Dataset Information

Microarray analysis reveals differential gene expression in hybrid sunflower species.

ABSTRACT: This paper describes the creation of a cDNA microarray for annual sunflowers and its use to elucidate patterns of gene expression in Helianthus annuus, Helianthus petiolaris, and the homoploid hybrid species Helianthus deserticola. The array comprises 3743 ESTs (expressed sequence tags) representing approximately 2897 unique genes. It has an average clone/EST identity rate of 91%, is applicable across species boundaries within the annual sunflowers, and shows patterns of gene expression that are highly reproducible according to real-time RT-PCR (reverse transcription-polymerase chain reaction) results. Overall, 12.8% of genes on the array showed statistically significant differential expression across the three species. Helianthus deserticola displayed transgressive, or extreme, expression for 58 genes, with roughly equal numbers exhibiting up- or down-regulation relative to both parental species. Transport-related proteins were strongly over-represented among the transgressively expressed genes, which makes functional sense given the extreme desert floor habitat of H. deserticola. The potential adaptive value of differential gene expression was evaluated for five genes in two populations of early generation (BC2) hybrids between the parental species grown in the H. deserticola habitat. One gene (a G protein-coupled receptor) had a significant association with fitness and maps close to a QTL controlling traits that may be adaptive in the desert habitat.

SUBMITTER: Lai Z

PROVIDER: S-EPMC2536761 | biostudies-literature | 2006 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Microarray analysis reveals differential gene expression in hybrid sunflower species.

Lai Zhao Z Gross Briana L BL Zou Yi Y Andrews Justen J Rieseberg Loren H LH

Molecular ecology 20060401 5

This paper describes the creation of a cDNA microarray for annual sunflowers and its use to elucidate patterns of gene expression in Helianthus annuus, Helianthus petiolaris, and the homoploid hybrid species Helianthus deserticola. The array comprises 3743 ESTs (expressed sequence tags) representing approximately 2897 unique genes. It has an average clone/EST identity rate of 91%, is applicable across species boundaries within the annual sunflowers, and shows patterns of gene expression that are ...[more]

PMID: 16626449

Similar Datasets

Project description:BACKGROUND: Identification of differentially expressed genes is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increasingly popular to solve this type of problems. These models show good performance in accommodating noise, variability and low replication of microarray data. However, the correlation between different fluorescent signals measured from a gene spot is ignored, which can diversely affect the data analysis step. In fact, the intensities of the two signals are significantly correlated across samples. The larger the log-transformed intensities are, the smaller the correlation is. RESULTS: Motivated by the complicated error relations in microarray data, we propose a multivariate hierarchical Bayesian framework for data analysis in the replicated microarray experiments. Gene expression data are modelled by a multivariate normal distribution, parameterized by the corresponding mean vectors and covariance matrixes with a conjugate prior distribution. Within the Bayesian framework, a generalized likelihood ratio test (GLRT) is also developed to infer the gene expression patterns. Simulation studies show that the proposed approach presents better operating characteristics and lower false discovery rate (FDR) than existing methods, especially when the correlation coefficient is large. The approach is illustrated with two examples of microarray analysis. The proposed method successfully detects significant genes closely related to the experimental states, which are verified by the biological information. CONCLUSIONS: The multivariate Bayesian model, compatible with the dependence between mean and variance in the univariate Bayesian model, relaxes the constant coefficient of variation assumption between measurements by adding a covariance structure. This model improves the identification of differentially expressed genes significantly since the Bayesian model fit well with the microarray data.

Project description:Influenza, a communicable disease, affects thousands of people worldwide. Young children, elderly, immunocompromised individuals and pregnant women are at higher risk for being infected by the influenza virus. Our study aims to highlight differentially expressed genes in influenza disease compared to influenza vaccination, including variability due to age and sex. To accomplish our goals, we conducted a meta-analysis using publicly available microarray expression data. Our inclusion criteria included subjects with influenza, subjects who received the influenza vaccine and healthy controls. We curated 18 microarray datasets for a total of 3,481 samples (1,277 controls, 297 influenza infection, 1,907 influenza vaccination). We pre-processed the raw microarray expression data in R using packages available to pre-process Affymetrix and Illumina microarray platforms. We used a Box-Cox power transformation of the data prior to our down-stream analysis to identify differentially expressed genes. Statistical analyses were based on linear mixed effects model with all study factors and successive likelihood ratio tests (LRT) to identify differentially-expressed genes. We filtered LRT results by disease (Bonferroni adjusted p < 0.05) and used a two-tailed 10% quantile cutoff to identify biologically significant genes. Furthermore, we assessed age and sex effects on the disease genes by filtering for genes with a statistically significant (Bonferroni adjusted p < 0.05) interaction between disease and age, and disease and sex. We identified 4,889 statistically significant genes when we filtered the LRT results by disease factor, and gene enrichment analysis (gene ontology and pathways) included innate immune response, viral process, defense response to virus, Hematopoietic cell lineage and NF-kappa B signaling pathway. Our quantile filtered gene lists comprised of 978 genes each associated with influenza infection and vaccination. We also identified 907 and 48 genes with statistically significant (Bonferroni adjusted p < 0.05) disease-age and disease-sex interactions, respectively. Our meta-analysis approach highlights key gene signatures and their associated pathways for both influenza infection and vaccination. We also were able to identify genes with an age and sex effect. This gives potential for improving current vaccines and exploring genes that are expressed equally across ages when considering universal vaccinations for influenza.

Dataset Information

Microarray analysis reveals differential gene expression in hybrid sunflower species.

Publications

Microarray analysis reveals differential gene expression in hybrid sunflower species.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure