Andrew D. Johnson

Allelic Expression Imbalance of Human mu Opioid Receptor (OPRM1) Caused by Variant A118G

As a primary target for opioid drugs and peptides, the mu opioid receptor (OPRM1) plays a key role in pain perception and addiction. Genetic variants of OPRM1 have been implicated in predisposition to drug addiction, in particular the single nucleotide polymorphism A118G, leading to an N40D substitution, with an allele frequency of 10–32%, and uncertain functions. We have measured allele-specific mRNA expression of OPRM1 in human autopsy brain tissues, using A118G as a marker. In 8 heterozygous samples measured, the A118 mRNA allele was 1.5–2.5-fold more abundant than the G118 allele. Transfection into Chinese hamster ovary cells of a cDNA representing only the coding region of OPRM1, carrying adenosine, guanosine, cytidine, and thymidine in position 118, resulted in 1.5-fold lower mRNA levels only for OPRM1-G118, and more than 10-fold lower OPRM1 protein levels, measured by Western blotting and receptor binding assay. After transfection and inhibition of transcription with actinomycin D, analysis of mRNA turnover failed to reveal differences in mRNA stability between A118 and G118 alleles, indicating a defect in transcription or mRNA maturation. These results indicate that OPRM1-G118 is a functional variant with deleterious effects on both mRNA and protein yield. Clarifying the functional relevance of polymorphisms associated with susceptibility to a complex disorder such as drug addiction provides a foundation for clinical association studies.

Synergy Between 3′Azido-3′deoxythymidine and Paclitaxel in Human Pharynx FaDu Cells

AbstractPurpose. We recently demonstrated simultaneous targeting of telomere and telomerase as a novel cancer therapeutic approach, and that telomerase inhibitors such as 3′azido-3′deoxythymidine (AZT) significantly enhanced the antitumor activity of paclitaxel, which causes telomere erosion, in telomerase-positive human pharynx FaDu tumors in vitro and in vivo (1). The present study evaluated the synergy between AZT and paclitaxel to identify optimal combinations for future clinical evaluation. Methods. FaDu cells were incubated with or without AZT for 24 h and then treated with AZT with or without paclitaxel for an additional 48 h. Under these conditions, single agent paclitaxel produced a 60% maximum reduction of cell number (IC50 was 7.3 nM), and single agent AZT produced a 97% reduction (IC50 was 5.6 μM). Synergy was evaluated using fixed-concentration and fixed-ratio methods, and data were analyzed by the combination index method. Results. The results indicate a concentration-dependent synergy between the two drugs; the synergy was higher for combinations containing greater paclitaxel-to-AZT concentration ratios and increased with the level of drug effect. For example, in combinations containing 1 μM AZT, synergy was 1.3-fold at the 20% effect level and 3.1-fold at the 60% effect level. Because the major antitumor activity, determined by comparing the posttreatment cell number to the pretreatment cell number, was antiproliferation at the 20% effect level and cell kill at the 60% effect level, our results suggest that AZT mainly enhances the cell kill effect of paclitaxel. Conclusion. In summary, the present study demonstrates a synergistic interaction between paclitaxel and AZT and supports a combination using a low and nontoxic AZT dose in combination with a therapeutically active dose of paclitaxel.

Large scale genotype–phenotype correlation analysis based on phylogenetic trees

We provide two methods for identifying changes in genotype that are correlated with changes in a phenotype implied by phylogenetic trees. The first method, VENN, works when the number of branches over which the change occurred are modest. VENN looks for genetic changes that are completely penetrant with phenotype changes on a tree. The second method, CCTSWEEP, allows for a partial matching between changes in phenotypes and genotypes and provides a score for each change using Maddisons concentrated changes test. The mutations that are highly correlated with phenotypic change can be ranked by score. We use these methods to find SNPs correlated with resistance to Bacillus anthracis in inbred mouse strains. Our findings are consistent with the current biological literature, and also suggest potential novel candidate genes.

A Quantitative Assay of Telomerase Activity

AbstractPurpose. Telomerase is a ribonucleoprotein that extends telomeres at the ends of chromosome. Increased telomerase activity is associated with cellular immortality. The currently available assay for telomerase, i.e., telomeric repeat amplification protocol (TRAP), consists of 2 steps: (a) telomerase-mediated extension of an oligonucleotide primer by the enzyme-containing extracts of cells and tissues, and (b) amplification of the telomerase-extended primer products by polymerase chain reaction (PCR) and detection of the PCR products. It is generally accepted that the current TRAP assay lacks quantitative precision. The present study was to develop a quantitative telomerase assay with greater precision and sensitivity. Methods. This new method used the primer extension method as in TRAP, plus the following modifications: (a) used a lysis buffer that yielded complete lysis of nuclei; (b) removal of PCR inhibitors by phenol/chloroform extraction after primer extension; and (c) used primers for the internal standard that were designed to reduce their competition with the telomerase products for PCR. Results. The modified method showed a good correlation (r2 = 0.99, P < 0.001) between telomerase amount (expressed as total protein in cell lysate) and its activity (expressed as telomerase products). Compared to the conventional TRAP, the new method (a) was more sensitive (average of 5.5-fold in cultured cancer cells and >5.9-fold in patient tumors), (b) had a lower inter- and intra-day variability (>3-fold), and (c) showed a 2 to 4-fold broader range of linearity in the standard curve. The higher assay sensitivity further enabled the use of a nonradioactive method, i.e., ethidium bromide staining of DNA, to detect the TRAP products, as opposed to the use of radioactive nucleotide and the more labor-intensive autoradiography mandated by the conventional TRAP. Conclusion. We report here a quantitative assay for telomerase activity in cultured human cancer cells and patient tumors.

An XML-based system for synthesis of data from disparate databases.

Abstract Diverse data sets have become key building blocks of translational biomedical research. Data types captured and referenced by sophisticated research studies include high throughput genomic and proteomic data, laboratory data, data from imagery, and outcome data. In this paper, the authors present the application of an XML-based data management system to support integration of data from disparate data sources and large data sets. This system facilitates management of XML schemas and on-demand creation and management of XML databases that conform to these schemas. They illustrate the use of this system in an application for genotype–phenotype correlation analyses. This application implements a method of phenotype–genotype correlation based on phylogenetic optimization of large data sets of mouse SNPs and phenotypic data. The application workflow requires the management and integration of genomic information and phenotypic data from external data repositories and from the results of phenotype–genotype correlation analyses. Our implementation supports the process of carrying out a complex workflow that includes large-scale phylogenetic tree optimizations and application of Maddisons concentrated changes test to large phylogenetic tree data sets. The data management system also allows collaborators to share data in a uniform way and supports complex queries that target data sets.

Statistical Model for Prediction of Hearing Loss in Patients Receiving Cisplatin Chemotherapy

IMPORTANCE This statistical model might be used to predict cisplatin-induced hearing loss, particularly in patients undergoing concomitant radiotherapy. OBJECTIVE To create a statistical model based on pretreatment hearing thresholds to provide an individual probability for hearing loss from cisplatin therapy and, secondarily, to investigate the use of hearing classification schemes as predictive tools for hearing loss. DESIGN Retrospective case-control study. SETTING Tertiary care medical center. PARTICIPANTS A total of 112 subjects receiving chemotherapy and audiometric evaluation were evaluated for the study. Of these subjects, 31 met inclusion criteria for analysis. MAIN OUTCOME MEASURES The primary outcome measurement was a statistical model providing the probability of hearing loss following the use of cisplatin chemotherapy. RESULTS Fifteen of the 31 subjects had significant hearing loss following cisplatin chemotherapy. American Academy of Otolaryngology-Head and Neck Society and Gardner-Robertson hearing classification schemes revealed little change in hearing grades between pretreatment and posttreatment evaluations for subjects with or without hearing loss. The Chang hearing classification scheme could effectively be used as a predictive tool in determining hearing loss with a sensitivity of 73.33%. Pretreatment hearing thresholds were used to generate a statistical model, based on quadratic approximation, to predict hearing loss (C statistic = 0.842, cross-validated = 0.835). The validity of the model improved when only subjects who received concurrent head and neck irradiation were included in the analysis (C statistic = 0.91). A calculated cutoff of 0.45 for predicted probability has a cross-validated sensitivity and specificity of 80%. CONCLUSIONS AND RELEVANCE Pretreatment hearing thresholds can be used as a predictive tool for cisplatin-induced hearing loss, particularly with concomitant radiotherapy.

Large-scale phylogenetic analysis on current HPC architectures

Phylogenetic inference is considered a grand challenge in Bioinformatics due to its immense computational requirements. The increasing popularity and availability of large multi-gene alignments as well as comprehensive datasets of single nucleotide polymorphisms (SNPs) in current biological studies, coupled with rapid accumulation of sequence data in general, pose new challenges for high performance computing. By example of RAxML, which is currently among the fastest and most accurate programs for phylogenetic inference under the Maximum Likelihood (ML) criterion, we demonstrate how the phylogenetic ML function can be efficiently scaled to current supercomputer architectures like the IBM BlueGene/L (BG/L) and SGI Altix. This is achieved by simultaneous exploitation of coarseand fine-grained parallelism which is inherent to every ML-based biological analysis. Performance is assessed using datasets consisting of 270 sequences and 566,470 base pairs (haplotype map dataset), and 2,182 sequences and 51,089 base pairs, respectively. To the best of our knowledge, these are the largest datasets analyzed under ML to date. Experimental results indicate that the fine-grained parallelization scales well up to 1,024 processors. Moreover, a larger number of processors can be efficiently exploited by a combination of coarseand fine-grained parallelism. We also demonstrate that our parallelization scales equally well on an AMD Opteron cluster with a less favorable network latency to processor speed ratio. Finally, we underline the practical relevance of our approach by including a biological discussion of the results from the haplotype map dataset analysis, which revealed novel biological insights via phylogenetic inference.