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      Genomic responses in mouse models greatly mimic human inflammatory diseases

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      Proceedings of the National Academy of Sciences

      Proceedings of the National Academy of Sciences

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          Abstract

          The use of mice as animal models has long been considered essential in modern biomedical research, but the role of mouse models in research was challenged by a recent report that genomic responses in mouse models poorly mimic human inflammatory diseases. Here we reevaluated the same gene expression datasets used in the previous study by focusing on genes whose expression levels were significantly changed in both humans and mice. Contrary to the previous findings, the gene expression levels in the mouse models showed extraordinarily significant correlations with those of the human conditions (Spearman's rank correlation coefficient: 0.43-0.68; genes changed in the same direction: 77-93%; P = 6.5 × 10(-11) to 1.2 × 10(-35)). Moreover, meta-analysis of those datasets revealed a number of pathways/biogroups commonly regulated by multiple conditions in humans and mice. These findings demonstrate that gene expression patterns in mouse models closely recapitulate those in human inflammatory conditions and strongly argue for the utility of mice as animal models of human disorders.

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          Most cited references 21

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          The sepsis seesaw: tilting toward immunosuppression.

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            Is Open Access

            Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data

            Background The investigation of the interconnections between the molecular and genetic events that govern biological systems is essential if we are to understand the development of disease and design effective novel treatments. Microarray and next-generation sequencing technologies have the potential to provide this information. However, taking full advantage of these approaches requires that biological connections be made across large quantities of highly heterogeneous genomic datasets. Leveraging the increasingly huge quantities of genomic data in the public domain is fast becoming one of the key challenges in the research community today. Methodology/Results We have developed a novel data mining framework that enables researchers to use this growing collection of public high-throughput data to investigate any set of genes or proteins. The connectivity between molecular states across thousands of heterogeneous datasets from microarrays and other genomic platforms is determined through a combination of rank-based enrichment statistics, meta-analyses, and biomedical ontologies. We address data quality concerns through dataset replication and meta-analysis and ensure that the majority of the findings are derived using multiple lines of evidence. As an example of our strategy and the utility of this framework, we apply our data mining approach to explore the biology of brown fat within the context of the thousands of publicly available gene expression datasets. Conclusions Our work presents a practical strategy for organizing, mining, and correlating global collections of large-scale genomic data to explore normal and disease biology. Using a hypothesis-free approach, we demonstrate how a data-driven analysis across very large collections of genomic data can reveal novel discoveries and evidence to support existing hypothesis.
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              Genomic expression profiling across the pediatric systemic inflammatory response syndrome, sepsis, and septic shock spectrum.

              To advance our biological understanding of pediatric septic shock, we measured the genome-level expression profiles of critically ill children representing the systemic inflammatory response syndrome (SIRS), sepsis, and septic shock spectrum. Prospective observational study involving microarray-based bioinformatics. Multiple pediatric intensive care units in the United States. Children
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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                January 27 2015
                January 27 2015
                January 27 2015
                August 04 2014
                : 112
                : 4
                : 1167-1172
                Article
                10.1073/pnas.1401965111
                4313832
                25092317
                © 2014

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