No abstract

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.

To identify the molecular mechanisms by which EBVassociated 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 oncogenemediated 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. (Cancer Res 2006; 66(16): 7999-8006)

Decreased lens movement with age could be in part secondary to extralenticular age-related changes, such as loss of ciliary body forward movement. Ciliary body centripetal movement may not be the limiting component in accommodation in the older eye.

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.