delayed by the inherently slow growth rate of these organisms. This chapter reviews the general techniques behind WGS and their optimization, existing techniques for species-level identification and the advantages of WGS for this purpose, and a variety of useful tools for the genomic characterization of mycobacterial strains.

A synthesis of bacterial and archaeal phenotypic trait data.

A synthesis of phenotypic and quantitative genomic traits is provided for bacteria and archaea, in the form of a scripted, reproducible workflow that standardizes and merges 26 sources. The resulting unified dataset covers 14 phenotypic traits, 5 quantitative genomic traits, and 4 environmental characteristics for approximately 170,000 strain-level and 15,000 species-aggregated records. It spans all habitats including soils, marine and fresh waters and sediments, host-associated and thermal. Trait data can find use in clarifying major dimensions of ecological strategy variation across species. They can also be used in conjunction with species and abundance sampling to characterize trait mixtures in communities and responses of traits along environmental gradients.

Characterization of the First mecA-Positive Multidrug-Resistant Staphylococcus pseudintermedius Isolated from an Argentinian Patient.

Staphylococcus pseudintermedius is commonly associated with colonization or infection in dogs, and was identified as a novel species within the genus Staphylococcus in 2006. Methicillin resistance emerged in S. pseudintermedius during the last decade. We describe here a genomic characterization of the first methicillin-resistant S. pseudintermedius (MRSP) recovered from a human patient in Argentina. The strain was phenotypically identified as MRSP 8510 by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) and antimicrobial susceptibility testing. We assessed genetic characterization by mecA PCR, SCCmec (staphylococcal chromosomal cassette) typing, and whole-genome sequencing. MRSP 8510 was phenotypically resistant to six classes of antimicrobial agents, consistent with the genes found in its genome. We concluded that MRSP 8510 was a multidrug-resistant ST1412 isolate. This study highlights the importance of the detection and characterization of pathogens with potential risks of zoonotic transmission to humans, as they may constitute a reservoir of genes associated with antimicrobial resistance.

The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities.

The PathoSystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center funded by the National Institute of Allergy and Infectious Diseases (https://www.patricbrc.org). PATRIC supports bioinformatic analyses of all bacteria with a special emphasis on pathogens, offering a rich comparative analysis environment that provides users with access to over 250 000 uniformly annotated and publicly available genomes with curated metadata. PATRIC offers web-based visualization and comparative analysis tools, a private workspace in which users can analyze their own data in the context of the public collections, services that streamline complex bioinformatic workflows and command-line tools for bulk data analysis. Over the past several years, as genomic and other omics-related experiments have become more cost-effective and widespread, we have observed considerable growth in the usage of and demand for easy-to-use, publicly available bioinformatic tools and services. Here we report the recent updates to the PATRIC resource, including new web-based comparative analysis tools, eight new services and the release of a command-line interface to access, query and analyze data.

Helicobacter pylori Infections in the Bronx, New York: Surveying Antibiotic Susceptibility and Strain Lineage by Whole-Genome Sequencing.

The emergence of drug resistance in Helicobacter pylori has resulted in a greater need for susceptibility-guided treatment. While the alleles associated with resistance to clarithromycin and levofloxacin have been defined, there are limited data regarding the molecular mechanisms underlying resistance to other antimicrobials. Using H. pylori isolates from 42 clinical specimens, we compared phenotypic and whole-genome sequencing (WGS)-based detection of resistance. Phenotypic resistance correlated with the presence of alleles of 23S rRNA (A2142G/A2143G) for clarithromycin (kappa coefficient, 0.84; 95% confidence interval [CI], 0.67 to 1.0) and gyrA (N87I/N87K/D91Y/D91N/D91G/D99N) for levofloxacin (kappa coefficient, 0.90; 95% CI, 0.77 to 1.0). Phenotypic resistance to amoxicillin in three isolates correlated with mutations in pbp1, pbp2, and/or pbp3 within coding regions near known amoxicillin binding motifs. All isolates were phenotypically susceptible to tetracycline, although four bore a mutation in 16S rRNA (A926G). For metronidazole, nonsense mutations and R16H substitutions in rdxA correlated with phenotypic resistance (kappa coefficient, 0.76; 95% CI, 0.56 to 0.96). Previously identified mutations in the rpoB rifampin resistance-determining region (RRDR) were not present, but 14 novel mutations outside the RRDR were found in rifampin-resistant isolates. WGS also allowed for strain lineage determination, which may be important for future studies in associating precise MICs with specific resistance alleles. In summary, WGS allows for broad analyses of H. pylori isolates, and our findings support the use of WGS for the detection of clarithromycin and levofloxacin resistance. Additional studies are warranted to better define mutations conferring resistance to amoxicillin, tetracycline, and rifampin, but combinatorial analyses for rdxA gene truncations and R16H mutations have utility for determining metronidazole resistance.

Identification and molecular epidemiology of methicillin resistant Staphylococcus pseudintermedius strains isolated from canine clinical samples in Argentina.

BACKGROUND: Staphylococcus pseudintermedius is the leading cause of pyoderma in dogs and the frequent use of antimicrobial treatment is associated to the development of resistance to nearly all classes of antibiotics. Despite S. pseudintermedius significance, our understanding of the molecular mechanism of ß-lactam resistance and its genetic diversity remains limited. We aimed to: i) determine the phenotypic resistance profile of methicillin resistant Staphylococcus pseudintermedius (MRSP) isolated from infected dogs in three different veterinary hospitals in Buenos Aires, Argentina; ii) identify the SCCmec elements and resistance genes; and iii) analyze the clonal relationship between isolates and in regard of dominant lineages found in the world. RESULTS: In addition to the differential levels of ß-lactam resistance, MRSP isolates (n = 10) showed resistance to 5-6 families of antibiotics, and were therefore categorized as multidrug-resistant. All the isolates were variant of SCCmec V homologous to S. aureus; additional SCCmecFinder analysis classified five of the genomes as SCCmec type V (5C2&5) with mecA (encodes for PBP2a), mecRI and mecI and all the genes closely related to the reference SCCmec type V S. aureus TSGH17 strain. In the remaining five strains, mecA was present, although other genes associated with SCCmec V including mecR1 and mecI were missing. PBP2a was inducible in low level resistance strains (MRSP 8151), and constitutively expressed in MRSP 8150, suggesting different mecA regulatory mechanisms. MRSP isolates showed significant genetic diversity: eight PFGE clonal types and six multilocus-sequence typing (MLST) sequence types (STs) (339, 649, 919, 920, 921 and 922), including four new STs genetically distinct from STs reported in other geographic areas. Comparative genomics and phylogenetic analyses of the MRSP showed a correlation between the genetic content and the phenotypes, and established the genetic relationship between the isolates. CONCLUSIONS: MRSP could be a threat to animal health due to it concerning level of antimicrobial resistance. Our study highlights genetic and epidemiological aspects of multidrug-resistant MRSP strains from Argentina showing high degree of correlation between the resistance genes and the phenotype of the isolates and, furthermore, they appeared evolutionary closer to major worldwide reported ST68 and ST71.

PATRIC as a unique resource for studying antimicrobial resistance.

The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org) is designed to provide researchers with the tools and services that they need to perform genomic and other 'omic' data analyses. In response to mounting concern over antimicrobial resistance (AMR), the PATRIC team has been developing new tools that help researchers understand AMR and its genetic determinants. To support comparative analyses, we have added AMR phenotype data to over 15 000 genomes in the PATRIC database, often assembling genomes from reads in public archives and collecting their associated AMR panel data from the literature to augment the collection. We have also been using this collection of AMR metadata to build machine learning-based classifiers that can predict the AMR phenotypes and the genomic regions associated with resistance for genomes being submitted to the annotation service. Likewise, we have undertaken a large AMR protein annotation effort by manually curating data from the literature and public repositories. This collection of 7370 AMR reference proteins, which contains many protein annotations (functional roles) that are unique to PATRIC and RAST, has been manually curated so that it projects stably across genomes. The collection currently projects to 1 610 744 proteins in the PATRIC database. Finally, the PATRIC Web site has been expanded to enable AMR-based custom page views so that researchers can easily explore AMR data and design experiments based on whole genomes or individual genes.

Comparative mangrove metagenome reveals global prevalence of heavy metals and antibiotic resistome across different ecosystems.

The mangrove ecosystem harbors a complex microbial community that plays crucial role in biogeochemical cycles. In this study, we analyzed mangrove sediments from India using de novo whole metagenome next generation sequencing (NGS) and compared their taxonomic and functional community structures to mangrove metagenomics samples from Brazil and Saudi Arabia. The most abundant phyla in the mangroves of all three countries was Proteobacteria, followed by Firmicutes and Bacteroidetes. A total of 1,942 genes were found to be common across all the mangrove sediments from each of the three countries. The mangrove resistome consistently showed high resistance to fluoroquinolone and acriflavine. A comparative study of the mangrove resistome with other ecosystems shows a higher frequency of heavy metal resistance in mangrove and terrestrial samples. Ocean samples had a higher abundance of drug resistance genes with fluoroquinolone and methicillin resistance genes being as high as 28.178% ± 3.619 and 10.776% ± 1.823. Genes involved in cobalt-zinc-cadmium resistance were higher in the mangrove (23.495% ± 4.701) and terrestrial (27.479% ± 4.605) ecosystems. Our comparative analysis of samples collected from a variety of habitats shows that genes involved in resistance to both heavy metals and antibiotics are ubiquitous, irrespective of the ecosystem examined.

Cell Division in genus Corynebacterium: protein-protein interaction and molecular docking of SepF and FtsZ in the understanding of cytokinesis in pathogenic species.

The genus Corynebacterium includes species of great importance in medical, veterinary and biotechnological fields. The genus-specific families (PLfams) from PATRIC have been used to observe conserved proteins associated to all species. Our results showed a large number of conserved proteins that are associated with the cellular division process. Was not observe in our results other proteins like FtsA and ZapA that interact with FtsZ. Our findings point that SepF overlaps the function of this proteins explored by molecular docking, protein-protein interaction and sequence analysis. Transcriptomic analysis showed that these two (Sepf and FtsZ) proteins can be expressed in different conditions together. The work presents novelties on molecules participating in the cell division event, from the interaction of FtsZ and SepF, as new therapeutic targets.

Author Correction: Searching for signatures across microbial communities: Metagenomic analysis of soil samples from mangrove and other ecosystems.

A correction to this article has been published and is linked from the HTML version of this paper. The error has been fixed in the paper.

Comparative Genomics for Prokaryotes.

Bacteria and archaea, collectively known as prokaryotes, have in general genomes that are much smaller than those of eukaryotes. As a result, thousands of these genomes have been sequenced. In prokaryotes, gene architecture lacks the intron-exon structure of eukaryotic genes (with an occasional exception). These two facts mean that there is an abundance of data for prokaryotic genomes, and that they are easier to study than the more complex eukaryotic genomes. In this chapter, we provide an overview of genome comparison tools that have been developed primarily (sometimes exclusively) for prokaryotic genomes. We cover methods that use only the DNA sequences, methods that use only the gene content, and methods that use both data types.

Assembly, Annotation, and Comparative Genomics in PATRIC, the All Bacterial Bioinformatics Resource Center.

In the "big data" era, research biologists are faced with analyzing new types that usually require some level of computational expertise. A number of programs and pipelines exist, but acquiring the expertise to run them, and then understanding the output can be a challenge.The Pathosystems Resource Integration Center (PATRIC, www.patricbrc.org ) has created an end-to-end analysis platform that allows researchers to take their raw reads, assemble a genome, annotate it, and then use a suite of user-friendly tools to compare it to any public data that is available in the repository. With close to 113,000 bacterial and more than 1000 archaeal genomes, PATRIC creates a unique research experience with "virtual integration" of private and public data. PATRIC contains many diverse tools and functionalities to explore both genome-scale and gene expression data, but the main focus of this chapter is on assembly, annotation, and the downstream comparative analysis functionality that is freely available in the resource.

Insight of Genus Corynebacterium: Ascertaining the Role of Pathogenic and Non-pathogenic Species.

This review gathers recent information about genomic and transcriptomic studies in the Corynebacterium genus, exploring, for example, prediction of pathogenicity islands and stress response in different pathogenic and non-pathogenic species. In addition, is described several phylogeny studies to Corynebacterium, exploring since the identification of species until biological speciation in one species belonging to the genus Corynebacterium. Important concepts associated with virulence highlighting the role of Pld protein and Tox gene. The adhesion, characteristic of virulence factor, was described using the sortase mechanism that is associated to anchorage to the cell wall. In addition, survival inside the host cell and some diseases, were too addressed for pathogenic corynebacteria, while important biochemical pathways and biotechnological applications retain the focus of this review for non-pathogenic corynebacteria. Concluding, this review broadly explores characteristics in genus Corynebacterium showing to have strong relevance inside the medical, veterinary, and biotechnology field.

Searching for signatures across microbial communities: Metagenomic analysis of soil samples from mangrove and other ecosystems.

In this study, we categorize the microbial community in mangrove sediment samples from four different locations within a vast mangrove system in Kerala, India. We compared this data to other samples taken from the other known mangrove data, a tropical rainforest, and ocean sediment. An examination of the microbial communities from a large mangrove forest that stretches across southwestern India showed strong similarities across the higher taxonomic levels. When ocean sediment and a single isolate from a tropical rain forest were included in the analysis, a strong pattern emerged with Bacteria from the phylum Proteobacteria being the prominent taxon among the forest samples. The ocean samples were predominantly Archaea, with Euryarchaeota as the dominant phylum. Principal component and functional analyses grouped the samples isolated from forests, including those from disparate mangrove forests and the tropical rain forest, from the ocean. Our findings show similar patterns in samples were isolated from forests, and these were distinct from the ocean sediment isolates. The taxonomic structure was maintained to the level of class, and functional analysis of the genes present also displayed these similarities. Our report for the first time shows the richness of microbial diversity in the Kerala coast and its differences with tropical rain forest and ocean microbiome.