Proteomic characterization of the dynamic KSR-2 interactome, a signaling scaffold complex in MAPK pathway.

KSR-1 is a scaffold protein that is essential for Ras-induced activation of the highly conserved RAF-MEK-ERK kinase module. Previously, we identified a close homolog of KSR-1, called KSR-2, through structural homology-based data mining. In order to further understand the role of KSR-2 in MAPK signaling, we undertook a functional proteomics approach to elucidate the dynamic composition of the KSR-2 functional complex in HEK-293 cells under conditions with and without TNF-alpha stimulation. We found nearly 100 proteins that were potentially associated with KSR-2 complex and 43 proteins that were likely recruited to the super molecular complex after TNF-alpha treatment. Our results indicate that KSR-2 may act as a scaffold protein similar as KSR-1 to mediate the MAPK core (RAF-MEK-ERK) signaling but with a distinct RAF isoform specificity, namely KSR-2 may only mediate the A-RAF signaling while KSR-1 is responsible for transducing signals only from c-RAF. In addition, KSR-2 may be involved in the activation of many MAPK downstream signaling molecules such as p38 MAPK, IKAP, AIF, and proteins involved in ubiquitin-proteasome, apoptosis, cell cycle control, and DNA synthesis and repair pathways, as well as mediating crosstalks between MAPK and several other signaling pathways, including PI3K and insulin signaling. While interactions with these molecules are not known for KSR-1, it's reasonable to hypothesize that KSR-1 may also play a similar role in mediating these downstream signaling pathways.

Fluopack screening platform for unbiased cellular phenotype profiling.

Gene and compound functions are often interrogated by perturbation. However, we have limited methods to capture associated phenotypes in an unbiased and holistic manner. Here, we describe Fluopack screening as a novel platform enabling the profiling of subcellular phenotypes associated with perturbation. Our approach leverages imaging of a panel of fluorescent chemical probes to survey cellular processes in an unbiased and high throughput fashion. Segmentation-free, whole image analysis applied to Fluopack images identifies probes revealing distinct phenotypes upon perturbation, thereby informing on the function and mechanism of action of perturbagens. This chemical biology approach allows to interrogate phenotypes that tend to be overlooked by other methods, such as lipid trafficking and ion concentration inside the cell. Fluopack screening is a powerful approach to study orphan protein function, as exemplified by the characterization of TMEM41B as novel regulator of lipid mobilization.

A Scalable Pipeline for High-Throughput Flow Cytometry.

Flow cytometry (FC) provides high-content data for a variety of applications, including phenotypic analysis of cell surface and intracellular markers, characterization of cell supernatant or lysates, and gene expression analysis. Historically, sample preparation, acquisition, and analysis have presented as a bottleneck for running such types of assays at scale. This article will outline the solutions that have been implemented at Novartis which have allowed high-throughput FC to be successfully conducted and analyzed for a variety of cell-based assays. While these experiments were generally conducted to measure phenotypic responses from a well-characterized and information-rich small molecular probe library known as the Mechanism-of-Action (MoA) Box, they are broadly applicable to any type of test sample. The article focuses on application of automated methods for FC sample preparation in 384-well assay plates. It also highlights a pipeline for analyzing large volumes of FC data, covering a visualization approach that facilitates review of screen-level data by dynamically embedding FlowJo (FJ) workspace images for each sample into a Spotfire file, directly linking them to the metric being observed. Finally, an application of these methods to a screen for MHC-I expression upregulators is discussed.

Jenkins-CI, an Open-Source Continuous Integration System, as a Scientific Data and Image-Processing Platform.

High-throughput screening generates large volumes of heterogeneous data that require a diverse set of computational tools for management, processing, and analysis. Building integrated, scalable, and robust computational workflows for such applications is challenging but highly valuable. Scientific data integration and pipelining facilitate standardized data processing, collaboration, and reuse of best practices. We describe how Jenkins-CI, an "off-the-shelf," open-source, continuous integration system, is used to build pipelines for processing images and associated data from high-content screening (HCS). Jenkins-CI provides numerous plugins for standard compute tasks, and its design allows the quick integration of external scientific applications. Using Jenkins-CI, we integrated CellProfiler, an open-source image-processing platform, with various HCS utilities and a high-performance Linux cluster. The platform is web-accessible, facilitates access and sharing of high-performance compute resources, and automates previously cumbersome data and image-processing tasks. Imaging pipelines developed using the desktop CellProfiler client can be managed and shared through a centralized Jenkins-CI repository. Pipelines and managed data are annotated to facilitate collaboration and reuse. Limitations with Jenkins-CI (primarily around the user interface) were addressed through the selection of helper plugins from the Jenkins-CI community.

Recent advances in quantitative high throughput and high content data analysis.

INTRODUCTION: High throughput screening has become a basic technique with which to explore biological systems. Advances in technology, including increased screening capacity, as well as methods that generate multiparametric readouts, are driving the need for improvements in the analysis of data sets derived from such screens. AREAS COVERED: This article covers the recent advances in the analysis of high throughput screening data sets from arrayed samples, as well as the recent advances in the analysis of cell-by-cell data sets derived from image or flow cytometry application. Screening multiple genomic reagents targeting any given gene creates additional challenges and so methods that prioritize individual gene targets have been developed. The article reviews many of the open source data analysis methods that are now available and which are helping to define a consensus on the best practices to use when analyzing screening data. EXPERT OPINION: As data sets become larger, and more complex, the need for easily accessible data analysis tools will continue to grow. The presentation of such complex data sets, to facilitate quality control monitoring and interpretation of the results will require the development of novel visualizations. In addition, advanced statistical and machine learning algorithms that can help identify patterns, correlations and the best features in massive data sets will be required. The ease of use for these tools will be important, as they will need to be used iteratively by laboratory scientists to improve the outcomes of complex analyses.

Informatic tools for proteome profiling.

In recent years, the practice of proteomics research has experienced a dramatic shift within the pharmaceutical and biotechnology industry with the widespread implementation of novel applications. The areas of interest extend all the way from discovery of novel drug, vaccine, and diagnostic targets, characterization of protein-based products, toxicology, and identification of surrogate markers of activity in clinical research, to the ability to provide information on the mechanisms of drug action. The power of two-dimensional gel electrophoresis as well as advances in mass spectrometric techniques combined with sequence database correlation have enabled speed and accuracy in identification of proteins in complex mixtures. This article surveys currently available software and informatic tools related to these methods for proteome profiling. The broad acceptance of these technologies, however, has not been accompanied by significant advances in the informatics and software tools necessary to support the analysis and management of the massive amounts of data generated in the process. In this context, this article also discusses the importance of relational databases for protein identification data management.

The multidimensional perturbation value: a single metric to measure similarity and activity of treatments in high-throughput multidimensional screens.

Screens using high-throughput, information-rich technologies such as microarrays, high-content screening (HCS), and next-generation sequencing (NGS) have become increasingly widespread. Compared with single-readout assays, these methods produce a more comprehensive picture of the effects of screened treatments. However, interpreting such multidimensional readouts is challenging. Univariate statistics such as t-tests and Z-factors cannot easily be applied to multidimensional profiles, leaving no obvious way to answer common screening questions such as "Is treatment X active in this assay?" and "Is treatment X different from (or equivalent to) treatment Y?" We have developed a simple, straightforward metric, the multidimensional perturbation value (mp-value), which can be used to answer these questions. Here, we demonstrate application of the mp-value to three data sets: a multiplexed gene expression screen of compounds and genomic reagents, a microarray-based gene expression screen of compounds, and an HCS compound screen. In all data sets, active treatments were successfully identified using the mp-value, and simulations and follow-up analyses supported the mp-value's statistical and biological validity. We believe the mp-value represents a promising way to simplify the analysis of multidimensional data while taking full advantage of its richness.

Proteomic analysis and identification of Streptococcus pyogenes surface-associated proteins.

Streptococcus pyogenes is a gram-positive human pathogen that causes a wide spectrum of disease, placing a significant burden on public health. Bacterial surface-associated proteins play crucial roles in host-pathogen interactions and pathogenesis and are important targets for the immune system. The identification of these proteins for vaccine development is an important goal of bacterial proteomics. Here we describe a method of proteolytic digestion of surface-exposed proteins to identify surface antigens of S. pyogenes. Peptides generated by trypsin digestion were analyzed by multidimensional tandem mass spectrometry. This approach allowed the identification of 79 proteins on the bacterial surface, including 14 proteins containing cell wall-anchoring motifs, 12 lipoproteins, 9 secreted proteins, 22 membrane-associated proteins, 1 bacteriophage-associated protein, and 21 proteins commonly identified as cytoplasmic. Thirty-three of these proteins have not been previously identified as cell surface associated in S. pyogenes. Several proteins were expressed in Escherichia coli, and the purified proteins were used to generate specific mouse antisera for use in a whole-cell enzyme-linked immunosorbent assay. The immunoreactivity of specific antisera to some of these antigens confirmed their surface localization. The data reported here will provide guidance in the development of a novel vaccine to prevent infections caused by S. pyogenes.

Systemically administered rhBMP-2 promotes MSC activity and reverses bone and cartilage loss in osteopenic mice.

Osteoporosis is a disease manifested in drastic bone loss resulting in osteopenia and high risk for fractures. This disease is generally divided into two subtypes. The first, post-menopausal (type I) osteoporosis, is primarily related to estrogen deficiency. The second, senile (type II) osteoporosis, is mostly related to aging. Decreased bone formation, as well as increased bone resorption and turnover, are thought to play roles in the pathophysiology of both types of osteoporosis. In this study, we demonstrate in murine models for both type I (estrogen deficiency) and type II (senile) osteopenia/osteoporosis that reduced bone formation is related to a decrease in adult mesenchymal stem cell (AMSC) number, osteogenic activity, and proliferation. Decreased proliferation is coupled with increased apoptosis in AMSC cultures obtained from osteopenic mice. Recombinant human bone morphogenetic protein (rhBMP-2) is a highly osteoinductive protein, promoting osteogenic differentiation of AMSCs. Systemic intra-peritoneal (i.p.) injections of rhBMP-2 into osteopenic mice were able to reverse this phenotype in the bones of these animals. Moreover, this change in bone mass was coupled to an increase in AMSCs numbers, osteogenic activity, and proliferation as well as a decrease in apoptosis. Bone formation activity was increased as well. However, the magnitude of this response to rhBMP-2 varied among different stains of mice. In old osteopenic BALB/c male mice (type II osteoporosis model), rhBMP-2 systemic treatment also restored both articular and epiphyseal cartilage width to the levels seen in young mice. In summary, our study shows that AMSCs are a good target for systemically active anabolic compounds like rhBMP-2.