MAID : An effect size based model for microarray data integration across laboratories and platforms

<p>Abstract</p> <p>Background</p> <p>Gene expression profiling has the potential to unravel molecular mechanisms behind gene regulation and identify gene targets for therapeutic interventions. As microarray technology matures, the number of microarray studies has increased, resulting in many different datasets available for any given disease. The increase in sensitivity and reliability of measurements of gene expression changes can be improved through a systematic integration of different microarray datasets that address the same or similar biological questions.</p> <p>Results</p> <p>Traditional effect size models can not be used to integrate array data that directly compare treatment to control samples expressed as log ratios of gene expressions. Here we extend the traditional effect size model to integrate as many array datasets as possible. The extended effect size model (MAID) can integrate any array datatype generated with either single or two channel arrays using either direct or indirect designs across different laboratories and platforms. The model uses two standardized indices, the standard effect size score for experiments with two groups of data, and a new standardized index that measures the difference in gene expression between treatment and control groups for one sample data with replicate arrays. The statistical significance of treatment effect across studies for each gene is determined by appropriate permutation methods depending on the type of data integrated. We apply our method to three different expression datasets from two different laboratories generated using three different array platforms and two different experimental designs. Our results indicate that the proposed integration model produces an increase in statistical power for identifying differentially expressed genes when integrating data across experiments and when compared to other integration models. We also show that genes found to be significant using our data integration method are of direct biological relevance to the three experiments integrated.</p> <p>Conclusion</p> <p>High-throughput genomics data provide a rich and complex source of information that could play a key role in deciphering intricate molecular networks behind disease. Here we propose an extension of the traditional effect size model to allow the integration of as many array experiments as possible with the aim of increasing the statistical power for identifying differentially expressed genes.</p

Distinct cellular responses differentiating alcohol- and hepatitis C virus-induced liver cirrhosis

BACKGROUND: Little is known at the molecular level concerning the differences and/or similarities between alcohol and hepatitis C virus induced liver disease. Global transcriptional profiling using oligonucleotide microarrays was therefore performed on liver biopsies from patients with cirrhosis caused by either chronic alcohol consumption or chronic hepatitis C virus (HCV). RESULTS: Global gene expression patterns varied significantly depending upon etiology of liver disease, with a greater number of differentially regulated genes seen in HCV-infected patients. Many of the gene expression changes specifically observed in HCV-infected cirrhotic livers were expectedly associated with activation of the innate antiviral immune response. We also compared severity (CTP class) of cirrhosis for each etiology and identified gene expression patterns that differentiated ethanol-induced cirrhosis by class. CTP class A ethanol-cirrhotic livers showed unique expression patterns for genes implicated in the inflammatory response, including those related to macrophage activation and migration, as well as lipid metabolism and oxidative stress genes. CONCLUSION: Stages of liver cirrhosis could be differentiated based on gene expression patterns in ethanol-induced, but not HCV-induced, disease. In addition to genes specifically regulating the innate antiviral immune response, mechanisms responsible for differentiating chronic liver damage due to HCV or ethanol may be closely related to regulation of lipid metabolism and to effects of macrophage activation on deposition of extracellular matrix components

Functional gene analysis of individual response to challenge of SIVmac239 in M. mulatta PBMC culture

AbstractIt has previously been shown in macaques that individual animals exhibit varying responses to challenge with the same strain of SIV. We attempted to elucidate these differences using functional genomics and correlate them to biological response. Unfractionated PBMC from three rhesus macaques were isolated, activated, and infected with SIVmac239. Interestingly, one of the three animals used for these experiments exhibited a completely unique response to infection relative to the other two. After repeated attempts to infect the PBMC from this animal, little or no infectivity was seen across the time points considered, and corresponding to this apparent lack of infection, few genes were seen to be differentially expressed when compared to mock-infected cells. For the remaining two animals, gene expression analysis showed that while they exhibited responses for the same groups of pathways, these responses included differences specific to the individual animal at the gene level. In instances where the patterns of differential gene expression differed between these animals, the genes being differentially expressed were associated with the same categories of biological process, mainly immune response and cell signaling. At the pathway level, these animals again exhibited similar responses that could be predicted based on the experimental conditions. Even in these expected results, the degree of response and the specific genes being regulated differed greatly from animal to animal. The differences in gene expression on an individual level have the potential to be used as markers in identification of animals suitable for lentiviral infection experiments. Our results highlight the importance of individual variation in response to viral challenge

High-density rhesus macaque oligonucleotide microarray design using early-stage rhesus genome sequence information and human genome annotations

BACKGROUND: Until recently, few genomic reagents specific for non-human primate research have been available. To address this need, we have constructed a macaque-specific high-density oligonucleotide microarray by using highly fragmented low-pass sequence contigs from the rhesus genome project together with the detailed sequence and exon structure of the human genome. Using this method, we designed oligonucleotide probes to over 17,000 distinct rhesus/human gene orthologs and increased by four-fold the number of available genes relative to our first-generation expressed sequence tag (EST)-derived array. RESULTS: We constructed a database containing 248,000 exon sequences from 23,000 human RefSeq genes and compared each human exon with its best matching sequence in the January 2005 version of the rhesus genome project list of 486,000 DNA contigs. Best matching rhesus exon sequences for each of the 23,000 human genes were then concatenated in the proper order and orientation to produce a rhesus "virtual transcriptome." Microarray probes were designed, one per gene, to the region closest to the 3' untranslated region (UTR) of each rhesus virtual transcript. Each probe was compared to a composite rhesus/human transcript database to test for cross-hybridization potential yielding a final probe set representing 18,296 rhesus/human gene orthologs, including transcript variants, and over 17,000 distinct genes. We hybridized mRNA from rhesus brain and spleen to both the EST- and genome-derived microarrays. Besides four-fold greater gene coverage, the genome-derived array also showed greater mean signal intensities for genes present on both arrays. Genome-derived probes showed 99.4% identity when compared to 4,767 rhesus GenBank sequence tag site (STS) sequences indicating that early stage low-pass versions of complex genomes are of sufficient quality to yield valuable functional genomic information when combined with finished genome information from a closely related species. CONCLUSION: The number of different genes represented on microarrays for unfinished genomes can be greatly increased by matching known gene transcript annotations from a closely related species with sequence data from the unfinished genome. Signal intensity on both EST- and genome-derived arrays was highly correlated with probe distance from the 3' UTR, information often missing from ESTs yet present in early-stage genome projects

Genomic Analysis Reveals a Potential Role for Cell Cycle Perturbation in HCV-Mediated Apoptosis of Cultured Hepatocytes

The mechanisms of liver injury associated with chronic HCV infection, as well as the individual roles of both viral and host factors, are not clearly defined. However, it is becoming increasingly clear that direct cytopathic effects, in addition to immune-mediated processes, play an important role in liver injury. Gene expression profiling during multiple time-points of acute HCV infection of cultured Huh-7.5 cells was performed to gain insight into the cellular mechanism of HCV-associated cytopathic effect. Maximal induction of cell-death–related genes and appearance of activated caspase-3 in HCV-infected cells coincided with peak viral replication, suggesting a link between viral load and apoptosis. Gene ontology analysis revealed that many of the cell-death genes function to induce apoptosis in response to cell cycle arrest. Labeling of dividing cells in culture followed by flow cytometry also demonstrated the presence of significantly fewer cells in S-phase in HCV-infected relative to mock cultures, suggesting HCV infection is associated with delayed cell cycle progression. Regulation of numerous genes involved in anti-oxidative stress response and TGF-β1 signaling suggest these as possible causes of delayed cell cycle progression. Significantly, a subset of cell-death genes regulated during in vitro HCV infection was similarly regulated specifically in liver tissue from a cohort of HCV-infected liver transplant patients with rapidly progressive fibrosis. Collectively, these data suggest that HCV mediates direct cytopathic effects through deregulation of the cell cycle and that this process may contribute to liver disease progression. This in vitro system could be utilized to further define the cellular mechanism of this perturbation

MAID : An effect size based model for microarray data integration across laboratories and platforms-3

Ntile-Quantile plot of the observed (black curve) vs expected Q quantiles (red curve), the expected Q values are from the distribution, where l designates the number of experiments. The difference between the observed and the expected Q quantiles are large and show that a random effect model should be considered for data integration.<p><b>Copyright information:</b></p><p>Taken from "MAID : An effect size based model for microarray data integration across laboratories and platforms"</p><p>http://www.biomedcentral.com/1471-2105/9/305</p><p>BMC Bioinformatics 2008;9():305-305.</p><p>Published online 10 Jul 2008</p><p>PMCID:PMC2483727.</p><p></p

MAID : An effect size based model for microarray data integration across laboratories and platforms-5

F false positives (()), figure 6b shows the same plot as figure 6a with the expected number of false positives () ≤ 21.<p><b>Copyright information:</b></p><p>Taken from "MAID : An effect size based model for microarray data integration across laboratories and platforms"</p><p>http://www.biomedcentral.com/1471-2105/9/305</p><p>BMC Bioinformatics 2008;9():305-305.</p><p>Published online 10 Jul 2008</p><p>PMCID:PMC2483727.</p><p></p

MAID : An effect size based model for microarray data integration across laboratories and platforms-1

Ee experiments are measuring similar differences between conditions.<p><b>Copyright information:</b></p><p>Taken from "MAID : An effect size based model for microarray data integration across laboratories and platforms"</p><p>http://www.biomedcentral.com/1471-2105/9/305</p><p>BMC Bioinformatics 2008;9():305-305.</p><p>Published online 10 Jul 2008</p><p>PMCID:PMC2483727.</p><p></p

MAID : An effect size based model for microarray data integration across laboratories and platforms-4

<p><b>Copyright information:</b></p><p>Taken from "MAID : An effect size based model for microarray data integration across laboratories and platforms"</p><p>http://www.biomedcentral.com/1471-2105/9/305</p><p>BMC Bioinformatics 2008;9():305-305.</p><p>Published online 10 Jul 2008</p><p>PMCID:PMC2483727.</p><p></p