Search

Background Use of in silico bioinformatics analyses has led to important leads in the complex nature of alcoholism at the genomic, epigenomic, and proteomic level, but has not previously been successfully translated to the development of effective pharmacotherapies. In this study, a bioinformatics approach led to the discovery of neuroimmune pathways as an age-specific druggable target. Minocycline, a neuroimmune modulator, reduced high ethanol ( Et OH) drinking in adult, but not adolescent, mice as predicted a priori. Methods Age and sex-divergent effects in alcohol consumption were quantified in FVB/ NJ × C57 BL/6 J F1 mice given access to 20% alcohol using a 4 h/d, 4-day drinking-in- dark ( DID) paradigm. In silico bioinformatics pathway overrepresentation analysis for age-specific effects of alcohol in brain was performed using gene expression data collected in control and DID-treated, adolescent and adult, male mice. Minocycline (50 mg/kg i.p., once daily) or saline alone was tested for an effect on Et OH intake in the F1 and C57 BL/6 J ( B6) mice across both age and gender groups. Effects of minocycline on the pharmacokinetic properties of alcohol were evaluated by comparing the rates of Et OH elimination between the saline- and minocycline-treated F1 and B6 mice. Results Age and gender differences in DID consumption were identified. Only males showed a clear developmental increase difference in drinking over time. In silico analyses revealed neuroimmune-related pathways as significantly overrepresented in adult, but not in adolescent, male mice. As predicted, minocycline treatment reduced drinking in adult, but not adolescent, mice. The age effect was present for both genders, and in both the F1 and B6 mice. Minocycline had no effect on the pharmacokinetic elimination of Et OH. Conclusions Our results are a proof of concept that bioinformatics analysis of brain gene expression can lead to the generation of new hypotheses and a positive translational outcome for individualized pharmacotherapeutic treatment of high alcohol consumption. [ABSTRACT FROM AUTHOR]

Clusters of time series data may change location and memberships over time; in gene expression data, this occurs as groups of genes or samples respond differently to stimuli or experimental conditions at different times. In order to uncover this underlying temporal structure, we consider dynamic clusters with time-dependent parameters which split and merge over time, enabling cluster memberships to change. These interesting time-dependent structures are useful in understanding the development of organisms or complex organs, and could not be identified using traditional clustering methods. In cell cycle data, these time-dependent structure may provide links between genes and stages of the cell cycle, whilst in developmental data sets they may highlight key developmental transitions. [ABSTRACT FROM AUTHOR]

This article introduces an agglomerative Bayesian model-based clustering algorithm which outputs a nested sequence of two-way cluster configurations for an input matrix of data. Each two-way cluster configuration in the output hierarchy is specified by a row configuration and a column configuration whose Cartesian product partitions the data matrix. Variable selection is incorporated into the algorithm by identifying row clusters which form distinct groups defined by the column clusters, through the use of a mixture model. A primitive similarity measure between the two clusters is the multiplicative change in model posterior probability implied by their merger, and the hierarchy is formed by iteratively merging the cluster pair which maximize some fixed monotonic function of this quantity. A naive implementation of the algorithm would be to choose this function to be the identity function. However, when applying this naive algorithm to gene expression data where the number of genes being studied typically far exceeds the number of experimental samples available, this imbalanced dimensionality of the data results in an algorithmic bias toward merging samples. To counteract this bias, alternative functions of the similarity measure are considered which prevent degenerative behavior of the algorithm. The resulting improvements in the output cluster configurations are demonstrated on simulated data and the method is then applied to real gene expression data. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012 [ABSTRACT FROM AUTHOR]