Inferring the Sign of Kinase-Substrate Interactions by Combining Quantitative Phosphoproteomics with a Literature-Based Mammalian Kinome Network

Protein phosphorylation is a reversible post-translational modification commonly used by cell signaling networks to transmit information about the extracellular environment into intracellular organelles for the regulation of the activity and sorting of proteins within the cell. For this study we reconstructed a literature-based mammalian kinase-substrate network from several online resources. The interactions within this directed graph network connect kinases to their substrates, through specific phosphosites including kinase-kinase regulatory interactions. However, the "signs" of links, activation or inhibition of the substrate upon phosphorylation, within this network are mostly unknown. Here we show how we can infer the "signs" indirectly using data from quantitative phosphoproteomics experiments applied to mammalian cells combined with the literature-based kinase-substrate network. Our inference method was able to predict the sign for 321 links and 153 phosphosites on 120 kinases, resulting in signed and directed subnetwork of mammalian kinase-kinase interactions. Such an approach can rapidly advance the reconstruction of cell signaling pathways and networks regulating mammalian cells.Comment: 5 page, 3 figures, IEEE-BIBE confrenc

Genes2WordCloud: a quick way to identify biological themes from gene lists and free text

<p>Abstract</p> <p>Background</p> <p>Word-clouds recently emerged on the web as a solution for quickly summarizing text by maximizing the display of most relevant terms about a specific topic in the minimum amount of space. As biologists are faced with the daunting amount of new research data commonly presented in textual formats, word-clouds can be used to summarize and represent biological and/or biomedical content for various applications.</p> <p>Results</p> <p>Genes2WordCloud is a web application that enables users to quickly identify biological themes from gene lists and research relevant text by constructing and displaying word-clouds. It provides users with several different options and ideas for the sources that can be used to generate a word-cloud. Different options for rendering and coloring the word-clouds give users the flexibility to quickly generate customized word-clouds of their choice.</p> <p>Methods</p> <p>Genes2WordCloud is a word-cloud generator and a word-cloud viewer that is based on WordCram implemented using Java, Processing, AJAX, mySQL, and PHP. Text is fetched from several sources and then processed to extract the most relevant terms with their computed weights based on word frequencies. Genes2WordCloud is freely available for use online; it is open source software and is available for installation on any web-site along with supporting documentation at <url>http://www.maayanlab.net/G2W</url>.</p> <p>Conclusions</p> <p>Genes2WordCloud provides a useful way to summarize and visualize large amounts of textual biological data or to find biological themes from several different sources. The open source availability of the software enables users to implement customized word-clouds on their own web-sites and desktop applications.</p

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases

<p>Abstract</p> <p>Background</p> <p>In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP), generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes.</p> <p>Results</p> <p>Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list.</p> <p>Conclusion</p> <p>Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.</p

Chronic Morphine Alters the Presynaptic Protein Profile: Identification of Novel Molecular Targets Using Proteomics and Network Analysis

Opiates produce significant and persistent changes in synaptic transmission; knowledge of the proteins involved in these changes may help to understand the molecular mechanisms underlying opiate dependence. Using an integrated quantitative proteomics and systems biology approach, we explored changes in the presynaptic protein profile following a paradigm of chronic morphine administration that leads to the development of dependence. For this, we isolated presynaptic fractions from the striata of rats treated with saline or escalating doses of morphine, and analyzed the proteins in these fractions using differential isotopic labeling. We identified 30 proteins that were significantly altered by morphine and integrated them into a protein-protein interaction (PPI) network representing potential morphine-regulated protein complexes. Graph theory-based analysis of this network revealed clusters of densely connected and functionally related morphine-regulated clusters of proteins. One of the clusters contained molecular chaperones thought to be involved in regulation of neurotransmission. Within this cluster, cysteine-string protein (CSP) and the heat shock protein Hsc70 were downregulated by morphine. Interestingly, Hsp90, a heat shock protein that normally interacts with CSP and Hsc70, was upregulated by morphine. Moreover, treatment with the selective Hsp90 inhibitor, geldanamycin, decreased the somatic signs of naloxone-precipitated morphine withdrawal, suggesting that Hsp90 upregulation at the presynapse plays a role in the expression of morphine dependence. Thus, integration of proteomics, network analysis, and behavioral studies has provided a greater understanding of morphine-induced alterations in synaptic composition, and identified a potential novel therapeutic target for opiate dependence

SNAVI: Desktop application for analysis and visualization of large-scale signaling networks

<p>Abstract</p> <p>Background</p> <p>Studies of cellular signaling indicate that signal transduction pathways combine to form large networks of interactions. Viewing protein-protein and ligand-protein interactions as graphs (networks), where biomolecules are represented as nodes and their interactions are represented as links, is a promising approach for integrating experimental results from different sources to achieve a systematic understanding of the molecular mechanisms driving cell phenotype. The emergence of large-scale signaling networks provides an opportunity for topological statistical analysis while visualization of such networks represents a challenge.</p> <p>Results</p> <p>SNAVI is Windows-based desktop application that implements standard network analysis methods to compute the clustering, connectivity distribution, and detection of network motifs, as well as provides means to visualize networks and network motifs. SNAVI is capable of generating linked web pages from network datasets loaded in text format. SNAVI can also create networks from lists of gene or protein names.</p> <p>Conclusion</p> <p>SNAVI is a useful tool for analyzing, visualizing and sharing cell signaling data. SNAVI is open source free software. The installation may be downloaded from: <url>http://snavi.googlecode.com</url>. The source code can be accessed from: <url>http://snavi.googlecode.com/svn/trunk</url></p

Temporal proteomic profiling of postnatal human cortical development.

Healthy cortical development depends on precise regulation of transcription and translation. However, the dynamics of how proteins are expressed, function and interact across postnatal human cortical development remain poorly understood. We surveyed the proteomic landscape of 69 dorsolateral prefrontal cortex samples across seven stages of postnatal life and integrated these data with paired transcriptome data. We detected 911 proteins by liquid chromatography-mass spectrometry, and 83 were significantly associated with postnatal age (FDR < 5%). Network analysis identified three modules of co-regulated proteins correlated with age, including two modules with increasing expression involved in gliogenesis and NADH metabolism and one neurogenesis-related module with decreasing expression throughout development. Integration with paired transcriptome data revealed that these age-related protein modules overlapped with RNA modules and displayed collinear developmental trajectories. Importantly, RNA expression profiles that are dynamically regulated throughout cortical development display tighter correlations with their respective translated protein expression compared to those RNA profiles that are not. Moreover, the correspondence between RNA and protein expression significantly decreases as a function of cortical aging, especially for genes involved in myelination and cytoskeleton organization. Finally, we used this data resource to elucidate the functional impact of genetic risk loci for intellectual disability, converging on gliogenesis, myelination and ATP-metabolism modules in the proteome and transcriptome. We share all data in an interactive, searchable companion website. Collectively, our findings reveal dynamic aspects of protein regulation and provide new insights into brain development, maturation, and disease