David B. Dahl

A metagenomic study of diet-dependent interaction between gut microbiota and host in infants reveals differences in immune response

BackgroundGut microbiota and the host exist in a mutualistic relationship, with the functional composition of the microbiota strongly affecting the health and well-being of the host. Thus, it is important to develop a synthetic approach to study the host transcriptome and the microbiome simultaneously. Early microbial colonization in infants is critically important for directing neonatal intestinal and immune development, and is especially attractive for studying the development of human-commensal interactions. Here we report the results from a simultaneous study of the gut microbiome and host epithelial transcriptome of three-month-old exclusively breast- and formula-fed infants.ResultsVariation in both host mRNA expression and the microbiome phylogenetic and functional profiles was observed between breast- and formula-fed infants. To examine the interdependent relationship between host epithelial cell gene expression and bacterial metagenomic-based profiles, the host transcriptome and functionally profiled microbiome data were subjected to novel multivariate statistical analyses. Gut microbiota metagenome virulence characteristics concurrently varied with immunity-related gene expression in epithelial cells between the formula-fed and the breast-fed infants.ConclusionsOur data provide insight into the integrated responses of the host transcriptome and microbiome to dietary substrates in the early neonatal period. We demonstrate that differences in diet can affect, via gut colonization, host expression of genes associated with the innate immune system. Furthermore, the methodology presented in this study can be adapted to assess other host-commensal and host-pathogen interactions using genomic and transcriptomic data, providing a synthetic genomics-based picture of host-commensal relationships.

Genome-Wide Expression Profiling Reveals EBV-Associated Inhibition of MHC Class I Expression in Nasopharyngeal Carcinoma

To identify the molecular mechanisms by which EBV-associated epithelial cancers are maintained, we measured the expression of essentially all human genes and all latent EBV genes in a collection of 31 laser-captured, microdissected nasopharyngeal carcinoma (NPC) tissue samples and 10 normal nasopharyngeal tissues. Global gene expression profiles clearly distinguished tumors from normal healthy epithelium. Expression levels of six viral genes (EBNA1, EBNA2, EBNA3A, EBNA3B, LMP1, and LMP2A) were correlated among themselves and strongly inversely correlated with the expression of a large subset of host genes. Among the human genes whose inhibition was most strongly correlated with increased EBV gene expression were multiple MHC class I HLA genes involved in regulating immune response via antigen presentation. The association between EBV gene expression and inhibition of MHC class I HLA expression implies that antigen display is either directly inhibited by EBV, facilitating immune evasion by tumor cells, and/or that tumor cells with inhibited presentation are selected for their ability to sustain higher levels of EBV to take maximum advantage of EBV oncogene-mediated tumor-promoting actions. Our data clearly reflect such tumor promotion, showing that deregulation of key proteins involved in apoptosis (BCL2-related protein A1 and Fas apoptotic inhibitory molecule), cell cycle checkpoints (AKIP, SCYL1, and NIN), and metastasis (matrix metalloproteinase 1) is closely correlated with the levels of EBV gene expression in NPC.

Cellular phenotypes of age-associated skeletal muscle mitochondrial abnormalities in rhesus monkeys

Rhesus monkey vastus lateralis muscle was examined histologically for age-associated electron transport system (ETS) abnormalities: fibers lacking cytochrome c oxidase activity (COX(-)) and/or exhibiting succinate dehydrogenase hyperreactivity (SDH(++)). Two hundred serial cross-sections (spanning 1600 microm) were obtained and analyzed for ETS abnormalities at regular intervals. The abundance and length of ETS abnormal regions increased with age. Extrapolating the data to the entire length of the fiber, up to 60% of the fibers were estimated to display ETS abnormalities in the oldest animal studied (34 years) compared to 4% in a young adult animal (11 years). ETS abnormal phenotypes varied with age and fiber type. Middle-aged animals primarily exhibited the COX(-) phenotype, while COX(-)/SDH(++) abnormalities were more common in old animals. Transition region phenotype was affected by fiber type with type 2 fibers first displaying COX(-) and then COX(-)/SDH(++) while type 1 fibers progressed from normal to SDH(++) and then to COX(-)/SDH(++). In situ hybridizations studies revealed an association of ETS abnormalities with deletions of the mitochondrial genome. By measuring cross-sectional area along the length of ETS abnormal fibers, we demonstrated that some of these fibers exhibit atrophy. Our data suggest mitochondrial (mtDNA) deletions and associated ETS abnormalities are contributors to age-associated fiber atrophy.

Multiple Hypothesis Testing by Clustering Treatment Effects

Multiple hypothesis testing and clustering have been the subject of extensive research in high-dimensional inference, yet these problems usually have been treated separately. By defining true clusters in terms of shared parameter values, we could improve the sensitivity of individual tests, because more data bearing on the same parameter values are available. We develop and evaluate a hybrid methodology that uses clustering information to increase testing sensitivity and accommodates uncertainty in the true clustering. To investigate the potential efficacy of the hybrid approach, we first study a stylized example in which each object is evaluated with a standard z score but different objects are connected by shared parameter values. We show that there is increased testing power when the clustering is estimated sufficiently well. We next develop a model-based analysis using a conjugate Dirichlet process mixture model. The method is general, but for specificity we focus attention on microarray gene expression data, to which both clustering and multiple testing methods are actively applied. Clusters provide the means for sharing information among genes, and the hybrid methodology averages over uncertainty in these clusters through Markov chain sampling. Simulations show that the hybrid method performs substantially better than other methods when clustering is heavy or moderate and performs well even under weak clustering. The proposed method is illustrated on microarray data from a study of the effects of aging on gene expression in heart tissue.

Assessing Side-Chain Perturbations of the Protein Backbone: A Knowledge-Based Classification of Residue Ramachandran Space

Grouping the 20 residues is a classic strategy to discover ordered patterns and insights about the fundamental nature of proteins, their structure, and how they fold. Usually, this categorization is based on the biophysical and/or structural properties of a residues side-chain group. We extend this approach to understand the effects of side chains on backbone conformation and to perform a knowledge-based classification of amino acids by comparing their backbone phi, psi distributions in different types of secondary structure. At this finer, more specific resolution, torsion angle data are often sparse and discontinuous (especially for nonhelical classes) even though a comprehensive set of protein structures is used. To ensure the precision of Ramachandran plot comparisons, we applied a rigorous Bayesian density estimation method that produces continuous estimates of the backbone phi, psi distributions. Based on this statistical modeling, a robust hierarchical clustering was performed using a divergence score to measure the similarity between plots. There were seven general groups based on the clusters from the complete Ramachandran data: nonpolar/beta-branched (Ile and Val), AsX (Asn and Asp), long (Met, Gln, Arg, Glu, Lys, and Leu), aromatic (Phe, Tyr, His, and Cys), small (Ala and Ser), bulky (Thr and Trp), and, lastly, the singletons of Gly and Pro. At the level of secondary structure (helix, sheet, turn, and coil), these groups remain somewhat consistent, although there are a few significant variations. Besides the expected uniqueness of the Gly and Pro distributions, the nonpolar/beta-branched and AsX clusters were very consistent across all types of secondary structure. Effectively, this consistency across the secondary structure classes implies that side-chain steric effects strongly influence a residues backbone torsion angle conformation. These results help to explain the plasticity of amino acid substitutions on protein structure and should help in protein design and structure evaluation.

Density Estimation for Protein Conformation Angles Using a Bivariate von Mises Distribution and Bayesian Nonparametrics

Please see the online supplemental material for a correction to this article. Interest in predicting protein backbone conformational angles has prompted the development of modeling and inference procedures for bivariate angular distributions. We present a Bayesian approach to density estimation for bivariate angular data that uses a Dirichlet process mixture model and a bivariate von Mises distribution. We derive the necessary full conditional distributions to fit the model, as well as the details for sampling from the posterior predictive distribution. We show how our density estimation method makes it possible to improve current approaches for protein structure prediction by comparing the performance of the so-called “whole” and “half” position distributions. Current methods in the field are based on whole position distributions, as density estimation for the half positions requires techniques, such as ours, that can provide good estimates for small datasets. With our method we are able to demonstrate that half position data provides a better approximation for the distribution of conformational angles at a given sequence position, therefore providing increased efficiency and accuracy in structure prediction.

The contributions of molecular framework to IMS collision cross-sections of gas-phase peptide ions.

Molecular dynamics (MD) is an essential tool for correlating collision cross-section data determined by ion mobility spectrometry (IMS) with candidate (calculated) structures. Conventional methods used for ion structure determination rely on comparing the measured cross-sections with the calculated collision cross-section for the lowest energy structure(s) taken from a large pool of candidate structures generated through multiple tiers of simulated annealing. We are developing methods to evaluate candidate structures from an ensemble of many conformations rather than the lowest energy structure. Here, we describe computational simulations and clustering methods to assign backbone conformations for singly-protonated ions of the model peptide (NH2-Met-Ile-Phe-Ala-Gly-Ile-Lys-COOH) formed by both MALDI and ESI, and compare the structures of MIFAGIK derivatives to test the ‘sensitivity’ of the cluster analysis method. Cluster analysis suggests that [MIFAGIK + H]+ ions formed by MALDI have a predominantly turn structure even though the low-energy ions prefer partial helical conformers. Although the ions formed by ESI have collision cross-sections that are different from those formed by MALDI, the results of cluster analysis indicate that the ions backbone structures are similar. Chemical modifications (N-acetyl, methylester as well as addition of Boc or Fmoc groups) to MIFAGIK alter the distribution of various conformers; the most dramatic changes are observed for the [M + Na]+ ion, which show a strong preference for random coil conformers owing to the strong solvation by the backbone amide groups.

A survey of mental health care provider's and managed care organization attitudes toward, familiarity with, and use of group interventions.

Abstract Managed Care has had a significant impact on delivery systems for mental health services. Direct and indirect persuasion to provide more cost-effective treatments has been one consequence. The cost-saving qualities and the effectiveness of group interventions have produced clear expectations for an increased use of therapy groups. This study compared perceptions and uses of group treatments on a national sample of managed care organizations and mental health providers. Because group psychotherapy encompasses such a broad definition, five specific types of group interventions were defined: problem-focused homogenous, process-oriented heterogeneous, psycho-educational, self-help, and short-term groups. Implications of differences and similarities between directors of managed care organizations and treatment providers are examined and discussed across five response categories (familiarity/training, perceived effectiveness, likelihood of reimbursement/referral, daily use, and expectation for future use).

Spiked Dirichlet Process Prior for Bayesian Multiple Hypothesis Testing in Random Effects Models.

We propose a Bayesian method for multiple hypothesis testing in random effects models that uses Dirichlet process (DP) priors for a nonparametric treatment of the random effects distribution. We consider a general model formulation which accommodates a variety of multiple treatment conditions. A key feature of our method is the use of a product of spiked distributions, i.e., mixtures of a point-mass and continuous distributions, as the centering distribution for the DP prior. Adopting these spiked centering priors readily accommodates sharp null hypotheses and allows for the estimation of the posterior probabilities of such hypotheses. Dirichlet process mixture models naturally borrow information across objects through model-based clustering while inference on single hypotheses averages over clustering uncertainty. We demonstrate via a simulation study that our method yields increased sensitivity in multiple hypothesis testing and produces a lower proportion of false discoveries than other competitive methods. While our modeling framework is general, here we present an application in the context of gene expression from microarray experiments. In our application, the modeling framework allows simultaneous inference on the parameters governing differential expression and inference on the clustering of genes. We use experimental data on the transcriptional response to oxidative stress in mouse heart muscle and compare the results from our procedure with existing nonparametric Bayesian methods that provide only a ranking of the genes by their evidence for differential expression.

Simultaneous inference for multiple testing and clustering via a Dirichlet process mixture model

We propose a Bayesian nonparametric regression model that exploits clustering for increased sensitivity in multiple hypothesis testing. We build on the recently proposed BEMMA (Bayesian Effects Models for Microarrays) method which is able to model dependence among objects through clustering and then estimates hypothesis-testing parameters averaged over clustering uncertainty. We propose several improvements. First, we separate the clustering of the regression coefficients from the part of the model that accommodates heteroscedasticity. Second, our model accommodates a wider class of experimental designs, such as permitting covariates and not requiring independent sampling. Third, we provide a more satisfactory treatment of nuisance parameters and some hyperparameters. Finally, we do not require the arbitrary designation of a reference treatment. The proposed method is compared in a simulation study to ANOVA and the BEMMA methods.