Functional characterization and genomic organization of the human Na+-sulfate cotransporter hNaS2 gene (SLC13A4)

Sulfate plays an essential role in human growth and development. Here, we characterized the functional properties of the human Na+-sulfate cotransporter (hNaS2), determined its tissue distribution, and identified its gene (SLC13A4) structure. Expression of hNaS2 protein in Xenopus oocytes led to a Na+-dependent transport of sulfate that was inhibited by thiosulfate, phosphate, molybdate. selenate and tungstate, but not by oxalate, citrate, succinate, phenol red or DIDS. Transport kinetics of hNaS2 determined a K, for sulfate of 0.38 mM, suggestive of a high affinity sulfate transporter. Na+ kinetics determined a Hill coefficient of 1.6 +/- 0.6, suggesting a Na: SO42- stoichiometry of 2:1. hNaS2 mRNA was highly expressed in placenta and testis, with intermediate levels in brain and lower levels found in the heart, thymus, and liver. The SLC13A4 gene contains 16 exons, spanning over 47 kb in length. Its 5'-flanking region contains CAAT- and GC-box motifs, and a number of putative transcription factor binding sites, including GATA-1, AP-1, and AP-2 consensus sequences. This is the first study to characterize hNaS2 transport kinetics, define its tissue distribution, and resolve its gene (SLC13A4) structure and 5' flanking region. (C) 2004 Elsevier Inc. All rights reserved.

On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples

This paper considers a model-based approach to the clustering of tissue samples of a very large number of genes from microarray experiments. It is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. Frequently in practice, there are also clinical data available on those cases on which the tissue samples have been obtained. Here we investigate how to use the clinical data in conjunction with the microarray gene expression data to cluster the tissue samples. We propose two mixture model-based approaches in which the number of components in the mixture model corresponds to the number of clusters to be imposed on the tissue samples. One approach specifies the components of the mixture model to be the conditional distributions of the microarray data given the clinical data with the mixing proportions also conditioned on the latter data. Another takes the components of the mixture model to represent the joint distributions of the clinical and microarray data. The approaches are demonstrated on some breast cancer data, as studied recently in van't Veer et al. (2002).