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The microsporidian Nosema bombycis is an obligate intracellular pathogen of the silkworm Bombyx mori, causing the epidemic disease Pebrine and extensive economic losses in sericulture. Although N. bombycis forms spores with rigid spore walls that protect against various environmental pressures, ingested spores germinate immediately under the extremely alkaline host gut condition (Lepidoptera gut pH > 10.5), which is a key developmental turning point from dormant state to infected state. However, to date this process remains poorly understood due to the complexity of the animal digestive tract and the lack of genetic tools for microsporidia. Here we show, using an in vitro spore germination model, how the proteome of N. bombycis changes during germination, analyse specific metabolic pathways employed in detail, and validate key functional proteins in vivo in silkworms. By a label-free quantitative proteomics approach that is directly based on high-resolution mass spectrometry (MS) data, a total of 1136 proteins were identified with high confidence, with 127 proteins being significantly changed in comparison to non-germinated spores. Among them, structural proteins including polar tube protein 1 and 3 and spore wall protein (SWP) 4 and 30 were found to be significantly down-regulated, but SWP9 significantly up-regulated. Some nucleases like polynucleotide kinase/phosphatase and flap endonucleases 1, together with a panel of hydrolases involved in protein degradation and RNA cleavage were overrepresented too upon germination, which implied that they might play important roles during spore germination. The differentially regulated trends of these genes were validated, respectively, by quantitative RT-PCR and 3 proteins of interest were confirmed by Western blotting analyses in vitro and in vivo. Furthermore, the pathway analysis showed that abundant up- and down-regulations appear involved in the glycolysis, pentose phosphate pathway, purine, and pyrimidine metabolism, suggesting preparations of energy generation and substance synthesis for the following invasion and proliferation inside the host. This report, to our knowledge, provides the first proteomic landscape of N. bombycis spores, and also a stepping stone on the way to further study of the unique infection mode of this economically important pathogen and other microsporidia in general. [ABSTRACT FROM AUTHOR]

Background: Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. Results: In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs) and Support Vector Machines (SVMs) were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. Conclusions: A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression. [ABSTRACT FROM AUTHOR]