Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification

<p>Abstract</p> <p>Background</p> <p>Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of identification and annotation of bimodal genes in the human and mouse genomes. These switch-like genes consist of 15% of known human genes, and are enriched with genes coding for extracellular and membrane proteins. It is of interest to determine the prediction potential of bimodal genes for class discovery in large-scale datasets.</p> <p>Results</p> <p>Use of a model-based clustering algorithm accurately classified more than 400 microarray samples into 19 different tissue types on the basis of bimodal gene expression. Bimodal expression patterns were also highly effective in differentiating between infectious diseases in model-based clustering of microarray data. Supervised classification with feature selection restricted to switch-like genes also recognized tissue specific and infectious disease specific signatures in independent test datasets reserved for validation. Determination of "on" and "off" states of switch-like genes in various tissues and diseases allowed for the identification of activated/deactivated pathways. Activated switch-like genes in neural, skeletal muscle and cardiac muscle tissue tend to have tissue-specific roles. A majority of activated genes in infectious disease are involved in processes related to the immune response.</p> <p>Conclusion</p> <p>Switch-like bimodal gene sets capture genome-wide signatures from microarray data in health and infectious disease. A subset of bimodal genes coding for extracellular and membrane proteins are associated with tissue specificity, indicating a potential role for them as biomarkers provided that expression is altered in the onset of disease. Furthermore, we provide evidence that bimodal genes are involved in temporally and spatially active mechanisms including tissue-specific functions and response of the immune system to invading pathogens.</p

Fibroblast-specific expression of AC6 enhances β-adrenergic and prostacyclin signaling and blunts bleomycin-induced pulmonary fibrosis

Pulmonary fibroblasts regulate extracellular matrix production and degradation and are critical in maintenance of lung structure, function, and repair, but they also play a central role in lung fibrosis. cAMP-elevating agents inhibit cytokine- and growth factor-stimulated myofibroblast differentiation and collagen synthesis in pulmonary fibroblasts. In the present study, we overexpressed adenylyl cyclase 6 (AC6) in pulmonary fibroblasts and measured cAMP production and collagen synthesis. AC6 overexpression enhanced cAMP production and the inhibition of collagen synthesis mediated by isoproterenol and beraprost, but not the responses to butaprost or PGE2. To examine if increased AC6 expression would impact the development of fibrosis in an animal model, we generated transgenic mice that overexpress AC6 under a fibroblast-specific promoter, FTS1. Lung fibrosis was induced in FTS1-AC6+/− mice and littermate controls by intratracheal instillation of saline or bleomycin. Wild-type mice treated with bleomycin showed extensive peribronchial and interstitial fibrosis and collagen deposition. By contrast, FTS1-AC6+/− mice displayed decreased fibrotic development, lymphocyte infiltration (as determined by pathological scoring), and lung collagen content. Thus, AC6 overexpression inhibits fibrogenesis in the lung by reducing pulmonary fibroblast-mediated collagen synthesis and myofibroblast differentiation. Because AC6 overexpression does not lead to enhanced basal or PGE2-stimulated levels of cAMP, we conclude that endogenous catecholamines or prostacyclin is produced during bleomycin-induced lung fibrosis and that these signals have antifibrotic potential

Microbial planktonic communities in the Red Sea: high levels of spatial and temporal variability shaped by nutrient availability and turbulence

The semi-enclosed nature of the Red Sea (20.2 degrees N-38.5 degrees N) makes it a natural laboratory to study the influence of environmental gradients on microbial communities. This study investigates the composition and structure of microbial prokaryotes and eukaryotes using molecular methods, targeting ribosomal RNA genes across different regions and seasons. The interaction between spatial and temporal scales results in different scenarios of turbulence and nutrient conditions allowing for testing of ecological theory that categorizes the response of the plankton community to these variations. The prokaryotic reads are mainly comprised of Cyanobacteria and Proteobacteria (Alpha and Gamma), with eukaryotic reads dominated by Dinophyceae and Syndiniophyceae. Periodic increases in the proportion of Mamiellophyceae and Bacillariophyceae reads were associated with alterations in the physical oceanography leading to nutrient increases either through the influx of Gulf of Aden Intermediate Water (south in the fall) or through water column mixing processes (north in the spring). We observed that in general dissimilarity amongst microbial communities increased when nutrient concentrations were higher, whereas richness (observed OTUs) was higher in scenarios of higher turbulence. Maximum abundance models showed the differential responses of dominant taxa to temperature giving an indication how taxa will respond as waters become warmer and more oligotrophic