Activation of heat shock and antioxidant responses by the natural product celastrol: transcriptional signatures of a thiol-targeted molecule.

Stress response pathways allow cells to sense and respond to environmental changes and adverse pathophysiological states. Pharmacological modulation of cellular stress pathways has implications in the treatment of human diseases, including neurodegenerative disorders, cardiovascular disease, and cancer. The quinone methide triterpene celastrol, derived from a traditional Chinese medicinal herb, has numerous pharmacological properties, and it is a potent activator of the mammalian heat shock transcription factor HSF1. However, its mode of action and spectrum of cellular targets are poorly understood. We show here that celastrol activates Hsf1 in Saccharomyces cerevisiae at a similar effective concentration seen in mammalian cells. Transcriptional profiling revealed that celastrol treatment induces a battery of oxidant defense genes in addition to heat shock genes. Celastrol activated the yeast Yap1 oxidant defense transcription factor via the carboxy-terminal redox center that responds to electrophilic compounds. Antioxidant response genes were likewise induced in mammalian cells, demonstrating that the activation of two major cell stress pathways by celastrol is conserved. We report that celastrol's biological effects, including inhibition of glucocorticoid receptor activity, can be blocked by the addition of excess free thiol, suggesting a chemical mechanism for biological activity based on modification of key reactive thiols by this natural product.

Detecting the signature of natural selection with microsatellites

Natural selection is one of the major factors in the evolution of all organisms. Detecting the signature of natural selection has been a central theme in evolutionary genetics. With the availability of microsatellite data, it is of interest to study how natural selection can be detected with microsatellites. ^ The overall aim of this research is to detect signatures of natural selection with data on genetic variation at microsatellite loci. The null hypothesis to be tested is the neutral mutation theory of molecular evolution, which states that different alleles at a locus have equivalent effects on fitness. Currently used tests of this hypothesis based on data on genetic polymorphism in natural populations presume that mutations at the loci follow the infinite allele/site models (IAM, ISM), in the sense that at each site at most only one mutation event is recorded, and each mutation leads to an allele not seen before in the population. Microsatellite loci, which are abundant in the genome, do not obey these mutation models, since the new alleles at such loci can be created either by contraction or expansion of tandem repeat sizes of core motifs. Since the current genome map is mainly composed of microsatellite loci and this class of loci is still most commonly studied in the context of human genome diversity, this research explores how the current test procedures for testing the neutral mutation hypothesis should be modified to take into account a generalized model of forward-backward stepwise mutations. In addition, recent literature also suggested that past demographic history of populations, presence of population substructure, and varying rates of mutations across loci all have confounding effects for detecting signatures of natural selection. ^ The effects of the stepwise mutation model and other confounding factors on detecting signature of natural selection are the main results of the research. ^

STUDIES ON THE PATTERNS OF NUCLEOTIDE AND AMINO ACID SUBSTITUTION (ELECTROPHORESIS, MUTATION RATE, SELECTION, BASE, COMPOSITION)

Theoretical and empirical studies were conducted on the pattern of nucleotide and amino acid substitution in evolution, taking into account the effects of mutation at the nucleotide level and purifying selection at the amino acid level. A theoretical model for predicting the evolutionary change in electrophoretic mobility of a protein was also developed by using information on the pattern of amino acid substitution. The specific problems studied and the main results obtained are as follows: (1) Estimation of the pattern of nucleotide substitution in DNA nuclear genomes. The pattern of point mutations and nucleotide substitutions among the four different nucleotides are inferred from the evolutionary changes of pseudogenes and functional genes, respectively. Both patterns are non-random, the rate of change varying considerably with nucleotide pair, and that in both cases transitions occur somewhat more frequently than transversions. In protein evolution, substitution occurs more often between amino acids with similar physico-chemical properties than between dissimilar amino acids. (2) Estimation of the pattern of nucleotide substitution in RNA genomes. The majority of mutations in retroviruses accumulate at the reverse transcription stage. Selection at the amino acid level is very weak, and almost non-existent between synonymous codons. The pattern of mutation is very different from that in DNA genomes. Nevertheless, the pattern of purifying selection at the amino acid level is similar to that in DNA genomes, although selection intensity is much weaker. (3) Evaluation of the determinants of molecular evolutionary rates in protein-coding genes. Based on rates of nucleotide substitution for mammalian genes, the rate of amino acid substitution of a protein is determined by its amino acid composition. The content of glycine is shown to correlate strongly and negatively with the rate of substitution. Empirical formulae, called indices of mutability, are developed in order to predict the rate of molecular evolution of a protein from data on its amino acid sequence. (4) Studies on the evolutionary patterns of electrophoretic mobility of proteins. A theoretical model was constructed that predicts the electric charge of a protein at any given pH and its isoelectric point from data on its primary and quaternary structures. Using this model, the evolutionary change in electrophoretic mobilities of different proteins and the expected amount of electrophoretically hidden genetic variation were studied. In the absence of selection for the pI value, proteins will on the average evolve toward a mildly basic pI. (Abstract shortened with permission of author.) ^

Statistical and methodological challenges for disaster preparedness and medical needs assessment in Rio Grande Valley of Texas

In recent years, disaster preparedness through assessment of medical and special needs persons (MSNP) has taken a center place in public eye in effect of frequent natural disasters such as hurricanes, storm surge or tsunami due to climate change and increased human activity on our planet. Statistical methods complex survey design and analysis have equally gained significance as a consequence. However, there exist many challenges still, to infer such assessments over the target population for policy level advocacy and implementation. ^ Objective. This study discusses the use of some of the statistical methods for disaster preparedness and medical needs assessment to facilitate local and state governments for its policy level decision making and logistic support to avoid any loss of life and property in future calamities. ^ Methods. In order to obtain precise and unbiased estimates for Medical Special Needs Persons (MSNP) and disaster preparedness for evacuation in Rio Grande Valley (RGV) of Texas, a stratified and cluster-randomized multi-stage sampling design was implemented. US School of Public Health, Brownsville surveyed 3088 households in three counties namely Cameron, Hidalgo, and Willacy. Multiple statistical methods were implemented and estimates were obtained taking into count probability of selection and clustering effects. Statistical methods for data analysis discussed were Multivariate Linear Regression (MLR), Survey Linear Regression (Svy-Reg), Generalized Estimation Equation (GEE) and Multilevel Mixed Models (MLM) all with and without sampling weights. ^ Results. Estimated population for RGV was 1,146,796. There were 51.5% female, 90% Hispanic, 73% married, 56% unemployed and 37% with their personal transport. 40% people attained education up to elementary school, another 42% reaching high school and only 18% went to college. Median household income is less than $15,000/year. MSNP estimated to be 44,196 (3.98%) [95% CI: 39,029; 51,123]. All statistical models are in concordance with MSNP estimates ranging from 44,000 to 48,000. MSNP estimates for statistical methods are: MLR (47,707; 95% CI: 42,462; 52,999), MLR with weights (45,882; 95% CI: 39,792; 51,972), Bootstrap Regression (47,730; 95% CI: 41,629; 53,785), GEE (47,649; 95% CI: 41,629; 53,670), GEE with weights (45,076; 95% CI: 39,029; 51,123), Svy-Reg (44,196; 95% CI: 40,004; 48,390) and MLM (46,513; 95% CI: 39,869; 53,157). ^ Conclusion. RGV is a flood zone, most susceptible to hurricanes and other natural disasters. People in the region are mostly Hispanic, under-educated with least income levels in the U.S. In case of any disaster people in large are incapacitated with only 37% have their personal transport to take care of MSNP. Local and state government’s intervention in terms of planning, preparation and support for evacuation is necessary in any such disaster to avoid loss of precious human life. ^ Key words: Complex Surveys, statistical methods, multilevel models, cluster randomized, sampling weights, raking, survey regression, generalized estimation equations (GEE), random effects, Intracluster correlation coefficient (ICC).^