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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. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Hydrogen (H2) is the primary electron donor for methane formation in ruminants, but the H2-producing organisms involved are largely uncharacterized. This work integrated studies of microbial physiology and genomics to characterize rumen bacterial isolate NK3A20 of the family Lachnospiraceae. Isolate NK3A20 was the first recognized isolate of the NK3A20 group, which is among the ten most abundant bacterial genera in 16S rRNA gene surveys of rumen microbiota. NK3A20 produced acetate, butyrate, H2, and formate from glucose. The end product ratios varied when grown with different substrates and at different H2 partial pressures. NK3A20 produced butyrate as a major product using glucose or under high H2 partial pressures and switched to mainly acetate in the presence of galacturonic acid (an oxidized sugar) or in coculture with a methanogen. Growth with galacturonic acid was faster at elevated H2 concentrations, while elevated H2 slowed growth with glucose. Genome analyses revealed the presence of multiple hydrogenases including a membrane-bound Ech hydrogenase, an electron bifurcating butyryl-CoA dehydrogenase (Bcd-Etf), and an Rnf complex that may be involved in modulating the observed metabolic pathway changes, providing insight into H2 formation in the rumen. [ABSTRACT FROM AUTHOR]

Microbial genomics studies focusing on the dynamics of selection have often used a small number of distant genomes. As a result, they could only analyze mutations that had become fixed during the divergence between species. However, thousands of genomes of some species are now available in public databases, thanks to high-throughput sequencing. These data provide a more complete picture of the polymorphisms segregating within a species, offering a unique insight into the processes that shape the recent evolution of a species. In this study, we present GLASS (Gene-Level Amino-acid Score Shift), a selection test that is based on the predicted effects of amino acid changes. By comparing the distribution of effects of mutations observed in a gene to the expectation in the absence of selection, GLASS can quantify the intensity of selection. We applied GLASS to a dataset of 60,472 Escherichia coli strains and used this to reexamine the longstanding debate about the role of essentiality versus expression level in the rate of protein evolution. We found that selection has contrasting shortterm and long-term dynamics, with essential genes being subject to strong purifying selection in the short term, while expression level determines the rate of gene evolution in the long term. GLASS also found an overrepresentation of inactivating mutations in specific transcription factors, such as efflux pump repressors, which is consistent with selection for antibiotic resistance. These gene-inactivating polymorphisms do not reach fixation, suggesting another contrast between short-term fitness gains and long-term counterselection. [ABSTRACT FROM AUTHOR]

Bacterial genomes exhibit widespread horizontal gene transfer, resulting in highly variable genome content that complicates the inference of genetic interactions. In this study, we develop a method for detecting coevolving genes from large datasets of bacterial genomes based on pairwise comparisons of closely related individuals, analogous to a pedigree study in eukaryotic populations. We apply our method to pairs of genes from the Staphylococcus aureus accessory genome of over 75,000 annotated gene families using a database of over 40,000 whole genomes. We find many pairs of genes that appear to be gained or lost in a coordinated manner, as well as pairs where the gain of one gene is associated with the loss of the other. These pairs form networks of rapidly coevolving genes, primarily consisting of genes involved in virulence, mechanisms of horizontal gene transfer, and antibiotic resistance, particularly the SCCmec complex. While we focus on gene gain and loss, our method can also detect genes that tend to acquire substitutions in tandem, or genotype-phenotype or phenotype-phenotype coevolution. Finally, we present the R package DeCoTUR that allows for the computation of our method. [ABSTRACT FROM AUTHOR]

Microbe domestication has a major applied relevance but is still poorly understood from an evolutionary perspective. The yeast Torulaspora delbrueckii is gaining importance for biotechnology but little is known about its population structure, variation in gene content or possible domestication routes. Here, we show that T. delbrueckii is composed of five major clades. Among the three European clades, a lineage associated with the wild arboreal niche is sister to the two other lineages that are linked to anthropic environments, one to wine fermentations and the other to diverse sources including dairy products and bread dough (Mix‐Anthropic clade). Using 64 genomes we assembled the pangenome and the variable genome of T. delbrueckii. A comparison with Saccharomyces cerevisiae indicated that the weight of the variable genome in the pangenome of T. delbrueckii is considerably smaller. An association of gene content and ecology supported the hypothesis that the Mix‐Anthropic clade has the most specialized genome and indicated that some of the exclusive genes were implicated in galactose and maltose utilization. More detailed analyses traced the acquisition of a cluster of GAL genes in strains associated with dairy products and the expansion and functional diversification of MAL genes in strains isolated from bread dough. In contrast to S. cerevisiae, domestication in T. delbrueckii is not primarily driven by alcoholic fermentation but rather by adaptation to dairy and bread‐production niches. This study expands our views on the processes of microbe domestication and on the trajectories leading to adaptation to anthropic niches. [ABSTRACT FROM AUTHOR]

The emergence of resistance to antiviral drugs increasingly used to treat SARS-CoV-2 infections has been recognised as a significant threat to COVID-19 control. In addition, some SARS-CoV-2 variants of concern appear to be intrinsically resistant to several classes of these antiviral agents. Therefore, there is a critical need for rapid recognition of clinically relevant polymorphisms in SARS-CoV-2 genomes associated with significant reduction of drug activity in virus neutralisation experiments. Here we present SABRes, a bioinformatic tool, which leverages on expanding public datasets of SARS-CoV-2 genomes and allows detection of drug resistance mutations in consensus genomes as well as in viral subpopulations. We have applied SABRes to detect resistance-conferring mutations in 25,197 genomes generated over the course of the SARS-CoV-2 pandemic in Australia and identified 299 genomes containing resistance conferring mutations to the five antiviral therapeutics that retain effectiveness against currently circulating strains of SARS-CoV-2 – Sotrovimab, Bebtelovimab, Remdesivir, Nirmatrelvir and Molnupiravir. These genomes accounted for a 1.18% prevalence of resistant isolates discovered by SABRes, including 80 genomes with resistance conferring mutations found in viral subpopulations. Timely recognition of these mutations within subpopulations is critical as these mutations can provide an advantage under selective pressure and presents an important step forward in our ability to monitor SARS-CoV-2 drug resistance. [ABSTRACT FROM AUTHOR]

Motivation The discovery of the genetic features that underly a phenotype is a fundamental task in microbial genomics. With the growing number of microbial genomes that are paired with phenotypic data, new challenges, and opportunities are arising for genotype-phenotype inference. Phylogenetic approaches are frequently used to adjust for the population structure of microbes but scaling them to trees with thousands of leaves representing heterogeneous populations is highly challenging. This greatly hinders the identification of prevalent genetic features that contribute to phenotypes that are observed in a wide diversity of species. Results In this study, Evolink was developed as an approach to rapidly identify genotypes associated with phenotypes in large-scale multispecies microbial datasets. Compared with other similar tools, Evolink was consistently among the top-performing methods in terms of precision and sensitivity when applied to simulated and real-world flagella datasets. In addition, Evolink significantly outperformed all other approaches in terms of computation time. Application of Evolink on flagella and gram-staining datasets revealed findings that are consistent with known markers and supported by the literature. In conclusion, Evolink can rapidly detect phenotype-associated genotypes across multiple species, demonstrating its potential to be broadly utilized to identify gene families associated with traits of interest. Availability and implementation The source code, docker container, and web server for Evolink are freely available at https://github.com/nlm-irp-jianglab/Evolink. [ABSTRACT FROM AUTHOR]

Microbial single-cell genomics (SCG) provides access to the genomes of rare and uncultured microorganisms and is a complementary method to metagenomics. Due to the femtogram-levels of DNA in a single microbial cell, sequencing the genome requires whole genome amplification (WGA) as a preliminary step. However, the most common WGA method, multiple displacement amplification (MDA), is known to be costly and biased against specific genomic regions, preventing high-throughput applications and resulting in uneven genome coverage. Thus, obtaining high-quality genomes from many taxa, especially minority members of microbial communities, becomes difficult. Here, we present a volume reduction approach that significantly reduces costs while improving genome coverage and uniformity of DNA amplification products in standard 384-well plates. Our results demonstrate that further volume reduction in specialized and complex setups (e.g., microfluidic chips) is likely unnecessary to obtain higher-quality microbial genomes. This volume reduction method makes SCG more feasible for future studies, thus helping to broaden our knowledge on the diversity and function of understudied and uncharacterized microorganisms in the environment. [ABSTRACT FROM AUTHOR]

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

Background Microbial culture collections play a key role in taxonomy by studying the diversity of their strains and providing well-characterized biological material to the scientific community for fundamental and applied research. These microbial resource centers thus need to implement new standards in species delineation, including whole-genome sequencing and phylogenomics. In this context, the genomic needs of the Belgian Coordinated Collections of Microorganisms were studied, resulting in the GEN-ERA toolbox. The latter is a unified cluster of bioinformatic workflows dedicated to both bacteria and small eukaryotes (e.g. yeasts). Findings This public toolbox allows researchers without a specific training in bioinformatics to perform robust phylogenomic analyses. Hence, it facilitates all steps from genome downloading and quality assessment, including genomic contamination estimation, to tree reconstruction. It also offers workflows for average nucleotide identity comparisons and metabolic modeling. Technical details Nextflow workflows are launched by a single command and are available on the GEN-ERA GitHub repository (https://github.com/Lcornet/GENERA). All the workflows are based on Singularity containers to increase reproducibility. Testing The toolbox was developed for a diversity of microorganisms, including bacteria and fungi. It was further tested on an empirical dataset of 18 (meta)genomes of early branching Cyanobacteria, providing the most up-to-date phylogenomic analysis of the Gloeobacterales order, the first group to diverge in the evolutionary tree of Cyanobacteria. Conclusion The GEN-ERA toolbox can be used to infer completely reproducible comparative genomic and metabolic analyses on prokaryotes and small eukaryotes. Although designed for routine bioinformatics of culture collections, it can also be used by all researchers interested in microbial taxonomy, as exemplified by our case study on Gloeobacterales. [ABSTRACT FROM AUTHOR]

Monkeypox has been a neglected, zoonotic tropical disease for over 50 years. Since the 2022 global outbreak, hundreds of human clinical samples have been subjected to next-generation sequencing (NGS) worldwide with raw data deposited in public repositories. However, sequence analysis for in-depth investigation of viral evolution remains hindered by the lack of a curated, whole genome Monkeypox virus (MPXV) database (DB) and efficient bioinformatics pipelines. To address this, we developed a customized MPXV DB for integration with "ready-to-use" workflows in the CLC Microbial Genomics Module for whole genomic and metagenomic analysis. After database construction (218 MPXV genomes), whole genome alignment, pairwise comparison, and evolutionary analysis of all genomes were analyzed to autogenerate tabular outputs and visual displays (collective runtime: 16 min). The clinical utility of the MPXV DB was demonstrated by using a Chimpanzee fecal, hybrid-capture NGS dataset (publicly available) for metagenomic, phylogenomic, and viral/host integration analysis. The clinically relevant MPXV DB embedded in CLC workflows proved to be a rapid method of sequence analysis useful for phylogenomic exploration and a wide range of applications in translational science. [ABSTRACT FROM AUTHOR]

Human papillomavirus (HPV) integration within the host genome may contribute to carcinogenesis through various disruptive mechanisms. With next-generation sequencing (NGS), identification of viral and host genomic breakpoints and chimeric sequences are now possible. However, a simple, streamlined bioinformatics workflow has been non-existent until recently. Here, we tested two new, automated workflows in CLC Microbial Genomics, i.e., Viral Hybrid Capture (VHC) Data Analysis and Viral Integration Site (VIS) Identification for software performance and efficiency. The workflows embedded with HPV and human reference genomes were used to analyze a publicly available NGS dataset derived from pre- and cancerous HPV+ cervical cytology of 21 Gabonese women. The VHC and VIS workflow median runtimes were 19 and 7 min per sample, respectively. The VIS dynamic graphical outputs included read mappings, virus-host genomic breakpoints, and virus-host integration circular plots. Key findings, including disrupted and nearby genes, were summarized in an auto-generated report. Overall, the VHC and VIS workflows proved to be a rapid and accurate means of localizing viral-host integration site(s) and identifying disrupted and neighboring human genes. Applying HPV VIS-mapping to pre- or invasive tumors will advance our understanding of viral oncogenesis and facilitate the discovery of prognostic biomarkers and therapeutic targets. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Tick-borne relapsing fever (TBRF) is a globally prevalent, yet under-studied vector-borne disease transmitted by soft and hard bodied ticks. While soft TBRF (sTBRF) spirochetes have been described for over a century, our understanding of the molecular mechanisms facilitating vector and host adaptation is poorly understood. This is due to the complexity of their small (~ 1.5 Mb) but fragmented genomes that typically consist of a linear chromosome and both linear and circular plasmids. A majority of sTBRF spirochete genomes' plasmid sequences are either missing or are deposited as unassembled sequences. Consequently, our goal was to generate complete, plasmid-resolved genomes for a comparative analysis of sTBRF species of the Western Hemisphere. Results: Utilizing a Borrelia specific pipeline, genomes of sTBRF spirochetes from the Western Hemisphere were sequenced and assembled using a combination of short- and long-read sequencing technologies. Included in the analysis were the two recently isolated species from Central and South America, Borrelia puertoricensis n. sp. and Borrelia venezuelensis, respectively. Plasmid analyses identified diverse sequences that clustered plasmids into 30 families; however, only three families were conserved and syntenic across all species. We also compared two species, B. venezuelensis and Borrelia turicatae, which were isolated ~ 6,800 km apart and from different tick vector species but were previously reported to be genetically similar. Conclusions: To truly understand the biological differences observed between species of TBRF spirochetes, complete chromosome and plasmid sequences are needed. This comparative genomic analysis highlights high chromosomal synteny across the species yet diverse plasmid composition. This was particularly true for B. turicatae and B. venezuelensis, which had high average nucleotide identity yet extensive plasmid diversity. These findings are foundational for future endeavors to evaluate the role of plasmids in vector and host adaptation. [ABSTRACT FROM AUTHOR]

Background: Development of new pan-genome analysis tools is important, as the pangenome of a microbial species has become an important method to define the diversity of a selected taxon, most commonly a species, in the last years. This enables comparison of strains from different ecological niches and can be used to define the functional potential in a bacterial population. It gives us a much better view of microbial genomics than can be gained from singular genomes which after all are just single representatives of a much more varied population. Results: We present Panakeia, a tool which strives to be easy to use and providing a detailed view of the pangenome structure which can efficiently be utilised for discovery, or further in-depth analysis, of features of interest. It analyses synteny and multiple structural patterns of the pangenome, giving insights into the biological diversity and evolution of the studied taxon. Panakeia hence provides both broad and detailed information on the structure of a pangenome, for diverse and highly clonal populations of bacteria. Conclusions: Previously published pangenome tools often reduce the information to a presence/absence matrix of unconnected genes or generate massive hard to interpret output graphs. However, Panakeia includes synteny and structural information and presents it in a way that can readily be used for further analysis. Panakeia can be downloaded at https://github.com/BioSina/Panakeiatogether with a detailed User Guide. [ABSTRACT FROM AUTHOR]

In 2022, the burden of cholera--an acute watery diarrheal disease caused by Vibrio cholerae serogroup O1 (or more rarely O139) bacteria, which produce cholera toxin--remains high in many African and Asian countries. In the last few years, microbial genomics has made it possible to define the bacterial populations responsible for cholera more precisely. It has been shown that the current, seventh pandemic is due to a single lineage with a reservoir in the countries of the Bay of Bengal (India and Bangladesh). There have been several transmissions of the causal agent of cholera from this region to Africa, Asia and Latin America, suggesting a human-to-human transmission of the disease. Microbial genetics can help to fight this scourge by providing insight into cholera epidemiology and through its use in disease monitoring, thereby contributing to the achievement of the World Health Organization's goal of reducing cholera deaths by 90% by 2030. [ABSTRACT FROM AUTHOR]

Genome-wide functional genetic studies of food-borne pathogens could provide a comprehensive understanding of the mechanisms underline their survival and pathogenesis in the harsh environments during food processing and infections. Traditional methods such as physical mutagenesis, chemical mutagenesis, and labeled DNA fragment insertion mutation techniques are time-consuming, labor-intensive and low precision. Although RNA-Seq and other omics methods enable high-throughput research on a genome-wide scale, the obtained results of gene transcription or expression cannot establish direct causal connection between the genetic determinants and particular functions. Transposon sequencing (Tn-seq) is an emerging microbial functional genomic technology based on transposon mutagenesis and high-throughput sequencing technologies, which is able to quantitatively track millions of independent mutants in a single experiment, and therefore efficiently map the "genotype-phenotype" relationship and the genetic interaction networks of a bacterium. This article summarizes the common types and hazards of food-borne pathogens, the development of Tn-seq technology, and its applications in studying the physiology, pathogenesis, and environmental survival of food-borne pathogens. • Food-borne pathogens are a common concern of food safety worldwide. • Transposon sequencing (Tn-seq) is an emerging microbial functional genomics technology. • Tn-seq can efficiently establish "genotype-phenotype" correspondence. • The development and applications of Tn-seq are reviewed. [ABSTRACT FROM AUTHOR]

Background: Functional genome annotation is the process of labelling functional genomic regions with descriptive information. Manual curation can produce higher quality genome annotations than fully automated methods. Manual annotation efforts are time-consuming and complex; however, software can help reduce these drawbacks. Results: We created Manual Annotation Studio (MAS) to improve the efficiency of the process of manual functional annotation prokaryotic and viral genomes. MAS allows users to upload unannotated genomes, provides an interface to edit and upload annotations, tracks annotation history and progress, and saves data to a relational database. MAS provides users with pertinent information through a simple point and click interface to execute and visualize results for multiple homology search tools (blastp, rpsblast, and HHsearch) against multiple databases (Swiss-Prot, nr, CDD, PDB, and an internally generated database). MAS was designed to accept connections over the local area network (LAN) of a lab or organization so multiple users can access it simultaneously. MAS can take advantage of high-performance computing (HPC) clusters by interfacing with SGE or SLURM and data can be exported from MAS in a variety of formats (FASTA, GenBank, GFF, and excel). Conclusions: MAS streamlines and provides structure to manual functional annotation projects. MAS enhances the ability of users to generate, interpret, and compare results from multiple tools. The structure that MAS provides can improve project organization and reduce annotation errors. MAS is ideal for team-based annotation projects because it facilitates collaboration. [ABSTRACT FROM AUTHOR]

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]

Prokaryote genomics started in earnest in 1995, with the complete sequences of two small bacterial genomes, those of Haemophilus influenzae and Mycoplasma genitalium. During the next quarter century, the prokaryote genome database has been growing exponentially, with no saturation in sight. For most of these 25 years, genome sequencing remained limited to cultivable microbes. Together with next-generation sequencing methods, advances in metagenomics and single-cell genomics have lifted this limitation, providing for an increasingly unbiased characterization of the global prokaryote diversity. Advances in computational genomics followed the progress of genome sequencing, even if occasionally lagging behind. Several major new branches of bacteria and archaea were discovered, including Asgard archaea, the apparent closest relatives of eukaryotes and expansive groups of bacteria and archaea with small genomes thought to be symbionts of other prokaryotes. Comparative analysis of numerous prokaryote genomes spanning a wide range of evolutionary distances changed the conceptual foundations of microbiology, supplanting the notion of species genomes with fixed gene sets with that of dynamic pangenomes and the notion of a single Tree of Life (ToL) with a statistical tree-like trend among individual gene trees. Strides were also made towards a theory and quantitative laws of prokaryote genome evolution. The database of prokaryote genomes has been growing exponentially for 25 years. There is no saturation of prokaryote diversity currently in sight. Most prokaryotes have dynamic, 'open' pangenomes. Metagenomics makes key contributions to the study of prokaryote diversity. There is no single Tree of Life but a tree-like trend in evolution is discernible. [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]

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]

Nutrient supply regulates the activity of phytoplankton, but the global biogeography of nutrient limitation and co-limitation is poorly understood. Prochlorococcus adapt to local environments by gene gains and losses, and we used genomic changes as an indicator of adaptation to nutrient stress. We collected metagenomes from all major ocean regions as part of the Global Ocean Ship-based Hydrographic Investigations Program (Bio-GO-SHIP) and quantified shifts in genes involved in nitrogen, phosphorus, and iron assimilation. We found regional transitions in stress type and severity as well as widespread co-stress. Prochlorococcus stress genes, bottle experiments, and Earth system model predictions were correlated. We propose that the biogeography of multinutrient stress is stoichiometrically linked by controls on nitrogen fixation. Our omics-based description of phytoplankton resource use provides a nuanced and highly resolved description of nutrient stress in the global ocean. [ABSTRACT FROM AUTHOR]

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]

Latin binomials, popularised in the 18th century by the Swedish naturalist Linnaeus, have stood the test of time in providing a stable, clear, and memorable system of nomenclature across biology. However, relentless and ever-deeper exploration and analysis of the microbial world has created an urgent need for huge numbers of new names for Archaea and Bacteria. Manual creation of such names remains difficult and slow and typically relies on expert-driven nomenclatural quality control. Keen to ensure that the legacy of Linnaeus lives on in the age of microbial genomics and metagenomics, we propose an automated approach, employing combinatorial concatenation of roots from Latin and Greek to create linguistically correct names for genera and species that can be used off the shelf as needed. As proof of principle, we document over a million new names for Bacteria and Archaea. We are confident that our approach provides a road map for how to create new names for decades to come. Microbiology has entered a golden era of discovery, with exponential growth in the identification of new species, genera, and high-level taxa through culturomics, genomics, and metagenomics. This creates an urgent unmet need for new taxonomic names for Archaea and Bacteria. Currently, creation of well-formed names relies on time-consuming nomenclatorial quality control by a dwindling pool of experts conversant with classical languages and the International Code of Nomenclature of Prokaryotes. These problems are compounded by the custom of creating names on an as-needed, just-in-time-fashion. Here, we outline a novel approach with three features: creation of names en masse before they are tied to taxa; combinatorial concatenation of roots from Latin and Greek, drawing on stocks of roots with relevant meanings; computerised automation of the creation of new names. [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]

Virtually every biological system is governed by complex relations among its components. Identifying such relations requires a rigorous or heuristics-based search for patterns among variables/features of a system. Various algorithms have been developed to identify two-dimensional (involving two variables) patterns employing correlation, covariation, mutual information, etc. It seems obvious, however, that comprehensive descriptions of complex biological systems need also to include more complicated multivariable relations, which can only be described using patterns that simultaneously embrace 3, 4, and more variables. The goal of this manuscript is to (a) introduce a novel type of associations (multivariable Boolean patterns) that can be manifested between features of complex systems but cannot be identified (described) by traditional pair-vise metrics; (b) propose patterns classification method, and (c) provide a novel definition of the pattern's strength (pattern's score) able to accommodate heterogeneous multi-omics data. To demonstrate the presence of such patterns, we performed a search for all possible 2-, 3-, and 4-dimensional patterns in historical data from the Human Microbiome Project (15 body sites) and collection of H. pylori genomes associated with gastric ulcers, gastritis, and duodenal ulcers. In all datasets under consideration, we were able to identify hundreds of statistically significant multivariable patterns. These results suggest that such patterns can be common in microbial genomics/microbiomics systems. [ABSTRACT FROM AUTHOR]

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

This review follows on from the International Conference on One Health Antimicrobial Resistance (ICOHAR 2019), where strategies to improve the fundamental understanding and management of antimicrobial resistance at the interface between humans, animals and the environment were discussed. This review identifies alternatives to antimicrobials in a One Health context, noting how advances in genomic technologies are assisting their development and enabling more targeted use of antimicrobials. Key articles on the use of microbiota modulation, livestock breeding and gene editing, vaccination, antivirulence strategies and bacteriophage therapy are discussed. Antimicrobials are central for disease control, but reducing their use is paramount as a result of the rise of transmissible antimicrobial resistance. This review discusses antimicrobial alternatives in the context of improved understanding of fundamental host–pathogen and microbiota interactions using genomic tools. Host and microbial genomics and other novel technologies play an important role in devising disease control strategies for healthier animals and humans that in turn reduce our reliance on antimicrobials. Summary of alternatives to antibiotics in One Health context. Image 1 [ABSTRACT FROM AUTHOR]

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]

Next-generation-sequencing (NGS) is being increasingly used and implemented at clinical microbiology and reference laboratories, heavily relying on bioinformatics for data processing, analysis, interpretation, and communication. Professionals with various expertise (infectious diseases, epidemiology, microbiology, genomics, bioinformatics) therefore need to work hand-in-hand and develop a common language to ensure efficient and critical use of genomics and metagenomics. Finally, A Lebrand stressed the importance of using a different pipeline according to the objective, whether using NGS data to assemble a genome (genome assembly), to type strains (typing), to perform taxonomic assignment (metagenomics), to predict resistance-encoding genes (resistome) or to predict genes implicated in virulence (virulome). Claire Bertelli (Lausanne, Switzerland), Head of bioinformatics at the Genomics and metagenomics Laboratory of the Institute of Microbiology of the University of Lausanne, did an overview of the bioinformatics approaches used to predict resistance and virulence. [Extracted from the article]

An important goal in microbial computational genomics is to identify crucial events in the evolution of a gene that severely alter the duplication, loss, and mobilization patterns of the gene within the genomes in which it disseminates. In this article, we formalize this microbiological goal as a new pattern-matching problem in the domain of gene tree and species tree reconciliation, denoted "Reconciliation-Scenario Altering Mutation (RSAM) Discovery." We propose an O (m ⋅ n ⋅ k) time algorithm to solve this new problem, wheremandnare the number of vertices of the input gene tree and species tree, respectively, andkis a user-specified parameter that bounds from above the number of optimal solutions of interest. The algorithm first constructs a hypergraph representing thekhighest scoring reconciliation scenarios between the given gene tree and species tree, and then interrogates this hypergraph for subtrees matching a prespecified RSAM pattern. Our algorithm is optimal in the sense that the number of hypernodes in the hypergraph can be lower bounded by Ω (m ⋅ n ⋅ k). We implement the new algorithm as a tool, called RSAM-finder, and demonstrate its application to the identification of RSAMs in toxins and drug resistance elements across a data set spanning hundreds of species. [ABSTRACT FROM AUTHOR]

Populations are fundamental units of ecology and evolution, but can we define them for bacteria and archaea in a biologically meaningful way? Here, we review why population structure is difficult to recognize in microbes and how recent advances in measuring contemporary gene flow allow us to identify clearly delineated populations among collections of closely related genomes. Such structure can arise from preferential gene flow caused by coexistence and genetic similarity, defining populations based on biological mechanisms. We show that such gene flow units are sufficiently genetically isolated for specific adaptations to spread, making them also ecological units that are differentially adapted compared to their closest relatives. We discuss the implications of these observations for measuring bacterial and archaeal diversity in the environment. We show that operational taxonomic units defined by 16S rRNA gene sequencing have woefully poor resolution for ecologically defined populations and propose monophyletic clusters of nearly identical ribosomal protein genes as an alternative measure for population mapping in community ecological studies employing metagenomics. These population-based approaches have the potential to provide much-needed clarity in interpreting the vast microbial diversity in human and environmental microbiomes. [ABSTRACT FROM AUTHOR]

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

Anstract: Background: The recent development of metagenomic sequencing makes it possible to massively sequence microbial genomes including viral genomes without the need for laboratory culture. Existing reference-based and gene homology-based methods are not efficient in identifying unknown viruses or short viral sequences from metagenomic data. Methods: Here we developed a reference-free and alignment-free machine learning method, DeepVirFinder, for identifying viral sequences in metagenomic data using deep learning. Results: Trained based on sequences from viral RefSeq discovered before May 2015, and evaluated on those discovered after that date, DeepVirFinder outperformed the state-of-the-art method VirFinder at all contig lengths, achieving AUROC 0.93, 0.95, 0.97, and 0.98 for 300, 500, 1000, and 3000 bp sequences respectively. Enlarging the training data with additional millions of purified viral sequences from metavirome samples further improved the accuracy for identifying virus groups that are under-represented. Applying DeepVirFinder to real human gut metagenomic samples, we identified 51,138 viral sequences belonging to 175 bins in patients with colorectal carcinoma (CRC). Ten bins were found associated with the cancer status, suggesting viruses may play important roles in CRC. Conclusions: Powered by deep learning and high throughput sequencing metagenomic data, DeepVirFinder significantly improved the accuracy of viral identification and will assist the study of viruses in the era of metagenomics. [ABSTRACT FROM AUTHOR]

Humans are exposed to potentially harmful amounts of the neurotoxin monomethylmercury (MMHg) through consumption of marine fish and mammals. However, the pathways of MMHg production and bioaccumulation in the ocean remain elusive. In anaerobic environments, inorganic mercury (Hg) can be methylated to MMHg through an enzymatic pathway involving the hgcAB gene cluster. Recently, hgcA‐like genes have been discovered in oxygenated marine water, suggesting the hgcAB methylation pathway, or a close analog, may also be relevant in the ocean. Using polymerase chain reaction amplification and shotgun metagenomics, we searched for but did not find the hgcAB gene cluster in Arctic Ocean seawater. However, we detected Hg‐cycling genes from the mer operon (including organomercury lyase, merB), and hgcA‐like paralogs (i.e., cdhD) in Arctic Ocean metagenomes. Our analysis of Hg biogeochemistry and marine microbial genomics suggests that various microorganisms and metabolisms, and not just the hgcAB pathway, are important for Hg methylation in the ocean. [ABSTRACT FROM AUTHOR]

Biological, ecological, social, and technological systems are complex structures with multiple interacting parts, often represented by networks. Correlation matrices describing interdependency of the variables in such structures provide key information for comparison and classification of such systems. Classification based on correlation matrices could supplement or improve classification based on variable values, since the former reveals similarities in system structures, while the latter relies on the similarities in system states. Importantly, this approach of clustering correlation matrices is different from clustering elements of the correlation matrices, because our goal is to compare and cluster multiple networks–not the nodes within the networks. A novel approach for clustering correlation matrices, named "snakes-&-dragons," is introduced and illustrated by examples from neuroscience, human microbiome, and macroeconomics. [ABSTRACT FROM AUTHOR]

Early clinical successes are driving enthusiasm for fecal microbiota transplantation (FMT), the transfer of healthy gut bacteria through whole stool, as emerging research is linking the microbiome to many different diseases. However, preliminary trials have yielded mixed results and suggest that heterogeneity in donor stool may play a role in patient response. Thus, clinical trials may fail because an ineffective donor was chosen rather than because FMT is not appropriate for the indication. Here, we describe a conceptual framework to guide rational donor selection to increase the likelihood that FMT clinical trials will succeed. We argue that the mechanism by which the microbiome is hypothesized to be associated with a given indication should inform how healthy donors are selected for FMT trials, categorizing these mechanisms into four disease models and presenting associated donor selection strategies. We next walk through examples based on previously published FMT trials and ongoing investigations to illustrate how donor selection might occur in practice. Finally, we show that typical FMT trials are not powered to discover individual taxa mediating patient responses, suggesting that clinicians should develop targeted hypotheses for retrospective analyses and design their clinical trials accordingly. Moving forward, developing and applying novel clinical trial design methodologies like rational donor selection will be necessary to ensure that FMT successfully translates into clinical impact. [ABSTRACT FROM AUTHOR]

There is growing awareness within the scientific community of the strong connection between the inflammation in the intestine and the pathogenesis of Parkinson's disease (PD). In previous studies we developed a PD animal model exposing pup rats to permethrin (PERM) pesticide. Here, we intended to explore whether in our animal model there were changes in gut permeability, fecal microbiota and hepatic injury. Moreover, we tested if the co-treatment with an electrolyzed reduced (ERW) was effective to protect against alterations induced by PERM. Rats (from postnatal day 6 to 21) were gavaged daily with PERM, PERM+ERW or vehicle and gut, liver and feces were analyzed in 2-months-old rats. Increased gut permeability, measured by FITC-dextran assay, was detected in PERM group compared to control and PERM+ERW groups. In duodenum and ileum, concentration of occludin was higher in control group than those measured in PERM group, whereas only in duodenum ZO-1 was higher in control than those measured in PERM and PERM+ERW groups. Number of inflammatory focis and neutrophils as well as iNOS protein levels were higher in livers of PERM-treated rats than in those of PERM+ERW and control rats. Fecal microbiota analysis revealed that Lachnospira was less abundant and Defluviitaleaceae more abundant in the PERM group, whereas the co-treatment with ERW was protective against PERM treatment since the abundances in Lachnospira and Defluviitaleaceae were similar to those in the control group. Higher abundances of butyrate- producing bacteria such as Blautia, U.m. of Lachnospiraceae family, U.m. of Ruminococcaceae family, Papillibacter, Roseburia, Intestinimonas, Shuttleworthia together with higher butyric acid levels were detected in PERM+ERW group compared to the other groups. In conclusion, the PD animal model showed increased intestinal permeability together with hepatic inflammation correlated with altered gut microbiota. The positive effects of ERW co-treatment observed in gut, liver and brain of rats were linked to changes on gut microbiota. [ABSTRACT FROM AUTHOR]

In order to gain an evolutionary perspective on host-parasite-microbe interactions, evolutionary studies encompassing microbes across host and parasite species are necessary to identify patterns of cospeciation and speciation following host shifts. With appropriate experimental systems, geographic variations affecting the role of microbes in host-parasite interactions can be assessed by using a complete cross-experimental design, in which hosts from different localities are infected with parasites from their corresponding localities in the presence of either microbes isolated from the same localities or microbes from different test localities. Finally, when possible, experimental evolution of parasites and hosts in the presence or absence of the identified microbes can been used to test the effect of specific microbes on the evolution of the system and to identify mechanisms involved in parasite-microbe interaction. Phase one will compile information on previously characterized parasite-associated microbes and parasite-microbe interactions (already partially reviewed in [[15]-[16], [53]]), mine genomic and transcriptomic databases to detect microbial sequences, and characterize the complete microbiome of a set of parasites representing diverse taxa and environments. [Extracted from the article]

The human gut microbiome can influence health through the brain-gut-microbiome axis. Growing evidence suggests that the gut microbiome can influence sleep quality. Previous studies that have examined sleep deprivation and the human gut microbiome have yielded conflicting results. A recent study found that sleep deprivation leads to changes in gut microbiome composition while a different study found that sleep deprivation does not lead to changes in gut microbiome. Accordingly, the relationship between sleep physiology and the gut microbiome remains unclear. To address this uncertainty, we used actigraphy to quantify sleep measures coupled with gut microbiome sampling to determine how the gut microbiome correlates with various measures of sleep physiology. We measured immune system biomarkers and carried out a neurobehavioral assessment as these variables might modify the relationship between sleep and gut microbiome composition. We found that total microbiome diversity was positively correlated with increased sleep efficiency and total sleep time, and was negatively correlated with wake after sleep onset. We found positive correlations between total microbiome diversity and interleukin-6, a cytokine previously noted for its effects on sleep. Analysis of microbiome composition revealed that within phyla richness of Bacteroidetes and Firmicutes were positively correlated with sleep efficiency, interleukin-6 concentrations and abstract thinking. Finally, we found that several taxa (Lachnospiraceae, Corynebacterium, and Blautia) were negatively correlated with sleep measures. Our findings initiate linkages between gut microbiome composition, sleep physiology, the immune system and cognition. They may lead to mechanisms to improve sleep through the manipulation of the gut microbiome. [ABSTRACT FROM AUTHOR]

Antimicrobial drug concentrations in the gastrointestinal tract likely drive antimicrobial resistance in enteric bacteria. Our objective was to determine the concentration of ceftiofur and its metabolites in the gastrointestinal tract of steers treated with ceftiofur crystalline-free acid (CCFA) or ceftiofur hydrochloride (CHCL), determine the effect of these drugs on the minimum inhibitory concentration (MIC) of fecal Escherichia coli, and evaluate shifts in the microbiome. Steers were administered either a single dose (6.6 mg/kg) of CCFA or 2.2 mg/kg of CHCL every 24 hours for 3 days. Ceftiofur and its metabolites were measured in the plasma, interstitium, ileum and colon. The concentration and MIC of fecal E. coli and the fecal microbiota composition were assessed after treatment. The maximum concentration of ceftiofur was higher in all sampled locations of steers treated with CHCL. Measurable drug persisted longer in the intestine of CCFA-treated steers. There was a significant decrease in E. coli concentration (P = 0.002) within 24 hours that persisted for 2 weeks after CCFA treatment. In CHCL-treated steers, the mean MIC of ceftiofur in E. coli peaked at 48 hours (mean MIC = 20.45 ug/ml, 95% CI = 10.29–40.63 ug/ml), and in CCFA-treated steers, mean MIC peaked at 96 hours (mean MIC = 10.68 ug/ml, 95% CI = 5.47–20.85 ug/ml). Shifts in the microbiome of steers in both groups were due to reductions in Firmicutes and increases in Bacteroidetes. CCFA leads to prolonged, low intestinal drug concentrations, and is associated with decreased E. coli concentration, an increased MIC of ceftiofur in E. coli at specific time points, and shifts in the fecal microbiota. CHCL led to higher intestinal drug concentrations over a shorter duration. Effects on E. coli concentration and the microbiome were smaller in this group, but the increase in the MIC of ceftiofur in fecal E. coli was similar. [ABSTRACT FROM AUTHOR]

Background: Evidence indicates that human circadian rhythm is affected by the intestinal microbiota, and establishment of the circadian rhythm begins during fetal development. However, the relationship between maternal fermented food intake and infant sleep duration has not been previously investigated. In this study, we examined whether dietary consumption of fermented food during pregnancy is associated with infant sleep duration at 1 year of age. Methods: This birth cohort study used data from a nationwide government-funded study called The Japan Environment and Children’s Study (JECS). After exclusions from a dataset comprising 104,065 JECS records, we evaluated 72,624 mother-child pairs where the child was 1 year old. We investigated the association between dietary intake of fermented foods during pregnancy and infant sleep duration of less than 11 h at 1 year of age. Results: Multivariable logistic regression showed that maternal intake of fermented food, especially miso, during the pregnancy was independently associated with reduced risk of infant sleep duration of less than 11 h. Conclusions: Further research, including interventional studies, is warranted to confirm the association between consumption of fermented foods during pregnancy and sufficient infant sleep duration. Trial registration: . [ABSTRACT FROM AUTHOR]

Liquid biopsy of plasma is a simple and non-invasive technology that holds great promise in biomedical research. It is based on the analysis of nucleic acid-based biomarkers with predictive potential. In the present work, we have combined this concept with the FTA technology for sentinel mussels. We found that hemocytes collected from liquid biopsies can be readily fixed on FTA cards and used for long-term transcriptome analysis. We also showed that liquid biopsy is easily adaptable for metagenomic analysis of bacterial profiles of mussels. We finally provide evidence that liquid biopsies contained circulating cell-free DNA (ccfDNA) which can be used as an easily accessible genomic reservoir. Sampling of FTA-fixed circulating nucleic acids is stable at room temperature and does not necessitate a cold-chain protection. It showed comparable performance to frozen samples and is ideally adapted for sampling in remote areas, most notably in polar regions threatened by anthropogenic activities. From an ethical point of view, this minimally-invasive and non-lethal approach further reduces incidental mortality associated with conventional tissue sampling. This liquid biopsy-based approach should thus facilitate biobanking activities and development of omics-based biomarkers in mussels to assess the quality of aquatic ecosystems. [ABSTRACT FROM AUTHOR]