A novel method for determining microflora composition using dynamic phylogenetic analysis of 16S ribosomal RNA deep sequencing data

Deep sequencing of the 16S rRNA gene provides a comprehensive view of bacterial communities in a particular environment and has expanded our ability to study the impact of the microflora on human health and disease. Current analysis methods rely on comparisons of the sequences generated with an expanding but limited set of annotated 16S rRNA sequences or phylogenic clustering of sequences based on arbitrary similarity cutoffs. We describe a novel approach to characterize bacterial composition using deep sequencing of 16S rRNA gene. Our method defines operational taxonomic units based on phylogenetic tree reconstruction and dynamic clustering of sequences using solely sequencing data. These OTUs can be used to identify differences in bacteria abundance between environments. This approach can perform better than previous phylogenetic methods and will significantly improve our understanding of the microfloral role on human diseases by providing a comprehensive analysis of the microbial composition from various bacterial communities.     

Combining counts and incidence data: an efficient approach for estimating the log-normal species abundance distribution and diversity indices

Obtaining accurate estimates of diversity indices is difficult because the number of species encountered in a sample increases with sampling intensity. We introduce a novel method that requires that the presence of species in a sample to be assessed while the counts of the number of individuals per species are only required for just a small part of the sample. To account for species included as incidence data in the species abundance distribution, we modify the likelihood function of the classical Poisson log-normal distribution. Using simulated community assemblages, we contrast diversity estimates based on a community sample, a subsample randomly extracted from the community sample, and a mixture sample where incidence data are added to a subsample. We show that the mixture sampling approach provides more accurate estimates than the subsample and at little extra cost. Diversity indices estimated from a freshwater zooplankton community sampled using the mixture approach show the same pattern of results as the simulation study. Our method efficiently increases the accuracy of diversity estimates and comprehension of the left tail of the species abundance distribution. We show how to choose the scale of sample size needed for a compromise between information gained, accuracy of the estimates and cost expended when assessing biological diversity. The sample size estimates are obtained from key community characteristics, such as the expected number of species in the community, the expected number of individuals in a sample and the evenness of the community.     

Effects of plant biomass, plant diversity, and water content on bacterial communities in soil lysimeters: implications for the determinants of bacterial diversity

Soils may comprise tens of thousands to millions of bacterial species. It is still unclear whether this high level of diversity is governed by functional redundancy or by a multitude of ecological niches. In order to address this question, we analyzed the reproducibility of bacterial community composition after different experimental manipulations. Soil lysimeters were planted with four different types of plant communities, and the water content was adjusted. Group-specific phylogenetic fingerprinting by PCR-denaturing gradient gel electrophoresis revealed clear differences in the composition of Alphaproteobacteria, Betaproteobacteria, Bacteroidetes, Chloroflexi, Planctomycetes, and Verrucomicrobia populations in soils without plants compared to that of populations in planted soils, whereas no influence of plant species composition on bacterial diversity could be discerned. These results indicate that the presence of higher plant species affects the species composition of bacterial groups in a reproducible manner and even outside of the rhizosphere. In contrast, the environmental factors tested did not affect the composition of Acidobacteria, Actinobacteria, Archaea, and Firmicutes populations. One-third (52 out of 160) of the sequence types were found to be specifically and reproducibly associated with the absence or presence of plants. Unexpectedly, this was also true for numerous minor constituents of the soil bacterial assemblage. Subsequently, one of the low-abundance phylotypes (beta10) was selected for studying the interdependence under particular experimental conditions and the underlying causes in more detail. This so-far-uncultured phylotype of the Betaproteobacteria species represented up to 0.18% of all bacterial cells in planted lysimeters compared to 0.017% in unplanted systems. A cultured representative of this phylotype exhibited high physiological flexibility and was capable of utilizing major constituents of root exudates. Our results suggest that the bacterial species composition in soil is determined to a significant extent by abiotic and biotic factors, rather than by mere chance, thereby reflecting a multitude of distinct ecological niches.     

Recovery of human gut microbiota genomes with third-generation sequencing

Human gut microbiota modulates normal physiological functions, such as maintenance of barrier homeostasis and modulation of metabolism, as well as various chronic diseases including type 2 diabetes and gastrointestinal cancer. Despite decades of research, the composition of the gut microbiota remains poorly understood. Here, we established an effective extraction method to obtain high quality gut microbiota genomes, and analyzed them with third-generation sequencing technology. We acquired a large quantity of data from each sample and assembled large numbers of reliable contigs. With this approach, we constructed tens of completed bacterial genomes in which there were several new bacteria species. We also identified a new conditional pathogen, Enterococcus tongjius, which is a member of Enterococci. This work provided a novel and reliable approach to recover gut microbiota genomes, facilitating the discovery of new bacteria species and furthering our understanding of the microbiome that underlies human health and diseases.Subject terms: DNA sequencing, Mechanisms of disease     

Proportional mixture of two rarefaction/extrapolation curves to forecast biodiversity changes under landscape transformation

Progressive habitat transformation causes global changes in landscape biodiversity patterns, but can be hard to quantify. Rarefaction/extrapolation approaches can quantify within‐habitat biodiversity, but may not be useful for cases in which one habitat type is progressively transformed into another habitat type. To quantify biodiversity patterns in such transformed landscapes, we use Hill numbers to analyse individual‐based species abundance data or replicated, sample‐based incidence data. Given biodiversity data from two distinct habitat types, when a specified proportion of original habitat is transformed, our approach utilises a proportional mixture of two within‐habitat rarefaction/extrapolation curves to analytically predict biodiversity changes, with bootstrap confidence intervals to assess sampling uncertainty. We also derive analytic formulas for assessing species composition (i.e. the numbers of shared and unique species) for any mixture of the two habitat types. Our analytical and numerical analyses revealed that species unique to each habitat type are the most important determinants of landscape biodiversity patterns.     

Phylogenetic trees based on gene content

Comparing gene content between species can be a useful approach for reconstructing phylogenetic trees. In this paper, we derive a maximum-likelihood estimation of evolutionary distance between species under a simple model of gene genesis and gene loss. Using simulated data on a biological tree with 107 taxa (and on a number of randomly generated trees), we compare the accuracy of tree reconstruction using this ML distance measure to an earlier ad hoc distance. We then compare these distance-based approaches to a character-based tree reconstruction method (Dollo parsimony) which seems well suited to the analysis of gene content data. To simplify simulations, we give a formal proof of the well-known 'fact' that the Dollo parsimony score is independent of the choice of root. Our results show a consistent trend, with the character-based method and ML distance measure outperforming the earlier ad hoc distance method. AVAILABILITY: http://www.ab.informatik.uni-tuebingen.de/software/genecontent/welcome_en.html     

Nonhomogeneous model of sequence evolution indicates independent origins of primary endosymbionts within the enterobacteriales (gamma-Proteobacteria)

Standard methods of phylogenetic reconstruction are based on models that assume homogeneity of nucleotide composition among taxa. However, this assumption is often violated in biological data sets. In this study, we examine possible effects of nucleotide heterogeneity among lineages on the phylogenetic reconstruction of a bacterial group that spans a wide range of genomic nucleotide contents: obligately endosymbiotic bacteria and free-living or commensal species in the gamma-Proteobacteria. We focus on AT-rich primary endosymbionts to better understand the origins of obligately intracellular lifestyles. Previous phylogenetic analyses of this bacterial group point to the importance of accounting for base compositional variation in estimating relationships, particularly between endosymbiotic and free-living taxa. Here, we develop an approach to compare susceptibility of various phylogenetic reconstruction methods to the effects of nucleotide heterogeneity. First, we identify candidate trees of gamma-Proteobacteria groEL and 16S rRNA using approaches that assume homogeneous and stationary base composition, including Bayesian, maximum likelihood, parsimony, and distance methods. We then create permutations of the resulting candidate trees by varying the placement of the AT-rich endosymbiont Buchnera. These permutations are evaluated under the nonhomogeneous and nonstationary maximum likelihood model of Galtier and Gouy, which allows equilibrium base content to vary among examined lineages. Our results show that commonly used phylogenetic methods produce incongruent trees of the Enterobacteriales, and that the placement of Buchnera is especially unstable. However, under a nonhomogeneous model, various groEL and 16S rRNA phylogenies that separate Buchnera from other AT-rich endosymbionts (Blochmannia and Wigglesworthia) have consistently and significantly higher likelihood scores. Blochmannia and Wigglesworthia appear to have evolved from secondary endosymbionts, and represent an origin of primary endosymbiosis that is independent from Buchnera. This application of a nonhomogeneous model offers a computationally feasible way to test specific phylogenetic hypotheses for taxa with heterogeneous and nonstationary base composition.     

Environmental barcoding: a next-generation sequencing approach for biomonitoring applications using river benthos

Timely and accurate biodiversity analysis poses an ongoing challenge for the success of biomonitoring programs. Morphology-based identification of bioindicator taxa is time consuming, and rarely supports species-level resolution especially for immature life stages. Much work has been done in the past decade to develop alternative approaches for biodiversity analysis using DNA sequence-based approaches such as molecular phylogenetics and DNA barcoding. On-going assembly of DNA barcode reference libraries will provide the basis for a DNA-based identification system. The use of recently introduced next-generation sequencing (NGS) approaches in biodiversity science has the potential to further extend the application of DNA information for routine biomonitoring applications to an unprecedented scale. Here we demonstrate the feasibility of using 454 massively parallel pyrosequencing for species-level analysis of freshwater benthic macroinvertebrate taxa commonly used for biomonitoring. We designed our experiments in order to directly compare morphology-based, Sanger sequencing DNA barcoding, and next-generation environmental barcoding approaches. Our results show the ability of 454 pyrosequencing of mini-barcodes to accurately identify all species with more than 1% abundance in the pooled mixture. Although the approach failed to identify 6 rare species in the mixture, the presence of sequences from 9 species that were not represented by individuals in the mixture provides evidence that DNA based analysis may yet provide a valuable approach in finding rare species in bulk environmental samples. We further demonstrate the application of the environmental barcoding approach by comparing benthic macroinvertebrates from an urban region to those obtained from a conservation area. Although considerable effort will be required to robustly optimize NGS tools to identify species from bulk environmental samples, our results indicate the potential of an environmental barcoding approach for biomonitoring programs.     

Inter- and intraspecific phytochemical variation correlate with epiphytic flower and leaf bacterial communities

Microbes associated with flowers and leaves affect plant health and fitness and modify the chemical phenotypes of plants with consequences for interactions of plants with their environment. However, the drivers of bacterial communities colonizing above-ground parts of grassland plants in the field remain largely unknown. We therefore examined the relationships between phytochemistry and the epiphytic bacterial community composition of flowers and leaves of Ranunculus acris and Trifolium pratense. On 252 plant individuals, we characterized primary and specialized metabolites, that is, surface sugars, volatile organic compounds (VOCs), and metabolic fingerprints, as well as epiphytic flower and leaf bacterial communities. The genomic potential of bacterial colonizers concerning metabolic capacities was assessed using bacterial reference genomes. Phytochemical composition displayed pronounced variation within and between plant species and organs, which explained part of the variation in bacterial community composition. Correlation network analysis suggests strain-specific correlations with metabolites. Analysis of bacterial reference genomes revealed taxon-specific metabolic capabilities that corresponded with genes involved in glycolysis and adaptation to osmotic stress. Our results show relationships between phytochemistry and the flower and leaf bacterial microbiomes suggesting that plants provide chemical niches for distinct bacterial communities. In turn, bacteria may induce alterations in the plants' chemical phenotype. Thus, our study may stimulate further research on the mechanisms of trait-based community assembly in epiphytic bacteria.     

DNA methylation arrays as surrogate measures of cell mixture distribution

ABSTRACT: BACKGROUND: There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls. RESULTS: Here we present a method, similar to regression calibration, for inferring changes in the distribution of white blood cells between different subpopulations (e.g. cases and controls) using DNA methylation signatures, in combination with a previously obtained external validation set consisting of signatures from purified leukocyte samples. We validate the fundamental idea in a cell mixture reconstruction experiment, then demonstrate our method on DNA methylation data sets from several studies, including data from a Head and Neck Squamous Cell Carcinoma (HNSCC) study and an ovarian cancer study. Our method produces results consistent with prior biological findings, thereby validating the approach. CONCLUSIONS: Our method, in combination with an appropriate external validation set, promises new opportunities for large-scale immunological studies of both disease states and noxious exposures.     

Deriving site-specific species pools from large databases

Defining the species pool of a community is crucial for many types of ecological analyses, providing a foundation to metacommunity, null modelling or dark diversity frameworks. It is a challenge to derive the species pool empirically from large and heterogeneous databases. Here, we propose a method to define a site-specific species pool (SSSP), i.e. the probabilistic set of species that may co-occur with the species of a target community. Using large databases with geo-referenced records that comprise full plant community surveys, our approach characterizes each site by its own species pool without requiring a pre-defined habitat classification. We calculate the probabilities of each species in the database to occur in the target community using Beals’ index of sociological favourability, and then build sample-based rarefaction curves from neighbouring records with similar species composition to estimate the asymptotic species pool size. A corresponding number of species is then selected among the species having the highest occurrence probability, thus defining both size and composition of the species pool. We tested the robustness of this approach by comparing SSSPs obtained with different spatial extents and dissimilarity thresholds, fitting different models to the rarefaction curves, and comparing the results obtained when using Beals co-occurrence probabilities or presence/absence data. As an example application, we calculated the SSSPs for all calcareous grassland records in the German Vegetation Reference Database, and show how our method could be used to 1) produce grain-dependent estimations of species richness across plots, 2) derive scalable maps of species richness and 3) define the full list of species composing the SSSP of each target site. By deriving the species pool exclusively from community characteristics, the SSSP framework presented here provides a robust approach to bridge biodiversity estimations across spatial scales.     

Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method

Accurate and large‐scale prediction of protein–protein interactions directly from amino‐acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino‐acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two‐component systems and comprehensively reconstruct two‐component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome‐wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome‐wide two‐component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of ‘hub’ nodes that distribute and integrate signals to and from up to tens of different interaction partners.     

Total bacterial and species-specific 16S rDNA micro-array quantification of complex samples

AIMS: We describe a novel DNA-micro-array-based method that targets 16S rDNA to quantify changes in both the total bacterial DNA and the species-specific DNA composition. METHODS AND RESULTS: Quantifications were achieved by combining competitive PCR for quantifying total bacterial DNA with quantification of species-specific DNA composition based on signature 16S rDNA sequences. We constructed 11 different probes, which were evaluated on 21 different strains, in addition to complex samples. The signals obtained with sequence-specific labelling of the probes corresponded well with what should be expected based on 16S rDNA phylogenetic reconstruction. The quantification of species-specific DNA composition showed that the micro-array approach could be used to accurately determine differential growth of bacteria in mixed samples. We analysed samples containing mixtures of Lactococcus lactis and different species of propionibacteria during a 2-week incubation period. Lactococcus lactis grew fast, reaching a maximum after 12 h, Propionibacterium acidipropionici and Propionibacterium freudenreichii reached a maximum after 48 h, whereas Propionibacterium jensenii showed a slow increase during the whole growth period. The 16S rDNA total bacterial DNA quantification was compared with real-time PCR, absorbance measurements (ABS600) and colony forming units (CFU). CONCLUSION: The accuracy of the array approach was in the same range or better than the alternative techniques. The potential of the 16S rDNA micro-array method was further demonstrated using a liquid cheese model. SIGNIFICANCE AND IMPACT OF THE STUDY: This is to our knowledge the first time quantification of the total bacterial DNA and the species-specific DNA compositions of mixed populations have been achieved in the same assay.     

Rapid quantitative profiling of complex microbial populations

Diverse and complex microbial ecosystems are found in virtually every environment on earth, yet we know very little about their composition and ecology. Comprehensive identification and quantification of the constituents of these microbial communities—a ‘census’—is an essential foundation for understanding their biology. To address this problem, we developed, tested and optimized a DNA oligonucleotide microarray composed of 10 462 small subunit (SSU) ribosomal DNA (rDNA) probes (7167 unique sequences) selected to provide quantitative information on the taxonomic composition of diverse microbial populations. Using our optimized experimental approach, this microarray enabled detection and quantification of individual bacterial species present at fractional abundances of <0.1% in complex synthetic mixtures. The estimates of bacterial species abundance obtained using this microarray are similar to those obtained by phylogenetic analysis of SSU rDNA sequences from the same samples—the current ‘gold standard’ method for profiling microbial communities. Furthermore, probes designed to represent higher order taxonomic groups of bacterial species reliably detected microbes for which there were no species-specific probes. This simple, rapid microarray procedure can be used to explore and systematically characterize complex microbial communities, such as those found within the human body.     

Proteomics-based compositional analysis of complex cellulase-hemicellulase mixtures

Efficient deconstruction of cellulosic biomass to fermentable sugars for fuel and chemical production is accomplished by a complex mixture of cellulases, hemicellulases, and accessory enzymes (e.g., >50 extracellular proteins). Cellulolytic enzyme mixtures, produced industrially mostly using fungi like Trichoderma reesei, are poorly characterized in terms of their protein composition and its correlation to hydrolytic activity on cellulosic biomass. The secretomes of commercial glycosyl hydrolase-producing microbes was explored using a proteomics approach with high-throughput quantification using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Here, we show that proteomics-based spectral counting approach is a reasonably accurate and rapid analytical technique that can be used to determine protein composition of complex glycosyl hydrolase mixtures that also correlates with the specific activity of individual enzymes present within the mixture. For example, a strong linear correlation was seen between Avicelase activity and total cellobiohydrolase content. Reliable, quantitative and cheaper analytical methods that provide insight into the cellulosic biomass degrading fungal and bacterial secretomes would lead to further improvements toward commercialization of plant biomass-derived fuels and chemicals.     

Conspecific and Heterospecific Plant Densities at Small-Scale Can Drive Plant-Pollinator Interactions

Generalist pollinators are important in many habitats, but little research has been done on small-scale spatial variation in interactions between them and the plants that they visit. Here, using a spatially explicit approach, we examined whether multiple species of flowering plants occurring within a single meadow showed spatial structure in their generalist pollinator assemblages.We report the results for eight plant species for which at least 200 individual visits were recorded. We found that for all of these species, the proportions of their general pollinator assemblages accounted for by particular functional groups showed spatial heterogeneity at the scale of tens of metres. This heterogeneity was connected either with no or only subtle changes of vegetation and flowering species composition. In five of these species, differences in conspecific plant density influenced the pollinator communities (with greater dominance of main pollinators at low-conspecific plant densities). The density of heterospecific plant individuals influenced the pollinator spectrum in one case.Our results indicate that the picture of plant-pollinator interactions provided by averaging data within large plots may be misleading and that within-site spatial heterogeneity should be accounted for in terms of sampling effort allocation and analysis. Moreover, spatially structured plant-pollinator interactions may have important ecological and evolutionary consequences, especially for plant population biology.     

Greedy Selection of Species for Ancestral State Reconstruction on Phylogenies: Elimination Is Better than Insertion

Accurate reconstruction of ancestral character states on a phylogeny is crucial in many genomics studies. We study how to select species to achieve the best reconstruction of ancestral character states on a phylogeny. We first show that the marginal maximum likelihood has the monotonicity property that more taxa give better reconstruction, but the Fitch method does not have it even on an ultrametric phylogeny. We further validate a greedy approach for species selection using simulation. The validation tests indicate that backward greedy selection outperforms forward greedy selection. In addition, by applying our selection strategy, we obtain a set of the ten most informative species for the reconstruction of the genomic sequence of the so-called boreoeutherian ancestor of placental mammals. This study has broad relevance in comparative genomics and paleogenomics since limited research resources do not allow researchers to sequence the large number of descendant species required to reconstruct an ancestral sequence.     

A Regression-Based Differential Expression Detection Algorithm for Microarray Studies with Ultra-Low Sample Size

Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED). Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality.     

Effects of Plant Biomass, Plant Diversity, and Water Content on Bacterial Communities in Soil Lysimeters: Implications for the Determinants of Bacterial Diversity

Soils may comprise tens of thousands to millions of bacterial species. It is still unclear whether this high level of diversity is governed by functional redundancy or by a multitude of ecological niches. In order to address this question, we analyzed the reproducibility of bacterial community composition after different experimental manipulations. Soil lysimeters were planted with four different types of plant communities, and the water content was adjusted. Group-specific phylogenetic fingerprinting by PCR-denaturing gradient gel electrophoresis revealed clear differences in the composition of Alphaproteobacteria, Betaproteobacteria, Bacteroidetes, Chloroflexi, Planctomycetes, and Verrucomicrobia populations in soils without plants compared to that of populations in planted soils, whereas no influence of plant species composition on bacterial diversity could be discerned. These results indicate that the presence of higher plant species affects the species composition of bacterial groups in a reproducible manner and even outside of the rhizosphere. In contrast, the environmental factors tested did not affect the composition of Acidobacteria, Actinobacteria, Archaea, and Firmicutes populations. One-third (52 out of 160) of the sequence types were found to be specifically and reproducibly associated with the absence or presence of plants. Unexpectedly, this was also true for numerous minor constituents of the soil bacterial assemblage. Subsequently, one of the low-abundance phylotypes (beta10) was selected for studying the interdependence under particular experimental conditions and the underlying causes in more detail. This so-far-uncultured phylotype of the Betaproteobacteria species represented up to 0.18% of all bacterial cells in planted lysimeters compared to 0.017% in unplanted systems. A cultured representative of this phylotype exhibited high physiological flexibility and was capable of utilizing major constituents of root exudates. Our results suggest that the bacterial species composition in soil is determined to a significant extent by abiotic and biotic factors, rather than by mere chance, thereby reflecting a multitude of distinct ecological niches.