A challenging task in the study of the secretory pathway is the identification and localization of new proteins to increase our understanding of the functions of different organelles. Previous proteomic studies of the endomembrane system have been hindered by contaminating proteins, making it impossible to assign proteins to organelles. Here we have used the localization of organelle proteins by the isotope tagging technique in conjunction with isotope tags for relative and absolute quantitation and 2D liquid chromatography for the simultaneous assignment of proteins to multiple subcellular compartments. With this approach, the density gradient distributions of 689 proteins from Arabidopsis thaliana were determined, enabling confident and simultaneous localization of 527 proteins to the endoplasmic reticulum, Golgi apparatus, vacuolar membrane, plasma membrane, or mitochondria and plastids. This parallel analysis of endomembrane components has enabled protein steady-state distributions to be determined. Consequently, genuine organelle residents have been distinguished from contaminating proteins and proteins in transit through the secretory pathway.endomembrane ͉ localization of organelle proteins by isotope tagging ͉ isotope tags for relative and absolute quantitation ͉ organelle proteomics P roteins are spatially organized according to their functions within the eukaryotic cell. Therefore, protein localization is an important step toward assigning functions to the thousands of uncharacterized proteins predicted by the genome-sequencing projects. Proteomics provides powerful tools for characterizing the protein contents of organelles. Confident protein localization, however, requires that either organelle preparations are free of contaminants or that techniques are used to discriminate between genuine organelle residents and contaminating proteins (1). Although reasonably pure preparations of some organelles, such as mitochondria, can be achieved, the components of the endomembrane system so far have proved recalcitrant to purification (2, 3). The constituent organelles of the endomembrane system have similar sizes and densities, making them difficult to separate. In addition, the proteins that reside within this system are in a constant state of flux. Endomembrane proteins traffic through the system en route to their final destination; for example, plasma membrane (PM) proteins travel although the endoplasmic reticulum (ER) and the Golgi apparatus before reaching the cell surface. Proteins within the endomembrane system also cycle between compartments; for example, ER residents continuously escape to the Golgi apparatus and are retrieved in COPI vesicles (4). Consequently, it is not sufficient merely to identify the proteins within a single organelle-enriched fraction. Instead, the steady-state distributions of proteins within the whole endomembrane system must be determined if a realistic insight into the subcellular localization of endomembrane proteins is to be achieved.Localization of organelle proteins by...

The goal of this group is to define the reporting requirements associated with the statistical analysis (including univariate, multivariate, informatics, machine learning etc.) of metabolite data with respect to other measured/collected experimental data (often called metadata). These definitions will embrace as many aspects of a complete metabolomics study as possible at this time. In chronological order this will include: Experimental Design, both in terms of sample collection/matching, and data acquisition scheduling of samples through whichever spectroscopic technology used; Deconvolution (if required); Pre-processing, for example, data cleaning, outlier detection, row/column scaling, or other transformations; Definition and parameterization of subsequent

We have profiled, for the first time, an evolving human metastatic microenvironment, measuring gene expression, matrisome proteomics, cytokine and chemokine levels, cellularity, ECM organization and biomechanical properties, all on the same sample. Using biopsies of high-grade serous ovarian cancer (HGSOC) metastases that ranged from minimal to extensive disease, we show how non-malignant cell densities and cytokine networks evolve with disease progression. Multivariate integration of the different components allowed us to define for the first time, gene and protein profiles that predict extent of disease and tissue stiffness, whilst also revealing the complexity and dynamic nature of matrisome remodeling during development of metastases. Although we studied a single metastatic site from one human malignancy, a pattern of expression of 22 matrisome genes distinguished patients with a shorter overall survival in ovarian and twelve other primary solid cancers, suggesting that there may be a common matrix response to human cancer.

Metals are needed by at least one-quarter of all proteins. Although metallochaperones insert the correct metal into some proteins, they have not been found for the vast majority, and the view is that most metalloproteins acquire their metals directly from cellular pools. However, some metals form more stable complexes with proteins than do others. For instance, as described in the Irving-Williams series, Cu(2+) and Zn(2+) typically form more stable complexes than Mn(2+). Thus it is unclear what cellular mechanisms manage metal acquisition by most nascent proteins. To investigate this question, we identified the most abundant Cu(2+)-protein, CucA (Cu(2+)-cupin A), and the most abundant Mn(2+)-protein, MncA (Mn(2+)-cupin A), in the periplasm of the cyanobacterium Synechocystis PCC 6803. Each of these newly identified proteins binds its respective metal via identical ligands within a cupin fold. Consistent with the Irving-Williams series, MncA only binds Mn(2+) after folding in solutions containing at least a 10(4) times molar excess of Mn(2+) over Cu(2+) or Zn(2+). However once MncA has bound Mn(2+), the metal does not exchange with Cu(2+). MncA and CucA have signal peptides for different export pathways into the periplasm, Tat and Sec respectively. Export by the Tat pathway allows MncA to fold in the cytoplasm, which contains only tightly bound copper or Zn(2+) (refs 10-12) but micromolar Mn(2+) (ref. 13). In contrast, CucA folds in the periplasm to acquire Cu(2+). These results reveal a mechanism whereby the compartment in which a protein folds overrides its binding preference to control its metal content. They explain why the cytoplasm must contain only tightly bound and buffered copper and Zn(2+).

Background: iTRAQ™ technology for protein quantitation using mass spectrometry is a recent, powerful means of determining relative protein levels in up to four samples simultaneously. Although protein identification of samples generated using iTRAQ may be carried out using any current identification software, the quantitation calculations have been restricted to the ProQuant software supplied by Applied Biosciences. i-Tracker software has been developed to extract reporter ion peak ratios from non-centroided tandem MS peak lists in a format easily linked to the results of protein identification tools such as Mascot and Sequest. Such functionality is currently not provided by ProQuant, which is restricted to matching quantitative information to the peptide identifications from Applied Biosciences' Interrogator™ software.

Identification of proteins by tandem mass spectrometry requires a database of the proteins that could be in the sample. This is available for model species (e.g. humans) but not for non-model species. Ideally, for a non-model species the sequencing of expressed mRNA would generate a protein database for mass spectrometry based identification, allowing detection of genes and proteins using high throughput sequencing and protein identification technologies. Here we use human cells infected with human adenovirus as a complex and dynamic model to demonstrate this approach is robust. Our Proteomics Informed by Transcriptomics technique identifies >99% of over 3700 distinct proteins identified using traditional analysis reliant on comprehensive human and adenovirus protein lists. This facilitates high throughput acquisition of direct evidence for transcripts and proteins in non-model species. Critically, we show this approach can also be used to highlight genes and proteins undergoing dynamic changes in post transcriptional protein stability.

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Ziehl-Neelsen (ZN) staining for the diagnosis of tuberculosis (TB) is time-consuming and operator dependent and lacks sensitivity. A new method is urgently needed. We investigated the potential of an electronic nose (EN) (gas sensor array) comprising 14 conducting polymers to detect different Mycobacterium spp. and Pseudomonas aeruginosa in the headspaces of cultures, spiked sputa, and sputum samples from 330 cultureproven and human immunodeficiency virus-tested TB and non-TB patients. The data were analyzed using principal-component analysis, discriminant function analysis, and artificial neural networks. The EN differentiated between different Mycobacterium spp. and between mycobacteria and other lung pathogens both in culture and in spiked sputum samples. The detection limit in culture and spiked sputa was found to be 1 ؋ 10 4 mycobacteria ml ؊1 . After training of the neural network with 196 sputum samples, 134 samples (55 M. tuberculosis culture-positive samples and 79 culture-negative samples) were used to challenge the model. The EN correctly predicted 89% of culture-positive patients; the six false negatives were the four ZN-negative and two ZN-positive patients. The specificity and sensitivity of the described method were 91% and 89%, respectively, compared to culture. At present, the reasons for the false negatives and false positives are unknown, but they could well be due to the nonoptimized system used here. This study has shown the ability of an electronic nose to detect M. tuberculosis in clinical specimens and opens the way to making this method a rapid and automated system for the early diagnosis of respiratory infections.