Detection of carbapenemase producing Acinetobacter baumannii ST19 from Georgia and Ukraine carrying bla OXA-23, bla OXA-72, and/or bla NDM-5, December 2019 to June 2023.

In 2003-2023, amid 5,436 Acinetobacter baumannii isolates collected globally through the Multidrug-Resistant Organism Repository and Surveillance Network, 97 were ST19PAS, 34 of which carbapenem-resistant. Strains (n = 32) sampled after 2019 harboured either bla OXA-23, bla OXA-72, and/or bla NDM-5. Phylogenetic analysis of the 97 isolates and 11 publicly available ST19 genomes revealed three sub-lineages of carbapenemase-producing isolates from mainly Ukraine and Georgia, including an epidemic clone carrying all three carbapenemase genes. Infection control and global surveillance of carbapenem-resistant A. baumannii remain important.

Microbial genomics: a potential toolkit for forensic investigations.

Microbial forensics is a new discipline of science that analyzes evidence related to biological crime through the uniqueness and abundance of microorganisms and their toxins. Microorganisms remain alive longer than any other trace of biological evidence, such as DNA, fingerprints, and fibers, because of the protective cell membrane or capsules. Microbiological research has opened up various possibilities for forensic investigations of microbial flora. Current molecular technologies, including DNA sequencing, whole-genome sequencing, metagenomics, DNA fingerprinting, and molecular phylogeny, provide valid results for forensic investigations. Recent advancements in genome sequencing technologies, genetic data generation, and bioinformatic tools have significantly improved microbial sampling methods and forensic analyses. In this review, we discuss the applications of microbial genomic tools and technologies in forensic investigations, including human identification, geolocation, and causes of death.

zDB: bacterial comparative genomics made easy.

The analysis and comparison of genomes rely on different tools for tasks such as annotation, orthology prediction, and phylogenetic inference. Most tools are specialized for a single task, and additional efforts are necessary to integrate and visualize the results. To fill this gap, we developed zDB, an application integrating a Nextflow analysis pipeline and a Python visualization platform built on the Django framework. The application is available on GitHub (https://github.com/metagenlab/zDB) and from the bioconda channel. Starting from annotated Genbank files, zDB identifies orthologs and infers a phylogeny for each orthogroup. A species phylogeny is also constructed from shared single-copy orthologs. The results can be enriched with Pfam protein domain prediction, Cluster of Orthologs Genes and Kyoto Encyclopedia of Genes and Genomes annotations, and Swissprot homologs. The web application allows searching for specific genes or annotations, running Blast queries, and comparing genomic regions and whole genomes. The metabolic capacities of organisms can be compared at either the module or pathway levels. Finally, users can run queries to examine the conservation of specific genes or annotations across a chosen subset of genomes and display the results as a list of genes, Venn diagram, or heatmaps. Those features make zDB useful for both bioinformaticians and researchers more accustomed to laboratory research.IMPORTANCEGenome comparison and analysis rely on many independent tools, leaving to scientists the burden to integrate and visualize their results for interpretation. To alleviate this burden, we have built zDB, a comparative genomics tool that includes both an analysis pipeline and a visualization platform. The analysis pipeline automates gene annotation, orthology prediction, and phylogenetic inference, while the visualization platform allows scientists to easily explore the results in a web browser. Among other features, the interface allows users to visually compare whole genomes and targeted regions, assess the conservation of genes or metabolic pathways, perform Blast searches, or look for specific annotations. Altogether, this tool will be useful for a broad range of applications in comparative studies between two and hundred genomes. Furthermore, it is designed to allow sharing of data sets easily at a local or international scale, thereby supporting exploratory analyses for non-bioinformaticians on the genome of their favorite organisms.

AMRViz enables seamless genomics analysis and visualization of antimicrobial resistance.

We have developed AMRViz, a toolkit for analyzing, visualizing, and managing bacterial genomics samples. The toolkit is bundled with the current best practice analysis pipeline allowing researchers to perform comprehensive analysis of a collection of samples directly from raw sequencing data with a single command line. The analysis results in a report showing the genome structure, genome annotations, antibiotic resistance and virulence profile for each sample. The pan-genome of all samples of the collection is analyzed to identify core- and accessory-genes. Phylogenies of the whole genome as well as all gene clusters are also generated. The toolkit provides a web-based visualization dashboard allowing researchers to interactively examine various aspects of the analysis results. Availability: AMRViz is implemented in Python and NodeJS, and is publicly available under open source MIT license at https://github.com/amromics/amrviz .

AnnoView enables large-scale analysis, comparison, and visualization of microbial gene neighborhoods.

The analysis and comparison of gene neighborhoods is a powerful approach for exploring microbial genome structure, function, and evolution. Although numerous tools exist for genome visualization and comparison, genome exploration across large genomic databases or user-generated datasets remains a challenge. Here, we introduce AnnoView, a web server designed for interactive exploration of gene neighborhoods across the bacterial and archaeal tree of life. Our server offers users the ability to identify, compare, and visualize gene neighborhoods of interest from 30 238 bacterial genomes and 1672 archaeal genomes, through integration with the comprehensive Genome Taxonomy Database and AnnoTree databases. Identified gene neighborhoods can be visualized using pre-computed functional annotations from different sources such as KEGG, Pfam and TIGRFAM, or clustered based on similarity. Alternatively, users can upload and explore their own custom genomic datasets in GBK, GFF or CSV format, or use AnnoView as a genome browser for relatively small genomes (e.g. viruses and plasmids). Ultimately, we anticipate that AnnoView will catalyze biological discovery by enabling user-friendly search, comparison, and visualization of genomic data. AnnoView is available at http://annoview.uwaterloo.ca.

Defining the phylogenetics and resistome of the major Clostridioides difficile ribotypes circulating in Australia.

Clostridioides difficile infection (CDI) remains a significant public health threat globally. New interventions to treat CDI rely on an understanding of the evolution and epidemiology of circulating strains. Here we provide longitudinal genomic data on strain diversity, transmission dynamics and antimicrobial resistance (AMR) of C. difficile ribotypes (RTs) 014/020 (n=169), 002 (n=77) and 056 (n=36), the three most prominent C. difficile strains causing CDI in Australia. Genome scrutiny showed that AMR was uncommon in these lineages, with resistance-conferring alleles present in only 15/169 RT014/020 strains (8.9 %), 1/36 RT056 strains (2.78 %) and none of 77 RT002 strains. Notably, ~90 % of strains were resistant to MLSB agents in vitro, but only ~5.9 % harboured known resistance alleles, highlighting an incongruence between AMR genotype and phenotype. Core genome analyses revealed all three RTs contained genetically heterogeneous strain populations with limited evidence of clonal transmission between CDI cases. The average number of pairwise core genome SNP (cgSNP) differences within each RT group ranged from 23.3 (RT056, ST34, n=36) to 115.6 (RT002, ST8, n=77) and 315.9 (RT014/020, STs 2, 13, 14, 49, n=169). Just 19 clonal groups (encompassing 40 isolates), defined as isolates differing by ≤2 cgSNPs, were identified across all three RTs (RT014/020, n=14; RT002, n=3; RT056, n=2). Of these clonal groups, 63 % (12/19) comprised isolates from the same Australian State and 37 % (7/19) comprised isolates from different States. The low number of plausible transmission events found for these major RTs (and previously documented populations in animal and environmental sources/reservoirs) points to widespread and persistent community sources of diverse C. difficile strains as opposed to ongoing nationwide healthcare outbreaks dominated by a single clone. Together, these data provide new insights into the evolution of major lineages causing CDI in Australia and highlight the urgent need for enhanced surveillance, and for public health interventions to move beyond the healthcare setting and into a One Health paradigm to effectively combat this complex pathogen.

SaLTy: a novel Staphylococcus aureus Lineage Typer.

Staphylococcus aureus asymptomatically colonises 30 % of humans but can also cause a range of diseases, which can be fatal. In 2017 S. aureus was associated with 20 000 deaths in the USA alone. Dividing S. aureus isolates into smaller sub-groups can reveal the emergence of distinct sub-populations with varying potential to cause infections. Despite multiple molecular typing methods categorising such sub-groups, they do not take full advantage of S. aureus genome sequences when describing the fundamental population structure of the species. In this study, we developed Staphylococcus aureus Lineage Typing (SaLTy), which rapidly divides the species into 61 phylogenetically congruent lineages. Alleles of three core genes were identified that uniquely define the 61 lineages and were used for SaLTy typing. SaLTy was validated on 5000 genomes and 99.12 % (4956/5000) of isolates were assigned the correct lineage. We compared SaLTy lineages to previously calculated clonal complexes (CCs) from BIGSdb (n=21 173). SALTy improves on CCs by grouping isolates congruently with phylogenetic structure. SaLTy lineages were further used to describe the carriage of Staphylococcal chromosomal cassette containing mecA (SCCmec) which is carried by methicillin-resistant S. aureus (MRSA). Most lineages had isolates lacking SCCmec and the four largest lineages varied in SCCmec over time. Classifying isolates into SaLTy lineages, which were further SCCmec typed, allowed SaLTy to describe high-level MRSA epidemiology. We provide SaLTy as a simple typing method that defines phylogenetic lineages (https://github.com/LanLab/SaLTy). SaLTy is highly accurate and can quickly analyse large amounts of S. aureus genome data. SaLTy will aid the characterisation of S. aureus populations and ongoing surveillance of sub-groups that threaten human health.

Long COVID across SARS-CoV-2 variants, lineages, and sublineages.

This prospective study aimed to determine the prevalence of long COVID in patients hospitalized for SARS-CoV-2 infection from March 2020 to July 2022 and assess the impact of different viral lineages. A total of 2,524 patients were followed up for 12 months, with persistent symptoms reported in 35.2% at one month, decreasing thereafter. Omicron variant patients initially showed higher symptom intensity, but this trend diminished over time. Certain viral lineages, notably Delta lineages AY.126 and AY.43, and Omicron sublineages BA.1.17, BA.2.56, and BA.5.1, consistently correlated with more severe symptoms. Overall, long COVID prevalence and severity were similar across SARS-CoV-2 variants. Specific lineages may influence post-COVID sequelae persistence and severity.

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.

Classification of Neisseria meningitidis genomes with a bag-of-words approach and machine learning.

Whole genome sequencing of bacteria is important to enable strain classification. Using entire genomes as an input to machine learning (ML) models would allow rapid classification of strains while using information from multiple genetic elements. We developed a "bag-of-words" approach to encode, using SentencePiece or k-mer tokenization, entire bacterial genomes and analyze these with ML. Initial model selection identified SentencePiece with 8,000 and 32,000 words as the best approach for genome tokenization. We then classified in Neisseria meningitidis genomes the capsule B group genotype with 99.6% accuracy and the multifactor invasive phenotype with 90.2% accuracy, in an independent test set. Subsequently, in silico knockouts of 2,808 genes confirmed that the ML model predictions aligned with our current understanding of the underlying biology. To our knowledge, this is the first ML method using entire bacterial genomes to classify strains and identify genes considered relevant by the classifier.

Microbial composition and function in reclaimed mine sites along a reclamation chronosequence become increasingly similar to undisturbed reference sites.

Mine reclamation historically focuses on enhancing plant coverage to improve below and aboveground ecology. However, there is a great need to study the role of soil microorganisms in mine reclamation, particularly long-term studies that track the succession of microbial communities. Here, we investigate the trajectory of microbial communities of mining sites reclaimed between three and 26 years. We used high-throughput amplicon sequencing to characterize the bacterial and fungal communities. We quantified how similar the reclaimed sites were to unmined, undisturbed reference sites and explored the trajectory of microbial communities along the reclamation chronosequence. We also examined the ecological processes that shape the assembly of bacterial communities. Finally, we investigated the functional potential of the microbial communities through metagenomic sequencing. Our results reveal that the reclamation age significantly impacted the community compositions of bacterial and fungal communities. As the reclamation age increases, bacterial and fungal communities become similar to the unmined, undisturbed reference site, suggesting a favorable succession in microbial communities. The bacterial community assembly was also significantly impacted by reclamation age and was primarily driven by stochastic processes, indicating a lesser influence of environmental properties on the bacterial community. Furthermore, our read-based metagenomic analysis showed that the microbial communities' functional potential increasingly became similar to the reference sites. Additionally, we found that the plant richness increased with the reclamation age. Overall, our study shows that both above- and belowground ecological properties of reclaimed mine sites trend towards undisturbed sites with increasing reclamation age. Further, it demonstrates the importance of microbial genomics in tracking the trajectory of ecosystem reclamation.

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 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.

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.

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.

aKmerBroom: Ancient oral DNA decontamination using Bloom filters on k-mer sets.

Dental calculus samples are modeled as a mixture of DNA coming from dental plaque and contaminants. Current computational decontamination methods such as Recentrifuge and DeconSeq require either a reference database or sequenced negative controls, and therefore have limited use cases. We present a reference-free decontamination tool tailored for the removal of contaminant DNA of ancient oral sample called aKmerBroom. Our tool builds a Bloom filter of known ancient and modern oral k-mers, then scans an input set of ancient metagenomic reads using multiple passes to iteratively retain reads likely to be of oral origin. On synthetic data, aKmerBroom achieves over 89.53% sensitivity and 94.00% specificity. On real datasets, aKmerBroom shows higher read retainment (+60% on average) than other methods. We anticipate aKmerBroom will be a valuable tool for the processing of ancient oral samples as it will prevent contaminated datasets from being completely discarded in downstream analyses.

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.

Review: Towards the next-generation models of the rumen microbiome for enhancing predictive power and guiding sustainable production strategies.

The rumen ecosystem harbours a galaxy of microbes working in syntrophy to carry out a metabolic cascade of hydrolytic and fermentative reactions. This fermentation process allows ruminants to harvest nutrients from a wide range of feedstuff otherwise inaccessible to the host. The interconnection between the ruminant and its rumen microbiota shapes key animal phenotypes such as feed efficiency and methane emissions and suggests the potential of reducing methane emissions and enhancing feed conversion into animal products by manipulating the rumen microbiota. Whilst significant technological progress in omics techniques has increased our knowledge of the rumen microbiota and its genome (microbiome), translating omics knowledge into effective microbial manipulation strategies remains a great challenge. This challenge can be addressed by modelling approaches integrating causality principles and thus going beyond current correlation-based approaches applied to analyse rumen microbial genomic data. However, existing rumen models are not yet adapted to capitalise on microbial genomic information. This gap between the rumen microbiota available omics data and the way microbial metabolism is represented in the existing rumen models needs to be filled to enhance rumen understanding and produce better predictive models with capabilities for guiding nutritional strategies. To fill this gap, the integration of computational biology tools and mathematical modelling frameworks is needed to translate the information of the metabolic potential of the rumen microbes (inferred from their genomes) into a mathematical object. In this paper, we aim to discuss the potential use of two modelling approaches for the integration of microbial genomic information into dynamic models. The first modelling approach explores the theory of state observers to integrate microbial time series data into rumen fermentation models. The second approach is based on the genome-scale network reconstructions of rumen microbes. For a given microorganism, the network reconstruction produces a stoichiometry matrix of the metabolism. This matrix is the core of the so-called genome-scale metabolic models which can be exploited by a plethora of methods comprised within the constraint-based reconstruction and analysis approaches. We will discuss how these methods can be used to produce the next-generation models of the rumen microbiome.

Analysis of the milk kefir pan-metagenome reveals four community types, core species, and associated metabolic pathways.

A comprehensive metagenomics-based investigation of the microorganisms present within milk kefir communities from across the globe was carried out with a view to defining the milk kefir pan-metagenome, including details relating to core and non-core components. Milk kefir samples, generated by inoculating full fat, pasteurized cow's milk with 64 kefir grains sourced from 25 different countries, were analyzed. We identified core features, including a consistent pattern of domination by representatives from the species Lactobacillus helveticus or the sub-species Lactobacillus kefiranofaciens subsp. kefiranofaciens, Lactococcus lactis subsp. lactis or Lla. cremoris subsp. cremoris in each kefir. Notably, even in kefirs where the lactococci did not dominate, they and 51 associated metabolic pathways were identified across all metagenomes. These insights can contribute to future efforts to create tailored kefir-based microbial communities for different applications and assist regulators and producers to ensure that kefir products have a microbial composition that reflects the artisanal beverage.

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.