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Presentation Discussion

  • Icon for: Teresa Wang

    Teresa Wang

    May 21, 2012 | 04:51 p.m.

    Hi everyone! Thanks for visiting this entry. My team and I are excited to share our research findings with you. Don’t hesitate to ask questions about this project, or about bioinformatics in general!

  • Icon for: Joseph Gerrein

    Joseph Gerrein

    May 22, 2012 | 11:21 a.m.

    Great presentation! How did you pick out differentially expressed genes using the time course data, as in what kind of statistical test did you use? Also, how did you do the pathway enrichment?



  • Icon for: Teresa Wang

    Teresa Wang

    May 23, 2012 | 03:08 p.m.

    Hi Joe! Thanks for your interest. We analyzed the custom-array time course data using several different techniques, one of which incorporates a linear model to adjust for changes in gene expression with time. One major challenge analysis for this project was that most non-human primates were not consistently sampled over time (sometimes due to complications with drawing blood, other times because they were more susceptible to the lethal dose of infection than others). Pathway enrichment was investigated using the Thus, aside from using a statistical test to filter for genes, we created a filter such that each probe had to have a fold change of plus/minus three in at least two arrays relative to baseline expression levels.
    For pathway enrichment, we integrated our results with tools offered by the Database for Annotation, Visualization and Integrated Discovery (DAVID) and Kyoto Encyclopedia of Genes and Genomes (KEGG).

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Icon for: Teresa Wang


Boston University
Years in Grad School: 2

Bioinformatics for Biodefense: Comparison of Global Transcriptional Host Responses across Non-Human Primates Infected with Anthrax, Poxviruses, and Filoviruses

The World Health Organization recognizes Anthrax, Monkeypox, Smallpox, Ebola, and Marburg as high-risk infectious agents. In addition, the United States Centers for Disease Control has characterized four out of five of these agents as Category A bio-threats. These pathogens are characterized by mortality rates that range between ten and ninety percent. Furthermore, while all of these pathogens are known to trigger an unbalanced immune response in the host, there has been no analysis comparing how the global peripheral immune response varies with each type of infection. To address this question, we examined the host responses infected non-human primates. Utilizing custom microarray technology, we investigated the regulation of 18,000 genes through relative host mRNA levels throughout the course of each infection. A computational pipeline was developed to process each sample, comparing post-infection gene expression levels to a their pre-infection baseline. Genes whose expression varied significantly from baseline were selected for functional enrichment and pathway analysis. Comparing these results allowed us to elucidate commonalities and differences between the five infection mechanisms. For instance, our analysis confirms the modulation of particular immune response pathways. Specifically, we were able to identify the interferon responsive STAT1 gene and the Toll-Like Receptor Pathway as prime conserved components that are altered in across all infections. This work represents a comparative genomic expression analysis of the temporal pathogen-host responses in the context of five separate in vivo primate infection studies. Our results will guide future experiments that provide insight into disease pathogenesis and potential therapeutic targets.