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      Identification of Common Biological Pathways and Drug Targets Across Multiple Respiratory Viruses Based on Human Host Gene Expression Analysis


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          Pandemic and seasonal respiratory viruses are a major global health concern. Given the genetic diversity of respiratory viruses and the emergence of drug resistant strains, the targeted disruption of human host-virus interactions is a potential therapeutic strategy for treating multi-viral infections. The availability of large-scale genomic datasets focused on host-pathogen interactions can be used to discover novel drug targets as well as potential opportunities for drug repositioning.


          In this study, we performed a large-scale analysis of microarray datasets involving host response to infections by influenza A virus, respiratory syncytial virus, rhinovirus, SARS-coronavirus, metapneumonia virus, coxsackievirus and cytomegalovirus. Common genes and pathways were found through a rigorous, iterative analysis pipeline where relevant host mRNA expression datasets were identified, analyzed for quality and gene differential expression, then mapped to pathways for enrichment analysis. Possible repurposed drugs targets were found through database and literature searches. A total of 67 common biological pathways were identified among the seven different respiratory viruses analyzed, representing fifteen laboratories, nine different cell types, and seven different array platforms. A large overlap in the general immune response was observed among the top twenty of these 67 pathways, adding validation to our analysis strategy. Of the top five pathways, we found 53 differentially expressed genes affected by at least five of the seven viruses. We suggest five new therapeutic indications for existing small molecules or biological agents targeting proteins encoded by the genes F3, IL1B, TNF, CASP1 and MMP9. Pathway enrichment analysis also identified a potential novel host response, the Parkin-Ubiquitin Proteasomal System (Parkin-UPS) pathway, which is known to be involved in the progression of neurodegenerative Parkinson's disease.


          Our study suggests that multiple and diverse respiratory viruses invoke several common host response pathways. Further analysis of these pathways suggests potential opportunities for therapeutic intervention.

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          Most cited references116

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          Missing value estimation methods for DNA microarrays.

          Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data. We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1--20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions.
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            Highly pathogenic H5N1 influenza virus can enter the central nervous system and induce neuroinflammation and neurodegeneration.

            One of the greatest influenza pandemic threats at this time is posed by the highly pathogenic H5N1 avian influenza viruses. To date, 61% of the 433 known human cases of H5N1 infection have proved fatal. Animals infected by H5N1 viruses have demonstrated acute neurological signs ranging from mild encephalitis to motor disturbances to coma. However, no studies have examined the longer-term neurologic consequences of H5N1 infection among surviving hosts. Using the C57BL/6J mouse, a mouse strain that can be infected by the A/Vietnam/1203/04 H5N1 virus without adaptation, we show that this virus travels from the peripheral nervous system into the CNS to higher levels of the neuroaxis. In regions infected by H5N1 virus, we observe activation of microglia and alpha-synuclein phosphorylation and aggregation that persists long after resolution of the infection. We also observe a significant loss of dopaminergic neurons in the substantia nigra pars compacta 60 days after infection. Our results suggest that a pandemic H5N1 pathogen, or other neurotropic influenza virus, could initiate CNS disorders of protein aggregation including Parkinson's and Alzheimer's diseases.
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              Global functional profiling of gene expression.

              The typical result of a microarray experiment is a list of tens or hundreds of genes found to be differentially regulated in the condition under study. Independent of the methods used to select these genes, the common task faced by any researcher is to translate these lists of genes into a better understanding of the biological phenomena involved. Currently, this is done through a tedious combination of searches through the literature and a number of public databases. We developed Onto-Express (OE) as a novel tool able to automatically translate such lists of differentially regulated genes into functional profiles characterizing the impact of the condition studied. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function, and chromosome location. Statistical significance values are calculated for each category. We demonstrate the validity and the utility of this comprehensive global analysis of gene function by analyzing two breast cancer datasets from two separate laboratories. OE was able to identify correctly all biological processes postulated by the original authors, as well as discover novel relevant mechanisms.

                Author and article information

                Role: Editor
                PLoS One
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                14 March 2012
                : 7
                : 3
                : e33174
                [1 ]Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
                [2 ]Computational Biology, Quantitative Sciences, GlaxoSmithKline, King of Prussia, Pennsylvania, United States of America
                [3 ]Center for Integrated Bioinformatics, Drexel University, Philadelphia, Pennsylvania, United States of America
                [4 ]Computational Biology, Quantitative Sciences, GlaxoSmithKline, Collegeville, Pennsylvania, United States of America
                Faculty of Biochemistry Biophysics and Biotechnology, Jagiellonian University, Poland
                Author notes

                Conceived and designed the experiments: SS MM-S JB. Performed the experiments: SS. Analyzed the data: SS WD AT. Wrote the paper: SS MM-S WD AT JB.

                Smith 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.
                : 17 October 2011
                : 8 February 2012
                Page count
                Pages: 15
                Research Article
                Computational Biology
                Drugs and Devices
                Drug Research and Development
                Infectious Diseases
                Viral Diseases



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