The millions of deaths from cholera during the past 200 y, coupled with the morbidity and mortality of cholera in Haiti since October 2010, are grim reminders that Vibrio cholerae , the etiologic agent of cholera, remains a scourge. We report the isolation of both V . cholerae O1 and non-O1/O139 early in the Haiti cholera epidemic from samples collected from victims in 18 towns across eight Arrondissements of Haiti. The results showed two distinct populations of V. cholerae coexisted in Haiti early in the epidemic. As non-O1/O139 V . cholerae was the sole pathogen isolated from 21% of the clinical specimens, its role in this epidemic, either alone or in concert with V . cholerae O1, cannot be dismissed. A genomic approach was used to examine similarities and differences among the Haitian V . cholerae O1 and V . cholerae non-O1/O139 strains. A total of 47 V . cholerae O1 and 29 V . cholerae non-O1/O139 isolates from patients and the environment were sequenced. Comparative genome analyses of the 76 genomes and eight reference strains of V . cholerae isolated in concurrent epidemics outside Haiti and 27 V . cholerae genomes available in the public database demonstrated substantial diversity of V. cholerae and ongoing flux within its genome.

A fundamental question in biology is whether the network of interactions that regulate gene expression can be modeled by existing mathematical techniques. Studies of the ability to predict a gene's state based on the states of other genes suggest that it may be possible to abstract sufficient information to build models of the system that retain steady-state behavioral characteristics of the real system. This study tests this possibility by: (i) constructing a finite state homogeneous Markov chain model using a small set of interesting genes; (ii) estimating the model parameters based on the observed experimental data; (iii) exploring the dynamics of this small genetic regulatory network by analyzing its steady-state (long-run) behavior and comparing the resulting model behavior to the observed behavior of the original system. The data used in this study are from a survey of melanoma where predictive relationships (coefficient of determination, CoD) between 587 genes from 31 samples were examined. Ten genes with strong interactive connectivity were chosen to formulate a finite state Markov chain on the basis of their role as drivers in the acquisition of an invasive phenotype in melanoma cells. Simulations with different perturbation probabilities and different iteration times were run. Following convergence of the chain to steady-state behavior, millions of samples of the results of further transitions were collected to estimate the steady-state distribution of network. In these samples, only a limited number of states possessed significant probability of occurrence. This behavior is nicely congruent with biological behavior, as cells appear to occupy only a negligible portion of the state space available to them. The model produced both some of the exact state vectors observed in the data, and also a number of state vectors that were near neighbors of the state vectors from the original data. By combining these similar states, a good representation of the observed states in the original data could be achieved. From this study, we find that, in this limited context, Markov chain simulation emulates well the dynamic behavior of a small regulatory network.

No abstract

Human saliva is clinically informative of both oral and general health. Since next generation shotgun sequencing (NGS) is now widely used to identify and quantify bacteria, we investigated the bacterial flora of saliva microbiomes of two healthy volunteers and five datasets from the Human Microbiome Project, along with a control dataset containing short NGS reads from bacterial species representative of the bacterial flora of human saliva. GENIUS, a system designed to identify and quantify bacterial species using unassembled short NGS reads was used to identify the bacterial species comprising the microbiomes of the saliva samples and datasets. Results, achieved within minutes and at greater than 90% accuracy, showed more than 175 bacterial species comprised the bacterial flora of human saliva, including bacteria known to be commensal human flora but also Haemophilus influenzae, Neisseria meningitidis, Streptococcus pneumoniae, and Gamma proteobacteria. Basic Local Alignment Search Tool (BLASTn) analysis in parallel, reported ca. five times more species than those actually comprising the in silico sample. Both GENIUSand BLAST analyses of saliva samples identified major genera comprising the bacterial flora of saliva, but GENIUS provided a more precise description of species composition, identifying to strain in most cases and delivered results at least 10,000 times faster. Therefore, GENIUS offers a facile and accurate system for identification and quantification of bacterial species and/or strains in metagenomic samples.

The RNA-binding protein TIAR (related to TIA-1 [T-cell-restricted intracellular antigen 1]) was shown to associate with subsets of mRNAs bearing U-rich sequences in their 3 untranslated regions. TIAR can function as a translational repressor, particularly in response to cytotoxic agents. Using unstressed colon cancer cells, collections of mRNAs associated with TIAR were isolated by immunoprecipitation (IP) of (TIAR-RNA) ribonucleoprotein (RNP) complexes, identified by microarray analysis, and used to elucidate a common signature motif present among TIAR target transcripts. The predicted TIAR motif was an unexpectedly cytosine-rich, 28-to 32-nucleotide-long element forming a stem and a loop of variable size with an additional side loop. The ability of TIAR to bind an RNA oligonucleotide with a representative C-rich TIAR motif sequence was verified in vitro using surface plasmon resonance. By this analysis, TIAR containing two or three RNA recognition domains (TIAR12 and TIAR123) showed low but significant binding to the C-rich sequence. In vivo, insertion of the C-rich motif into a heterologous reporter strongly suppressed its translation in cultured cells. Using this signature motif, an additional ϳ2,209 UniGene targets were identified (2.0% of the total UniGene database). A subset of specific mRNAs were validated by RNP IP analysis. Interestingly, in response to treatment with short-wavelength UV light (UVC), a stress agent causing DNA damage, each of these target mRNAs bearing C-rich motifs dissociated from TIAR. In turn, expression of the encoded proteins was elevated in a TIAR-dependent manner. In sum, we report the identification of a C-rich signature motif present in TIAR target mRNAs whose association with TIAR decreases following exposure to a stress-causing agent.

The DDN MATLAB toolbox and experiment data are available at http://www.cbil.ece.vt.edu/software.htm.

Factors affecting gene expression in the brain

The transcriptional profiles of five regions of the central nervous system (CNS) of mice varying in age, gender and dietary intake were measured by microarray. The resulting data provide insights into the mechanisms of age-, diet-and gender-related CNS plasticity and vulnerability in mammals.

Temporal and tissue-specific alterations in gene expression have profound effects on aging of multicellular organisms. However, much remains unknown about the patterns of molecular changes in different tissues and how different tissues interact with each other during aging. Previous genomic studies on invertebrate aging mostly utilized the whole body or body parts and limited age-points, and failed to address tissue-specific aging. Here we measured genome-wide expression profiles of aging in Drosophila melanogaster for seven tissues representing nervous, muscular, digestive, renal, reproductive, and storage systems at six adult ages. In each tissue, we identified hundreds of age-related genes exhibiting significant changes of transcript levels with age. The age-related genes showed clear tissue-specific patterns: <10% of them in each tissue were in common with any other tissue; <20% of the biological processes enriched with the age-related genes were in common between any two tissues. A significant portion of the age-related genes were those involved in physiological functions regulated by the corresponding tissue. Nevertheless, we identified some overlaps of the age-related functional groups among tissues, suggesting certain common molecular mechanisms that regulate aging in different tissues. This study is one of the first that defined global, temporal, and spatial changes associated with aging from multiple tissues at multiple ages, showing that different tissues age in different patterns in an organism. The spatial and temporal transcriptome data presented in this study provide a basis and a valuable resource for further genetic and genomic investigation of tissue-specific regulation of aging.