A metagenomic strategy for harnessing the chemical repertoire of the human microbiome

Science ◽  
2019 ◽  
Vol 366 (6471) ◽  
pp. eaax9176 ◽  
Author(s):  
Yuki Sugimoto ◽  
Francine R. Camacho ◽  
Shuo Wang ◽  
Pranatchareeya Chankhamjon ◽  
Arman Odabas ◽  
...  
Keyword(s):  
Small Molecules ◽  
Host Defense ◽  
Computational Algorithm ◽  
Human Microbiome ◽  
Antibacterial Activities ◽  
Biologically Active ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Niche Competition ◽  
Made In

Extensive progress has been made in determining the effects of the microbiome on human physiology and disease, but the underlying molecules and mechanisms governing these effects remain largely unexplored. Here, we combine a new computational algorithm with synthetic biology to access biologically active small molecules encoded directly in human microbiome–derived metagenomic sequencing data. We discover that members of a clinically used class of molecules are widely encoded in the human microbiome and that they exert potent antibacterial activities against neighboring microbes, implying a possible role in niche competition and host defense. Our approach paves the way toward a systematic unveiling of the chemical repertoire encoded by the human microbiome and provides a generalizable platform for discovering molecular mediators of microbiome-host and microbiome-microbiome interactions.

scholarly journals Biology and taxonomy of crAss-like bacteriophages, the most abundant virus in the human gut

2018 ◽  
Author(s):  
Emma Guerin ◽  
Andrey Shkoporov ◽  
Stephen R. Stockdale ◽  
Adam G. Clooney ◽  
Feargal J. Ryan ◽  
...  
Keyword(s):  
Classification Scheme ◽  
Ex Vivo ◽  
Human Microbiome ◽  
Biological Entity ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Human Gut ◽  
Taxonomic Analysis ◽  
Faecal Samples ◽  
Phage Capsid

AbstractCrAssphage is yet to be cultured even though it represents the most abundant virus in the gut microbiota of humans. Recently, sequence based classification was performed on distantly related crAss-like phages from multiple environments, leading to the proposal of a familial level taxonomic group [Yutin N, et al. (2018) Discovery of an expansive bacteriophage family that includes the most abundant viruses from the human gut. Nat Microbiol 3(1):38–46]. Here, we assembled the metagenomic sequencing reads from 702 human faecal virome/phageome samples and obtained 98 complete circular crAss-like phage genomes and 145 contigs ≥70kb. In silico comparative genomics and taxonomic analysis was performed, resulting in a classification scheme of crAss-like phages from human faecal microbiomes into 4 candidate subfamilies composed of 10 candidate genera. Moreover, laboratory analysis was performed on faecal samples from an individual harbouring 7 distinct crAss-like phages. We achieved propagation of crAss-like phages in ex vivo human faecal fermentations and visualised Podoviridae virions by electron microscopy. Furthermore, detection of a crAss-like phage capsid protein could be linked to metagenomic sequencing data confirming crAss-like phage structural annotations.SignificanceCrAssphage is the most abundant biological entity in the human gut, but it remains uncultured in the laboratory and its host(s) is unknown. CrAssphage was not identified in metagenomic studies for many years as its sequence is so different from anything present in databases. To this day, it can only be detected from sequences assembled from metagenomics or viromic datasets (crAss – cross Assembly). In this study, we identified 243 new crAss-like phages from human faecal metagenomic studies. Taxonomic analysis of these crAss-like phages highlighted their extensive diversity within the human microbiome. We also present the first propagation of crAssphage in faecal fermentations and provide the first electron micrographs of this extraordinary bacteriophage.

scholarly journals Insights into the Human Virome Using CRISPR Spacers from Microbiomes

Viruses ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 479 ◽  
Author(s):  
Claudio Hidalgo-Cantabrana ◽  
Rosemary Sanozky-Dawes ◽  
Rodolphe Barrangou
Keyword(s):  
Pathogenic Bacteria ◽  
Human Microbiome ◽  
Adaptive Immune System ◽  
Metagenomic Data ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Adaptive Immune ◽  
Associated Proteins ◽  
Health And Disease ◽  
Generation Sequencing

Due to recent advances in next-generation sequencing over the past decade, our understanding of the human microbiome and its relationship to health and disease has increased dramatically. Yet, our insights into the human virome, and its interplay with important microbes that impact human health, is relatively limited. Prokaryotic and eukaryotic viruses are present throughout the human body, comprising a large and diverse population which influences several niches and impacts our health at various body sites. The presence of prokaryotic viruses like phages, has been documented at many different body sites, with the human gut being the richest ecological niche. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and associated proteins constitute the adaptive immune system of bacteria, which prevents attack by invasive nucleic acid. CRISPR-Cas systems function by uptake and integration of foreign genetic element sequences into the CRISPR array, which constitutes a genomic archive of iterative vaccination events. Consequently, CRISPR spacers can be investigated to reconstruct interplay between viruses and bacteria, and metagenomic sequencing data can be exploited to provide insights into host-phage interactions within a niche. Here, we show how the CRISPR spacer content of commensal and pathogenic bacteria can be used to determine the evidence of their phage exposure. This framework opens new opportunities for investigating host-virus dynamics in metagenomic data, and highlights the need to dedicate more efforts for virome sampling and sequencing.

scholarly journals MG-MLST: Characterizing the Microbiome at the Strain Level in Metagenomic Data

2020 ◽  
Vol 8 (5) ◽  
pp. 684
Author(s):  
Nathanael J. Bangayan ◽  
Baochen Shi ◽  
Jerry Trinh ◽  
Emma Barnard ◽  
Gabriela Kasimatis ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Human Microbiome ◽  
Shotgun Sequencing ◽  
Strain Level ◽  
Multi Locus Sequence Typing ◽  
Metagenomic Data ◽  
Sequencing Analysis ◽  
Metagenomic Sequencing ◽  
Healthy Skin ◽  
Sequencing Data

The microbiome plays an important role in human physiology. The composition of the human microbiome has been described at the phylum, class, genus, and species levels, however, it is largely unknown at the strain level. The importance of strain-level differences in microbial communities has been increasingly recognized in understanding disease associations. Current methods for identifying strain populations often require deep metagenomic sequencing and a comprehensive set of reference genomes. In this study, we developed a method, metagenomic multi-locus sequence typing (MG-MLST), to determine strain-level composition in a microbial community by combining high-throughput sequencing with multi-locus sequence typing (MLST). We used a commensal bacterium, Propionibacterium acnes, as an example to test the ability of MG-MLST in identifying the strain composition. Using simulated communities, MG-MLST accurately predicted the strain populations in all samples. We further validated the method using MLST gene amplicon libraries and metagenomic shotgun sequencing data of clinical skin samples. MG-MLST yielded consistent results of the strain composition to those obtained from nearly full-length 16S rRNA clone libraries and metagenomic shotgun sequencing analysis. When comparing strain-level differences between acne and healthy skin microbiomes, we demonstrated that strains of RT2/6 were highly associated with healthy skin, consistent with previous findings. In summary, MG-MLST provides a quantitative analysis of the strain populations in the microbiome with diversity and richness. It can be applied to microbiome studies to reveal strain-level differences between groups, which are critical in many microorganism-related diseases.

scholarly journals Microbiome Maps: Hilbert Curve Visualizations of Metagenomic Profiles

2021 ◽  
Author(s):  
Camilo Valdes ◽  
Vitalii Stebliankin ◽  
Daniel Ruiz-Perez ◽  
Ji In Park ◽  
Hajeong Lee ◽  
...  
Keyword(s):  
Human Microbiome ◽  
Human Microbiome Project ◽  
Supplementary Information ◽  
Metagenomic Sequencing ◽  
Hilbert Curve ◽  
Sequencing Data ◽  
Space Filling ◽  
Space Filling Curve ◽  
Spatio Temporal ◽  
Disease Study

AbstractMotivationAbundance profiles from metagenomic sequencing data synthesize information from billions of sequenced reads coming from thousands of microbial genomes. Analyzing and understanding these profiles can be a challenge since the data they represent is complex. Particularly challenging is their visualization, as existing techniques are inadequate when the taxa number in the thousands. We present a technique for succinct visualization of abundance profiles using a space-filling curve that transforms a profile into an interpretable 2D image.ResultsJasper is a tool for visualizing profiles from metagenomic whole-genome sequencing and 16S, and orders taxa along a space-filling Hilbert curve. The result is a “Microbiome Map”, where each position in the image represents the abundance of a single taxon from a reference collection. Jasper can order the taxa in one of two ways, and depending on the ordering, the microbiome maps can highlight “hot spots” of microbes that are either dominant in taxonomic clades or to the biological conditions under study.We use Jasper to visualize samples from the Human Microbiome Project and from a Chronic Kidney Disease study, and discuss a variety of ways in which the microbiome maps can be an invaluable tool to visualize spatial, temporal, disease, and differential profiles. Our approach can create detailed microbiome maps involving hundreds of thousands of microbial reference genomes with the potential to unravel latent relationships (taxonomic, spatio-temporal, functional, and other) that could remain hidden using traditional visualization techniques. The maps can be converted into animated movies that bring to life the dynamicity of microbiomes.AvailabilityJasper will be available as free software from the Mac App Store and biorg.cs.fiu.edu/jasperSupplementary informationSupplementary materials are available at biorg.cs.fiu.edu/jasper

Visualizing 3D Molecular Structures Using an Augmented Reality App

2019 ◽  
Author(s):  
Kristina Eriksen ◽  
Bjarne Nielsen ◽  
Michael Pittelkow
Keyword(s):  
Augmented Reality ◽  
Small Molecules ◽  
Computer Modelling ◽  
Simple Procedure ◽  
3D Structure ◽  
Molecular Structures ◽  
Free Software ◽  
Programming Skills ◽  
2D Space ◽  
Made In

<p>We present a simple procedure to make an augmented reality app to visualize any 3D chemical model. The molecular structure may be based on data from crystallographic data or from computer modelling. This guide is made in such a way, that no programming skills are needed and the procedure uses free software and is a way to visualize 3D structures that are normally difficult to comprehend in the 2D space of paper. The process can be applied to make 3D representation of any 2D object, and we envisage the app to be useful when visualizing simple stereochemical problems, when presenting a complex 3D structure on a poster presentation or even in audio-visual presentations. The method works for all molecules including small molecules, supramolecular structures, MOFs and biomacromolecules.</p>

scholarly journals Characterizing and Evaluating the Zoonotic Potential of Novel Viruses Discovered in Vampire Bats

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 252
Author(s):  
Laura M. Bergner ◽  
Nardus Mollentze ◽  
Richard J. Orton ◽  
Carlos Tello ◽  
Alice Broos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Phylogenetic Analyses ◽  
Human Infection ◽  
Machine Learning Algorithms ◽  
Zoonotic Potential ◽  
Metagenomic Sequencing ◽  
Learning Models ◽  
Sequencing Data ◽  
Vampire Bats ◽  
Machine Learning Models

The contemporary surge in metagenomic sequencing has transformed knowledge of viral diversity in wildlife. However, evaluating which newly discovered viruses pose sufficient risk of infecting humans to merit detailed laboratory characterization and surveillance remains largely speculative. Machine learning algorithms have been developed to address this imbalance by ranking the relative likelihood of human infection based on viral genome sequences, but are not yet routinely applied to viruses at the time of their discovery. Here, we characterized viral genomes detected through metagenomic sequencing of feces and saliva from common vampire bats (Desmodus rotundus) and used these data as a case study in evaluating zoonotic potential using molecular sequencing data. Of 58 detected viral families, including 17 which infect mammals, the only known zoonosis detected was rabies virus; however, additional genomes were detected from the families Hepeviridae, Coronaviridae, Reoviridae, Astroviridae and Picornaviridae, all of which contain human-infecting species. In phylogenetic analyses, novel vampire bat viruses most frequently grouped with other bat viruses that are not currently known to infect humans. In agreement, machine learning models built from only phylogenetic information ranked all novel viruses similarly, yielding little insight into zoonotic potential. In contrast, genome composition-based machine learning models estimated different levels of zoonotic potential, even for closely related viruses, categorizing one out of four detected hepeviruses and two out of three picornaviruses as having high priority for further research. We highlight the value of evaluating zoonotic potential beyond ad hoc consideration of phylogeny and provide surveillance recommendations for novel viruses in a wildlife host which has frequent contact with humans and domestic animals.

scholarly journals METAMVGL: a multi-view graph-based metagenomic contig binning algorithm by integrating assembly and paired-end graphs

2021 ◽  
Vol 22 (S10) ◽  
Author(s):  
Zhenmiao Zhang ◽  
Lu Zhang
Keyword(s):  
De Novo ◽  
Label Propagation ◽  
Next Generation Sequencing Data ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Fecal Samples ◽  
Microbial Genomes ◽  
Metagenome Assembly ◽  
High Chance ◽  
Mock Communities

Abstract Background Due to the complexity of microbial communities, de novo assembly on next generation sequencing data is commonly unable to produce complete microbial genomes. Metagenome assembly binning becomes an essential step that could group the fragmented contigs into clusters to represent microbial genomes based on contigs’ nucleotide compositions and read depths. These features work well on the long contigs, but are not stable for the short ones. Contigs can be linked by sequence overlap (assembly graph) or by the paired-end reads aligned to them (PE graph), where the linked contigs have high chance to be derived from the same clusters. Results We developed METAMVGL, a multi-view graph-based metagenomic contig binning algorithm by integrating both assembly and PE graphs. It could strikingly rescue the short contigs and correct the binning errors from dead ends. METAMVGL learns the two graphs’ weights automatically and predicts the contig labels in a uniform multi-view label propagation framework. In experiments, we observed METAMVGL made use of significantly more high-confidence edges from the combined graph and linked dead ends to the main graph. It also outperformed many state-of-the-art contig binning algorithms, including MaxBin2, MetaBAT2, MyCC, CONCOCT, SolidBin and GraphBin on the metagenomic sequencing data from simulation, two mock communities and Sharon infant fecal samples. Conclusions Our findings demonstrate METAMVGL outstandingly improves the short contig binning and outperforms the other existing contig binning tools on the metagenomic sequencing data from simulation, mock communities and infant fecal samples.

scholarly journals SCAPP: an algorithm for improved plasmid assembly in metagenomes

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
David Pellow ◽  
Alvah Zorea ◽  
Maraike Probst ◽  
Ori Furman ◽  
Arik Segal ◽  
...  
Keyword(s):  
Bacterial Species ◽  
Bacterial Genome ◽  
Biological Knowledge ◽  
Assessment Procedure ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Human Gut ◽  
Double Stranded Dna ◽  
Wide Range ◽  
Python Package

Abstract Background Metagenomic sequencing has led to the identification and assembly of many new bacterial genome sequences. These bacteria often contain plasmids: usually small, circular double-stranded DNA molecules that may transfer across bacterial species and confer antibiotic resistance. These plasmids are generally less studied and understood than their bacterial hosts. Part of the reason for this is insufficient computational tools enabling the analysis of plasmids in metagenomic samples. Results We developed SCAPP (Sequence Contents-Aware Plasmid Peeler)—an algorithm and tool to assemble plasmid sequences from metagenomic sequencing. SCAPP builds on some key ideas from the Recycler algorithm while improving plasmid assemblies by integrating biological knowledge about plasmids. We compared the performance of SCAPP to Recycler and metaplasmidSPAdes on simulated metagenomes, real human gut microbiome samples, and a human gut plasmidome dataset that we generated. We also created plasmidome and metagenome data from the same cow rumen sample and used the parallel sequencing data to create a novel assessment procedure. Overall, SCAPP outperformed Recycler and metaplasmidSPAdes across this wide range of datasets. Conclusions SCAPP is an easy to use Python package that enables the assembly of full plasmid sequences from metagenomic samples. It outperformed existing metagenomic plasmid assemblers in most cases and assembled novel and clinically relevant plasmids in samples we generated such as a human gut plasmidome. SCAPP is open-source software available from: https://github.com/Shamir-Lab/SCAPP.

Efficient Synthesis of Novel N-[4-Methyl-3-(4-(5-phenyl-1,3,4-oxadiazol-2-yl)phenyl)thiazol-2(3H)-ylidene]benzamide Hybrid Ring System as Potential Antibacterial Agents

2021 ◽  
Vol 17 ◽  
Author(s):  
Hummera Rafique ◽  
Aamer Saeed ◽  
Muhammad Naseem ◽  
Tauqeer Riaz ◽  
Fouzia Perveen ◽  
...  
Keyword(s):  
Bacterial Resistance ◽  
Antioxidant Activities ◽  
Antibacterial Properties ◽  
Antibacterial Activities ◽  
Structural Features ◽  
Ring System ◽  
Biologically Active ◽  
Thiazole Ring ◽  
Spectroscopic Techniques ◽  
Hybrid Ring

Background: Heterocyclic compounds display versatile biological applications, so the aim of this paper was to prepare biologically important heterocycles with enhanced bacterial resistance and to evaluate for their various structural features that are responsible for their biological properties. Objective: The objective was to synthesize bacterial resistance compounds with enhanced antibacterial properties. Method: Ester moiety containing thiazole ring was converted into its hydrazide derivatives. These heterocyclic derivatives were cyclized into another ring oxadiazole; hence a hybrid ring system of two biologically active rings was prepared. Result: All the synthesized compounds were characterized by spectroscopic techniques and were screened for their antibacterial potential; they possess significant antibacterial activities. Conclusion: New hybrid heterocyclic ring systems were synthesized by cyclization of hydrazide derivatives by adopting two step strategy in good yields. All the synthesized compounds were evaluated for their antioxidant activities; they showed moderate to significant activities. QSAR and Molecular docking studies were performed to determine the mode of interaction. Experimental and computational data is in accordance with the determined antibacterial activities.

scholarly journals Evaluation of the CosmosID Bioinformatics Platform for Prosthetic Joint-Associated Sonicate Fluid Shotgun Metagenomic Data Analysis

2018 ◽  
Vol 57 (2) ◽  
Author(s):  
Qun Yan ◽  
Yu Mi Wi ◽  
Matthew J. Thoendel ◽  
Yash S. Raval ◽  
Kerryl E. Greenwood-Quaintance ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Metagenomic Data ◽  
Metagenomic Sequencing ◽  
Antibacterial Resistance ◽  
Sequencing Data ◽  
Bacterial Detection ◽  
Shotgun Metagenomic Sequencing ◽  
Prosthetic Joint ◽  
Validation Set ◽  
Fluid Culture

ABSTRACT We previously demonstrated that shotgun metagenomic sequencing can detect bacteria in sonicate fluid, providing a diagnosis of prosthetic joint infection (PJI). A limitation of the approach that we used is that data analysis was time-consuming and specialized bioinformatics expertise was required, both of which are barriers to routine clinical use. Fortunately, automated commercial analytic platforms that can interpret shotgun metagenomic data are emerging. In this study, we evaluated the CosmosID bioinformatics platform using shotgun metagenomic sequencing data derived from 408 sonicate fluid samples from our prior study with the goal of evaluating the platform vis-à-vis bacterial detection and antibiotic resistance gene detection for predicting staphylococcal antibacterial susceptibility. Samples were divided into a derivation set and a validation set, each consisting of 204 samples; results from the derivation set were used to establish cutoffs, which were then tested in the validation set for identifying pathogens and predicting staphylococcal antibacterial resistance. Metagenomic analysis detected bacteria in 94.8% (109/115) of sonicate fluid culture-positive PJIs and 37.8% (37/98) of sonicate fluid culture-negative PJIs. Metagenomic analysis showed sensitivities ranging from 65.7 to 85.0% for predicting staphylococcal antibacterial resistance. In conclusion, the CosmosID platform has the potential to provide fast, reliable bacterial detection and identification from metagenomic shotgun sequencing data derived from sonicate fluid for the diagnosis of PJI. Strategies for metagenomic detection of antibiotic resistance genes for predicting staphylococcal antibacterial resistance need further development.

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