Summary

从土壤、根际和根 Endosphere 提取细菌群落数据的根菌群探析

Published: May 02, 2018
doi:

Summary

在这里, 我们描述了获取土壤、根际和根 endosphere 微生物的扩增子序列数据的协议。该信息可用于研究植物相关微生物群落的组成和多样性, 适合广泛的植物种类使用。

Abstract

植物宿主和相关微生物之间的密切互动对确定植物的适应性至关重要, 并能促进对非生物胁迫和疾病的耐受性提高。由于植物菌群可以是高度复杂的, 低成本, 高通量的方法, 如基于扩增子的 16S rRNA 基因测序, 往往更倾向于表征其微生物组成和多样性。然而, 在进行此类实验时, 选择适当的方法对于减少可能使样本和研究之间的分析和比较变得困难的偏见至关重要。本议定书详细描述了从土壤、根际和根样本收集和提取 DNA 的标准化方法。此外, 我们还强调了一个建立完善的 16S rRNA 扩增子测序管道, 允许探索这些样本中细菌群落的组成, 并可以很容易地适应其他标记基因。这条管道已经验证了各种植物种类, 包括高粱, 玉米, 小麦, 草莓和龙舌兰, 并可以帮助克服与植物细胞器污染相关的问题。

Introduction

植物相关的微生物由细菌、古菌、病毒、真菌和其他真核微生物组成的动态和复杂的微生物群落组成。除了它们在引起植物病害方面的良好研究作用外, 植物相关的微生物还可以通过提高对生物和非生命胁迫的耐受性, 促进养分供应, 并通过激素的生产。因此, 特殊的兴趣存在于与植物根系 endospheres、豆科和周围土壤相关联的分类。虽然有些微生物可以在实验室产生的培养基上隔离培养, 但许多人不能, 部分原因是它们可能依赖与其他微生物的共生关系, 生长非常缓慢, 或者需要在实验室环境中不能复制的条件。由于它绕过了耕种的需要, 而且相对便宜和高通量, 基于序列的环境和宿主相关微生物样本的系统发育分析已成为测定微生物群落的首选方法。组成。

不同的下一代测序平台1提供的适当排序技术的选择取决于用户的需求, 其中包括: 所需的覆盖率、扩增子长度、预期的社区多样性, 以及排序错误率, 读长, 和每运行成本/megabase。另一个需要在基于扩增子的测序实验中考虑的变量是什么基因将被放大, 以及将使用什么引物。在设计或选择底漆时, 研究人员常常被迫在放大的普遍性和由此产生的 amplicons 所能实现的分类分辨率之间做出权衡。因此, 这些类型的研究往往选择了引物和标记, 有选择地针对微生物群的特定子集。对细菌群落组成的评估通常是通过对细菌 16S rRNA 基因23的一个或多个高变区域进行排序而完成的。在本研究中, 我们描述了一个基于扩增子的测序协议, 它是针对 16S rRNA 基因的 500 bp V3-V4 区域而开发的, 它允许对细菌类群进行广泛的扩增, 同时也为区分不同的分类群。此外, 该协议可以很容易地适应使用其他底漆集, 如那些针对真菌的 ITS2 标志或 18S rRNA 亚单位的真核生物。

虽然其他方法, 如猎枪 metagenomics, metatranscriptomics 和单细胞测序, 提供其他优势, 包括解决微生物基因组和更直接测量社区功能, 这些技术通常更比此处描述的系统系统分析更昂贵和计算密集型4。此外, 在根样本上执行猎枪 metagenomics 和 metatranscriptomics 产生的读数占宿主植物基因组的百分比很大, 克服此限制的方法仍在开发中5,6

与任何实验平台一样, 基于扩增子的分析可以引入一些潜在的偏差, 在实验设计和数据分析过程中应该考虑到这一点。这些方法包括样本采集、DNA 提取、PCR 引物的选择以及如何进行库准备。不同的方法可以显著地影响生成的可用数据量, 也会阻碍研究结果的比较。例如, 去除根际细菌的方法7和使用不同的提取技术或选择 DNA 提取套件8,9已被证明大大影响下游分析, 这导致不同的结论关于哪些微生物存在和他们的相对丰度。由于可以自定义基于扩增子的分析, 所以在研究中进行比较可能具有挑战性。地球微生物学项目建议, 研究复杂系统的研究人员, 如植物相关的微生物群将受益于标准化协议的发展, 作为一种手段, 以尽量减少因应用而引起的变异性研究1011之间的不同方法。在这里, 我们讨论了以上的许多主题, 并提出了适当的最佳做法的建议。

该协议展示了从高粱双色收集土壤、根际和根样本的过程, 并使用建立良好的 dna 隔离套件11提取 dna。此外, 我们的协议还包括一个详细的扩增子排序工作流, 使用一个常用的 “通用” 平台来确定细菌群落的结构12, 13, 14.在最近发表的关于18种单子叶植物物种的根、根际和相关土壤的研究中, 本议定书已得到验证, 用于广泛的植物寄主, 其中包括高粱双色、玉米、小麦15。该方法也得到了验证, 以用于其他标记基因, 这表明其成功地应用于研究的真菌 ITS2 标记基因在龙舌兰微生物组学16,17和草莓微生物群18

Protocol

1. 根 Endosphere、根际和土壤样品的收集和分离 进入现场之前, 高压釜超纯水 (每样至少90毫升水) 进行杀菌。通过将6.75 克的25毫升的2PO4、8.75 克的 K2HPO4和 X-100 的1毫升, 为无菌水的1升, 准备真菌去除缓冲 (每样样品至少可达到每个采样率的约为 mL)。使用0.2 µm 孔径的真空过滤器消毒缓冲器。 对于步骤1.2 至 1.5, 请穿戴干净的手套, 在任何时候都用乙醇消毒…

Representative Results

执行建议的协议应导致一个索引配对端读取的数据集, 可以匹配回每个样本, 并分配给细菌操作分类学单位 (OTU) 或精确序列变体 (ESV, 也称为扩增子序列变体 (ASV) 和次操作分类单元 (sOTU)), 取决于下游分析。为了获得高质量的序列数据, 必须在每个步骤中都注意保持样本之间的一致性, 并尽量减少在样品处理或库准备过程中引入任何潜在偏差。在收集、处理和提取样本 (步骤1?…

Discussion

该协议展示了一个建立的管道, 用于探索根 endosphere、根际和土壤微生物群落组成, 从野外取样到样品处理和下游测序。研究根相关的微生物提出了独特的挑战, 部分原因是从土壤取样的固有困难。土壤在物理和化学性质上是高度可变的, 不同的土壤条件可以用少量毫米2829来分隔。这可能导致从相邻取样点收集的样本, 它们的微生物群落组成和活动有相?…

Divulgazioni

The authors have nothing to disclose.

Acknowledgements

这项工作由美国农业部 (2030-21430-008-00D) 资助。TS 由 NSF 研究生研究奖学金计划支持。

Materials

0.1-10/20 µL filtered micropipette tips USA Scientific 1120-3810 Can substitute with equivalent from other suppliers.
1.5 mL microcentrifuge tubes USA Scientific 1615-5510 Can substitute with equivalent from other suppliers.
10 µL multi-channel pipette Eppendorf 3122000027 Can substitute with equivalent from other suppliers.
10 µL, 100 µL, and 1000 µL micropipettes Eppendorf 3120000909 Can substitute with equivalent from other suppliers.
100 µL multi-channel pipette Eppendorf 3122000043 Can substitute with equivalent from other suppliers.
1000 µL filtered micropipette tips USA Scientific 1122-1830 Can substitute with equivalent from other suppliers.
2 mL microcentrifuge tubes USA Scientific 1620-2700 Can substitute with equivalent from other suppliers.
2 mm soil sieve Forestry Suppliers 60141009 Can substitute with equivalent from other suppliers.
200 µL filtered micropipette tips USA Scientific 1120-8810 Can substitute with equivalent from other suppliers.
25 mL reservoirs VWR International LLC 89094-664 Can substitute with equivalent from other suppliers.
50 mL conical vials Thermo Fisher Scientific 352098 Can substitute with equivalent from other suppliers.
500 mL vacuum filters (0.2 µm pore size) VWR International LLC 156-4020
96-well microplates USA Scientific 655900
96-well PCR plates BioRad HSP9631
Agencourt AMPure XP beads Thermo Fisher Scientific NC9933872 Instructions for use:
https://www.beckmancoulter.com/wsrportal/ajax/downloadDocument/B37419AA.pdf?autonomyId=TP_DOC_150180&documentName=B37419AA.pdf
Aluminum foil Boardwalk 7124 Can substitute with equivalent from other suppliers.
Analytical scale with 0.001 g resolution Ohaus Pioneer PA323 Can substitute with equivalent from other suppliers.
Bioruptor Plus ultrasonicator Diagenode B01020001
Bovine Serum Albumin (BSA) 20 mg/mL New England Biolabs B9000S
Centrifuge Eppendorf 5811000908 Including 50mL and 96-well plate bucket adapters
Cryogenic gloves Millipore Sigma Z183490 Can substitute with equivalent from other suppliers.
DNeasy PowerClean kit (optional) Qiagen Inc. 12877-50 Previously MoBio
DNeasy PowerSoil kit Qiagen Inc. 12888-100 Previously MoBio
Dry ice Any NA
DynaMag-2 magnet Thermo Fisher Scientific 12321D Do not substitute
Ethanol VWR International LLC 89125-188 Can substitute with equivalent from other suppliers.
Gallon size freezer bags Ziploc NA Can substitute with equivalent from other suppliers.
Gemini EM Microplate Reader Molecular Devices EM Can use another fluorometer that reads 96-well plates from the top.
K2HPO4 Sigma-Aldrich P3786
KH2PO4 Sigma-Aldrich P5655
Lab coat Workrite J1367 Can substitute with equivalent from other suppliers.
Liquid N2 Any NA Can substitute with equivalent from other suppliers.
Liquid N2 dewar Thermo Fisher Scientific 4150-9000 Can substitute with equivalent from other suppliers.
Milli-Q ultrapure water purification system Millipore Sigma SYNS0R0WW
Mini-centrifuge Eppendorf 5404000014
Molecular grade water Thermo Fisher Scientific 4387937 Can substitute with equivalent from other suppliers.
Mortars VWR International LLC 89038-150 Can substitute with equivalent from other suppliers.
Nitrile gloves Thermo Fisher Scientific 19167032B Can substitute with equivalent from other suppliers.
Paper towels VWR International LLC BWK6212 Can substitute with equivalent from other suppliers.
PCR plate sealing film Thermo Fisher Scientific NC9684493
PCR strip tubes USA Scientific 1402-2700
Pestles VWR International LLC 89038-166 Can substitute with equivalent from other suppliers.
Plastic spatulas LevGo, Inc. 17211
Platinum Hot Start PCR Master Mix (2x) Thermo Fisher Scientific 13000014
PNAs – chloroplast and mitochondrial PNA Bio NA Make sure to verify sequence bioinformatically
Protective eyewear Millipore Sigma Z759015 Can substitute with equivalent from other suppliers.
Qubit 3.0 Fluorometer Thermo Fisher Scientific Q33216
Qubit dsDNA HS assay kit Thermo Fisher Scientific Q32854
Rubber mallet (optional) Ace Hardware 2258622 Can substitute with equivalent from other suppliers.
Shears or scissors VWR International LLC 89259-936 Can substitute with equivalent from other suppliers.
Shovel Home Depot 2597400 Can substitute with equivalent from other suppliers.
Soil core collector (small diameter: <1 inch) Ben Meadows 221700 Can substitute with equivalent from other suppliers.
Spray bottles Santa Cruz Biotechnology sc-395278 Can substitute with equivalent from other suppliers.
Standard desalted barcoded primers (10 µM) (Table 1) IDT NA 4 nmole Ultramer DNA Oligo with standard desalting. NGS adapter and sequencing primer (Table 1) are designed for use with Illumina MiSeq using v3 chemistry.
Thermocycler Thermo Fisher Scientific E950040015 Can substitute with equivalent from other suppliers.
Triton X-100 Sigma-Aldrich X100 Can substitute with equivalent from other suppliers.
Weigh boats Spectrum Chemicals B6001W Can substitute with equivalent from other suppliers.

Riferimenti

  1. Goodwin, S., McPherson, J. D., McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet. 17 (6), 333-351 (2016).
  2. Soergel, D. A. W., Dey, N., Knight, R., Brenner, S. E. Selection of primers for optimal taxonomic classification of environmental 16S rRNA gene sequences. ISME J. 6 (7), 1440-1444 (2012).
  3. Takahashi, S., Tomita, J., Nishioka, K., Hisada, T., Nishijima, M. Development of a Prokaryotic Universal Primer for Simultaneous Analysis of Bacteria and Archaea Using Next-Generation Sequencing. PLoS One. 9 (8), e105592 (2014).
  4. Poretsky, R., Rodriguez-R, L. M., Luo, C., Tsementzi, D., Konstantinidis, K. T. Strengths and Limitations of 16S rRNA Gene Amplicon Sequencing in Revealing Temporal Microbial Community Dynamics. PLoS One. 9 (4), e93827 (2014).
  5. Sharpton, T. J. An introduction to the analysis of shotgun metagenomic data. Front Plant Sci. 5, 209 (2014).
  6. Jiao, J. -. Y., Wang, H. -. X., Zeng, Y., Shen, Y. -. M. Enrichment for microbes living in association with plant tissues. J Appl Microbiol. 100 (4), 830-837 (2006).
  7. Richter-Heitmann, T., Eickhorst, T., Knauth, S., Friedrich, M. W., Schmidt, H. Evaluation of Strategies to Separate Root-Associated Microbial Communities: A Crucial Choice in Rhizobiome Research. Front Microbiol. 7, 773 (2016).
  8. Mahmoudi, N., Slater, G. F., Fulthorpe, R. R., Mahmoudi, N., Slater, G. F., Fulthorpe, R. R. Comparison of commercial DNA extraction kits for isolation and purification of bacterial and eukaryotic DNA from PAH-contaminated soils. Can J Microbiol. 5709, 623-628 (2011).
  9. Vishnivetskaya, T. A., et al. Commercial DNA extraction kits impact observed microbial community composition in permafrost samples. FEMS Microbiol Ecol. 87 (1), 217-230 (2014).
  10. Busby, P. E., et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biol. 15 (3), e2001793 (2017).
  11. Thompson, L. R., et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. , (2017).
  12. 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).
  13. Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K., Schloss, P. D. Development of a Dual-Index Sequencing Strategy and Curation Pipeline for Analyzing Amplicon Sequence Data on the MiSeq Illumina Sequencing Platform. Appl Environ Microb. 79 (17), 5112-5120 (2013).
  14. Degnan, P. H., Ochman, H. Illumina-based analysis of microbial community diversity. ISME J. 6 (1), 183-194 (2012).
  15. Naylor, D., DeGraaf, S., Purdom, E., Coleman-Derr, D. Drought and host selection influence bacterial community dynamics in the grass root microbiome. ISME J. , (2017).
  16. Desgarennes, D., Garrido, E., Torres-Gomez, M. J., Peña-Cabriales, J. J., Partida-Martinez, L. P. Diazotrophic potential among bacterial communities associated with wild and cultivated Agave species. FEMS Microbiol Ecol. 90 (3), 844-857 (2014).
  17. Coleman-Derr, D., et al. Plant compartment and biogeography affect microbiome composition in cultivated and native Agave species. New Phytol. 209 (2), 798-811 (2016).
  18. De Tender, C., et al. Dynamics in the Strawberry Rhizosphere Microbiome in Response to Biochar and Botrytis cinerea Leaf Infection. Front Microbiol. 7, 2062 (2016).
  19. Kapp, J. R., et al. Variation in pre-PCR processing of FFPE samples leads to discrepancies in BRAF and EGFR mutation detection: a diagnostic RING trial. J Clin Pathol. 68 (2), 111-118 (2015).
  20. Simbolo, M., et al. DNA qualification workflow for next generation sequencing of histopathological samples. PLoS One. 8 (6), e62692 (2013).
  21. O’Neill, M., McPartlin, J., Arthure, K., Riedel, S., McMillan, N. Comparison of the TLDA with the Nanodrop and the reference Qubit system. J Phys Conf Ser. 307 (1), 012047 (2011).
  22. Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., Holmes, S. P. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 13 (7), 581-583 (2016).
  23. Amir, A., et al. Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns. mSystems. 2 (2), e00191-e00116 (2017).
  24. Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv. , 081257 (2016).
  25. Nguyen, N. -. P., Warnow, T., Pop, M., White, B. A perspective on 16S rRNA operational taxonomic unit clustering using sequence similarity. NPJ Biofilms Microbiomes. 2, 16004 (2016).
  26. Callahan, B. J., McMurdie, P. J., Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. , (2017).
  27. Lundberg, D. S., Yourstone, S., Mieczkowski, P., Jones, C. D., Dangl, J. L. Practical innovations for high-throughput amplicon sequencing. Nat Methods. 10 (10), 999-1002 (2013).
  28. O’Brien, S. L., et al. Spatial scale drives patterns in soil bacterial diversity. Environ Microbiol. 18 (6), 2039-2051 (2016).
  29. Fierer, N., Lennon, J. T. The generation and maintenance of diversity in microbial communities. Am J Bot. 98 (3), 439-448 (2011).
  30. Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 15 (10), 579-590 (2017).
  31. Buckley, D. H., Schmidt, T. M. Diversity and dynamics of microbial communities in soils from agro-ecosystems. Environ Microbiol. 5 (6), 441-452 (2003).
  32. Salter, S. J., et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).
  33. Weiss, S., Amir, A., Hyde, E. R., Metcalf, J. L., Song, S. J., Knight, R. Tracking down the sources of experimental contamination in microbiome studies. Genome Biol. 15 (12), 564 (2014).
  34. Sutlović, D., Definis Gojanović, M., Andelinović, S., Gugić, D., Primorac, D. Taq polymerase reverses inhibition of quantitative real time polymerase chain reaction by humic acid. Croat Med J. 46 (4), 556-562 (2005).
  35. Sutlovic, D., Gamulin, S., Definis-Gojanovic, M., Gugic, D., Andjelinovic, S. Interaction of humic acids with human DNA: proposed mechanisms and kinetics. Electrophoresis. 29 (7), 1467-1472 (2008).
  36. Aleklett, K., Leff, J. W., Fierer, N., Hart, M. Wild plant species growing closely connected in a subalpine meadow host distinct root-associated bacterial communities. PeerJ. 3, e804 (2015).
  37. Bogas, A. C., et al. Endophytic bacterial diversity in the phyllosphere of Amazon Paullinia cupana associated with asymptomatic and symptomatic anthracnose. Springerplus. 4, 258 (2015).
  38. Hiscox, J., Savoury, M., Müller, C. T., Lindahl, B. D., Rogers, H. J., Boddy, L. Priority effects during fungal community establishment in beech wood. ISME J. 9 (10), 2246-2260 (2015).
  39. Zhang, T., Yao, Y. -. F. Endophytic Fungal Communities Associated with Vascular Plants in the High Arctic Zone Are Highly Diverse and Host-Plant Specific. PLoS One. 10 (6), e0130051 (2015).
  40. Ghyselinck, J., Pfeiffer, S., Heylen, K., Sessitsch, A., De Vos, P. The effect of primer choice and short read sequences on the outcome of 16S rRNA gene based diversity studies. PLoS One. 8 (8), e71360 (2013).
  41. Wintzingerode, F., Landt, O., Ehrlich, A., Göbel, U. B. Peptide nucleic acid-mediated PCR clamping as a useful supplement in the determination of microbial diversity. Appl Environ Microb. 66 (2), 549-557 (2000).
  42. Cruaud, P., Vigneron, A., Lucchetti-Miganeh, C., Ciron, P. E., Godfroy, A., Cambon-Bonavita, M. -. A. Influence of DNA extraction method, 16S rRNA targeted hypervariable regions, and sample origin on microbial diversity detected by 454 pyrosequencing in marine chemosynthetic ecosystems. Appl Environ Microb. 80 (15), 4626-4639 (2014).
  43. Yang, B., Wang, Y., Qian, P. -. Y. Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis. BMC Bioinformatics. 17, 135 (2016).

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Citazione di questo articolo
Simmons, T., Caddell, D. F., Deng, S., Coleman-Derr, D. Exploring the Root Microbiome: Extracting Bacterial Community Data from the Soil, Rhizosphere, and Root Endosphere. J. Vis. Exp. (135), e57561, doi:10.3791/57561 (2018).

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