Proteomics of Human Dendritic Cell Subsets Reveals Subset-Specific Surface Markers and Differential Inflammasome Function.

Dendritic cells (DCs) play a key role in orchestrating adaptive immune responses. In human blood, three distinct subsets exist: plasmacytoid DCs (pDCs) and BDCA3+ and CD1c+ myeloid DCs. In addition, a DC-like CD16+ monocyte has been reported. Although RNA-expression profiles have been previously compared, protein expression data may provide a different picture. Here, we exploited label-free quantitative mass spectrometry to compare and identify differences in primary human DC subset proteins. Moreover, we integrated these proteomic data with existing mRNA data to derive robust cell-specific expression signatures with more than 400 differentially expressed proteins between subsets, forming a solid basis for investigation of subset-specific functions. We illustrated this by extracting subset identification markers and by demonstrating that pDCs lack caspase-1 and only express low levels of other inflammasome-related proteins. In accordance, pDCs were incapable of interleukin (IL)-1β secretion in response to ATP

RAPID: Resource of Asian Primary Immunodeficiency Diseases

Availability of a freely accessible, dynamic and integrated database for primary immunodeficiency diseases (PID) is important both for researchers as well as clinicians. To build a PID informational platform and also as a part of action to initiate a network of PID research in Asia, we have constructed a web-based compendium of molecular alterations in PID, named Resource of Asian Primary Immunodeficiency Diseases (RAPID), which is available as a worldwide web resource at http://rapid.rcai.riken.jp/. It hosts information on sequence variations and expression at the mRNA and protein levels of all genes reported to be involved in PID patients. The main objective of this database is to provide detailed information pertaining to genes and proteins involved in primary immunodeficiency diseases along with other relevant information about protein–protein interactions, mouse studies and microarray gene-expression profiles in various organs and cells of the immune system. RAPID also hosts a tool, mutation viewer, to predict deleterious and novel mutations and also to obtain mutation-based 3D structures for PID genes. Thus, information contained in this database should help physicians and other biomedical investigators to further investigate the role of these molecules in PID

Mutation@A Glance: An Integrative Web Application for Analysing Mutations from Human Genetic Diseases

Although mutation analysis serves as a key part in making a definitive diagnosis about a genetic disease, it still remains a time-consuming step to interpret their biological implications through integration of various lines of archived information about genes in question. To expedite this evaluation step of disease-causing genetic variations, here we developed Mutation@A Glance (http://rapid.rcai.riken.jp/mutation/), a highly integrated web-based analysis tool for analysing human disease mutations; it implements a user-friendly graphical interface to visualize about 40 000 known disease-associated mutations and genetic polymorphisms from more than 2600 protein-coding human disease-causing genes. Mutation@A Glance locates already known genetic variation data individually on the nucleotide and the amino acid sequences and makes it possible to cross-reference them with tertiary and/or quaternary protein structures and various functional features associated with specific amino acid residues in the proteins. We showed that the disease-associated missense mutations had a stronger tendency to reside in positions relevant to the structure/function of proteins than neutral genetic variations. From a practical viewpoint, Mutation@A Glance could certainly function as a ‘one-stop’ analysis platform for newly determined DNA sequences, which enables us to readily identify and evaluate new genetic variations by integrating multiple lines of information about the disease-causing candidate genes

Human Protein Reference Database—2009 update

Human Protein Reference Database (HPRD—http://www.hprd.org/), initially described in 2003, is a database of curated proteomic information pertaining to human proteins. We have recently added a number of new features in HPRD. These include PhosphoMotif Finder, which allows users to find the presence of over 320 experimentally verified phosphorylation motifs in proteins of interest. Another new feature is a protein distributed annotation system—Human Proteinpedia (http://www.humanproteinpedia.org/)—through which laboratories can submit their data, which is mapped onto protein entries in HPRD. Over 75 laboratories involved in proteomics research have already participated in this effort by submitting data for over 15 000 human proteins. The submitted data includes mass spectrometry and protein microarray-derived data, among other data types. Finally, HPRD is also linked to a compendium of human signaling pathways developed by our group, NetPath (http://www.netpath.org/), which currently contains annotations for several cancer and immune signaling pathways. Since the last update, more than 5500 new protein sequences have been added, making HPRD a comprehensive resource for studying the human proteome

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein–protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes

Extracellular vesicles secreted by Saccharomyces cerevisiae are involved in cell wall remodelling

Extracellular vesicles (EVs) are membranous vesicles that are released by cells. In this study, the role of the Endosomal Sorting Complex Required for Transport (ESCRT) machinery in the biogenesis of yeast EVs was examined. Knockout of components of the ESCRT machinery altered the morphology and size of EVs as well as decreased the abundance of EVs. In contrast, strains with deletions in cell wall biosynthesis genes, produced more EVs than wildtype. Proteomic analysis highlighted the depletion of ESCRT components and enrichment of cell wall remodelling enzymes, glucan synthase subunit Fks1 and chitin synthase Chs3, in yeast EVs. Interestingly, EVs containing Fks1 and Chs3 rescued the yeast cells from antifungal molecules. However, EVs from fks1∆ or chs3∆ or the vps23∆chs3∆ double knockout strain were unable to rescue the yeast cells as compared to vps23∆ EVs. Overall, we have identified a potential role for yeast EVs in cell wall remodelling.Kening Zhao, Mark Bleackley, David Chisanga, Lahiru Gangoda, Pamali Fonseka, Michael Liem, Hina Kalra, Haidar Al Saffar, Shivakumar Keerthikumar, Ching-Seng Ang, Christopher G. Adda, Lanzhou Jiang, Kuok Yap, Ivan K. Poon, Peter Lock, Vincent Bulone, Marilyn Anderson, Suresh Mathivana

The impact of circulating preeclampsia-associated extracellular vesicles on the migratory activity and phenotype of THP-1 monocytic cells

Intercellular communication via extracellular vesicles (EVs) and their target cells, especially immune cells, results in functional and phenotype changes that consequently may play a significant role in various physiological states and the pathogenesis of immune-mediated disorders. Monocytes are the most prominent environment-sensing immune cells in circulation, skilled to shape their microenvironments via cytokine secretion and further differentiation. Both the circulating monocyte subset distribution and the blood plasma EV pattern are characteristic for preeclampsia, a pregnancy induced immune-mediated hypertensive disorder. We hypothesized that preeclampsia-associated EVs (PE-EVs) induced functional and phenotypic alterations of monocytes. First, we proved EV binding and uptake by THP-1 cells. Cellular origin and protein cargo of circulating PE-EVs were characterized by flow cytometry and mass spectrometry. An altered phagocytosis-associated molecular pattern was found on 12.5 K fraction of PE-EVs: an elevated CD47 "don't eat me" signal (p < 0.01) and decreased exofacial phosphatidylserine "eat-me" signal (p < 0.001) were found along with decreased uptake of these PE-EVs (p < 0.05). The 12.5 K fraction of PE-EVs induced significantly lower chemotaxis (p < 0.01) and cell motility but accelerated cell adhesion of THP-1 cells (p < 0.05). The 12.5 K fraction of PE-EVs induced altered monocyte functions suggest that circulating EVs may have a role in the pathogenesis of preeclampsia

svdPPCS: an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification

<p>Abstract</p> <p>Background</p> <p>Comparative analysis of gene expression profiling of multiple biological categories, such as different species of organisms or different kinds of tissue, promises to enhance the fundamental understanding of the universality as well as the specialization of mechanisms and related biological themes. Grouping genes with a similar expression pattern or exhibiting co-expression together is a starting point in understanding and analyzing gene expression data. In recent literature, gene module level analysis is advocated in order to understand biological network design and system behaviors in disease and life processes; however, practical difficulties often lie in the implementation of existing methods.</p> <p>Results</p> <p>Using the singular value decomposition (SVD) technique, we developed a new computational tool, named svdPPCS (<b>SVD</b>-based <b>P</b>attern <b>P</b>airing and <b>C</b>hart <b>S</b>plitting), to identify conserved and divergent co-expression modules of two sets of microarray experiments. In the proposed methods, gene modules are identified by splitting the two-way chart coordinated with a pair of left singular vectors factorized from the gene expression matrices of the two biological categories. Importantly, the cutoffs are determined by a data-driven algorithm using the well-defined statistic, SVD-p. The implementation was illustrated on two time series microarray data sets generated from the samples of accessory gland (ACG) and malpighian tubule (MT) tissues of the line W¹¹⁸of <it>M. drosophila</it>. Two conserved modules and six divergent modules, each of which has a unique characteristic profile across tissue kinds and aging processes, were identified. The number of genes contained in these models ranged from five to a few hundred. Three to over a hundred GO terms were over-represented in individual modules with FDR < 0.1. One divergent module suggested the tissue-specific relationship between the expressions of mitochondrion-related genes and the aging process. This finding, together with others, may be of biological significance. The validity of the proposed SVD-based method was further verified by a simulation study, as well as the comparisons with regression analysis and cubic spline regression analysis plus PAM based clustering.</p> <p>Conclusions</p> <p>svdPPCS is a novel computational tool for the comparative analysis of transcriptional profiling. It especially fits the comparison of time series data of related organisms or different tissues of the same organism under equivalent or similar experimental conditions. The general scheme can be directly extended to the comparisons of multiple data sets. It also can be applied to the integration of data sets from different platforms and of different sources.</p

NetPath: a public resource of curated signal transduction pathways

NetPath, a novel community resource of curated human signaling pathways is presented and its utility demonstrated using immune signaling data