scholarly journals A large-scale metagenomic survey dataset of the post-weaning piglet gut lumen

GigaScience ◽  
2021 ◽  
Vol 10 (6) ◽  
Author(s):  
Daniela Gaio ◽  
Matthew Z DeMaere ◽  
Kay Anantanawat ◽  
Graeme J Eamens ◽  
Michael Liu ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Gut Microbiota ◽  
Antibiotic Treatment ◽  
Large Scale ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Sample Collection ◽  
Microbial Composition ◽  
Intensive Farming ◽  
Wide Range

Abstract Background Early weaning and intensive farming practices predispose piglets to the development of infectious and often lethal diseases, against which antibiotics are used. Besides contributing to the build-up of antimicrobial resistance, antibiotics are known to modulate the gut microbial composition. As an alternative to antibiotic treatment, studies have previously investigated the potential of probiotics for the prevention of postweaning diarrhea. In order to describe the post-weaning gut microbiota, and to study the effects of two probiotics formulations and of intramuscular antibiotic treatment on the gut microbiota, we sampled and processed over 800 faecal time-series samples from 126 piglets and 42 sows. Results Here we report on the largest shotgun metagenomic dataset of the pig gut lumen microbiome to date, consisting of >8 Tbp of shotgun metagenomic sequencing data. The animal trial, the workflow from sample collection to sample processing, and the preparation of libraries for sequencing, are described in detail. We provide a preliminary analysis of the dataset, centered on a taxonomic profiling of the samples, and a 16S-based beta diversity analysis of the mothers and the piglets in the first 5 weeks after weaning. Conclusions This study was conducted to generate a publicly available databank of the faecal metagenome of weaner piglets aged between 3 and 9 weeks old, treated with different probiotic formulations and intramuscular antibiotic treatment. Besides investigating the effects of the probiotic and intramuscular antibiotic treatment, the dataset can be explored to assess a wide range of ecological questions with regards to antimicrobial resistance, host-associated microbial and phage communities, and their dynamics during the aging of the host.

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.

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).

IMMU-09. HUMAN MICROBIOTA INFLUENCE THE EFFICACY OF IMMUNOTHERAPY IN A MOUSE MODEL OF GLIOBLASTOMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi93-vi94
Author(s):  
Kory Dees ◽  
Hyunmin Koo ◽  
James Humphreys ◽  
Joseph Hakim ◽  
David Crossman ◽  
...  
Keyword(s):  
Mouse Model ◽  
Microbial Communities ◽  
Mouse Models ◽  
Gut Microbiome ◽  
Positive Response ◽  
Checkpoint Inhibitors ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Microbial Composition ◽  
Healthy Human

Abstract Although immunotherapy works well in glioblastoma (GBM) pre-clinical mouse models, the therapy has unfortunately not demonstrated efficacy in humans. In melanoma and other cancers, the composition of the gut microbiome has been shown to determine responsiveness or resistance to immune checkpoint inhibitors (anti-PD-1). Most pre-clinical cancer studies have been done in mouse models using mouse gut microbiomes, but there are significant differences between mouse and human microbial gut compositions. To address this inconsistency, we developed a novel humanized microbiome (HuM) model to study the response to immunotherapy in a pre-clinical mouse model of GBM. We used five healthy human donors for fecal transplantation of gnotobiotic mice. After the transplanted microbiomes stabilized, the mice were bred to generate five independent humanized mouse lines (HuM1-HuM5). 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. Interestingly, we found that the HuM lines responded differently to anti-PD-1. Specifically, we demonstrate that HuM2 and HuM3 mice are responsive to anti-PD-1 and displayed significantly increased survival compared to isotype controls, while HuM1, HuM4, and HuM5 mice are resistant to anti-PD-1. These mice are genetically identical, and only differ in the composition of the gut microbiome. In a correlative experiment, we found that disrupting the responder HuM2 microbiome with antibiotics abrogated the positive response to anti-PD-1, indicating that HuM2 microbiota must be present in the mice to elicit the positive response to anti-PD-1 in the GBM model. The question remains of whether the “responsive” microbial communities in HuM2 and HuM3 can be therapeutically exploited and applicable in other tumor models, or if the “resistant” microbial communities in HuM1, HuM4, and HuM5 can be depleted and/or replaced. Future studies will assess responder microbial transplants as a method of enhancing immunotherapy.

scholarly journals PinAPL-Py: A comprehensive web-application for the analysis of CRISPR/Cas9 screens

2017 ◽  
Author(s):  
Philipp N. Spahn ◽  
Tyler Bath ◽  
Ryan J. Weiss ◽  
Jihoon Kim ◽  
Jeffrey D. Esko ◽  
...  
Keyword(s):  
Web Application ◽  
Large Scale ◽  
Sequencing Data ◽  
Bioinformatic Tools ◽  
Link Type ◽  
Screening Experiments ◽  
Independent Analysis ◽  
Wide Range ◽  
Set Up ◽  
Sequence Quality

AbstractBackgroundLarge-scale genetic screens using CRISPR/Cas9 technology have emerged as a major tool for functional genomics. With its increased popularity, experimental biologists frequently acquire large sequencing datasets for which they often do not have an easy analysis option. While a few bioinformatic tools have been developed for this purpose, their utility is still hindered either due to limited functionality or the requirement of bioinformatic expertise.ResultsTo make sequencing data analysis of CRISPR/Cas9 screens more accessible to a wide range of scientists, we developed a Platform-independent Analysis of Pooled Screens using Python (PinAPL-Py), which is operated as an intuitive web-service. PinAPL-Py implements state-of-the-art tools and statistical models, assembled in a comprehensive workflow covering sequence quality control, automated sgRNA sequence extraction, alignment, sgRNA enrichment/depletion analysis and gene ranking. The workflow is set up to use a variety of popular sgRNA libraries as well as custom libraries that can be easily uploaded. Various analysis options are offered, suitable to analyze a large variety of CRISPR/Cas9 screening experiments. Analysis output includes ranked lists of sgRNAs and genes, and publication-ready plots.ConclusionsPinAPL-Py helps to advance genome-wide screening efforts by combining comprehensive functionality with user-friendly implementation. PinAPL-Py is freely accessible at http://pinapl-py.ucsd.edu with instructions, documentation and test datasets. The source code is available at https://github.com/LewisLabUCSD/PinAPL-Py

scholarly journals A Co-abundant Gene Analysis for Identification of Gut Microbial Strains Associated with Immune Responses to Oral Cholera Vaccine

2021 ◽  
Author(s):  
Fred J. Heller ◽  
Hasan Al Banna ◽  
M. Hasanul Kaisar ◽  
Denise Chac ◽  
Fahima Chowdhury ◽  
...  
Keyword(s):  
Gut Microbiota ◽  
Immune Responses ◽  
Bacterial Genome ◽  
Short Chain Fatty Acids ◽  
Metagenomic Sequencing ◽  
Vaccine Adjuvants ◽  
Sequencing Data ◽  
Bacterial Strains ◽  
Vaccine Response ◽  
Protein Coding

Background: Oral cholera vaccines (OCVs) are an important tool for reduction of the worldwide cholera burden, but some individuals who receive an OCV do not develop protective immune responses. The gut microbiota is a potential explanation for these differences. Components of the gut microbiota associated with differences in OCV response have not been identified. Results: We used metagenomic sequencing to identify predicted protein-coding genes in the gut microbiota at the time of OCV administration, and then measured immune responses to vaccination. Vaccine recipients were classified as OCV 'responders' if they developed a post-vaccination increase in memory B cell populations that produce IgA or IgG specific for cholera toxin and the V. cholerae O-specific polysaccharide. We next analyzed microbial genes seen at similar abundances across individual samples and classified these into co-abundant gene groupings (CAGs), and correlated CAGs with OCV responses. Next, to identify specific bacterial strains associated with OCV responses, we mapped CAGs to bacterial genomes and generated a 'priority score' for each strain detected in the study population. This score reflects both the number of CAGs aligning to a specific bacterial genome and the strength of the association between the CAGs and the vaccine response. This strain-level analysis revealed relationships between the gut microbiota and immune response to OCV that were not detected at the genus or species level. Bacterial strains which produce short-chain fatty acids and those with sphingolipid-containing cell membranes were correlated with more robust immune responses to vaccination. Conclusion: Our study demonstrates a method for translating metagenomic sequencing data into strain-specific results associated with a biological outcome. Using this approach, we identified strains for the study of bacterial-derived molecules or metabolites associated with immune responses; such agents might have potential utility as vaccine adjuvants.

scholarly journals Community composition and development of the post-weaning piglet gut microbiome

2020 ◽  
Author(s):  
Daniela Gaio ◽  
Matthew Z DeMaere ◽  
Kay Anantanawat ◽  
Graeme J Eamens ◽  
Michael Liu ◽  
...  
Keyword(s):  
Microbial Community ◽  
Gut Microbiota ◽  
Community Composition ◽  
Antibiotic Treatment ◽  
Gut Microbiome ◽  
Treatment Effects ◽  
Sequence Data ◽  
Microbial Community Composition ◽  
Dominant Factor ◽  
Microbial Composition

Abstract BackgroundEarly weaning and intensive farming practices predispose piglets to the development of infectious and often lethal diseases, against which antibiotics are used. Besides contributing to the build-up of antimicrobial resistance, antibiotics are known to modulate the gut microbial composition. Studies have previously investigated the effects of probiotics as alternatives to antibiotic treatment for the prevention of post-weaning diarrhea. In order to describe the post-weaning gut microbiota, and the effects of two probiotics formulations and of intramuscular antibiotic treatment on the gut microbiota, we processed over 800 faecal time-series samples from 126 piglets and 42 sows, generating over 8Tbp of metagenomic shotgun sequence data. Here we describe the animal trial procedures, the generation of our metagenomic dataset and the analysis of the microbial community composition using a phylogenetic framework.ResultsFactors such as age, litter effects, and breed, by significantly correlating with gut microbial community shifts, can be major confounding factors in the assessment of treatment effects. Intramuscular antibiotic treatment and probiotic treatments were found to correlate with alpha and beta diversity, as well as with a transient establishment of Mollicutes and Lactobacillales, respectively. We found the abundance of certain taxa to correlate with weight gain.ConclusionsOur findings demonstrate that breed, litter, and age, are important contributors to variation in the community composition, and that treatment effects of the antibiotic and probiotic treatments were subtle, while host age was the dominant factor in shaping the gut microbiota of piglets after weaning. The current study shows, by means of a phylogenetic diversity framework, that the post-weaning pig gut microbiome appears to follow a highly structured developmental program with characteristic post-weaning changes that can distinguish hosts that were born as little as two days apart in the second month of life.

scholarly journals Rapid inference of direct interactions in large-scale ecological networks from heterogeneous microbial sequencing data

2018 ◽  
Author(s):  
Janko Tackmann ◽  
João Frederico Matias Rodrigues ◽  
Christian von Mering
Keyword(s):  
Graphical Models ◽  
Large Scale ◽  
Study Data ◽  
Microbial Interactions ◽  
Data Sets ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Human Gut ◽  
Data Set ◽  
Seamless Integration

AbstractThe recent explosion of metagenomic sequencing data opens the door towards the modeling of microbial ecosystems in unprecedented detail. In particular, co-occurrence based prediction of ecological interactions could strongly benefit from this development. However, current methods fall short on several fronts: univariate tools do not distinguish between direct and indirect interactions, resulting in excessive false positives, while approaches with better resolution are so far computationally highly limited. Furthermore, confounding variables typical for cross-study data sets are rarely addressed. We present FlashWeave, a new approach based on a flexible Probabilistic Graphical Models framework to infer highly resolved direct microbial interactions from massive heterogeneous microbial abundance data sets with seamless integration of metadata. On a variety of benchmarks, FlashWeave outperforms state-of-the-art methods by several orders of magnitude in terms of speed while generally providing increased accuracy. We apply FlashWeave to a cross-study data set of 69 818 publicly available human gut samples, resulting in one of the largest and most diverse models of microbial interactions in the human gut to date.

scholarly journals TMOD-19. INDIVIDUAL SPECIFIC HUMAN GUT MICROBE COMMUNITIES INFLUENCE RESPONSE TO IMMUNOTHERAPY IN A HUMANIZED MICROBIOME MOUSE MODEL OF GLIOMA

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii232-ii232
Author(s):  
Kory Dees ◽  
Hyunmin Koo ◽  
J Fraser Humphreys ◽  
Joseph Hakim ◽  
David Crossman ◽  
...  
Keyword(s):  
Mouse Model ◽  
Microbial Community Composition ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Microbial Composition ◽  
Healthy Human ◽  
Fecal Transplantation ◽  
Microbiome Composition ◽  
Gut Microbe ◽  
Mouse Lines

Abstract Although immunotherapy works well in glioblastoma (GBM) pre-clinical mouse models, the therapy has not demonstrated efficacy in GBM patients. Since recent studies have linked the gut microbial composition to the success with immunotherapy for other cancers, we utilized a novel humanized microbiome (HuM) model in order to study the response to immunotherapy in a pre-clinical mouse model of GBM. We used five healthy human donors for fecal transplantation of gnotobiotic mice since it is now recognized that microbe strain level differences render individual humans with a unique microbial community composition. After the transplanted microbiomes stabilized, the mice were bred to generate 5 independent humanized mouse lines (humanized microbiome HuM1-HuM5). Analysis of shotgun metagenomic sequencing data from fecal samples revealed a unique microbiome composition with significant differences in diversity and microbial composition among HuM1-HuM5 lines. We next analyzed the growth of intracranial glioma cells in the HuM lines. All HuM mouse lines were susceptible to GBM transplantation, and exhibited similar median survival ranging from 19-26 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 non-responders to anti-PD-1 resulting in the death of the mice from the intracranial tumors, 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 HuM2 and HuM3 were closely related. Detailed taxonomic comparison analysis at the top 5 across all HuM mouse lines revealed that Bacteroides cellulosilyticus was commonly found between HuM2 and HuM3 with high abundances. The results of our study establish the utility of humanized microbiome mice as avatars to delineate features of the host interaction with gut microbe communities needed for effective immunotherapy against GBM.

scholarly journals Exploring Gut Microbiota in Patients with Colorectal Disease Based on 16S rRNA Gene Amplicon and Shallow Metagenomic Sequencing

2021 ◽  
Vol 8 ◽  
Author(s):  
Yuanfeng Liu ◽  
Xiang Li ◽  
Yudie Yang ◽  
Ye Liu ◽  
Shijun Wang ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
Large Scale ◽  
Central China ◽  
Colorectal Disease ◽  
Rrna Gene ◽  
Metagenomic Sequencing ◽  
Network Analyses ◽  
Significant Difference ◽  
Colorectal Diseases

The gastrointestinal tract, the largest human microbial reservoir, is highly dynamic. The gut microbes play essential roles in causing colorectal diseases. In the present study, we explored potential keystone taxa during the development of colorectal diseases in central China. Fecal samples of some patients were collected and were allocated to the adenoma (Group A), colorectal cancer (Group C), and hemorrhoid (Group H) groups. The 16S rRNA amplicon and shallow metagenomic sequencing (SMS) strategies were used to recover the gut microbiota. Microbial diversities obtained from 16S rRNA amplicon and SMS data were similar. Group C had the highest diversity, although no significant difference in diversity was observed among the groups. The most dominant phyla in the gut microbiota of patients with colorectal diseases were Bacteroidetes, Firmicutes, and Proteobacteria, accounting for >95% of microbes in the samples. The most abundant genera in the samples were Bacteroides, Prevotella, and Escherichia/Shigella, and further species-level and network analyses identified certain potential keystone taxa in each group. Some of the dominant species, such as Prevotella copri, Bacteroides dorei, and Bacteroides vulgatus, could be responsible for causing colorectal diseases. The SMS data recovered diverse antibiotic resistance genes of tetracycline, macrolide, and beta-lactam, which could be a result of antibiotic overuse. This study explored the gut microbiota of patients with three different types of colorectal diseases, and the microbial diversity results obtained from 16S rRNA amplicon sequencing and SMS data were found to be similar. However, the findings of this study are based on a limited sample size, which warrants further large-scale studies. The recovery of gut microbiota profiles in patients with colorectal diseases could be beneficial for future diagnosis and treatment with modulation of the gut microbiota. Moreover, SMS data can provide accurate species- and gene-level information, and it is economical. It can therefore be widely applied in future clinical metagenomic studies.

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