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Dynamism in gene expression across multiple studies.

ABSTRACT: In this study we develop methods of examining gene expression dynamics, how and when genes change expression, and demonstrate their application in a meta-analysis involving over 29,000 microarrays. By defining measures across many experimental conditions, we have a new way of characterizing dynamics, complementary to measures looking at changes in absolute variation or breadth of tissues showing expression. We show conservation in overall patterns of dynamism across three species (human, mouse, and rat) and show associations with known disease-related genes. We discuss the enriched functional properties of the sets of genes showing different patterns of dynamics and show that the differences in expression dynamics is associated with the variety of different transcription factor regulatory sites. These results can influence thinking about the selection of genes for microarray design and the analysis of measurements of mRNA expression variation in a global context of expression dynamics across many conditions, as genes that are rarely differentially expressed between experimental conditions may be the subject of increased scrutiny when they significantly vary in expression between experimental subsets.

SUBMITTER: Morgan AA

PROVIDER: S-EPMC2825768 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Dynamism in gene expression across multiple studies.

Morgan Alexander A AA Dudley Joel T JT Deshpande Tarangini T Butte Atul J AJ

Physiological genomics 20091117 3

In this study we develop methods of examining gene expression dynamics, how and when genes change expression, and demonstrate their application in a meta-analysis involving over 29,000 microarrays. By defining measures across many experimental conditions, we have a new way of characterizing dynamics, complementary to measures looking at changes in absolute variation or breadth of tissues showing expression. We show conservation in overall patterns of dynamism across three species (human, mouse, ...[more]

PMID: 19920211

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Project description:Methicillin-resistant Staphylococcus aureus (MRSA) is a deleterious human pathogen responsible for severe morbidity and mortality worldwide. The pathogen has attained high priority in the World Health Organization (WHO) - Multidrug-resistant (MDR) pathogens list. Emerging MDR strains of S. aureus are clinically challenging due to failure in conventional antibiotic therapy. Biofilm formation is one of the underlying mechanisms behind the antibiotic resistance. Hence, attenuating biofilm formation has become an alternative strategy to control persistent infections. The current study is probably the first that focuses on the antibiofilm and antivirulence potential of myrtenol against MRSA and its clinical isolates. Myrtenol exhibited a concentration-dependent biofilm inhibition without causing any harmful effect on cell growth and viability. Further, microscopic analysis validated the biofilm inhibitory efficacy of myrtenol against MRSA. In addition, myrtenol inhibited the synthesis of major virulence factors including slime, lipase, α-hemolysin, staphyloxanthin and autolysin. Inhibition of staphyloxanthin in turn sensitized the MRSA cells to healthy human blood and hydrogen peroxide (H2O2). Notably, myrtenol treated cells were deficient in extracellular DNA (eDNA) mediated autoaggregation as eDNA releasing autolysis was impaired by myrtenol. Biofilm disruptive activity on preformed biofilms was observed at concentrations higher than minimum biofilm inhibitory concentration (MBIC) of myrtenol. Also, the non-cytotoxic effect of myrtenol on human peripheral blood mononuclear cell (PBMC) was evidenced by trypan blue and Alamar blue assays. Transcriptional analysis unveiled the down-regulation of global regulator sarA and sarA mediated virulence genes upon myrtenol treatment, which is well correlated with results of phenotypic assays. Thus, the results of the present study revealed the sarA mediated antibiofilm and antivirulence potential of myrtenol against MRSA.

Project description:BackgroundThe increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing.ResultsFor each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In particular, the better predictive results of DV in across platform classification indicate higher robustness of the classifier when trained on single channel data and applied to gene expression ratios.ConclusionsWe present a systematic evaluation of strategies for the integration of independent microarray studies in a classification task. Our findings in across studies classification may guide further research aiming on the construction of more robust and reliable methods for stratification and diagnosis in clinical practice.

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Dynamism in gene expression across multiple studies.

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Dynamism in gene expression across multiple studies.

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