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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.

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.

Whole-genome sequencing (WGS)-based outbreak investigation has proven to be a valuable method for the surveillance of bacterial pathogens. Its utility has been successfully demonstrated using both gene-by-gene (cgMLST or wgMLST) and single-nucleotide polymorphism (SNP)-based approaches. Among the obstacles of implementing a WGS-based routine surveillance is the need for an exchange of large volumes of sequencing data, as well as a widespread reluctance to share sequence and metadata in public repositories, together with a lacking standardization of suitable bioinformatic tools and workflows. To address these issues, we present chewieSnake, an intuitive and simple-to-use cgMLST workflow. ChewieSnake builds on the allele calling software chewBBACA and extends it by the concept of allele hashing. The resulting hashed allele profiles can be readily compared between laboratories without the need of a central allele nomenclature. The workflow fully automates the computation of the allele distance matrix, cluster membership, and phylogeny and summarizes all important findings in an interactive HTML report. Furthermore, chewieSnake can join allele profiles generated at different laboratories and identify shared clusters, including a stable and intercommunicable cluster nomenclature, thus facilitating a joint outbreak investigation. We demonstrate the feasibility of the proposed approach with a thorough method comparison using publically available sequencing data for Salmonella enterica. However, chewieSnake is readily applicable to all bacterial taxa, provided that a suitable cgMLST scheme is available. The workflow is freely available as an open-source tool and can be easily installed via conda or docker.

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.

Background: Pathogen whole genome sequencing (WGS) is being incorporated into public health surveillance and disease control systems worldwide and has the potential to make significant contributions to infectious disease surveillance, outbreak investigation and infection prevention and control. However, to date, there are limited data regarding (i) the optimal models for integration of genomic data into epidemiological investigations and (ii) how to quantify and evaluate public health impacts resulting from genomic epidemiological investigations. Methods: We developed the Pathogen Genomics in Public HeAlth Surveillance Evaluation (PG-PHASE) Framework to guide examination of the use of WGS in public health surveillance and disease control. We illustrate the use of this framework with three pathogens as case studies: Listeria monocytogenes, Mycobacterium tuberculosis and SARS-CoV-2. Results: The framework utilises an adaptable whole-of-system approach towards understanding how interconnected elements in the public health application of pathogen genomics contribute to public health processes and outcomes. The three phases of the PG-PHASE Framework are designed to support understanding of WGS laboratory processes, analysis, reporting and data sharing, and how genomic data are utilised in public health practice across all stages, from the decision to send an isolate or sample for sequencing to the use of sequence data in public health surveillance, investigation and decision-making. Importantly, the phases can be used separately or in conjunction, depending on the need of the evaluator. Subsequent to conducting evaluation underpinned by the framework, avenues may be developed for strategic investment or interventions to improve utilisation of whole genome sequencing. Conclusions: Comprehensive evaluation is critical to support health departments, public health laboratories and other stakeholders to successfully incorporate microbial genomics into public health practice. The PG-PHASE Framework aims to assist public health laboratories, health departments and authorities who are either considering transitioning to whole genome sequencing or intending to assess the integration of WGS in public health practice, including the capacity to detect and respond to outbreaks and associated costs, challenges and facilitators in the utilisation of microbial genomics and public health impacts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Studying how novel phenotypes originate and evolve is fundamental to the field of evolutionary biology as it allows us to understand how organismal diversity is generated and maintained. However, determining the basis of novel phenotypes is challenging as it involves orchestrated changes at multiple biological levels. Here, we aim to overcome this challenge by using a comparative species framework combining behavioral, gene expression, and genomic analyses to understand the evolutionary novel egg-laying substrate-choice behavior of the invasive pest species Drosophila suzukii. First, we used egg-laying behavioral assays to understand the evolution of ripe fruit oviposition preference in D. suzukii compared with closely related species D. subpulchrella and D. biarmipes as well as D. melanogaster. We show that D. subpulchrella and D. biarmipes lay eggs on both ripe and rotten fruits, suggesting that the transition to ripe fruit preference was gradual. Second, using two-choice oviposition assays, we studied how D. suzukii , D. subpulchrella , D. biarmipes , and D. melanogaster differentially process key sensory cues distinguishing ripe from rotten fruit during egg-laying. We found that D. suzukii 's preference for ripe fruit is in part mediated through a species-specific preference for stiff substrates. Last, we sequenced and annotated a high-quality genome for D. subpulchrella. Using comparative genomic approaches, we identified candidate genes involved in D. suzukii 's ability to seek out and target ripe fruits. Our results provide detail to the stepwise evolution of pest activity in D. suzukii , indicating important cues used by this species when finding a host, and the molecular mechanisms potentially underlying their adaptation to a new ecological niche. [ABSTRACT FROM AUTHOR]

DNA sequencing technologies provide unprecedented opportunities to analyze within-host evolution of microorganism populations. Often, within-host populations are analyzed via pooled sequencing of the population, which contains multiple individuals or "haplotypes." However, current next-generation sequencing instruments, in conjunction with single-molecule barcoded linked-reads, cannot distinguish long haplotypes directly. Computational reconstruction of haplotypes from pooled sequencing has been attempted in virology, bacterial genomics, metagenomics, and human genetics, using algorithms based on either cross-host genetic sharing or within-host genomic reads. Here, we describe PoolHapX, a flexible computational approach that integrates information from both genetic sharing and genomic sequencing. We demonstrated that PoolHapX outperforms state-of-the-art tools tailored to specific organismal systems, and is robust to within-host evolution. Importantly, together with barcoded linked-reads, PoolHapX can infer whole-chromosome-scale haplotypes from 50 pools each containing 12 different haplotypes. By analyzing real data, we uncovered dynamic variations in the evolutionary processes of within-patient HIV populations previously unobserved in single position-based analysis. [ABSTRACT FROM AUTHOR]

Summary: Whole metagenomic shotgun (WMS) sequencing has dramatically enhanced our ability to study microbial genomics. The possibility to unveil the genetic makeup of bacteria that cannot be easily isolated has significantly expanded our microbiological horizon. Here, we report an approach aimed at uncovering novel bacterial species by the use of targeted WMS sequencing. Employing in silico data retrieved from metabolic modelling to formulate a chemically defined medium (CDM), we were able to isolate and subsequently sequence the genomes of six putative novel species of bacteria from the gut of non‐human primates. [ABSTRACT FROM AUTHOR]

Whole-genome sequencing (WGS)-based outbreak investigation has proven to be a valuable method for the surveillance of bacterial pathogens. Its utility has been successfully demonstrated using both gene-by-gene (cgMLST or wgMLST) and single-nucleotide polymorphism (SNP)-based approaches. Among the obstacles of implementing a WGS-based routine surveillance is the need for an exchange of large volumes of sequencing data, as well as a widespread reluctance to share sequence and metadata in public repositories, together with a lacking standardization of suitable bioinformatic tools and workflows. To address these issues, we present chewieSnake , an intuitive and simple-to-use cgMLST workflow. ChewieSnake builds on the allele calling software chewBBACA and extends it by the concept of allele hashing. The resulting hashed allele profiles can be readily compared between laboratories without the need of a central allele nomenclature. The workflow fully automates the computation of the allele distance matrix, cluster membership, and phylogeny and summarizes all important findings in an interactive HTML report. Furthermore, chewieSnake can join allele profiles generated at different laboratories and identify shared clusters, including a stable and intercommunicable cluster nomenclature, thus facilitating a joint outbreak investigation. We demonstrate the feasibility of the proposed approach with a thorough method comparison using publically available sequencing data for Salmonella enterica. However, chewieSnake is readily applicable to all bacterial taxa, provided that a suitable cgMLST scheme is available. The workflow is freely available as an open-source tool and can be easily installed via conda or docker. [ABSTRACT FROM AUTHOR]

Covering: up to mid-2020 Terpenoids, also called isoprenoids, are the largest and most structurally diverse family of natural products. Found in all domains of life, there are over 80 000 known compounds. The majority of characterized terpenoids, which include some of the most well known, pharmaceutically relevant, and commercially valuable natural products, are produced by plants and fungi. Comparatively, terpenoids of bacterial origin are rare. This is counter-intuitive to the fact that recent microbial genomics revealed that almost all bacteria have the biosynthetic potential to create the C5 building blocks necessary for terpenoid biosynthesis. In this review, we catalogue terpenoids produced by bacteria. We collected 1062 natural products, consisting of both primary and secondary metabolites, and classified them into two major families and 55 distinct subfamilies. To highlight the structural and chemical space of bacterial terpenoids, we discuss their structures, biosynthesis, and biological activities. Although the bacterial terpenome is relatively small, it presents a fascinating dichotomy for future research. Similarities between bacterial and non-bacterial terpenoids and their biosynthetic pathways provides alternative model systems for detailed characterization while the abundance of novel skeletons, biosynthetic pathways, and bioactivies presents new opportunities for drug discovery, genome mining, and enzymology. [ABSTRACT FROM AUTHOR]

Although nanopore long-read length could reach up to 2Mbp with special genome extraction methods (Ammer-Herrmenau et al., [2]), N50 by traditional bacterial genome extraction methods is normally <50 kb (Li et al., [10]; Ruh et al., [12]). Keywords: antimicrobial resistance; microbial genomics; long-read sequencing; mobile genetic elements; single-molecule analysis EN antimicrobial resistance microbial genomics long-read sequencing mobile genetic elements single-molecule analysis 1 3 3 05/16/22 20220512 NES 220512 The emergence, dissemination, and evolution of antimicrobial-resistant pathogens among animals, humans, and the environment have caused serious public health concern and is exacerbating globally (Hernando-Amado et al., [7]; Antimicrobial Resistance Collaborators, [3]). They found that using QitanTech nanopore sequencing data, most MDR bacterial genome structures could be well-resolved successfully including cointegrate plasmids, although high accurate Illumina short-read data is necessary to avoid indel errors. [Extracted from the article]

Abstract Background Short-read sequencing technologies have long been the work-horse of microbiome analysis. Continuing technological advances are making the application of long-read sequencing to metagenomic samples increasingly feasible. Results We demonstrate that whole bacterial chromosomes can be obtained from an enriched community, by application of MinION sequencing to a sample from an EBPR bioreactor, producing 6 Gb of sequence that assembles into multiple closed bacterial chromosomes. We provide a simple pipeline for processing such data, which includes a new approach to correcting erroneous frame-shifts. Conclusions Advances in long-read sequencing technology and corresponding algorithms will allow the routine extraction of whole chromosomes from environmental samples, providing a more detailed picture of individual members of a microbiome.

Abstract Background Malaria parasites are genetically polymorphic and phenotypically plastic. In studying transcriptome variation among parasites from different infections, it is challenging to overcome potentially confounding technical and biological variation between samples. We investigate variation in the major human parasite Plasmodium falciparum, generating RNA-seq data on multiple independent replicate sample preparations of merozoite-containing intra-erythrocytic schizonts from a panel of clinical isolates and from long-term laboratory-adapted clones, with a goal of robustly identifying differentially expressed genes. Results Analysis of biological sample replicates shows that increased numbers improve the true discovery rate of differentially expressed genes, and that six independent replicates of each parasite line allowed identification of most differences that could be detected with larger numbers. For highly expressed genes, focusing on the top quartile at schizont stages, there was more power to detect differences. Comparing cultured clinical isolates and laboratory-adapted clones, genes more highly expressed in the laboratory-adapted clones include those encoding an AP2 transcription factor (PF3D7_0420300), a ubiquitin-binding protein and two putative methyl transferases. In contrast, higher expression in clinical isolates was seen for the merozoite surface protein gene dblmsp2, proposed to be a marker of schizonts forming merozoites committed to sexual differentiation. Variable expression was extremely strongly, but not exclusively, associated with genes known to be targeted by Heterochromatin Protein 1. Clinical isolates show variable expression of several known merozoite invasion ligands, as well as other genes for which new RT-qPCR assays validate the quantitation and allow characterisation in samples with more limited material. Expression levels of these genes vary among schizont preparations of different clinical isolates in the first ex vivo cycle in patient erythrocytes, but mean levels are similar to those in continuously cultured clinical isolates. Conclusions Analysis of multiple biological sample replicates greatly improves identification of genes variably expressed between different cultured parasite lines. Clinical isolates recently established in culture show differences from long-term adapted clones in transcript levels of particular genes, and are suitable for analyses requiring biological replicates to understand parasite phenotypes and variable expression likely to be relevant in nature.

Abstract Background Staphylococcus aureus is an opportunistic pathogen and a leading cause of nosocomial infections. It can acquire resistance to all the antibiotics that entered the clinics to date, and the World Health Organization defined it as a high-priority pathogen for research and development of new antibiotics. A deeper understanding of the genetic variability of S. aureus in clinical settings would lead to a better comprehension of its pathogenic potential and improved strategies to contrast its virulence and resistance. However, the number of comprehensive studies addressing clinical cohorts of S. aureus infections by simultaneously looking at the epidemiology, phylogenetic reconstruction, genomic characterisation, and transmission pathways of infective clones is currently low, thus limiting global surveillance and epidemiological monitoring. Methods We applied whole-genome shotgun sequencing (WGS) to 184 S. aureus isolates from 135 patients treated in different operative units of an Italian paediatric hospital over a timespan of 3 years, including both methicillin-resistant S. aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) from different infection types. We typed known and unknown clones from their genomes by multilocus sequence typing (MLST), Staphylococcal Cassette Chromosome mec (SCCmec), Staphylococcal protein A gene (spa), and Panton-Valentine Leukocidin (PVL), and we inferred their whole-genome phylogeny. We explored the prevalence of virulence and antibiotic resistance genes in our cohort, and the conservation of genes encoding vaccine candidates. We also performed a timed phylogenetic investigation for a potential outbreak of a newly emerging nosocomial clone. Results The phylogeny of the 135 single-patient S. aureus isolates showed a high level of diversity, including 80 different lineages, and co-presence of local, global, livestock-associated, and hypervirulent clones. Five of these clones do not have representative genomes in public databases. Variability in the epidemiology is mirrored by variability in the SCCmec cassettes, with some novel variants of the type IV cassette carrying extra antibiotic resistances. Virulence and resistance genes were unevenly distributed across different clones and infection types, with highly resistant and lowly virulent clones showing strong association with chronic diseases, and highly virulent strains only reported in acute infections. Antigens included in vaccine formulations undergoing clinical trials were conserved at different levels in our cohort, with only a few highly prevalent genes fully conserved, potentially explaining the difficulty of developing a vaccine against S. aureus. We also found a recently diverged ST1-SCCmecIV-t127 PVL− clone suspected to be hospital-specific, but time-resolved integrative phylogenetic analysis refuted this hypothesis and suggested that this quickly emerging lineage was acquired independently by patients. Conclusions Whole genome sequencing allowed us to study the epidemiology and genomic repertoire of S. aureus in a clinical setting and provided evidence of its often underestimated complexity. Some virulence factors and clones are specific of disease types, but the variability and dispensability of many antigens considered for vaccine development together with the quickly changing epidemiology of S. aureus makes it very challenging to develop full-coverage therapies and vaccines. Expanding WGS-based surveillance of S. aureus to many more hospitals would allow the identification of specific strains representing the main burden of infection and therefore reassessing the efforts for the discovery of new treatments and clinical practices.

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.

Summary: In this study, six bacterial isolates with variable GC, including Escherichia coli as mesophilic reference strain, were selected to compare hybrid assembly strategies based on next-generation sequencing (NGS) of short reads, single-tube long-fragment reads (stLFR) sequencing, and Oxford Nanopore Technologies (ONT) sequencing platforms. We obtained the complete genomes using the hybrid assembler Unicycler based on the NGS and ONT reads; others were de novo assembled using NGS, stLFR, and ONT reads by using different strategies. The contiguity, accuracy, completeness, sequencing costs, and DNA material requirements of the investigated strategies were compared systematically. Although all sequencing data could be assembled into accurate whole-genome sequences, the stLFR sequencing data yield a scaffold with more contiguity with more completeness of gene function than NGS sequencing assemblies. Our research provides a low-cost chromosome-level genome assembly strategy for large-scale sequencing of extremophile genomes with different GC contents.

A dataset of four draft genome sequences of Bifidobacterium strains is presented. All four genome assemblies are high-quality drafts characterized by high completeness and low contamination levels. GC content of the genomes varied in the range between 59.27% and 62.77%. Genome sequences were annotated for further functional and taxonomical analyses of the respective Bifidobacterium strains. Genetic determinants of probiotic capabilities, including the genes, related to utilization of human milk oligosaccharides and mucin, as well as the genes, encoding bile salt hydrolase were identified. The genome of B. bifidum VKPM=Ac-1784 has been shown to possess two bacteriocin gene clusters. The dataset expands knowledge on genomic diversity of probiotic strains of Bifidobacterium genus. The dataset is available under PRJNA656137 accession number in NCBI database and under zyv26t6x5r accession number in Mendeley Data repository.

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.

The biological mechanisms underlying emotional distress in HIV infection are likely to be complex but remain understudied. We investigated whether dysbiotic signatures in the gut microbiome of persons living with HIV (PLWH) are associated with their emotional status. We retrospectively examined the gut microbiome and clinical evaluation of 129 adults (94 PLWH and 35 HIV−) enrolled at UC San Diego's HIV Neurobehavioral Research Program. A subset of participants (32 PLWH vs. 13 HIV−) underwent an emotional assessment using the NIH Toolbox Emotion Battery summarized by three composite scores (negative affect, social satisfaction, and psychological well-being). We then sequenced the 16S rDNA V3-V4 regions from stool and performed taxonomic assignment using CLC Microbial Genomics Module. The gut microbiota profiles were evaluated in relation to participants' emotional assessment. All analyses were done in R statistical software. We found that the relative abundance of aerotolerant bacteria was significantly higher in PLWH (p < 0.01) and was associated with a lifetime major depression diagnosis independently of HIV status (p = 0.05). Moreover, PLWH experienced significantly worse psychological well-being (p = 0.02), less social satisfaction (p = 0.03), and more negative affect (p = 0.02). Higher levels of aerotolerant bacteria were associated with worse psychological well-being (rho = -0.35, p = 0.02), less social satisfaction (r = − 0.42, p < 0.01), and more negative affect (rho = 0.46, p < 0.01). The association of aerotolerant bacteria with social satisfaction and negative affect was independent of HIV status (p < 0.05, for both). The over-representation of aerotolerant bacteria in the gut may reflect worse oxidative stress and barrier defects and may contribute to emotional distress during HIV infection. [ABSTRACT FROM AUTHOR]

Metagenomics is a segment of conventional microbial genomics dedicated to the sequencing and analysis of combined genomic DNA of entire environmental samples. The most critical step of the metagenomic data analysis is the reconstruction of individual genes and genomes of the microorganisms in the communities using metagenomic assemblers – computational programs that put together small fragments of sequenced DNA generated by sequencing instruments. Here, we describe the challenges of metagenomic assembly, a wide spectrum of applications in which metagenomic assemblies were used to better understand the ecology and evolution of microbial ecosystems, and present one of the most efficient microbial assemblers, SPAdes that was upgraded to become applicable for metagenomics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Deepwater Horizon oil spill represents one of the most damaging environmental catastrophes of our generation. It contaminated vast areas of the open ocean, the deep sea, and the shoreline of the Gulf region and disrupted its ecosystems, with both residual and long-term impacts. At the core of all of these ecosystems are microbial communities that perform essential biogeochemical processes and ecosystem services such as carbon and nutrient cycling. Despite their importance, relatively little was known about marine microbes that degrade hydrocarbons in the Gulf of Mexico prior to the Deepwater Horizon spill, nor the effect of hydrocarbons on the microbiology of the Gulf region. Research carried out through the Gulf of Mexico Research Initiative (GoMRI) revealed cooperative microbial communities operating at the heart of bioremediation services with highly adaptive and complex dynamics. In addition, these efforts established new methods for assessing and monitoring ecosystem health, whereby microbial population genetics can serve as indicators of biogeochemical disruptions and/or restoration status in marine and coastal environments. Although much research is still needed to fully understand and engage microbially mediated bioremediation services, GoMRI constructed a strong foundation of methods, discoveries, and overarching principles to build upon. These insights and tools will help scientists better prepare for, and respond to, future environmental catastrophes, from oil tanker spills to long-term disruptions of climate change. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: Although the deep-sea bathymodiolin mussels have been intensively studied as a model of animal-bacteria symbiosis, it remains challenging to assess the host-symbiont interactions due to the complexity of the symbiotic tissue—the gill. Using cold-seep mussel Gigantidas platifrons as a model, we isolated the symbiont harboring bacteriocytes and profiled the transcriptomes of the three major parts of the symbiosis—the gill, the bacteriocyte, and the symbiont. This breakdown of the complex symbiotic tissue allowed us to characterize the host-symbiont interactions further. Our data showed that the gill's non-symbiotic parts play crucial roles in maintaining and protecting the symbiosis; the bacteriocytes supply the symbiont with metabolites, control symbiont population, and shelter the symbiont from phage infection; the symbiont dedicates to the methane oxidation and energy production. This study demonstrates that the bathymodiolin symbiosis interacts at the tissue, cellular, and molecular level, maintaining high efficiency and harmonic chemosynthetic micro niche.

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.

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.

Dimension reduction is often a preliminary step in the analysis of data sets with a large number of variables. Most classical, both supervised and unsupervised, dimension reduction methods such as principal component analysis (PCA), independent component analysis (ICA) or sliced inverse regression (SIR) can be formulated using one, two or several different scatter matrix functionals. Scatter matrices can be seen as different measures of multivariate dispersion and might highlight different features of the data and when compared might reveal interesting structures. Such analysis then searches for a projection onto an interesting (signal) part of the data, and it is also important to know the correct dimension of the signal subspace. These approaches usually make either no model assumptions or work in wide classes of semiparametric models. Theoretical results in the literature are however limited to the case where the sample size exceeds the number of variables which is hardly ever true for data sets encountered in bioinformatics. In this paper, we briefly review the relevant literature and explore if the dimension reduction tools can be used to find relevant and interesting subspaces for small-n-large-p data sets. We illustrate the methods with a microarray dataset of prostate cancer patients and healthy controls.

Rapid detection of antibiotic-resistant bacteria in blood cultures is critical for the timely treatment of patients with sepsis. The aim of this study was to develop a simple method for the rapid detection of drug-resistant bacteria from blood cultures and to evaluate its performance. We developed an optical microscopy-based microcolony detection method (MCD) for the rapid detection of antibiotic-resistant bacterial colonies in media. This method was tested using staphylococci resistant to methicillin and gram-negative bacilli resistant to third-generation cephalosporins and carbapenem. The results of the investigations of clinical samples using this method were compared with the drug susceptibility testing results for each of the 457 isolates, which included 134 staphylococci and 323 g-negative bacilli. The MCD was successful in detecting antibiotic-resistant bacterial growth from culture-positive blood samples in approximately 3 h. The sensitivity/specificity for methicillin-resistant staphylococci was 100%/97.2%. In the case of gram-negative bacilli, the sensitivity/specificity values for bacteria resistant to ceftriaxone, ceftazidime, and carbapenem were 100%/98.7%, 100%/89.3%, and 100%/90.9%, respectively. Therefore, MCD is a clinically useful screening method for the efficient and rapid detection of antibiotic-resistant bacteria and can be easily implemented in laboratories.

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.

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could influenced the work reported in this paper., (Crown Copyright © 2024. Published by Elsevier B.V. All rights reserved.)

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., Competing Interests: None to declare., (© 2024 Published by Elsevier Inc.)

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., Competing Interests: M.P. and S.B. disclose that their postdoctoral grant at the University of Pisa, Italy, is funded by GSK. Outside of the submitted work, S.B. also discloses having received grants from the University of Siena, Italy and the University of Tuscia, Viterbo, Italy; payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing or educational events from the University of Siena, Italy, University of Bari, Italy, and the Italica Academy srl. A.P., A.B., G.R., A.M., and M.B. are employed by GSK. M.S. was an intern at GSK during the time of the study. G.R. holds shares in GSK and in Novartis AG. A.M. holds shares in GSK. C.P. and A.S. disclose that GSK commissioned this research. The authors declare no other financial and non-financial relationships and activities and no other conflicts of interest., (© 2024 GSK.)

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., Competing Interests: The authors declare no conflict of interest.

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Estimation of post-mortem time interval (PMI) is a key parameter in the forensic investigation which poses a huge challenge to the medico-legal experts. The succession of microbes within different parts of the human body after death has shown huge potential in the determination of PMI. Human body harbors trillions of microorganisms as commensals. With the death of an individual when biological functions are stopped, these microorganisms behave contrarily along with the invasion of degrading microbes from the environment. Human cadaver becomes a rich source of nutrients due to autolysis of cells, which attracts various invading microorganisms as well as macroorganisms. At different stages of degradation, the succession of microorganisms differs significantly which can be explored for accurate PMI estimation. With the advent of microbial genomics technique and reduction in the cost of DNA sequencing, thanatomicrobiome and epinecrotic community analysis have gained huge attention in PMI estimation. The article summarizes different sources of microorganisms in a human cadaver, their succession pattern, and analytical techniques for application in the field of microbial forensics. Key points: • Thanatomicrobiome and epinecrotic microbiome develop in postmortem human body. • Lack of metabolic, immune, neuroendocrine systems facilitate microbial succession. • Analysis of postmortem microbial communities predicts accurate PMI. [ABSTRACT FROM AUTHOR]

Summary: The recent prevalence of high-throughput sequencing has been producing numerous prokaryotic community structure datasets. Although the trait-based approach is useful to interpret those datasets from ecological perspectives, available trait information is biased toward culturable prokaryotes, especially those of clinical and public health relevance, and thus may not represent the breadth of microbiota found across many of Earth's environments. To facilitate habitat-based analysis free of such bias, here we report a ready-to-use prokaryotic habitat database, ProkAtlas. ProkAtlas comprehensively links 16S rRNA gene sequences to prokaryotic habitats, using public shotgun metagenome datasets. We also developed a computational pipeline for habitat-based analysis of given prokaryotic community structures. After confirmation of the method effectiveness using 16S rRNA gene sequence datasets from individual genomes and the Earth Microbiome Project, we showed its validness and effectiveness in drawing ecological insights by applying it to six empirical prokaryotic community datasets from soil, aquatic, and human gut samples.

ABSTRACT 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.

Introduction: Rotavirus is the leading cause of diarrhoea in young children in India, responsible for an estimated 21357 mean numbers of deaths in 2010. Various genotypes of rotaviruses evolved due to mutational changes have been recognized. In this study, we determined the genotypes of rotaviruses involved in diarrhea in Goa and Meghalaya states of India. Methods: The dsRNA of rotaviruses was extracted from stool samples and detected by Ribonucleic Acid-Polyacrylamide gel electrophoresis (RNA PAGE) and Reverse transcription-polymerase Chain Reaction (RT-PCR) targeting the partial VP7 gene. The full length VP7 and partial VP4 genes of rotavirus strains were amplified by RT-PCR followed by nucleotide sequencing. The RotaC classification tool was used to determine the genotypes. Results: The positivity of rotavirus by PAGE and RT-PCR was observed to be 43.10% and 39.65% in Goa and 38% and 36% in Meghalaya, respectively. Though long electrophoretic profile was appeared to be the most predominant rotavirus type in circulation in these two states, 96% of long and 84.61% short electropherotype profiles could be detected by RT-PCR. The dsRNA of rotavirus extracted from 36 samples could be transcribed and amplified by beg9end9 primers for G genotyping, while, 41 by con3con2 primers for P genotyping. G1P[8] and G1P[6] genotypes were commonly circulated in Goa and G1P[8] and G1P[4] genotypes in Meghalaya. On nucleotide analysis, 6 samples from Goa showed G1 genotype specificity, while, 3 showed P[8] specificity indicating the G1P[8] rotavirus circulating in Goa. In Meghalaya state, 3 strains showed P[8] and 2 showed P[4] genotype specificity. The majority of the G and P genotypes were closely related to each other and G1 genotypes appeared in two separate clusters, while, P[8] and P[4] appeared in the respective clusters. Conclusion: The circulation of G1P[8], G1P[6] genotypes in Goa and the presence of G1P[8] and G1P[4] genotypes in Meghalaya was observed.

Early diagnosis of SARS-CoV-2 infected patients is essential to control the dynamics of the COVID-19 pandemic. We develop a rapid and accurate one-step multiplex TaqMan probe-based real-time RT-PCR assay, along with a computational tool to systematically analyse the data. Our assay could detect to a limit of 15 copies of SARS-CoV-2 transcripts—based on experiments performed by spiking total human RNA with in vitro synthesized viral transcripts. The assay was evaluated by performing 184 validations for the SARS-CoV-2 Nucleocapsid gene and human RNase P as an internal control reference gene with dilutions ranging from 1-100 ng for human RNA on a cohort of 26 clinical samples. 5 of 26 patients were confirmed to be infected with SARS-CoV-2, while 21 tested negative, consistent with the standards. The accuracy of the assay was found to be 100% sensitive and 100% specific based on the 26 clinical samples that need to be further verified using a large number of clinical samples. In summary, we present a rapid, easy to implement real-time PCR based assay with automated analysis using a novel COVID qPCR Analyzer tool with graphical user interface (GUI) to analyze the raw qRT-PCR data in an unbiased manner at a cost of under $3 per reaction and turnaround time of less than 2h, to enable in-house SARS-CoV-2 testing across laboratories.

Summary: Long-read sequencing techniques, such as the Oxford Nanopore Technology, can generate reads that are tens of kilobases in length and are therefore particularly relevant for microbiome studies. However, owing to the higher per-base error rates than typical short-read sequencing, the application of long-read sequencing on microbiomes remains largely unexplored. Here we deeply sequenced two human microbiota mock community samples (HM-276D and HM-277D) from the Human Microbiome Project. We showed that assembly programs consistently achieved high accuracy (∼99%) and completeness (∼99%) for bacterial strains with adequate coverage. We also found that long-read sequencing provides accurate estimates of species-level abundance (R = 0.94 for 20 bacteria with abundance ranging from 0.005% to 64%). Our results not only demonstrate the feasibility of characterizing complete microbial genomes and populations from error-prone Nanopore sequencing data but also highlight necessary bioinformatics improvements for future metagenomics tool development.

The wide use of pesticides in agriculture expose microbiota to stressful conditions that require the development of survival strategies. The bacterial response to many pollutants has not been elucidated in detail, as well as the evolutionary processes that occur to build adapted communities. The purpose of this study was to evaluate the bacterial population structure and adaptation strategies in planktonic and biofilm communities in limited environments, as tanks containing water used for washing herbicide containers. This biodiversity, with high percentage of nonculturable microorganisms, was characterized based on habitat and abiotic parameters using molecular and bioinformatics tools. According to water and wastewater standards, the physicochemical conditions of the tank water were inadequate for survival of the identified bacteria, which had to develop survival strategies in this hostile environment. The biodiversity decreased in the transition from planktonic to biofilm samples, indicating a possible association between genetic drift and selection of individuals that survive under stressful conditions, such as heating in water and the presence of chlorine, fluorine and agrochemicals over a six-month period. The abundance of Enterobacter, Acinetobacter and Pseudomonas in biofilms from water tanks was linked to essential processes, deduced from the genes attributed to these taxonomic units, and related to biofilm formation, structure and membrane transport, quorum sensing and xenobiotic degradation. These characteristics were randomly combined and fixed in the biofilm community. Thus, communities of biofilm bacteria obtained under these environmental conditions serve as interesting models for studying herbicide biodegradation kinetics and the prospects of consortia suitable for use in bioremediation in reservoirs containing herbicide-contaminated wastewater, as biofilters containing biofilm communities capable of degrading herbicides.

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.

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.

The Candida (C.) albicans complex includes C. albicans, C. dubliniensis, C. stellatoidea, and C. africana, with the last mentioned as an important emerging agent of vulvovaginal candidiasis (VVC). The aim of the study was to identify C. africana and C. dubliniensis and assess their drug susceptibility in vaginitis. One-hundred Candida isolates of the C. albicans complex from women diagnosed with vaginitis and from vaginal samples in the culture collection of a medical mycology laboratory were examined. Species of the C. albicans complex were identified with conventional and molecular methods using polymerase chain reaction (PCR) for amplification and sequencing of the internal transcribed spacer (ITS) region, PCR for partial amplification of hyphal wall protein 1 (HWP1) gene and duplex PCR. The effects of antifungal drugs were evaluated according to standard broth microdilution protocols.Ninety-seven C. albicans (97%) and three C. africana (3%) isolates were identified. Results of susceptibility testing revealed one isolate of C. africana to be resistant to both clotrimazole and fluconazole, and one showed reduced susceptibility to itraconazole.Identification of Candida species especially C. africana in vaginitis is crucial, there are varying levels of resistance to antifungal drugs.