Yeast membrane proteomics using leucine metabolic labelling: Bioinformatic data processing and exemplary application to the ER-intramembrane protease Ypf1.

We describe in detail the usage of leucine metabolic labelling in yeast in order to monitor quantitative proteome alterations, e.g. upon removal of a protease. Since laboratory yeast strains are typically leucine auxotroph, metabolic labelling with trideuterated leucine (d3-leucine) is a straightforward, cost-effective, and ubiquitously applicable strategy for quantitative proteomic studies, similar to the widely used arginine/lysine metabolic labelling method for mammalian cells. We showcase the usage of advanced peptide quantification using the FeatureFinderMultiplex algorithm (part of the OpenMS software package) for robust and reliable quantification. Furthermore, we present an OpenMS bioinformatics data analysis workflow that combines accurate quantification with high proteome coverage. In order to enable visualization, peptide-mapping, and sharing of quantitative proteomic data, especially for membrane-spanning and cell-surface proteins, we further developed the web-application Proteator (http://proteator.appspot.com). Due to its simplicity and robustness, we expect metabolic leucine labelling in yeast to be of great interest to the research community. As an exemplary application, we show the identification of the copper transporter Ctr1 as a putative substrate of the ER-intramembrane protease Ypf1 by yeast membrane proteomics using d3-leucine isotopic labelling.

Disseminated Tumor Cells Persist in the Bone Marrow of Breast Cancer Patients through Sustained Activation of the Unfolded Protein Response.

Disseminated tumor cells (DTC), which share mesenchymal and epithelial properties, are considered to be metastasis-initiating cells in breast cancer. However, the mechanisms supporting DTC survival are poorly understood. DTC extravasation into the bone marrow may be encouraged by low oxygen concentrations that trigger metabolic and molecular alterations contributing to DTC survival. Here, we investigated how the unfolded protein response (UPR), an important cytoprotective program induced by hypoxia, affects the behavior of stressed cancer cells. DTC cell lines established from the bone marrow of patients with breast cancer (BC-M1), lung cancer, (LC-M1), and prostate cancer (PC-E1) were subjected to hypoxic and hypoglycemic conditions. BC-M1 and LC-M1 exhibiting mesenchymal and epithelial properties adapted readily to hypoxia and glucose starvation. Upregulation of UPR proteins, such as the glucose-regulated protein Grp78, induced the formation of filamentous networks, resulting in proliferative advantages and sustained survival under total glucose deprivation. High Grp78 expression correlated with mesenchymal attributes of breast and lung cancer cells and with poor differentiation in clinical samples of primary breast and lung carcinomas. In DTCs isolated from bone marrow specimens from breast cancer patients, Grp78-positive stress granules were observed, consistent with the likelihood these cells were exposed to acute cell stress. Overall, our findings provide the first evidence that the UPR is activated in DTC in the bone marrow from cancer patients, warranting further study of this cell stress pathway as a predictive biomarker for recurrent metastatic disease.

Toward improved peptide feature detection in quantitative proteomics using stable isotope labeling.

Reliable detection of peptides in LC-MS data is a key algorithmic step in the analysis of quantitative proteomics experiments. While highly abundant peptides can be detected reliably by most modern software tools, there is much less agreement on medium and low-intensity peptides in a sample. The choice of software tools can have a big impact on the quantification of proteins, especially for proteins that appear in lower concentrations. However, in many experiments, it is precisely this region of less abundant but substantially regulated proteins that holds the biggest potential for discoveries. This is particularly true for discovery proteomics in the pharmacological sector with a specific interest in key regulatory proteins. In this viewpoint article, we discuss how the development of novel software algorithms allows us to study this region of the proteome with increased confidence. Reliable results are one of many aspects to be considered when deciding on a bioinformatics software platform. Deployment into existing IT infrastructures, compatibility with other software packages, scalability, automation, flexibility, and support need to be considered and are briefly addressed in this viewpoint article.