Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

Bolyen, Evan; Rideout, Jai Ram; Dillon, Matthew R.; Bokulich, Nicholas A.; Abnet, Christian C.; Al‐Ghalith, Gabriel A.; Alexander, Harriet; Alm, Eric J.; Arumugam, Manimozhiyan; Asnicar, Francesco; Bai, Yang; Bisanz, Jordan E.; Bittinger, Kyle; Brejnrod, Asker; Brislawn, Colin; Brown, C. Titus; Callahan, Benjamin J.; Caraballo‐Rodríguez, Andrés Mauricio; Chase, John; Cope, Emily K.; Silva, Ricardo R. da; Diener, Christian; Dorrestein, Pieter C.; Douglas, Gavin M.; Durall, Daniel M.; Duvallet, Claire; Edwardson, Christian F.; Ernst, Madeleine; Estaki, Mehrbod; Fouquier, Jennifer; Gauglitz, Julia M.; Gibbons, Sean M.; Gibson, Deanna L.; González, Antonio; Gorlick, Kestrel; Guo, Jiarong; Hillmann, Benjamin; Holmes, Susan; Holste, Hannes; Huttenhower, Curtis; Huttley, Gavin A.; Janssen, Stefan; Jarmusch, Alan K.; Jiang, Lingjing; Kaehler, Benjamin; Kang, Kyo Bin; Keefe, Christopher R; Keim, Paul; Kelley, Scott T.; Knights, Dan; Koester, Irina; Kościółek, Tomasz; Kreps, Jorden; Langille, Morgan G. I.; Lee, Joslynn; Ley, Ruth E.; Liu, Yong‐Xin; Loftfield, Erikka; Lozupone, Catherine; Maher, Massoud; Marotz, Clarisse; Martin, Bryan D.; McDonald, Daniel; McIver, Lauren J.; Melnik, Alexey V.; Metcalf, Jessica L.; Morgan, Sydney; Morton, Jamie; Naimey, Ahmad Turan; Navas-Molina, José A.; Nothias, Louis Félix; Orchanian, Stephanie B.; Pearson, Talima; Peoples, Samuel L; Petras, Daniel; Preuss, Mary L.; Pruesse, Elmar; Rasmussen, Lasse Buur; Rivers, Adam R.; Robeson, Michael S.; Rosenthal, Patrick; Segata, Nicola; Shaffer, Michael J.; Shiffer, Arron; Sinha, Rashmi; Song, Se Jin; Spear, John R.; Swafford, Austin D.; Thompson, Luke R.; Torres, Pedro J.; Trinh, Pauline; Tripathi, Anupriya; Turnbaugh, Peter J.; Ul-Hasan, Sabah; Hooft, Justin J. J. van der; Vargas, Fernando; Vázquez-Baeza, Yoshiki; Vogtmann, Emily; Hippel, Max von; Walters, William A.; Wan, Yunhu; Wang, Mingxun; Warren, Jonathan; Weber, Kyle C.; Williamson, Charles H. D.; Willis, Amy; Xu, Zhenjiang; Zaneveld, Jesse; Zhang, Yilong; Zhu, Quanyin; Knight, Rob; Caporaso, J. Gregory

doi:10.1038/s41587-019-0209-9

Cited by 14,483 publications

(9,162 citation statements)

References 28 publications

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“…Sequence data for the amplicons were analysed using the QIIME2 platform, Version 2018.11 (Bolyen et al, ). For all paired reads, the first 20 bases of both sequences were trimmed (to remove primer sequences) and the bases after position 200 were truncated (to remove low‐quality sequence data), and potential amplicon sequencing errors were corrected using DADA2 (Callahan et al, ) to produce an amplicon sequence variant (ASV) dataset.…”

Section: Methodsmentioning

confidence: 99%

Rhizosphere modelling reveals spatiotemporal distribution of daidzein shaping soybean rhizosphere bacterial community

Okutani

Hamamoto

Aoki

et al. 2020

Plant Cell & Environment

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Plant roots nurture a wide variety of microbes via exudation of metabolites, shaping the rhizosphere's microbial community. Despite the importance of plant specialized metabolites in the assemblage and function of microbial communities in the rhizosphere, little is known of how far the effects of these metabolites extend through the soil. We employed a fluid model to simulate the spatiotemporal distribution of daidzein, an isoflavone secreted from soybean roots, and validated using soybeans grown in a rhizobox. We then analysed how daidzein affects bacterial communities using soils artificially treated with daidzein. Simulation of daidzein distribution showed that it was only present within a few millimetres of root surfaces. After 14 days in a rhizobox, daidzein was only present within 2 mm of root surfaces. Soils with different concentrations of daidzein showed different community composition, with reduced α‐diversity in daidzein‐treated soils. Bacterial communities of daidzein‐treated soils were closer to those of the soybean rhizosphere than those of bulk soils. This study highlighted the limited distribution of daidzein within a few millimetres of root surfaces and demonstrated a novel role of daidzein in assembling bacterial communities in the rhizosphere by acting as more of a repellant than an attractant.

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Section: Methodsmentioning

confidence: 99%

Rhizosphere modelling reveals spatiotemporal distribution of daidzein shaping soybean rhizosphere bacterial community

Okutani

Hamamoto

Aoki

et al. 2020

Plant Cell & Environment

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“…16S rRNA gene amplicon sequencing reads underwent taxonomic assignment using QIIME2 (Bolyen et al, ) which includes vsearch dereplicate‐sequences function for dereplication and q2‐feature‐classifier for taxonomic assignment and the SILVA v128 database (Quast et al., ) while default parameters of QIIME (Caporaso et al., ) with closed‐reference clustering for OTU picking with 97% identity and UNITE database version 7.2 (Nilsson et al., ) were used for ITS amplicon analysis.…”

Section: Methodsmentioning

confidence: 99%

Microbial composition of Kombucha determined using amplicon sequencing and shotgun metagenomics

Arıkan

Mitchell

Finn

et al. 2020

Journal of Food Science

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Kombucha, a fermented tea generated from the co-culture of yeasts and bacteria, has gained worldwide popularity in recent years due to its potential benefits to human health. As a result, many studies have attempted to characterize both its biochemical properties and microbial composition. Here, we have applied a combination of whole metagenome sequencing (WMS) and amplicon (16S rRNA and Internal Transcribed Spacer 1 [ITS1]) sequencing to investigate the microbial communities of homemade Kombucha fermentations from day 3 to day 15. We identified the dominant bacterial genus as Komagataeibacter and dominant fungal genus as Zygosaccharomyces in all samples at all time points. Furthermore, we recovered three near complete Komagataeibacter genomes and one Zygosaccharomyces bailii genome and then predicted their functional properties. Also, we determined the broad taxonomic and functional profile of plasmids found within the Kombucha microbial communities. Overall, this study provides a detailed description of the taxonomic and functional systems of the Kombucha microbial community. Based on this, we conject that the functional complementarity enables metabolic cross talks between Komagataeibacter species and Z. bailii, which helps establish the sustained a relatively low diversity ecosystem in Kombucha.

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“…The unmapped reads to chloroplast, mitochondria, and cabbage lopper were used in the analysis. The 16S amplicon microbiome analysis was carried out using the Qiime2 program (qiime2‐2018‐4; https://docs.qiime2.org; Bolyen et al, ). Amplicon sequence variants (ASV) were called using the DADA2 program (‐‐p‐trim‐left‐f 9, ‐‐p‐trim‐left‐r 9, ‐‐p‐trunc‐len‐f 240, ‐‐p‐trunc‐len‐r 240, –p‐n‐reads‐learn 2,000,000) within Qiime2, and chimeric variants were filtered out.…”

Section: Methodsmentioning

confidence: 99%

Cabbage looper (Trichoplusia ni Hübner) labial glands contain unique bacterial flora in contrast with their alimentary canal, mandibular glands, and Malpighian tubules

Lawrence¹,

Novak²,

Shao

et al. 2020

MicrobiologyOpen

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In recent years, several studies have examined the gut microbiome of lepidopteran larvae and how factors such as host plant affect it, and in turn, how gut bacteria affect host plant responses to herbivory. In addition, other studies have detailed how secretions of the labial (salivary) glands can alter host plant defense responses. We examined the gut microbiome of the cabbage looper (Trichoplusia ni) feeding on collards (Brassica oleracea) and separately analyzed the microbiomes of various organs that open directly into the alimentary canal, including the labial glands, mandibular glands, and the Malpighian tubules. In this study, the gut microbiome of T. ni was found to be generally consistent with those of other lepidopteran larvae in prior studies. The greatest diversity of bacteria appeared in the Firmicutes, Actinobacteria, Proteobacteria, and Bacteriodetes. Well‐represented genera included Staphylococcus, Streptococcus, Corynebacterium, Pseudomonas, Diaphorobacter, Methylobacterium, Flavobacterium, and Cloacibacterium. Across all organs, two amplicon sequence variants (ASVs) associated with the genera Diaphorobacter and Cloacibacterium appeared to be most abundant. In terms of the most prevalent ASVs, the alimentary canal, Malpighian tubules, and mandibular glands appeared to have similar complements of bacteria, with relatively few significant differences evident. However, aside from the Diaphorobacter and Cloacibacterium ASVs common to all the organs, the labial glands appeared to possess a distinctive complement of bacteria which was absent or poorly represented in the other organs. Among these were representatives of the Pseudomonas, Flavobacterium, Caulobacterium, Anaerococcus, and Methylobacterium. These results suggest that the labial glands present bacteria with different selective pressures than those occurring in the mandibular gland, Malpighian tubules and the alimentary canal. Given the documented effects that labial gland secretions and the gut microbiome can exert on host plant defenses, the effects exerted by the bacteria inhabiting the labial glands themselves deserve further study.

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Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

Cited by 14,483 publications

References 28 publications

Rhizosphere modelling reveals spatiotemporal distribution of daidzein shaping soybean rhizosphere bacterial community

Rhizosphere modelling reveals spatiotemporal distribution of daidzein shaping soybean rhizosphere bacterial community

Microbial composition of Kombucha determined using amplicon sequencing and shotgun metagenomics

Cabbage looper (Trichoplusia ni Hübner) labial glands contain unique bacterial flora in contrast with their alimentary canal, mandibular glands, and Malpighian tubules

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