PCR-SSCP技术在嗜盐放线菌链单孢菌属快速筛选中的应用

为提高嗜盐放线茵的研究效率,快速、准确的从大量分离菌株中去除重复菌株、筛选出目的菌株,在特异性引物快速定属的基础上,以嗜盐放线茵链单孢茵属的34株菌株为研究对象,采用与PCR相结合的单链构象多态性分析(PCR-SSCP),扩增出16S rRNA基因中的两个高变区,根据结果对34株菌株进行聚类分析,并将其16S rRNA基因片段测序予以验证.结果表明,聚类后34株菌株可大致分为3类,且与16S rRNA基因片段分析结果一致.从而可快速去除重复菌株并反映出菌株间的系统进化关系.同时实验数据可构建成库,使后续分离菌株的筛选工作只需比对数据即可完成,利于提高工作效率,降低实验成本.     

云南江城和黑井盐矿沉积物未培养放线菌多样性比较

类群特异性引物的应用使得研究者可以对感兴趣的微生物类群进行针对性研究.围绕云南江城和黑井两个地区的3个盐矿样点沉积物中放线菌的多样性和群落组成,我们通过放线菌特异性引物对总DNA进行16S rRNA基因扩增,经过克隆文库构建,利用酶切并选择其中不同带型的133个克隆的16S rRNA基因插入片段进行测序.系统发育分析和统计学结果表明,两地放线菌16S rRNA基因克隆广泛分布于整个放线菌门,同时发现部分序列可能属于放线菌的新类群.分析结果还预示,江城和黑井两地盐矿虽处云南不同地域含盐区,但两地未培养放线菌物种多样性和系统发育关系均较为相似.     

16S rRNA基因在微生物生态学中的应用

16S rRNA(Small subunit ribosomal RNA)基因是对原核微生物进行系统进化分类研究时最常用的分子标志物(Biomarker),广泛应用于微生物生态学研究中。近些年来随着高通量测序技术及数据分析方法等的不断进步,大量基于16S rRNA基因的研究使得微生物生态学得到了快速发展,然而使用16S rRNA基因作为分子标志物时也存在诸多问题,比如水平基因转移、多拷贝的异质性、基因扩增效率的差异、数据分析方法的选择等,这些问题影响了微生物群落组成和多样性分析时的准确性。对当前使用16S rRNA基因分析微生物群落组成和多样性的进展情况做一总结,重点讨论当前存在的主要问题以及各种分析方法的发展,尤其是与高通量测序技术有关的实验和数据处理问题。     

24株鸡源丁酸梭菌的分离鉴定及耐药基因与毒力基因携带情况

[目的]旨在对鸡源丁酸梭菌进行分离鉴定与安全性评估.[方法]利用厌氧培养方法对源自汶上芦花鸡与SPF鸡粪便样品进行丁酸梭菌的分离与纯化,挑选可疑菌落进行微生物质谱鉴定,进一步通过16S rRNA基因测序进行鉴定,16S rRNA测序结果与NCBI核苷酸数据库中丁酸梭菌的16S rRNA序列进行同源性分析;同时,进行所有...     

引物应用策略：基于2对古菌16S rRNA基因通用引物对环境样品古菌多样性分析

【目的】找到适宜的16S rRNA基因通用引物应用策略,应对复杂环境微生物多样性调查,尤其目前高速发展的高通量测序技术带来的巨大挑战。【方法】用Oligocheck软件分别将两对应试的古菌16S rRNA基因通用引物与RDP(Ribosomal database project)数据库中古菌16S rRNA基因序列进行匹配比对。用两对应试引物分别构建海洋沉积物样品的古菌16S rRNA基因文库。【结果】软件匹配结果显示引物f109/r958与目的基因的匹配程度高于引物f21/r958。该结果与古菌16S rRNA基因文库RFLP分析、古菌多样性指数分析结果相吻合。数据还表明,2对引物的综合文库能更好满足该沉积物样品的古菌多样性分析。【结论】选用与数据库中目的基因匹配性高的通用引物和多个引物的联合使用,可以有效提高环境样品微生物多样性调查的分辨率。     

高脂饮食中添加短链菊粉对小鼠肠道菌群的影响

目的:探究高脂饮食中添加短链菊粉对小鼠肠道菌群的影响。方法:选择8周龄雄性小鼠,5只喂食高脂饲料,5只喂食10%菊粉复合型高脂饲料,喂食8周后收集小鼠粪便,检测小鼠粪便中三种主要的短链脂肪酸。同时,提取小鼠粪便中的细菌基因组,对菌群基因组16S rRNA基因V4高变区进行测序,对数据进行PCoA分析、Alpha多样性分析、LEfSe分析和16S功能预测。结果:菊粉添加后,小鼠粪便中含有的细菌DNA量增多,短链脂肪酸增加。菊粉组和对照组PCoA图可以看到明显聚类。菊粉组物种多样性低于对照组。菊粉组小鼠粪便中S24_7菌科丰度上升;Lachnospiraceae(毛螺菌科),Ruminococcaceae(瘤胃菌科)和Deferribacteraceae(脱铁杆菌科)丰度下降。16S基因功能预测发现22个第二层级的KEGG通路发生变化。结论:高脂饮食情况下短链菊粉的添加会改变小鼠肠道菌群,继而影响肠道菌群的功能。     

利用12S rRNA、COI基因分子标记鉴定少棘巨蜈蚣干燥体

本研究旨在利用线粒体DNA上的12S rRNA基因、COI基因分子标记鉴定少棘巨蜈蚣(Scolopendra subspinipes mutilans L. Koch)干燥体。通过提取少棘巨蜈蚣干燥体样本DNA,PCR扩增线粒体DNA上的12S r RNA基因、COI基因片段,对PCR产物进行电泳检测及测序分析,测序结果在GenBank上进行BLAST搜索,同源性分析,并用MEGA7.0软件对所有实验样品及少棘巨蜈蚣的近缘物种进行遗传距离分析、构建邻接(NJ)树以验证序列比对结果。结果表明,从少棘巨蜈蚣(Scolopendra subspinipes mutilans L. Koch)干燥体中成功地提取到了基因组总DNA,并成功扩增出了用于动物种属鉴定的12S rRNA、COI基因片段。但所得蜈蚣样本12S rRNA基因片段序列与NCBI的GenBank中的物种的同源性无90%以上的。通过文献调研发现尚无蜈蚣12S rRNA基因片段的相关报道,将该序列作为新的基因序列注册到NCBI基因数据库中,该基因片段序列的GenBank登录号为JN558832.1。这说明利用线粒体DNA上的12S rRNA、COI基因DNA分子标记皆可准确鉴定动物种属。本研究得到的12S rRNA基因片段序列可作为后续准确鉴定少棘巨蜈蚣药材的分子标记参考。     

基于16S rRNA基因测序分析微生物群落多样性

微生物群落多样性的研究对于挖掘微生物资源,探索微生物群落功能,阐明微生物群落与生境间的关系具有重要意义。随着宏基因组概念的提出以及测序技术的快速发展,16S rRNA基因测序在微生物群落多样性的研究中已被广泛应用。文中系统地介绍了16S rRNA基因测序分析流程中的四个重要环节,包括测序平台与扩增区的选择、测序数据预处理以及多样性分析方法,就其面临的问题与挑战进行了探讨并对未来的研究方向进行了展望,以期为微生物群落多样性相关研究提供参考。     

应用基因芯片技术检测非综合征型耳聋基因突变

目的:应用遗传性耳聋基因芯片对散发性聋患者进行分子病因学检测,评估其在遗传性耳聋快速基因诊断中的可靠性。方法:门诊收集散发性聋患者10例,取外周血,提取基因组DNA,用遗传性耳聋基因芯片检测4个中国人中常见的耳聋相关基因中的9个热点突变,包括GJB2(35delG、176del16bp、235delC及299delAT)、GJB3(C538T)、SLC26A4(IVS7-2AG、A2168G)和线粒体DNA 12S rRNA(A1555G、C1494T)。同时,PCR扩增GJB2、线粒体12S rRNA基因全序列,DNA测序,以验证基因芯片检测结果的准确性。结果:在10名耳聋患者中,基因芯片方法检出1例携带线粒体DNA 12S rRNA C1494T突变;2例GJB2基因235delC纯合突变;2例235delC杂合突变;SLC26A4基因和GJB3基因未检出突变。基因芯片的结果与测序结果完全一致。结论:遗传性耳聋基因芯片技术对中国人常见耳聋相关基因热点突变的检出率高,结果准确、可靠,具有快速、高通量、高准确性、低成本等特点,能够满足临床耳聋基因检测的要求,同时结合产前诊断技术能有效预防耳聋患儿的出生,因而具有广阔的临床应用前景。     

16S rRNA基因和COI基因序列分析在石斑鱼物种鉴定中的应用

对台湾海峡常见的8种石斑鱼进行了16S rRNA基因和COI基因的序列测定,并通过GenBank和BOLD两个数据库进行鱼种鉴定.序列分析表明,COI基因较16S rRNA基因有更大的分辨率;两个基因序列在GenBank中的搜索结果和COI基因序列在两个数据库的搜索结果大部分一致,但仍有部分差异.建议同时使用COI和16S rRNA两种保守基因,进行序列测定,然后在GenBank和BOLD SYSTEMS数据库进行搜索,选择一致的鉴定物种作为鉴定结果.     

Simultaneous analysis of seven 16S rRNA hypervariable gene regions increases efficiency in marine bacterial diversity detection

Environmental DNA sequencing is the gold standard to reveal microbial community structures. In most applications, a one-fragment PCR approach is applied to amplify a taxonomic marker gene, usually a hypervariable region of the 16S rRNA gene. We used a new reverse complement (RC)-PCR-based assay that amplifies seven out of the nine hypervariable regions of the 16S rRNA gene, to interrogate bacterial communities in sediment samples collected from different coastal marine sites with an impact gradient. In parallel, we employed a traditional one-fragment analysis of the hypervariable V3–V4 region to investigate whether the RC-PCR reveals more of the ‘unseen’ diversity obtained by the one-fragment approach. As a benchmark for the full deck of diversity, we subjected the samples to PCR-free metagenomic sequencing. None of the two PCR-based approaches recorded the full taxonomic repertoire obtained from the metagenomics datasets. However, the RC-PCR approach detected 2.8 times more bacterial genera compared to the near-saturation sequenced V3–V4 samples. RC-PCR is an ideal compromise between the standard one-fragment approach and metagenomics sequencing and may guide future environmental sequencing studies, in which bacterial diversity is a central subject.     

Exploring Microbial Diversity and Taxonomy Using SSU rRNA Hypervariable Tag Sequencing

Massively parallel pyrosequencing of hypervariable regions from small subunit ribosomal RNA (SSU rRNA) genes can sample a microbial community two or three orders of magnitude more deeply per dollar and per hour than capillary sequencing of full-length SSU rRNA. As with full-length rRNA surveys, each sequence read is a tag surrogate for a single microbe. However, rather than assigning taxonomy by creating gene trees de novo that include all experimental sequences and certain reference taxa, we compare the hypervariable region tags to an extensive database of rRNA sequences and assign taxonomy based on the best match in a Global Alignment for Sequence Taxonomy (GAST) process. The resulting taxonomic census provides information on both composition and diversity of the microbial community. To determine the effectiveness of using only hypervariable region tags for assessing microbial community membership, we compared the taxonomy assigned to the V3 and V6 hypervariable regions with the taxonomy assigned to full-length SSU rRNA sequences isolated from both the human gut and a deep-sea hydrothermal vent. The hypervariable region tags and full-length rRNA sequences provided equivalent taxonomy and measures of relative abundance of microbial communities, even for tags up to 15% divergent from their nearest reference match. The greater sampling depth per dollar afforded by massively parallel pyrosequencing reveals many more members of the “rare biosphere” than does capillary sequencing of the full-length gene. In addition, tag sequencing eliminates cloning bias and the sequences are short enough to be completely sequenced in a single read, maximizing the number of organisms sampled in a run while minimizing chimera formation. This technique allows the cost-effective exploration of changes in microbial community structure, including the rare biosphere, over space and time and can be applied immediately to initiatives, such as the Human Microbiome Project.     

Taxonomic Precision of Different Hypervariable Regions of 16S rRNA Gene and Annotation Methods for Functional Bacterial Groups in Biological Wastewater Treatment

High throughput sequencing of 16S rRNA gene leads us into a deeper understanding on bacterial diversity for complex environmental samples, but introduces blurring due to the relatively low taxonomic capability of short read. For wastewater treatment plant, only those functional bacterial genera categorized as nutrient remediators, bulk/foaming species, and potential pathogens are significant to biological wastewater treatment and environmental impacts. Precise taxonomic assignment of these bacteria at least at genus level is important for microbial ecological research and routine wastewater treatment monitoring. Therefore, the focus of this study was to evaluate the taxonomic precisions of different ribosomal RNA (rRNA) gene hypervariable regions generated from a mix activated sludge sample. In addition, three commonly used classification methods including RDP Classifier, BLAST-based best-hit annotation, and the lowest common ancestor annotation by MEGAN were evaluated by comparing their consistency. Under an unsupervised way, analysis of consistency among different classification methods suggests there are no hypervariable regions with good taxonomic coverage for all genera. Taxonomic assignment based on certain regions of the 16S rRNA genes, e.g. the V1&V2 regions – provide fairly consistent taxonomic assignment for a relatively wide range of genera. Hence, it is recommended to use these regions for studying functional groups in activated sludge. Moreover, the inconsistency among methods also demonstrated that a specific method might not be suitable for identification of some bacterial genera using certain 16S rRNA gene regions. As a general rule, drawing conclusions based only on one sequencing region and one classification method should be avoided due to the potential false negative results.     

Analysis of the Cystic Fibrosis Lung Microbiota via Serial Illumina Sequencing of Bacterial 16S rRNA Hypervariable Regions

The characterization of bacterial communities using DNA sequencing has revolutionized our ability to study microbes in nature and discover the ways in which microbial communities affect ecosystem functioning and human health. Here we describe Serial Illumina Sequencing (SI-Seq): a method for deep sequencing of the bacterial 16S rRNA gene using next-generation sequencing technology. SI-Seq serially sequences portions of the V5, V6 and V7 hypervariable regions from barcoded 16S rRNA amplicons using an Illumina short-read genome analyzer. SI-Seq obtains taxonomic resolution similar to 454 pyrosequencing for a fraction of the cost, and can produce hundreds of thousands of reads per sample even with very high multiplexing. We validated SI-Seq using single species and mock community controls, and via a comparison to cystic fibrosis lung microbiota sequenced using 454 FLX Titanium. Our control runs show that SI-Seq has a dynamic range of at least five orders of magnitude, can classify >96% of sequences to the genus level, and performs just as well as 454 and paired-end Illumina methods in estimation of standard microbial ecology diversity measurements. We illustrate the utility of SI-Seq in a pilot sample of central airway secretion samples from cystic fibrosis patients.     

16S Classifier: A Tool for Fast and Accurate Taxonomic Classification of 16S rRNA Hypervariable Regions in Metagenomic Datasets

The diversity of microbial species in a metagenomic study is commonly assessed using 16S rRNA gene sequencing. With the rapid developments in genome sequencing technologies, the focus has shifted towards the sequencing of hypervariable regions of 16S rRNA gene instead of full length gene sequencing. Therefore, 16S Classifier is developed using a machine learning method, Random Forest, for faster and accurate taxonomic classification of short hypervariable regions of 16S rRNA sequence. It displayed precision values of up to 0.91 on training datasets and the precision values of up to 0.98 on the test dataset. On real metagenomic datasets, it showed up to 99.7% accuracy at the phylum level and up to 99.0% accuracy at the genus level. 16S Classifier is available freely at http://metagenomics.iiserb.ac.in/16Sclassifier and http://metabiosys.iiserb.ac.in/16Sclassifier.     

Variable copy number, intra-genomic heterogeneities and lateral transfers of the 16S rRNA gene in Pseudomonas

Even though the 16S rRNA gene is the most commonly used taxonomic marker in microbial ecology, its poor resolution is still not fully understood at the intra-genus level. In this work, the number of rRNA gene operons, intra-genomic heterogeneities and lateral transfers were investigated at a fine-scale resolution, throughout the Pseudomonas genus. In addition to nineteen sequenced Pseudomonas strains, we determined the 16S rRNA copy number in four other Pseudomonas strains by Southern hybridization and Pulsed-Field Gel Electrophoresis, and studied the intra-genomic heterogeneities by Denaturing Gradient Gel Electrophoresis and sequencing. Although the variable copy number (from four to seven) seems to be correlated with the evolutionary distance, some close strains in the P. fluorescens lineage showed a different number of 16S rRNA genes, whereas all the strains in the P. aeruginosa lineage displayed the same number of genes (four copies). Further study of the intra-genomic heterogeneities revealed that most of the Pseudomonas strains (15 out of 19 strains) had at least two different 16S rRNA alleles. A great difference (5 or 19 nucleotides, essentially grouped near the V1 hypervariable region) was observed only in two sequenced strains. In one of our strains studied (MFY30 strain), we found a difference of 12 nucleotides (grouped in the V3 hypervariable region) between copies of the 16S rRNA gene. Finally, occurrence of partial lateral transfers of the 16S rRNA gene was further investigated in 1803 full-length sequences of Pseudomonas available in the databases. Remarkably, we found that the two most variable regions (the V1 and V3 hypervariable regions) had probably been laterally transferred from another evolutionary distant Pseudomonas strain for at least 48.3 and 41.6% of the 16S rRNA sequences, respectively. In conclusion, we strongly recommend removing these regions of the 16S rRNA gene during the intra-genus diversity studies.     

Short-Read Assembly of Full-Length 16S Amplicons Reveals Bacterial Diversity in Subsurface Sediments

In microbial ecology, a fundamental question relates to how community diversity and composition change in response to perturbation. Most studies have had limited ability to deeply sample community structure (e.g. Sanger-sequenced 16S rRNA libraries), or have had limited taxonomic resolution (e.g. studies based on 16S rRNA hypervariable region sequencing). Here, we combine the higher taxonomic resolution of near-full-length 16S rRNA gene amplicons with the economics and sensitivity of short-read sequencing to assay the abundance and identity of organisms that represent as little as 0.01% of sediment bacterial communities. We used a new version of EMIRGE optimized for large data size to reconstruct near-full-length 16S rRNA genes from amplicons sheared and sequenced with Illumina technology. The approach allowed us to differentiate the community composition among samples acquired before perturbation, after acetate amendment shifted the predominant metabolism to iron reduction, and once sulfate reduction began. Results were highly reproducible across technical replicates, and identified specific taxa that responded to the perturbation. All samples contain very high alpha diversity and abundant organisms from phyla without cultivated representatives. Surprisingly, at the time points measured, there was no strong loss of evenness, despite the selective pressure of acetate amendment and change in the terminal electron accepting process. However, community membership was altered significantly. The method allows for sensitive, accurate profiling of the “long tail” of low abundance organisms that exist in many microbial communities, and can resolve population dynamics in response to environmental change.     

Development of an Analysis Pipeline Characterizing Multiple Hypervariable Regions of 16S rRNA Using Mock Samples

Objectives

There is much speculation on which hypervariable region provides the highest bacterial specificity in 16S rRNA sequencing. The optimum solution to prevent bias and to obtain a comprehensive view of complex bacterial communities would be to sequence the entire 16S rRNA gene; however, this is not possible with second generation standard library design and short-read next-generation sequencing technology.

Methods

This paper examines a new process using seven hypervariable or V regions of the 16S rRNA (six amplicons: V2, V3, V4, V6-7, V8, and V9) processed simultaneously on the Ion Torrent Personal Genome Machine (Life Technologies, Grand Island, NY). Four mock samples were amplified using the 16S Ion Metagenomics Kit^™ (Life Technologies) and their sequencing data is subjected to a novel analytical pipeline.

Results

Results are presented at family and genus level. The Kullback-Leibler divergence (D_KL), a measure of the departure of the computed from the nominal bacterial distribution in the mock samples, was used to infer which region performed best at the family and genus levels. Three different hypervariable regions, V2, V4, and V6-7, produced the lowest divergence compared to the known mock sample. The V9 region gave the highest (worst) average D_KL while the V4 gave the lowest (best) average D_KL. In addition to having a high D_KL, the V9 region in both the forward and reverse directions performed the worst finding only 17% and 53% of the known family level and 12% and 47% of the genus level bacteria, while results from the forward and reverse V4 region identified all 17 family level bacteria.

Conclusions

The results of our analysis have shown that our sequencing methods using 6 hypervariable regions of the 16S rRNA and subsequent analysis is valid. This method also allowed for the assessment of how well each of the variable regions might perform simultaneously. Our findings will provide the basis for future work intended to assess microbial abundance at different time points throughout a clinical protocol.