scholarly journals Accurate and sensitive detection of microbial eukaryotes from metagenomic shotgun sequencing

2020 ◽  
Author(s):  
Abigail L. Lind ◽  
Katherine S. Pollard
Keyword(s):  
Shotgun Sequencing ◽  
Marker Genes ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Dna And Rna ◽  
Paired Samples ◽  
Microbial Eukaryotes ◽  
Shotgun Metagenomic Sequencing ◽  
Active Transcription ◽  
Eukaryotic Genomes

AbstractMicrobial eukaryotes are found alongside bacteria and archaea in natural microbial systems, including host-associated microbiomes. While microbial eukaryotes are critical to these communities, they are often not included in metagenomic analyses. Here we present EukDetect, a bioinformatics method to identify eukaryotes in shotgun metagenomic sequencing data. Our approach uses a database of universal marker genes, which we curated from all 2,571 currently available fungal, protist, worm and other diverse eukaryotic genomes. EukDetect is accurate and sensitive, has a broad taxonomic coverage of microbial eukaryotes, and is resilient against bacterial contamination in eukaryotic genomes. Using EukDetect, we describe the spatial distribution of eukaryotes along the human gastrointestinal tract, showing that fungi and protists are present in the lumen and mucosa throughout the large intestine. We discover that there is a succession of eukaryotes that colonize the human gut during the first years of life, similar to patterns of developmental succession observed in gut bacteria. By comparing DNA and RNA sequencing of paired samples from human stool, we find that many eukaryotes continue active transcription after passage through the gut, while others do not, suggesting they are dormant or nonviable. Finally, we observe eukaryotes in Arabidopsis leaf samples, many of which are not identifiable from public protein databases. Thus, EukDetect provides an automated and reliable way to characterize eukaryotes in shotgun sequencing datasets from diverse microbiomes.

scholarly journals Accurate and sensitive detection of microbial eukaryotes from whole metagenome shotgun sequencing

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Abigail L. Lind ◽  
Katherine S. Pollard
Keyword(s):  
Gene Families ◽  
Shotgun Sequencing ◽  
Metagenomic Data ◽  
Marker Genes ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Dna And Rna ◽  
Paired Samples ◽  
Microbial Eukaryotes ◽  
Conserved Gene

Abstract Background Microbial eukaryotes are found alongside bacteria and archaea in natural microbial systems, including host-associated microbiomes. While microbial eukaryotes are critical to these communities, they are challenging to study with shotgun sequencing techniques and are therefore often excluded. Results Here, we present EukDetect, a bioinformatics method to identify eukaryotes in shotgun metagenomic sequencing data. Our approach uses a database of 521,824 universal marker genes from 241 conserved gene families, which we curated from 3713 fungal, protist, non-vertebrate metazoan, and non-streptophyte archaeplastida genomes and transcriptomes. EukDetect has a broad taxonomic coverage of microbial eukaryotes, performs well on low-abundance and closely related species, and is resilient against bacterial contamination in eukaryotic genomes. Using EukDetect, we describe the spatial distribution of eukaryotes along the human gastrointestinal tract, showing that fungi and protists are present in the lumen and mucosa throughout the large intestine. We discover that there is a succession of eukaryotes that colonize the human gut during the first years of life, mirroring patterns of developmental succession observed in gut bacteria. By comparing DNA and RNA sequencing of paired samples from human stool, we find that many eukaryotes continue active transcription after passage through the gut, though some do not, suggesting they are dormant or nonviable. We analyze metagenomic data from the Baltic Sea and find that eukaryotes differ across locations and salinity gradients. Finally, we observe eukaryotes in Arabidopsis leaf samples, many of which are not identifiable from public protein databases. Conclusions EukDetect provides an automated and reliable way to characterize eukaryotes in shotgun sequencing datasets from diverse microbiomes. We demonstrate that it enables discoveries that would be missed or clouded by false positives with standard shotgun sequence analysis. EukDetect will greatly advance our understanding of how microbial eukaryotes contribute to microbiomes.

PStrain: an iterative microbial strains profiling algorithm for shotgun metagenomic sequencing data

2020 ◽  
Author(s):  
Shuai Wang ◽  
Yiqi Jiang ◽  
Shuaicheng Li
Keyword(s):  
Optimization Method ◽  
Supplementary Information ◽  
Marker Genes ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Shotgun Metagenomic Sequencing ◽  
Genotype Frequencies ◽  
Microbial Strains ◽  
And Control ◽  
First Time

Abstract Motivation The microbial community plays an essential role in human diseases and physiological activities. The functions of microbes can differ due to strain-level differences in the genome sequences. Shotgun metagenomic sequencing allows us to profile the strains in microbial communities practically. However, current methods are underdeveloped due to the highly similar sequences among strains. We observe that strains genotypes at the same single nucleotide variant (SNV) locus can be speculated by the genotype frequencies. Also, the variants in different loci covered by the same reads can provide evidence that they reside on the same strain. Results These insights inspire us to design PStrain, an optimization method that utilizes genotype frequencies and the reads which cover multiple SNV loci to profile strains iteratively based on SNVs in a set of MetaPhlAn2 marker genes. Compared to the state-of-art methods, PStrain, on average, improved the performance of inferring strains abundances and genotypes by 87.75% and 59.45%, respectively. We have applied the PStrain package to the dataset with two cohorts of colorectal cancer (CRC) and found that the sequences of Bacteroides coprocola strains are significantly different between CRC and control samples, which is the first time to report the potential role of B.coprocola in the gut microbiota of CRC. Availabilityand implementation https://github.com/wshuai294/PStrain. Supplementary information Supplementary data are available at Bioinformatics online.

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.

scholarly journals Human gut microbial communities dictate efficacy of anti-PD-1 therapy in a humanized microbiome mouse model of glioma

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Kory J Dees ◽  
Hyunmin Koo ◽  
J Fraser Humphreys ◽  
Joseph A Hakim ◽  
David K Crossman ◽  
...  
Keyword(s):  
Mouse Model ◽  
Microbial Communities ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Microbial Composition ◽  
Healthy Human ◽  
Fecal Transplantation ◽  
Host Interaction ◽  
Shotgun Metagenomic Sequencing ◽  
Mouse Lines

Abstract Background Although immunotherapy works well in glioblastoma (GBM) preclinical mouse models, the therapy has not demonstrated efficacy in humans. To address this anomaly, we developed a novel humanized microbiome (HuM) model to study the response to immunotherapy in a preclinical mouse model of GBM. Methods We used 5 healthy human donors for fecal transplantation of gnotobiotic mice. After the transplanted microbiomes stabilized, the mice were bred to generate 5 independent humanized mouse lines (HuM1-HuM5). Results Analysis of shotgun metagenomic sequencing data from fecal samples revealed a unique microbiome with significant differences in diversity and microbial composition among HuM1-HuM5 lines. All HuM mouse lines were susceptible to GBM transplantation, and exhibited similar median survival ranging from 19 to 26 days. Interestingly, we found that HuM lines responded differently to the immune checkpoint inhibitor anti-PD-1. Specifically, we demonstrate that HuM1, HuM4, and HuM5 mice are nonresponders to anti-PD-1, while HuM2 and HuM3 mice are responsive to anti-PD-1 and displayed significantly increased survival compared to isotype controls. Bray-Curtis cluster analysis of the 5 HuM gut microbial communities revealed that responders HuM2 and HuM3 were closely related, and detailed taxonomic comparison analysis revealed that Bacteroides cellulosilyticus was commonly found in HuM2 and HuM3 with high abundances. Conclusions The results of our study establish the utility of humanized microbiome mice as avatars to delineate features of the host interaction with gut microbial communities needed for effective immunotherapy against GBM.

scholarly journals Yoghurt Consumption is Associated With Transient Changes in the Composition of the Human Gut Microbiome

2020 ◽  
Author(s):  
Caroline Ivanne Le Roy ◽  
Alexander Kurilshikov ◽  
Emily Leeming ◽  
Alessia Visconti ◽  
Ruth Bowyer ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
Visceral Fat ◽  
Gut Microbiome ◽  
Body Weight Gain ◽  
Healthy Eating Index ◽  
Rrna Gene ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Shotgun Metagenomic Sequencing

Abstract Background: Yoghurt contains live bacteria that could contribute via modulation of the gut microbiota to its reported beneficial effects such as reduced body weight gain and lower incidence of type 2 diabetes. To date, the association between yoghurt consumption and the composition of the gut microbiota is underexplored. Here we used clinical variables, metabolomics, 16S rRNA and shotgun metagenomic sequencing data collected on over 1000 predominantly female UK twins to define the link between the gut microbiota and yoghurt-associated health benefits. Results: According to food frequency questionnaires (FFQ), 73% of subjects consumed yoghurt. Consumers presented a healthier diet pattern (healthy eating index: beta = 2.17±0.34; P = 2.72x10-10) and improved metabolic health characterised by reduced visceral fat (beta = -28.18±11.71 g; P = 0.01). According to 16S rRNA gene analyses and whole shotgun metagenomic sequencing approach consistent taxonomic variations were observed with yoghurt consumption. More specifically, we identified higher abundance of species used as yoghurt starters Streptococcus thermophilus (beta = 0.41±0.051; P = 6.14x10-12) and sometimes added Bifidobacterium animalis subsp. lactis (beta = 0.30±0.052; P = 1.49x10-8) in the gut of yoghurt consumers. Replication in 1103 volunteers from the LifeLines-DEEP cohort confirmed the increase of S. thermophilus among yoghurt consumers. Using food records collected the day prior to faecal sampling we showed that increase in these two yoghurt bacteria could be transient. Metabolomics analysis revealed that B. animalis subsp. lactis was associated with 13 faecal metabolites including a 3-hydroxyoctanoic acid, known to be involved in the regulation of gut inflammation.Conclusions: Yoghurt consumption is associated with reduced visceral fat mass and changes in gut microbiome including transient increase of yoghurt-contained species (i.e. S. thermophilus and B. lactis).

scholarly journals A Genome-Phenome Association study in native microbiomes identifies a mechanism for cytosine modification in DNA and RNA

2021 ◽  
Author(s):  
Weiwei Yang ◽  
Yu-Cheng Lin ◽  
William Johnson ◽  
Nan Dai ◽  
Peter Wiegele ◽  
...  
Keyword(s):  
Metagenomic Sequencing ◽  
Microbiome Composition ◽  
Dna And Rna ◽  
Shotgun Metagenomic Sequencing ◽  
A Genome ◽  
Powerful Approach ◽  
Unbiased Manner ◽  
Versatile Tool ◽  
Functional Phenotype ◽  
Cytosine Modification

Shotgun metagenomic sequencing is a powerful approach to study microbiomes in an unbiased manner and of increasing relevance for identifying novel enzymatic functions. However, the potential of metagenomics to relate from microbiome composition to function has thus far been underutilized. Here, we introduce the Metagenomics Genome-Phenome Association (MetaGPA) study framework, which allows to link genetic information in metagenomes with a dedicated functional phenotype. We applied MetaGPA to identify enzymes associated with cytosine modifications in environmental samples. From the 2365 genes that met our significance criteria, we confirm known pathways for cytosine modifications and proposed novel cytosine-modifying mechanisms. Specifically, we characterized and identified a novel nucleic acid modifying enzyme, 5-hydroxymethylcytosine carbamoyltransferase, that catalyzes the formation of a previously unknown cytosine modification, 5-carbamoyloxymethylcytosine, in DNA and RNA. Our work introduces MetaGPA as a novel and versatile tool for advancing functional metagenomics.

scholarly journals Targeted high-resolution taxonomic identification of Bifidobacterium longum subsp. infantis using human milk oligosaccharide metabolizing genes

2021 ◽  
Author(s):  
Lauren Tso ◽  
Kevin S Bonham ◽  
Alyssa Fishbein ◽  
Sophie Rowland ◽  
Vanja Klepac-Ceraj ◽  
...  
Keyword(s):  
Human Milk ◽  
Bifidobacterium Longum ◽  
Gene Clusters ◽  
Species Group ◽  
Human Breast Milk ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Specific Primers ◽  
Breastfed Infants ◽  
Shotgun Metagenomic Sequencing

Bifidobacterium longum subsp. infantis (B. infantis) is one of few microorganisms capable of metabolizing human breast milk and is a pioneer colonizer in the guts of breastfed infants. One current challenge is differentiating B. infantis from its close relatives, B. longum and B. suis, by molecular methods. These two organisms are classified in the same species group as B. infantis but do not share the ability to metabolize human milk oligosaccharides (HMOs). Here, we compared HMO-metabolizing genes across different Bifidobacterium genomes to develop B. infantis specific primers and determine if they alone can be used to quickly characterize B. infantis with shotgun metagenomic sequencing data. We showed that B. infantis is uniquely identified by the presence of five HMO-metabolizing gene clusters, used this characterization to test for its prevalence in infants, and validated the results using the B. infantis-specific primers. By examining stool samples from a cohort of US children and pregnant women using shotgun metagenomic sequencing, we observed that only 18 of 204 (8.8%) of children under 2 years old harbored B. infantis. These results highlight the importance of developing and improving approaches to identify B. infantis. A more accurate characterization may provide insights into regional differences of B. infantis prevalence in infant gut microbiota.

scholarly journals Shotgun metagenomic sequencing data of sunflower rhizosphere microbial community in South Africa

Data in Brief ◽  
2020 ◽  
Vol 31 ◽  
pp. 105831
Author(s):  
Olubukola Oluranti Babalola ◽  
Temitayo Tosin Alawiye ◽  
Carlos Rodriguez Lopez ◽  
Ayansina Segun Ayangbenro
Keyword(s):  
South Africa ◽  
Microbial Community ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Shotgun Metagenomic Sequencing ◽  
Rhizosphere Microbial Community

scholarly journals Identification of core and rare species in metagenome samples based on shotgun metagenomic sequencing, Fourier transforms and spectral comparisons

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Marie-Madlen Pust ◽  
Burkhard Tümmler
Keyword(s):  
Microbial Communities ◽  
Rare Species ◽  
Fourier Transforms ◽  
Species Level ◽  
Reference Frequency ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Discrete Fourier Transforms ◽  
False Discovery ◽  
Shotgun Metagenomic Sequencing

AbstractIn shotgun metagenomic sequencing applications, low signal-to-noise ratios may complicate species-level differentiation of genetically similar core species and impede high-confidence detection of rare species. However, core and rare species can take pivotal roles in their habitats and should hence be studied as one entity to gain insights into the total potential of microbial communities in terms of taxonomy and functionality. Here, we offer a solution towards increased species-level specificity, decreased false discovery and omission rates of core and rare species in complex metagenomic samples by introducing the rare species identifier (raspir) tool. The python software is based on discrete Fourier transforms and spectral comparisons of biological and reference frequency signals obtained from real and ideal distributions of short DNA reads mapping towards circular reference genomes. Simulation-based testing of raspir enabled the detection of rare species with genome coverages of less than 0.2%. Species-level differentiation of rare Escherichia coli and Shigella spp., as well as the clear delineation between human Streptococcus spp. was feasible with low false discovery (1.3%) and omission rates (13%). Publicly available human placenta sequencing data were reanalysed with raspir. Raspir was unable to identify placental microbial communities, reinforcing the sterile womb paradigm.

scholarly journals Comprehensive discovery of CRISPR-targeted terminally redundant sequences in the human gut metagenome: Viruses, plasmids, and more

2021 ◽  
Vol 17 (10) ◽  
pp. e1009428
Author(s):  
Ryota Sugimoto ◽  
Luca Nishimura ◽  
Phuong Thanh Nguyen ◽  
Jumpei Ito ◽  
Nicholas F. Parrish ◽  
...  
Keyword(s):  
De Novo ◽  
Sequence Similarity ◽  
Metagenomic Data ◽  
Marker Genes ◽  
Biological Entity ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Human Gut ◽  
Protein Coding ◽  
Viral Sequences

Viruses are the most numerous biological entity, existing in all environments and infecting all cellular organisms. Compared with cellular life, the evolution and origin of viruses are poorly understood; viruses are enormously diverse, and most lack sequence similarity to cellular genes. To uncover viral sequences without relying on either reference viral sequences from databases or marker genes that characterize specific viral taxa, we developed an analysis pipeline for virus inference based on clustered regularly interspaced short palindromic repeats (CRISPR). CRISPR is a prokaryotic nucleic acid restriction system that stores the memory of previous exposure. Our protocol can infer CRISPR-targeted sequences, including viruses, plasmids, and previously uncharacterized elements, and predict their hosts using unassembled short-read metagenomic sequencing data. By analyzing human gut metagenomic data, we extracted 11,391 terminally redundant CRISPR-targeted sequences, which are likely complete circular genomes. The sequences included 2,154 tailed-phage genomes, together with 257 complete crAssphage genomes, 11 genomes larger than 200 kilobases, 766 genomes of Microviridae species, 56 genomes of Inoviridae species, and 95 previously uncharacterized circular small genomes that have no reliably predicted protein-coding gene. We predicted the host(s) of approximately 70% of the discovered genomes at the taxonomic level of phylum by linking protospacers to taxonomically assigned CRISPR direct repeats. These results demonstrate that our protocol is efficient for de novo inference of CRISPR-targeted sequences and their host prediction.

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