A Bayesian Network Driven Approach to Model the Transcriptional Response to Nitric Oxide in Saccharomyces cerevisiae

The transcriptional response to exogenously supplied nitric oxide in Saccharomyces cerevisiae was modeled using an integrated framework of Bayesian network learning and experimental feedback. A Bayesian network learning algorithm was used to generate network models of transcriptional output, followed by model verification and revision through experimentation. Using this framework, we generated a network model of the yeast transcriptional response to nitric oxide and a panel of other environmental signals. We discovered two environmental triggers, the diauxic shift and glucose repression, that affected the observed transcriptional profile. The computational method predicted the transcriptional control of yeast flavohemoglobin YHB1 by glucose repression, which was subsequently experimentally verified. A freely available software application, ExpressionNet, was developed to derive Bayesian network models from a combination of gene expression profile clusters, genetic information and experimental conditions

Histoplasma capsulatum proteome response to decreased iron availability

<p>Abstract</p> <p>Background</p> <p>A fundamental pathogenic feature of the fungus <it>Histoplasma capsulatum </it>is its ability to evade innate and adaptive immune defenses. Once ingested by macrophages the organism is faced with several hostile environmental conditions including iron limitation. <it>H. capsulatum </it>can establish a persistent state within the macrophage. A gap in knowledge exists because the identities and number of proteins regulated by the organism under host conditions has yet to be defined. Lack of such knowledge is an important problem because until these proteins are identified it is unlikely that they can be targeted as new and innovative treatment for histoplasmosis.</p> <p>Results</p> <p>To investigate the proteomic response by <it>H. capsulatum </it>to decreasing iron availability we have created <it>H. capsulatum </it>protein/genomic databases compatible with current mass spectrometric (MS) search engines. Databases were assembled from the <it>H. capsulatum </it>G217B strain genome using gene prediction programs and expressed sequence tag (EST) libraries. Searching these databases with MS data generated from two dimensional (2D) in-gel digestions of proteins resulted in over 50% more proteins identified compared to searching the publicly available fungal databases alone. Using 2D gel electrophoresis combined with statistical analysis we discovered 42 <it>H. capsulatum </it>proteins whose abundance was significantly modulated when iron concentrations were lowered. Altered proteins were identified by mass spectrometry and database searching to be involved in glycolysis, the tricarboxylic acid cycle, lysine metabolism, protein synthesis, and one protein sequence whose function was unknown.</p> <p>Conclusion</p> <p>We have created a bioinformatics platform for <it>H. capsulatum </it>and demonstrated the utility of a proteomic approach by identifying a shift in metabolism the organism utilizes to cope with the hostile conditions provided by the host. We have shown that enzyme transcripts regulated by other fungal pathogens in response to lowering iron availability are also regulated in <it>H. capsulatum </it>at the protein level. We also identified <it>H. capsulatum </it>proteins sensitive to iron level reductions which have yet to be connected to iron availability in other pathogens. These data also indicate the complexity of the response by <it>H. capsulatum </it>to nutritional deprivation. Finally, we demonstrate the importance of a strain specific gene/protein database for <it>H. capsulatum </it>proteomic analysis.</p