Application of MUTIC to the exploration of gene expression data in prostate cancer

We show here an example of the application of a novel method, MUTIC (model utilization-based clustering), used for identifying complex interactions between genes or gene categories based on gene expression data. The method deals with binary categorical data which consist of a set of gene expression profiles divided into two biologically meaningful categories. It does not require data from multiple time points. Gene expression profiles are represented by feature vectors whose component features are either gene expression values, or averaged expression values corresponding to gene ontology or protein information resource categories. A supervised learning algorithm (genetic programming) is used to learn an ensemble of classification models distinguishing the two categories based on the feature vectors corresponding to their members. Each feature is associated with a "model utilization vector", which has an entry for each high-quality classification model found, indicating whether or not the feature was used in that model. These utilization vectors are then clustered using a variant of hierarchical clustering called Omniclust. The result is a set of model utilization-based clusters, in which features are gathered together if they are often considered together by classification models - which may be because they are co-expressed, or may be for subtler reasons involving multi-gene interactions. The MUTIC method is illustrated here by applying it to a dataset regarding gene expression in prostate cancer and control samples. Compared to traditional expression-based clustering, MUTIC yields clusters that have higher mathematical quality (in the sense of homogeneity and separation) and that also yield novel insights into the underlying biological processes.

CaV 3.1 and CaV 3.3 account for T-type Ca2+ current in GH3 cells

T-type Ca2+ channels are important for cell signaling by a variety of cells. We report here the electrophysiological and molecular characteristics of the whole-cell Ca2+ current in GH3 clonal pituitary cells. The current inactivation at 0 mV was described by a single exponential function with a time constant of 18.32 ñ 1.87 ms (N = 16). The I-V relationship measured with Ca2+ as a charge carrier was shifted to the left when we applied a conditioning pre-pulse of up to -120 mV, indicating that a low voltage-activated current may be present in GH3 cells. Transient currents were first activated at -50 mV and peaked around -20 mV. The half-maximal voltage activation and the slope factors for the two conditions are -35.02 ñ 2.4 and 6.7 ñ 0.3 mV (pre-pulse of -120 mV, N = 15), and -27.0 ñ 0.97 and 7.5 ñ 0.7 mV (pre-pulse of -40 mV, N = 9). The 8-mV shift in the activation mid-point was statistically significant (P < 0.05). The tail currents decayed bi-exponentially suggesting two different T-type Ca2+ channel populations. RT-PCR revealed the presence of a1G (CaV3.1) and a1I (CaV3.3) T-type Ca2+ channel mRNA transcripts.