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      Large-scale profiling of signalling pathways reveals an asthma specific signature in bronchial smooth muscle cells

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          Abstract

          Background

          Bronchial smooth muscle (BSM) cells from asthmatic patients maintain in vitro a distinct hyper-reactive (“primed”) phenotype, characterized by increased release of pro-inflammatory factors and mediators, as well as hyperplasia and/or hypertrophy. This “primed” phenotype helps to understand pathogenesis of asthma, as changes in BSM function are essential for manifestation of allergic and inflammatory responses and airway wall remodelling.

          Objective

          To identify signalling pathways in cultured primary BSMs of asthma patients and non-asthmatic subjects by genome wide profiling of differentially expressed mRNAs and activated intracellular signalling pathways (ISPs).

          Methods

          Transcriptome profiling by cap-analysis-of-gene-expression (CAGE), which permits selection of preferentially capped mRNAs most likely to be translated into proteins, was performed in human BSM cells from asthmatic (n=8) and non-asthmatic (n=6) subjects and OncoFinder tool were then exploited for identification of ISP deregulations.

          Results

          CAGE revealed >600 RNAs differentially expressed in asthma vs control cells (p≤0.005), with asthma samples showing a high degree of similarity among them. Comprehensive ISP activation analysis revealed that among 269 pathways analysed, 145 (p<0.05) or 103 (p<0.01) are differentially active in asthma, with profiles that clearly characterize BSM cells of asthmatic individuals. Notably, we identified 7 clusters of coherently acting pathways functionally related to the disease, with ISPs down-regulated in asthma mostly targeting cell death-promoting pathways and up-regulated ones affecting cell growth and proliferation, inflammatory response, control of smooth muscle contraction and hypoxia-related signalization.

          Conclusions

          These first-time results can now be exploited toward development of novel therapeutic strategies targeting ISP signatures linked to asthma pathophysiology.

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          Most cited references38

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          MADE4: an R package for multivariate analysis of gene expression data.

          MADE4, microarray ade4, is a software package that facilitates multivariate analysis of microarray gene-expression data. MADE4 accepts a wide variety of gene-expression data formats. MADE4 takes advantage of the extensive multivariate statistical and graphical functions in the R package ade4, extending these for application to microarray data. In addition, MADE4 provides new graphical and visualization tools that aid in interpretation of multivariate analysis of microarray data.
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            Pathway-based personalized analysis of cancer.

            We introduce Pathifier, an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. We demonstrate the algorithm's performance on three colorectal cancer datasets and two glioblastoma multiforme datasets and show that our multipathway-based representation is reproducible, preserves much of the original information, and allows inference of complex biologically significant information. We discovered several pathways that were significantly associated with survival of glioblastoma patients and two whose scores are predictive of survival in colorectal cancer: CXCR3-mediated signaling and oxidative phosphorylation. We also identified a subclass of proneural and neural glioblastoma with significantly better survival, and an EGF receptor-deregulated subclass of colon cancers.
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              5' end-centered expression profiling using cap-analysis gene expression and next-generation sequencing.

              Cap-analysis gene expression (CAGE) provides accurate high-throughput measurement of RNA expression. CAGE allows mapping of all the initiation sites of both capped coding and noncoding RNAs. In addition, transcriptional start sites within promoters are characterized at single-nucleotide resolution. The latter allows the regulatory inputs driving gene expression to be studied, which in turn enables the construction of transcriptional networks. Here we provide an optimized protocol for the construction of CAGE libraries on the basis of the preparation of 27-nt-long tags corresponding to initial bases at the 5' ends of capped RNAs. We have optimized the methods using simple steps based on filtration, which altogether takes 4 d to complete. The CAGE tags can be readily sequenced with Illumina sequencers, and upon modification they are also amenable to sequencing using other platforms.
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                Author and article information

                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                3 May 2016
                5 February 2016
                : 7
                : 18
                : 25150-25161
                Affiliations
                1 Laboratory of Molecular Medicine and Genomics, Department of Medicine and Surgery, University of Salerno, Baronissi (SA), Italy
                2 Genomix4Life Srl, Campus of Medicine, University of Salerno, Baronissi (SA), Italy
                3 Laboratory of Bioinformatics, D. Rogachyov Federal Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
                4 Pathway Pharmaceuticals, Wan Chai, Hong Kong, Hong Kong SAR
                5 Group for Genomic Regulation of Cell Signalling Systems, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
                6 Insilico Medicine, Inc, ETC, Johns Hopkins University, Baltimore, MD, USA
                7 Department of Biomedicine, University Hospital Basel, Basel, Switzerland
                8 Institute of Biomedical Technologies, National Research Council, Segregate, (MI), Italy
                9 Molecular Pathology and Medical Genomics Unit, ‘SS. Giovanni di Dio e Ruggi d'Aragona - Schola Medica Salernitana’ University Hospital, Salerno, (SA), Italy
                Author notes
                Correspondence to: Pieter Borger, pieter.borger@ 123456unibas.ch
                Alessandro Weisz, aweisz@ 123456unisa.it
                Article
                7209
                10.18632/oncotarget.7209
                5039037
                26863634
                4b586f38-2fb5-4932-9601-c1f6679927db
                Copyright: © 2016 Alexandrova et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 7 December 2015
                : 26 January 2016
                Categories
                Research Paper

                Oncology & Radiotherapy
                asthma,smooth muscle cells,signalling pathways,cage
                Oncology & Radiotherapy
                asthma, smooth muscle cells, signalling pathways, cage

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