Metagenomic Samples Research Articles

The rootstock shape microbial diversity and functionality in the rhizosphere of Vitis vinifera L. cultivar Falanghina.

The rhizosphere effect occurring at the root-soil interface has increasingly been shown to play a key role in plant fitness and soil functionality, influencing plants resilience. Here, for the first time, we investigated whether the rootstock genotype on which Vitis vinifera L. cultivar Falanghina is grafted can influence the rhizosphere microbiome. Specifically, we evaluated to which extent the 5BB and 1103P rootstocks are able to shape microbial diversity of rhizosphere environment. Moreover, we explored the potential function of microbial community and its shift under plant genotype influence. We investigated seven vineyards subjected to the same pedo-climatic conditions, similar age, training system and management and collected twelve rhizosphere soil samples for metagenomic analyses and composite soil samples for physical-chemical properties. In this study, we used 16S rRNA gene-based metagenomic analysis to investigate the rhizosphere bacterial diversity and composition. Liner discriminant analysis effect size (LEFSe) was conducted for metagenomic biomarker discovery. The functional composition of sampled communities was determined using PICRUSt, which is based on marker gene sequencing profiles. Soil analyses involved the determination of texture, pH, Cation Exchange Capacity (CSC), Organic Carbon (OC), electrical conductivity (EC), calcium (Ca), magnesium (Mg), potassium (K) content, Phosphorous (P), nitrogen (N). The latter revealed that soil features were quite homogenous. The metagenomic data showed that the bacterial alpha-diversity (Observed OTUs) significantly increased in 1103P rhizosphere microbiota. Irrespective of cultivar, Pseudomonadota was the dominant phylum, followed by Actinomycetota > Bacteroidota > Thermoproteota. However, Actinomycetota was the major marker phyla differentiating the rhizosphere microbial communities associated with the different rootstock types. At the genus level, several taxa belonging to Actinomycetota and Alphaproteobacteria classes were enriched in 1103P genotype rhizosphere. Investigating the potential functional profile, we found that most key enzyme-encoding genes involved in N cycling were significantly more abundant in 5BB rootstock rhizosphere soil. However, we found that 1103P rhizosphere was enriched in genes involved in C cycle and Plant Growth Promotion (PGP) functionality. Our results suggest that the different rootstocks not only recruit specific bacterial communities, but also specific functional traits within the same environment.

Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions

BackgroundA growing body of evidence suggests that the gut microbiota is strongly linked to general human health. Microbiome-directed interventions, such as diet and exercise, are acknowledged as a viable and achievable strategy for preventing disorders and improving human health. However, due to the significant inter-individual diversity of the gut microbiota between subjects, lifestyle recommendations are expected to have distinct and highly variable impacts to the microbiome structure.ResultsHere, through a large-scale meta-analysis including 1448 shotgun metagenomics samples obtained longitudinally from 396 individuals during lifestyle studies, we revealed Bacteroides stercoris, Prevotella copri, and Bacteroides vulgatus as biomarkers of microbiota’s resistance to structural changes, and aromatic and non-aromatic amino acid biosynthesis as important regulator of microbiome dynamics. We established criteria for distinguishing between significant compositional changes from normal microbiota fluctuation and classified individuals based on their level of response. We further developed a machine learning model for predicting “responders” and “non-responders” independently of the type of intervention with an area under the curve of up to 0.86 in external validation cohorts of different ethnicities.ConclusionsWe propose here that microbiome-based stratification is possible for identifying individuals with highly plastic or highly resistant microbial structures. Identifying subjects that will not respond to generalized lifestyle therapeutic interventions targeting the restructuring of gut microbiota is important to ensure that primary end-points of clinical studies are reached.2m7TsyyzrZ6DMn35dcSWgYVideo

Comparison of 6 DNA extraction methods for isolation of high yield of high molecular weight DNA suitable for shotgun metagenomics Nanopore sequencing to detect bacteria

BackgroundOxford Nanopore Technologies (ONT) offers an accessible platform for long-read sequencing, which improves the reconstruction of genomes and helps to resolve complex genomic contexts, especially in the case of metagenome analysis. To take the best advantage of long-read sequencing, DNA extraction methods must be able to isolate pure high molecular weight (HMW) DNA from complex metagenomics samples, without introducing any bias. New methods released on the market, and protocols developed at the research level, were specifically designed for this application and need to be assessed.ResultsIn this study, with different bacterial cocktail mixes, analyzed as pure or spiked in a synthetic fecal matrix, we evaluated the performances of 6 DNA extraction methods using various cells lysis and purification techniques, from quick and easy, to more time-consuming and gentle protocols, including a portable method for on-site application. In addition to the comparison of the quality, quantity and purity of the extracted DNA, the performance obtained when doing Nanopore sequencing on a MinION flow cell was also tested. From the obtained results, the Quick-DNA HMW MagBead Kit (Zymo Research) was selected as producing the best yield of pure HMW DNA. Furthermore, this kit allowed an accurate detection, by Nanopore sequencing, of almost all the bacterial species present in a complex mock community.ConclusionAmongst the 6 tested methods, the Quick-DNA HMW MagBead Kit (Zymo Research) was considered as the most suitable for Nanopore sequencing and would be recommended for bacterial metagenomics studies using this technology.

Facilitating accessible, rapid, and appropriate processing of ancient metagenomic data with AMDirT

Background: Access to sample-level metadata is important when selecting public metagenomic sequencing datasets for reuse in new biological analyses. The Standards, Precautions, and Advances in Ancient Metagenomics community (SPAAM, https://spaam-community.github.io) has previously published AncientMetagenomeDir, a collection of curated and standardised sample metadata tables for metagenomic and microbial genome datasets generated from ancient samples. However, while sample-level information is useful for identifying relevant samples for inclusion in new projects, Next Generation Sequencing (NGS) library construction and sequencing metadata are also essential for appropriately reprocessing ancient metagenomic data. Currently, recovering information for downloading and preparing such data is difficult when laboratory and bioinformatic metadata is heterogeneously recorded in prose-based publications. Methods: Through a series of community-based hackathon events, AncientMetagenomeDir was updated to provide standardised library-level metadata of existing and new ancient metagenomic samples. In tandem, the companion tool 'AMDirT' was developed to facilitate automated metadata curation and data validation, as well as rapid data filtering and downloading. Results: AncientMetagenomeDir was extended to include standardised metadata of over 5000 ancient metagenomic libraries. The companion tool 'AMDirT' provides both graphical- and command-line interface based access to such metadata for users from a wide range of computational backgrounds. We also report on errors with metadata reporting that appear to commonly occur during data upload and provide suggestions on how to improve the quality of data sharing by the community. Conclusions: Together, both standardised metadata and tooling will help towards easier incorporation and reuse of public ancient metagenomic datasets into future analyses.

Benchmarking State-of-the-Art Approaches for Norovirus Genome Assembly in Metagenome Sample.

A recently published article in BMCGenomics by Fuentes-Trillo et al. contains a comparison of assembly approaches of several noroviral samples via different tools and preprocessing strategies. It turned out that the study used outdated versions of tools as well as tools that were not designed for the viral assembly task. In order to improve the suboptimal assemblies, authors suggested different sophisticated preprocessing strategies that seem to make only minor contributions to the results. We have reproduced the analysis using state-of-the-art tools designed for viral assembly, and we demonstrate that tools from the SPAdes toolkit (rnaviralSPAdes and coronaSPAdes) allow one to assemble the samples from the original study into a single contig without any additional preprocessing.

AMAnD: an automated metagenome anomaly detection methodology utilizing DeepSVDD neural networks.

The composition of metagenomic communities within the human body often reflects localized medical conditions such as upper respiratory diseases and gastrointestinal diseases. Fast and accurate computational tools to flag anomalous metagenomic samples from typical samples are desirable to understand different phenotypes, especially in contexts where repeated, long-duration temporal sampling is done. Here, we present Automated Metagenome Anomaly Detection (AMAnD), which utilizes two types of Deep Support Vector Data Description (DeepSVDD) models; one trained on taxonomic feature space output by the Pan-Genomics for Infectious Agents (PanGIA) taxonomy classifier and one trained on kmer frequency counts. AMAnD's semi-supervised one-class approach makes no assumptions about what an anomaly may look like, allowing the flagging of potentially novel anomaly types. Three diverse datasets are profiled. The first dataset is hosted on the National Center for Biotechnology Information's (NCBI) Sequence Read Archive (SRA) and contains nasopharyngeal swabs from healthy and COVID-19-positive patients. The second dataset is also hosted on SRA and contains gut microbiome samples from normal controls and from patients with slow transit constipation (STC). AMAnD can learn a typical healthy nasopharyngeal or gut microbiome profile and reliably flag the anomalous COVID+ or STC samples in both feature spaces. The final dataset is a synthetic metagenome created by the Critical Assessment of Metagenome Annotation Simulator (CAMISIM). A control dataset of 50 well-characterized organisms was submitted to CAMISIM to generate 100 synthetic control class samples. The experimental conditions included 12 different spiked-in contaminants that are taxonomically similar to organisms present in the laboratory blank sample ranging from one strain tree branch taxonomic distance away to one family tree branch taxonomic distance away. This experiment was repeated in triplicate at three different coverage levels to probe the dependence on sample coverage. AMAnD was again able to flag the contaminant inserts as anomalous. AMAnD's assumption-free flagging of metagenomic anomalies, the real-time model training update potential of the deep learning approach, and the strong performance even with lightweight models of low sample cardinality would make AMAnD well-suited to a wide array of applied metagenomics biosurveillance use-cases, from environmental to clinical utility.

A Metagenomic Time-Series Approach to Assess the Ecological Stability of Microbial Mats in a Seasonally Fluctuating Environment

Microbial mats are complex ecological assemblages that have been present in the rock record since the Precambrian and can still be found in extant marginalized environments. These structures are considered highly stable ecosystems. In this study, we evaluate the ecological stability of dome-forming microbial mats in a modern, water-level fluctuating, hypersaline pond located in the Cuatro Ciénegas Basin, Mexico. We conducted metagenomic sampling of the site from 2016 to 2019 and detected 2250 genera of Bacteria and Archaea, with only <20 belonging to the abundant taxa (>1%). The microbial community was dominated by Proteobacteria, Euryarchaeota, Bacteroidetes, Firmicutes, and Cyanobacteria, and was compositionally sensitive to disturbances, leading to high taxonomic replacement even at the phylum level, with a significant increase in Archaea from sim 1-4% to sim 33% throughout the 2016-2019 study period. Although a core community represented most of the microbial community (>75%), relative abundances shifted significantly between samples, as demonstrated by changes in the abundance of Coleofasciculus from 10.2% in 2017 to 0.05% in 2019. Although functional differences between seasons were subtle, co-occurrence networks suggest differential ecological interactions between the seasons, with the addition of a new module during the rainy season and the potential shift in hub taxa. Functional composition was slightly more similar between samples, but basic processes such as carbohydrate, amino acid, and nucleic acid metabolisms were widely distributed among samples. Major carbon fixation processes included sulfur oxidation, nitrogen fixation, and photosynthesis (both oxygenic and anoxygenic), as well as the Wood-Ljundgahl and Calvin cycles.

Finding phylogeny-aware and biologically meaningful averages of metagenomic samples: L2UniFrac.

Metagenomic samples have high spatiotemporal variability. Hence, it is useful to summarize and characterize the microbial makeup of a given environment in a way that is biologically reasonable and interpretable. The UniFrac metric has been a robust and widely used metric for measuring the variability between metagenomic samples. We propose that the characterization of metagenomic environments can be improved by finding the average, a.k.a. the barycenter, among the samples with respect to the UniFrac distance. However, it is possible that such a UniFrac-average includes negative entries, making it no longer a valid representation of a metagenomic community. To overcome this intrinsic issue, we propose a special version of the UniFrac metric, termed L2UniFrac, which inherits the phylogenetic nature of the traditional UniFrac and with respect to which one can easily compute the average, producing biologically meaningful environment-specific "representative samples." We demonstrate the usefulness of such representative samples as well as the extended usage of L2UniFrac in efficient clustering of metagenomic samples, and provide mathematical characterizations and proofs to the desired properties of L2UniFrac. A prototype implementation is provided at https://github.com/KoslickiLab/L2-UniFrac.git. All figures, data, and analysis can be reproduced at https://github.com/KoslickiLab/L2-UniFrac-Paper.

Bakdrive: identifying a minimum set of bacterial species driving interactions across multiple microbial communities.

Interactions among microbes within microbial communities have been shown to play crucial roles in human health. In spite of recent progress, low-level knowledge of bacteria driving microbial interactions within microbiomes remains unknown, limiting our ability to fully decipher and control microbial communities. We present a novel approach for identifying species driving interactions within microbiomes. Bakdrive infers ecological networks of given metagenomic sequencing samples and identifies minimum sets of driver species (MDS) using control theory. Bakdrive has three key innovations in this space: (i) it leverages inherent information from metagenomic sequencing samples to identify driver species, (ii) it explicitly takes host-specific variation into consideration, and (iii) it does not require a known ecological network. In extensive simulated data, we demonstrate identifying driver species identified from healthy donor samples and introducing them to the disease samples, we can restore the gut microbiome in recurrent Clostridioides difficile (rCDI) infection patients to a healthy state. We also applied Bakdrive to two real datasets, rCDI and Crohn's disease patients, uncovering driver species consistent with previous work. Bakdrive represents a novel approach for capturing microbial interactions. Bakdrive is open-source and available at: https://gitlab.com/treangenlab/bakdrive.

Species-Level Taxonomic Characterization of Uncultured Core Gut Microbiota of Plateau Pika.

Rarely has the vast diversity of bacteria on Earth been profiled, particularly on inaccessible plateaus. These uncultured microbes, which are also known as "microbial dark matter," may play crucial roles in maintaining the ecosystem and are linked to human health, regarding pathogenicity and prebioticity. The plateau pika (Ochotona curzoniae) is a small burrowing steppe lagomorph that is endemic to the Qinghai-Tibetan Plateau and is a keystone species in the maintenance of ecological balance. We used a combination of full-length 16S rRNA amplicon sequencing, shotgun metagenomics, and metabolomics to elucidate the species-level community structure and the metabolic potential of the gut microbiota of the plateau pika. Using a full-length 16S rRNA metataxonomic approach, we clustered 618 (166 ± 35 per sample) operational phylogenetic units (OPUs) from 105 plateau pika samples and assigned them to 215 known species, 226 potentially new species, and 177 higher hierarchical taxa. Notably, 39 abundant OPUs (over 60% total relative abundance) are found in over 90% of the samples, thereby representing a "core microbiota." They are all classified as novel microbial lineages, from the class to the species level. Using metagenomic reads, we independently assembled and binned 109 high-quality, species-level genome bins (SGBs). Then, a precise taxonomic assignment was performed to clarify the phylogenetic consistency of the SGBs and the 16S rRNA amplicons. Thus, the majority of the core microbes possess their genomes. SGBs belonging to the genus Treponema, the families Muribaculaceae, Lachnospiraceae, and Oscillospiraceae, and the order Eubacteriales are abundant in the metagenomic samples. In addition, multiple CAZymes are detected in these SGBs, indicating their efficient utilization of plant biomass. As the most widely connected metabolite with the core microbiota, tryptophan may relate to host environmental adaptation. Our investigation allows for a greater comprehension of the composition and functional capacity of the gut microbiota of the plateau pika. IMPORTANCE The great majority of microbial species remain uncultured, severely limiting their taxonomic characterization and biological understanding. The plateau pika (Ochotona curzoniae) is a small burrowing steppe lagomorph that is endemic to the Qinghai-Tibetan Plateau and is considered to be the keystone species in the maintenance of ecological stability. We comprehensively investigated the gut microbiota of the plateau pika via a multiomics endeavor. Combining full-length 16S rRNA metataxonomics, shotgun metagenomics, and metabolomics, we elucidated the species-level taxonomic assignment of the core uncultured intestinal microbiota of the plateau pika and revealed their correlation to host nutritional metabolism and adaptation. Our findings provide insights into the microbial diversity and biological significance of alpine animals.

Terabase-Scale Coassembly of a Tropical Soil Microbiome.

Petabases of environmental metagenomic data are publicly available, presenting an opportunity to characterize complex environments and discover novel lineages of life. Metagenome coassembly, in which many metagenomic samples from an environment are simultaneously analyzed to infer the underlying genomes' sequences, is an essential tool for achieving this goal. We applied MetaHipMer2, a distributed metagenome assembler that runs on supercomputing clusters, to coassemble 3.4 terabases (Tbp) of metagenome data from a tropical soil in the Luquillo Experimental Forest (LEF), Puerto Rico. The resulting coassembly yielded 39 high-quality (>90% complete, <5% contaminated, with predicted 23S, 16S, and 5S rRNA genes and ≥18 tRNAs) metagenome-assembled genomes (MAGs), including two from the candidate phylum Eremiobacterota. Another 268 medium-quality (≥50% complete, <10% contaminated) MAGs were extracted, including the candidate phyla Dependentiae, Dormibacterota, and Methylomirabilota. In total, 307 medium- or higher-quality MAGs were assigned to 23 phyla, compared to 294 MAGs assigned to nine phyla in the same samples individually assembled. The low-quality (<50% complete, <10% contaminated) MAGs from the coassembly revealed a 49% complete rare biosphere microbe from the candidate phylum FCPU426 among other low-abundance microbes, an 81% complete fungal genome from the phylum Ascomycota, and 30 partial eukaryotic MAGs with ≥10% completeness, possibly representing protist lineages. A total of 22,254 viruses, many of them low abundance, were identified. Estimation of metagenome coverage and diversity indicates that we may have characterized ≥87.5% of the sequence diversity in this humid tropical soil and indicates the value of future terabase-scale sequencing and coassembly of complex environments. IMPORTANCE Petabases of reads are being produced by environmental metagenome sequencing. An essential step in analyzing these data is metagenome assembly, the computational reconstruction of genome sequences from microbial communities. "Coassembly" of metagenomic sequence data, in which multiple samples are assembled together, enables more complete detection of microbial genomes in an environment than "multiassembly," in which samples are assembled individually. To demonstrate the potential for coassembling terabases of metagenome data to drive biological discovery, we applied MetaHipMer2, a distributed metagenome assembler that runs on supercomputing clusters, to coassemble 3.4 Tbp of reads from a humid tropical soil environment. The resulting coassembly, its functional annotation, and analysis are presented here. The coassembly yielded more, and phylogenetically more diverse, microbial, eukaryotic, and viral genomes than the multiassembly of the same data. Our resource may facilitate the discovery of novel microbial biology in tropical soils and demonstrates the value of terabase-scale metagenome sequencing.

Detecting fecal microbiota transplantation–associated infection transmission using shotgun metagenomic sequencing and clonality analysis

Background: Fecal microbiota transplantation (FMT) is a widely used modality for safe and effective treatment of recurrent Clostridium difficile infections, and FMT is being explored for the treatment of additional indications including gastrointestinal diseases and neurological disorders. Although microbiota-based therapies like FMT utilize rigorous donor screening procedures, these procedures are limited in resolution and scope, and there remains a risk of transmission of FMT-associated infectious agents from donor stool to a FMT recipient. Critically, these health concerns led the FDA to issue a 2019 safety alert for the transmission risks associated with FMT and to update its guidelines for screening and reporting. In a suspected transmission event, there is uncertainty around the source of infection; thus, methods are needed to rapidly determine whether a patient’s infection is linked to the donor stool product. Methods: Here, we developed a laboratory service sequencing and bioinformatics pipeline within our CLIA-certified laboratory for investigating suspected FMT infection transmission by measuring genomic relatedness. Our pipeline performs deep sequencing of a metagenomic sample, whole-genome sequencing (WGS) of an isolate derived from the implicated patient infection and determines the genomic relatedness between the 2 using a SNP-based analysis. The workflow was validated in silico with synthetic metagenomic samples spiked-in with WGS of clinically relevant isolate strains at varying abundance. Results: The sample and sequencing library preparation workflow was optimized across a panel of metagenomic and mock fecal microbiome samples demonstrating reproducible and reduced-bias sequencing of metagenomic samples. Our pipeline demonstrates high sensitivity and specificity for clonality calls when a spiked in isolate genome achieves 5× depth for &gt;50% of the genome. We also demonstrated an interplay between abundance rate and sequencing depth for determining a clonality limit of detection. Conclusions: Taken together, our pipeline represents a new method that can support the clinical efforts of FMT and other microbiota-based therapies.

Ecology of Endozoicomonadaceae in three coral genera across the Pacific Ocean

Health and resilience of the coral holobiont depend on diverse bacterial communities often dominated by key marine symbionts of the Endozoicomonadaceae family. The factors controlling their distribution and their functional diversity remain, however, poorly known. Here, we study the ecology of Endozoicomonadaceae at an ocean basin-scale by sampling specimens from three coral genera (Pocillopora, Porites, Millepora) on 99 reefs from 32 islands across the Pacific Ocean. The analysis of 2447 metabarcoding and 270 metagenomic samples reveals that each coral genus harbored a distinct new species of Endozoicomonadaceae. These species are composed of nine lineages that have distinct biogeographic patterns. The most common one, found in Pocillopora, appears to be a globally distributed symbiont with distinct metabolic capabilities, including the synthesis of amino acids and vitamins not produced by the host. The other lineages are structured partly by the host genetic lineage in Pocillopora and mainly by the geographic location in Porites. Millepora is more rarely associated to Endozoicomonadaceae. Our results show that different coral genera exhibit distinct strategies of host-Endozoicomonadaceae associations that are defined at the bacteria lineage level.

Novel CRISPR-Associated Gene-Editing Systems Discovered in Metagenomic Samples Enable Efficient and Specific Genome Engineering.

Development of medicines using gene editing has been hampered by enzymological and immunological impediments. We described previously the discovery and characterization of improved, novel gene-editing systems from metagenomic data. In this study, we substantially advance this work with three such gene-editing systems, demonstrating their utility for cell therapy development. All three systems are capable of reproducible, high-frequency gene editing in primary immune cells. In human T cells, disruption of the T cell receptor (TCR) alpha-chain was induced in >95% of cells, both paralogs of the TCR beta-chain in >90% of cells, and >90% knockout of β2-microglobulin, TIGIT, FAS, and PDCD1. Simultaneous double knockout of TRAC and TRBC was obtained at a frequency equal to that of the single edits. Gene editing with our systems had minimal effect on T cell viability. Furthermore, we integrate a chimeric antigen receptor (CAR) construct into TRAC (up to ∼60% of T cells), and demonstrate CAR expression and cytotoxicity. We next applied our novel gene-editing tools to natural killer (NK) cells, B cells, hematopoietic stem cells, and induced pluripotent stem cells, generating similarly efficient cell-engineering outcomes including the creation of active CAR-NK cells. Interrogation of our gene-editing systems' specificity reveals a profile comparable with or better than Cas9. Finally, our nucleases lack preexisting humoral and T cell-based immunity, consistent with their sourcing from nonhuman pathogens. In all, we show these new gene-editing systems have the activity, specificity, and translatability necessary for use in cell therapy development.

Dissecting the metal resistance genes contributed by virome from mining-affected metal contaminated soils

Virus-borne auxiliary genes have been demonstrated to influence their hosts’ characteristics and flexibility, resulting in an elevated ability to withstand diverse conditions and competition. However, the research on the soil viral community and its influencing factors is not extensive. The effects of bacterium-phage co-evolution in the metal contaminated environment have yet to be fully understood. In our research, numerous viral genes with conserved functional residues/motifs working on detoxifying heavy metals were pinpointed in the virome recovered from 58 contaminated soil metagenomic samples of mineral area, including tellurite resistance genes (e.g., terC, terD), copper resistance genes (e.g., copC, copA), and arsenate resistance genes (e.g., arsC). Moreover, viral proteins involved in key processes of nutrient uptake and metabolism, cellular function, polysaccharides production and biomineralization were also detected, which may also contribute to the response of microbial community to heavy metal stress. Our research showed that viruses have helped their hosts to acquire novel metal-resistance abilities through horizontal gene transfer (HGT) during the adaptation to challenging metal-rich environments. This was also evidenced by the high Codon adaption index (CAI) values across metal-resistance-related genes. This study has advanced our understanding on virus-host interplay within heavy metal contaminated soils.

Antibiotic use in infants within the first year of life is associated with the appearance of antibiotic-resistant genes in their feces.

Antibiotic resistance, an increasing challenge, is not only a national threat but also a global threat. Carriage of resistance genes is not limited to adults alone, various microbiota niches present in the body system of children have been found to harbor bacteria carrying resistant genes, especially, their gut microbiota. This study aims to identify selected antibiotic-resistant genes from the fecal samples of infants and the association of antibiotics use with the occurrence of resistant genes in the infant's gut. A total number of 172 metagenomic DNA samples previously extracted from stool samples of 28 Nigerian babies longitudinally within their first year of life were screened for the presence of ESBL genes (blaSHV, blaTEM, and blaCTX-M), PMQR genes (qnrA, qnrB, qnrS, qepA), ribosomal protection protein tetracycline resistance gene, (RPP) β-lactamase (blaZ), macrolide (ermA, ermB, mefA/E), aminoglycoside modifying enzymes gentR (aac(6')/aph(2″)) and dfrA genes by PCR. Nineteen (19) of the 28 babies used antibiotics during the study. The association between antibiotic use by the babies within the first year of life and occurrence of resistant genes were analyzed by Spearman rank correlation. One hundred and twenty-two (122) samples (71%) out of the 172 isolates had antibiotic-resistance genes. PMQR genes were absent in all the samples. Three isolates had blaTEM gene, nine isolates had blaSHV gene, six isolates had blaCTX-M gene and 19 isolates had dfrA gene, 31 samples had tet gene, 29 samples had mef gene, 27 samples had ermB gene, four samples had ermA gene, 13 samples had blaZ gene and 16 samples had aac gene. The babies whose samples had resistant genes used antibiotics in the same months the samples were collected. Interestingly, the 11 babies whose samples had the dfrA gene all used antibiotics in the same months their samples were collected but none of them used trimethoprim/sulfamethoxazole antibiotic. The overall correlation matrix of the babies showed a strong association between antibiotic use (AU) and antibiotic use presence of resistance genes (AUPRG) with a coefficient of 0.89. Antibiotic-resistant genes are present in the gut of infants and their occurrence is strongly connected with antibiotic use by infants.

Detection of Phytophthora, Pythium, Globisporangium, Hyaloperonospora, and Plasmopara Species in High-Throughput Sequencing Data by In Silico and In Vitro Analysis Using Microbe Finder (MiFi)

Species identification of plant pathogenic oomycetes based on morphology is challenging because of the limited number of variable characters available. Hence, pure culture isolation followed by internal transcribed spacer sequencing is routinely conducted for diagnostic purposes. E-probe Diagnostic Nucleic acid Analysis (EDNA), a pipeline that couples high-throughput sequencing and metagenomic sequence analysis, is a detection system used to identify targets without relying on often time-consuming serological and molecular approaches. The EDNA pipeline, implemented through the Microbe Finder (MiFi), was used to design short species-specific sequences (e-probes) with sizes from 40 to 80 nucleotides and to detect plant pathogenic oomycetes in metagenomic data. To avoid false positives and increase target specificity, we removed the e-probes with sequences similar to nontarget species in NCBI's nucleotide database. The designed e-probes were tested using in silico simulated metagenomes with different target pathogen and host sequence levels. E-probes of Phytophthora nicotianae were further tested with metagenomes generated using nanopore sequencing. In silico detection with 100% sensitivity was obtained at 0.01 to 1% pathogen read abundances with the three e-probe lengths tested. MiFi also successfully identified Ph. nicotianae in the sequenced metagenomes with nanopore DNA sequencing. Thus, EDNA-MiFi could enable rapid detection (&lt;24 h from sample collection to diagnosis) without requiring post-sequencing processing. EDNA, as implemented in the MiFi web application, is a powerful tool for detecting oomycetes from metagenomic samples. Our study shows the outstanding potential of EDNA-MiFi coupled with nanopore sequencing for plant and possible on-site pathogen diagnosis. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Influence of Bifidobacterium breve on the Glycaemic Control, Lipid Profile and Microbiome of Type 2 Diabetic Subjects: A Preliminary Randomized Clinical Trial.

Type 2 diabetes mellitus (T2DM) is one of the most highly prevalent metabolic disorders worldwide. Uncontrolled T2DM can lead to other health threats such as cardiac arrest, lower-limb amputation, blindness, stroke, impaired kidney function, and microvascular and macrovascular complications. Many studies have demonstrated the association between gut microbiota and diabetes development and probiotic supplementation in improving glycemic properties in T2DM. The study aimed to evaluate the influence of Bifidobacterium breve supplementation on glycemic control, lipid profile, and microbiome of T2DM subjects. Forty participants were randomly divided into two groups, and they received probiotics (50 × 109 CFU/day) or placebo interventions (corn starch; 10 mg/day) for 12 weeks. The changes in the blood-urea nitrogen (BUN), aspartate aminotransferase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), fasting blood sugar (FBS), glycated hemoglobin (HbA1c), total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), creatinine levels, and other factors such as body-mass index, visceral fat, body fat, and body weight were assessed at baseline and after 12 weeks. B. breve supplementation significantly reduced BUN, creatinine, LDL, TG, and HbA1c levels compared to the placebo group. Significant changes were observed in the microbiome of the probiotic-treated group compared to the placebo group. Firmicutes and proteobacteria were predominant in the placebo and probiotic-treated groups. Genera Streptococcus, Butyricicoccus, and species Eubacterium hallii were significantly reduced in the probiotic-treated group compared to the placebo. Overall results suggested that B. breve supplementation could prevent worsening of representative clinical parameters in T2DM subjects. The current study has limitations, including fewer subjects, a single probiotic strain, and fewer metagenomic samples for microbiome analysis. Therefore, the results of the current study require further validation using more experimental subjects.

GENETIC BARCODING FOR SURVEILLANCE OF VIRULENCE PLASMIDS IN METAGENOMIC SAMPLES OF MICE GUT MICROFLORA

Outbreaks of nosocomial infections strike the public health system around the world. Current surveillance systems for infectious agents either take too long to identify pathogens or lack the necessary sensitivity to distinguish the virulent strains from the benign ones in the resident microflora. To address this problem, we propose a method for improving the sensitivity and specificity of detection of virulent clonal lines of bacteria or individual virulence plasmids in metagenomic samples. In this study, we used previously developed computational tools, Barcode Generator and Barcoding 2.0, to analyse gut microflora of laboratory mice infected with a pathogenic multidrug-resistant S. aureus and treated with cefazolin, an iodine-containing complex CC-195, and by a combinatorial treatment. We searched the metagenomic samples for representatives of pathogenic microflora using diagnostic genetic barcodes, which were designed based on the whole genome sequences of a collection of clinical isolates. Our results demonstrated a practical applicability of these diagnostic barcodes to monitor clonal lines of pathogens and even individual virulence plasmids in the environment. We also found that the novel drug CC-195 promotes a speedy restoration of the normal gut microflora disturbed by infection and administration of the antibiotic cefazolin.

UltraSEQ, a Universal Bioinformatic Platform for Information-Based Clinical Metagenomics and Beyond.

Applied metagenomics is a powerful emerging capability enabling the untargeted detection of pathogens, and its application in clinical diagnostics promises to alleviate the limitations of current targeted assays. While metagenomics offers a hypothesis-free approach to identify any pathogen, including unculturable and potentially novel pathogens, its application in clinical diagnostics has so far been limited by workflow-specific requirements, computational constraints, and lengthy expert review requirements. To address these challenges, we developed UltraSEQ, a first-of-its-kind accurate and scalable metagenomic bioinformatic tool for potential clinical diagnostics and biosurveillance utility. Here, we present the results of the evaluation of our novel UltraSEQ pipeline using an in silico-synthesized metagenome, mock microbial community data sets, and publicly available clinical data sets from samples of different infection types, including both short-read and long-read sequencing data. Our results show that UltraSEQ successfully detected all expected species across the tree of life in the in silico sample and detected all 10 bacterial and fungal species in the mock microbial community data set. For clinical data sets, even without requiring data set-specific configuration setting changes, background sample subtraction, or prior sample information, UltraSEQ achieved an overall accuracy of 91%. Furthermore, as an initial demonstration with a limited patient sample set, we show UltraSEQ's ability to provide antibiotic resistance and virulence factor genotypes that are consistent with phenotypic results. Taken together, the above-described results demonstrate that the UltraSEQ platform offers a transformative approach for microbial and metagenomic sample characterization, employing a biologically informed detection logic, deep metadata, and a flexible system architecture for the classification and characterization of taxonomic origin, gene function, and user-defined functions, including disease-causing infections. IMPORTANCE Traditional clinical microbiology-based diagnostic tests rely on targeted methods that can detect only one to a few preselected organisms or slow, culture-based methods. Although widely used today, these methods have several limitations, resulting in rates of cases of an unknown etiology of infection of >50% for several disease types. Massive developments in sequencing technologies have made it possible to apply metagenomic methods to clinical diagnostics, but current offerings are limited to a specific disease type or sequencer workflow and/or require laboratory-specific controls. The limitations associated with current clinical metagenomic offerings result from the fact that the backend bioinformatic pipelines are optimized for the specific parameters described above, resulting in an excess of unmaintained, redundant, and niche tools that lack standardization and explainable outputs. In this paper, we demonstrate that UltraSEQ uses a novel, information-based approach that enables accurate, evidence-based predictions for diagnosis as well as the functional characterization of a sample.