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Biomedical subjects

L M Shi

Publications and source records attributed to L M Shi.

25 records · Page 2Linked to original sources
PubMed1988

Hybrid origin of Japanese mice "Mus musculus molossinus": evidence from restriction analysis of mitochondrial DNA.

The Japanese mouse, Mus musculus molossinus, has long been considered an independent subspecies of the house mouse. A survey of restriction-site haplotypes of mitochondrial DNA (mtDNA) showed that Japanese mice have two main maternal lineages. The most common haplotype is closely related to the mtDNA of the European subspecies M. m. musculus. The other common haplotype and two minor ones are closely related to each other and to the mtDNA of an Asiatic subspecies, M. m. castaneus. Two other rare variants are probably the result of recent contamination by European M. m. domesticus. The musculus type of mtDNA is found in the southern two-thirds of Japan, whereas the common castaneus type is found in the northern third and the minor variants are found sporadically throughout Japan. The castaneus mtDNA lineage had a few minor variants, whereas the musculus lineage was completely monomorphic. By contrast, the native population of M. m. castaneus and the Chinese and Korean musculus populations were highly polymorphic. These results suggest that M. m. molossinus is a hybrid between ancestral colonies, possibly very small, of M. m. musculus and M. m. castaneus, rather than an independent subspecies.

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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.

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PubMedDate not supplied

Mining the NCI anticancer drug discovery databases: genetic function approximation for the QSAR study of anticancer ellipticine analogues.

The U.S. National Cancer Institute (NCI) conducts a drug discovery program in which approximately 10,000 compounds are screened every year in vitro against a panel of 60 human cancer cell lines from different organs of origin. Since 1990, approximately 63,000 compounds have been tested, and their patterns of activity profiled. Recently, we analyzed the antitumor activity patterns of 112 ellipticine analogues using a hierarchical clustering algorithm. Dramatic coherence between molecular structures and activity patterns was observed qualitatively from the cluster tree. In the present study, we further investigate the quantitative structure-activity relationships (QSAR) of these compounds, in particular with respect to the influence of p53-status and the CNS cell selectivity of the activity patterns. Independent variables (i.e., chemical structural descriptors of the ellipticine analogues) were calculated from the Cerius2 molecular modeling package. Important structural descriptors, including partial atomic charges on the ellipticine ring-forming atoms, were identified by the recently developed genetic function approximation (GFA) method. For our data set, the GFA method gave better correlation and cross-validation results (R2 and CVR2 were usually approximately 0.3 higher) than did classical stepwise linear regression. A procedure for improving the performance of GFA is proposed, and the relative advantages and disadvantages of using GFA for QSAR studies are discussed.

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PubMedDate not supplied

Mining and visualizing large anticancer drug discovery databases.

In order to find more effective anticancer drugs, the U.S. National Cancer Institute (NCI) screens a large number of compounds in vitro against 60 human cancer cell lines from different organs of origin. About 70,000 compounds have been tested in the program since 1990, and each tested compound can be characterized by a vector (i.e., "fingerprint") of 60 anticancer activity, or -[log(GI50)], values. GI50 is the concentration required to inhibit cell growth by 50% compared with untreated controls. Although cell growth inhibitory activity for a single cell line is not very informative, activity patterns across the 60 cell lines can provide incisive information on the mechanisms of action of screened compounds and also on molecular targets and modulators of activity within the cancer cells. Various statistical and artificial intelligence methods, including principal component analysis, hierarchical cluster analysis, stepwise linear regression, multidimensional scaling, neural network modeling, and genetic function approximation, among others, can be used to analyze this large activity database. Mining the database can provide useful information: (a) for the development of anticancer drugs; (b) for a better understanding of the molecular pharmacology of cancer; and (c) for improvement of the drug discovery process.

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