Interactions between serotonergic genotype and amino acid transmitter receptor expression in human alcoholics

Serotonin can modulate the activity of neural reward pathways that are strongly implicated in mediating the effects of chronic alcohol misuse, and its treatment, in human subjects. In previous work and as discussed elsewhere at this meeting, we and others have found consistent differences in the parameters of GABA and glutamate receptors, and the expression of their component subunit transcripts and proteins, in areas of the alcoholic brain that are altered by alcoholism. We did not fi nd clear changes in GABA and glutamate transport function in such samples, but a series of microarray analyses showed consistent upregulation of the presynaptic GABA/betaine transporter SLC6A12. Microarray studies showed no signifi cant differences in the expression of transcripts associated with 5HT transmission; however, only a small number of such elements were present on the arrays. Here we partitioned GABAA and NMDA pharmacology, and subunit mRNA and protein expression, measured in samples of frontal and motor cortex obtained at autopsy from alcoholics without comorbid disease, alcoholics with liver cirrhosis, and controls, according to 5HTTLPR (SLC6A4) and 5HT1B (HTR1B) polymorphisms. We found no effect of these genotypes on the expression of GABAA receptor gene products, but there was a signifi cant mRNA Transcript X Area X Group X 5HTTLPR Interaction with NMDA subunit isoform expression measured by Real Time PCR with GAPDH normalization. Further analysis showed the effect to be selective for alcoholics with cirrhosis, to be most marked in the pathologically vulnerable frontal cortex, and to vary with subunit transcript (F2,76 = 6.545, P = 0.002). NR1 expression was most affected, followed by NR2A, with NR2B expression least altered. Pilot data suggest 5HT1B genotype may also modulate NMDA subunit expression. Interactions between amino acid and serotonin transmission may infl uence susceptibility to alcohol dependence or pathogenesis

Multilevel modelling for inference of genetic regulatory networks

Time-course experiments with microarrays are often used to study dynamic biological systems and genetic regulatory networks (GRNs) that model how genes influence each other in cell-level development of organisms. The inference for GRNs provides important insights into the fundamental biological processes such as growth and is useful in disease diagnosis and genomic drug design. Due to the experimental design, multilevel data hierarchies are often present in time-course gene expression data. Most existing methods, however, ignore the dependency of the expression measurements over time and the correlation among gene expression profiles. Such independence assumptions violate regulatory interactions and can result in overlooking certain important subject effects and lead to spurious inference for regulatory networks or mechanisms. In this paper, a multilevel mixed-effects model is adopted to incorporate data hierarchies in the analysis of time-course data, where temporal and subject effects are both assumed to be random. The method starts with the clustering of genes by fitting the mixture model within the multilevel random-effects model framework using the expectation-maximization (EM) algorithm. The network of regulatory interactions is then determined by searching for regulatory control elements (activators and inhibitors) shared by the clusters of co-expressed genes, based on a time-lagged correlation coefficients measurement. The method is applied to two real time-course datasets from the budding yeast (Saccharomyces cerevisiae) genome. It is shown that the proposed method provides clusters of cell-cycle regulated genes that are supported by existing gene function annotations, and hence enables inference on regulatory interactions for the genetic network.