“Environmental proteomics, also referred to as metaproteom


“Environmental proteomics, also referred to as metaproteomics, is an emerging technology to study the structure and function of microbial communities. Here, we applied semi-quantitative label-free proteomics using one-dimensional gel electrophoresis combined with LC-MS/MS and normalized

spectral counting together with fluorescence in situ hybridization and confocal laser scanning microscopy to characterize the metaproteome of the lung lichen symbiosis Lobaria pulmonaria. In addition to the myco- BMS202 mouse and photobiont, L. pulmonaria harbors proteins from a highly diverse prokaryotic community, which is dominated by Proteobacteria and including also Archaea. While fungal proteins are most dominant (75.4% of all assigned spectra), about the same amount of spectra were assigned to prokaryotic proteins (10%)

and to the green algal photobiont (9%). While the latter proteins were found to be mainly associated with energy and carbohydrate metabolism, a major proportion of fungal and bacterial proteins appeared to be involved in PTMs and protein turnover and other diverse functions.”
“Due to the lack of precise markers indicative of its occurrence and progression, coronary artery disease (CAD), the most common type of heart diseases, is currently associated with high mortality in the United States. To systemically identify novel protein biomarkers associated with CAD progression for early diagnosis and possible therapeutic

intervention, we employed an iTRAQ-based quantitative proteomic approach to analyze the proteome changes in Selleck Gilteritinib the plasma collected from a pair Lck of wild-type versus apolipoprotein E knockout (APOE(-/-)) mice which were fed with a high fat diet. In a multiplex manner, iTRAQ serves as the quantitative ‘in-spectra’ marker for ‘cross-sample’ comparisons to determine the differentially expressed/secreted proteins caused by APOE knock-out. To obtain the most comprehensive proteomic data sets from this CAD-associated mouse model, we applied both MALDI and ESI-based mass spectrometric (MS) platforms coupled with two different schemes of multidimensional liquid chromatography (2-D LC) separation. We then comparatively analyzed a series of the plasma samples collected at 6 and 12wk of age after the mice were fed with fat diets, where the 6- or 12-wk time point represents the early or intermediate phase of the fat-induced CAD, respectively. We then categorized those proteins showing abundance changes in accordance with APOE depletion. Several proteins such as the gamma and beta chains of fibrinogen, apolipoprotein B, apolipoprotein C-I, and thrombospondin-4 were among the previously known CAD markers identified by other methods. Our results suggested that these unbiased proteomic methods are both feasible and a practical means of discovering potential biomarkers associated with CAD progression.

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