Bacterial Genome Sequences Research Articles

Development of a long-read next generation sequencing workflow for improved characterization of fastidious respiratory mycoplasmas.

Mycoplasma cynos and Mycoplasma felis are often associated with canine and feline infectious respiratory disease in dogs and cats, respectively. Mycoplasmas have a reduced genome and dearth of many biosynthetic pathways, making them dependent on rich medium for growth. Due to this fastidious nature, mycoplasmas have been historically underdiagnosed. The aim of this study was to develop a cost-effective and accurate sequencing workflow for genotypic characterization of clinical isolates of M. cynos and M. felis using a rapid long-read sequencing platform. We explored the following critical aspects of bacterial whole genome sequencing, including: (i) five solid and liquid-based culture approaches based on a specialized media formulation for Mycoplasma culture, (ii) three DNA extraction methods modified for long-read sequencing purposes, and (iii) two de novo assembly platforms, Flye and Canu, as key components of a bioinformatics pipeline. DNA extraction method 1, a solid-phase and column-based kit with enzymatic lysis, provided the best DNA quality and concentration followed by high coverage and sequencing contiguity. This was obtained with a culture volume of 45 ml in modified Hayflick's broth incubated for 48 h. DNA extracted directly from colonies on agar or from small broth volumes (6 ml) did not meet the criteria required for long-read sequencing. Overall, Flye generated more contiguous assemblies than the Canu assembler and was more time efficient. This 4-5 day sample-to-sequence workflow provides the scientific and clinical communities with a more comprehensive tool than laborious conventional methods for complete genomic characterization of M. cynos and M. felis clinical isolates.

A comprehensive update to the Mycobacterium tuberculosis H37Rv reference genome

H37Rv is the most widely used Mycobacterium tuberculosis strain, and its genome is globally used as the M. tuberculosis reference sequence. Here, we present Bact-Builder, a pipeline that uses consensus building to generate complete and accurate bacterial genome sequences and apply it to three independently cultured and sequenced H37Rv aliquots of a single laboratory stock. Two of the 4,417,942 base-pair long H37Rv assemblies are 100% identical, with the third differing by a single nucleotide. Compared to the existing H37Rv reference, the new sequence contains ~6.4 kb additional base pairs, encoding ten new regions that include insertions in PE/PPE genes and new paralogs of esxN and esxJ, which are differentially expressed compared to the reference genes. New sequencing and de novo assemblies with Bact-Builder confirm that all 10 regions, plus small additional polymorphisms, are also present in the commonly used H37Rv strains NR123, TMC102, and H37Rv1998. Thus, Bact-Builder shows promise as an improved method to perform accurate and reproducible de novo assemblies of bacterial genomes, and our work provides important updates to the primary M. tuberculosis reference genome.

CRISPR in Modulating Antibiotic Resistance of ESKAPE Pathogens.

The ESKAPE (Enterococcus spp., Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, andEnterobacterspp.) isolates both from the clinical settings and food products are demonstrated to gain resistance to multiple antimicrobials. Therefore, the ESKAPE pathogens pose a serious threat to public health, which warrants specific attention to developing alternative novel therapeutics. The clustered regularly interspaced short palindromic repeats associated (CRISPR-Cas) system is one of the novel methods for managing antibiotic-resistant strains. Specific Cas nucleases can be programmed against bacterial genomic sequences to decrease bacterial resistance to antibiotics. Moreover, a few CRISPR-Cas nucleases have the ability to the sequence-specific killing of bacterial strains. However, some pathogens acquire antibiotic resistance due to the presence of the CRISPR-Cas system. In brief, there is a wide range of functional diversity of CRISPR-Cas systems in bacterial pathogens. Hence, to be an effective and safe infection treatment strategy, a comprehensive understanding of the role of CRISPR-Cas systems in modulating antibiotic resistance in ESKAPE pathogens is essential. The present review summarizes all the mechanisms by which CRISPR confers and prevents antibiotic resistance in ESKAPE. The review also emphasizes the relationship between CRISPR-Cas systems, biofilm formation, and antibiotic resistance in ESKAPE.

IProm70: A Convolutional Neural Network-based Tool for σ70 Promoter Classification

<p>Background: A promoter is a DNA regulatory region typically found upstream of a gene that plays a significant role in gene transcription regulation. Due to their function in transcription initiation, sigma (σ) promoter sequences in bacterial genomes are important. σ70 is among the most notable sigma factors. Therefore, the precise recognition of the σ70 promoter is essential in bioinformatics. <p> Objective: Several methods for predicting σ70 promoters have been developed. However, the performance of these approaches needs to be enhanced. This study proposes a convolutional neural network (CNN) based model iProm70 to predict σ70 promoter sequences from a bacterial genome. <p> Methods: This CNN-based method employs a one-hot encoding scheme to identify promoters. The CNN model comprises three convolution layers, followed by max-pooling and a dropout layer. The architecture tool was trained and tested on a benchmark and an independent dataset. We used four assessment measures to determine the prediction performance. <p> Results: It achieved 96.10% accuracy, and the area under the receiver operating characteristic curve was 0.99. <p> Conclusion: According to the comparative results, iProm70 outperforms the current approaches for defining σ70 promoter. A publicly accessible online web server is created, and it is accessible at the website: http://nsclbio.jbnu.ac.kr/tools/Prom70-CNN/.</p>

Sequencing of Enteric Bacteria: Library Preparation Procedure Matters for Accurate Identification and Characterization.

Enzymatic library preparation kits are increasingly used for bacterial whole genome sequencing. While they offer a rapid workflow, the transposases used in the kits are recognized to be somewhat biased. The aim of this study was to optimize and validate a protocol for the Illumina DNA Prep kit (formerly Nextera DNA Flex) for sequencing enteric pathogens and compare its performance against the Nextera XT kit. One hundred forty-three strains of Campylobacter, Escherichia, Listeria, Salmonella, Shigella, and Vibrio were prepared with both methods and sequenced on the Illumina MiSeq using 300 and/or 500 cycle chemistries. Sequences were compared using core genome multilocus sequence typing (cgMLST), 7-gene multilocus sequence typing (MLST), and detection of markers encoding serotype, virulence, and antimicrobial resistance. Sequences for one Escherichia strain were downsampled to determine the minimum coverage required for the analyses. While organism-specific differences were observed, the Prep libraries generated longer average read lengths and less fragmented assemblies compared to the XT libraries. In downstream analysis, the most notable difference between the kits was observed for Escherichia, particularly for the 300 cycle sequences. The O group was not predicted in 32% and 4% of XT sequences when using blast and kmer algorithms, respectively, while the O group was predicted from all Prep sequences regardless of the algorithm. In addition, the ehxA gene was not detected in 6% of XT sequences and 34% were missing one or more of the type III secretion systems and/or plasmid-associated genes, which were detected in the Prep sequences. The coverage downsampling revealed that acceptable assembly quality and allele detection was achieved at 30 × coverage with the Prep libraries, whereas 40-50 × coverage was required for the XT libraries. The better performance of the Prep libraries was attributed to more even coverage, particularly in genome regions low in GC content.

Datasets for benchmarking antimicrobial resistance genes in bacterial metagenomic and whole genome sequencing

Whole genome sequencing (WGS) is a key tool in identifying and characterising disease-associated bacteria across clinical, agricultural, and environmental contexts. One increasingly common use of genomic and metagenomic sequencing is in identifying the type and range of antimicrobial resistance (AMR) genes present in bacterial isolates in order to make predictions regarding their AMR phenotype. However, there are a large number of alternative bioinformatics software and pipelines available, which can lead to dissimilar results. It is, therefore, vital that researchers carefully evaluate their genomic and metagenomic AMR analysis methods using a common dataset. To this end, as part of the Microbial Bioinformatics Hackathon and Workshop 2021, a ‘gold standard’ reference genomic and simulated metagenomic dataset was generated containing raw sequence reads mapped against their corresponding reference genome from a range of 174 potentially pathogenic bacteria. These datasets and their accompanying metadata are freely available for use in benchmarking studies of bacteria and their antimicrobial resistance genes and will help improve tool development for the identification of AMR genes in complex samples.

A network approach to decipher the dynamics of Lysobacteraceae plasmid gene sharing.

Plasmids provide an efficient vehicle for gene sharing among bacterial populations, playing a key role in bacterial evolution. Network approaches are particularly suitable to represent multipartite relationships and are useful tools to characterize plasmid-mediated gene sharing events. The bacterial family Lysobacteraceae includes plant commensal, plant pathogenic and opportunistic human pathogens for which plasmid-mediated adaptation has been reported. We searched for homologues of plasmid gene sequences from this family in the entire diversity of available bacterial genome sequences and built a network of plasmid gene sharing from the results. While plasmid genes are openly shared between the bacteria of the family Lysobacteraceae, taxonomy strongly defined the boundaries of these exchanges, which only barely reached other families. Most inferred plasmid gene sharing events involved a few genes only, and evidence of full plasmid transfers were restricted to taxonomically closely related taxa. We detected multiple plasmid-chromosome gene transfers, including the known sharing of a heavy metal resistance transposon. In the network, bacterial lifestyles shaped substructures of isolates colonizing specific ecological niches and harbouring specific types of resistance genes. Genes associated with pathogenicity or antibiotic and metal resistance were among those that most importantly structured the network, highlighting the imprints of human-mediated selective pressure on pathogenic populations. A massive sequencing effort on environmental Lysobacteraceae is therefore required to refine our understanding of how this reservoir fuels the emergence and the spread of genes among this family and its potential impact on plant, animal and human health.

Bioinformatic prospecting and synthesis of a bifunctional lipopeptide antibiotic that evades resistance.

Emerging resistance to currently used antibiotics is a global public health crisis. Because most of the biosynthetic capacity within the bacterial kingdom has remained silent in previous antibiotic discovery efforts, uncharacterized biosynthetic gene clusters found in bacterial genome-sequencing studies remain an appealing source of antibiotics with distinctive modes of action. Here, we report the discovery of a naturally inspired lipopeptide antibiotic called cilagicin, which we chemically synthesized on the basis of a detailed bioinformatic analysis of the cil biosynthetic gene cluster. Cilagicin's ability to sequester two distinct, indispensable undecaprenyl phosphates used in cell wall biosynthesis, together with the absence of detectable resistance in laboratory tests and among multidrug-resistant clinical isolates, makes it an appealing candidate for combating antibiotic-resistant pathogens.

VRprofile2: detection of antibiotic resistance-associated mobilome in bacterial pathogens.

VRprofile2 is an updated pipeline that rapidly identifies diverse mobile genetic elements in bacterial genome sequences. Compared with the previous version, three major improvements were made. First, the user-friendly visualization could aid users in investigating the antibiotic resistance gene cassettes in conjunction with various mobile elements in the multiple resistance region with mosaic structure. VRprofile2 could compare the predicted mobile elements to the collected known mobile elements with similar architecture. A new mobilome indicator was proposed to give an overall estimation of the mobilome size in individual bacterial genomes. Second, the relationship between antibiotic resistance genes, mobile elements, and host strains would be efficiently examined with the aid of predicted strain's sequence typing, the incompatibility group and the transferability of plasmids. Finally, the updated back-end database, MobilomeDB2, now collected nearly a thousand active mobile elements retrieved from literature or based on prediction. The pre-computed results of the antibiotic resistance gene-carrying mobile elements of >5500 ESKAPEE genomes were also provided. We expect that VRprofile2 will provide better support for researchers interested in bacterial mobile elements and the dissemination of antibiotic resistance. VRprofile2 is freely available to all users without any login requirement at https://tool2-mml.sjtu.edu.cn/VRprofile.

Identifying Mucus‐Degrading Microbes Within the Human Gut Microbiota

IntroductionMucus provides a critical barrier for the colonic epithelium by excluding microbes and antigens from direct interaction with epithelial cells, providing a lubricant for stool, and limiting the diffusion of harmful compounds. This barrier function is due in part to the extensive glycan structures found in mucus. Mucin protein is heavily O‐glycosylated with core structures containing α‐ and β‐ linked N‐acetyl‐galactosamine, N‐acetyl‐glucosamine, and galactose. These core structures are elongated and commonly modified by α‐linked fucose, sialic acid and sulfate residues. The sialic acid and sulfate residues protect the underlying mucin glycans from degradation. However, some gut microbes harbor glycosyl hydrolases (GHs) that enzymatically degrade mucin glycans. The released oligosaccharides can then be used as a primary carbohydrate source for the mucus‐associated microbiota. Despite the growing number of bacterial genome sequences available, our knowledge of the mucin‐degrading capacity of human gut commensal microbes remains fragmented. The aim of the present study was to systematically examine the CAZyme mucin‐degrading profiles of the human gut microbiota.Methods & ResultsUsing the CAZy database, we identified 13,156 genomes harboring at least one gene copy of GH families involved in mucin degradation. Commensal microbes were identified using the Human Microbiome Project consortium in the Joint Genomes Institute Integrated Microbial Genomes database, resulting in 4,385 genomes for downstream analysis. Within the Verrucomicrobia phlyum, all Akkermansia glycaniphila and muciniphila genomes harbored gene copies of sialidases (GH33), fucosidases (GH29, GH95), endo‐β‐1,4‐galactosidases (GH2, 35), mucin core GHs (GH84 and GH89), endo‐acting O‐glycanases (GH16), and sulfatases (GH20, GH2, GH42); consistent with their known ability to degrade mucus. Interestingly, the only representative of the Lentisphaerae phylum, Victivallales, harbored a GH profile that closely mirrored Akkermanisa. In the Actinobacteria phlyum, we found that several Actinomadura, Actinomyces, Bifidobacteria, Streptacidiphilus and Streptomyces species contained gene copies of mucin‐degrading GHs. Within the Bacteroidetes phylum, we identified mucin degrading GHs in Alistipes, Alloprevotella, Bacteroides, Fermenitomonas Parabacteroides, Prevotella and Phocaeicola species. Firmicutes contained Abiotrophia, Blautia, Enterococcus, Paenibacillus, Ruminococcus, Streptococcus, and Viridibacillus species with mucin‐degrading GHs. Far fewer mucin‐degrading GHs were observed in the Proteobacteria phylum, with only 3‐4 GHs families found in Klebsiella, Mixta and Enterobacter. We confirmed the mucin‐degrading capability of 26 representative gut microbes by growing these bacteria in a chemically defined media lacking glucose supplemented with porcine intestinal mucus.ConclusionsThese data greatly expand our knowledge of mucin degradation within the human gut microbiota.

An Improved Genome Sequence Resource of Bipolaris maydis, Causal Agent of Southern Corn Leaf Blight.

An Improved Genome Sequence Resource of Bipolaris maydis, Causal Agent of Southern Corn Leaf Blight.

Patients with Primary and Secondary Bile Duct Stones Harbor Distinct Biliary Microbial Composition and Metabolic Potential

IntroductionCholelithiasis has a high incidence worldwide and limited treatment options due to its poorly understood pathogenesis. Furthermore, the role of biliary microbiota in cholelithiasis remains understudied. To address these questions, we performed microbial sequencing from biliary samples from primary bile duct stone (PBDS) and secondary bile duct stone (SBDS) patients.ResultsWe analyzed in total 45 biliary samples, including those from cholelithiasis patients with PBDS or SBDS and people with other digestive diseases. 16S rRNA sequencing showed the bacteria family Alcaligenaceae increased in relative abundance in the lithiasis group compared with the non-lithiasis group. In addition, the PBDS group showed significantly lower bacterial diversity than SBDS, with Propionibacteriaceae, Sphingomonadaceae, and Lactobacillaceae as the most significant bacteria families decreased in relative abundance. We further performed whole metagenomic shotgun sequencing (wMGS) and found increased ability of biofilm synthesis and the ability to sense external stimuli in PBDS based on functional annotation of mapped reads. From genome-resolved analysis of the samples, we identified 36 high-quality draft bacterial genome sequences with completion ≥70% and contamination ≤10%. Most of these genomes were classified into Proteobacteria, Firmicutes, or Actinobacteria.ConclusionsOur findings indicated that there is a subtle impact on biliary microbiome from cholelithiasis while the difference is more pronounced between the PBDS and SBDS. It was revealed that the diversity of biliary microbiota in PBDS is lower, while some metabolic pathways are up-regulated, including those linked to higher incidence of different types of cancer, providing new insights for the understanding of cholelithiasis with different origin.

DEPhT: a novel approach for efficient prophage discovery and precise extraction.

Advances in genome sequencing have produced hundreds of thousands of bacterial genome sequences, many of which have integrated prophages derived from temperate bacteriophages. These prophages play key roles by influencing bacterial metabolism, pathogenicity, antibiotic resistance, and defense against viral attack. However, they vary considerably even among related bacterial strains, and they are challenging to identify computationally and to extract precisely for comparative genomic analyses. Here, we describe DEPhT, a multimodal tool for prophage discovery and extraction. It has three run modes that facilitate rapid screening of large numbers of bacterial genomes, precise extraction of prophage sequences, and prophage annotation. DEPhT uses genomic architectural features that discriminate between phage and bacterial sequences for efficient prophage discovery, and targeted homology searches for precise prophage extraction. DEPhT is designed for prophage discovery in Mycobacterium genomes but can be adapted broadly to other bacteria. We deploy DEPhT to demonstrate that prophages are prevalent in Mycobacterium strains but are absent not only from the few well-characterized Mycobacterium tuberculosis strains, but also are absent from all ∼30 000 sequenced M. tuberculosis strains.

Algorisms used for in silico finishing of bacterial genomes based on short-read assemblage implemented in GenoFinisher, AceFileViewer, and ShortReadManager.

In these days, for bacterial genome sequence determination, ultralong reads with homopolymeric troubles are used in combinations with short reads, resulting in genomic sequences with possible incorrect uniformity of repeat sequences. We have been determining complete bacterial genomic sequences based on NGS short reads and Newbler assemblage by utilizing functions implemented in 3 software GenoFinisher, AceFileViewer, and ShortReadManager without conducting additional experiments for gap closing, proving the concept that NGS short reads enclose enough information to determine complete genome sequences. Although some manual in silico tasks are to be conducted, they will ultimately be solved in a single pipeline. In this review, we describe the tools and implemented ideas that have enabled complete sequence determination solely based on short reads, which would be useful for establishing the basis for the future development of a short-read-based assembler that enables complete and accurate genome sequence determination at a lower cost.

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.

Genome wide association study of Escherichia coli bloodstream infection isolates identifies genetic determinants for the portal of entry but not fatal outcome.

Escherichia coli is an important cause of bloodstream infections (BSI), which is of concern given its high mortality and increasing worldwide prevalence. Finding bacterial genetic variants that might contribute to patient death is of interest to better understand infection progression and implement diagnostic methods that specifically look for those factors. E. coli samples isolated from patients with BSI are an ideal dataset to systematically search for those variants, as long as the influence of host factors such as comorbidities are taken into account. Here we performed a genome-wide association study (GWAS) using data from 912 patients with E. coli BSI from hospitals in Paris, France. We looked for associations between bacterial genetic variants and three patient outcomes (death at 28 days, septic shock and admission to intensive care unit), as well as two portals of entry (urinary and digestive tract), using various clinical variables from each patient to account for host factors. We did not find any association between genetic variants and patient outcomes, potentially confirming the strong influence of host factors in influencing the course of BSI; we however found a strong association between the papGII operon and entrance of E. coli through the urinary tract, which demonstrates the power of bacterial GWAS when applied to actual clinical data. Despite the lack of associations between E. coli genetic variants and patient outcomes, we estimate that increasing the sample size by one order of magnitude could lead to the discovery of some putative causal variants. Given the wide adoption of bacterial genome sequencing of clinical isolates, such sample sizes may be soon available.

A Large-Scale Genome-Based Survey of Acidophilic Bacteria Suggests That Genome Streamlining Is an Adaption for Life at Low pH.

The genome streamlining theory suggests that reduction of microbial genome size optimizes energy utilization in stressful environments. Although this hypothesis has been explored in several cases of low-nutrient (oligotrophic) and high-temperature environments, little work has been carried out on microorganisms from low-pH environments, and what has been reported is inconclusive. In this study, we performed a large-scale comparative genomics investigation of more than 260 bacterial high-quality genome sequences of acidophiles, together with genomes of their closest phylogenetic relatives that live at circum-neutral pH. A statistically supported correlation is reported between reduction of genome size and decreasing pH that we demonstrate is due to gene loss and reduced gene sizes. This trend is independent from other genome size constraints such as temperature and G + C content. Genome streamlining in the evolution of acidophilic bacteria is thus supported by our results. The analyses of predicted Clusters of Orthologous Genes (COG) categories and subcellular location predictions indicate that acidophiles have a lower representation of genes encoding extracellular proteins, signal transduction mechanisms, and proteins with unknown function but are enriched in inner membrane proteins, chaperones, basic metabolism, and core cellular functions. Contrary to other reports for genome streamlining, there was no significant change in paralog frequencies across pH. However, a detailed analysis of COG categories revealed a higher proportion of genes in acidophiles in the following categories: “replication and repair,” “amino acid transport,” and “intracellular trafficking”. This study brings increasing clarity regarding the genomic adaptations of acidophiles to life at low pH while putting elements, such as the reduction of average gene size, under the spotlight of streamlining theory.

PHISDetector: A Tool to Detect Diverse In Silico Phage–host Interaction Signals for Virome Studies

Phage–microbe interactions are appealing systems to study coevolution, and have also been increasingly emphasized due to their roles in human health, disease, and the development of novel therapeutics. Phage–microbe interactions leave diverse signals in bacterial and phage genomic sequences, defined as phage–host interaction signals (PHISs), which include clustered regularly interspaced short palindromic repeats (CRISPR) targeting, prophage, and protein–protein interaction signals. In the present study, we developed a novel tool phage–host interaction signal detector (PHISDetector) to predict phage–host interactions by detecting and integrating diverse in silico PHISs, and scoring the probability of phage–host interactions using machine learning models based on PHIS features. We evaluated the performance of PHISDetector on multiple benchmark datasets and application cases. When tested on a dataset of 758 annotated phage–host pairs, PHISDetector yields the prediction accuracies of 0.51 and 0.73 at the species and genus levels, respectively, outperforming other phage–host prediction tools. When applied to on 125,842 metagenomic viral contigs (mVCs) derived from 3042 geographically diverse samples, a detection rate of 54.54% could be achieved. Furthermore, PHISDetector could predict infecting phages for 85.6% of 368 multidrug-resistant (MDR) bacteria and 30% of 454 human gut bacteria obtained from the National Institutes of Health (NIH) Human Microbiome Project (HMP). The PHISDetector can be run either as a web server (http://www.microbiome-bigdata.com/PHISDetector/) for general users to study individual inputs or as a stand-alone version (https://github.com/HIT-ImmunologyLab/PHISDetector) to process massive phage contigs from virome studies.

Development of a bacterial regulatory motif database

O b j e c t i v e s . The amount of data generated by modern methods of high-throughput sequencing is such that their analysis is performed mainly in automatic mode. In particular, the use of newly decoded genomic sequences is possible only after the annotation of functional elements of the genome, which, as a rule, is performed by automatic pipelines. Such annotation pipelines do a good job to identify the genes, but none of them annotate regulatory elements. Without these elements it is not possible to understand when and how genes can be expressed. Information on the regulatory elements of bacteria is collected in several specialized databases (RegulonDB, CollecTF, Prodoric2, etc.), however, only a part of this information can be used for annotation of regulatory elements, and only for a very limited range of bacteria. Previously, we proposed a clear formal criterion for applying regulatory information to any bacterial genome. Such a criterion is the CR tag, a sequence of amino acid residues of a transcriptional regulator that specifically contacts the nitrogenous bases of regulatory element in genomic DNA. The mathematical model of a regulatory element (motif) associated with a CR tag can be correctly applied to annotate similar elements in any genomes encoding a transcriptional regulator with an identical CR tag. The accumulation of motifs associated with CR tags raised the question of their ordered storage for the convenience of subsequent use in the annotation of genomic sequences. Since no one of well-known databases uses the concept of CR tags, a new database ought to be developed. Thus, the goal of this work is to create a database with information about bacterial transcription factors and DNA sequences recognized by them, suitable for annotation of regulatory sequences in bacterial genomes.M e t h o d s . Infological modeling of the subject area was carried out using the IDEF1X methodology. The database was developed using the Microsoft SQL Server DBMS. A cross-platform application for importing data into a database is written in C++ using Qt technology.Re s u l t s . As a result of the study of the subject area, a relational data model was developed and implemented in the Microsoft SQL Server DBMS, which allows holistic storage of information about accumulated transcription regulation motifs in bacteria, including information about the publications confirming their correctness. To automate the process of entering accumulated data, a cross-platform application was developed for importing structured data on transcription factors.Co n c l u s i o n . The main difference of the developed database is the use of CR-tag concept. Records of mathematical models of regulatory elements (motifs) in the database are associated with a CR tag and, therefore, can be correctly used to annotate similar elements in any genomes encoding a transcriptional regulator with an identical CR tag. The developed database will provide structured and holistic data storage, as well as their quick search when used in the pipeline for automatic annotation of regulatory elements in bacterial genomic sequences.

Phylodynamic Inference of Bacterial Outbreak Parameters Using Nanopore Sequencing.

Nanopore sequencing and phylodynamic modeling have been used to reconstruct the transmission dynamics of viral epidemics, but their application to bacterial pathogens has remained challenging. Cost-effective bacterial genome sequencing and variant calling on nanopore platforms would greatly enhance surveillance and outbreak response in communities without access to sequencing infrastructure. Here, we adapt random forest models for single nucleotide polymorphism (SNP) polishing developed by Sanderson and colleagues (2020. High precision Neisseria gonorrhoeae variant and antimicrobial resistance calling from metagenomic nanopore sequencing. Genome Res. 30(9):1354–1363) to estimate divergence and effective reproduction numbers (Re) of two methicillin-resistant Staphylococcus aureus (MRSA) outbreaks from remote communities in Far North Queensland and Papua New Guinea (PNG; n = 159). Successive barcoded panels of S. aureus isolates (2 × 12 per MinION) sequenced at low coverage (>5× to 10×) provided sufficient data to accurately infer genotypes with high recall when compared with Illumina references. Random forest models achieved high resolution on ST93 outbreak sequence types (>90% accuracy and precision) and enabled phylodynamic inference of epidemiological parameters using birth–death skyline models. Our method reproduced phylogenetic topology, origin of the outbreaks, and indications of epidemic growth (Re > 1). Nextflow pipelines implement SNP polisher training, evaluation, and outbreak alignments, enabling reconstruction of within-lineage transmission dynamics for infection control of bacterial disease outbreaks on portable nanopore platforms. Our study shows that nanopore technology can be used for bacterial outbreak reconstruction at competitive costs, providing opportunities for infection control in hospitals and communities without access to sequencing infrastructure, such as in remote northern Australia and PNG.