Search

Dental calculus and other preserved microbiome substrates are an attractive target for dietary reconstruction in past populations through a variety of physical, chemical, and molecular means. Recently, studies have attempted to reconstruct diet from archaeological dental calculus using archaeogenetic techniques. While dental calculus may provide a relatively stable environment for DNA preservation, the detection of plants and animals possibly consumed by an individual through DNA analysis is primarily hindered by microbial richness and incomplete reference databases. Moreover, high genomic similarity within eukaryotic groups 1. Introduction 2. Materials and methods 3. Results 3.1. Effects of low read counts on damage patterns 3.2. Identification of dietary reads from synthetic datasets 3.3. Identification of dietary reads from real datasets 4. Discussion and conclusions 4.1. Checklist for authors and reviewers 4.1.1. Field/storage contamination 4.1.2. Is the laboratory methodology suitable for aDNA authentication? 4.1.3. Is the database and pipeline suitable? 4.1.4. Is the taxonomic resolution analyzed sufficient for reliable interpretation? 4.1.5. Have unexpected taxa been reported and evaluated? 4.1.6. Have laboratory controls been evaluated alongside samples? 4.1.7. Has evidence of aDNA authenticity been provided? 4.1.8. Is there other evidence supporting that dietary organisms were consumed?

10 pages, 2 figures, 3 boxes, Ocean microbes are fundamental for the functioning of the Earth system. Yet, our understanding of how they are reacting to global change in terms of evolution is limited. Microbes typically grow in large populations and reproduce quickly, which may allow them to rapidly adapt to environmental stressors compared to larger organisms. However, genetic evidence of contemporary evolution in wild microbes is scarce. We must begin coordinated efforts to establish new microbial time-series and explore novel tools, experiments and data to fill this knowledge gap. The development of coordinated microbial “genomic” observatories will provide the unprecedented opportunity to track contemporary microbial evolution in the ocean and explore the role of evolution in enabling wild microbes to respond to global change, GB received funding from the European Union's Horizon 2020 research 434 and innovation program under the Marie Skłodowska-Curie grant agreement no. 895338. This work was supported by the project MINIME (PID2019-105775RB-I00, AEI, Spain) to RL as well as funding from the Spanish government through the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

Grapevines that are used for winemaking host a diverse range of microorganisms that make up their microbiome. The microbes that inhabit the grapevine have been used by winemakers to produce wine for centuries, although modern wine producers often rely on inoculated microorganisms such as Saccharomyces cerevisiae. In the Australian wine industry, there is a movement towards returning to the utilisation of the microbiome for wine fermentation. With the recent increase in the understanding of the role of the grapevine microbiome in grapevine health, fermentation and subsequent wine sensory traits, the microbial world offers a new level of complexity that can be harnessed for winemaking. In order to develop and maintain a desired vineyard micro-biodiversity, extensive microbial monitoring is required. Here we discuss the utilisation of a viability selection dye in order to distinguish between microorganisms that are live and associated with the host, and relic signals generated from non-living sources.

Although it is clinically important for acute respiratory tract (co)infections to have rapid and accurate diagnosis, it is critical that respiratory medicine understand the advantages of current laboratory methods. In this study we tested nasopharyngeal samples (n=29) with a commercially available PCR assay and compared the results with hybridization capture-based mNGS workflow. Detection criteria for positive PCR samples was Ct40% target coverage, median depth of 1X and RPKM>10. A high degree of concordance (98.33% PPA and 100% NPA) was recorded. However, mNGS yielded positive 29 additional microorganisms (23 bacteria, 4 viruses, and 2 fungi) beyond PCR. We then characterize the microorganisms of each method into three phenotypic categories using the IDbyDNA Explify® Platform for consideration of infectivity and trafficking potential to the lower respiratory region. The findings are significant for providing a comprehensive yet clinically relevant microbiology profile of acute upper respiratory infection, especially important in immunocompetent respiratory cases or where traditional syndromic approaches fail to identify pathogenicity. Accordingly, this technology can be used to supplement current syndromic-based tests and data can quickly and effectively be phenotypically characterized for trafficking potential, clinical (co)infection, and comorbid consideration—with promise to reduce morbidity and mortality.HighlightsWhat are the main findings?Although there was a high concordance between methodologies, a hybridization capture-based mNGS workflow was able to detect 29 additional upper respiratory microorganisms versus PCR.Identified microorganisms were rapidly characterized into three phenotypic groups for infectivity and trafficking potential.What is the implication of the main finding?A hybridization capture-based mNGS workflow can provide a comprehensive yet clinically relevant microbiology profile of acute upper respiratory infectionDeciphering upper respiratory microbiota with phenotypic grouping has potential to provide respiratory medicine a tool to better manage immunocompetent and complex respiratory cases.

Introduction: Invading extended-spectrum beta-lactamase-producing Escherichia coli (ESBL-PE), non-ESBL E. coli, and other bacteria form a complex environment in the gut. The duration and dynamics of ESBL-PE colonization varies among individuals. Understanding the factors associated with colonization may lead to decolonization strategies. In this study, we aim to identify (i) single E. coli strains and (ii) microbiome networks that correlate with retention or decline of colonization, and (iii) pan-sensitive E. coli strains that potentially could be used to displace ESBL-PE during colonization. Methods and analysis: We recruit healthy travellers to Southeast Asia for a one-year prospective observational follow-up study. We collect and biobank stool, serum, and peripheral blood mononuclear cells (PBMCs) at predefined timepoints. Additional information is collected with questionnaires. We determine the colonization status with ESBL-PE and non-ESBL E. coli and quantify cell densities in stools and ratios over time. We characterize multiple single bacterial isolates per patient and timepoint using whole genome sequencing (WGS) and 16S/ITS amplicon-based and shotgun metagenomics. We determine phylogenetic relationships between isolates, antimicrobial resistance (AMR; phenotypic and genotypic), and virulence genes. We describe the bacterial and fungal stool microbiome alpha and beta diversity on 16S/ITS metagenomic data. We describe patterns in microbiome dynamics to identify features associated with protection or risk of ESBL-PE colonization. Ethics and dissemination: The study is registered (clinicaltrials.gov; NCT04764500 on 09/02/2019) and approved by the Ethics Committee (EKNZ project ID 2019-00044). We will present anonymized results at conferences and in scientific journals. Bacterial sequencing data will be shared via publicly accessible databases according to FAIR principles., MICROBIOLOGY RESEARCH, 14 (1)

Objectives The aim of this study is to evaluate the effectiveness of a CRISPR-based human and bacterial ribosomal RNA (rRNA) depletion kit (JUMPCODE Genomics) on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shotgun metagenomic sequencing in weakly positive respiratory samples. Methods Shotgun metagenomics was performed on 40 respiratory specimens collected from solid organ transplant patients and deceased intensive care unit patients at UCLA Medical Center in late 2020 to early 2021. Human and bacterial rRNA depletion was performed on remnant library pools prior to sequencing by Illumina MiSeq. Data quality was analyzed using Geneious Prime, whereas the identification of SARS-CoV-2 variants and lineages was determined by Pangolin. Results The average genome coverage of the rRNA-depleted respiratory specimens increased from 72.55% to 93.71% in overall samples and from 29.3% to 83.3% in 15 samples that failed to achieve sufficient genome coverage using the standard method. Moreover, rRNA depletion enhanced genome coverage to over 85% in 11 (73.3%) of 15 low viral load samples with cycle threshold values up to 35, resulting in the identification of genotypes. Conclusion The CRISPR-based human and bacterial rRNA depletion enhanced the sensitivity of SARS-CoV-2 shotgun metagenomic sequencing, especially in low viral load samples.

Antarctica and the Arctic are the coldest places, containing a high diversity of microorganisms, including viruses, which are important components of polar ecosystems. However, owing to the difficulties in obtaining access to animal and environmental samples, the current knowledge of viromes in polar regions is still limited. To better understand polar viromes, this study performed a retrospective analysis using metagenomic sequencing data of animal feces from Antarctica and frozen soil from the Arctic collected during 2012-2014. The results reveal diverse communities of DNA and RNA viruses from at least 23 families from Antarctic animal feces and 16 families from Arctic soils. Although the viral communities from Antarctica and the Arctic show a large diversity, they have genetic similarities with known viruses from different ecosystems and organisms with similar viral proteins. Phylogenetic analysis of Microviridae, Parvoviridae, and Larvidaviridae was further performed, and complete genomic sequences of two novel circular replication-associated protein (rep)-encoding single-stranded (CRESS) DNA viruses closely related to Circoviridae were identified. These results reveal the high diversity, complexity, and novelty of viral communities from polar regions, and suggested the genetic similarity and functional correlations of viromes between the Antarctica and Arctic. Variations in viral families in Arctic soils, Arctic freshwater, and Antarctic soils are discussed. These findings improve our understanding of polar viromes and suggest the importance of performing follow-up in-depth investigations of animal and environmental samples from Antarctica and the Arctic, which would reveal the substantial role of these viruses in the global viral community.

Pangolins are the only animals other than bats proposed to have been infected with SARS-CoV-2related coronaviruses (SARS2r-CoVs) prior to the COVID-19 pandemic. Here we examine thenovel SARS2r-CoV we previously identified in game animal metatranscriptomic datasetssequenced by He et al. (2022) and find that sections of the partial genome phylogenetically groupwith Guangxi (GX) pangolin CoVs (GX PCoVs), while the full RdRp sequence groups with bat-SL-CoVZC45. While the novel SARS2r-CoV is found in 6 pangolin datasets, the same CoV isalso found in 10 additional NGS datasets from 5 separate mammalian species and is likelyrelated to contamination by a laboratory researched virus. Absence of bat mitochondrialsequences from the datasets, the fragmentary nature of the virus sequence and the presence of apartial sequence of a cloning vector attached to a SARS2r-CoV read suggests that it has beencloned. We find that NGS datasets containing the novel SARS2r-CoV are contaminated withsignificant Homo sapiens genetic material, and numerous viruses not associated with the hostanimals sampled. We further identify the dominant human haplogroup of the contaminatingH.sapiens genetic material to be F1c1a1, which is of East Asian provenance. The association ofthis novel CoV with both bat CoV and the Guangxi pangolin CoV (GX PCoV) clades is animportant step towards identifying the origin of the GX PCoVs.

Metagenomic next-generation sequencing (mNGS) is increasingly used for the clinical diagnosis of infectious diseases, but there is a paucity of data regarding the application of mNGS in the early diagnosis of infected pancreatic necrosis (IPN).To investigate the clinical application value of mNGS in the pathogenic diagnosis of IPN.Forty-two patients with suspected IPN were prospectively and consecutively enrolled from August 2019 to August 2021. Blood samples were collected for mNGS and microbial culture simultaneously during fever (T ≥ 38.5 °C). For patients who had indications of surgical interventions, peri-pancreatic specimens were collected for mNGS and microbial culture simultaneously during the first surgical intervention to confirm IPN. The clinical performance of mNGS and microbial culture were compared.A total of 21 patients (50.0%) were confirmed to have IPN during hospitalization. The sensitivity of blood mNGS was significantly higher than blood culture (95.2% vs. 23.8%, P 0.001) in diagnosing IPN. The negative predictive value of blood mNGS was 90.0%. The turnaround time of mNGS was significantly shorter than that of microbial culture [(37.70 ± 1.44) vs. (115.23 ± 8.79) h, P 0.01] and the average costs of mNGS accounted for 1.7% of the average total cost of hospitalization. The survival analysis demonstrates that the positive blood mNGS result was not associated with increased mortality (P = 0.119).With more valuable diagnostic performance and shorter turnaround time, clinical mNGS represents a potential step forward in the early diagnosis of IPN.

Gut microbes (GMs), dominated by bacteria, play important roles in many physiological processes. The structures and functions of GMs are closely related to human health, the occurrence and development of diseases and the rapid recovery of the body. Gastrointestinal cancers are the major diseases affecting human health worldwide. With the development of metagenomic technology and the wide application of new generation sequencing technology, a large number of studies suggest that complex GMs are related to the occurrence and development of gastrointestinal cancers. Fecal microbiota transplantation (FMT) and probiotics can treat and prevent the occurrence of gastrointestinal cancers. This article reviews the latest research progress of microbes in gastrointestinal cancers from the perspectives of the correlation, the influence mechanism and the application, so as to provide new directions for the prevention, early diagnosis and treatment of gastrointestinal cancers.

Motivation Recovery of metagenome-assembled genomes (MAGs) from shotgun metagenomic data is an important task for the comprehensive analysis of microbial communities from variable sources. Single binning tools differ in their ability to leverage specific aspects in MAG reconstruction, the use of ensemble binning refinement tools is often time consuming and computational demand increases with community complexity. We introduce MAGScoT, a fast, lightweight and accurate implementation for the reconstruction of highest-quality MAGs from the output of multiple genome-binning tools. Results MAGScoT outperforms popular bin-refinement solutions in terms of quality and quantity of MAGs as well as computation time and resource consumption. Availability and implementation MAGScoT is available via GitHub (https://github.com/ikmb/MAGScoT) and as an easy-to-use Docker container (https://hub.docker.com/repository/docker/ikmb/magscot). Supplementary information Supplementary data are available at Bioinformatics online.

Metagenomic data generated from environmental samples is increasingly common in the analysis of modern and ancient biological communities. To obtain taxonomic profiles from this type of data, DNA sequences are aligned against large genomic reference databases and the lowest common ancestor (LCA) needs to be inferred for each sequence with multiple alignments. To date, efforts have mainly focused on improving the speed, sensitivity and specificity of alignment tools, and little effort has been applied to the LCA algorithm that generates the taxonomic profiles from alignments. We present ngsLCA, a command-line toolkit with two separate modules: the main program (in C/C++) performing LCA inference, and an R package for generating tables and visualisations of the taxonomic profiles. ngsLCA processed large datasets in BAM/SAM alignment format 4–11 times faster and used less memory compared to other available programs. It is compatible with the NCBI taxonomy and has flexible parameter settings. Furthermore, the toolkit offers functions for filtering, contamination removal, taxonomic clustering, and multiple ways of visualising the generated taxonomic profiles. ngsLCA bridges a gap in current metagenomic analyses by supplying a computationally light, easy-to-use, accurate, fast and flexible LCA algorithm with R functions for processing and illustrating the taxonomic profiles.

Reduced availability of agricultural water has spurred increased interest in using recycled irrigation water for U.S. food crop production. However, there are significant knowledge gaps concerning the microbiological quality of these water sources. To address these gaps, we used 16S rRNA gene and metagenomic sequencing to characterize taxonomic and functional variations (e.g., antimicrobial resistance) in bacterial communities across diverse recycled and surface water irrigation sources. We collected 1 L water samples (n = 410) between 2016 and 2018 from the Mid-Atlantic (12 sites) and Southwest (10 sites) U.S. Samples were filtered, and DNA was extracted. The V3-V4 regions of the 16S rRNA gene were then PCR amplified and sequenced. Metagenomic sequencing was also performed to characterize antibiotic, metal, and biocide resistance genes. Bacterial alpha and beta diversities were significantly different (p < 0.001) across water types and seasons. Pathogenic bacteria, such as Salmonella enterica, Staphylococcus aureus, and Aeromonas hydrophilia were observed across sample types. The most common antibiotic resistance genes identified coded against macrolides/lincosamides/streptogramins, aminoglycosides, rifampin and elfamycins, and their read counts fluctuated across seasons. We also observed multi-metal and multi-biocide resistance across all water types. To our knowledge, this is the most comprehensive longitudinal study to date of U.S. recycled water and surface water used for irrigation. Our findings improve understanding of the potential differences in the risk of exposure to bacterial pathogens and antibiotic resistance genes originating from diverse irrigation water sources across seasons and U.S. regions.

Medication is a major determinant of human gut microbiome structure, and its overuse increases the risks of morbidity and mortality. However, effects of certain commonly prescribed drugs and multiple medications on the gut microbiome are still underinvestigated.We performed shotgun metagenomic analysis of fecal samples from 4198 individuals in the Japanese 4D (Disease, Drug, Diet, Daily life) microbiome project. A total of 759 drugs were profiled, and other metadata, such as anthropometrics, lifestyles, diets, physical activities, and diseases, were prospectively collected. Second fecal samples were collected from 243 individuals to assess the effects of drug initiation and discontinuation on the microbiome.We found that numerous drugs across different treatment categories influence the microbiome; more than 70% of the drugs we profiled had not been examined before. Individuals exposed to multiple drugs, polypharmacy, showed distinct gut microbiome structures harboring significantly more abundant upper gastrointestinal species and several nosocomial pathobionts due to additive drug effects. Polypharmacy was also associated with microbial functions, including the reduction of short-chain fatty acid metabolism and increased bacterial stress responses. Even nonantibiotic drugs were significantly correlated with an increased antimicrobial resistance potential through polypharmacy. Notably, a 2-time points dataset revealed the alteration and recovery of the microbiome in response to drug initiation and cessation, corroborating the observed drug-microbe associations in the cross-sectional cohort.Our large-scale metagenomics unravels extensive and disruptive impacts of individual and multiple drug exposures on the human gut microbiome, providing a drug-microbe catalog as a basis for a deeper understanding of the role of the microbiome in drug efficacy and toxicity.

Viruses of bacteria (bacteriophages or phage) have broad effects on bacterial ecology and evolution in nature that mediate microbial interactions, shape bacterial diversity, and influence nutrient cycling and ecosystem function. The unrelenting impact of phages within the microbial realm is the result, in large part, of their ability to rapidly evolve in response to bacterial host dynamics. The knowledge gained from laboratory systems, typically using pairwise interactions between single-host and single-phage systems, has made clear that phages coevolve with their bacterial hosts rapidly, somewhat predictably, and primarily by counteradapting to host resistance. Recent advancement in metagenomics approaches, as well as a shifting focus toward natural microbial communities and host-associated microbiomes, is beginning to uncover the full picture of phage evolution and ecology within more complex settings. As these data reach their full potential, it will be critical to ask when and how insights gained from studies of phage evolution in vitro can be meaningfully applied to understanding bacteria-phage interactions in nature. In this review, we explore the myriad ways that phagesshape and are themselves shaped by bacterial host populations and communities, with a particular focus on observed and predicted differences between the laboratory and complex microbial communities.

Plastic pollution exacerbated by the excessive use of synthetic plastics and its recalcitrance has been recognized among the most pressing global threats. Microbial degradation of plastics has gained attention as a possible eco-friendly countermeasure, as several studies have shown microbial metabolic capabilities as potential degraders of various synthetic plastics. However, still defined biochemical mechanisms of biodegradation for the most plastics remain elusive, because the widely used culture-dependent approach can access only a very limited amount of the metabolic potential in each microbiome. A culture-independent approach, including metagenomics, is becoming increasingly important in the mining of novel plastic-degrading enzymes, considering its more expanded coverage on the microbial metabolism in microbiomes. Here, we described the advantages and drawbacks associated with four different metagenomics approaches (microbial community analysis, functional metagenomics, targeted gene sequencing, and whole metagenome sequencing) for the mining of plastic-degrading microorganisms and enzymes from the plastisphere. Among these approaches, whole metagenome sequencing has been recognized among the most powerful tools that allow researchers access to the entire metabolic potential of a microbiome. Accordingly, we suggest strategies that will help to identify plastisphere-enriched sequences as de novo plastic-degrading enzymes using the whole metagenome sequencing approach. We anticipate that new strategies for metagenomics approaches will continue to be developed and facilitate to identify novel plastic-degrading microorganisms and enzymes from microbiomes.

Electroautotrophic microorganisms have attracted great attention since they exhibit a new type of primary production. Here, in situ electrochemical cultivation was conducted using the naturally occurring electromotive forces at a deep-sea hydrothermal vent. The voltage and current generation originating from the resulting microbial activity was observed for 12 days of deployment, with fluctuation in response to tidal cycles. A novel bacterium belonging to the genus Thiomicrorhabdus dominated the microbial community specifically enriched on the cathode. Metagenomic analysis provided the draft genome of the bacterium and the gene repertoire indicated that the bacterium has the potential for thio-autotrophic growth, which is a typical physiological feature of the members of the genus, while the bacterium had a unique gene cluster encoding multi-heme cytochrome c proteins responsible for extracellular electron transfer. Herein, we propose this bacterium as a new species, specifically enriched during electricity generation, as ‘Candidatus Thiomicrorhabdus electrophagus’. This finding suggests the natural occurrence of electrosynthetic microbial populations using the geoelectricity in deep-sea hydrothermal environments.

This study aimed to evaluate the clinical value of metagenomic next-generation sequencing (mNGS) in patients with suspected lower respiratory tract infections.This retrospective study reviewed patients with suspected lower respiratory tract infections at the Wuhan Union Hospital. Data including demographic, laboratory, and radiological profiles; treatment; and outcomes were recorded and analyzed.mNGS identified pathogenic microbes in 100/140 (71.4%) patients, although 135 (96.4%) had received empiric antibiotic treatment before the mNGS tests. Single bacterial infection (35/100, 35%) was the most common type of infection in patients with positive mNGS results, followed by single fungal infection (14/100, 14%), bacterial-viral coinfection (14/100, 14%), single viral infection (12/100, 12%), bacterial-fungal coinfection (9/100, 9%), fungal-viral coinfection (9/100, 9%), and bacterial-fungal-viral coinfection (7/100, 7%). Moreover, compared with culture test, mNGS showed higher sensitivity (63/85, 74.1% vs 22/85, 25.9% P = 0.001) and lower processing time (24 hours vs 48 hours). Antibiotic treatment was adjusted or confirmed based on the mNGS results in 123 (87.9%) patients, including five (3.6%), 33 (23.6%), and 85 (60.7%) patients, in whom treatment was downgraded, upgraded, and unchanged, respectively. Almost all patients, regardless of escalation, de-escalation, or no change in treatment, showed significant improvement in clinical symptoms and inflammatory indicators. In addition, 17 (12.1%) patients were referred to Wuhan Pulmonary Hospital for further treatment because of confirmed or suspected tuberculosis.mNGS could be a promising technique for microbiological diagnosis and antibiotic management, potentially improving outcomes for patients.

The diagnosis of bacterial infections continues to rely on culture, a slow process in which antibiotic susceptibility profiles of potential pathogens are made available to clinicians 48 hours after sampling, at best. Recently, clinical metagenomics, the metagenomic sequencing of samples with the purpose of identifying microorganisms and determining their susceptibility to antimicrobials, has emerged as a potential diagnostic tool that could prove faster than culture. Clinical metagenomics indeed has the potential to detect antibiotic resistance genes (ARGs) and mutations associated with resistance. Nevertheless, many challenges have yet to be overcome in order to make rapid phenotypic inference of antibiotic susceptibility from metagenomic data a reality.The objective of this narrative review is to discuss the challenges underlying the phenotypic inference of antibiotic susceptibility from metagenomic data.We conducted a narrative review using published articles available in the National Center for Biotechnology Information PubMed database.We review the current ARG databases with a specific emphasis on those which now provide associations with phenotypic data. Next, we discuss the bioinformatic tools designed to identify ARGs in metagenomes. We then report on the performance of phenotypic inference from genomic data and the issue predicting the expression of ARGs. Finally, we address the challenge of linking an ARG to this host.Significant improvements have recently been made in associating ARG and phenotype, and the inference of susceptibility from genomic data has been demonstrated in pathogenic bacteria such as Staphylococci and Enterobacterales. Resistance involving gene expression is more challenging however, and inferring susceptibility from species such as Pseudomonas aeruginosa remains difficult. Future research directions include the consideration of gene expression via RNA sequencing and machine learning.

Motivation Bacteriophages and plasmids usually coexist with their host bacteria in microbial communities and play important roles in microbial evolution. Accurately identifying sequence contigs as phages, plasmids and bacterial chromosomes in mixed metagenomic assemblies is critical for further unraveling their functions. Many classification tools have been developed for identifying either phages or plasmids in metagenomic assemblies. However, only two classifiers, PPR-Meta and viralVerify, were proposed to simultaneously identify phages and plasmids in mixed metagenomic assemblies. Due to the very high fraction of chromosome contigs in the assemblies, both tools achieve high precision in the classification of chromosomes but perform poorly in classifying phages and plasmids. Short contigs in these assemblies are often wrongly classified or classified as uncertain. Results Here we present 3CAC, a new three-class classifier that improves the precision of phage and plasmid classification. 3CAC starts with an initial three-class classification generated by existing classifiers and improves the classification of short contigs and contigs with low confidence classification by using proximity in the assembly graph. Evaluation on simulated metagenomes and on real human gut microbiome samples showed that 3CAC outperformed PPR-Meta and viralVerify in both precision and recall, and increased F1-score by 10–60 percentage points. Availability and implementation The 3CAC software is available on https://github.com/Shamir-Lab/3CAC. Supplementary information Supplementary data are available at Bioinformatics online.

Marine micro-organisms are a promising source for novel natural compounds with many medical and biotechnological applications. Here, we demonstrate limitations and recent strategies for investigating the marine microbial community for novel bioactive metabolites, specifically those of antimicrobial potential. These strategies include culture-dependent methods such as modifying the standard culture media, including changing the gelling agent, dissolving vehicle, media supplementation and preparation to access a broader range of bacterial diversity from marine samples. Furthermore, we discuss strategies such as in situ cultivation, dilution-to-extinction cultivation and long-term incubation. We are presenting recent applications of culture-independent methods such as genome mining, proteomics profiling and the application of metagenomics as a novel strategy for structure confirmation in the discovery of the marine micro-organism for novel antimicrobial metabolites. We present this review as a simple guide and a helpful resource for those who seek to enter the challenging field of applied marine microbiology.

Identifying pathogens in patients with pulmonary infection (PI) has always been a major challenge in medicine. Compared with sputum and throat swabs, bronchoalveolar lavage fluid (BALF) can better reflect the actual state of the lungs. However, there has not been a meta-analysis of the diagnostic efficacy of metagenomic next-generation sequencing (mNGS) in detecting pathogens in BALF from patients with PIs.Data sources were PubMed, Web of Science, Embase, and the China National Knowledge Infrastructure. The pooled sensitivity and specificity were estimated using random-effects or fixed-effect models. Subgroup analysis was performed to reveal the effect of potential explanatory factors on the diagnostic performance measures.The pooled sensitivity was 78% (95% confidence interval [CI]: 67-87%; ImNGS has high diagnostic performance for BALF pathogens in patients with PIs, especially in critically ill or immunocompromised patients.

Colorectal cancer (CRC) is the third leading cancer globally. Metagenomics has been widely used to analyze the association between the gut microbiota and CRC based on bacterial genus- or species-level comparisons, providing evidence of dysbiosis in CRC development. However, this kind of analysis is unable to provide strain-level information for understanding the individual role of a species in CRC. Here, we used culturomics to isolate CRC mucosal samples and selected 158 Escherichia coli strains to reveal their differences in both genomics and functions by means of phylogenetic analysis and inflammatory induction based on cell and animal experiments. Through genomic comparison, these strains were divided into five phylogroups. The representative strains of each phylogroup significantly induced different levels of cytokine secretion by human leukemic monocyte (THP-1 cell)-based Transwell and animal experiments. Further bioinformatic analysis revealed different profiles of single-nucleotide polymorphisms, genes, and metabolic pathways in the different phylogroups, which can improve the current understanding of the phenotypic differences between these strains. The strain differences revealed in both genomics and functions indicate that the microbiota’s function at the strain level should be investigated in order to understand the interacting mechanisms between hosts and gut bacteria.

The demand for novel, robust microbial biocatalysts for use in industrial and pharmaceutical applications continues to increase rapidly. As a result, there is a need to develop advanced tools and technologies to exploit the vast metabolic potential of unculturable microorganisms found in various environments. Single-cell and functional metagenomics studies can explore the enzymatic potential of entire microbial communities in a given environment without the need to culture the microorganisms. This approach has contributed substantially to the discovery of unique microbial genes for industrial and medical applications. Functional metagenomics involves the extraction of microbial DNA directly from environmental samples, constructing expression libraries comprising the entire microbial genome, and screening of the libraries for the presence of desired phenotypes. In this study, lipolytic enzymes from the Red Sea were targeted. A high-throughput single-cell microfluidic platform combined with a laser-based fluorescent screening bioassay was employed to discover new genes encoding lipolytic enzymes. Analysis of the metagenomic library led to the identification of three microbial genes encoding lipases based on their functional similarity and sequence homology to known lipases. The results demonstrated that microfluidics is a robust technology that can be used for screening in functional metagenomics. The results also indicate that the Red Sea is a promising, under-investigated source of new genes and gene products.

Geneticist Petri Auvinen and his research team are using DNA samples to find out what has been happening in the Baltic ecosystem during the past 10,000 years.

Stroke leads to gut bacterial dysbiosis that impacts the post-stroke outcome. The gut microbiome also contains a high abundance of viruses which might play a crucial role in disease progression and recovery by modulating the metabolism of both host and host’s gut bacteria. We presently analyzed the virome composition (viruses and phages) by shotgun metagenomics in the fecal samples obtained at 1 day of reperfusion following transient focal ischemia in adult mice. Viral genomes, viral auxiliary metabolic genes, and viral protein networks were compared between stroke and sham conditions (stroke vs sham, exclusive to sham and exclusive to stroke). Following focal ischemia, abundances of 2 viral taxa decreased, and 5 viral taxa increased compared with the sham. Furthermore, the abundance of Clostridia-like phages and Erysipelatoclostridiaceae-like phages were altered in the stroke compared with the sham cohorts. This is the first report to show that the gut virome responds acutely to stroke.

Motivation Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a ‘theater of activity’ (ToA). An important question is, to what degree does the taxonomic and functional content of the former depend on the (details of the) latter? Here, we investigate a related technical question: Given a taxonomic and/or functional profile estimated from metagenomic sequencing data, how to predict the associated ToA? We present a deep-learning approach to this question. We use both taxonomic and functional profiles as input. We apply node2vec to embed hierarchical taxonomic profiles into numerical vectors. We then perform dimension reduction using clustering, to address the sparseness of the taxonomic data and thus make the problem more amenable to deep-learning algorithms. Functional features are combined with textual descriptions of protein families or domains. We present an ensemble deep-learning framework DeepToA for predicting the ToA of amicrobial community, based on taxonomic and functional profiles. We use SHAP (SHapley Additive exPlanations) values to determine which taxonomic and functional features are important for the prediction. Results Based on 7560 metagenomic profiles downloaded from MGnify, classified into 10 different theaters of activity, we demonstrate that DeepToA has an accuracy of 98.30%. We show that adding textual information to functional features increases the accuracy. Availability and implementation Our approach is available at http://ab.inf.uni-tuebingen.de/software/deeptoa. Supplementary information Supplementary data are available at Bioinformatics online.

Isolating barriers that drive speciation are commonly studied in the context of two-species hybrid zones. There is however evidence that more complex introgressive relationships are common in nature. Here, we use field observations and genomic analysis, including the sequencing and assembly of a novel reference genome, to study an emergent hybrid zone involving two colliding hybrid zones of three woodpecker species: Red-breasted, Red-naped, and Yellow-bellied Sapsuckers (Sphyrapicus ruber, S. nuchalis, and S. varius). Surveys of the area surrounding Prince George, British Columbia, Canada, show that all three species are sympatric, and Genotyping-by-Sequencing identifies hybrids from each species pair and birds with ancestry from all three species. Observations of mate pair phenotypes and genotypes provide evidence for assortative mating, though there is some heterospecific pairing. Hybridization is more extensive in this tri-species hybrid zone than in two di-species hybrid zones. However, there is no evidence of a hybrid swarm and admixture is constrained to contact zones, so we classify this region as a tension zone and invoke selection against hybrids as a likely mechanism maintaining species boundaries. Analysis of sapsucker age classes does not show disadvantages in hybrid survival to adulthood, so we speculate the selection upholding the tension zone may involve hybrid fecundity. Gene flow among all sapsuckers in di-species hybrid zones suggests introgression likely occurred before the formation of this tri-species hybrid zone, and might result from bridge hybridization, vagrancies, or other three-species interactions.

Metagenomics and total RNA sequencing (total RNA-Seq) have the potential to improve the taxonomic identification of diverse microbial communities, which could allow for the incorporation of microbes into routine ecological assessments. However, these target-PCR-free techniques require more testing and optimization. In this study, we processed metagenomics and total RNA-Seq data from a commercially available microbial mock community using 672 data-processing workflows, identified the most accurate data-processing tools, and compared their microbial identification accuracy at equal and increasing sequencing depths. The accuracy of data-processing tools substantially varied among replicates. Total RNA-Seq was more accurate than metagenomics at equal sequencing depths and even at sequencing depths almost one order of magnitude lower than those of metagenomics. We show that while data-processing tools require further exploration, total RNA-Seq might be a favorable alternative to metagenomics for target-PCR-free taxonomic identifications of microbial communities and might enable a substantial reduction in sequencing costs while maintaining accuracy. This could be particularly an advantage for routine ecological assessments, which require cost-effective yet accurate methods, and might allow for the incorporation of microbes into ecological assessments.

Conventional bioprocess models for wastewater treatment are based on aggregated bulk biomass concentrations and do not incorporate microbial physiological diversity. Such a broad aggregation of microbial functional groups can fail to predict ecosystem dynamics when high levels of physiological diversity exist within trophic guilds. For instance, functional diversity among nitrite-oxidizing bacteria (NOB) can obfuscate engineering strategies for their out-selection in activated sludge (AS), which is desirable to promote energy-efficient nitrogen removal. Here, we hypothesized that different NOB populations within AS can have different physiological traits that drive process performance, which we tested by estimating biokinetic growth parameters using a combination of highly replicated respirometry, genome-resolved metagenomics, and process modeling. A lab-scale AS reactor subjected to a selective pressure for over 90 days experienced resilience of NOB activity. We recovered three coexisting

High bacterial community diversity and complexity greatly challenge the cost-efficient monitoring of clinically prevalent antibiotic-resistant bacteria, which are usually present as rare and important populations involved in the environmental dissemination of clinical resistance. Here, we introduce culture-enriched phenotypic metagenomics that integrates culture enrichment, phenotypic screening, and metagenomic analyses as an emerging standardized methodology for targeted resistome monitoring and apply it to decipher the extended-spectrum β-lactam resistome in a municipal wastewater treatment plant (WWTP) and its receiving river. The results showed that clinically prevalent carbapenemase genes (e.g., the NDM and KPC families) and extended-spectrum β-lactamase genes (e.g., the CTX-M, TEM, and OXA families) were prevalent in the WWTP and showed prominent potential in horizontal dissemination. Strikingly, carbapenem and polymyxin resistance genes co-occurred in the highly virulent nosocomial pathogens

The skeleton of reef-building coral harbors diverse microbial communities that could compensate for metabolic deficiencies caused by the loss of algal endosymbionts, i.e., coral bleaching. However, it is unknown to what extent endolith taxonomic diversity and functional potential might contribute to thermal resilience. Here we exposed Goniastrea edwardsi and Porites lutea, two common reef‐building corals from the central Red Sea to a 17-day long heat stress. Using hyperspectral imaging, marker gene/metagenomic sequencing, and NanoSIMS, we characterized their endolithic microbiomes together with 15N and 13C assimilation of two skeletal compartments: the endolithic band directly below the coral tissue and the deep skeleton. The bleaching-resistant G. edwardsi was associated with endolithic microbiomes of greater functional diversity and redundancy that exhibited lower N and C assimilation than endoliths in the bleaching-sensitive P. lutea. We propose that the lower endolithic primary productivity in G. edwardsi can be attributed to the dominance of chemolithotrophs. Lower primary production within the skeleton may prevent unbalanced nutrient fluxes to coral tissues under heat stress, thereby preserving nutrient-limiting conditions characteristic of a stable coral-algal symbiosis. Our findings link coral endolithic microbiome structure and function to bleaching susceptibility, providing new avenues for understanding and eventually mitigating reef loss.

Since the number of studies of the microbial communities related to food and food-associated matrices almost completely reliant on next-generation sequencing techniques is rising, evaluations of these high-throughput methods are critical. Currently, the two most used sequencing methods to profile the microbiota of complex samples, including food and food-related matrices, are the 16S ribosomal RNA (rRNA) metabarcoding and the whole metagenome sequencing (WMS), both of which are powerful tools for the monitoring of foodborne pathogens and the investigation of the microbiome. Herein, the microbial profiles of 20 bulk tank milk filters from different dairy farms were investigated using both the full-length 16S (FL-16S) rRNA metabarcoding, a third-generation sequencing method whose application in food and food-related matrices is yet in its infancy, and the WMS, to evaluate the correlation and the reliability of these two methods to explore the microbiome of food-related matrices. Metabarcoding and metagenomic data were generated on a MinION platform (Oxford Nanopore Technologies) and on a Illumina NovaSeq 6000 platform, respectively. Our findings support the greater resolution of WMS in terms of both increased detection of bacterial taxa and enhanced detection of diversity; in contrast, FL-16S rRNA metabarcoding has proven to be a promising, less expensive, and more practical tool to profile most abundant taxa. The significant correlation of the two technologies both in terms of taxa diversity and richness, together with the similar profiles defined for both highly abundant taxa and core microbiomes, including

Microbial organisms play important roles in many aspects of human health and diseases. Encouraged by the numerous studies that show the association between microbiomes and human diseases, computational and machine learning methods have been recently developed to generate and utilize microbiome features for prediction of host phenotypes such as disease versus healthy cancer immunotherapy responder versus nonresponder. We have previously developed a

To identify gut and oral metagenomic signatures that accurately predict pancreatic ductal carcinoma (PDAC) and to validate these signatures in independent cohorts.We conducted a multinational study and performed shotgun metagenomic analysis of fecal and salivary samples collected from patients with treatment-naïve PDAC and non-PDAC controls in Japan, Spain, and Germany. Taxonomic and functional profiles of the microbiomes were characterized, and metagenomic classifiers to predict PDAC were constructed and validated in external datasets.Comparative metagenomics revealed dysbiosis of both the gut and oral microbiomes and identified 30 gut and 18 oral species significantly associated with PDAC in the Japanese cohort. These microbial signatures achieved high area under the curve values of 0.78 to 0.82. The prediction model trained on the Japanese gut microbiome also had high predictive ability in Spanish and German cohorts, with respective area under the curve values of 0.74 and 0.83, validating its high confidence and versatility for PDAC prediction. Significant enrichments of Streptococcus and Veillonella spp and a depletion of Faecalibacterium prausnitzii were common gut signatures for PDAC in all the 3 cohorts. Prospective follow-up data revealed that patients with certain gut and oral microbial species were at higher risk of PDAC-related mortality. Finally, 58 bacteriophages that could infect microbial species consistently enriched in patients with PDAC across the 3 countries were identified.Metagenomics targeting the gut and oral microbiomes can provide a powerful source of biomarkers for identifying individuals with PDAC and their prognoses. The identification of shared gut microbial signatures for PDAC in Asian and European cohorts indicates the presence of robust and global gut microbial biomarkers.

Conventional culture-based techniques are largely inadequate in elucidating the microbiota contained in an environment, due to low recovery within a complex bacterial community. This limitation has been mitigated by the use of next-generation sequencing (NGS)-based approaches thereby facilitating the identification and classification of both culturable and uncultivable microorganisms. Amplicon targeted NGS methods, such as 16S ribosomal RNA (16S rRNA) and shotgun metagenomics, are increasingly being applied in various settings such as in food production environments to decipher the microbial consortium therein. Even though multiple food matrices/food production environments have been studied, the low-moisture environment associated with bakery food production remains to be investigated. To address this knowledge gap, in this study, we investigated the microbiome associated with two bakery production sites (designated as A and B) located in Ireland using 16S rRNA-amplicon-based sequencing. Amplicons corresponding to a hypervariable region contained within the 16S rRNA gene were amplified from DNA samples purified from environmental swabs and ingredients collected at both sites at various stages (preparation, production, postproduction, and storage) across the bakery production chain, over three seasons (winter, spring, and summer). These amplicons were sequenced, and data were analyzed using the mothur pipeline and visualized using MicrobiomeAnalyst and a series of R packages. The top seven bacterial phyla identified at both sites were composed of Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, Deinococcus-Thermus, Patescibacteria, and Verrucomicrobia. In addition, the phyla Tenericutes (Mycoplasmatota) and Acidobacteria were observed only in samples taken at site B. Different bacterial compositions were identified at each stage of production. These same bacteria were also found to be present in the final processed food suggesting the influence of the environment on the food matrix. This study is the first demonstration of the utility of 16S rRNA amplicon-based sequencing to describe the microbiota associated with bakery processing environments.

Given hydroxylamine accumulation in various nitrification systems and its potential mechanism in regulating the subsequent denitrification process were unraveled in this study. Hydroxylamine (0.5 mgN/L) immediately induced nitrite accumulation of activated sludge by inhibiting the activities of nitrite reductases and their electron transport modules (Complex III and cytochrome c). Moreover, long-term exposure to 0.5-2.5 mgN/L hydroxylamine accelerated the functional transformation from denitrification to denitratation under low C/N conditions. However, genome-centric metagenomics indicated that a genotypic complete rather than truncated denitrifier

Motivation Phage–host associations play important roles in microbial communities. But in natural communities, as opposed to culture-based lab studies where phages are discovered and characterized metagenomically, their hosts are generally not known. Several programs have been developed for predicting which phage infects which host based on various sequence similarity measures or machine learning approaches. These are often based on whole viral and host genomes, but in metagenomics-based studies, we rarely have whole genomes but rather must rely on contigs that are sometimes as short as hundreds of bp long. Therefore, we need programs that predict hosts of phage contigs on the basis of these short contigs. Although most existing programs can be applied to metagenomic datasets for these predictions, their accuracies are generally low. Here, we develop ContigNet, a convolutional neural network-based model capable of predicting phage–host matches based on relatively short contigs, and compare it to previously published VirHostMatcher (VHM) and WIsH. Results On the validation set, ContigNet achieves 72–85% area under the receiver operating characteristic curve (AUROC) scores, compared to the maximum of 68% by VHM or WIsH for contigs of lengths between 200 bps to 50 kbps. We also apply the model to the Metagenomic Gut Virus (MGV) catalogue, a dataset containing a wide range of draft genomes from metagenomic samples and achieve 60–70% AUROC scores compared to that of VHM and WIsH of 52%. Surprisingly, ContigNet can also be used to predict plasmid-host contig associations with high accuracy, indicating a similar genetic exchange between mobile genetic elements and their hosts. Availability and implementation The source code of ContigNet and related datasets can be downloaded from https://github.com/tianqitang1/ContigNet.

Motivation Metagenome-assembled genomes (MAGs) have substantially extended our understanding of microbial functionality. However, 16S rRNA genes, which are commonly used in phylogenetic analysis and environmental surveys, are often missing from MAGs. Here, we developed MarkerMAG, a pipeline that links 16S rRNA genes to MAGs using paired-end sequencing reads. Results Assessment of MarkerMAG on three benchmarking metagenomic datasets with various degrees of complexity shows substantial increases in the number of MAGs with 16S rRNA genes and a 100% assignment accuracy. MarkerMAG also estimates the copy number of 16S rRNA genes in MAGs with high accuracy. Assessments on three real metagenomic datasets demonstrate 1.1- to 14.2-fold increases in the number of MAGs with 16S rRNA genes. We also show that MarkerMAG-improved MAGs increase the accuracy of functional prediction from 16S rRNA gene amplicon data. MarkerMAG is helpful in connecting information in MAG databases with those in 16S rRNA databases and surveys and hence contributes to our increasing understanding of microbial diversity, function and phylogeny. Availability and implementation MarkerMAG is implemented in Python3 and freely available at https://github.com/songweizhi/MarkerMAG. Supplementary information Supplementary data are available at Bioinformatics online.

The phyllosphere, defined as the aerial parts of plants, is one of the most prevalent microbial habitats on earth. The microorganisms present on the phyllosphere can have several interactions with the plant. The phyllosphere represents then a unique niche where microorganisms have evolved through time in that stressful environment and may have acquired the ability to degrade lignocellulosic plant cell walls in order to survive to oligotrophic conditions. The dynamic lignocellulolytic potential of two phyllospheric microbial consortia (wheat straw and wheat bran) has been studied. The microbial diversity rapidly changed between the native phyllospheres and the final degrading microbial consortia after 48 h of culture. Indeed, the initial microbial consortia was dominated by the Ralstonia (35·8%) and Micrococcus (75·2%) genera for the wheat bran and wheat straw whereas they were dominated by Candidatus phytoplasma (59%) and Acinetobacter (31·8%) in the final degrading microbial consortia respectively. Culturable experiments leading to the isolation of several new lignocellulolytic isolates (belonging to Moraxella and Atlantibacter genera) and metagenomic reconstruction of the microbial consortia highlighted the existence of an unpredicted microbial diversity involved in lignocellulose fractionation but also the existence of new pathways in known genera (presence of CE2 for Acinetobacter, several AAs for Pseudomonas and several GHs for Bacillus in different metagenomes-assembled genomes). The phyllosphere from agricultural co-products represents then a new niche as a lignocellulolytic degrading ecosystem.

The skin, as the body’s outermost layer of cells, plays a crucial dual role in protecting against foreign pathogens while providing a habitat for commensal microbes. Despite the skin’s harsh conditions, which include desiccation, acidity, and scarce nutrients, the skin hosts a diverse community of bacteria, fungi, and viruses. Prior work associating fluctuations in the skin microbiome with health and disease has been limited by our limited understanding of the skin microbiome composition and functions. One way to characterise skin microbial diversity is through metagenomics. A previous investigation of the skin microbiome found that more than half of the sequenced skin metagenomic reads did not align to reference genomes, complicating the analysis of skin metagenomic datasets. To address this issue, we combined bacterial cultivation and metagenomic sequencing to create the Skin Microbial Genome Collection (SMGC), the most comprehensive catalogue of prokaryotic, eukaryotic, and viral genomes from the skin. The SMGC allows for the classification of a median of 85% of skin metagenomic sequencing reads, providing a comprehensive view of skin microbial diversity. Using the SMGC, we investigated the skin microbiome in atopic dermatitis, a prevalent inflammatory skin condition characterized by recurring episodes of red, itchy, and swollen skin. Atopic dermatitis flares have been associated with the proliferation of various staphylococcal species, with only S. aureus strains cultured from atopic dermatitis inducing inflammation in a mouse model. Our extensive genomic survey of the skin microbiome in atopic dermatitis, supported by cultured isolates from the same samples, identified Staphylococcus strains and genomic loci associated with higher disease severity. Our work also showed that the Staphylococcus strains found in AD are influenced by factors such as geography and strain sharing within households. Additionally, our examination of the mobilome of multiple Staphylococcus species colonising the same individuals revealed widespread inter-species transfer of genetic material, highlighting the fluid nature of staphylococcal genetic composition. In conclusion, our work shows how novel genomic approaches and the integration of sequencing data can be used to characterise the skin microbiome at an unparalleled resolution, allowing for new insights into how skin microbes vary in health and disease.

It is generally assumed that viruses outnumber cells on Earth by at least tenfold. Virus-to-microbe ratios (VMR) are largely based on counts of fluorescently labelled virus-like particles. However, these exclude intracellular viruses and potentially include false positives (DNA-containing vesicles, gene-transfer agents, unspecifically stained inert particles). Here, we develop a metagenome-based VMR estimate (mVRM) that accounts for DNA viruses across all stages of their replication cycles (virion, intracellular lytic and lysogenic) by using normalised RPKM (reads per kilobase of gene sequence per million of mapped metagenome reads) counts of the major capsid protein (MCP) genes and cellular universal single-copy genes (USCGs) as proxies for virus and cell counts, respectively. After benchmarking this strategy using mock metagenomes with increasing VMR, we inferred mVMR across different biomes. To properly estimate mVMR in aquatic ecosystems, we generated metagenomes from co-occurring cellular and viral fractions (>50 kDa-200 µm size-range) in freshwater, seawater and solar saltern ponds (10 metagenomes, 2 control metaviromes). Viruses outnumbered cells in freshwater by ∼13 fold and in plankton from marine and saline waters by ∼2-4 fold. However, across an additional set of 121 diverse non-aquatic metagenomes including microbial mats, microbialites, soils, freshwater and marine sediments and metazoan-associated microbiomes, viruses, on average, outnumbered cells by barely two-fold. Although viruses likely are the most diverse biological entities on Earth, their global numbers might be closer to those of cells than previously estimated.

Perennial grains provide various ecosystem services compared to the annual counterparts thanks to their extensive root system and permanent soil cover. However, little is known about the evolution and diversification of perennial grains rhizosphere and its ecological functions over time. In this study, a suite of -OMICSs - metagenomics, enzymomics, metabolomics and lipidomics - was used to compare the rhizosphere environment of four perennial wheat lines at the first and fourth year of growth in comparison with an annual durum wheat cultivar and the parental species Thinopyrum intermedium. We hypothesized that wheat perenniality has a greater role in shaping the rhizobiome composition, biomass, diversity, and activity than plant genotypes because perenniality affects the quality and quantity of C input – mainly root exudates – hence modulating the plant-microbes crosstalk. In support of this hypothesis, the continuous supply of sugars in the rhizosphere along the years created a favorable environment for microbial growth which is reflected in a higher microbial biomass and enzymatic activity. Moreover, modification in the rhizosphere metabolome and lipidome over the years led to changes in the microbial community composition favoring the coexistence of more diverse microbial taxa, increasing plant tolerance to biotic and abiotic stresses. Despite the dominance of the perenniality effect, our data underlined that the OK72 line rhizobiome distinguished from the others by the increase in abundance of Pseudomonas spp., most of which are known as potential beneficial microorganisms, identifying this line as a suitable candidate for the study and selection of new perennial wheat lines.

BackgroundAtrial fibrillation (AF) is an important heart rhythm disorder in aging populations. The gut microbiome composition has been previously related to cardiovascular disease risk factors. Whether the gut microbial profile is also associated with the risk of AF remains unknown.MethodsWe examined the associations of prevalent and incident AF with gut microbiota in the FINRISK 2002 study, a random population sample of 6763 individuals. We replicated our findings in an independent case-control cohort of 138 individuals in Hamburg, Germany.FindingsMultivariable-adjusted regression models revealed that prevalent AF (N=116) was associated with nine microbial genera. Incident AF (N=539) over a median follow-up of 15 years was associated with eight microbial genera with false discovery rate (FDR)-corrected P&nbsp

With blood stream infections (BSIs) representing a major cause of mortality and morbidity worldwide, blood cultures play a crucial role in diagnosis, but their clinical application is dampened by the long turn-around time and the detection of only culturable pathogens. In this study, we developed and validated a shotgun metagenomics next-generation sequencing (mNGS) test directly from positive blood culture fluid, allowing for the identification of fastidious or slow growing microorganisms more rapidly. The test was built based on previously validated next-generation sequencing tests, which rely on several key marker genes for bacterial and fungal identification. The new test utilizes an open-source metagenomics CZ-ID platform for the initial analysis to generate the most likely candidate species, which is then used as a reference genome for downstream, confirmatory analysis. This approach is innovative because it takes advantage of an open-source software’s agnostic taxonomic calling capability while still relying on the more established and previously validated marker gene-based identification scheme, increasing the confidence in the final results. The test showed high accuracy (100%, 30/30) for both bacterial and fungal microorganisms. We further demonstrated its clinical utility especially for anaerobes and mycobacteria that are either fastidious, slow growing, or unusual. Although applicable in only limited settings, the Positive Blood Culture mNGS test provides an incremental improvement in solving the unmet clinical needs for the diagnosis of challenging BSIs.

Molecular biology methods and technologies have advanced substantially over the past decade. These new molecular methods should be incorporated among the standard tools of planetary protection (PP) and could be validated for incorporation by 2026. To address the feasibility of applying modern molecular techniques to such an application, NASA conducted a technology workshop with private industry partners, academics, and government agency stakeholders, along with NASA staff and contractors. The technical discussions and presentations of the Multi-Mission Metagenomics Technology Development Workshop focused on modernizing and supplementing the current PP assays. The goals of the workshop were to assess the state of metagenomics and other advanced molecular techniques in the context of providing a validated framework to supplement the bacterial endospore-based NASA Standard Assay and to identify knowledge and technology gaps. In particular, workshop participants were tasked with discussing metagenomics as a stand-alone technology to provide rapid and comprehensive analysis of total nucleic acids and viable microorganisms on spacecraft surfaces, thereby allowing for the development of tailored and cost-effective microbial reduction plans for each hardware item on a spacecraft. Workshop participants recommended metagenomics approaches as the only data source that can adequately feed into quantitative microbial risk assessment models for evaluating the risk of forward (exploring extraterrestrial planet) and back (Earth harmful biological) contamination. Participants were unanimous that a metagenomics workflow, in tandem with rapid targeted quantitative (digital) PCR, represents a revolutionary advance over existing methods for the assessment of microbial bioburden on spacecraft surfaces. The workshop highlighted low biomass sampling, reagent contamination, and inconsistent bioinformatics data analysis as key areas for technology development. Finally, it was concluded that implementing metagenomics as an additional workflow for addressing concerns of NASA's robotic mission will represent a dramatic improvement in technology advancement for PP and will benefit future missions where mission success is affected by backward and forward contamination.

Background The prediction of bacteriophage sequences in metagenomic datasets has become a topic of considerable interest, leading to the development of many novel bioinformatic tools. A comparative analysis of ten state-of-the-art phage identification tools was performed to inform their usage in microbiome research. Methods Artificial contigs generated from complete RefSeq genomes representing phages, plasmids, and chromosomes, and a previously sequenced mock community containing four phage species, were used to evaluate the precision, recall, and F1 scores of the tools. We also generated a dataset of randomly shuffled sequences to quantify false-positive calls. In addition, a set of previously simulated viromes was used to assess diversity bias in each tool’s output. Results VIBRANT and VirSorter2 achieved the highest F1 scores (0.93) in the RefSeq artificial contigs dataset, with several other tools also performing well. Kraken2 had the highest F1 score (0.86) in the mock community benchmark by a large margin (0.3 higher than DeepVirFinder in second place), mainly due to its high precision (0.96). Generally, k-mer-based tools performed better than reference similarity tools and gene-based methods. Several tools, most notably PPR-Meta, called a high number of false positives in the randomly shuffled sequences. When analysing the diversity of the genomes that each tool predicted from a virome set, most tools produced a viral genome set that had similar alpha- and beta-diversity patterns to the original population, with Seeker being a notable exception. Conclusions This study provides key metrics used to assess performance of phage detection tools, offers a framework for further comparison of additional viral discovery tools, and discusses optimal strategies for using these tools. We highlight that the choice of tool for identification of phages in metagenomic datasets, as well as their parameters, can bias the results and provide pointers for different use case scenarios. We have also made our benchmarking dataset available for download in order to facilitate future comparisons of phage identification tools.

The human gut microbiome harbors substantial ecological diversity at the species level as well as at the strain level within species. In healthy hosts, species abundance fluctuations in the microbiome are thought to be stable, and these fluctuations can be described by macroecological laws. However, it is less clear how strain abundances change over time. An open question is whether individual strains behave like species themselves, exhibiting stability and following the macroecological relationships known to hold at the species level, or whether strains have different dynamics, perhaps due to the relatively close phylogenetic relatedness of cocolonizing lineages. Here, we analyze the daily dynamics of intraspecific genetic variation in the gut microbiomes of four healthy, densely longitudinally sampled hosts. First, we find that the overall genetic diversity of a large majority of species is stationary over time despite short-term fluctuations. Next, we show that fluctuations in abundances in approximately 80% of strains analyzed can be predicted with a stochastic logistic model (SLM), an ecological model of a population experiencing environmental fluctuations around a fixed carrying capacity, which has previously been shown to capture statistical properties of species abundance fluctuations. The success of this model indicates that strain abundances typically fluctuate around a fixed carrying capacity, suggesting that most strains are dynamically stable. Finally, we find that the strain abundances follow several empirical macroecological laws known to hold at the species level. Together, our results suggest that macroecological properties of the human gut microbiome, including its stability, emerge at the level of strains. IMPORTANCE To date, there has been an intense focus on the ecological dynamics of the human gut microbiome at the species level. However, there is considerable genetic diversity within species at the strain level, and these intraspecific differences can have important phenotypic effects on the host, impacting the ability to digest certain foods and metabolize drugs. Thus, to fully understand how the gut microbiome operates in times of health and sickness, its ecological dynamics may need to be quantified at the level of strains. Here, we show that a large majority of strains maintain stable abundances for periods of months to years, exhibiting fluctuations in abundance that can be well described by macroecological laws known to hold at the species level, while a smaller percentage of strains undergo rapid, directional changes in abundance. Overall, our work indicates that strains are an important unit of ecological organization in the human gut microbiome.