人乳样品靶向16S 测序方法的研究
Summary
提出了一种半自动化工作流, 用于从人乳和其他低生物量样品类型中进行 16S rRNA 的目标排序。
Abstract
随着相对便宜、快速和高吞吐量测序的发展, 微生物群落的研究变得越来越普遍。然而, 与所有这些技术一样, 可重现的结果取决于实验室工作流, 其中包含适当的预防措施和控制。这是特别重要的低生物量样本, 污染细菌 DNA 可以产生误导的结果。本文详细介绍了一种半自动的工作流程, 即使用16S 核糖体 RNA (rRNA) V4 区域的目标测序, 在低到中吞吐量范围内, 识别出人类母乳样本中的微生物。该协议描述了从全牛乳样品的制备方法, 包括: 样品裂解、核酸提取、16S rRNA 基因 V4 区的扩增和质量控制措施的库准备。重要的是, 该议定书和讨论考虑了对低生物量样品的制备和分析具有显著意义的问题, 包括适当的阳性和阴性对照、PCR 抑制剂去除、环境样品污染、试剂或实验源和实验性最佳实践, 旨在确保重现性。虽然所描述的议定书是特定于人乳样本, 但它适用于许多低和高生物量样本类型, 包括收集在拭子上的样本, 冷冻整齐, 或稳定在保存缓冲区。
Introduction
殖民人类的微生物群落被认为对人类健康和疾病影响新陈代谢、免疫发展、对疾病的易感性以及对疫苗接种和药物治疗的反应至关重要1, 2. 为了解微生物群对人类健康的影响而作出的努力目前强调识别与定义的解剖隔间 (即、皮肤、肠道、口腔、等) 相关的微生物, 以及内部的本地化站点。这些舱室3,4。这些调查工作的基础是迅速出现并增加了下一代测序技术的可获得性, 为样品的微生物遗传量 (微生物) 分析提供了一个大规模的并行平台。对于许多生理样本, 相关的菌群既复杂又丰富 (即, 大便), 但对于一些样本, 微生物群是由低生物量 (即, 人乳, 下呼吸道) 所代表的, 其中敏感度、实验性文物和可能的污染成为主要问题。微生物研究和适当实验设计的共同挑战是多个评论文章的主题5,6,7,8。
本文介绍了一种基于 rRNA 16S V4 区域9的目标测序的鲁棒实验管道, 以表征人乳的微生物群。微生物菌群分析的人奶不仅是复杂的先天低生物量的10, 但另外由高级别的人类 DNA 背景11,12,13,14和PCR 抑制剂的潜在结转15,16在提取的核酸。该协议依赖于商业上可用的提取工具包和半自动平台, 可帮助最大限度地减少样本准备批次之间的可变性。它包含了一个定义良好的细菌模拟社区, 它与样品一起处理, 作为质量控制来验证协议中的每一步, 并提供一个独立的管道健壮性度量。虽然所描述的协议是特定于人乳样本, 它很容易适应其他样本类型, 包括大便, 直肠, 阴道, 皮肤, 晕和口服拭子10,17, 并可以作为一个起点希望进行微生物分析的研究人员。
Protocol
对于所有的协议步骤, 必须佩戴适当的个人防护设备 (PPE), 并需要采取严格的防污染措施。观察从预放大工作区到放大后工作区域的工作流程, 以尽量减少样品的污染。所有使用的用品是无菌的, 没有 RNase, DNase, DNA 和热原。所有吸管提示都被过滤。提供了协议步骤的流程图 (图 1)。 1. 样品裂解 注: 样品裂解和核酸提取是在洁净室环境中?…
Representative Results
此处介绍的协议包括重要的质量控制 (QC) 步骤, 以确保所生成的数据符合协议灵敏度、特异性和污染控制的基准。该协议的第一个 QC 步骤是 16S V4 区域的 PCR 放大 (图 2)。通过电泳分析了每种样品的 PCR 产物的一个µL, 以确认其在 315-450 bp (图 2、红色箭头) 的预期尺寸范围内。一些人奶样本产生了较低数量的特定产品 (<strong class="xf…
Discussion
16S rRNA 的目标下一代测序是一种广泛使用的、快速的微生物特征18技术。但是, 许多因素, 包括批处理效果、环境污染、样本交叉污染、灵敏度和重现性都会对实验结果产生负面影响, 并混淆其解释7,19,20. 为了最好地促进强健的16S 分析, 微生物组工作流必须包括良好的实验设计、使用适当的控制、工作流步骤的?…
Disclosures
The authors have nothing to disclose.
Acknowledgements
我们要感谢 Helty Adisetiyo, 博士和 Shangxin 杨博士的发展的协议。对国际妇幼青少年艾滋病临床试验组 (IMPAACT) 的总体支助是由国家卫生研究院 (NIH) 国家过敏和传染病研究所 (NIAID) 根据奖励数字提供的。UM1AI068632 (IMPAACT), UM1AI068616 (IMPAACT SDMC) 和 UM1AI106716 (IMPAACT LC), 与来自尤尼斯·肯尼迪-国家儿童健康和人类发展研究所 (发育研究院) 和国家心理健康研究所 (镍氢) 的共同资助。内容完全是作者的责任, 不一定代表 NIH 的官方观点。
Materials
| AllPrep RNA/DNA Mini Kit | Qiagen | 80204 | DNA/RNA extraction kit |
| Eliminase | Fisher Scientific | 435532 | RNase, DNase, DNA decontaminant |
| Thermo Mixer | Fisher Scientific | temperature-controlled vortexer | |
| Buffer RLT plus | Qiagen | 1053393 | guanidinium thiocyanate lysis buffer/ Part of Allprep kit |
| ß-Mercaptoethanol | Sigma Aldrich | 63689-25ML-F | ß-ME is a reducing agent that will irreversibly denature RNases by reducing disulfide bonds |
| LME Beads | MP Biomedicals | 116914050 | bead tube |
| QIAgen TissueLyzer | Qiagen | 85300 | automated sample disruptor adapter set |
| QIAshredder column | Qiagen | 79654 | |
| QIAgen RB tube | manufacturer's microcentrifuge tube in kit | ||
| QIAcube and related plasticware | Qiagen | 9001292 | automated DNA/RNA purification instrument |
| DNA exitus plus | Applichem | A7089 | non-enzymatic decontamination solution |
| EB Buffer | Qiagen | 19086 | elution buffer |
| QIAgility and related plasticware | Qiagen | 9001532 | robotic liquid handler |
| PCR water | MO BIO | 17000- | |
| 5PRIME HotMasterMix | Quantabio | 2200400 | |
| Barcoded reverse primers | Eurofin | No Catalog #'s | designed and ordered |
| 96 well PCR plate | USA scientific | 1402-9708 | |
| Tapestation 2200 and related plasticware | Agilent | G2964AA | automated DNA/RNA fragment analyzer |
| D1000 reagents for Tapestation | Agilent | 5067-5585 | Sample buffer and ladder are part of this kit |
| OneStep PCR Inhibitor Removal Kit | Zymo Research | 50444470 | PCR inhibitor removal is done per the manufacturer's instructions. |
| QIAquick PCR Purification Kit | Qiagen | 28104 | DNA clean up kit: silica-membrane-based purification of PCR products |
| Qubit dsDNA HS Assay Kit | Thermo Fisher | Q32854 | dimethylsulfoxide-based dilution buffer and dye are part of this kit. |
| Qubit Fluorometer | Thermo Fisher | Q33216 | |
| NanoDrop | Thermo Fisher | microvolume spectrophotometer | |
| MiSeq 300 V2 kit | Illumina | 15033624/15033626 | |
| MiSeq | Illumina | No Catalog #'s | next generation sequencer |
References
- Ahern, P. P., Faith, J. J., Gordon, J. I. Mining the human gut microbiota for effector strains that shape the immune system. Immunity. 40 (6), 815-823 (2014).
- Postler, T. S., Ghosh, S. Understanding the Holobiont: How Microbial Metabolites Affect Human Health and Shape the Immune System. Cell Metab. , (2017).
- Hall, M. W., et al. Inter-personal diversity and temporal dynamics of dental, tongue, and salivary microbiota in the healthy oral cavity. NPJ Biofilms Microbiomes. 3, 2 (2017).
- Perez Perez, G. I., et al. Body Site Is a More Determinant Factor than Human Population Diversity in the Healthy Skin Microbiome. PLoS One. 11 (4), e0151990 (2016).
- Hamady, M., Knight, R. Microbial community profiling for human microbiome projects: Tools, techniques, and challenges. Genome Res. 19 (7), 1141-1152 (2009).
- Human Microbiome Project, C. A framework for human microbiome research. Nature. 486 (7402), 215-221 (2012).
- Kim, D., et al. Optimizing methods and dodging pitfalls in microbiome research. Microbiome. 5 (1), 52 (2017).
- Hugerth, L. W., Andersson, A. F. Analysing Microbial Community Composition through Amplicon Sequencing: From Sampling to Hypothesis Testing. Front Microbiol. 8, 1561 (2017).
- 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).
- Pannaraj, P. S., et al. Association Between Breast Milk Bacterial Communities and Establishment and Development of the Infant Gut Microbiome. JAMA Pediatr. , (2017).
- Ho, F. C., Wong, R. L., Lawton, J. W. Human colostral and breast milk cells. A light and electron microscopic study. Acta Paediatr Scand. 68 (3), 389-396 (1979).
- Parmely, M. J., Beer, A. E., Billingham, R. E. In vitro studies on the T-lymphocyte population of human milk. J Exp Med. 144 (2), 358-370 (1976).
- Sabbaj, S., et al. Human immunodeficiency virus-specific CD8(+) T cells in human breast milk. J Virol. 76 (15), 7365-7373 (2002).
- Jimenez, E., et al. Metagenomic Analysis of Milk of Healthy and Mastitis-Suffering Women. J Hum Lact. 31 (3), 406-415 (2015).
- Ghosh, M. K., et al. Quantitation of human immunodeficiency virus type 1 in breast milk. J Clin Microbiol. 41 (6), 2465-2470 (2003).
- Lim, N. Y., Roco, C. A., Frostegard, A. Transparent DNA/RNA Co-extraction Workflow Protocol Suitable for Inhibitor-Rich Environmental Samples That Focuses on Complete DNA Removal for Transcriptomic Analyses. Front Microbiol. 7, 1588 (2016).
- Bender, J. M., et al. Maternal HIV infection influences the microbiome of HIV-uninfected infants. Sci Transl Med. 8 (349), 349ra100 (2016).
- Cox, M. J., Cookson, W. O., Moffatt, M. F. Sequencing the human microbiome in health and disease. Hum Mol Genet. 22 (R1), R88-R94 (2013).
- Lauder, A. P., et al. Comparison of placenta samples with contamination controls does not provide evidence for a distinct placenta microbiota. Microbiome. 4 (1), 29 (2016).
- Salter, S. J., et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).
- Walker, A. W., et al. 16S rRNA gene-based profiling of the human infant gut microbiota is strongly influenced by sample processing and PCR primer choice. Microbiome. 3, 26 (2015).
- Glassing, A., Dowd, S. E., Galandiuk, S., Davis, B., Chiodini, R. J. Inherent bacterial DNA contamination of extraction and sequencing reagents may affect interpretation of microbiota in low bacterial biomass samples. Gut Pathog. 8, 24 (2016).
- Kennedy, K., Hall, M. W., Lynch, M. D., Moreno-Hagelsieb, G., Neufeld, J. D. Evaluating bias of illumina-based bacterial 16S rRNA gene profiles. Appl Environ Microbiol. 80 (18), 5717-5722 (2014).
- Weiss, S., et al. Tracking down the sources of experimental contamination in microbiome studies. Genome Biol. 15 (12), 564 (2014).
- Aho, V. T., et al. The microbiome of the human lower airways: a next generation sequencing perspective. World Allergy Organ J. 8 (1), 23 (2015).
- Charlson, E. S., et al. Topographical continuity of bacterial populations in the healthy human respiratory tract. Am J Respir Crit Care Med. 184 (8), 957-963 (2011).
- Bittinger, K., et al. Improved characterization of medically relevant fungi in the human respiratory tract using next-generation sequencing. Genome Biol. 15 (10), 487 (2014).
- Jervis-Bardy, J., et al. Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data. Microbiome. 3, 19 (2015).
- Knights, D., et al. Bayesian community-wide culture-independent microbial source tracking. Nat Methods. 8 (9), 761-763 (2011).
- Lazarevic, V., Gaia, N., Girard, M., Schrenzel, J. Decontamination of 16S rRNA gene amplicon sequence datasets based on bacterial load assessment by qPCR. BMC Microbiol. 16, 73 (2016).
- Kurilshikov, A., Wijmenga, C., Fu, J., Zhernakova, A. Host Genetics and Gut Microbiome: Challenges and Perspectives. Trends Immunol. 38 (9), 633-647 (2017).
- . Mock microbial communities Available from: https://www.atcc.org/en/Products/Microbiome_Standards.aspx?utm_id=t170601524l1 (2017)
Cite This Article
Tobin, N. H., Woodward, C., Zabih, S., Lee, D. J., Li, F., Aldrovandi, G. M. A Method for Targeted 16S Sequencing of Human Milk Samples. J. Vis. Exp. (133), e56974, doi:10.3791/56974 (2018).