Importance of replication in analyzing time-series gene expression data: Corticosteroid dynamics and circadian patterns in rat liver

<p>Abstract</p> <p>Background</p> <p>Microarray technology is a powerful and widely accepted experimental technique in molecular biology that allows studying genome wide transcriptional responses. However, experimental data usually contain potential sources of uncertainty and thus many experiments are now designed with repeated measurements to better assess such inherent variability. Many computational methods have been proposed to account for the variability in replicates. As yet, there is no model to output expression profiles accounting for replicate information so that a variety of computational models that take the expression profiles as the input data can explore this information without any modification.</p> <p>Results</p> <p>We propose a methodology which integrates replicate variability into expression profiles, to generate so-called 'true' expression profiles. The study addresses two issues: (i) develop a statistical model that can estimate 'true' expression profiles which are more robust than the average profile, and (ii) extend our previous micro-clustering which was designed specifically for clustering time-series expression data. The model utilizes a previously proposed error model and the concept of 'relative difference'. The clustering effectiveness is demonstrated through synthetic data where several methods are compared. We subsequently analyze <it>in vivo </it>rat data to elucidate circadian transcriptional dynamics as well as liver-specific corticosteroid induced changes in gene expression.</p> <p>Conclusions</p> <p>We have proposed a model which integrates the error information from repeated measurements into the expression profiles. Through numerous synthetic and real time-series data, we demonstrated the ability of the approach to improve the clustering performance and assist in the identification and selection of informative expression motifs.</p

Comparative analysis of acute and chronic corticosteroid pharmacogenomic effects in rat liver: Transcriptional dynamics and regulatory structures

<p>Abstract</p> <p>Background</p> <p>Comprehensively understanding corticosteroid pharmacogenomic effects is an essential step towards an insight into the underlying molecular mechanisms for both beneficial and detrimental clinical effects. Nevertheless, even in a single tissue different methods of corticosteroid administration can induce different patterns of expression and regulatory control structures. Therefore, rich <it>in vivo </it>datasets of pharmacological time-series with two dosing regimens sampled from rat liver are examined for temporal patterns of changes in gene expression and their regulatory commonalities.</p> <p>Results</p> <p>The study addresses two issues, including (1) identifying significant transcriptional modules coupled with dynamic expression patterns and (2) predicting relevant common transcriptional controls to better understand the underlying mechanisms of corticosteroid adverse effects. Following the orientation of meta-analysis, an extended computational approach that explores the concept of agreement matrix from consensus clustering has been proposed with the aims of identifying gene clusters that share common expression patterns across multiple dosing regimens as well as handling challenges in the analysis of microarray data from heterogeneous sources, e.g. different platforms and time-grids in this study. Six significant transcriptional modules coupled with typical patterns of expression have been identified. Functional analysis reveals that virtually all enriched functions (gene ontologies, pathways) in these modules are shown to be related to metabolic processes, implying the importance of these modules in adverse effects under the administration of corticosteroids. Relevant putative transcriptional regulators (e.g. RXRF, FKHD, SP1F) are also predicted to provide another source of information towards better understanding the complexities of expression patterns and the underlying regulatory mechanisms of those modules.</p> <p>Conclusions</p> <p>We have proposed a framework to identify significant coexpressed clusters of genes across multiple conditions experimented from different microarray platforms, time-grids, and also tissues if applicable. Analysis on rich <it>in vivo </it>datasets of corticosteroid time-series yielded significant insights into the pharmacogenomic effects of corticosteroids, especially the relevance to metabolic side-effects. This has been illustrated through enriched metabolic functions in those transcriptional modules and the presence of GRE binding motifs in those enriched pathways, providing significant modules for further analysis on pharmacogenomic corticosteroid effects.</p

Circadian signatures in rat liver: from gene expression to pathways

<p>Abstract</p> <p>Background</p> <p>Circadian rhythms are 24 hour oscillations in many behavioural, physiological, cellular and molecular processes that are controlled by an endogenous clock which is entrained to environmental factors including light, food and stress. Transcriptional analyses of circadian patterns demonstrate that genes showing circadian rhythms are part of a wide variety of biological pathways.</p> <p>Pathway activity method can identify the significant pattern of the gene expression levels within a pathway. In this method, the overall gene expression levels are translated to a reduced form, pathway activity levels, via singular value decomposition (SVD). A given pathway represented by pathway activity levels can then be as analyzed using the same approaches used for analyzing gene expression levels. We propose to use pathway activity method across time to identify underlying circadian pattern of pathways.</p> <p>Results</p> <p>We used synthetic data to demonstrate that pathway activity analysis can evaluate the underlying circadian pattern within a pathway even when circadian patterns cannot be captured by the individual gene expression levels. In addition, we illustrated that pathway activity formulation should be coupled with a significance analysis to distinguish biologically significant information from random deviations. Next, we performed pathway activity level analysis on a rich time series of transcriptional profiling in rat liver. The over-represented five specific patterns of pathway activity levels, which cannot be explained by random event, exhibited circadian rhythms. The identification of the circadian signatures at the pathway level identified 78 pathways related to energy metabolism, amino acid metabolism, lipid metabolism and DNA replication and protein synthesis, which are biologically relevant in rat liver. Further, we observed tight coordination between cholesterol biosynthesis and bile acid biosynthesis as well as between folate biosynthesis, one carbon pool by folate and purine-pyrimidine metabolism. These coupled pathways are parts of a sequential reaction series where the product of one pathway is the substrate of another pathway.</p> <p>Conclusions</p> <p>Rather than assessing the importance of a single gene beforehand and map these genes onto pathways, we instead examined the orchestrated change within a pathway. Pathway activity level analysis could reveal the underlying circadian dynamics in the microarray data with an unsupervised approach and biologically relevant results were obtained.</p

Identification of Global Transcriptional Dynamics

One of the challenges in exploiting high throughput measurement techniques such as microarrays is the conversion of the vast amounts of data obtained into relevant knowledge. Of particular importance is the identification of the intrinsic response of a transcriptional experiment and the characterization of the underlying dynamics.The proposed algorithm seeks to provide the researcher a summary as to various aspects relating to the dynamic progression of a biological system, rather than that of individual genes. The approach is based on the identification of smaller number of expression motifs that define the transcriptional state of the system which quantifies the deviation of the cellular response from a control state in the presence of an external perturbation. The approach is demonstrated with a number of data sets including a synthetic base case and four animal studies. The synthetic dataset will be used to establish the response of the algorithm on a "null" dataset, whereas the four different experimental datasets represent a spectrum of possible time course experiments in terms of the degree of perturbation associated with the experiment as well as representing a wide range of temporal sampling strategies. This wide range of experimental datasets will thus allow us to explore the performance of the proposed algorithm and determine its ability identify relevant information.In this work, we present a computational approach which operates on high throughput temporal gene expression data to assess the information content of the experiment, identify dynamic markers of important processes associated with the experimental perturbation, and summarize in a concise manner the evolution of the system over time with respect to the experimental perturbation

A probabilistic assessment of health risks associated with short-term exposure to tropospheric ozone

The work described in this report is part of a larger risk assessment sponsored by the U.S. Environmental Protection Agency. Earlier efforts developed exposure-response relationships for acute health effects among populations engaged in heavy exertion. Those efforts also developed a probabilistic national ambient air quality standards exposure model and a general methodology for integrating probabilistic exposure-response relation- ships and exposure estimates to calculate overall risk results. Recently published data make it possible to model additional health endpoints (for exposure at moderate exertion), including hospital admissions. New air quality and exposure estimates for alternative national ambient air quality standards for ozone are combined with exposure-response models to produce the risk results for hospital admissions and acute health effects. Sample results explain the methodology and introduce risk output formats