Tolerance and cross-tolerance to neurocognitive effects of THC and alcohol in heavy cannabis users

and cross-tolerance to neurocognitive effects of THC and alcohol in heavy cannabis user

Systematical Detection of Significant Genes in Microarray Data by Incorporating Gene Interaction Relationship in Biological Systems

Many methods, including parametric, nonparametric, and Bayesian methods, have been used for detecting differentially expressed genes based on the assumption that biological systems are linear, which ignores the nonlinear characteristics of most biological systems. More importantly, those methods do not simultaneously consider means, variances, and high moments, resulting in relatively high false positive rate. To overcome the limitations, the SWang test is proposed to determine differentially expressed genes according to the equality of distributions between case and control. Our method not only latently incorporates functional relationships among genes to consider nonlinear biological system but also considers the mean, variance, skewness, and kurtosis of expression profiles simultaneously. To illustrate biological significance of high moments, we construct a nonlinear gene interaction model, demonstrating that skewness and kurtosis could contain useful information of function association among genes in microarrays. Simulations and real microarray results show that false positive rate of SWang is lower than currently popular methods (T-test, F-test, SAM, and Fold-change) with much higher statistical power. Additionally, SWang can uniquely detect significant genes in real microarray data with imperceptible differential expression but higher variety in kurtosis and skewness. Those identified genes were confirmed with previous published literature or RT-PCR experiments performed in our lab

Analyses of In Vivo Interaction and Mobility of Two Spliceosomal Proteins Using FRAP and BiFC

U1-70K, a U1 snRNP-specific protein, and serine/arginine-rich (SR) proteins are components of the spliceosome and play critical roles in both constitutive and alternative pre-mRNA splicing. However, the mobility properties of U1-70K, its in vivo interaction with SR proteins, and the mobility of the U1-70K-SR protein complex have not been studied in any system. Here, we studied the in vivo interaction of U1-70K with an SR protein (SR45) and the mobility of the U1-70K/SR protein complex using bimolecular fluorescence complementation (BiFC) and fluorescence recovery after photobleaching (FRAP). Our results show that U1-70K exchanges between speckles and the nucleoplasmic pool very rapidly and that this exchange is sensitive to ongoing transcription and phosphorylation. BiFC analyses showed that U1-70K and SR45 interacted primarily in speckles and that this interaction is mediated by the RS1 or RS2 domain of SR45. FRAP analyses showed considerably slower recovery of the SR45/U1-70K complex than either protein alone indicating that SR45/U1-70K complexes remain in the speckles for a longer duration. Furthermore, FRAP analyses with SR45/U1-70K complex in the presence of inhibitors of phosphorylation did not reveal any significant change compared to control cells, suggesting that the mobility of the complex is not affected by the status of protein phosphorylation. These results indicate that U1-70K, like SR splicing factors, moves rapidly in the nucleus ensuring its availability at various sites of splicing. Furthermore, although it appears that U1-70K moves by diffusion its mobility is regulated by phosphorylation and transcription

Acute effects of MDMA (3,4-methylenedioxymethamphetamine) on EEG oscillations: alone and in combination with ethanol or THC (delta-9-tetrahydrocannabinol)

Item does not contain fulltextRATIONALE: Typical users of 3,4-methylenedioxymethamphetamine (MDMA or "ecstasy") are polydrug users, combining MDMA with alcohol or cannabis [most active compound: delta-9-tetrahydrocannabinol (THC)]. OBJECTIVES: The aim of the present study was to investigate whether co-administration of alcohol or THC with MDMA differentially affects ongoing electroencephalogram (EEG) oscillations compared to the administration of each drug alone. METHODS: In two separate experiments, 16 volunteers received four different drug conditions: (1) MDMA (100 mg); (2) alcohol clamp (blood alcohol concentration = 0.6 per thousand) or THC (inhalation of 4, 6 and 6 mg, interval of 1.5 h); (3) MDMA in combination with alcohol or THC; and (4) placebo. Before and after drug administration, electroencephalography was recorded during an eyes closed resting state. RESULTS: Theta and alpha power increased after alcohol intake compared to placebo and reduced after MDMA intake. No interaction between alcohol and MDMA was found. Significant MDMA x THC effects for theta and lower-1-alpha power indicated that the power attenuation after the combined intake of MDMA and THC was less than the sum of each drug alone. For the lower-2-alpha band, the intake of MDMA or THC alone did not significantly affect power, but the intake of combined MDMA and THC significantly decreased lower-2-alpha power. CONCLUSIONS: The present findings indicate that the combined intake of MDMA and THC, but not of MDMA and alcohol, affects ongoing EEG oscillations differently than the sum of either one drug alone. Changes in ongoing EEG oscillations may be related to the impaired task performance that has often been reported after drug intake

Do Dispersing Monkeys Follow Kin? Evidence from Gray-cheeked Mangabeys (Lophocebus albigena)

Among social vertebrates, immigrants may incur a substantial fitness cost when they attempt to join a new group. Dispersers could reduce that cost, or increase their probability of mating via coalition formation, by immigrating into groups containing first- or second-degree relatives. We here examine whether dispersing males tend to move into groups containing fathers or brothers in gray-cheeked mangabeys (Lophocebus albigena) in Kibale National Park, Uganda. We sampled blood from 21 subadult and adult male mangabeys in 7 social groups and genotyped them at 17 microsatellite loci. Twelve genotyped males dispersed to groups containing other genotyped adult males during the study; in only 1 case did the group contain a probable male relative. Contrary to the prediction that dispersing males would follow kin, relatively few adult male dyads were likely first- or second-degree relatives; opportunities for kin-biased dispersal by mangabeys appear to be rare. During 4 yr of observation, adult brothers shared a group only once, and for only 6 wk. Mean relatedness among adult males sharing a group was lower than that among males in different groups. Randomization tests indicate that closely related males share groups no more often than expected by chance, although these tests had limited power. We suggest that the demographic conditions that allow kin-biased dispersal to evolve do not occur in mangabeys, may be unusual among primates, and are worth further attention

Effects of the Distribution of Female Primates on the Number of Males

The spatiotemporal distribution of females is thought to drive variation in mating systems, and hence plays a central role in understanding animal behavior, ecology and evolution. Previous research has focused on investigating the links between female spatiotemporal distribution and the number of males in haplorhine primates. However, important questions remain concerning the importance of spatial cohesion, the generality of the pattern across haplorhine and strepsirrhine primates, and the consistency of previous findings given phylogenetic uncertainty. To address these issues, we examined how the spatiotemporal distribution of females influences the number of males in primate groups using an expanded comparative dataset and recent advances in Bayesian phylogenetic and statistical methods. Specifically, we investigated the effect of female distributional factors (female number, spatial cohesion, estrous synchrony, breeding season duration and breeding seasonality) on the number of males in primate groups. Using Bayesian approaches to control for uncertainty in phylogeny and the model of trait evolution, we found that the number of females exerted a strong influence on the number of males in primate groups. In a multiple regression model that controlled for female number, we found support for temporal effects, particularly involving female estrous synchrony: the number of males increases when females are more synchronously receptive. Similarly, the number of males increases in species with shorter birth seasons, suggesting that greater breeding seasonality makes defense of females more difficult for male primates. When comparing primate suborders, we found only weak evidence for differences in traits between haplorhines and strepsirrhines, and including suborder in the statistical models did not affect our conclusions or give compelling evidence for different effects in haplorhines and strepsirrhines. Collectively, these results demonstrate that male monopolization is driven primarily by the number of females in groups, and secondarily by synchrony of female reproduction within groups

Systems Integration of Biodefense Omics Data for Analysis of Pathogen-Host Interactions and Identification of Potential Targets

The NIAID (National Institute for Allergy and Infectious Diseases) Biodefense Proteomics program aims to identify targets for potential vaccines, therapeutics, and diagnostics for agents of concern in bioterrorism, including bacterial, parasitic, and viral pathogens. The program includes seven Proteomics Research Centers, generating diverse types of pathogen-host data, including mass spectrometry, microarray transcriptional profiles, protein interactions, protein structures and biological reagents. The Biodefense Resource Center (www.proteomicsresource.org) has developed a bioinformatics framework, employing a protein-centric approach to integrate and support mining and analysis of the large and heterogeneous data. Underlying this approach is a data warehouse with comprehensive protein + gene identifier and name mappings and annotations extracted from over 100 molecular databases. Value-added annotations are provided for key proteins from experimental findings using controlled vocabulary. The availability of pathogen and host omics data in an integrated framework allows global analysis of the data and comparisons across different experiments and organisms, as illustrated in several case studies presented here. (1) The identification of a hypothetical protein with differential gene and protein expressions in two host systems (mouse macrophage and human HeLa cells) infected by different bacterial (Bacillus anthracis and Salmonella typhimurium) and viral (orthopox) pathogens suggesting that this protein can be prioritized for additional analysis and functional characterization. (2) The analysis of a vaccinia-human protein interaction network supplemented with protein accumulation levels led to the identification of human Keratin, type II cytoskeletal 4 protein as a potential therapeutic target. (3) Comparison of complete genomes from pathogenic variants coupled with experimental information on complete proteomes allowed the identification and prioritization of ten potential diagnostic targets from Bacillus anthracis. The integrative analysis across data sets from multiple centers can reveal potential functional significance and hidden relationships between pathogen and host proteins, thereby providing a systems approach to basic understanding of pathogenicity and target identification

Interaction among apoptosis-associated sequence variants and joint effects on aggressive prostate cancer

<p>Abstract</p> <p>Background</p> <p>Molecular and epidemiological evidence demonstrate that altered gene expression and single nucleotide polymorphisms in the apoptotic pathway are linked to many cancers. Yet, few studies emphasize the interaction of variant apoptotic genes and their joint modifying effects on prostate cancer (PCA) outcomes. An exhaustive assessment of all the possible two-, three- and four-way gene-gene interactions is computationally burdensome. This statistical conundrum stems from the prohibitive amount of data needed to account for multiple hypothesis testing.</p> <p>Methods</p> <p>To address this issue, we systematically prioritized and evaluated individual effects and complex interactions among 172 apoptotic SNPs in relation to PCA risk and aggressive disease (i.e., Gleason score ≥ 7 and tumor stages III/IV). Single and joint modifying effects on PCA outcomes among European-American men were analyzed using statistical epistasis networks coupled with multi-factor dimensionality reduction (SEN-guided MDR). The case-control study design included 1,175 incident PCA cases and 1,111 controls from the prostate, lung, colo-rectal, and ovarian (PLCO) cancer screening trial. Moreover, a subset analysis of PCA cases consisted of 688 aggressive and 488 non-aggressive PCA cases. SNP profiles were obtained using the NCI Cancer Genetic Markers of Susceptibility (CGEMS) data portal. Main effects were assessed using logistic regression (LR) models. Prior to modeling interactions, SEN was used to pre-process our genetic data. SEN used network science to reduce our analysis from > 36 million to < 13,000 SNP interactions. Interactions were visualized, evaluated, and validated using entropy-based MDR. All parametric and non-parametric models were adjusted for age, family history of PCA, and multiple hypothesis testing.</p> <p>Results</p> <p>Following LR modeling, eleven and thirteen sequence variants were associated with PCA risk and aggressive disease, respectively. However, none of these markers remained significant after we adjusted for multiple comparisons. Nevertheless, we detected a modest synergistic interaction between <it>AKT3 rs2125230-PRKCQ rs571715 </it>and disease aggressiveness using SEN-guided MDR (p = 0.011).</p> <p>Conclusions</p> <p>In summary, entropy-based SEN-guided MDR facilitated the logical prioritization and evaluation of apoptotic SNPs in relation to aggressive PCA. The suggestive interaction between <it>AKT3-PRKCQ </it>and aggressive PCA requires further validation using independent observational studies.</p

A biology-based approach for quantitative structure-activity relationships (QSARs) in ecotoxicity.

Quantitative structure-activity relationships (QSARs) for ecotoxicity can be used to fill data gaps and limit toxicity testing on animals. QSAR development may additionally reveal mechanistic information based on observed patterns in the data. However, the use of descriptive summary statistics for toxicity, such as the 4-day LC50 for fish, introduces bias and ignores valuable kinetic information in the data. Biology-based methods use all of the toxicity data in time to derive time-independent and unbiased parameter estimates. Such an approach offers whole new opportunities for mechanism-based QSAR development. In this paper, we apply the hazard model from DEBtox to analyse survival data for fathead minnows (Pimephales promelas). Different modes of action resulted in different patterns in the parameter estimates, and therefore, the toxicity data by themselves reveal insight into the actual mechanism of toxic action