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

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.

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

Neuro-Oncology Advances ◽

10.1093/noajnl/vdab023 ◽

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.

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

Journal of Clinical Microbiology ◽

10.1128/jcm.01182-18 ◽

2018 ◽

Vol 57 (2) ◽

Cited By ~ 8

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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Accurate Reconstruction of Microbial Strains from Metagenomic Sequencing Using Representative Reference Genomes

10.1101/215707 ◽

2017 ◽

Cited By ~ 2

Author(s):

Zhemin Zhou ◽

Nina Luhmann ◽

Nabil-Fareed Alikhan ◽

Christopher Quince ◽

Mark Achtman

Keyword(s):

Evaluation Studies ◽

Species Level ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Reference Databases ◽

Microbial Strains ◽

Taxonomic Assignments ◽

Taxonomic Groups ◽

Reference Genomes ◽

Recent Evaluation

AbstractExploring the genetic diversity of microbes within the environment through metagenomic sequencing first requires classifying these reads into taxonomic groups. Current methods compare these sequencing data with existing biased and limited reference databases. Several recent evaluation studies demonstrate that current methods either lack sufficient sensitivity for species-level assignments or suffer from false positives, overestimating the number of species in the metagenome. Both are especially problematic for the identification of low-abundance microbial species, e. g. detecting pathogens in ancient metagenomic samples. We present a new method, SPARSE, which improves taxonomic assignments of metagenomic reads. SPARSE balances existing biased reference databases by grouping reference genomes into similarity-based hierarchical clusters, implemented as an efficient incremental data structure. SPARSE assigns reads to these clusters using a probabilistic model, which specifically penalizes non-specific mappings of reads from unknown sources and hence reduces false-positive assignments. Our evaluation on simulated datasets from two recent evaluation studies demonstrated the improved precision of SPARSE in comparison to other methods for species-level classification. In a third simulation, our method successfully differentiated multiple co-existing Escherichia coli strains from the same sample. In real archaeological datasets, SPARSE identified ancient pathogens with ≤ 0.02% abundance, consistent with published findings that required additional sequencing data. In these datasets, other methods either missed targeted pathogens or reported non-existent ones. SPARSE and all evaluation scripts are available at https://github.com/zheminzhou/SPARSE.

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

10.21203/rs.3.rs-99718/v1 ◽

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).

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

Neuro-Oncology ◽

10.1093/neuonc/noab196.369 ◽

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.

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Phylogenetic Molecular Ecological Network of Soil Microbial Communities in Response to Elevated CO2

mBio ◽

10.1128/mbio.00122-11 ◽

2011 ◽

Vol 2 (4) ◽

Cited By ~ 322

Author(s):

Jizhong Zhou ◽

Ye Deng ◽

Feng Luo ◽

Zhili He ◽

Yunfeng Yang

Keyword(s):

Microbial Communities ◽

Microbial Ecology ◽

High Throughput ◽

Network Topology ◽

Ecosystem Functioning ◽

Soil Microbial Communities ◽

Microbial Populations ◽

Metagenomic Sequencing ◽

Sequencing Data ◽

Soil Microbial

ABSTRACT Understanding the interactions among different species and their responses to environmental changes, such as elevated atmospheric concentrations of CO2, is a central goal in ecology but is poorly understood in microbial ecology. Here we describe a novel random matrix theory (RMT)-based conceptual framework to discern phylogenetic molecular ecological networks using metagenomic sequencing data of 16S rRNA genes from grassland soil microbial communities, which were sampled from a long-term free-air CO2 enrichment experimental facility at the Cedar Creek Ecosystem Science Reserve in Minnesota. Our experimental results demonstrated that an RMT-based network approach is very useful in delineating phylogenetic molecular ecological networks of microbial communities based on high-throughput metagenomic sequencing data. The structure of the identified networks under ambient and elevated CO2 levels was substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher-order organization, topological roles of individual nodes, and network hubs, suggesting that the network interactions among different phylogenetic groups/populations were markedly changed. Also, the changes in network structure were significantly correlated with soil carbon and nitrogen contents, indicating the potential importance of network interactions in ecosystem functioning. In addition, based on network topology, microbial populations potentially most important to community structure and ecosystem functioning can be discerned. The novel approach described in this study is important not only for research on biodiversity, microbial ecology, and systems microbiology but also for microbial community studies in human health, global change, and environmental management. IMPORTANCE The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning, but very little is known about the network interactions in a microbial community, owing to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomic technologies can rapidly produce a massive amount of data, but one of the greatest difficulties is deciding how to extract, analyze, synthesize, and transform such a vast amount of information into biological knowledge. This study provides a novel conceptual framework to identify microbial interactions and key populations based on high-throughput metagenomic sequencing data. This study is among the first to document that the network interactions among different phylogenetic populations in soil microbial communities were substantially changed by a global change such as an elevated CO2 level. The framework developed will allow microbiologists to address research questions which could not be approached previously, and hence, it could represent a new direction in microbial ecology research.

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

10.1101/2021.06.04.447087 ◽

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.

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

Data in Brief ◽

10.1016/j.dib.2020.105831 ◽

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

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Abundance tracking by long-read nanopore sequencing of complex microbial communities in samples from 20 different biogas/wastewater plants

10.21203/rs.2.17734/v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Christian Brandt ◽

Erik Bongcam-Rudloff ◽

Bettina Müller

Keyword(s):

Microbial Communities ◽

Fold Increase ◽

Green Energy ◽

Dramatic Improvement ◽

Metagenomic Sequencing ◽

Nanopore Sequencing ◽

Sequencing Data ◽

Long Reads ◽

Long Read ◽

Water Reactors

Abstract Anaerobic digestion (AD) has long been critical technology for green energy, but the majority of the microorganisms involved are unknown and not cultivable, which makes abundance tracking difficult. Developments in nanopore sequencing make it a promising approach for monitoring microbial communities via metagenomic sequencing. For reliable monitoring of AD via long reads, a robust protocol for obtaining less fragmented, high-quality DNA, while preserving bacterial composition, was established. Samples from 20 different biogas/waste-water reactors were investigated and a median of 20 Gb sequencing data per flow cell were retrieved for each reactor. Using the GTDB index allowed sufficient characterisation of abundance of bacteria and archaea in biogas reactors. A dramatic improvement (1.8- to 13-fold increase) in taxonomic classification was achieved using the GTDB-based index compared with the RefSeq index. Ongoing efforts in GTDB to achieve more phylogenetically coherent taxonomic species definitions, including meta-assembled genomes, give a clear advantage over conventional classification databases such as RefSeq. Unlike conventional 16S rRNA studies, metagenomic read classification allows abundance of the unknown microbial fraction to be monitored.

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Comparative analysis of 16S rRNA gene and metagenome sequencing in pediatric gut microbiomes

10.1101/2021.02.20.432118 ◽

2021 ◽

Author(s):

Danielle Peterson ◽

Kevin S. Bonham ◽

Sophie Rowland ◽

Cassandra W. Pattanayak ◽

Vanja Klepac-Ceraj ◽

...

Keyword(s):

16S Rrna ◽

Microbial Communities ◽

Gut Microbiome ◽

Age Groups ◽

Alpha Diversity ◽

Sequencing Depth ◽

Human Infant ◽

Rrna Gene ◽

Metagenomic Sequencing ◽

Shotgun Metagenomic Sequencing

AbstractThe colonization of the human gut microbiome begins at birth, and, over time, these microbial communities become increasingly complex. Most of what we currently know about the human microbiome, especially in early stages of development, was described using culture-independent sequencing methods that allow us to identify the taxonomic composition of microbial communities using genomic techniques, such as amplicon or shotgun metagenomic sequencing. Each method has distinct tradeoffs, but there has not been a direct comparison of the utility of these methods in stool samples from very young children, which have different features than those of adults. We compared the effects of profiling the human infant gut microbiome with 16S rRNA amplicon versus shotgun metagenomic sequencing techniques in 130 fecal samples; younger than 15, 15-30, and older than 30 months of age. We demonstrate that observed changes in alpha-diversity and beta-diversity with age occur to similar extents using both profiling methods. We also show that 16S rRNA profiling identified a larger number of genera and we find several genera that are missed or underrepresented by each profiling method. We present the link between alpha diversity and shotgun metagenomic sequencing depth for children of different ages. These findings provide a guide for selecting an appropriate method and sequencing depth for the three studied age groups.

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