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Multiplexed Component Analysis to Identify Genes Contributing to the Immune Response during Acute SIV Infection.

ABSTRACT: Immune response genes play an important role during acute HIV and SIV infection. Using an SIV macaque model of AIDS and CNS disease, our overall goal was to assess how the expression of genes associated with immune and inflammatory responses are longitudinally changed in different organs or cells during SIV infection. To compare RNA expression of a panel of 88 immune-related genes across time points and among three tissues - spleen, mesenteric lymph nodes (MLN) and peripheral blood mononuclear cells (PBMC) - we designed a set of Nanostring probes. To identify significant genes during acute SIV infection and to investigate whether these genes are tissue-specific or have global roles, we introduce a novel multiplexed component analysis (MCA) method. This combines multivariate analysis methods with multiple preprocessing methods to create a set of 12 "judges"; each judge emphasizes particular types of change in gene expression to which cells could respond, for example, the absolute or relative size of expression change from baseline. Compared to bivariate analysis methods, our MCA method improved classification rates. This analysis allows us to identify three categories of genes: (a) consensus genes likely to contribute highly to the immune response; (b) genes that would contribute highly to the immune response only if certain assumptions are met - e.g. that the cell responds to relative expression change rather than absolute expression change; and (c) genes whose contribution to immune response appears to be modest. We then compared the results across the three tissues of interest; some genes are consistently highly-contributing in all tissues, while others are specific for certain tissues. Our analysis identified CCL8, CXCL10, CXCL11, MxA, OAS2, and OAS1 as top contributing genes, all of which are stimulated by type I interferon. This suggests that the cytokine storm during acute SIV infection is a systemic innate immune response against viral replication. Furthermore, these genes have approximately equal contributions to all tissues, making them possible candidates to be used as non-invasive biomarkers in studying PBMCs instead of MLN and spleen during acute SIV infection experiments. We identified clusters of genes that co-vary together and studied their correlation with regard to other gene clusters. We also developed novel methods to faithfully visualize multi-gene correlations on two-dimensional polar plots, and to visualize tissue specificity of gene expression responses.

SUBMITTER: Hosseini I

PROVIDER: S-EPMC4436129 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Multiplexed Component Analysis to Identify Genes Contributing to the Immune Response during Acute SIV Infection.

Hosseini Iraj I Gama Lucio L Mac Gabhann Feilim F

PloS one 20150518 5

Immune response genes play an important role during acute HIV and SIV infection. Using an SIV macaque model of AIDS and CNS disease, our overall goal was to assess how the expression of genes associated with immune and inflammatory responses are longitudinally changed in different organs or cells during SIV infection. To compare RNA expression of a panel of 88 immune-related genes across time points and among three tissues - spleen, mesenteric lymph nodes (MLN) and peripheral blood mononuclear c ...[more]

PMID: 25984721

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Project description:Expression profiling by NanoString nCounter gene expression system Inflammatory response genes play an important role during acute HIV and SIV infection. Using an SIV macaque model for AIDS and CNS disease, we measured mRNA expression of 92 genes before and at multiple time points during acute SIV infection in three tissues: Peripheral blood mononuclear cells, mesenteric lymph nodes (MLN) and peripheral blood mononuclear cells (PBMC). Studying the overall changes of mRNA expressions over time or analyzing the correlation between the gene measurements and SIV RNA in plasma can result in limited interpretations. This is due to several reasons including but not limited to: 1) lack of prior information on how cells react to changes in gene expressions; and 2) biological responses typically involve many genes working together. To approach this problem, we combine multiple preprocessing methods with two multivariate analysis methods, namely principal component analysis and partial least square regression, to create a multiplexed set of 12 “judges”. Each of the judges simultaneously observes all the variables and emphasizes a unique type of gene expression response that could be significant, for example, depending on whether the cell responds to the absolute or relative size of gene expression changes. By incorporating multiple potential biological models of response, it is possible to identify genes that are consistently ranked as high “contributing” genes in different scenarios, i.e., genes that have a higher weight when we classify the data based on different classification schemes. We then use statistical analysis to verify that the consistently high-ranking genes are also statistically significant. We also investigate whether genes are tissue-specific, identify clusters of genes that co-vary together and study their correlation with regard to other gene clusters. The multiplex component analysis method introduced in this paper is a powerful tool to analyze complex gene datasets, identify significant genes, and generate testable hypotheses. In this study we inoculated 20 pig-tailed macaques with two SIV strains (SIV/17E-Fr and the swarm SIV/DeltaB670), and mock-inoculated 4 animals for control. Infected animals were sacrificed at 4 time points, as following: 6 animals sacrificed at 4 days, 6 at 7 days, 6 at 14 days and 2 at 21 days post-infection. Animals were perfused before necropsy to decrease blood contamination in the tissues. Organs were then culled, and RNA was extraction for NanosString analysis using a set of 92 probes specifically generated for macaque genes. Data were then analyzed by multiple preprocessing methods with two multivariate analysis methods. The following files contains raw data for the listed samples, respectively; GAMA Nanostring RAW MLN.xlsx - all MLN samples GAMA Nanostring RAW PBMC.xlsx - all PBMC samples GAMA Nanostring RAW Spleen 1.xls - PT01-04SP, PT71-74SP, PT141-144SP GAMA Nanostring RAW Spleen 2.xls - PT41-44SP, PT75-76SP, PT145-146SP, PT211-212SP GAMA Nanostring RAW Spleen 3.xls - PT45-46SP Please note that, in the associated publication, we describe a multiplexed component analysis to identify genes that highly contribute to inflammation during SIV acute infection. This analysis was done by compiling results generated by different types of data transformations and data normalizations (more than 12 for each tissue) and the relevant algorithms/mathematical processes will be discussed in detail. Therefore, only raw data is presented in this record (i.e. each sample data table contains raw data).

Dataset Information

Multiplexed Component Analysis to Identify Genes Contributing to the Immune Response during Acute SIV Infection.

Publications

Multiplexed Component Analysis to Identify Genes Contributing to the Immune Response during Acute SIV Infection.

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