Myosin-Like Proteins in S. cerevisiae: Multifunctional, Structural Components of the Nuclear Envelope

The nuclear envelope (NE) separates the genetic material from the rest of the cell, delimits and defines the nucleus, organizes the intranuclear architecture and serves as a regulator for multiple nuclear processes. In all eukaryotes, filamentous coiled-coil proteins are associated with the intranuclear surface of the NE and are integral to proper nuclear function. One such protein, called Tpr in vertebrates, attaches to the NPC and appears to form the nuclear basket structure, is conserved throughout all eukaryotes. The two yeast homologs of Tpr are termed Mlp1p and Mlp2p. The Mlp proteins also attach to the nuclear face of the NPC and form a layer underneath the NE. For my thesis work I examined the structure and function of the Mlp proteins. A proteomic study of Mlp associated complexes revealed that the Mlp proteins interact predominantly with components of the NPC, the mRNA transport and processing machinery, and the spindle pole body (SPB; the yeast microtubule organizing center). Structural and microscopic analyses show that the Mlp proteins may form the nuclear basket in yeast, as well as interconnect NPCs into a network. Finally, a detailed functional study demonstrated that Mlp2p binds directly to the SPB and promotes the incorporation of components into the core structure of the SPB. The data presented in this thesis supports a model in which the Mlp proteins integrate the NPCs and the SPBs into a continuous structure at the nuclear periphery. This network supports the stability of the NE and, by its interaction with soluble factors, directly influences nuclear functions like SPB maintenance and mRNP metabolism

Rapid isolation of functionally intact nuclei from the yeast Saccharomyces [preprint]

Most available methods for nuclear isolation entail lengthy procedures that are difficult to master and generally emphasize yield and enrichment over nuclear preservation, thus limiting their utility for further studies. Here we demonstrate a novel and robust method to rapidly isolate well-preserved yeast nuclei. The method can be easily adapted to multiple preparation scales depending on experimental need and it can readily be performed on multiple samples by a single researcher in one day. We show that the nuclei fraction is strongly enriched and that the resulting nuclei are free from contaminating endoplasmic reticulum and other cell debris. EM studies show that preservation of nuclear morphology is exquisite, making it possible to study peripheral nuclear pore components such as the cytoplasmic filaments and the basket, whose structure is generally difficult to maintain ex vivo. In addition, incubation of isolated nuclei with bulk transport substrates of different sizes and with import cargo indicates that the nuclear envelope is intact and nuclear pores retain their capacity to bind transport substrates. Our results suggest that this preparation procedure will greatly facilitate studies of the yeast nucleus which have been difficult to establish and to multiplex to date

Analysis of growth factor signaling in genetically diverse breast cancer lines

Background: Soluble growth factors present in the microenvironment play a major role in tumor development, invasion, metastasis, and responsiveness to targeted therapies. While the biochemistry of growth factor-dependent signal transduction has been studied extensively in individual cell types, relatively little systematic data are available across genetically diverse cell lines. Results: We describe a quantitative and comparative dataset focused on immediate-early signaling that regulates the AKT (AKT1/2/3) and ERK (MAPK1/3) pathways in a canonical panel of well-characterized breast cancer lines. We also provide interactive web-based tools to facilitate follow-on analysis of the data. Our findings show that breast cancers are diverse with respect to ligand sensitivity and signaling biochemistry. Surprisingly, triple negative breast cancers (TNBCs; which express low levels of ErbB2, progesterone and estrogen receptors) are the most broadly responsive to growth factors and HER2amp cancers (which overexpress ErbB2) the least. The ratio of ERK to AKT activation varies with ligand and subtype, with a systematic bias in favor of ERK in hormone receptor positive (HR+) cells. The factors that correlate with growth factor responsiveness depend on whether fold-change or absolute activity is considered the key biological variable, and they differ between ERK and AKT pathways. Conclusions: Responses to growth factors are highly diverse across breast cancer cell lines, even within the same subtype. A simple four-part heuristic suggests that diversity arises from variation in receptor abundance, an ERK/AKT bias that depends on ligand identity, a set of factors common to all receptors that varies in abundance or activity with cell line, and an “indirect negative regulation” by ErbB2. This analysis sets the stage for the development of a mechanistic and predictive model of growth factor signaling in diverse cancer lines. Interactive tools for looking up these results and downloading raw data are available at http://lincs.hms.harvard.edu/niepel-bmcbiol-2014/

GRcalculator: an online tool for calculating and mining dose–response data

Background: Quantifying the response of cell lines to drugs or other perturbagens is the cornerstone of pre-clinical drug development and pharmacogenomics as well as a means to study factors that contribute to sensitivity and resistance. In dividing cells, traditional metrics derived from dose–response curves such as IC 50, AUC, and E max, are confounded by the number of cell divisions taking place during the assay, which varies widely for biological and experimental reasons. Hafner et al. (Nat Meth 13:521–627, 2016) recently proposed an alternative way to quantify drug response, normalized growth rate (GR) inhibition, that is robust to such confounders. Adoption of the GR method is expected to improve the reproducibility of dose–response assays and the reliability of pharmacogenomic associations (Hafner et al. 500–502, 2017). Results: We describe here an interactive website (www.grcalculator.org) for calculation, analysis, and visualization of dose–response data using the GR approach and for comparison of GR and traditional metrics. Data can be user-supplied or derived from published datasets. The web tools are implemented in the form of three integrated Shiny applications (grcalculator, grbrowser, and grtutorial) deployed through a Shiny server. Intuitive graphical user interfaces (GUIs) allow for interactive analysis and visualization of data. The Shiny applications make use of two R packages (shinyLi and GRmetrics) specifically developed for this purpose. The GRmetrics R package is also available via Bioconductor and can be used for offline data analysis and visualization. Source code for the Shiny applications and associated packages (shinyLi and GRmetrics) can be accessed at www.github.com/uc-bd2k/grcalculator and www.github.com/datarail/gr_metrics. Conclusions: GRcalculator is a powerful, user-friendly, and free tool to facilitate analysis of dose–response data. It generates publication-ready figures and provides a unified platform for investigators to analyze dose–response data across diverse cell types and perturbagens (including drugs, biological ligands, RNAi, etc.). GRcalculator also provides access to data collected by the NIH LINCS Program (http://www.lincsproject.org/) and other public domain datasets. The GRmetrics Bioconductor package provides computationally trained users with a platform for offline analysis of dose–response data and facilitates inclusion of GR metrics calculations within existing R analysis pipelines. These tools are therefore well suited to users in academia as well as industry. Electronic supplementary material The online version of this article (10.1186/s12885-017-3689-3) contains supplementary material, which is available to authorized users

LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites

Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling

More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program (http://www.lincsproject.org/) in which molecular data (reduced dimensionality transcript L1000 profiles) were recorded across dose and time in parallel with phenotypic data on cellular cytostasis and cytotoxicity. We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response

Characterization of Torin2, an ATP-Competitive Inhibitor of mTOR, ATM, and ATR

mTOR is a highly conserved serine/threonine protein kinase that serves as a central regulator of cell growth, survival, and autophagy. Deregulation of the PI3K/Akt/mTOR signaling pathway occurs commonly in cancer and numerous inhibitors targeting the ATP-binding site of these kinases are currently undergoing clinical evaluation. Here, we report the characterization of Torin2, a second-generation ATP-competitive inhibitor that is potent and selective for mTOR with a superior pharmacokinetic profile to previous inhibitors. Torin2 inhibited mTORC1-dependent T389 phosphorylation on S6K (RPS6KB1) with an EC[subscript 50] of 250 pmol/L with approximately 800-fold selectivity for cellular mTOR versus phosphoinositide 3-kinase (PI3K). Torin2 also exhibited potent biochemical and cellular activity against phosphatidylinositol-3 kinase–like kinase (PIKK) family kinases including ATM (EC[subscript 50], 28 nmol/L), ATR (EC[subscript 50], 35 nmol/L), and DNA-PK (EC[subscript 50], 118 nmol/L; PRKDC), the inhibition of which sensitized cells to Irradiation. Similar to the earlier generation compound Torin1 and in contrast to other reported mTOR inhibitors, Torin2 inhibited mTOR kinase and mTORC1 signaling activities in a sustained manner suggestive of a slow dissociation from the kinase. Cancer cell treatment with Torin2 for 24 hours resulted in a prolonged block in negative feedback and consequent T308 phosphorylation on Akt. These effects were associated with strong growth inhibition in vitro. Single-agent treatment with Torin2 in vivo did not yield significant efficacy against KRAS-driven lung tumors, but the combination of Torin2 with mitogen-activated protein/extracellular signal–regulated kinase (MEK) inhibitor AZD6244 yielded a significant growth inhibition. Taken together, our findings establish Torin2 as a strong candidate for clinical evaluation in a broad number of oncologic settings where mTOR signaling has a pathogenic role