AFFILIATIONS AMONG STEROID-RECEPTORS AS REVEALED BY MULTIVARIATE-ANALYSIS OF STEROID-BINDING DATA

To illustrate the informative value of descriptive multivariate analys is in biochemical screening, we have analyzed several data matrices re lating to the binding of steroids to the estrogen, progestin, androgen , glucocorticoid and mineralocorticoid receptors in different organs a nd species. We first compared dendrograms of steroid hormone receptors , that were obtained by an automatic hierarchical classification analy sis of the binding data, to published phylogenetic trees of nuclear re ceptors based on amino-acid sequence analysis. The former classificati on describes the affiliations among the receptors as given by the bind ing specificity of a population of 187 steroids in a traditional cytos ol binding assay (an indirect comparison of ligand binding sites); the latter describes the affiliations among the receptors as given by a c omparison of selected primary sequences involved in ligand-dependent r egulation of transactivation and dimerization. A similar hierarchical classification was also performed on the binding data of 62 steroids t o myometrium cytosol from different species in order to show to what e xtent the progesterone-binding proteins in these species are affiliate d. Hierarchical clustering methods classify each type of variable (rec eptor or steroid) independently. In order to be able to correlate both types of variable (receptors and steroids) on single-display graphs, it is necessary to resort to correspondence factorial analysis (CFA). CFA ranks the information content within the experimental system, high lighting major correlations and disclosing secondary correlations by e liminating redundant information and background noise. This multivaria te method, applied to the analysis of published data, illustrated the particular specificity of estrogen binding in human vagina and raised the question of the nature of the binding protein in this tissue. Our examples are based on small data tables that can and have been analyze d de visu. However, it is certain that such descriptive multivariate t echniques are indispensable for the analysis of large data banks not o nly to define structure-activity relationships but to estimate the deg rees of affiliation among the biological variables being measured. Kno wledge of such affiliations will help to organize available informatio n in a context where the complexity of the biological systems under st udy is becoming increasingly apparent.