Estimation of bacterial diversity using next generation sequencing of 16S rDNA: a comparison of different workflows

The bacterial density of clinical rectal swabs is highly variable, correlates with sequencing contamination, and predicts patient risk of extraintestinal infection

BACKGROUND

In ecology, population density is a key feature of community analysis. Yet in studies of the gut microbiome, bacterial density is rarely reported. Studies of hospitalized patients commonly use rectal swabs for microbiome analysis, yet variation in their bacterial density-and the clinical and methodologic significance of this variation-remains undetermined. We used an ultra-sensitive quantification approach-droplet digital PCR (ddPCR)-to quantify bacterial density in rectal swabs from 118 hospitalized patients. We compared bacterial density with bacterial community composition (via 16S rRNA amplicon sequencing) and clinical data to determine if variation in bacterial density has methodological, clinical, and prognostic significance.

RESULTS

Bacterial density in rectal swab specimens was highly variable, spanning five orders of magnitude (1.2 × 10⁴-3.2 × 10⁹ 16S rRNA gene copies/sample). Low bacterial density was strongly correlated with the detection of sequencing contamination (Spearman ρ = - 0.95, p < 10^-16). Low-density rectal swab communities were dominated by peri-rectal skin bacteria and sequencing contaminants (p < 0.01), suggesting that some variation in bacterial density is explained by sampling variation. Yet bacterial density was also associated with important clinical exposures, conditions, and outcomes. Bacterial density was lower among patients who had received piperacillin-tazobactam (p = 0.017) and increased among patients with multiple medical comorbidities (Charlson score, p = 0.0040) and advanced age (p = 0.043). Bacterial density at the time of hospital admission was independently associated with subsequent extraintestinal infection (p = 0.0028), even when controlled for severity of illness and comorbidities.

CONCLUSIONS

The bacterial density of rectal swabs is highly variable, and this variability is of methodological, clinical, and prognostic significance. Microbiome studies using rectal swabs are vulnerable to sequencing contamination and should include appropriate negative sequencing controls. Among hospitalized patients, gut bacterial density is associated with clinical exposures (antibiotics, comorbidities) and independently predicts infection risk. Bacterial density is an important and under-studied feature of gut microbiome community analysis. Video abstract.

The intratumoral microbiome: Characterization methods and functional impact

Live-pathogenic bacteria, which were identified inside tumors hundreds year ago, are key elements in modern cancer research. As they have a relatively accessible genome, they offer a multitude of metabolic engineering opportunities, useful in several clinical fields. Better understanding of the tumor microenvironment and its associated microbiome would help conceptualize new metabolically engineered species, triggering efficient therapeutic responses against cancer. Unfortunately, given the low microbial biomass nature of tumors, characterizing the tumor microbiome remains a challenge. Tumors have a high host versus bacterial DNA ratio, making it extremely complex to identify tumor-associated bacteria. Nevertheless, with the improvements in next-generation analytic tools, recent studies demonstrated the existence of intratumor bacteria inside defined tumors. It is now proven that each cancer subtype has a unique microbiome, characterized by bacterial communities with specific metabolic functions. This review provides a brief overview of the main approaches used to characterize the tumor microbiome, and of the recently proposed functions of intracellular bacteria identified in oncological entities. The therapeutic aspects of live-pathogenic microbes are also discussed, regarding the tumor microenvironment of each cancer type.

Microbial Community Dysbiosis and Functional Gene Content Changes in Apple Flowers due to Fire Blight

Despite the plant microbiota plays an important role in plant health, little is known about the potential interactions of the flower microbiota with pathogens. In this study, we investigated the microbial community of apple blossoms when infected with Erwinia amylovora. The long-read sequencing technology, which significantly increased the genome sequence resolution, thus enabling the characterization of fire blight-induced changes in the flower microbial community. Each sample showed a unique microbial community at the species level. Pantoea agglomerans and P. allii were the most predominant bacteria in healthy flowers, whereas E. amylovora comprised more than 90% of the microbial population in diseased flowers. Furthermore, gene function analysis revealed that glucose and xylose metabolism were enriched in diseased flowers. Overall, our results showed that the microbiome of apple blossoms is rich in specific bacteria, and the nutritional composition of flowers is important for the incidence and spread of bacterial disease.

Comparison of Methods for Picking the Operational Taxonomic Units From Amplicon Sequences

With the advent of next-generation sequencing technology, it has become convenient and cost efficient to thoroughly characterize the microbial diversity and taxonomic composition in various environmental samples. Millions of sequencing data can be generated, and how to utilize this enormous sequence resource has become a critical concern for microbial ecologists. One particular challenge is the OTUs (operational taxonomic units) picking in 16S rRNA sequence analysis. Lucky, this challenge can be directly addressed by sequence clustering that attempts to group similar sequences. Therefore, numerous clustering methods have been proposed to help to cluster 16S rRNA sequences into OTUs. However, each method has its clustering mechanism, and different methods produce diverse outputs. Even a slight parameter change for the same method can also generate distinct results, and how to choose an appropriate method has become a challenge for inexperienced users. A lot of time and resources can be wasted in selecting clustering tools and analyzing the clustering results. In this study, we introduced the recent advance of clustering methods for OTUs picking, which mainly focus on three aspects: (i) the principles of existing clustering algorithms, (ii) benchmark dataset construction for OTU picking and evaluation metrics, and (iii) the performance of different methods with various distance thresholds on benchmark datasets. This paper aims to assist biological researchers to select the reasonable clustering methods for analyzing their collected sequences and help algorithm developers to design more efficient sequences clustering methods.

Ecology, histopathology, and microbial ecology of a white-band disease outbreak in the threatened staghorn coral Acropora cervicornis

This study is a multi-pronged description of a temperature-induced outbreak of white-band disease (WBD) that occurred in Acropora cervicornis off northern Miami Beach, Florida (USA), from July to October 2014. We describe the ecology of the disease and examine diseased corals using both histopathology and next-generation bacterial 16S gene sequencing, making it possible to better understand the effect this disease has on the coral holobiont, and to address some of the seeming contradictions among previous studies of WBD that employed either a purely histological or molecular approach. The outbreak began in July 2014, as sea surface temperatures reached 29°C, and peaked in mid-September, a month after the sea surface temperature maximum. The microscopic anatomy of apparently healthy portions of colonies displaying active disease signs appeared normal except for some tissue atrophy and dissociation of mesenterial filaments deep within the branch. Structural changes were more pronounced in visibly diseased fragments, with atrophy, necrosis, and lysing of surface and basal body wall and polyp structures at the tissue-loss margin. The only bacteria evident microscopically in both diseased and apparently healthy tissues with Giemsa staining was a Rickettsiales-like organism (RLO) occupying mucocytes. Sequencing also identified bacteria belonging to the order Rickettsiales in all fragments. When compared to apparently healthy fragments, diseased fragments had more diverse bacterial communities made up of many previously suggested potential primary pathogens and secondary (opportunistic) colonizers. Interactions between elevated seawater temperatures, the coral host, and pathogenic members of the diseased microbiome all contribute to the coral displaying signs of WBD.

OTUX: V-region specific OTU database for improved 16S rRNA OTU picking and efficient cross-study taxonomic comparison of microbiomes

Many microbiome studies employ reference-based operational taxonomic unit (OTU)-picking methods, which in general, rely on databases cataloguing reference OTUs identified through clustering full-length 16S rRNA genes. Given that the rate of accumulation of mutations are not uniform throughout the length of a 16S rRNA gene across different taxonomic clades, results of OTU identification or taxonomic classification obtained using ‘short-read’ sequence queries (as generated by next-generation sequencing platforms) can be inconsistent and of suboptimal accuracy. De novo OTU clustering results too can significantly vary depending upon the hypervariable region (V-region) targeted for sequencing. As a consequence, comparison of microbiomes profiled in different scientific studies becomes difficult and often poses a challenge in analysing new findings in context of prior knowledge. The OTUX approach of reference-based OTU-picking proposes to overcome these limitations by using ‘customized’ OTU reference databases, which can cater to different sets of short-read sequences corresponding to different 16S V-regions. The results obtained with OTUX-approach (which are in terms of OTUX-OTU identifiers) can also be ‘mapped back’ or represented in terms of other OTU database identifiers/taxonomy, e.g. Greengenes, thus allowing for easy cross-study comparisons. Validation with simulated datasets indicates more efficient, accurate, and consistent taxonomic classifications obtained using OTUX-approach, as compared with conventional methods.

DMSC: A Dynamic Multi-Seeds Method for Clustering 16S rRNA Sequences Into OTUs

Next-generation sequencing (NGS)-based 16S rRNA sequencing by jointly using the PCR amplification and NGS technology is a cost-effective technique, which has been successfully used to study the phylogeny and taxonomy of samples from complex microbiomes or environments. Clustering 16S rRNA sequences into operational taxonomic units (OTUs) is often the first step for many downstream analyses. Heuristic clustering is one of the most widely employed approaches for generating OTUs. However, most heuristic OTUs clustering methods just select one single seed sequence to represent each cluster, resulting in their outcomes suffer from either overestimation of OTUs number or sensitivity to sequencing errors. In this paper, we present a novel dynamic multi-seeds clustering method (namely DMSC) to pick OTUs. DMSC first heuristically generates clusters according to the distance threshold. When the size of a cluster reaches the pre-defined minimum size, then DMSC selects the multi-core sequences (MCS) as the seeds that are defined as the n-core sequences (n ≥ 3), in which the distance between any two sequences is less than the distance threshold. A new sequence is assigned to the corresponding cluster depending on the average distance to MCS and the distance standard deviation within the MCS. If a new sequence is added to the cluster, dynamically update the MCS until no sequence is merged into the cluster. The new method DMSC was tested on several simulated and real-life sequence datasets and also compared with the traditional heuristic methods such as CD-HIT, UCLUST, and DBH. Experimental results in terms of the inferred OTUs number, normalized mutual information (NMI) and Matthew correlation coefficient (MCC) metrics demonstrate that DMSC can produce higher quality clusters with low memory usage and reduce OTU overestimation. Additionally, DMSC is also robust to the sequencing errors. The DMSC software can be freely downloaded from https://github.com/NWPU-903PR/DMSC.

The Ocular Microbiome: Molecular Characterisation of a Unique and Low Microbial Environment

Aim: The ocular surface is continually exposed to bacteria from the environment and traditional culture-based microbiological studies have isolated a low diversity of microorganisms from this region. The use of culture-independent methods to define the ocular microbiome, primarily involving 16S ribosomal RNA gene sequencing studies, have shown that the microbial communities present on the ocular surface have a greater diversity than previously reported. Method: A review of the literature on ocular microbiome research in health and disease. Results: Molecular techniques have been used to investigate the effect of contact lens wear and disease on the microbiota of the ocular surface and eyelids and the immunoregulatory role of the ocular surface microbiota. Studies have shown that compositional changes in the microbiota occur in ocular surface disorders such as blepharitis, trachoma and dry eye and also suggest a role of the ocular and non-ocular microbiome in retinal disease including age-related macular degeneration, glaucoma, uveitis and diabetic retinopathy. However, ocular microbiome studies need to recognise the potential for contamination to impact findings and carefully control each stage of the experimental procedure and to utilise statistical methods to identify contamination signals. Conclusion: The healthy ocular surface is characterised by a relatively stable, comparatively low diversity microbiome with recent findings that the bacteria of the ocular surface appear to have a role in maintaining homeostasis by modulating immune function.

Species-level bacterial community profiling of the healthy sinonasal microbiome using Pacific Biosciences sequencing of full-length 16S rRNA genes

BACKGROUND

Pan-bacterial 16S rRNA microbiome surveys performed with massively parallel DNA sequencing technologies have transformed community microbiological studies. Current 16S profiling methods, however, fail to provide sufficient taxonomic resolution and accuracy to adequately perform species-level associative studies for specific conditions. This is due to the amplification and sequencing of only short 16S rRNA gene regions, typically providing for only family- or genus-level taxonomy. Moreover, sequencing errors often inflate the number of taxa present. Pacific Biosciences' (PacBio's) long-read technology in particular suffers from high error rates per base. Herein, we present a microbiome analysis pipeline that takes advantage of PacBio circular consensus sequencing (CCS) technology to sequence and error correct full-length bacterial 16S rRNA genes, which provides high-fidelity species-level microbiome data.

RESULTS

Analysis of a mock community with 20 bacterial species demonstrated 100% specificity and sensitivity with regard to taxonomic classification. Examination of a 250-plus species mock community demonstrated correct species-level classification of > 90% of taxa, and relative abundances were accurately captured. The majority of the remaining taxa were demonstrated to be multiply, incorrectly, or incompletely classified. Using this methodology, we examined the microgeographic variation present among the microbiomes of six sinonasal sites, by both swab and biopsy, from the anterior nasal cavity to the sphenoid sinus from 12 subjects undergoing trans-sphenoidal hypophysectomy. We found greater variation among subjects than among sites within a subject, although significant within-individual differences were also observed. Propiniobacterium acnes (recently renamed Cutibacterium acnes) was the predominant species throughout, but was found at distinct relative abundances by site.

CONCLUSIONS

Our microbial composition analysis pipeline for single-molecule real-time 16S rRNA gene sequencing (MCSMRT, https://github.com/jpearl01/mcsmrt ) overcomes deficits of standard marker gene-based microbiome analyses by using CCS of entire 16S rRNA genes to provide increased taxonomic and phylogenetic resolution. Extensions of this approach to other marker genes could help refine taxonomic assignments of microbial species and improve reference databases, as well as strengthen the specificity of associations between microbial communities and dysbiotic states.

In Silico Analysis of the Quorum Sensing Metagenome in Environmental Biofilm Samples

Quorum sensing (QS) is a sophisticated cell to cell signaling mechanism mediated by small diffusible molecules called “autoinducers.” This phenomenon is well studied in bacteria, where different QS systems are described that differ between Gram-negative and Gram-positive bacteria. However, a common system to these groups was discovered, the autoinducer 2. QS has implications in biofilm formation, where the application of metagenomic techniques to study these phenomena may be useful to understand the communication networks established by the different components of the community, and to discover new targets for microbial control. Here we present an in silico screening of QS proteins in all publicly available biofilm metagenomes from the JGI database. We performed sequence, conserved motifs, phylogenetic, and three-dimensional structure analyses of the candidates, resulting in an effective strategy to search QS proteins in metagenomes sequences. The number of QS proteins present in each sample, and its phylogenetic affiliation, was clearly related to the bacterial diversity and the origin of the biofilm. The samples isolated from natural habitats presented clear differences with those from artificial habitats. Interesting findings have been made in the abundance of LuxR-like proteins finding an unbalanced ratio between the synthases and the receptor proteins in Bacteroidetes bacteria, pointing out the existence of “cheaters” in this group. Moreover, we have shown the presence of the LuxI/R QS system in bacteria from the Nitrospira taxonomic group. Finally, some undescribed proteins from the HdtS family have been found in Gamma-proteobacteria.

Planktonic microbial profiling in water samples from a Brazilian Amazonian reservoir

Our comprehension of the dynamics and diversity of freshwater planktonic bacterial communities is far from complete concerning the Brazilian Amazonian region. Therefore, reference studies are urgently needed. We mapped bacterial communities present in the planktonic communities of a freshwater artificial reservoir located in the western Amazonian basin. Two samples were obtained from rainy and dry seasons, the periods during which water quality and plankton diversity undergo the most significant changes. Hypervariable 16S rRNA and shotgun sequencing were performed to describe the first reference of a microbial community in an Amazonian lentic system. Microbial composition consisted mainly of Betaproteobacteria, Cyanobacteria, Alphaproteobacteria, and Actinobacteria in the dry period. The bacteria distribution in the rainy period was notably absent of Cyanobacteria. Microcystis was observed in the dry period in which the gene cluster for cyanotoxins was found. Iron acquisition gene group was higher in the sample from the rainy season. This work mapped the first inventory of the planktonic microbial community of a large water reservoir in the Amazon, providing a reference for future functional studies and determining other communities and how they interact.

Complex interactions between potentially pathogenic, opportunistic, and resident bacteria emerge during infection on a reef-building coral

Increased bacterial diversity on diseased corals can obscure disease etiology and complicate our understanding of pathogenesis. To untangle microbes that may cause white band disease signs from microbes responding to disease, we inoculated healthy Acropora cervicornis corals with an infectious dose from visibly diseased corals. We sampled these dosed corals and healthy controls over time for sequencing of the bacterial 16S region. Endozoicomonas were associated with healthy fragments from 4/10 colonies, dominating microbiomes before dosing and decreasing over time only in corals that displayed disease signs, suggesting a role in disease resistance. We grouped disease-associated bacteria by when they increased in abundance (primary vs secondary) and whether they originated in the dose (colonizers) or the previously healthy corals (responders). We found that all primary responders increased in all dosed corals regardless of final disease state and are therefore unlikely to cause disease signs. In contrast, primary colonizers in the families Pasteurellaceae and Francisellaceae increased solely in dosed corals that ultimately displayed disease signs, and may be infectious foreign bacteria involved in the development of disease signs. Moving away from a static comparison of diseased and healthy bacterial communities, we provide a framework to identify key players in other coral diseases.

Natural products and morphogenic activity of γ-Proteobacteria associated with the marine hydroid polyp Hydractinia echinata

Illumina 16S rRNA gene sequencing was used to profile the associated bacterial community of the marine hydroid Hydractinia echinata, a long-standing model system in developmental biology. 56 associated bacteria were isolated and evaluated for their antimicrobial activity. Three strains were selected for further in-depth chemical analysis leading to the identification of 17 natural products. Several γ-Proteobacteria were found to induce settlement of the motile larvae, but only six isolates induced the metamorphosis to the primary polyp stage within 24h. Our study paves the way to better understand how bacterial partners contribute to protection, homeostasis and propagation of the hydroid polyp.

A comprehensive evaluation of the sl1p pipeline for 16S rRNA gene sequencing analysis

BACKGROUND

Advances in next-generation sequencing technologies have allowed for detailed, molecular-based studies of microbial communities such as the human gut, soil, and ocean waters. Sequencing of the 16S rRNA gene, specific to prokaryotes, using universal PCR primers has become a common approach to studying the composition of these microbiota. However, the bioinformatic processing of the resulting millions of DNA sequences can be challenging, and a standardized protocol would aid in reproducible analyses.

METHODS

The short-read library 16S rRNA gene sequencing pipeline (sl1p, pronounced "slip") was designed with the purpose of mitigating this lack of reproducibility by combining pre-existing tools into a computational pipeline. This pipeline automates the processing of raw 16S rRNA gene sequencing data to create human-readable tables, graphs, and figures to make the collected data more readily accessible.

RESULTS

Data generated from mock communities were compared using eight OTU clustering algorithms, two taxon assignment approaches, and three 16S rRNA gene reference databases. While all of these algorithms and options are available to sl1p users, through testing with human-associated mock communities, AbundantOTU+, the RDP Classifier, and the Greengenes 2011 reference database were chosen as sl1p's defaults based on their ability to best represent the known input communities.

CONCLUSIONS

sl1p promotes reproducible research by providing a comprehensive log file, and reduces the computational knowledge needed by the user to process next-generation sequencing data. sl1p is freely available at https://bitbucket.org/fwhelan/sl1p .

DMclust, a Density-based Modularity Method for Accurate OTU Picking of 16S rRNA Sequences

Clustering 16S rRNA sequences into operational taxonomic units (OTUs) is a crucial step in analyzing metagenomic data. Although many methods have been developed, how to obtain an appropriate balance between clustering accuracy and computational efficiency is still a major challenge. A novel density-based modularity clustering method, called DMclust, is proposed in this paper to bin 16S rRNA sequences into OTUs with high clustering accuracy. The DMclust algorithm consists of four main phases. It first searches for the sequence dense group defined as n-sequence community, in which the distance between any two sequences is less than a threshold. Then these dense groups are used to construct a weighted network, where dense groups are viewed as nodes, each pair of dense groups is connected by an edge, and the distance of pairwise groups represents the weight of the edge. Then, a modularity-based community detection method is employed to generate the preclusters. Finally, the remaining sequences are assigned to their nearest preclusters to form OTUs. Compared with existing widely used methods, the experimental results on several metagenomic datasets show that DMclust has higher accurate clustering performance with acceptable memory usage.

ESPRIT-Forest: Parallel clustering of massive amplicon sequence data in subquadratic time

The rapid development of sequencing technology has led to an explosive accumulation of genomic sequence data. Clustering is often the first step to perform in sequence analysis, and hierarchical clustering is one of the most commonly used approaches for this purpose. However, it is currently computationally expensive to perform hierarchical clustering of extremely large sequence datasets due to its quadratic time and space complexities. In this paper we developed a new algorithm called ESPRIT-Forest for parallel hierarchical clustering of sequences. The algorithm achieves subquadratic time and space complexity and maintains a high clustering accuracy comparable to the standard method. The basic idea is to organize sequences into a pseudo-metric based partitioning tree for sub-linear time searching of nearest neighbors, and then use a new multiple-pair merging criterion to construct clusters in parallel using multiple threads. The new algorithm was tested on the human microbiome project (HMP) dataset, currently one of the largest published microbial 16S rRNA sequence dataset. Our experiment demonstrated that with the power of parallel computing it is now compu- tationally feasible to perform hierarchical clustering analysis of tens of millions of sequences. The software is available at http://www.acsu.buffalo.edu/∼yijunsun/lab/ESPRIT-Forest.html.

Second Era of OMICS in Caries Research: Moving Past the Phase of Disillusionment

Novel approaches using OMICS techniques enable a collective assessment of multiple related biological units, including genes, gene expression, proteins, and metabolites. In the past decade, next-generation sequencing ( NGS) technologies were improved by longer sequence reads and the development of genome databases and user-friendly pipelines for data analysis, all accessible at lower cost. This has generated an outburst of high-throughput data. The application of OMICS has provided more depth to existing hypotheses as well as new insights in the etiology of dental caries. For example, the determination of complete bacterial microbiomes of oral samples rather than selected species, together with oral metatranscriptome and metabolome analyses, supports the viewpoint of dysbiosis of the supragingival biofilms. In addition, metabolome studies have been instrumental in disclosing the contributions of major pathways for central carbon and amino acid metabolisms to biofilm pH homeostasis. New, often noncultured, oral streptococci have been identified, and their phenotypic characterization has revealed candidates for probiotic therapy. Although findings from OMICS research have been greatly informative, problems related to study design, data quality, integration, and reproducibility still need to be addressed. Also, the emergence and continuous updates of these computationally demanding technologies require expertise in advanced bioinformatics for reliable interpretation of data. Despite the obstacles cited above, OMICS research is expected to encourage the discovery of novel caries biomarkers and the development of next-generation diagnostics and therapies for caries control. These observations apply equally to the study of other oral diseases.

OptiClust, an Improved Method for Assigning Amplicon-Based Sequence Data to Operational Taxonomic Units

Assignment of 16S rRNA gene sequences to operational taxonomic units (OTUs) is a computational bottleneck in the process of analyzing microbial communities. Although this has been an active area of research, it has been difficult to overcome the time and memory demands while improving the quality of the OTU assignments. Here, we developed a new OTU assignment algorithm that iteratively reassigns sequences to new OTUs to optimize the Matthews correlation coefficient (MCC), a measure of the quality of OTU assignments. To assess the new algorithm, OptiClust, we compared it to 10 other algorithms using 16S rRNA gene sequences from two simulated and four natural communities. Using the OptiClust algorithm, the MCC values averaged 15.2 and 16.5% higher than the OTUs generated when we used the average neighbor and distance-based greedy clustering with VSEARCH, respectively. Furthermore, on average, OptiClust was 94.6 times faster than the average neighbor algorithm and just as fast as distance-based greedy clustering with VSEARCH. An empirical analysis of the efficiency of the algorithms showed that the time and memory required to perform the algorithm scaled quadratically with the number of unique sequences in the data set. The significant improvement in the quality of the OTU assignments over previously existing methods will significantly enhance downstream analysis by limiting the splitting of similar sequences into separate OTUs and merging of dissimilar sequences into the same OTU. The development of the OptiClust algorithm represents a significant advance that is likely to have numerous other applications. IMPORTANCE The analysis of microbial communities from diverse environments using 16S rRNA gene sequencing has expanded our knowledge of the biogeography of microorganisms. An important step in this analysis is the assignment of sequences into taxonomic groups based on their similarity to sequences in a database or based on their similarity to each other, irrespective of a database. In this study, we present a new algorithm for the latter approach. The algorithm, OptiClust, seeks to optimize a metric of assignment quality by shuffling sequences between taxonomic groups. We found that OptiClust produces more robust assignments and does so in a rapid and memory-efficient manner. This advance will allow for a more robust analysis of microbial communities and the factors that shape them.

Desert Perennial Shrubs Shape the Microbial-Community Miscellany in Laimosphere and Phyllosphere Space

Microbial function, composition, and distribution play a fundamental role in ecosystem ecology. The interaction between desert plants and their associated microbes is expected to greatly affect their response to changes in this harsh environment. Using comparative analyses, we studied the impact of three desert shrubs, Atriplex halimus (A), Artemisia herba-alba (AHA), and Hammada scoparia (HS), on soil- and leaf-associated microbial communities. DNA extracted from the leaf surface and soil samples collected beneath the shrubs were used to study associated microbial diversity using a sequencing survey of variable regions of bacterial 16S rRNA and fungal ribosomal internal transcribed spacer (ITS1). We found that the composition of bacterial and fungal orders is plant-type-specific, indicating that each plant type provides a suitable and unique microenvironment. The different adaptive ecophysiological properties of the three plant species and the differential effect on their associated microbial composition point to the role of adaptation in the shaping of microbial diversity. Overall, our findings suggest a link between plant ecophysiological adaptation as a "temporary host" and the biotic-community parameters in extreme xeric environments.

Considerations For Optimizing Microbiome Analysis Using a Marker Gene

Next-generation sequencing technologies have found a widespread use in the study of host–microbe interactions due to the increase in their throughput and their ever-decreasing costs. The analysis of human-associated microbial communities using a marker gene, particularly the 16S rRNA, has been greatly benefited from these technologies – the human gut microbiome research being a remarkable example of such analysis that has greatly expanded our understanding of microbe-mediated human health and disease, metabolism, and food absorption. 16S studies go through a series of in vitro and in silico steps that can greatly influence their outcomes. However, the lack of a standardized workflow has led to uncertainties regarding the transparency and reproducibility of gut microbiome studies. We, here, discuss the most common challenges in the archetypical 16S rRNA workflow, including the extraction of total DNA, its use as template in PCR with primers that amplify specific hypervariable regions of the gene, amplicon sequencing, the denoising and removal of low-quality reads, the detection and removal of chimeric sequences, the clustering of high-quality sequences into operational taxonomic units, and their taxonomic classification. We recommend the essential technical information that should be conveyed in publications for reproducibility of results and encourage non-experts to include procedures and available tools that mitigate most of the problems encountered in microbiome analysis.

Application of a Database-Independent Approach To Assess the Quality of Operational Taxonomic Unit Picking Methods

Assignment of 16S rRNA gene sequences to operational taxonomic units (OTUs) allows microbial ecologists to overcome the inconsistencies and biases within bacterial taxonomy and provides a strategy for clustering similar sequences that do not have representatives in a reference database. I have applied the Matthews correlation coefficient to assess the ability of 15 reference-independent and -dependent clustering algorithms to assign sequences to OTUs. This metric quantifies the ability of an algorithm to reflect the relationships between sequences without the use of a reference and can be applied to any data set or method. The most consistently robust method was the average neighbor algorithm; however, for some data sets, other algorithms matched its performance.

MtHc: a motif-based hierarchical method for clustering massive 16S rRNA sequences into OTUs

The recent sequencing revolution driven by high-throughput technologies has led to rapid accumulation of 16S rRNA sequences for microbial communities. Clustering short sequences into operational taxonomic units (OTUs) is an initial crucial process in analyzing metagenomic data. Although many methods have been proposed for OTU inferences, a major challenge is the balance between inference accuracy and computational efficiency. To address these challenges, we present a novel motif-based hierarchical method (namely MtHc) for clustering massive 16S rRNA sequences into OTUs with high clustering accuracy and low memory usage. Suppose all the 16S rRNA sequences can be used to construct a complete weighted network, where sequences are viewed as nodes, each pair of sequences is connected by an imaginary edge, and the distance of a pair of sequences represents the weight of the edge. MtHc consists of three main phrases. First, heuristically search the motif that is defined as n-node sub-graph (in the present study, n = 3, 4, 5), in which the distance between any two nodes is less than a threshold. Second, use the motif as a seed to form candidate clusters by computing the distances of other sequences with the motif. Finally, hierarchically merge the candidate clusters to generate the OTUs by only calculating the distances of motifs between two clusters. Compared with the existing methods on several simulated and real-life metagenomic datasets, we demonstrate that MtHc has higher clustering performance, less memory usage and robustness for setting parameters, and that it is more effective to handle the large-scale metagenomic datasets. The MtHC software can be freely download from for academic users.

Evaluation of the effects of intrapartum antibiotic prophylaxis on newborn intestinal microbiota using a sequencing approach targeted to multi hypervariable 16S rDNA regions

Different factors are known to influence the early gut colonization in newborns, among them the perinatal use of antibiotics. On the other hand, the effect on the baby of the administration of antibiotics to the mother during labor, referred to as intrapartum antibiotic prophylaxis (IAP), has received less attention, although routinely used in group B Streptococcus positive women to prevent the infection in newborns. In this work, the fecal microbiota of neonates born to mothers receiving IAP and of control subjects were compared taking advantage for the first time of high-throughput DNA sequencing technology. Seven different 16S rDNA hypervariable regions (V2, V3, V4, V6 + V7, V8, and V9) were amplified and sequenced using the Ion Torrent Personal Genome Machine. The results obtained showed significant differences in the microbial composition of newborns born to mothers who had received IAP, with a lower abundance of Actinobacteria and Bacteroidetes as well as an overrepresentation of Proteobacteria. Considering that the seven hypervariable regions showed different discriminant ability in the taxonomic identification, further analyses were performed on the V4 region evidencing in IAP infants a reduced microbial richness and biodiversity, as well as a lower number of bacterial families with a predominance of Enterobacteriaceae members. In addition, this analysis pointed out a significant reduction in Bifidobacterium spp. strains. The reduced abundance of these beneficial microorganisms, together with the increased amount of potentially pathogenic bacteria, may suggest that IAP infants are more exposed to gastrointestinal or generally health disorders later in age.

Determining threshold values for barcoding fungi: lessons from Cortinarius (Basidiomycota), a highly diverse and widespread ectomycorrhizal genus

Different distance-based threshold selection approaches were used to assess and compare use of the internal transcribed spacer (ITS) region to distinguish among 901 Cortinarius species represented by >3000 collections. Sources of error associated with genetic markers and selection approaches were explored and evaluated using MOTUs from genus and lineage based-alignments. Our study indicates that 1%-2% more species can be distinguished by using the full-length ITS barcode as compared to either the ITS1 or ITS2 regions alone. Optimal threshold values for different picking approaches and genetic marker lengths inferred from a subset of species containing major lineages ranged from 97.0% to 99.5% sequence similarity using clustering optimization and UNITE SH, and from 1% to 2% sequence dissimilarity with CROP. Errors for the optimal cutoff ranged from 0% to 70%, and these can be reduced to a maximum of 22% when excluding species lacking a barcode gap. A threshold value of 99% is suitable for distinguishing species in the majority of lineages in the genus using the entire ITS region but only 90% of the species could be identified using just the ITS1 or ITS2 region. Prior identification of species, lacking barcode gaps and their subsequent separate analyses, maximized the accuracy of threshold approaches.

De novo clustering methods outperform reference-based methods for assigning 16S rRNA gene sequences to operational taxonomic units

Background. 16S rRNA gene sequences are routinely assigned to operational taxonomic units (OTUs) that are then used to analyze complex microbial communities. A number of methods have been employed to carry out the assignment of 16S rRNA gene sequences to OTUs leading to confusion over which method is optimal. A recent study suggested that a clustering method should be selected based on its ability to generate stable OTU assignments that do not change as additional sequences are added to the dataset. In contrast, we contend that the quality of the OTU assignments, the ability of the method to properly represent the distances between the sequences, is more important. Methods. Our analysis implemented six de novo clustering algorithms including the single linkage, complete linkage, average linkage, abundance-based greedy clustering, distance-based greedy clustering, and Swarm and the open and closed-reference methods. Using two previously published datasets we used the Matthew's Correlation Coefficient (MCC) to assess the stability and quality of OTU assignments. Results. The stability of OTU assignments did not reflect the quality of the assignments. Depending on the dataset being analyzed, the average linkage and the distance and abundance-based greedy clustering methods generated OTUs that were more likely to represent the actual distances between sequences than the open and closed-reference methods. We also demonstrated that for the greedy algorithms VSEARCH produced assignments that were comparable to those produced by USEARCH making VSEARCH a viable free and open source alternative to USEARCH. Further interrogation of the reference-based methods indicated that when USEARCH or VSEARCH were used to identify the closest reference, the OTU assignments were sensitive to the order of the reference sequences because the reference sequences can be identical over the region being considered. More troubling was the observation that while both USEARCH and VSEARCH have a high level of sensitivity to detect reference sequences, the specificity of those matches was poor relative to the true best match. Discussion. Our analysis calls into question the quality and stability of OTU assignments generated by the open and closed-reference methods as implemented in current version of QIIME. This study demonstrates that de novo methods are the optimal method of assigning sequences into OTUs and that the quality of these assignments needs to be assessed for multiple methods to identify the optimal clustering method for a particular dataset.

Identification of Candidate Coral Pathogens on White Band Disease-Infected Staghorn Coral

Bacterial diseases affecting scleractinian corals pose an enormous threat to the health of coral reefs, yet we still have a limited understanding of the bacteria associated with coral diseases. White band disease is a bacterial disease that affects the two Caribbean acroporid corals, the staghorn coral Acropora cervicornis and the elkhorn coral A. palmate. Species of Vibrio and Rickettsia have both been identified as putative WBD pathogens. Here we used Illumina 16S rRNA gene sequencing to profile the bacterial communities associated with healthy and diseased A. cervicornis collected from four field sites during two different years. We also exposed corals in tanks to diseased and healthy (control) homogenates to reduce some of the natural variation of field-collected coral bacterial communities. Using a combination of multivariate analyses, we identified community-level changes between diseased and healthy corals in both the field-collected and tank-exposed datasets. We then identified changes in the abundances of individual operational taxonomic units (OTUs) between diseased and healthy corals. By comparing the diseased and healthy-associated bacteria in field-collected and tank-exposed corals, we were able to identify 16 healthy-associated OTUs and 106 consistently disease-associated OTUs, which are good candidates for putative WBD pathogens. A large percentage of these disease-associated OTUs belonged to the order Flavobacteriales. In addition, two of the putative pathogens identified here belong to orders previously suggested as WBD pathogens: Vibronales and Rickettsiales.

Toward accurate molecular identification of species in complex environmental samples: testing the performance of sequence filtering and clustering methods

Metabarcoding has the potential to become a rapid, sensitive, and effective approach for identifying species in complex environmental samples. Accurate molecular identification of species depends on the ability to generate operational taxonomic units (OTUs) that correspond to biological species. Due to the sometimes enormous estimates of biodiversity using this method, there is a great need to test the efficacy of data analysis methods used to derive OTUs. Here, we evaluate the performance of various methods for clustering length variable 18S amplicons from complex samples into OTUs using a mock community and a natural community of zooplankton species. We compare analytic procedures consisting of a combination of (1) stringent and relaxed data filtering, (2) singleton sequences included and removed, (3) three commonly used clustering algorithms (mothur, UCLUST, and UPARSE), and (4) three methods of treating alignment gaps when calculating sequence divergence. Depending on the combination of methods used, the number of OTUs varied by nearly two orders of magnitude for the mock community (60–5068 OTUs) and three orders of magnitude for the natural community (22–22191 OTUs). The use of relaxed filtering and the inclusion of singletons greatly inflated OTU numbers without increasing the ability to recover species. Our results also suggest that the method used to treat gaps when calculating sequence divergence can have a great impact on the number of OTUs. Our findings are particularly relevant to studies that cover taxonomically diverse species and employ markers such as rRNA genes in which length variation is extensive.

Impact of analytic provenance in genome analysis

Background

Many computational methods are available for assembly and annotation of newly sequenced microbial genomes. However, when new genomes are reported in the literature, there is frequently very little critical analysis of choices made during the sequence assembly and gene annotation stages. These choices have a direct impact on the biologically relevant products of a genomic analysis - for instance identification of common and differentiating regions among genomes in a comparison, or identification of enriched gene functional categories in a specific strain. Here, we examine the outcomes of different assembly and analysis steps in typical workflows in a comparison among strains of Vibrio vulnificus.

Results

Using six recently sequenced strains of V. vulnificus, we demonstrate the "alternate realities" of comparative genomics, and how they depend on the choice of a robust assembly method and accurate ab initio annotation. We apply several popular assemblers for paired-end Illumina data, and three well-regarded ab initio genefinders. We demonstrate significant differences in detected gene overlap among comparative genomics workflows that depend on these two steps. The divergence between workflows, even those using widely adopted methods, is obvious both at the single genome level and when a comparison is performed. In a typical example where multiple workflows are applied to the strain V. vulnificus CECT 4606, a workflow that uses the Velvet assembler and Glimmer gene finder identifies 3275 gene features, while a workflow that uses the Velvet assembler and the RAST annotation system identifies 5011 gene features. Only 3171 genes are identical between both workflows. When we examine 9 assembly/ annotation workflow scenarios as input to a three-way genome comparison, differentiating genes and even differentially represented functional categories change significantly from scenario to scenario.

Conclusions

Inconsistencies in genomic analysis can arise depending on the choices that are made during the assembly and annotation stages. These inconsistencies can have a significant impact on the interpretation of an individual genome's content. The impact is multiplied when comparison of content and function among multiple genomes is the goal. Tracking the analysis history of the data - its analytic provenance - is critical for reproducible analysis of genome data.

Limits to robustness and reproducibility in the demarcation of operational taxonomic units

The demarcation of operational taxonomic units (OTUs) from complex sequence data sets is a key step in contemporary studies of microbial ecology. However, as biologically motivated 'optimal' OTU-binning algorithms remain elusive, many conceptually distinct approaches continue to be used. Using a global data set of 887 870 bacterial 16S rRNA gene sequences, we objectively quantified biases introduced by several widely employed sequence clustering algorithms. We found that OTU-binning methods often provided surprisingly non-equivalent partitions of identical data sets, notably when clustering to the same nominal similarity thresholds; and we quantified the resulting impact on ecological data description for a well-defined human skin microbiome data set. We observed that some methods were very robust to varying clustering thresholds, while others were found to be highly susceptible even to slight threshold variations. Moreover, we comprehensively quantified the impact of the choice of 16S rRNA gene subregion, as well as of data set scope and context on algorithm performance. Our findings may contribute to an enhanced comparability of results across sequence-processing pipelines, and we arrive at recommendations towards higher levels of standardization in established workflows.

Variations in 16S rRNA-based microbiome profiling between pyrosequencing runs and between pyrosequencing facilities

Pyrosequencing of 16S rRNA gene amplicons on the 454 FLX Titanium platform has been widely used to analyze microbiomes in various environments. However, different results may stem from variations among sequencing runs or among sequencing facilities. This study aimed to evaluate these variations between different pyrosequencing runs by sequencing 16S rRNA gene amplicon libraries generated from three sets of rumen samples twice each on the 454 FLX Titanium system at two independent sequencing facilities. Similar relative abundances were found for predominant taxa represented by large numbers of sequence reads but not for minor taxa represented by small numbers of sequence reads. The two sequencing facilities revealed different bacterial profiles with respect to both predominant taxa and minor taxa, including the most predominant genus Prevotella, the family Lachnospiraceae, and the phylum Proteobacteria. Differences in primers used to generate amplicon libraries may be a major source of variations in microbiome profiling. Because different primers and regions of 16S rRNA genes are often used by different researchers, significant variations likely exist among studies. Quantitative interpretation for relative abundance of taxa, especially minor taxa, from prevalence of sequence reads and comparisons of results from different studies should be done with caution.

Comparative analysis of microbiome between accurately identified 16S rDNA and quantified bacteria in simulated samples

Although 16S rRNA gene (rDNA) sequencing is the gold standard for categorizing bacteria or characterizing microbial communities its clinical utility is limited by bias in metagenomic studies, in either the experiments or the data analyses. To evaluate the efficiency of current metagenomic methods, we sequenced seven simulated samples of ten bacterial species mixed at different concentrations. The V3 region of 16S rDNA was targeted and used to determine the distribution of bacterial species. The number of target sequences in individual simulated samples was in the range 1–1000 to provide a better reflection of natural microbial communities. However, for a given bacterial species present in the same proportion but at different concentrations, the observed percentage of 16S rDNAs was similar, except at very low concentrations that cannot be detected by real-time PCR. These results confirmed that the comparative microbiome in a sample characterized by 16S rDNA sequencing is sufficient to detect not only potential infectious pathogens, but also the relative proportion of 16S rDNA in the sample.

MSClust: A Multi-Seeds based Clustering algorithm for microbiome profiling using 16S rRNA sequence

Recent developments of next generation sequencing technologies have led to rapid accumulation of 16S rRNA sequences for microbiome profiling. One key step in data processing is to cluster short sequences into operational taxonomic units (OTUs). Although many methods have been proposed for OTU inferences, a major challenge is the balance between inference accuracy and computational efficiency, where inference accuracy is often sacrificed to accommodate the need to analyze large numbers of sequences. Inspired by the hierarchical clustering method and a modified greedy network clustering algorithm, we propose a novel multi-seeds based heuristic clustering method, named MSClust, for OTU inference. MSClust first adaptively selects multi-seeds instead of one seed for each candidate cluster, and the reads are then processed using a greedy clustering strategy. Through many numerical examples, we demonstrate that MSClust enjoys less memory usage, and better biological accuracy compared to existing heuristic clustering methods while preserving efficiency and scalability.

Analysis of metagenomic data containing high biodiversity levels

In this paper we have addressed the problem of analysing Next Generation Sequencing samples with an expected large biodiversity content. We analysed several well-known 16S rRNA datasets from experimental samples, including both large and short sequences, in numbers of tens of thousands, in addition to carefully crafted synthetic datasets containing more than 7000 OTUs. From this data analysis several patterns were identified and used to develop new guidelines for experimentation in conditions of high biodiversity. We analysed the suitability of different clustering packages for these type of situations, the problem of even sampling, the relative effectiveness of Chao1 and ACE estimators as well as their effect on sampling size for a variety of population distributions. As regards practical analysis procedures, we advocated an approach that retains as much high-quality experimental data as possible. By carefully applying selection rules combining the taxonomic assignment with clustering strategies, we derived a set of recommendations for ultra-sequencing data analysis at high biodiversity levels.

Can abundance of protists be inferred from sequence data: a case study of foraminifera

Protists are key players in microbial communities, yet our understanding of their role in ecosystem functioning is seriously impeded by difficulties in identification of protistan species and their quantification. Current microscopy-based methods used for determining the abundance of protists are tedious and often show a low taxonomic resolution. Recent development of next-generation sequencing technologies offered a very powerful tool for studying the richness of protistan communities. Still, the relationship between abundance of species and number of sequences remains subjected to various technical and biological biases. Here, we test the impact of some of these biological biases on sequence abundance of SSU rRNA gene in foraminifera. First, we quantified the rDNA copy number and rRNA expression level of three species of foraminifera by qPCR. Then, we prepared five mock communities with these species, two in equal proportions and three with one species ten times more abundant. The libraries of rDNA and cDNA of the mock communities were constructed, Sanger sequenced and the sequence abundance was calculated. The initial species proportions were compared to the raw sequence proportions as well as to the sequence abundance normalized by rDNA copy number and rRNA expression level per species. Our results showed that without normalization, all sequence data differed significantly from the initial proportions. After normalization, the congruence between the number of sequences and number of specimens was much better. We conclude that without normalization, species abundance determination based on sequence data was not possible because of the effect of biological biases. Nevertheless, by taking into account the variation of rDNA copy number and rRNA expression level we were able to infer species abundance, suggesting that our approach can be successful in controlled conditions.

The human gut microbiome: current knowledge, challenges, and future directions

The Human Genome Project was completed a decade ago, leaving a legacy of process, tools, and infrastructure now being turned to the study of the microbes that reside in and on the human body as determinants of health and disease, and has been branded "The Human Microbiome Project." Of the various niches under investigation, the human gut houses the most complex and abundant microbial community and is an arena for important host-microbial interactions that have both local and systemic impact. Initial studies of the human microbiome have been largely descriptive, a testing ground for innovative molecular techniques and new hypotheses. Methods for studying the microbiome have quickly evolved from low-resolution surveys of microbial community structure to high-definition description of composition, function, and ecology. Next-generation sequencing technologies combined with advanced bioinformatics place us at the doorstep of revolutionary insight into the composition, capability, and activity of the human intestinal microbiome. Renewed efforts to cultivate previously "uncultivable" microbes will be important to the overall understanding of gut ecology. There remain numerous methodological challenges to the effective study and understanding of the gut microbiome, largely relating to study design, sample collection, and the number of predictor variables. Strategic collaboration of clinicians, microbiologists, molecular biologists, computational scientists, and bioinformaticians is the ideal paradigm for success in this field. Meaningful interpretation of the gut microbiome requires that host genetic and environmental influences be controlled or accounted for. Understanding the gut microbiome in healthy humans is a foundation for discovering its influence in various important gastrointestinal and nutritional diseases (eg, inflammatory bowel disease, diabetes, and obesity), and for rational translation to human health gains.

Effect of Cry1Ab protein on rhizobacterial communities of Bt-maize over a four-year cultivation period

Background

Bt-maize is a transgenic variety of maize expressing the Cry toxin from Bacillus turingiensis. The potential accumulation of the relative effect of the transgenic modification and the cry toxin on the rhizobacterial communities of Bt-maize has been monitored over a period of four years.

Methodology/Principal Findings

The accumulative effects of the cultivation of this transgenic plant have been monitored by means of high throughput DNA pyrosequencing of the bacterial DNA coding for the 16S rRNA hypervariable V6 region from rhizobacterial communities. The obtained sequences were subjected to taxonomic, phylogenetic and taxonomic-independent diversity studies. The results obtained were consistent, indicating that variations detected in the rhizobacterial community structure were possibly due to climatic factors rather than to the presence of the Bt-gene. No variations were observed in the diversity estimates between non-Bt and Bt-maize.

Conclusions/Significance

The cultivation of Bt-maize during the four-year period did not change the maize rhizobacterial communities when compared to those of the non-Bt maize. This is the first study to be conducted with Bt-maize during such a long cultivation period and the first evaluation of rhizobacterial communities to be performed in this transgenic plant using Next Generation Sequencing.