Polycystin, the polycystic kidney disease 1 protein, is expressed by epithelial cells in fetal, adult, and polycystic kidney.

Polycystic kidney disease 1 (PKD1) is the major locus of the common genetic disorder autosomal dominant polycystic kidney disease. We have studied PKD1 mRNA, with an RNase protection assay, and found widespread expression in adult tissue, with high levels in brain and moderate signal in kidney. Expression of the PKD1 protein, polycystin, was assessed in kidney using monoclonal antibodies to a recombinant protein containing the C terminus of the molecule. In fetal and adult kidney, staining is restricted to epithelial cells. Expression in the developing nephron is most prominent in mature tubules, with lesser staining in Bowman's capsule and the proximal ureteric bud. In the nephrogenic zone, detectable signal was observed in comma- and S-shaped bodies as well as the distal branches of the ureteric bud. By contrast, uninduced mesenchyme and glomerular tufts showed no staining. In later fetal (>20 weeks) and adult kidney, strong staining persists in cortical tubules with moderate staining detected in the loops of Henle and collecting ducts. These results suggest that polycystin's major role is in the maintenance of renal epithelial differentiation and organization from early fetal life. Interestingly, polycystin expression, monitored at the mRNA level and by immunohistochemistry, appears higher in cystic epithelia, indicating that the disease does not result from complete loss of the protein.

The asymptotic distribution of canonical correlations and vectors in higher-order cointegrated models

The study of the large-sample distribution of the canonical correlations and variates in cointegrated models is extended from the first-order autoregression model to autoregression of any (finite) order. The cointegrated process considered here is nonstationary in some dimensions and stationary in some other directions, but the first difference (the “error-correction form”) is stationary. The asymptotic distribution of the canonical correlations between the first differences and the predictor variables as well as the corresponding canonical variables is obtained under the assumption that the process is Gaussian. The method of analysis is similar to that used for the first-order process.