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Haiminen N, Utro F, Seabolt E, Parida L. Functional profiling of COVID-19 respiratory tract microbiomes. Sci Rep 2021; 11:6433. [PMID: 33742096 PMCID: PMC7979704 DOI: 10.1038/s41598-021-85750-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Accepted: 03/03/2021] [Indexed: 12/13/2022] Open
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] [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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Bandoy DJDR, Weimer BC. Biological Machine Learning Combined with Campylobacter Population Genomics Reveals Virulence Gene Allelic Variants Cause Disease. Microorganisms 2020; 8:E549. [PMID: 32290186 PMCID: PMC7232492 DOI: 10.3390/microorganisms8040549] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 04/07/2020] [Accepted: 04/08/2020] [Indexed: 01/17/2023] Open
Abstract
Highly dimensional data generated from bacterial whole-genome sequencing is providing an unprecedented scale of information that requires an appropriate statistical analysis framework to infer biological function from populations of genomes. The application of genome-wide association study (GWAS) methods is an appropriate framework for bacterial population genome analysis that yields a list of candidate genes associated with a phenotype, but it provides an unranked measure of importance. Here, we validated a novel framework to define infection mechanism using the combination of GWAS, machine learning, and bacterial population genomics that ranked allelic variants that accurately identified disease. This approach parsed a dataset of 1.2 million single nucleotide polymorphisms (SNPs) and indels that resulted in an importance ranked list of associated alleles of porA in Campylobacter jejuni using spatiotemporal analysis over 30 years. We validated this approach using previously proven laboratory experimental alleles from an in vivo guinea pig abortion model. This framework, termed µPathML, defined intestinal and extraintestinal groups that have differential allelic porA variants that cause abortion. Divergent variants containing indels that defeated automated annotation were rescued using biological context and knowledge that resulted in defining rare, divergent variants that were maintained in the population over two continents and 30 years. This study defines the capability of machine learning coupled with GWAS and population genomics to simultaneously identify and rank alleles to define their role in infectious disease mechanisms.
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Affiliation(s)
- DJ Darwin R. Bandoy
- 100 K Pathogen Genome Project, Department of Population Health and Reproduction, School of Veterinary Medicine, University of California Davis, Davis, CA 95616, USA
- Department of Veterinary, Paraclinical Sciences, College of Veterinary Medicine, University of the Philippines Los Baños, Los Baños 4031, Philippines;
| | - Bart C. Weimer
- 100 K Pathogen Genome Project, Department of Population Health and Reproduction, School of Veterinary Medicine, University of California Davis, Davis, CA 95616, USA
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