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Mock community as an in situ positive control for amplicon sequencing of microbiotas from the same ecosystem

  • Proctor, L. Priorities for the next 10 years of human microbiome research. Nature 569(7758), 623–625 (2019).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Bahl, M. I., Bergström, A. & Licht, T. R. Freezing fecal samples prior to DNA extraction affects the Firmicutes to Bacteroidetes ratio determined by downstream quantitative PCR analysis. FEMS Microbiol. Lett. 329, 193–197 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Wu, X. et al. Metagenomic insights into nitrogen and phosphorus cycling at the soil aggregate scale driven by organic material amendments. Sci. Total Environ. 785, 147329 (2021).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Singh, B. K., Millard, P., Whiteley, A. S. & Murrell, J. C. Unravelling rhizosphere-microbial interactions: Opportunities and limitations. Trends Microbiol. 12, 386–393 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Methé, B. A. et al. A framework for human microbiome research. Nature 486, 215–221 (2012).

    Article 
    ADS 
    PubMed Central 

    Google Scholar 

  • Pascoe, E. L., Hauffe, H. C., Marchesi, J. R. & Perkins, S. E. Network analysis of gut microbiota literature: An overview of the research landscape in non-human animal studies. ISME J. 11, 2644–2651 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gilbert, J. A., Jansson, J. K. & Knight, R. Earth microbiome project and global systems biology. mSystems 3, e00217-17 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant–microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. 18(11), 607–621 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569(7758), 655–662 (2019).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chen, T. et al. A plant genetic network for preventing dysbiosis in the phyllosphere. Nature 580(7805), 653–657 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Holman, D. B. & Gzyl, K. E. A meta-analysis of the bovine gastrointestinal tract microbiota. FEMS Microbiol. Ecol. 95, 72 (2019).

    Article 

    Google Scholar 

  • Chen, L. et al. Plant growth–promoting bacteria improve maize growth through reshaping the rhizobacterial community in low-nitrogen and low-phosphorus soil. Biol. Fertil. Soils 57, 1075–1088. https://doi.org/10.1007/S00374-021-01598-6 (2021).

    Article 
    CAS 

    Google Scholar 

  • Sommer, F. et al. The gut microbiota modulates energy metabolism in the hibernating brown bear Ursus arctos. Cell Rep. 14, 1655–1661 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Hauffe, H. C. & Barelli, C. Conserve the germs: The gut microbiota and adaptive potential. Conserv. Genet. 20(1), 19–27 (2019).

    Article 

    Google Scholar 

  • Pollock, J., Glendinning, L., Wisedchanwet, T. & Watson, M. The madness of microbiome: Attempting to find consensus ‘best practice’ for 16S microbiome studies. Appl. Environ. Microbiol. 84(7), e02627-17 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551(7681), 457–463 (2017).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Costea, P. I. et al. Towards standards for human fecal sample processing in metagenomic studies. Nat. Biotechnol. 35, 1069–1076 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 2224 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Tourlousse, D. M. et al. Synthetic spike-in standards for high-throughput 16S rRNA gene Amplicon sequencing. Nucleic Acids Res. 45, e23–e23 (2017).

    PubMed 

    Google Scholar 

  • Thissen, J. B. et al. Axiom Microbiome Array, the next generation microarray for high-throughput pathogen and microbiome analysis. PLoS ONE 14, e0212045 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ducarmon, Q. R., Hornung, B. V. H., Geelen, A. R., Kuijper, E. J. & Zwittink, R. D. Toward standards in clinical microbiota studies: Comparison of three DNA extraction methods and two bioinformatic pipelines. mSystems 5, e00547-19 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ray, T. et al. The microbiome of common bedding materials before and after use on commercial dairy farms. Anim. Microbiome 4(1), 1–21 (2022).

    Article 
    MathSciNet 
    CAS 

    Google Scholar 

  • Akhremchuk, K. V. et al. Gut microbiome of healthy people and patients with hematological malignancies in Belarus. Microbiol. Indep. Res. J. (MIR J.) 9, 18–30 (2022).

    Article 

    Google Scholar 

  • Smets, W. et al. A method for simultaneous measurement of soil bacterial abundances and community composition via 16S rRNA gene sequencing. Soil Biol. Biochem. 96, 145–151 (2016).

    Article 
    CAS 

    Google Scholar 

  • Palmer, J. M., Jusino, M. A., Banik, M. T. & Lindner, D. L. Non-biological synthetic spike-in controls and the AMPtk software pipeline improve mycobiome data. PeerJ 6, e4925 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Alteio, L. V. et al. A critical perspective on interpreting amplicon sequencing data in soil ecological research. Soil Biol. Biochem. 160, 108357 (2021).

    Article 
    CAS 

    Google Scholar 

  • Stämmler, F. et al. Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome 4, 1–13 (2016).

    Article 

    Google Scholar 

  • Risely, A., Wilhelm, K., Clutton-Brock, T., Manser, M. B. & Sommer, S. Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats. Nat. Commun. 12(1), 1–12 (2021).

    Article 

    Google Scholar 

  • Risely, A., et al. Gut microbiota repeatability is contingent on temporal scale and age in wild meerkats. ecoevorxiv (2022). https://doi.org/10.32942/OSF.IO/DSQFR

  • Szóstak, N. et al. The standardisation of the approach to metagenomic human gut analysis: From sample collection to microbiome profiling. Sci. Rep. 12(1), 1–21 (2022).

    Article 

    Google Scholar 

  • Tourlousse, D. M. et al. Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing. Nucleic Acids Res. 45, e23 (2017).

    PubMed 

    Google Scholar 

  • Sheu, S. Y., Arun, A. B., Jiang, S. R., Young, C. C. & Chen, W. M. Allobacillus halotolerans gen. nov., sp. Nov. isolated from shrimp paste. Int. J. Syst. Evol. Microbiol. 61, 1023–1027 (2011).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Surendra, V., Bhawana, P., Suresh, K., Srinivas, T. N. R. & Anil Kumar, P. Imtechella halotolerans gen. nov., sp. nov., a member of the family Flavobacteriaceae isolated from estuarine water. Int. J. Syst. Evol. Microbiol. 62, 2624–2630 (2012).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Praeg, N. et al. The role of land management and elevation in shaping soil microbial communities: Insights from the Central European Alps. Soil Biol. Biochem. 150, 107951 (2020).

    Article 
    CAS 

    Google Scholar 

  • Albonico, F. et al. Raw milk and fecal microbiota of commercial Alpine dairy cows varies with herd, fat content and diet. PLoS ONE 15, e0237262 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Watson, S. E. et al. Global change-driven use of onshore habitat impacts polar bear faecal microbiota. ISME J. https://doi.org/10.1038/s41396-019-0480-2 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Huebner, K. L. et al. Effects of a Saccharomyces cerevisiae fermentation product on liver abscesses, fecal microbiome, and resistome in feedlot cattle raised without antibiotics. Sci. Rep. 9(1), 1–11 (2019).

    Article 

    Google Scholar 

  • Fan, P. et al. Host genetic effects upon the early gut microbiota in a bovine model with graduated spectrum of genetic variation. ISME J. 14(1), 302–317 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mtshali, K., Khumalo, Z. T. H., Kwenda, S., Arshad, I. & Thekisoe, O. M. M. Exploration and comparison of bacterial communities present in bovine faeces, milk and blood using 16S rRNA metagenomic sequencing. PLoS ONE 17, e0273799 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Johnson, J. S. et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun. 10(1), 5029 (2019).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Pei, A. Y. et al. Diversity of 16S rRNA genes within individual prokaryotic genomes. Appl. Environ. Microbiol. 76, 3886 (2010).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Stoler, N. & Nekrutenko, A. Sequencing error profiles of Illumina sequencing instruments. NAR Genomics Bioinforma. 3, lqab019 (2021).

    Article 

    Google Scholar 

  • Schirmer, M. et al. Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucleic Acids Res. 43, e37–e37 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • McLaren, M. R., Willis, A. D. & Callahan, B. J. Consistent and correctable bias in metagenomic sequencing experiments. Elife 8, e46923 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gonzalez, J. M., Portillo, M. C., Belda-Ferre, P. & Mira, A. Amplification by PCR artificially reduces the proportion of the rare biosphere in microbial communities. PLoS ONE 7, e29973 (2012).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gilbert, J. A., Jansson, J. K. & Knight, R. The earth microbiome project: Successes and aspirations. BMC Biol. 12, 1–4 (2014).

    Article 

    Google Scholar 

  • Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U.S.A. 108, 4516–4522 (2011).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, 1–8 (2011).

    Article 

    Google Scholar 

  • McDonald, D. et al. American gut: An open platform for citizen science microbiome research. mSystems 3, e00031-18 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Illumina. IMPORTANT NOTICE This document provides information for an application for 16S Metagenomic Sequencing Library Preparation Preparing 16S Ribosomal RNA Gene Amplicons for the Illumina MiSeq System.

  • Teng, F. et al. Impact of DNA extraction method and targeted 16S-rRNA hypervariable region on oral microbiota profiling. Sci. Rep. 8(1), 1–12 (2018).

    Article 
    ADS 

    Google Scholar 

  • Willis, C., Desai, D. & Laroche, J. Influence of 16S rRNA variable region on perceived diversity of marine microbial communities of the Northern North Atlantic. FEMS Microbiol. Lett. 366, fnz152 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chen, Z. et al. Impact of preservation method and 16S rRNA hypervariable region on gut microbiota profiling. mSystems 4, e00271-18 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Sanada, T. J. et al. Gut microbiota modification suppresses the development of pulmonary arterial hypertension in an SU5416/hypoxia rat model. Pulm. Circ. 10(3), 1–3. https://doi.org/10.1177/2045894020929147 (2020).

    Article 
    MathSciNet 
    CAS 

    Google Scholar 

  • Praeg, N., Schwinghammer, L. & Illmer, P. Larix decidua and additional light affect the methane balance of forest soil and the abundance of methanogenic and methanotrophic microorganisms. FEMS Microbiol. Lett. 366, 259 (2019).

    Article 

    Google Scholar 

  • Vandeputte, D. et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551(7681), 507–511 (2017).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Sanders, H. L. Marine benthic diversity: A comparative study. Am. Nat. 102, 243–282. https://doi.org/10.1086/282541 (2015).

    Article 

    Google Scholar 

  • Aitchison, J. The statistical analysis of compositional data. J. R. Stat. Soc. Ser. B 44, 139–160 (1982).

    MathSciNet 
    MATH 

    Google Scholar 

  • Stanaway, I. B. et al. Human oral buccal microbiomes are associated with farmworker status and azinphos-methyl agricultural pesticide exposure. Appl. Environ. Microbiol. 83, e02149-16 (2017).

    Article 
    PubMed 

    Google Scholar 

  • Grice, E. A. et al. A diversity profile of the human skin microbiota. Genome Res. 18, 1043–1050 (2008).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Payne, M. A. et al. Horizontal and vertical transfer of oral microbial dysbiosis and periodontal disease. J. Dent. Res. 98, 1503–1510 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Karasov, T. L. et al. The relationship between microbial population size and disease in the Arabidopsis thaliana phyllosphere. bioRxiv https://doi.org/10.1101/828814 (2020).

    Article 

    Google Scholar 

  • Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6(8), 1621–1624 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).

    Article 

    Google Scholar 

  • Albanese, D., Fontana, P., De Filippo, C., Cavalieri, D. & Donati, C. MICCA: A complete and accurate software for taxonomic profiling of metagenomic data. Sci. Rep. 5(1), 1–7 (2015).

    Article 

    Google Scholar 

  • Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv https://doi.org/10.1101/081257 (2016).

    Article 

    Google Scholar 

  • Team, R. C. R: A Language and Environment for Statistical Computing. (2019).

  • Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article 

    Google Scholar 

  • De Mendiburu, F. Agricolae: statistical procedures for agricultural research. R package version, 1(1). https://scholar.google.com/scholar?hl=it&as_sdt=0%2C5&q=Agricolae%3A+Statistical+Procedures+for+Agricultural+Research&btnG (2014).

  • Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5(7), 621–628 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Metsalu, T. & Vilo, J. ClustVis: A web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res. 43, W566–W570 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Gloor, G. B. & Reid, G. Compositional analysis: A valid approach to analyze microbiome high-throughput sequencing data. Can. J. Microbiol. https://doi.org/10.1139/cjm-2015-082162,692-703 (2016).

    Article 
    PubMed 

    Google Scholar 

  • McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O’Hara, R. B., Simpson, G. L., Solymos, P., Stevens M. H. H., Szöcs, E. & Wagner, H. vegan: Community Ecology Package. R package version 2.5-7. 2020 (2022).

  • Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag, New York.


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