Community-Driven Data Analysis Training for Biology.

The primary problem with the explosion of biomedical datasets is not the data, not computational resources, and not the required storage space, but the general lack of trained and skilled researchers to manipulate and analyze these data. Eliminating this problem requires development of comprehensive educational resources. Here we present a community-driven framework that enables modern, interactive teaching of data analytics in life sciences and facilitates the development of training materials. The key feature of our system is that it is not a static but a continuously improved collection of tutorials. By coupling tutorials with a web-based analysis framework, biomedical researchers can learn by performing computation themselves through a web browser without the need to install software or search for example datasets. Our ultimate goal is to expand the breadth of training materials to include fundamental statistical and data science topics and to precipitate a complete re-engineering of undergraduate and graduate curricula in life sciences. This project is accessible at https://training.galaxyproject.org.

Proteomic analysis of lung metastases in a murine breast cancer model reveals divergent influence of CTSB and CTSL overexpression.

Studies in the MMTV-PyMT (PyMT) breast cancer mouse model have shown a strong influence of the lysosomal cysteine cathepsins B or L on lung metastasis formation. Transgenic expression of human CTSB (tgCTSB) or CTSL (tgCTSL) both led to similar metastatic phenotypes with increased metastatic burden in the PyMT mice. However, recent studies in other tumor models proved marked differences in effects of either cathepsin on the proteome composition. We sought to analyze and compare proteome changes in the metastatic proteome of PyMT mice expressing either tgCTSB or tgCTSL to evaluate similarities and differences in those models. Performing an explorative, quantitative proteome comparison based on LC-MS/MS, we identified up to 3,000 proteins from murine lung metastases in three independent biological replicates per genotype. In both cases, when compared to wild-type (WT) mice, we noticed a pronounced impact of transgene cathepsin expression on the metastasis proteome. Highlights include increased moesin, integrin beta 1 and vinexin levels in the tgCTSB dataset and increased saposin and granulin levels in the tgCTSL dataset. Importantly, non-supervised hierarchical clustering clearly separated tgCTSB vs. tgCTSL induced proteome changes. In summary, tgCTSB and tgCTSL both display a strong and distinct impact on proteome composition of lung macrometastases in the PyMT model. Our observations suggest that they impact malignant behavior in distinct ways, thus further emphasizing interest into their tumor-contextual functionality.

Proteomic analysis of silenced cathepsin B expression suggests non-proteolytic cathepsin B functionality.

Cathepsin B (CTSB) is a lysosomal endo- and exopeptidase that is also secreted in high amounts by malignant and non-malignant cells. We determined the effect of CTSB on the tumor cell secretome by shRNA-mediated silencing of CTSB mRNA expression and subsequent proteomic LC-MS/MS analysis of the cell supernatants. We identified significant protein changes of 17 secreted or shed proteins. Notably, we found a general reduction in protein abundance of ADAM10 substrates and lysosomal proteins. We corroborated reduced amounts of soluble ADAM10 (sADAM10) and soluble APP (sAPP) in the two cancer cell lines MDA-MB-231 and U2OS by immunoblotting. Interestingly, reductions in sADAM10 and sAPP could be reversed by re-introducing a catalytically inactive variant of CTSB, suggesting a formerly unknown non-catalytic function of the protease.

miR-200c dampens cancer cell migration via regulation of protein kinase A subunits.

Expression of miR-200c is a molecular switch to determine cellular fate towards a mesenchymal or epithelial phenotype. miR-200c suppresses the early steps of tumor progression by preventing epithelial-mesenchymal transition (EMT) and intravasation of tumor cells. Unraveling the underlying molecular mechanisms might pinpoint to novel therapeutic options. To better understand these mechanisms it is crucial to identify targets of miR-200c. Here, we employ a combination of quantitative proteomic and bioinformatic strategies to identify novel miR-200c targets. We identify and confirm two subunits of the central cellular kinase protein kinase A (PKA), namely PRKAR1A and PRKACB, to be directly regulated by miR-200c. Notably, siRNA-mediated downregulation of both proteins phenocopies the migratory behavior of breast cancer cells after miR-200c overexpression. Patient data from publicly accessible databases supports a miR-200c-PKA axis. Thus, our study identifies the PKA heteroprotein as an important mediator of miR-200c induced repression of migration in breast cancer cells. By bioinformatics, we define a miRNA target cluster consisting of PRKAR1A, PRKAR2B, PRKACB, and COF2, which is targeted by a group of 14 miRNAs.

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.

Transmembrane collagen XVII modulates integrin dependent keratinocyte migration via PI3K/Rac1 signaling.

The hemidesmosomal transmembrane component collagen XVII (ColXVII) plays an important role in the anchorage of the epidermis to the underlying basement membrane. However, this adhesion protein seems to be also involved in the regulation of keratinocyte migration, since its expression in these cells is strongly elevated during reepithelialization of acute wounds and in the invasive front of squamous cell carcinoma, while its absence in ColXVII-deficient keratinocytes leads to altered cell motility. Using a genetic model of murine Col17a1⁻/⁻ keratinocytes we elucidated ColXVII mediated signaling pathways in cell adhesion and migration. Col17a1⁻/⁻ keratinocytes exhibited increased spreading on laminin 332 and accelerated, but less directed cell motility. These effects were accompanied by increased expression of the integrin subunits ß4 and ß1. The migratory phenotype, as evidenced by formation of multiple unstable lamellipodia, was associated with enhanced phosphoinositide 3-kinase (PI3K) activity. Dissection of the signaling pathway uncovered enhanced phosphorylation of the ß4 integrin subunit and the focal adhesion kinase (FAK) as activators of PI3K. This resulted in elevated Rac1 activity as a downstream consequence. These results provide mechanistic evidence that ColXVII coordinates keratinocyte adhesion and directed motility by interfering integrin dependent PI3K activation and by stabilizing lamellipodia at the leading edge of reepithelializing wounds and in invasive squamous cell carcinoma.