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Obregon-Gutierrez P, Bonillo-Lopez L, Correa-Fiz F, Sibila M, Segalés J, Kochanowski K, Aragon V. Gut-associated microbes are present and active in the pig nasal cavity. Sci Rep 2024; 14:8470. [PMID: 38605046 PMCID: PMC11009223 DOI: 10.1038/s41598-024-58681-9] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 04/02/2024] [Indexed: 04/13/2024] Open
Abstract
The nasal microbiota is a key contributor to animal health, and characterizing the nasal microbiota composition is an important step towards elucidating the role of its different members. Efforts to characterize the nasal microbiota composition of domestic pigs and other farm animals frequently report the presence of bacteria that are typically found in the gut, including many anaerobes from the Bacteroidales and Clostridiales orders. However, the in vivo role of these gut-microbiota associated taxa is currently unclear. Here, we tackled this issue by examining the prevalence, origin, and activity of these taxa in the nasal microbiota of piglets. First, analysis of the nasal microbiota of farm piglets sampled in this study, as well as various publicly available data sets, revealed that gut-microbiota associated taxa indeed constitute a substantial fraction of the pig nasal microbiota that is highly variable across individual animals. Second, comparison of herd-matched nasal and rectal samples at amplicon sequencing variant (ASV) level showed that these taxa are largely shared in the nasal and rectal microbiota, suggesting a common origin driven presumably by the transfer of fecal matter. Third, surgical sampling of the inner nasal tract showed that gut-microbiota associated taxa are found throughout the nasal cavity, indicating that these taxa do not stem from contaminations introduced during sampling with conventional nasal swabs. Finally, analysis of cDNA from the 16S rRNA gene in these nasal samples indicated that gut-microbiota associated taxa are indeed active in the pig nasal cavity. This study shows that gut-microbiota associated taxa are not only present, but also active, in the nasal cavity of domestic pigs, and paves the way for future efforts to elucidate the function of these taxa within the nasal microbiota.
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Affiliation(s)
- Pau Obregon-Gutierrez
- Centre de Recerca en Sanitat Animal (CReSA), Unitat Mixta d'Investigació IRTA-UAB en Sanitat Animal, Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra, 08193, Barcelona, Spain
- IRTA, Programa de Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra, 08193, Barcelona, Spain
- OIE Collaborating Centre for the Research and Control of Emerging and Re-Emerging Swine Diseases in Europe (IRTA-CReSA), Bellaterra, 08193, Barcelona, Spain
| | - Laura Bonillo-Lopez
- Centre de Recerca en Sanitat Animal (CReSA), Unitat Mixta d'Investigació IRTA-UAB en Sanitat Animal, Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra, 08193, Barcelona, Spain
- IRTA, Programa de Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra, 08193, Barcelona, Spain
- OIE Collaborating Centre for the Research and Control of Emerging and Re-Emerging Swine Diseases in Europe (IRTA-CReSA), Bellaterra, 08193, Barcelona, Spain
| | - Florencia Correa-Fiz
- Centre de Recerca en Sanitat Animal (CReSA), Unitat Mixta d'Investigació IRTA-UAB en Sanitat Animal, Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra, 08193, Barcelona, Spain
- IRTA, Programa de Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra, 08193, Barcelona, Spain
- OIE Collaborating Centre for the Research and Control of Emerging and Re-Emerging Swine Diseases in Europe (IRTA-CReSA), Bellaterra, 08193, Barcelona, Spain
| | - Marina Sibila
- Centre de Recerca en Sanitat Animal (CReSA), Unitat Mixta d'Investigació IRTA-UAB en Sanitat Animal, Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra, 08193, Barcelona, Spain
- IRTA, Programa de Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra, 08193, Barcelona, Spain
- OIE Collaborating Centre for the Research and Control of Emerging and Re-Emerging Swine Diseases in Europe (IRTA-CReSA), Bellaterra, 08193, Barcelona, Spain
| | - Joaquim Segalés
- Centre de Recerca en Sanitat Animal (CReSA), Unitat Mixta d'Investigació IRTA-UAB en Sanitat Animal, Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra, 08193, Barcelona, Spain
- OIE Collaborating Centre for the Research and Control of Emerging and Re-Emerging Swine Diseases in Europe (IRTA-CReSA), Bellaterra, 08193, Barcelona, Spain
- Departament de Sanitat i Anatomia Animals, Facultat de Veterinària, Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra, 08193, Barcelona, Spain
| | - Karl Kochanowski
- Centre de Recerca en Sanitat Animal (CReSA), Unitat Mixta d'Investigació IRTA-UAB en Sanitat Animal, Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra, 08193, Barcelona, Spain.
- IRTA, Programa de Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra, 08193, Barcelona, Spain.
- OIE Collaborating Centre for the Research and Control of Emerging and Re-Emerging Swine Diseases in Europe (IRTA-CReSA), Bellaterra, 08193, Barcelona, Spain.
| | - Virginia Aragon
- Centre de Recerca en Sanitat Animal (CReSA), Unitat Mixta d'Investigació IRTA-UAB en Sanitat Animal, Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra, 08193, Barcelona, Spain.
- IRTA, Programa de Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB), Bellaterra, 08193, Barcelona, Spain.
- OIE Collaborating Centre for the Research and Control of Emerging and Re-Emerging Swine Diseases in Europe (IRTA-CReSA), Bellaterra, 08193, Barcelona, Spain.
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2
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Kumar T, Bryant M, Cantrell K, Song SJ, McDonald D, Tubb HM, Farmer S, Lewis A, Lukacz ES, Brubaker L, Knight R. Effects of variation in sample storage conditions and swab order on 16S vaginal microbiome analyses. Microbiol Spectr 2024; 12:e0371223. [PMID: 38095462 PMCID: PMC10783137 DOI: 10.1128/spectrum.03712-23] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 11/20/2023] [Indexed: 01/13/2024] Open
Abstract
IMPORTANCE The composition of the human vaginal microbiome has been linked to a variety of medical conditions including yeast infection, bacterial vaginosis, and sexually transmitted infection. The vaginal microbiome is becoming increasingly acknowledged as a key factor in personal health, and it is essential to establish methods to collect and process accurate samples with self-collection techniques to allow large, population-based studies. In this study, we investigate if using AssayAssure Genelock, a nucleic acid preservative, introduces microbial biases in self-collected vaginal samples. To our knowledge, we also contribute some of the first evidence regarding the impacts of multiple swabs taken at one time point. Vaginal samples have relatively low biomass, so the ability to collect multiple swabs from a unique participant at a single time would greatly improve the replicability and data available for future studies. This will hopefully lay the groundwork to gain a more complete and accurate understanding of the vaginal microbiome.
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Affiliation(s)
- Tanya Kumar
- Medical Scientist Training Program, University of California San Diego, La Jolla, California, USA
| | - MacKenzie Bryant
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Kalen Cantrell
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA
| | - Se Jin Song
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Helena M. Tubb
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Sawyer Farmer
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Amanda Lewis
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, California, USA
| | - Emily S. Lukacz
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, California, USA
| | - Linda Brubaker
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, California, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
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3
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Austin GI, Park H, Meydan Y, Seeram D, Sezin T, Lou YC, Firek BA, Morowitz MJ, Banfield JF, Christiano AM, Pe'er I, Uhlemann AC, Shenhav L, Korem T. Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data. Nat Biotechnol 2023; 41:1820-1828. [PMID: 36928429 PMCID: PMC10504420 DOI: 10.1038/s41587-023-01696-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 01/23/2023] [Indexed: 03/18/2023]
Abstract
Sequencing-based approaches for the analysis of microbial communities are susceptible to contamination, which could mask biological signals or generate artifactual ones. Methods for in silico decontamination using controls are routinely used, but do not make optimal use of information shared across samples and cannot handle taxa that only partially originate in contamination or leakage of biological material into controls. Here we present Source tracking for Contamination Removal in microBiomes (SCRuB), a probabilistic in silico decontamination method that incorporates shared information across multiple samples and controls to precisely identify and remove contamination. We validate the accuracy of SCRuB in multiple data-driven simulations and experiments, including induced contamination, and demonstrate that it outperforms state-of-the-art methods by an average of 15-20 times. We showcase the robustness of SCRuB across multiple ecosystems, data types and sequencing depths. Demonstrating its applicability to microbiome research, SCRuB facilitates improved predictions of host phenotypes, most notably the prediction of treatment response in melanoma patients using decontaminated tumor microbiome data.
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Affiliation(s)
- George I Austin
- Department of Computer Science, Columbia University, New York, NY, USA
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Heekuk Park
- Division of Infectious Diseases, Columbia University Irving Medical Center, New York, NY, USA
| | - Yoli Meydan
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Dwayne Seeram
- Division of Infectious Diseases, Columbia University Irving Medical Center, New York, NY, USA
| | - Tanya Sezin
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, USA
| | - Yue Clare Lou
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
| | - Brian A Firek
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Michael J Morowitz
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jillian F Banfield
- Department of Earth and Planetary Science, University of California, Berkeley, CA, USA
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, USA
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Angela M Christiano
- Department of Dermatology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY, USA
| | - Itsik Pe'er
- Department of Computer Science, Columbia University, New York, NY, USA
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Data Science Institute, Columbia University, New York, NY, USA
| | - Anne-Catrin Uhlemann
- Division of Infectious Diseases, Columbia University Irving Medical Center, New York, NY, USA
| | - Liat Shenhav
- Center for Studies in Physics and Biology, Rockefeller University, New York, NY, USA.
| | - Tal Korem
- Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA.
- CIFAR Azrieli Global Scholars program, CIFAR, Toronto, Canada.
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4
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Liu W, Zhang T, Wang J, Zhao G, Hou Y. Protective Effect of Akkermansia muciniphila on the Preeclampsia-Like Mouse Model. Reprod Sci 2023; 30:2623-2633. [PMID: 36920671 DOI: 10.1007/s43032-023-01206-y] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 02/26/2023] [Indexed: 03/16/2023]
Abstract
Preeclampsia (PE) is known as a metabolism-related complication of pregnancy related to gut dysbiosis including the decreased abundance of Akkermansia muciniphila (A. muciniphila). However, the modulatory role of A. muciniphila as a supplement for PE remains ambiguous. This study investigated the effect of A. muciniphila administration on PE-like mice and its underlying mechanisms. A total of twenty-four C57BL/6 mice were randomly assigned into three groups. PE-like symptoms were induced by continuous injection of L-NAME intraperitoneally from gestational day (GD) 11 to GD18 combined with oral administration of pasteurized A. muciniphila during GD14-18 or not. Mice were sacrificed at GD19 to collect for further evaluation. Decreased A. muciniphila was observed in a successfully established PE-like model than normotensive pregnant control (NP), inversely correlated to increased systolic blood pressure blood and 24-h proteinuria. After supplementing with A. muciniphila, mice showed significantly minimized blood pressure and protein expression in urine, increased number of pups and weight of both embryos and placentas. In addition, colonies of bacteria, inflammatory cytokines (TNF-α and IL-6), and metabolic products of lipids including TC, FC, and TG were alleviated by A. muciniphila in the placentas. Among proteins linked with bowel barrier functions, diminished 2-AG and growing ZO-1 and occludin were attributable to A. muciniphila. Also, enhanced Treg/Th17 ratios were found in the intestines of mice treated with A. muciniphila. A. muciniphila facilitated alleviating PE-like symptoms and was beneficial as a novel probiotic therapeutic agent for PE.
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Affiliation(s)
- Wei Liu
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, Xiwu Road, Xincheng District, Xi'an, 710004, Shaanxi, China.
| | - Tingting Zhang
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, Xiwu Road, Xincheng District, Xi'an, 710004, Shaanxi, China
| | - Juanni Wang
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, Xiwu Road, Xincheng District, Xi'an, 710004, Shaanxi, China
| | - Gang Zhao
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, Xiwu Road, Xincheng District, Xi'an, 710004, Shaanxi, China
| | - Yuemin Hou
- Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, Xiwu Road, Xincheng District, Xi'an, 710004, Shaanxi, China
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5
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Kumar T, Bryant M, Cantrell K, Song SJ, McDonald D, Tubb HM, Farmer S, Lukacz ES, Brubaker L, Knight R. Effects of Variation in Urine Sample Storage Conditions on 16S Urogenital Microbiome Analyses. mSystems 2023; 8:e0102922. [PMID: 36475896 PMCID: PMC9948722 DOI: 10.1128/msystems.01029-22] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 11/07/2022] [Indexed: 12/13/2022] Open
Abstract
Replicability is a well-established challenge in microbiome research with a variety of contributing factors at all stages, from sample collection to code execution. Here, we focus on voided urine sample storage conditions for urogenital microbiome analysis. Using urine samples collected from 10 adult females, we investigated the microbiome preservation efficacy of AssayAssure Genelock (Genelock), compared with no preservative, under different temperature conditions. We varied temperature over 48 h in order to examine the impact of conditions samples may experience with home voided urine collection and shipping to a central biorepository. The following common lab and shipping conditions were investigated: -20°C, ambient temperature, 4°C, freeze-thaw cycle, and heat cycle. At 48 h, all samples were stored at -80°C until processing. After generating 16S rRNA gene amplicon sequencing data using the highly sensitive KatharoSeq protocol, we observed individual variation in both alpha and beta diversity metrics below interhuman differences, corroborating reports of individual microbiome variability in other specimen types. While there was no significant difference in beta diversity when comparing Genelock versus no preservative, we did observe a higher concordance with Genelock samples shipped at colder temperatures (-20°C and 4°C) when compared with the samples shipped at -20°C without preservative. Our results indicate that Genelock does not introduce a significant amount of microbial bias when used on a range of temperatures and is most effective at colder temperatures. IMPORTANCE The urogenital microbiome is an understudied yet important human microbiome niche. Research has been stimulated by the relatively recent discovery that urine is not sterile; urinary tract microbes have been linked to health problems, including urinary infections, incontinence, and cancer. The quality of life and economic impact of UTIs and urgency incontinence alone are enormous, with $3.5 billion and $82.6 billion, respectively, spent in the United States. annually. Given the low biomass of urine, novelty of the field, and limited reproducibility evidence, it is critical to study urine sample storage conditions to optimize scientific rigor. Efficient and reliable preservation methods inform methods for home self-sample collection and shipping, increasing the potential use in larger-scale studies. Here, we examined both buffer and temperature variation effects on 16S rRNA gene amplicon sequencing results from urogenital samples, providing data on the consequences of common storage methods on urogenital microbiome results.
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Affiliation(s)
- Tanya Kumar
- Medical Scientist Training Program, University of California San Diego, La Jolla, California, USA
| | - MacKenzie Bryant
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Kalen Cantrell
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA
| | - Se Jin Song
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA
| | - Daniel McDonald
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Helena M. Tubb
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Sawyer Farmer
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
| | - Emily S. Lukacz
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, California, USA
| | - Linda Brubaker
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, California, USA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, California, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, California, USA
- Department of Bioengineering, University of California San Diego, La Jolla, California, USA
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6
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Wang C, Yang S, Ma L, Li Y, Shi Y, Wang M, Shao L, Li Z, Wang Y. Alterations of Chinese women's skin microbiota associated with the aging process. Acta Biochim Biophys Sin (Shanghai) 2022; 54:1201-1205. [PMID: 35983972 PMCID: PMC9828269 DOI: 10.3724/abbs.2022105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Affiliation(s)
- Caixia Wang
- Shanghai Institute of TechnologyShanghai201418China,The Oriental Beauty Valley Research InstituteShanghai201403China
| | - Suzhen Yang
- Shandong Freda Biotech Co.LtdJinanShandong250101China
| | - Laiji Ma
- Shanghai Institute of TechnologyShanghai201418China,The Oriental Beauty Valley Research InstituteShanghai201403China
| | - Yan Li
- Shandong Freda Biotech Co.LtdJinanShandong250101China
| | - Yanqin Shi
- Shanghai Institute of TechnologyShanghai201418China,The Oriental Beauty Valley Research InstituteShanghai201403China
| | - Man Wang
- Shanghai Jiaotong University Affiliated Sixth People’s HospitalSouth CampusShanghai201499China
| | - Li Shao
- Shanghai Institute of TechnologyShanghai201418China,Correspondene address. Tel: +86-21-37199016; (L.S.) / Tel: +86-21-34293635; (Z.L.) / Tel: +86-21-37199016; (Y.W.) @
| | - Zongjie Li
- Shanghai Institute of TechnologyShanghai201418China,Correspondene address. Tel: +86-21-37199016; (L.S.) / Tel: +86-21-34293635; (Z.L.) / Tel: +86-21-37199016; (Y.W.) @
| | - Yue Wang
- Shanghai Institute of TechnologyShanghai201418China,The Oriental Beauty Valley Research InstituteShanghai201403China,Correspondene address. Tel: +86-21-37199016; (L.S.) / Tel: +86-21-34293635; (Z.L.) / Tel: +86-21-37199016; (Y.W.) @
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7
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Shaffer JP, Carpenter CS, Martino C, Salido RA, Minich JJ, Bryant M, Sanders K, Schwartz T, Humphrey G, Swafford AD, Knight R. A comparison of six DNA extraction protocols for 16S, ITS and shotgun metagenomic sequencing of microbial communities. Biotechniques 2022; 73:34-46. [PMID: 35713407 PMCID: PMC9361692 DOI: 10.2144/btn-2022-0032] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Microbial communities contain a broad phylogenetic diversity of organisms; however, the majority of methods center on describing bacteria and archaea. Fungi are important symbionts in many ecosystems and are potentially important members of the human microbiome, beyond those that can cause disease. To expand our analysis of microbial communities to include data from the fungal internal transcribed spacer (ITS) region, five candidate DNA extraction kits were compared against our standardized protocol for describing bacteria and archaea using 16S rRNA gene amplicon- and shotgun metagenomics sequencing. The results are presented considering a diverse panel of host-associated and environmental sample types and comparing the cost, processing time, well-to-well contamination, DNA yield, limit of detection and microbial community composition among protocols. Across all criteria, the MagMAX Microbiome kit was found to perform best. The PowerSoil Pro kit performed comparably but with increased cost per sample and overall processing time. The Zymo MagBead, NucleoMag Food and Norgen Stool kits were included.
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Affiliation(s)
- Justin P Shaffer
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Carolina S Carpenter
- Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
| | - Cameron Martino
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
- Bioinformatics & Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Rodolfo A Salido
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Jeremiah J Minich
- Marine Biology Research Division, University of California, San Diego, La Jolla, CA 92093, USA
| | - MacKenzie Bryant
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Karenina Sanders
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Tara Schwartz
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Gregory Humphrey
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Austin D Swafford
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
- InterOme, Inc. Carlsbad, CA 92008, USA
| | - Rob Knight
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA 92093, USA
- Micronoma Inc. San Diego, CA 92121, USA
- Department of Computer Science & Engineering, University of California, San Diego, La Jolla, CA 92093, USA
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8
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Armstrong G, Martino C, Morris J, Khaleghi B, Kang J, DeReus J, Zhu Q, Roush D, McDonald D, Gonazlez A, Shaffer JP, Carpenter C, Estaki M, Wandro S, Eilert S, Akel A, Eno J, Curewitz K, Swafford AD, Moshiri N, Rosing T, Knight R. Swapping Metagenomics Preprocessing Pipeline Components Offers Speed and Sensitivity Increases. mSystems 2022;:e0137821. [PMID: 35293792 DOI: 10.1128/msystems.01378-21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Increasing data volumes on high-throughput sequencing instruments such as the NovaSeq 6000 leads to long computational bottlenecks for common metagenomics data preprocessing tasks such as adaptor and primer trimming and host removal. Here, we test whether faster recently developed computational tools (Fastp and Minimap2) can replace widely used choices (Atropos and Bowtie2), obtaining dramatic accelerations with additional sensitivity and minimal loss of specificity for these tasks. Furthermore, the taxonomic tables resulting from downstream processing provide biologically comparable results. However, we demonstrate that for taxonomic assignment, Bowtie2’s specificity is still required. We suggest that periodic reevaluation of pipeline components, together with improvements to standardized APIs to chain them together, will greatly enhance the efficiency of common bioinformatics tasks while also facilitating incorporation of further optimized steps running on GPUs, FPGAs, or other architectures. We also note that a detailed exploration of available algorithms and pipeline components is an important step that should be taken before optimization of less efficient algorithms on advanced or nonstandard hardware. IMPORTANCE In shotgun metagenomics studies that seek to relate changes in microbial DNA across samples, processing the data on a computer often takes longer than obtaining the data from the sequencing instrument. Recently developed software packages that perform individual steps in the pipeline of data processing in principle offer speed advantages, but in practice they may contain pitfalls that prevent their use, for example, they may make approximations that introduce unacceptable errors in the data. Here, we show that differences in choices of these components can speed up overall data processing by 5-fold or more on the same hardware while maintaining a high degree of correctness, greatly reducing the time taken to interpret results. This is an important step for using the data in clinical settings, where the time taken to obtain the results may be critical for guiding treatment.
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Shaffer JP, Nothias LF, Thompson LR, Sanders JG, Salido RA, Couvillion SP, Brejnrod AD, Lejzerowicz F, Haiminen N, Huang S, Lutz HL, Zhu Q, Martino C, Morton JT, Karthikeyan S, Nothias-Esposito M, Dührkop K, Böcker S, Kim HW, Aksenov AA, Bittremieux W, Minich JJ, Marotz C, Bryant MM, Sanders K, Schwartz T, Humphrey G, Vásquez-Baeza Y, Tripathi A, Parida L, Carrieri AP, Beck KL, Das P, González A, McDonald D, Ladau J, Karst SM, Albertsen M, Ackermann G, DeReus J, Thomas T, Petras D, Shade A, Stegen J, Song SJ, Metz TO, Swafford AD, Dorrestein PC, Jansson JK, Gilbert JA, Knight R; Earth Microbiome Project 500 (EMP500) Consortium. Standardized multi-omics of Earth's microbiomes reveals microbial and metabolite diversity. Nat Microbiol 2022; 7:2128-50. [PMID: 36443458 DOI: 10.1038/s41564-022-01266-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 10/10/2022] [Indexed: 11/30/2022]
Abstract
Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth's environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.
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Torrington E, Free T, Sawyer A, Itano M. Welcome to the 72nd Volume of BioTechniques. Biotechniques 2021. [PMID: 34846162 DOI: 10.2144/btn-2021-0112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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Abstract
Bacterial communities in water, soil, and humans play an essential role in environmental ecology and human health. PCR-based amplicon analysis, such as 16S rRNA sequencing, is a fundamental tool for quantifying and studying microbial composition, dynamics, and interactions. However, given the complexity of microbial communities, a substantial number of samples becomes necessary for analyses that parse the factors that determine microbial composition. A common bottleneck in performing these kinds of experiments is genomic DNA (gDNA) extraction, which is time-consuming, expensive, and often biased based on the types of species present. Direct PCR method is a potentially simpler and more accurate alternative to gDNA extraction methods that do not require the intervening purification step. In this study, we evaluated three variations of direct PCR methods using diverse heterogeneous bacterial cultures, including both Gram-positive and Gram-negative species, ZymoBIOMICS microbial community standards, and groundwater. By comparing direct PCR methods with DNeasy Blood and Tissue Kits for microbial isolates and DNeasy PowerSoil Kits for microbial communities, we found that a specific variant of the direct PCR method exhibits an overall efficiency comparable to that of the conventional DNeasy PowerSoil protocol in the circumstances we tested. We also found that the method showed higher efficiency for extracting gDNA from the Gram-negative strains compared to DNeasy Blood and Tissue protocol. This direct PCR method is 1,600 times less expensive ($0.34 for 96 samples) and 10 times simpler (15 min hands-on time for 96 samples) than the DNeasy PowerSoil protocol. The direct PCR method can also be fully automated and is compatible with small-volume samples, thereby permitting scaling of samples and replicates needed to support high-throughput large-scale bacterial community analysis. IMPORTANCE Understanding bacterial interactions and assembly in complex microbial communities using 16S rRNA sequencing normally requires a large experimental load. However, the current DNA extraction methods, including cell disruption and genomic DNA purification, are normally biased, costly, time-consuming, labor-intensive, and not amenable to miniaturization by droplets or 1,536-well plates due to the significant DNA loss during the purification step for tiny-volume and low-cell-density samples. A direct PCR method could potentially solve these problems. In this study, we developed a direct PCR method which exhibits similar efficiency as the widely used method, the DNeasy PowerSoil protocol, while being 1,600 times less expensive and 10 times faster to execute. This simple, cost-effective, and automation-friendly direct-PCR-based 16S rRNA sequencing method allows us to study the dynamics, microbial interaction, and assembly of various microbial communities in a high-throughput fashion.
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Marotz C, Belda-Ferre P, Ali F, Das P, Huang S, Cantrell K, Jiang L, Martino C, Diner RE, Rahman G, McDonald D, Armstrong G, Kodera S, Donato S, Ecklu-Mensah G, Gottel N, Salas Garcia MC, Chiang LY, Salido RA, Shaffer JP, Bryant MK, Sanders K, Humphrey G, Ackermann G, Haiminen N, Beck KL, Kim HC, Carrieri AP, Parida L, Vázquez-Baeza Y, Torriani FJ, Knight R, Gilbert J, Sweeney DA, Allard SM. SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment. Microbiome 2021; 9:132. [PMID: 34103074 PMCID: PMC8186369 DOI: 10.1186/s40168-021-01083-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [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: 02/03/2021] [Accepted: 04/21/2021] [Indexed: 05/07/2023]
Abstract
BACKGROUND SARS-CoV-2 is an RNA virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Viruses exist in complex microbial environments, and recent studies have revealed both synergistic and antagonistic effects of specific bacterial taxa on viral prevalence and infectivity. We set out to test whether specific bacterial communities predict SARS-CoV-2 occurrence in a hospital setting. METHODS We collected 972 samples from hospitalized patients with COVID-19, their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and used these bacterial profiles to classify SARS-CoV-2 RNA detection with a random forest model. RESULTS Sixteen percent of surfaces from COVID-19 patient rooms had detectable SARS-CoV-2 RNA, although infectivity was not assessed. The highest prevalence was in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples more closely resembled the patient microbiome compared to floor samples, SARS-CoV-2 RNA was detected less often in bed rail samples (11%). SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity in both human and surface samples and higher biomass in floor samples. 16S microbial community profiles enabled high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool, and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia strongly predicted SARS-CoV-2 presence across sample types, with greater prevalence in positive surface and human samples, even when compared to samples from patients in other intensive care units prior to the COVID-19 pandemic. CONCLUSIONS These results contextualize the vast diversity of microbial niches where SARS-CoV-2 RNA is detected and identify specific bacterial taxa that associate with the viral RNA prevalence both in the host and hospital environment. Video Abstract.
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Affiliation(s)
- Clarisse Marotz
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Pedro Belda-Ferre
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Farhana Ali
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Promi Das
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Shi Huang
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Kalen Cantrell
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Lingjing Jiang
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
- Division of Biostatistics, University of California, San Diego, La Jolla, CA, USA
| | - Cameron Martino
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Rachel E Diner
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Gibraan Rahman
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Daniel McDonald
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - George Armstrong
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Sho Kodera
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Sonya Donato
- Microbiome Core, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Gertrude Ecklu-Mensah
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Neil Gottel
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Mariana C Salas Garcia
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
| | - Leslie Y Chiang
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Rodolfo A Salido
- Infection Prevention and Clinical Epidemiology Unit at UC San Diego Health, Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego, San Diego, CA, USA
| | - Justin P Shaffer
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Mac Kenzie Bryant
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Karenina Sanders
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Greg Humphrey
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Gail Ackermann
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Niina Haiminen
- IBM, T.J Watson Research Center, Yorktown Heights, New York, USA
| | - Kristen L Beck
- AI and Cognitive Software, IBM Research-Almaden, San Jose, CA, USA
| | - Ho-Cheol Kim
- AI and Cognitive Software, IBM Research-Almaden, San Jose, CA, USA
| | | | - Laxmi Parida
- IBM, T.J Watson Research Center, Yorktown Heights, New York, USA
| | - Yoshiki Vázquez-Baeza
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Francesca J Torriani
- Infection Prevention and Clinical Epidemiology Unit at UC San Diego Health, Division of Infectious Diseases and Global Public Health, Department of Medicine, UC San Diego, San Diego, CA, USA
| | - Rob Knight
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Jack Gilbert
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Daniel A Sweeney
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, University of California San Diego, La Jolla, CA, USA.
| | - Sarah M Allard
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA.
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
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