High-throughput cultivation and identification of bacteria from the plant root microbiota

Jingying Zhang, Yong-Xin Liu, Xiaoxuan Guo, Yuan Qin, Ruben Garrido-Oter, Paul Schulze-Lefert, Yang Bai

Published: 2021-01-12 DOI: 10.1038/s41596-020-00444-7

Supplementary information

Reporting Summary

Supplementary Table 1

Primer sequences used for bacterial identification. 16S rRNA gene primers 799F and 1193R are in gray. Well and plate barcodes are in red and blue, respectively. Illumina adapters (P5 and Read1, P7, Index and Read2) are in gray.

Supplementary Table 2

Mapping file.

Supplementary Table 3

Taxonomy and sequences of ASVs.

Supplementary Table 4

Read counts of ASVs in each well (ASV table).

Supplementary Table 5

Five best candidate wells corresponding to each ASV.

Supplementary Table 6

Data to generate anticipated results in Fig. 6.

Generation of mouse–human chimeric embryos

Genome-wide quantification of transcription factor binding at single-DNA-molecule resolution using methyl-transferase footprinting

Generation of CRISPR–Cas9-mediated genetic knockout human intestinal tissue–derived enteroid lines by lentivirus transduction and single-cell cloning

A puromycin-dependent activity-based sensing probe for histochemical staining of hydrogen peroxide in cells and animal tissues

Decoding personal biotic and abiotic airborne exposome

A clinically relevant model of focal embolic cerebral ischemia by thrombus and thrombolysis in rhesus monkeys

Bioengineering human intestinal mucosal grafts using patient-derived organoids, fibroblasts and scaffolds

Biomimetic tail-to-head terpene cyclizations using the resorcin[4]arene capsule catalyst

Neuroscience robotics for controlled induction and real-time assessment of hallucinations

Scanorama: integrating large and diverse single-cell transcriptomic datasets

Single-cell RNA sequencing (scRNA-seq)（单细胞RNA测序）

Data integration（数据集成）

Heterogeneity（异质性）

查看全部

Sections

Figures

References

Supplementary information

Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).
Bulgarelli, D. et al. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488, 91–95 (2012).
Lundberg, D. S. et al. Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86 (2012).
Peiffer, J. A. et al. Diversity and heritability of the maize rhizosphere microbiome under field conditions. Proc. Natl Acad. Sci. USA 110, 6548–6553 (2013).
Edwards, J. et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc. Natl Acad. Sci. USA 112, E911–E920 (2015).
Wang, W. et al. An Arabidopsis secondary metabolite directly targets expression of the bacterial type III secretion system to inhibit bacterial virulence. Cell Host Microbe 27, 601–613 (2020).
Durán, P. et al. Microbial interkingdom interactions in roots promote Arabidopsis survival. Cell 175, 973–983 (2018).
Carrión, V. J. et al. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science 366, 606–612 (2019).
Bai, Y. et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528, 364–369 (2015).
Lebeis, S. L. et al. Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa. Science 349, 860–864 (2015).
Zhang, J. et al. Root microbiota shift in rice correlates with resident time in the field and developmental stage. Sci. China Life Sci. 61, 613–621 (2018).
Zhang, J. et al. NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice. Nat. Biotechnol. 37, 676–684 (2019).
Castrillo, G. et al. Root microbiota drive direct integration of phosphate stress and immunity. Nature 543, 513–518 (2017).
Huang, A. C. et al. A specialized metabolic network selectively modulates Arabidopsis root microbiota. Science 364, eaau6389 (2019).
Yuan, J. et al. Root exudates drive the soil-borne legacy of aboveground pathogen infection. Microbiome 6, 156 (2018).
Lu, T. et al. Rhizosphere microorganisms can influence the timing of plant flowering. Microbiome 6, 231 (2018).
Kwak, M.-J. et al. Rhizosphere microbiome structure alters to enable wilt resistance in tomato. Nat. Biotechnol. 36, 1100 (2018).
Garrido-Oter, R. et al. Modular traits of the Rhizobiales root microbiota and their evolutionary relationship with symbiotic Rhizobia. Cell Host Microbe 24, 155–167 (2018).
Liu, Y.-X., Qin, Y. & Bai, Y. Reductionist synthetic community approaches in root microbiome research. Curr. Opin. Microbiol. 49, 97–102 (2019).
Lagier, J.-C. et al. Culturing the human microbiota and culturomics. Nat. Rev. Microbiol. 16, 540–550 (2018).
Pham, V. H. T. & Kim, J. Cultivation of unculturable soil bacteria. Trends Biotechnol. 30, 475–484 (2012).
Overmann, J., Abt, B. & Sikorski, J. Present and future of culturing bacteria. Annu. Rev. Microbiol. 71, 711–730 (2017).
Goodman, A. L. et al. Extensive personal human gut microbiota culture collections characterized and manipulated in gnotobiotic mice. Proc. Natl Acad. Sci. USA 108, 6252–6257 (2011).
Lagier, J.-C. et al. Culture of previously uncultured members of the human gut microbiota by culturomics. Nat. Microbiol 1, 16203 (2016).
Forster, S. C. et al. A human gut bacterial genome and culture collection for improved metagenomic analyses. Nat. Biotechnol. 37, 186–192 (2019).
Zou, Y. et al. 1,520 reference genomes from cultivated human gut bacteria enable functional microbiome analyses. Nat. Biotechnol. 37, 179–185 (2019).
Ji, P., Zhang, Y., Wang, J. & Zhao, F. MetaSort untangles metagenome assembly by reducing microbial community complexity. Nat. Commun. 8, 14306 (2017).
Wang, Y., Hammes, F., Boon, N., Chami, M. & Egli, T. Isolation and characterization of low nucleic acid (LNA)-content bacteria. ISME J. 3, 889–902 (2009).
Nichols, D. et al. Use of Ichip for high-throughput in situ cultivation of “uncultivable” microbial species. Appl. Environ. Microbiol. 76, 2445–2450 (2010).
Aoi, Y. et al. Hollow-fiber membrane chamber as a device for in situ environmental cultivation. Appl. Environ. Microbiol. 75, 3826–3833 (2009).
Zhang, F. et al. Microbiota transplantation: concept, methodology and strategy for its modernization. Protein Cell 9, 462–473 (2018).
Karasov, T. L. et al. Arabidopsis thaliana and Pseudomonas pathogens exhibit stable associations over evolutionary timescales. Cell Host Microbe 24, 168–179 (2018).
Oberhardt, M. A. et al. Harnessing the landscape of microbial culture media to predict new organism–media pairings. Nat. Commun. 6, 8493 (2015).
Atlas, R. M. Handbook of Microbiological Media (CRC Press, 2010).
Vartoukian, S. R., Palmer, R. M. & Wade, W. G. Strategies for culture of ‘unculturable’ bacteria. FEMS Microbiol. Lett. 309, 1–7 (2010).
Davis, K. E. R., Joseph, S. J. & Janssen, P. H. Effects of growth medium, inoculum size, and incubation time on culturability and isolation of soil bacteria. Appl. Environ. Microbiol. 71, 826–834 (2005).
Joseph, S. J., Hugenholtz, P., Sangwan, P., Osborne, C. A. & Janssen, P. H. Isolation and identification of Actinobacteria from surface-sterilized wheat roots. Appl. Environ. Microbiol. 69, 7210–7215 (2003).
Hacquard, S. et al. Microbiota and host nutrition across plant and animal kingdoms. Cell Host Microbe 17, 603–616 (2015).
Reasoner, D. J. & Geldreich, E. E. A new medium for the enumeration and subculture of bacteria from potable water. Appl. Environ. Microbiol 49, 1–7 (1985).
Coombs, J. T. & Franco, C. M. M. Isolation and identification of Actinobacteria from surface-sterilized wheat roots. Appl. Environ. Microbiol. 69, 5603–5608 (2003).
Travers, R. S., Martin, P. A. W. & Reichelderfer, C. F. Selective process for efficient isolation of soil Bacillus spp. Appl. Environ. Microbiol 53, 1263–1266 (1987).
Chelius, M. K. & Triplett, E. W. The diversity of archaea and bacteria in association with the roots of Zea mays L. Microb. Ecol. 41, 252–263 (2001).
Costea, P. I. et al. Towards standards for human fecal sample processing in metagenomic studies. Nat. Biotechnol. 35, 1069–1076 (2017).
Knight, R. et al. Best practices for analysing microbiomes. Nat. Rev. Microbiol. 16, 410–422 (2018).
Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821 (2020).
Liu, Y.-X. et al. A practical guide to amplicon and metagenomic analysis of microbiome data. Protein Cell http://doi.org/10.1007/s13238-020-00724-8 (2020).
Liu, Y.-X., Qin, Y., Guo, X. & Bai, Y. [Methods and applications for microbiome data analysis]. Yi Chuan 41, 845–862 (2019).
Qian, X.-B. et al. A guide to human microbiome research: study design, sample collection, and bioinformatics analysis. Chin. Med. J. 133, 1844–1855 (2020).
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
Chen, Q. et al. Recently duplicated sesterterpene (C25) gene clusters in Arabidopsis thaliana modulate root microbiota. Sci. China Life Sci. 62, 947–958 (2019).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Kolde, R. pheatmap: Pretty Heatmaps (2015).
Asnicar, F., Weingart, G., Tickle, T. L., Huttenhower, C. & Segata, N. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ 3, e1029 (2015).
Wang, Y. et al. GSA: Genome Sequence Archive. Genomics Proteom. Bioinforma. 15, 14–18 (2017).
Partners, N. G. D. C. M. Database resources of the National Genomics Data Center in 2020. Nucleic Acids Res. 48, D24–D33 (2020).
Cole, J. R. et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42, D633–D642 (2014).
Grüning, B. et al. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat. Methods 15, 475–476 (2018).
Sayers, E. W. et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 48, D9–D16 (2020).
Cook, C. E., Stroe, O., Cochrane, G., Birney, E. & Apweiler, R. The European Bioinformatics Institute in 2020: building a global infrastructure of interconnected data resources for the life sciences. Nucleic Acids Res. 48, D17–D23 (2019).