VIBRANT: Automated recovery, annotation and curation of microbial viruses, and evaluation of virome function from genomic sequences

Abstract Background Viruses are central to microbial community structure in all environments. The ability to generate large metagenomic assemblies of mixed microbial and viral sequences provides the opportunity to tease apart complex microbiome dynamics, but these analyses are currently limited by the tools available for analyses of viral genomes and assessing their metabolic impacts on microbiomes. Design Here we present VIBRANT, the first method to utilize a hybrid machine learning and protein similarity approach that is not reliant on sequence features for automated recovery and annotation of viruses, determination of genome quality and completeness, and characterization of virome function from metagenomic assemblies. VIBRANT uses neural networks of protein signatures and a novel v-score metric that circumvents traditional boundaries to maximize identification of lytic viral genomes and integrated proviruses, including highly diverse viruses. VIBRANT highlights viral auxiliary metabolic genes and metabolic pathways, thereby serving as a user-friendly platform for evaluating virome function. VIBRANT was trained and validated on reference virus datasets as well as microbiome and virome data. Results VIBRANT showed superior performance in recovering higher quality viruses and concurrently reduced the false identification of non-viral genome fragments in comparison to other virus identification programs, specifically VirSorter and VirFinder. When applied to 120,834 metagenomically derived viral sequences representing several human and natural environments, VIBRANT recovered an average of 94.5% of the viruses, whereas VirFinder and VirSorter achieved less powerful performance, averaging 48.1% and 56.0%, respectively. Similarly, VIBRANT identified more total viral sequence and proteins when applied to real metagenomes. When compared to PHASTER and Prophage Hunter for the ability to extract integrated provirus regions from host scaffolds, VIBRANT performed comparably and even identified proviruses that the other programs did not. To demonstrate applications of VIBRANT, we studied viromes associated with Crohn’s Disease to show that specific viral groups, namely Enterobacteriales-like viruses, as well as putative dysbiosis associated viral proteins are more abundant compared to healthy individuals, providing a possible viral link to maintenance of diseased states. Conclusions The ability to accurately recover viruses and explore viral impacts on microbial community metabolism will greatly advance our understanding of microbiomes, host-microbe interactions and ecosystem dynamics.

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VIBRANT: Automated recovery, annotation and curation of microbial viruses, and evaluation of virome function from genomic sequences

10.1101/855387 ◽

2019 ◽

Cited By ~ 8

Author(s):

Kristopher Kieft ◽

Zhichao Zhou ◽

Karthik Anantharaman

Keyword(s):

Microbial Community ◽

Ecosystem Dynamics ◽

Superior Performance ◽

Natural Environments ◽

Virus Identification ◽

Viral Genomes ◽

Reference Virus ◽

Microbe Interactions ◽

Host Microbe Interactions ◽

Viral Sequences

AbstractBackgroundViruses are central to microbial community structure in all environments. The ability to generate large metagenomic assemblies of mixed microbial and viral sequences provides the opportunity to tease apart complex microbiome dynamics, but these analyses are currently limited by the tools available for analyses of viral genomes and assessing their metabolic impacts on microbiomes.DesignHere we present VIBRANT, the first method to utilize a hybrid machine learning and protein similarity approach that is not reliant on sequence features for automated recovery and annotation of viruses, determination of genome quality and completeness, and characterization of virome function from metagenomic assemblies. VIBRANT uses neural networks of protein signatures and a novel v-score metric that circumvents traditional boundaries to maximize identification of lytic viral genomes and integrated proviruses, including highly diverse viruses. VIBRANT highlights viral auxiliary metabolic genes and metabolic pathways, thereby serving as a user-friendly platform for evaluating virome function. VIBRANT was trained and validated on reference virus datasets as well as microbiome and virome data.ResultsVIBRANT showed superior performance in recovering higher quality viruses and concurrently reduced the false identification of non-viral genome fragments in comparison to other virus identification programs, specifically VirSorter and VirFinder. When applied to 120,834 metagenomically derived viral sequences representing several human and natural environments, VIBRANT recovered an average of 94.5% of the viruses, whereas VirFinder and VirSorter achieved less powerful performance, averaging 48.1% and 56.0%, respectively. Similarly, VIBRANT identified more total viral sequence and proteins when applied to real metagenomes. When compared to PHASTER and Prophage Hunter for the ability to extract integrated provirus regions from host scaffolds, VIBRANT performed comparably and even identified proviruses that the other programs did not. To demonstrate applications of VIBRANT, we studied viromes associated with Crohn’s Disease to show that specific viral groups, namely Enterobacteriales-like viruses, as well as putative dysbiosis associated viral proteins are more abundant compared to healthy individuals, providing a possible viral link to maintenance of diseased states.ConclusionsThe ability to accurately recover viruses and explore viral impacts on microbial community metabolism will greatly advance our understanding of microbiomes, host-microbe interactions and ecosystem dynamics.

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VIBRANT: Automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequence

10.21203/rs.3.rs-16226/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Kristopher Kieft ◽

Zhichao Zhou ◽

Karthik Anantharaman

Keyword(s):

Microbial Community ◽

Genomic Sequence ◽

Ecosystem Dynamics ◽

Superior Performance ◽

Natural Environments ◽

Virus Identification ◽

Viral Genomes ◽

Community Function ◽

Viral Community ◽

Viral Sequences

Abstract Background: Viruses are central to microbial community structure in all environments. The ability to generate large metagenomic assemblies of mixed microbial and viral sequences provides the opportunity to tease apart complex microbiome dynamics, but these analyses are currently limited by the tools available for analyses of viral genomes and assessing their metabolic impacts on microbiomes. Design: Here we present VIBRANT, the first method to utilize a hybrid machine learning and protein similarity approach that is not reliant on sequence features for automated recovery and annotation of viruses, determination of genome quality and completeness, and characterization of viral community function from metagenomic assemblies. VIBRANT uses neural networks of protein signatures and a newly developed v-score metric that circumvents traditional boundaries to maximize identification of lytic viral genomes and integrated proviruses, including highly diverse viruses. VIBRANT highlights viral auxiliary metabolic genes and metabolic pathways, thereby serving as a user-friendly platform for evaluating viral community function. VIBRANT was trained and validated on reference virus datasets as well as microbiome and virome data. Results: VIBRANT showed superior performance in recovering higher quality viruses and concurrently reduced the false identification of non-viral genome fragments in comparison to other virus identification programs, specifically VirSorter, VirFinder and MARVEL. When applied to 120,834 metagenomically derived viral sequences representing several human and natural environments, VIBRANT recovered an average of 94% of the viruses, whereas VirFinder, VirSorter and MARVEL achieved less powerful performance, averaging 48%, 87% and 71%, respectively. Similarly, VIBRANT identified more total viral sequence and proteins when applied to real metagenomes. When compared to PHASTER, Prophage Hunter and VirSorter for the ability to extract integrated provirus regions from host scaffolds, VIBRANT performed comparably and even identified proviruses that the other programs did not. To demonstrate applications of VIBRANT, we studied viromes associated with Crohn’s Disease to show that specific viral groups, namely Enterobacteriales-like viruses, as well as putative dysbiosis associated viral proteins are more abundant compared to healthy individuals, providing a possible viral link to maintenance of diseased states. Conclusions: The ability to accurately recover viruses and explore viral impacts on microbial community metabolism will greatly advance our understanding of microbiomes, host-microbe interactions and ecosystem dynamics.

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Inferring directional relationships in microbial communities using signed Bayesian networks

BMC Genomics ◽

10.1186/s12864-020-07065-0 ◽

2020 ◽

Vol 21 (S6) ◽

Author(s):

Musfiqur Sazal ◽

Kalai Mathee ◽

Daniel Ruiz-Perez ◽

Trevor Cickovski ◽

Giri Narasimhan

Keyword(s):

Microbial Community ◽

Bayesian Networks ◽

Bayesian Network ◽

Microbial Communities ◽

Community Analysis ◽

Vital Role ◽

Microbe Interactions ◽

Health And Disease ◽

Colonization Patterns ◽

Host Microbe Interactions

Abstract Background Microbe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by a Bayesian Network (BN) about a microbial community. Unlike the previously proposed Co-occurrence Networks (CoNs), BNs are based on conditional dependencies and can help in revealing complex associations. Results In this paper, we propose a way of combining a BN and a CoN to construct a signed Bayesian Network (sBN). We report a surprising association between directed edges in signed BNs and known colonization orders. Conclusions BNs are powerful tools for community analysis and extracting influences and colonization patterns, even though the analysis only uses an abundance matrix with no temporal information. We conclude that directed edges in sBNs when combined with negative correlations are consistent with and strongly suggestive of colonization order.

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Inferring directional relationships in microbial communities using signed Bayesian networks

10.1101/2020.02.18.955344 ◽

2020 ◽

Cited By ~ 1

Author(s):

Musfiqur Sazal ◽

Kalai Mathee ◽

Daniel Ruiz-Perez ◽

Trevor Cickovski ◽

Giri Narasimhan

Keyword(s):

Microbial Community ◽

Bayesian Networks ◽

Bayesian Network ◽

Microbial Communities ◽

Community Analysis ◽

Vital Role ◽

Microbe Interactions ◽

Health And Disease ◽

Colonization Patterns ◽

Host Microbe Interactions

AbstractBackgroundMicrobe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by a Bayesian Network (BN) about a microbial community. Unlike the previously proposed Co-occurrence Networks (CoNs), BNs are based on conditional dependencies and can help in revealing complex associations.ResultsIn this paper, we propose a way of combining a BN and a CoN to construct a signed Bayesian Network (sBN). We report a surprising association between directed edges in signed BNs and known colonization orders.ConclusionsBNs are powerful tools for community analysis and extracting influences and colonization patterns, even though the analysis only uses an abundance matrix with no temporal information. We conclude that directed edges in sBNs when combined with negative correlations are consistent with and strongly suggestive of colonization order.

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Dual and Triple Epithelial Coculture Model Systems with Donor-Derived Microbiota and THP-1 Macrophages To Mimic Host-Microbe Interactions in the Human Sinonasal Cavities

mSphere ◽

10.1128/msphere.00916-19 ◽

2020 ◽

Vol 5 (1) ◽

Cited By ~ 1

Author(s):

Charlotte De Rudder ◽

Marta Calatayud Arroyo ◽

Sarah Lebeer ◽

Tom Van de Wiele

Keyword(s):

Microbial Community ◽

Epithelial Cells ◽

Respiratory Tract ◽

Model Systems ◽

Upper Respiratory Tract ◽

Microbe Interactions ◽

Upper Respiratory ◽

Host Microbe Interactions ◽

Nasal Microbiota

ABSTRACT The epithelium of the human sinonasal cavities is colonized by a diverse microbial community, modulating epithelial development and immune priming and playing a role in respiratory disease. Here, we present a novel in vitro approach enabling a 3-day coculture of differentiated Calu-3 respiratory epithelial cells with a donor-derived bacterial community, a commensal species (Lactobacillus sakei), or a pathobiont (Staphylococcus aureus). We also assessed how the incorporation of macrophage-like cells could have a steering effect on both epithelial cells and the microbial community. Inoculation of donor-derived microbiota in our experimental setup did not pose cytotoxic stress on the epithelial cell layers, as demonstrated by unaltered cytokine and lactate dehydrogenase release compared to a sterile control. Epithelial integrity of the differentiated Calu-3 cells was maintained as well, with no differences in transepithelial electrical resistance observed between coculture with donor-derived microbiota and a sterile control. Transition of nasal microbiota from in vivo to in vitro conditions maintained phylogenetic richness, and yet a decrease in phylogenetic and phenotypic diversity was noted. Additional inclusion and coculture of THP-1-derived macrophages did not alter phylogenetic diversity, and yet donor-independent shifts toward higher Moraxella and Mycoplasma abundance were observed, while phenotypic diversity was also increased. Our results demonstrate that coculture of differentiated airway epithelial cells with a healthy donor-derived nasal community is a viable strategy to mimic host-microbe interactions in the human upper respiratory tract. Importantly, including an immune component allowed us to study host-microbe interactions in the upper respiratory tract more in depth. IMPORTANCE Despite the relevance of the resident microbiota in sinonasal health and disease and the need for cross talk between immune and epithelial cells in the upper respiratory tract, these parameters have not been combined in a single in vitro model system. We have developed a coculture system of differentiated respiratory epithelium and natural nasal microbiota and incorporated an immune component. As indicated by absence of cytotoxicity and stable cytokine profiles and epithelial integrity, nasal microbiota from human origin appeared to be well tolerated by host cells, while microbial community composition remained representative for that of the human (sino)nasal cavity. Importantly, the introduction of macrophage-like cells enabled us to obtain a differential readout from the epithelial cells dependent on the donor microbial background to which the cells were exposed. We conclude that both model systems offer the means to investigate host-microbe interactions in the upper respiratory tract in a more representative way.

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Deterministic Assembly of Complex Bacterial Communities in Guts of Germ-Free Cockroaches

Applied and Environmental Microbiology ◽

10.1128/aem.03700-15 ◽

2015 ◽

Vol 82 (4) ◽

pp. 1256-1263 ◽

Cited By ~ 32

Author(s):

Aram Mikaelyan ◽

Claire L. Thompson ◽

Markus J. Hofer ◽

Andreas Brune

Keyword(s):

Microbial Community ◽

Gut Microbiota ◽

16S Rrna Genes ◽

Rrna Genes ◽

Experimental Proof ◽

Content Type ◽

Germ Free ◽

Microbe Interactions ◽

Host Microbe Interactions ◽

Axenic Conditions

ABSTRACTThe gut microbiota of termites plays important roles in the symbiotic digestion of lignocellulose. However, the factors shaping the microbial community structure remain poorly understood. Because termites cannot be raised under axenic conditions, we established the closely related cockroachShelfordella lateralisas a germ-free model to study microbial community assembly and host-microbe interactions. In this study, we determined the composition of the bacterial assemblages in cockroaches inoculated with the gut microbiota of termites and mice using pyrosequencing analysis of their 16S rRNA genes. Although the composition of the xenobiotic communities was influenced by the lineages present in the foreign inocula, their structure resembled that of conventional cockroaches. Bacterial taxa abundant in conventional cockroaches but rare in the foreign inocula, such asDysgonomonasandParabacteroidesspp., were selectively enriched in the xenobiotic communities. Donor-specific taxa, such as endomicrobia or spirochete lineages restricted to the gut microbiota of termites, however, either were unable to colonize germ-free cockroaches or formed only small populations. The exposure of xenobiotic cockroaches to conventional adults restored their normal microbiota, which indicated that autochthonous lineages outcompete foreign ones. Our results provide experimental proof that the assembly of a complex gut microbiota in insects is deterministic.

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