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Nayar G, Terrizzano I, Seabolt E, Agarwal A, Boucher C, Ruiz J, Slizovskiy IB, Kaufman JH, Noyes NR. ggMOB: Elucidation of genomic conjugative features and associated cargo genes across bacterial genera using genus-genus mobilization networks. Front Genet 2022; 13:1024577. [PMID: 36568361 PMCID: PMC9779932 DOI: 10.3389/fgene.2022.1024577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Accepted: 10/25/2022] [Indexed: 12/14/2022] Open
Abstract
Horizontal gene transfer mediated by conjugation is considered an important evolutionary mechanism of bacteria. It allows organisms to quickly evolve new phenotypic properties including antimicrobial resistance (AMR) and virulence. The frequency of conjugation-mediated cargo gene exchange has not yet been comprehensively studied within and between bacterial taxa. We developed a frequency-based network of genus-genus conjugation features and candidate cargo genes from whole-genome sequence data of over 180,000 bacterial genomes, representing 1,345 genera. Using our method, which we refer to as ggMOB, we revealed that over half of the bacterial genomes contained one or more known conjugation features that matched exactly to at least one other genome. Moreover, the proportion of genomes containing these conjugation features varied substantially by genus and conjugation feature. These results and the genus-level network structure can be viewed interactively in the ggMOB interface, which allows for user-defined filtering of conjugation features and candidate cargo genes. Using the network data, we observed that the ratio of AMR gene representation in conjugative versus non-conjugative genomes exceeded 5:1, confirming that conjugation is a critical force for AMR spread across genera. Finally, we demonstrated that clustering genomes by conjugation profile sometimes correlated well with classical phylogenetic structuring; but that in some cases the clustering was highly discordant, suggesting that the importance of the accessory genome in driving bacterial evolution may be highly variable across both time and taxonomy. These results can advance scientific understanding of bacterial evolution, and can be used as a starting point for probing genus-genus gene exchange within complex microbial communities that include unculturable bacteria. ggMOB is publicly available under the GNU licence at https://ruiz-hci-lab.github.io/ggMOB/.
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Affiliation(s)
- Gowri Nayar
- Department of Biomedical Informatics, Stanford University, Stanford, CA, United States
| | | | - Ed Seabolt
- IBM Research Almaden, San Jose, CA, United States
| | | | - Christina Boucher
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, United States
| | - Jaime Ruiz
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, United States
| | - Ilya B. Slizovskiy
- Department of Veterinary Population Medicine, University of Minnesota, Minneapolis, MN, United States
| | | | - Noelle R. Noyes
- Department of Veterinary Population Medicine, University of Minnesota, Minneapolis, MN, United States,*Correspondence: Noelle R. Noyes,
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Abstract
In response to the ongoing global pandemic, characterizing the molecular-level host interactions of the new coronavirus SARS-CoV-2 responsible for COVID-19 has been at the center of unprecedented scientific focus. However, when the virus enters the body it also interacts with the micro-organisms already inhabiting the host. Understanding the virus-host-microbiome interactions can yield additional insights into the biological processes perturbed by viral invasion. Alterations in the gut microbiome species and metabolites have been noted during respiratory viral infections, possibly impacting the lungs via gut-lung microbiome crosstalk. To better characterize microbial functions in the lower respiratory tract during COVID-19 infection, we carry out a functional analysis of previously published metatranscriptome sequencing data of bronchoalveolar lavage fluid from eight COVID-19 cases, twenty-five community-acquired pneumonia patients, and twenty healthy controls. The functional profiles resulting from comparing the sequences against annotated microbial protein domains clearly separate the cohorts. By examining the associated metabolic pathways, distinguishing functional signatures in COVID-19 respiratory tract microbiomes are identified, including decreased potential for lipid metabolism and glycan biosynthesis and metabolism pathways, and increased potential for carbohydrate metabolism pathways. The results include overlap between previous studies on COVID-19 microbiomes, including decrease in the glycosaminoglycan degradation pathway and increase in carbohydrate metabolism. The results also suggest novel connections to consider, possibly specific to the lower respiratory tract microbiome, calling for further research on microbial functions and host-microbiome interactions during SARS-CoV-2 infection.
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Affiliation(s)
- Niina Haiminen
- IBM T. J. Watson Research Center, Yorktown Heights, NY, USA
| | - Filippo Utro
- IBM T. J. Watson Research Center, Yorktown Heights, NY, USA
| | - Ed Seabolt
- IBM Almaden Research Center, San Jose, CA, USA
| | - Laxmi Parida
- IBM T. J. Watson Research Center, Yorktown Heights, NY, USA.
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Beck KL, Haiminen N, Chambliss D, Edlund S, Kunitomi M, Huang BC, Kong N, Ganesan B, Baker R, Markwell P, Kawas B, Davis M, Prill RJ, Krishnareddy H, Seabolt E, Marlowe CH, Pierre S, Quintanar A, Parida L, Dubois G, Kaufman J, Weimer BC. Monitoring the microbiome for food safety and quality using deep shotgun sequencing. NPJ Sci Food 2021; 5:3. [PMID: 33558514 PMCID: PMC7870667 DOI: 10.1038/s41538-020-00083-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 11/24/2020] [Indexed: 01/30/2023] Open
Abstract
In this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced the total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.96% during in silico validation. The pipeline identified 119 microbial genera per HPP sample on average with 65 genera present in all samples. The most abundant of these were Bacteroides, Clostridium, Lactococcus, Aeromonas, and Citrobacter. We also observed shifts in the microbial community corresponding to ingredient composition differences. When comparing culture-based results for Salmonella with total RNA sequencing, we found that Salmonella growth did not correlate with multiple sequence analyses. We conclude that microbiome sequencing is useful to characterize complex food microbial communities, while additional work is required for predicting specific species' viability from total RNA sequencing.
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Affiliation(s)
- Kristen L. Beck
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Niina Haiminen
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481554.9IBM T.J. Watson Research Center, Yorktown Heights, Ossining, NY USA
| | - David Chambliss
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Stefan Edlund
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Mark Kunitomi
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - B. Carol Huang
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.27860.3b0000 0004 1936 9684University of California Davis, School of Veterinary Medicine, 100 K Pathogen Genome Project, Davis, CA 95616 USA
| | - Nguyet Kong
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.27860.3b0000 0004 1936 9684University of California Davis, School of Veterinary Medicine, 100 K Pathogen Genome Project, Davis, CA 95616 USA
| | - Balasubramanian Ganesan
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,Mars Global Food Safety Center, Beijing, China ,grid.507690.dWisdom Health, A Division of Mars Petcare, Vancouver, WA USA
| | - Robert Baker
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,Mars Global Food Safety Center, Beijing, China
| | - Peter Markwell
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,Mars Global Food Safety Center, Beijing, China
| | - Ban Kawas
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Matthew Davis
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Robert J. Prill
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Harsha Krishnareddy
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Ed Seabolt
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Carl H. Marlowe
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.418312.d0000 0001 2187 1663Bio-Rad Laboratories, Hercules, CA USA
| | - Sophie Pierre
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481801.40000 0004 0623 3323Bio-Rad, Food Science Division, MArnes-La-Coquette, France
| | - André Quintanar
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481801.40000 0004 0623 3323Bio-Rad, Food Science Division, MArnes-La-Coquette, France
| | - Laxmi Parida
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481554.9IBM T.J. Watson Research Center, Yorktown Heights, Ossining, NY USA
| | - Geraud Dubois
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - James Kaufman
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.481551.cIBM Almaden Research Center, San Jose, CA USA
| | - Bart C. Weimer
- Consortium for Sequencing the Food Supply Chain, San Jose, CA USA ,grid.27860.3b0000 0004 1936 9684University of California Davis, School of Veterinary Medicine, 100 K Pathogen Genome Project, Davis, CA 95616 USA
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Gardiner LJ, Haiminen N, Utro F, Parida L, Seabolt E, Krishna R, Kaufman JH. Re-purposing software for functional characterization of the microbiome. Microbiome 2021; 9:4. [PMID: 33422152 PMCID: PMC7797099 DOI: 10.1186/s40168-020-00971-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 12/07/2020] [Indexed: 05/05/2023]
Abstract
BACKGROUND Widespread bioinformatic resource development generates a constantly evolving and abundant landscape of workflows and software. For analysis of the microbiome, workflows typically begin with taxonomic classification of the microorganisms that are present in a given environment. Additional investigation is then required to uncover the functionality of the microbial community, in order to characterize its currently or potentially active biological processes. Such functional analysis of metagenomic data can be computationally demanding for high-throughput sequencing experiments. Instead, we can directly compare sequencing reads to a functionally annotated database. However, since reads frequently match multiple sequences equally well, analyses benefit from a hierarchical annotation tree, e.g. for taxonomic classification where reads are assigned to the lowest taxonomic unit. RESULTS To facilitate functional microbiome analysis, we re-purpose well-known taxonomic classification tools to allow us to perform direct functional sequencing read classification with the added benefit of a functional hierarchy. To enable this, we develop and present a tree-shaped functional hierarchy representing the molecular function subset of the Gene Ontology annotation structure. We use this functional hierarchy to replace the standard phylogenetic taxonomy used by the classification tools and assign query sequences accurately to the lowest possible molecular function in the tree. We demonstrate this with simulated and experimental datasets, where we reveal new biological insights. CONCLUSIONS We demonstrate that improved functional classification of metagenomic sequencing reads is possible by re-purposing a range of taxonomic classification tools that are already well-established, in conjunction with either protein or nucleotide reference databases. We leverage the advances in speed, accuracy and efficiency that have been made for taxonomic classification and translate these benefits for the rapid functional classification of microbiomes. While we focus on a specific set of commonly used methods, the functional annotation approach has broad applicability across other sequence classification tools. We hope that re-purposing becomes a routine consideration during bioinformatic resource development. Video abstract.
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Affiliation(s)
| | - Niina Haiminen
- IBM T. J. Watson Research Center, Yorktown Heights, NY 10598 USA
| | - Filippo Utro
- IBM T. J. Watson Research Center, Yorktown Heights, NY 10598 USA
| | - Laxmi Parida
- IBM T. J. Watson Research Center, Yorktown Heights, NY 10598 USA
| | - Ed Seabolt
- IBM Almaden Research Center, San Jose, CA 95120 USA
| | - Ritesh Krishna
- IBM Research, The Hartree Centre, Warrington, WA4 4AD UK
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Utro F, Haiminen N, Siragusa E, Gardiner LJ, Seabolt E, Krishna R, Kaufman JH, Parida L. Hierarchically Labeled Database Indexing Allows Scalable Characterization of Microbiomes. iScience 2020; 23:100988. [PMID: 32248063 PMCID: PMC7125348 DOI: 10.1016/j.isci.2020.100988] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 03/02/2020] [Accepted: 03/11/2020] [Indexed: 12/14/2022] Open
Abstract
Increasingly available microbial reference data allow interpreting the composition and function of previously uncharacterized microbial communities in detail, via high-throughput sequencing analysis. However, efficient methods for read classification are required when the best database matches for short sequence reads are often shared among multiple reference sequences. Here, we take advantage of the fact that microbial sequences can be annotated relative to established tree structures, and we develop a highly scalable read classifier, PRROMenade, by enhancing the generalized Burrows-Wheeler transform with a labeling step to directly assign reads to the corresponding lowest taxonomic unit in an annotation tree. PRROMenade solves the multi-matching problem while allowing fast variable-size sequence classification for phylogenetic or functional annotation. Our simulations with 5% added differences from reference indicated only 1.5% error rate for PRROMenade functional classification. On metatranscriptomic data PRROMenade highlighted biologically relevant functional pathways related to diet-induced changes in the human gut microbiome.
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Affiliation(s)
- Filippo Utro
- IBM Research, T.J. Watson Research Center, Yorktown Heights, NY 10598, USA
| | - Niina Haiminen
- IBM Research, T.J. Watson Research Center, Yorktown Heights, NY 10598, USA
| | - Enrico Siragusa
- IBM Research, T.J. Watson Research Center, Yorktown Heights, NY 10598, USA
| | | | - Ed Seabolt
- IBM Research, Almaden Research Center, San Jose, CA 95120, USA
| | - Ritesh Krishna
- IBM Research, The Hartree Centre, Warrington, WA4 4AD, UK
| | - James H Kaufman
- IBM Research, Almaden Research Center, San Jose, CA 95120, USA.
| | - Laxmi Parida
- IBM Research, T.J. Watson Research Center, Yorktown Heights, NY 10598, USA.
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