Jianmei Wang

A genetic analysis of opioid-induced hyperalgesia in mice.

Background:Opioid-induced hyperalgesia (OIH) is a syndrome of increased sensitivity to noxious stimuli, seen after both the acute and chronic administration of opioids, that has been observed in humans and rodent models. This syndrome may reduce the clinical utility of opioids in treating acute and chronic pain. Methods:In these studies, the authors measured the propensity of 15 strains of inbred mice to develop mechanical manifestations of OIH. These data were subjected to in silico genetic analysis, which resulted in the association of haplotypic blocks within or near several known genes. Both pharmacologic agents and transgenic mice were used to confirm the functional association of the most strongly linked gene with OIH. Results:Both baseline mechanical nociceptive thresholds and the percentage changes in these thresholds after 4 days of morphine treatment were found to be highly strain dependent. The haplotypic blocks most strongly associated with the mechanical OIH data were located within the β2 adrenergic receptor gene (β2-AR). Using the selective β2-AR antagonist butoxamine, the authors observed a dose-dependent reversal of OIH. Furthermore, deletion of the β2-AR gene sharply reduced the mechanical allodynia present after morphine treatment in the wild-type mouse strain. Analysis of the associated β2-AR haplotypic block identified single nucleotide polymorphisms potentially explaining in part the strain specific differences in OIH. Conclusions:Genetic variants of the β2-AR gene seem to explain some part of the differences between various strains of mice to develop OIH. The association of this gene with OIH suggests specific pharmacologic strategies for reducing the impact of OIH on patients consuming opioids.

In silico pharmacogenetics of warfarin metabolism

Pharmacogenetic approaches can be instrumental for predicting individual differences in response to a therapeutic intervention. Here we used a recently developed murine haplotype-based computational method to identify a genetic factor regulating the metabolism of warfarin, a commonly prescribed anticoagulant with a narrow therapeutic index and a large variation in individual dosing. After quantification of warfarin and nine of its metabolites in plasma from 13 inbred mouse strains, we correlated strain-specific differences in 7-hydroxywarfarin accumulation with genetic variation within a chromosomal region encoding cytochrome P450 2C (Cyp2c) enzymes. This computational prediction was experimentally confirmed by showing that the rate-limiting step in biotransformation of warfarin to its 7-hydroxylated metabolite was inhibited by tolbutamide, a Cyp2c isoform-specific substrate, and that this transformation was mediated by expressed recombinant Cyp2c29. We show that genetic variants responsible for interindividual pharmacokinetic differences in drug metabolism can be identified by computational genetic analysis in mice.

In silico and in vitro pharmacogenetics: aldehyde oxidase rapidly metabolizes a p38 kinase inhibitor

The clinical development of a candidate p38 kinase inhibitor was terminated because of its unexpectedly rapid clearance in human subjects. Its short half-life and metabolic profile in human beings were vastly different from that in rats, dogs, and monkeys characterized during routine pre-clinical studies. Mice generated the predominant drug (4-hydroxylated) metabolite produced in human beings, which was not found in other species. The data from a murine in vitro drug biotransformation assay that used liver extracts from 14 inbred mouse strains were analyzed by haplotype-based computational genetic analysis. This led to the identification of aldehyde oxidase-1 (AOX1) as the enzyme responsible for the rapid metabolism of this drug. Specific enzyme inhibitors and expressed recombinant enzymes were used to confirm that AOX catalyzed the formation of the 4-hydroxylated drug metabolite in mouse and man. Genetic variation within Aox1 regulated the level of hepatic Aox1 mRNA, AOX1 protein, and enzyme activity among the inbred strains. Thus, computational murine pharmacogenetic analysis can facilitate the identification and characterization of drug metabolism pathways that are differentially utilized by humans and other species.

In silico and in vitro pharmacogenetic analysis in mice

Combining the experimental efficiency of a murine hepatic in vitro drug biotransformation system with in silico genetic analysis produces a model system that can rapidly analyze interindividual differences in drug metabolism. This model system was tested by using two clinically important drugs, testosterone and irinotecan, whose metabolism was previously well characterized. The metabolites produced after these drugs were incubated with hepatic in vitro biotransformation systems prepared from the 15 inbred mouse strains were measured. Strain-specific differences in the rate of 16α-hydroxytestosterone generation and irinotecan glucuronidation correlated with the pattern of genetic variation within Cyp2b9 and Ugt1a loci, respectively. These computational predictions were experimentally confirmed using expressed recombinant enzymes. The genetic changes affecting irinotecan metabolism in mice mirrored those in humans that are known to affect the pharmacokinetics and incidence of adverse responses to this medication.

Haplotype-Based Computational Genetic Analysis In Mice

A number of significant discoveries have resulted from genetic analysis of model experimental organisms. Improved methods for quantitative trait analysis, a process referred to as quantitative trait locus (QTL) mapping, have enabled investigators to make genetic discoveries. This mapping method requires the experimental generation of intercross progeny derived from two selected parental strains, chosen because they differ in a trait of interest. Through correlative analysis of the measured phenotype and genotype at multiple positions in the genome for each intercross progeny, regions of the genome responsible for the differences in the trait are identified. The genomic regions that quantitatively contribute to the trait are referred to as QTL. QTL analysis has been successfully used to map important traits in crop plants, cattle, fruit flies, mice, and many other model organisms. The statistical basis for QTL mapping has been thoroughly investigated (reviewed in ref. 1). Based on this statistical underpinning, experimental crosses using model organisms can be designed to reliably detect QTLs, even when the involved regions make a relatively small contribution to the trait being studied.

Cd14 SNPs regulate the innate immune response.

CD14 is a monocytic differentiation antigen that regulates innate immune responses to pathogens. Here, we show that murine Cd14 SNPs regulate the length of Cd14 mRNA and CD14 protein translation efficiency, and consequently the basal level of soluble CD14 (sCD14) and type I IFN production by murine macrophages. This has substantial downstream consequences for the innate immune response; the level of expression of at least 40 IFN-responsive murine genes was altered by this mechanism. We also observed that there was substantial variation in the length of human CD14 mRNAs and in their translation efficiency. sCD14 increased cytokine production by human dendritic cells (DCs), and sCD14-primed DCs augmented human CD4T cell proliferation. These findings may provide a mechanism for exploring the complex relationship between CD14 SNPs, serum sCD14 levels, and susceptibility to human infectious and allergic diseases.

Haplotype Structure of the Mouse Genome

Commonly available inbred mouse strains can be used to genetically model traits that vary in the human population, including those associated with disease susceptibility. In order to understand how genetic differences regulate trait variation in humans, we must first develop a detailed understanding of how genetic variation in the mouse produces the phenotypic differences among inbred mouse strains. The information obtained from analysis of experimental murine genetic models can direct biological experimentation, clinical research, and human genetic analysis. This “mouse to man” approach will increase our knowledge of the genes and pathways regulating important biological processes and disease susceptibility.

Special article: an optimistic prognosis for the clinical utility of laboratory test data.

It is hoped that anesthesiologists and other clinicians will be able to increasingly rely upon laboratory test data to improve the perioperative care of patients. However, it has been suggested that in order for a laboratory test to have clinically useful diagnostic performance characteristics (sensitivity and specificity), its performance must be considerably better than those that have been evaluated in most etiologic or epidemiologic studies. This pessimism about the clinical utility of laboratory tests is based upon the untested assumption that laboratory data are normally distributed within case and control populations.We evaluated the data distribution for 700 commonly ordered laboratory tests, and found that the vast majority (99%) do not have a normal distribution. The deviation from normal was most pronounced at extreme values, which had a large quantitative effect on laboratory test performance. At the sensitivity and specificity values required for diagnostic utility, the minimum required odds ratios for laboratory tests with a nonnormal data distribution were significantly smaller (by orders of magnitude) than for tests with a normal distribution.By evaluating the effect that the data distribution has on laboratory test performance, we have arrived at the more optimistic outlook that it is feasible to produce laboratory tests with diagnostically useful performance characteristics. We also show that moderate errors in the classification of outcome variables (e.g., death vs. survival at a specified end point) have a small impact on test performance, which is of importance for outcomes research that uses anesthesia information management systems. Because these analyses typically seek to identify factors associated with an undesirable outcome, the data distributions of the independent variables need to be considered when interpreting the odds ratios obtained from such investigations.