Single-molecule polymerase chain reaction reduces bias: application to DNA methylation analysis by bisulfite sequencing

The treatment of DNA with bisulfite, which converts C to U but leaves 5-methyl-C unchanged, forms the basis of many analytical techniques for DNA methylation analysis. Many techniques exist for measuring the methylation state of a single CpG but, for analysis of an entire region, cloning and sequencing remains the gold standard. However, biases in polymerase chain reaction (PCR) amplification and in cloning can skew the results. We hypothesized that single-molecule PCR (smPCR) amplification would eliminate the PCR amplification bias because competition between templates that amplify at different efficiencies no longer exists. The amplified products can be sequenced directly, thus eliminating cloning bias. We demonstrated this accurate and unbiased approach by analyzing a sample that was expected to contain a 50:50 ratio of methylated to unmethylated molecules: a region of the X-linked FMR1 gene from a human female cell line. We compared traditional cloning and sequencing to smPCR and sequencing. Sequencing smPCR products gave an expected methylated to unmethylated ratio of 48:52, whereas conventional cloning and sequencing gave a biased ratio of 72:28. Our results show that smPCR sequencing can eliminate both PCR and cloning bias and represents an attractive approach to bisulfite sequencing.     

Genome‐wide association studies on the phyllosphere microbiome: Embracing complexity in host–microbe interactions

Environmental sequencing shows that plants harbor complex communities of microbes that vary across environments. However, many approaches for mapping plant genetic variation to microbe‐related traits were developed in the relatively simple context of binary host–microbe interactions under controlled conditions. Recent advances in sequencing and statistics make genome‐wide association studies (GWAS) an increasingly promising approach for identifying the plant genetic variation associated with microbes in a community context. This review discusses early efforts on GWAS of the plant phyllosphere microbiome and the outlook for future studies based on human microbiome GWAS. A workflow for GWAS of the phyllosphere microbiome is then presented, with particular attention to how perspectives on the mechanisms, evolution and environmental dependence of plant–microbe interactions will influence the choice of traits to be mapped.     

Conditional Regression Based on a Multivariate Zero-Inflated Logistic-Normal Model for Microbiome Relative Abundance Data

The human microbiome plays critical roles in human health and has been linked to many diseases. While advanced sequencing technologies can characterize the composition of the microbiome in unprecedented detail, it remains challenging to disentangle the complex interplay between human microbiome and disease risk factors due to the complicated nature of microbiome data. Excessive numbers of zero values, high dimensionality, the hierarchical phylogenetic tree and compositional structure are compounded and consequently make existing methods inadequate to appropriately address these issues. We propose a multivariate two-part zero-inflated logistic-normal model to analyze the association of disease risk factors with individual microbial taxa and overall microbial community composition. This approach can naturally handle excessive numbers of zeros and the compositional data structure with the discrete part and the logistic-normal part of the model. For parameter estimation, an estimating equations approach is employed that enables us to address the complex inter-taxa correlation structure induced by the hierarchical phylogenetic tree structure and the compositional data structure. This model is able to incorporate standard regularization approaches to deal with high dimensionality. Simulation shows that our model outperforms existing methods. Our approach is also compared to others using the analysis of real microbiome data.     

Progress of analytical tools and techniques for human gut microbiome research

Massive DNA sequencing studies have expanded our insights and understanding of the ecological and functional characteristics of the gut microbiome. Advanced sequencing technologies allow us to understand the close association of the gut microbiome with human health and critical illnesses. In the future, analyses of the gut microbiome will provide key information associating with human individual health, which will help provide personalized health care for diseases. Numerous molecular biological analysis tools have been rapidly developed and employed for the gut microbiome researches; however, methodological differences among researchers lead to inconsistent data, limiting extensive share of data. It is therefore very essential to standardize the current methodologies and establish appropriate pipelines for human gut microbiome research. Herein, we review the methods and procedures currently available for studying the human gut microbiome, including fecal sample collection, metagenomic DNA extraction, massive DNA sequencing, and data analyses with bioinformatics. We believe that this review will contribute to the progress of gut microbiome research in the clinical and practical aspects of human health.     

Microbial Interaction Network Estimation via Bias-Corrected Graphical Lasso

With the increasing availability of microbiome 16S data, network estimation has become a useful approach to studying the interactions between microbial taxa. Network estimation on a set of variables is frequently explored using graphical models, in which the relationship between two variables is modeled via their conditional dependency given the other variables. Various methods for sparse inverse covariance estimation have been proposed to estimate graphical models in the high-dimensional setting, including graphical lasso. However, current methods do not address the compositional count nature of microbiome data, where abundances of microbial taxa are not directly measured, but are reflected by the observed counts in an error-prone manner. Adding to the challenge is that the sum of the counts within each sample, termed “sequencing depth,” is an experimental technicality that carries no biological information but can vary drastically across samples. To address these issues, we develop a new approach to network estimation, called BC-GLASSO (bias-corrected graphical lasso), which models the microbiome data using a logistic normal multinomial distribution with the sequencing depths explicitly incorporated, corrects the bias of the naive empirical covariance estimator arising from the heterogeneity in sequencing depths, and builds the inverse covariance estimator via graphical lasso. We demonstrate the advantage of BC-GLASSO over current approaches to microbial interaction network estimation under a variety of simulation scenarios. We also illustrate the efficacy of our method in an application to a human microbiome data set.

     

Skin microbiome: genomics-based insights into the diversity and role of skin microbes

Recent advances in DNA sequencing methodology have facilitated studies of human skin microbes that circumvent difficulties in isolating and characterizing fastidious microbes. Sequence-based approaches have identified a greater diversity of cutaneous bacteria than studies using traditional cultivation techniques. However, improved sequencing technologies and analytical methods are needed to study all skin microbes, including bacteria, archaea, fungi, viruses and mites, and how they interact with each other and their human hosts. This review discusses current skin microbiome research, with a primary focus on bacteria, and the challenges facing investigators striving to understand how skin microorganisms contribute to health and disease.     

The "most wanted" taxa from the human microbiome for whole genome sequencing

The goal of the Human Microbiome Project (HMP) is to generate a comprehensive catalog of human-associated microorganisms including reference genomes representing the most common species. Toward this goal, the HMP has characterized the microbial communities at 18 body habitats in a cohort of over 200 healthy volunteers using 16S rRNA gene (16S) sequencing and has generated nearly 1,000 reference genomes from human-associated microorganisms. To determine how well current reference genome collections capture the diversity observed among the healthy microbiome and to guide isolation and future sequencing of microbiome members, we compared the HMP's 16S data sets to several reference 16S collections to create a 'most wanted' list of taxa for sequencing. Our analysis revealed that the diversity of commonly occurring taxa within the HMP cohort microbiome is relatively modest, few novel taxa are represented by these OTUs and many common taxa among HMP volunteers recur across different populations of healthy humans. Taken together, these results suggest that it should be possible to perform whole-genome sequencing on a large fraction of the human microbiome, including the 'most wanted', and that these sequences should serve to support microbiome studies across multiple cohorts. Also, in stark contrast to other taxa, the 'most wanted' organisms are poorly represented among culture collections suggesting that novel culture- and single-cell-based methods will be required to isolate these organisms for sequencing.     

肠道微生物测序研究中不同实验来源差异的综合评价以及应用

肠道微生物测序研究具有将微生物结果转化为人类健康的巨大潜力。16S扩增子测序和宏基因组鸟枪测序(whole-metagenome shotgun, WMS)是微生物组研究中的两大主要方法,各具优势。然而,研究样品的异质性、测序仪和文库制备方法的差异如何影响肠道微生物测序结果的可重复性仍有待深入研究。该研究旨在通过比较粪便样本中微生物组成的差异,为测序技术的选择提供参考标准。3种广泛采用的测序仪的结果显示,WMS法中技术重复相关性(r=0.94)较高,而生物学重复相关性(r=0.69)较低。Bray-Curtis距离表明,生物学重复的差异大于技术重复(P<0.001)。此外,16S和WMS数据集间具有明显的分类学图谱差异。研究结果表明,同质化是样品DNA提取前的一个必要步骤;测序仪对分类学变异的贡献小于文库制备方法。我们开发了经验贝叶斯方法,即在计算中“借用信息”,利用标准化数据和(非)参数先验分布分析批次效应参数,提高了16S和WMS之间的群体可比性,为进一步应用于融合分析已发表的16S和微生物数据集提供了依据。     

DCMD: Distance-based classification using mixture distributions on microbiome data

Current advances in next-generation sequencing techniques have allowed researchers to conduct comprehensive research on the microbiome and human diseases, with recent studies identifying associations between the human microbiome and health outcomes for a number of chronic conditions. However, microbiome data structure, characterized by sparsity and skewness, presents challenges to building effective classifiers. To address this, we present an innovative approach for distance-based classification using mixture distributions (DCMD). The method aims to improve classification performance using microbiome community data, where the predictors are composed of sparse and heterogeneous count data. This approach models the inherent uncertainty in sparse counts by estimating a mixture distribution for the sample data and representing each observation as a distribution, conditional on observed counts and the estimated mixture, which are then used as inputs for distance-based classification. The method is implemented into a k-means classification and k-nearest neighbours framework. We develop two distance metrics that produce optimal results. The performance of the model is assessed using simulated and human microbiome study data, with results compared against a number of existing machine learning and distance-based classification approaches. The proposed method is competitive when compared to the other machine learning approaches, and shows a clear improvement over commonly used distance-based classifiers, underscoring the importance of modelling sparsity for achieving optimal results. The range of applicability and robustness make the proposed method a viable alternative for classification using sparse microbiome count data. The source code is available at https://github.com/kshestop/DCMD for academic use.     

A Model-Based Approach for Species Abundance Quantification Based on Shotgun Metagenomic Data

The human microbiome, which includes the collective microbes residing in or on the human body, has a profound influence on the human health. DNA sequencing technology has made the large-scale human microbiome studies possible by using shotgun metagenomic sequencing. One important aspect of data analysis of such metagenomic data is to quantify the bacterial abundances based on the metagenomic sequencing data. Existing methods almost always quantify such abundances one sample at a time, which ignore certain systematic differences in read coverage along the genomes due to GC contents, copy number variation and the bacterial origin of replication. In order to account for such differences in read counts, we propose a multi-sample Poisson model to quantify microbial abundances based on read counts that are assigned to species-specific taxonomic markers. Our model takes into account the marker-specific effects when normalizing the sequencing count data in order to obtain more accurate quantification of the species abundances. Compared to currently available methods on simulated data and real data sets, our method has demonstrated an improved accuracy in bacterial abundance quantification, which leads to more biologically interesting results from downstream data analysis.     

2020微生物组测序与分析专题序言

微生物是人体、动植物、土壤、沉积物、水体、空气等生境中最重要的生命体。对这些生境中微生物的分析已经成为一项基础的研究技术。微生物组测序与分析作为近年来快速发展的技术,已经在人类健康、环境污染治理、食品工业以及农牧业等领域得到了广泛应用。为了梳理和总结微生物组测序与分析技术的现状、发展状况和应用前景,本专题收录了16篇本领域的论文,分别从样本保存和处理、单菌基因组测序与分析、特殊生境中的微生物组特征分析、微生物组相关数据库和算法以及微生物组测序与分析专家共识等方面,详细介绍了微生物组测序与分析领域的发展态势,为推动我国微生物组测序与分析产业和科研的快速发展、促进微生物组相关产业的良性发展提供必要的参考。     

Gene discovery using mutagen-induced polymorphisms and deep sequencing: application to plant disease resistance

Next-generation sequencing technologies are accelerating gene discovery by combining multiple steps of mapping and cloning used in the traditional map-based approach into one step using DNA sequence polymorphisms existing between two different accessions/strains/backgrounds of the same species. The existing next-generation sequencing method, like the traditional one, requires the use of a segregating population from a cross of a mutant organism in one accession with a wild-type (WT) organism in a different accession. It therefore could potentially be limited by modification of mutant phenotypes in different accessions and/or by the lengthy process required to construct a particular mapping parent in a second accession. Here we present mapping and cloning of an enhancer mutation with next-generation sequencing on bulked segregants in the same accession using sequence polymorphisms induced by a chemical mutagen. This method complements the conventional cloning approach and makes forward genetics more feasible and powerful in molecularly dissecting biological processes in any organisms. The pipeline developed in this study can be used to clone causal genes in background of single mutants or higher order of mutants and in species with or without sequence information on multiple accessions.     

Characterization of the rat oral microbiome and the effects of dietary nitrate

The nitrate–nitrite–NO pathway to nitric oxide (NO) production is a symbiotic pathway in mammals that is dependent on nitrate reducing oral commensal bacteria. Studies suggest that by contributing NO to the mammalian host, the oral microbiome helps maintain cardiovascular health. To begin to understand how changes in oral microbiota affect physiological functions such as blood pressure, we have characterized the Wistar rat nitrate reducing oral microbiome. Using 16S rRNA gene sequencing and analysis we compare the native Wistar rat tongue microbiome to that of healthy humans and to that of rats with sodium nitrate and chlorhexidine mouthwash treatments. We demonstrate that the rat tongue microbiome is less diverse than the human tongue microbiome, but that the physiological activity is comparable, as sodium nitrate supplementation significantly lowered diastolic blood pressure in Wistar rats and also lowers blood pressure (diastolic and systolic) in humans. We also show for the first time that sodium nitrate supplementation alters the abundance of specific bacterial species on the tongue. Our results suggest that the changes in oral nitrate reducing bacteria may affect nitric oxide availability and physiological functions such as blood pressure. Understanding individual changes in human oral microbiome may offer novel dietary approaches to restore NO availability and blood pressure.     

Focus: Microbiome: Human Microbiota and Ophthalmic Disease

The human ocular surface, consisting of the cornea and conjunctiva, is colonized by an expansive, diverse microbial community. Molecular-based methods, such as 16S rRNA sequencing, has allowed for more comprehensive and precise identification of the species composition of the ocular surface microbiota compared to traditional culture-based methods. Evidence suggests that the normal microbiota plays a protective immunological role in preventing the proliferation of pathogenic species and thus, alterations in the homeostatic microbiome may be linked to ophthalmic pathologies. Further investigation of the ocular surface microbiome, as well as the microbiome of other areas of the body such as the oral mucosa and gut, and their role in the pathophysiology of diseases is a significant, emerging field of research, and may someday enable the development of novel probiotic approaches for the treatment and prevention of ophthalmic diseases.     

阴道微生物组：种群特征与疾病干预治疗

高通量测序技术的发展显著加快了对人类微生物组的理解。将人体微生物组与疾病关联,力求阐明疾病的发生进程,是推进个性化精准医疗的重要研究方向。近年来,栖居于女性阴道的微生物菌群日益受到关注,发现其生态失调与疾病发生、演变密不可分。文中综述了阴道微生物组与生殖道疾病发生、进展和治疗的最新进展,同时对阴道微生物组培养组、益生菌工程化改造以及合成菌群在阴道微生物组学研究以及疾病干预与治疗方面的前景进行了展望。     

Comparison of DNA extraction methods for microbial community profiling with an application to pediatric bronchoalveolar lavage samples

Barcoded amplicon sequencing is rapidly becoming a standard method for profiling microbial communities, including the human respiratory microbiome. While this approach has less bias than standard cultivation, several steps can introduce variation including the type of DNA extraction method used. Here we assessed five different extraction methods on pediatric bronchoalveolar lavage (BAL) samples and a mock community comprised of nine bacterial genera to determine method reproducibility and detection limits for these typically low complexity communities. Additionally, using the mock community, we were able to evaluate contamination and select a relative abundance cut-off threshold based on the geometric distribution that optimizes the trade off between detecting bona fide operational taxonomic units and filtering out spurious ones. Using this threshold, the majority of genera in the mock community were predictably detected by all extraction methods including the hard-to-lyse Gram-positive genus Staphylococcus. Differences between extraction methods were significantly greater than between technical replicates for both the mock community and BAL samples emphasizing the importance of using a standardized methodology for microbiome studies. However, regardless of method used, individual patients retained unique diagnostic profiles. Furthermore, despite being stored as raw frozen samples for over five years, community profiles from BAL samples were consistent with historical culturing results. The culture-independent profiling of these samples also identified a number of anaerobic genera that are gaining acceptance as being part of the respiratory microbiome. This study should help guide researchers to formulate sampling, extraction and analysis strategies for respiratory and other human microbiome samples.     

Inter-Individual Differences in the Oral Bacteriome Are Greater than Intra-Day Fluctuations in Individuals

Given the advent of massively parallel DNA sequencing, human microbiome is analyzed comprehensively by metagenomic approaches. However, the inter- and intra-individual variability and stability of the human microbiome remain poorly characterized, particularly at the intra-day level. This issue is of crucial importance for studies examining the effects of microbiome on human health. Here, we focused on bacteriome of oral plaques, for which repeated, time-controlled sampling is feasible. Eighty-one supragingival plaque subjects were collected from healthy individuals, examining multiple sites within the mouth at three time points (forenoon, evening, and night) over the course of 3 days. Bacterial composition was estimated by 16S rRNA sequencing and species-level profiling, resulting in identification of a total of 162 known bacterial species. We found that species compositions and their relative abundances were similar within individuals, and not between sampling time or tooth type. This suggests that species-level oral bacterial composition differs significantly between individuals, although the number of subjects is limited and the intra-individual variation also occurs. The majority of detected bacterial species (98.2%; 159/162), however, did not fluctuate over the course of the day, implying a largely stable oral microbiome on an intra-day time scale. In fact, the stability of this data set enabled us to estimate potential interactions between rare bacteria, with 40 co-occurrences supported by the existing literature. In summary, the present study provides a valuable basis for studies of the human microbiome, with significant implications in terms of biological and clinical outcomes.     

Identifying prognostic pairwise relationships among bacterial species in microbiome studies

In recent literature, the human microbiome has been shown to have a major influence on human health. To investigate this impact, scientists study the composition and abundance of bacterial species, commonly using 16S rRNA gene sequencing, among patients with and without a disease or condition. Methods for such investigations to date have focused on the association between individual bacterium and an outcome, and higher-order pairwise relationships or interactions among bacteria are often avoided due to the substantial increase in dimension and the potential for spurious correlations. However, overlooking such relationships ignores the environment of the microbiome, where there is dynamic cooperation and competition among bacteria. We present a method for identifying and ranking pairs of bacteria that have a differential dichotomized relationship across outcomes. Our approach, implemented in an R package PairSeek, uses the stability selection framework with data-driven dichotomized forms of the pairwise relationships. We illustrate the properties of the proposed method using a published oral cancer data set and a simulation study.     

Experimental and analytical tools for studying the human microbiome

The human microbiome substantially affects many aspects of human physiology, including metabolism, drug interactions and numerous diseases. This realization, coupled with ever-improving nucleotide sequencing technology, has precipitated the collection of diverse data sets that profile the microbiome. In the past 2 years, studies have begun to include sufficient numbers of subjects to provide the power to associate these microbiome features with clinical states using advanced algorithms, increasing the use of microbiome studies both individually and collectively. Here we discuss tools and strategies for microbiome studies, from primer selection to bioinformatics analysis.     

人类微生物组测序与比较研究

人体微生物群系影响人类的健康,与人类的各种疾病,如肥胖、糖尿病、冠心病、结肠癌等的发生具有密切的关系.近年来,人类微生物组研究日益受到重视.综述人类微生物组的测序和比较研究进展.