(489m) Comparative Analysis of Two Dosing Regimens of the Temporal Transcriptional Response of Liver to Methylprednisolone

Glucocorticoids are a class of steroid hormones that are present in almost every animal cell, playing a central role in regulating pathways for systemic energy metabolism. Because of their potent anti-inflammatory and immunosuppressive effects, synthetic glucocorticoids referred as corticosteroids (e.g. methylprednisolone) have been widely used in pharmacology as a therapeutic treatment for many diseases such as lupus, asthma, leukemia and organ transplantation [1]. However, beneficial effects derived from magnifying the physiological actions of endogenous glucocorticoids make long-term treatment with this class of drugs become risky for various side effects that include hyperglycemia, dyslipidemia, arteriosclerosis, muscle wasting, and osteoporosis [2-4]. Thus, a better understanding of corticosteroid pharmacogenomics is very essential not only in providing an insight into their underlying molecular mechanisms of action but also in aiding optimization of clinical therapies.

The first step towards a comprehensive understanding of how this drug alters systemic physiology and contributes to adverse effects is exploring the complexity of gene expression changes. Consequently, rich in vivo datasets of pharmacological time-series with two dosing regimens sampled from rat liver are examined for temporal patterns of changes in gene expression [5, 6]. The liver was selected because of its major role in both the efficacious and adverse effects of corticosteroids. The hypothesis here is that ?if two or more genes have the same temporal profiles in response to all dosing regimens, they will be more likely to be involved in a common regulatory mechanism' [5]. Therefore, we wish to provide a statistical and/or computational framework to identify gene clusters that have similar or different expression patterns across dosing regimens.

In this presentation, we first discuss a novel statistical model proposed to handle the rich information we possess in time-series replicated microarray data [7, 8]. By simply taking the average over replicated measurements, one can estimate the expression values of a gene across sampling time-points [9, 10]. However, these average profiles exclude the confidence to which the replicated microarrays provide. Although a number of approaches have been recently developed e.g. specifically-designed clustering methods [11, 12], novel similarity distance metrics [13, 14], they are limited in use for further analyses when the expression profiles are the required input. Therefore, we attempted to incorporate the error model [13] and the t-statistic model [15, 16] to estimate gene expression profiles in a more accurate manner, so-called ?true' expression profiles. The model is strongly supported by extensive testing on synthetic data and demonstrates higher clustering accuracy compared to standard methods, such as hierarchical clustering, kmeans, etc. as well as on real biological datasets.

We next examine the temporal responses of gene profiles in response to methylprednisolone administration for each and/or both dosing regimens. After applying ANOVA to pre-filter those genes that are significantly differentially expressed, the intersection from these two sets is further analyzed based on our previous work [17] to infer important gene clusters in response to methylprednisolone. The analysis aims at identifying dose-dependent and dose-independent expression profiles.

Consequently, our work aims at identifying gene clusters which characteristic dynamics in response to corticosteroid administration regimen and provide critical input for further analyses of transcriptional regulation.

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