Katherine A. Dunn

A new time-scale for ray-finned fish evolution

The Actinopterygii (ray-finned fishes) is the largest and most diverse vertebrate group, but little is agreed about the timing of its early evolution. Estimates using mitochondrial genomic data suggest that the major actinopterygian clades are much older than divergence dates implied by fossils. Here, the timing of the evolutionary origins of these clades is reinvestigated using morphological, and nuclear and mitochondrial genetic data. Results indicate that existing fossil-based estimates of the age of the crown-group Neopterygii, including the teleosts, Lepisosteus (gar) and Amia (bowfin), are at least 40 Myr too young. We present new palaeontological evidence that the neopterygian crown radiation is a Palaeozoic event, and demonstrate that conflicts between molecular and morphological data for the age of the Neopterygii result, in part, from missing fossil data. Although our molecular data also provide an older age estimate for the teleost crown, this range extension remains unsupported by the fossil evidence. Nuclear data from all relevant clades are used to demonstrate that the actinopterygian whole-genome duplication event is teleost-specific. While the date estimate of this event overlaps the probable range of the teleost stem group, a correlation between the genome duplication and the large-scale pattern of actinopterygian phylogeny remains elusive.

Multilocus Genotyping Assays for Single Nucleotide Polymorphism-Based Subtyping of Listeria monocytogenes Isolates

ABSTRACT Listeria monocytogenes is responsible for serious invasive illness associated with consumption of contaminated food and places a significant burden on public health and the agricultural economy. We recently developed a multilocus genotyping (MLGT) assay for high-throughput subtype determination of L. monocytogenes lineage I isolates based on interrogation of single nucleotide polymorphisms (SNPs) via multiplexed primer extension reactions. Here we report the development and validation of two additional MLGT assays that address the need for comprehensive DNA sequence-based subtyping of L. monocytogenes. The first of these novel MLGT assays targeted variation segregating within lineage II, while the second assay combined probes for lineage III strains with probes for strains representing a recently characterized fourth evolutionary lineage (IV) of L. monocytogenes. These assays were based on nucleotide variation identified in >3.8 Mb of comparative DNA sequence and consisted of 115 total probes that differentiated 93% of the 100 haplotypes defined by the multilocus sequence data. MLGT reproducibly typed the 173 isolates used in SNP discovery, and the 10,448 genotypes derived from MLGT analysis of these isolates were consistent with DNA sequence data. Application of the MLGT assays to assess subtype prevalence among isolates from ready-to-eat foods and food-processing facilities indicated a low frequency (6.3%) of epidemic clone subtypes and a substantial population of isolates (>30%) harboring mutations in inlA associated with attenuated virulence in cell culture and animal models. These mutations were restricted to serogroup 1/2 isolates, which may explain the overrepresentation of serotype 4b isolates in human listeriosis cases.

Phylogenomic analysis of natural selection pressure in Streptococcus genomes.

BackgroundIn comparative analyses of bacterial pathogens, it has been common practice to discriminate between two types of genes: (i) those shared by pathogens and their non-pathogenic relatives (core genes), and (ii) those found exclusively in pathogens (pathogen-specific accessory genes). Rather than attempting to a priori delineate genes into sets more or less relevant to pathogenicity, we took a broad approach to the analysis of Streptococcus species by investigating the strength of natural selection in all clusters of homologous genes. The genus Streptococcus is comprised of a wide variety of both pathogenic and commensal lineages, and we relate our findings to the pre-existing knowledge of Streptococcus virulence factors.ResultsOur analysis of 1730 gene clusters revealed 136 cases of positive Darwinian selection, which we suggest is most likely to result from an antagonistic interaction between the host and pathogen at the molecular level. A two-step validation procedure suggests that positive selection was robustly identified in our genomic survey. We found no evidence to support the notion that pathogen specific accessory genes are more likely to be subject to positive selection than core genes. Indeed, we even uncovered a few cases of essential gene evolution by positive selection. Among the gene clusters subject to positive selection, a large fraction (29%) can be connected to virulence. The most striking finding was that a considerable fraction of the positively selected genes are also known to have tissue specific patterns of expression during invasive disease. As current expression data is far from comprehensive, we suggest that this fraction was underestimated.ConclusionOur findings suggest that pathogen specific genes, although a popular focus of research, do not provide a complete picture of the evolutionary dynamics of virulence. The results of this study, and others, support the notion that the products of both core and accessory genes participate in complex networks that comprise the molecular basis of virulence. Future work should seek to understand the evolutionary dynamics of both core and accessory genes as a function of the networks in which they participate.

Seasonal assemblages and short-lived blooms in coastal north-west Atlantic Ocean bacterioplankton.

Temperate oceans are inhabited by diverse and temporally dynamic bacterioplankton communities. However, the role of the environment, resources and phytoplankton dynamics in shaping marine bacterioplankton communities at different time scales remains poorly constrained. Here, we combined time series observations (time scales of weeks to years) with molecular analysis of formalin-fixed samples from a coastal inlet of the north-west Atlantic Ocean to show that a combination of temperature, nitrate, small phytoplankton and Synechococcus abundances are best predictors for annual bacterioplankton community variability, explaining 38% of the variation. Using Bayesian mixed modelling, we identified assemblages of co-occurring bacteria associated with different seasonal periods, including the spring bloom (e.g. Polaribacter, Ulvibacter, Alteromonadales and ARCTIC96B-16) and the autumn bloom (e.g. OM42, OM25, OM38 and Arctic96A-1 clades of Alphaproteobacteria, and SAR86, OM60 and SAR92 clades of Gammaproteobacteria). Community variability over spring bloom development was best explained by silicate (32%)--an indication of rapid succession of bacterial taxa in response to diatom biomass--while nanophytoplankton as well as picophytoplankton abundance explained community variability (16-27%) over the transition into and out of the autumn bloom. Moreover, the seasonal structure was punctuated with short-lived blooms of rare bacteria including the KSA-1 clade of Sphingobacteria related to aromatic hydrocarbon-degrading bacteria.

BioMiCo: a supervised Bayesian model for inference of microbial community structure.

BackgroundMicrobiome samples often represent mixtures of communities, where each community is composed of overlapping assemblages of species. Such mixtures are complex, the number of species is huge and abundance information for many species is often sparse. Classical methods have a limited value for identifying complex features within such data.ResultsHere, we describe a novel hierarchical model for Bayesian inference of microbial communities (BioMiCo). The model takes abundance data derived from environmental DNA, and models the composition of each sample by a two-level hierarchy of mixture distributions constrained by Dirichlet priors. BioMiCo is supervised, using known features for samples and appropriate prior constraints to overcome the challenges posed by many variables, sparse data, and large numbers of rare species. The model is trained on a portion of the data, where it learns how assemblages of species are mixed to form communities and how assemblages are related to the known features of each sample. Training yields a model that can predict the features of new samples. We used BioMiCo to build models for three serially sampled datasets and tested their predictive accuracy across different time points. The first model was trained to predict both body site (hand, mouth, and gut) and individual human host. It was able to reliably distinguish these features across different time points. The second was trained on vaginal microbiomes to predict both the Nugent score and individual human host. We found that women having normal and elevated Nugent scores had distinct microbiome structures that persisted over time, with additional structure within women having elevated scores. The third was trained for the purpose of assessing seasonal transitions in a coastal bacterial community. Application of this model to a high-resolution time series permitted us to track the rate and time of community succession and accurately predict known ecosystem-level events.ConclusionBioMiCo provides a framework for learning the structure of microbial communities and for making predictions based on microbial assemblages. By training on carefully chosen features (abiotic or biotic), BioMiCo can be used to understand and predict transitions between complex communities composed of hundreds of microbial species.

Methods for selecting fixed-effect models for heterogeneous codon evolution, with comments on their application to gene and genome data

Models of codon evolution have proven useful for investigating the strength and direction of natural selection. In some cases, a priori biological knowledge has been used successfully to model heterogeneous evolutionary dynamics among codon sites. These are called fixed-effect models, and they require that all codon sites are assigned to one of several partitions which are permitted to have independent parameters for selection pressure, evolutionary rate, transition to transversion ratio or codon frequencies. For single gene analysis, partitions might be defined according to protein tertiary structure, and for multiple gene analysis partitions might be defined according to a genes functional category. Given a set of related fixed-effect models, the task of selecting the model that best fits the data is not trivial. In this study, we implement a set of fixed-effect codon models which allow for different levels of heterogeneity among partitions in the substitution process. We describe strategies for selecting among these models by a backward elimination procedure, Akaike information criterion (AIC) or a corrected Akaike information criterion (AICc). We evaluate the performance of these model selection methods via a simulation study, and make several recommendations for real data analysis. Our simulation study indicates that the backward elimination procedure can provide a reliable method for model selection in this setting. We also demonstrate the utility of these models by application to a single-gene dataset partitioned according to tertiary structure (abalone sperm lysin), and a multi-gene dataset partitioned according to the functional category of the gene (flagellar-related proteins of Listeria). Fixed-effect models have advantages and disadvantages. Fixed-effect models are desirable when data partitions are known to exhibit significant heterogeneity or when a statistical test of such heterogeneity is desired. They have the disadvantage of requiring a priori knowledge for partitioning sites. We recommend: (i) selection of models by using backward elimination rather than AIC or AICc, (ii) use a stringent cut-off, e.g., p = 0.0001, and (iii) conduct sensitivity analysis of results. With thoughtful application, fixed-effect codon models should provide a useful tool for large scale multi-gene analyses.BackgroundModels of codon evolution have proven useful for investigating the strength and direction of natural selection. In some cases, a priori biological knowledge has been used successfully to model heterogeneous evolutionary dynamics among codon sites. These are called fixed-effect models, and they require that all codon sites are assigned to one of several partitions which are permitted to have independent parameters for selection pressure, evolutionary rate, transition to transversion ratio or codon frequencies. For single gene analysis, partitions might be defined according to protein tertiary structure, and for multiple gene analysis partitions might be defined according to a genes functional category. Given a set of related fixed-effect models, the task of selecting the model that best fits the data is not trivial.ResultsIn this study, we implement a set of fixed-effect codon models which allow for different levels of heterogeneity among partitions in the substitution process. We describe strategies for selecting among these models by a backward elimination procedure, Akaike information criterion (AIC) or a corrected Akaike information criterion (AICc). We evaluate the performance of these model selection methods via a simulation study, and make several recommendations for real data analysis. Our simulation study indicates that the backward elimination procedure can provide a reliable method for model selection in this setting. We also demonstrate the utility of these models by application to a single-gene dataset partitioned according to tertiary structure (abalone sperm lysin), and a multi-gene dataset partitioned according to the functional category of the gene (flagellar-related proteins of Listeria).ConclusionFixed-effect models have advantages and disadvantages. Fixed-effect models are desirable when data partitions are known to exhibit significant heterogeneity or when a statistical test of such heterogeneity is desired. They have the disadvantage of requiring a priori knowledge for partitioning sites. We recommend: (i) selection of models by using backward elimination rather than AIC or AICc, (ii) use a stringent cut-off, e.g., p = 0.0001, and (iii) conduct sensitivity analysis of results. With thoughtful application, fixed-effect codon models should provide a useful tool for large scale multi-gene analyses.

BiomeNet: a Bayesian model for inference of metabolic divergence among microbial communities.

Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic subnetworks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems). The metabosystems are composed of mixtures of tightly connected metabolic subnetworks. BiomeNet differs from other unsupervised methods by allowing researchers to discriminate groups of samples through the metabolic patterns it discovers in the data, and by providing a framework for interpreting them. We describe a collapsed Gibbs sampler for inference of the mixture weights under BiomeNet, and we use simulation to validate the inference algorithm. Application of BiomeNet to human gut metagenomes revealed a metabosystem with greater prevalence among inflammatory bowel disease (IBD) patients. Based on the discriminatory subnetworks for this metabosystem, we inferred that the community is likely to be closely associated with the human gut epithelium, resistant to dietary interventions, and interfere with human uptake of an antioxidant connected to IBD. Because this metabosystem has a greater capacity to exploit host-associated glycans, we speculate that IBD-associated communities might arise from opportunist growth of bacteria that can circumvent the hosts nutrient-based mechanism for bacterial partner selection.

The Gut Microbiome of Pediatric Crohn's Disease Patients Differs from Healthy Controls in Genes That Can Influence the Balance Between a Healthy and Dysregulated Immune Response.

Background:Exclusive enteral nutrition (EEN) is a first-line therapy in pediatric Crohns disease (CD) thought to induce remission through changes in the gut microbiome. With microbiome assessment largely focused on microbial taxonomy and diversity, it remains unclear to what extent EEN induces functional changes that thereby contribute to its therapeutic effect. Methods:Fecal samples were collected from 15 pediatric CD patients prior to and after EEN treatment, as well as from 5 healthy controls. Metagenomic data were obtained via next-generation sequencing, and nonhuman reads were mapped to KEGG pathways, where possible. Pathway abundance was compared between CD patients and controls, and between CD patients that sustained remission (SR) and those that did not sustain remission (NSR). Results:Of 132 KEGG pathways identified, 8 pathways differed significantly between baseline CD patients and controls. Examination of these eight pathways showed SR patients had greater similarity to controls than NSR patients in all cases. Pathways fell into one of three groups: 1) no prior connection to IBD, 2) previously reported connection to IBD, and 3) known roles in innate immunity and immunoregulation. Conclusions:The microbiota of CD patients and controls represent alternative ecological states that have broad differences in functional capabilities, including xenobiotic and environmental pollutant degradation, succinate metavolism, and bacterial HtpG, all of which can affect barrier integrity and immune regulation. Moreover, our finding that SR patients were more similar to healthy controls suggests that community microbial function, as inferred from fecal microbiomes, could serve as a valuable diagnostic tool.

Early Changes in Microbial Community Structure Are Associated with Sustained Remission After Nutritional Treatment of Pediatric Crohn's Disease.

Background:Clinical remission achieved by exclusive enteral nutrition (EEN) is associated with marked microbiome changes. In this prospective study of exclusive enteral nutrition, we employ a hierarchical model of microbial community structure to distinguish between pediatric Crohns disease patients who achieved sustained remission (SR) and those who relapsed early (non-SR), after restarting a normal diet. Methods:Fecal samples were obtained from 10 patients (age 10–16) and from 5 healthy controls (age 9–14). The microbiota was assessed via 16S rRNA sequencing. In addition to standard measures of microbial biodiversity, we employed Bayesian methods to characterize the hierarchical community structure. Community structure between patients who sustained remission (wPCDAI <12.5) up to their 24-week follow-up (SR) was compared with patients that had not sustained remission (non-SR). Results:Microbial diversity was lower in Crohns disease patients relative to controls and lowest in patients who did not achieve SR. SR patients differed from non-SR patients in terms of the structure and prevalence of their microbial communities. The SR prevalent community contained a number of strains of Akkermansia muciniphila and Bacteroides and was limited in Proteobacteria, whereas the non-SR prevalent community had a large Proteobacteria component. Their communities were so different that a model trained to discriminate SR and non-SR had 80% classification accuracy, already at baseline sampling. Conclusions:Microbial community structure differs between healthy controls, patients who have an enduring response to exclusive enteral nutrition, and those who relapse early on introduction of normal diet. Our novel Bayesian approach to these differences is able to predict sustained remission after exclusive enteral nutrition.

Novel Strategies for Applied Metagenomics

Abstract:Detailed analyses of the gut microbiome and its effect on human physiology and disease are emerging, thanks to advances in high-throughput DNA-sequencing technology and the burgeoning field of metagenomics. Metagenomics examines the structure and functional potential of microbial communities in their native habitats through the direct isolation and analysis of community DNA. In inflammatory bowel disease, gut microbiome studies have shown an association with perturbations in community composition and, especially, function. In this review, we discuss the application of next-generation sequencing to microbiome research and highlight the importance of modeling microbiome structure and function to the future of inflammatory bowel disease research and treatment.