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W Tong

Publications and source records attributed to W Tong.

124 records · Page 7Linked to original sources
PubMed1976

Stimulatory actions of thyrotropin and dibutyryl cyclic AMP on transcription and translation in the regulation of thyroidal protein synthesis.

When beef thyroid cells were incubated with thyrotropin and then tested for protein synthesizing activity, the stimulatory effect of thyrotropin appeared in two distinct phases: first, an immediate stimulation which continued as long as thyrotropin was present, but died away promptly after withdrawal of the added thyrotropin; and second, a delayed stimulation with a lag period of 1 to 2 h, which persisted after thyrotropin withdrawal. The fast and the delayed effects each stimulated protein synthesis by about 25%, so that after 4 h of thyrotropin treatment, total stimulation amounted to 50% above basal levels of activity. The fast thyrotropin effect was not affected by actinomycin D or cordycepin, and hence is evidently not dependent upon induced RNA synthesis. In contrast, the delayed thyrotropin effect was completely prevented by actinomycin D or cordycepin. These findings suggest that the fast effect of thyrotropin occurs at the translational level to provide for acute adjustments in thyroglobulin production by regulating the rate of translation of existing mRNA. The delayed thyrotropin effect, by initiating certain transcriptional reactions could be the means for selectively inducing the production of specific enzymes or proteins. Both the fast and delayed effects of thyrotropin were faithfully reproduced by dibutyryl cyclic AMP. This finding seems to be the first in vitro demonstration of a cyclic AMP induced transcriptional response to a hormone in mammalian cells.

Animals↗
PubMed1975

Thyroidal autoregulation. Iodide-induced suppression of thyrotropin-stimulated cyclic AMP production and iodinating activity in thyroid cells.

In continuing our study of the thyroidal autoregulation phenomenon, we have investigated the effects of iodide on several thyroidal responses to thyrotropin. Thus, we have found that the 2--4-fold thyrotropin stimulation of protein iodination in beef thyroid cells was reduced about 30% by 4 h of preincubation with 10 muM iodide, and virtually abolished with 50 muM iodide. Similarly the 8-fold thyrotropin stimulation of cyclic AMP accumulation in the cells was reduced about 30% by 3 h of preincubation with 50 muM iodide. It appears therefore that the so-called autoregulation of the thyroid gland does include influences of iodide on the thyrotropin stimulation of cyclic AMP production, iodide transport, and protein iodination which can be demonstrated in vitro in the dispersed thyroid cell system. Two other effects of thyrotropin, namely, the stimulation of [14C]leucine incorporation into protein and of iodide efflux were not at all affected by treatment with excess iodide, and hence may not be subject to the autoregulatory influence of iodide.

Animals↗
PubMedDate not supplied

QSAR models using a large diverse set of estrogens.

Endocrine disruptors (EDs) have a variety of adverse effects in humans and animals. About 58,000 chemicals, most having little safety data, must be tested in a group of tiered assays. As assays will take years, it is important to develop rapid methods to help in priority setting. For application to large data sets, we have developed an integrated system that contains sequential four phases to predict the ability of chemicals to bind to the estrogen receptor (ER), a prevalent mechanism for estrogenic EDs. Here we report the results of evaluating two types of QSAR models for inclusion in phase III to quantitatively predict chemical binding to the ER. Our data set for the relative binding affinities (RBAs) to the ER consists of 130 chemicals covering a wide range of structural diversity and a 6 orders of magnitude spread of RBAs. CoMFA and HQSAR models were constructed and compared for performance. The CoMFA model had a r2 = 0.91 and a q2LOO = 0.66. HQSAR showed reduced performance compared to CoMFA with r2 = 0.76 and q2LOO = 0.59. A number of parameters were examined to improve the CoMFA model. Of these, a phenol indicator increased the q2LOO to 0.71. When up to 50% of the chemicals were left out in the leave-N-out cross-validation, the q2 remained significant. Finally, the models were tested by using two test sets; the q2pred for these were 0.71 and 0.62, a significant result which demonstrates the utility of the CoMFA model for predicting the RBAs of chemicals not included in the training set. If used in conjunction with phases I and II, which reduced the size of the data set dramatically by eliminating most inactive chemicals, the current CoMFA model (phase III) can be used to predict the RBA of chemicals with sufficient accuracy and to provide quantitative information for priority setting.

Animals↗
PubMedDate not supplied

Evaluation of quantitative structure-activity relationship methods for large-scale prediction of chemicals binding to the estrogen receptor.

Three different QSAR methods, Comparative Molecular Field Analysis (CoMFA), classical QSAR (utilizing the CODESSA program), and Hologram QSAR (HQSAR), are compared in terms of their potential for screening large data sets of chemicals as endocrine disrupting compounds (EDCs). While CoMFA and CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) have been commercially available for some time, HQSAR is a novel QSAR technique. HQSAR attempts to correlate molecular structure with biological activity for a series of compounds using molecular holograms constructed from counts of sub-structural molecular fragments. In addition to using r2 and q2 (cross-validated r2) in assessing the statistical quality of QSAR models, another statistical parameter was defined to be the ratio of the standard error to the activity range. The statistical quality of the QSAR models constructed using CoMFA and HQSAR techniques were comparable and were generally better than those produced with CODESSA. It is notable that only 2D-connectivity, bond and elemental atom-type information were considered in building HQSAR models. Since HQSAR requires no conformational analysis or structural alignment, it is straightforward to use and lends itself readily to the rapid screening of large numbers of compounds. Among the QSAR methods considered, HQSAR appears to offer many attractive features, such as speed, reproducibility and ease of use, which portend its utility for prioritizing large numbers of potential EDCs for subsequent toxicological testing and risk assessment.

Animals↗
PubMedDate not supplied

Comparative molecular field analysis (CoMFA) model using a large diverse set of natural, synthetic and environmental chemicals for binding to the androgen receptor.

A large number of natural, synthetic and environmental chemicals are capable of disrupting the endocrine systems of experimental animals, wildlife and humans. These so-called endocrine disrupting chemicals (EDCs), some mimic the functions of the endogenous androgens, have become a concern to the public health. Androgens play an important role in many physiological processes, including the development and maintenance of male sexual characteristics. A common mechanism for androgen to produce both normal and adverse effects is binding to the androgen receptor (AR). In this study, we used Comparative Molecular Field Analysis (CoMFA), a three-dimensional quantitative structure-activity relationship (3D-QSAR) technique, to examine AR-ligand binding affinities. A CoMFA model with r2 = 0.902 and q2 = 0.571 was developed using a large training data set containing 146 structurally diverse natural, synthetic, and environmental chemicals with a 10(6)-fold range of relative binding affinity (RBA). By comparing the binding characteristics derived from the CoMFA contour map with these observed in a human AR crystal structure, we found that the steric and electrostatic properties encoded in this training data set are necessary and sufficient to describe the RBA of AR ligands. Finally, the CoMFA model was challenged with an external test data set; the predicted results were close to the actual values with average difference of 0.637 logRBA. This study demonstrates the utility of this CoMFA model for real-world use in predicting the AR binding affinities of structurally diverse chemicals over a wide RBA range.

Androgens↗