Using life course charts to assess and compare trajectories of amphetamine type stimulant consumption in different user groups: a cross-sectional study

BackgroundAmphetamine-type stimulants (ATS) are the second most commonly used illicit drugs in Europe and globally. However, there is limited understanding of what shapes patterns of ATS use over the life course. The ATTUNE project “Understanding Pathways to Stimulant Use: a mixed methods examination of the individual, social and cultural factors shaping illicit stimulant use across Europe” aims to fill this gap. Here we report initial findings from the life course chart exercise conducted as part of qualitative interviews with ATS users and nonusers.MethodsTwo hundred seventy-nine in-depth qualitative interviews were conducted with five ATS user groups (current and former dependent users;current and former frequent users;non-frequent users) and one group of exposed non-ATS users in five European countries (Germany, UK, Poland, Netherlands and Czech Republic). As part of the interviews, we used life course charts to capture key life events and substance use histories. Life events were categorised as either positive, neutral or negative, and associated data were analysed systematically to identify differences between user groups. We applied statistical analysis of variance (ANOVA) and analysis of covariance (ANCOVA) to test for group differences.ResultsOut of 3547 life events documented, 1523 life events were categorised as neutral, 1005 life events as positive and 1019 life events as negative. Current and formerly dependent ATS users showed more negative life events for the entire life course after age adjustment. Although some group differences could be attributed to the individuals’ life course prior to first ATS use, most negative life events were associated with periods of ATS usage. A detailed analysis of the specific life domains reveals that dominantly, the social environment was affected by negative life events.ConclusionsFor non-dependent, frequent and non-frequent ATS users, negative life events from the period of ATS use do not become obvious in our analysed data. Besides preventing a pathway into ATS dependency, the aim of an intervention should be to reduce the harm by for example drug testing which offers also the opportunity for interventions to prevent developing a substance use dependency.For the group of dependent ATS users, our study suggests holistic, tailored interventions and specialist treatment services are needed, as a single, simple intervention is unlikely to cover all the life domains affected

Model-Based Deconvolution of Cell Cycle Time-Series Data Reveals Gene Expression Details at High Resolution

In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure “just-in-time” assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systematic error in the cell synchrony process and variance in the timing of cell division at the level of the single cell. Thus, gene or protein expression data collected from a population of synchronized cells is an inaccurate measure of what occurs in the average single-cell across a cell cycle. Here, we present a general computational method to extract “single-cell”-like information from population-level time-series expression data. This method removes the effects of 1) variance in growth rate and 2) variance in the physiological and developmental state of the cell. Moreover, this method represents an advance in the deconvolution of molecular expression data in its flexibility, minimal assumptions, and the use of a cross-validation analysis to determine the appropriate level of regularization. Applying our deconvolution algorithm to cell cycle gene expression data from the dimorphic bacterium Caulobacter crescentus, we recovered critical features of cell cycle regulation in essential genes, including ctrA and ftsZ, that were obscured in population-based measurements. In doing so, we highlight the problem with using population data alone to decipher cellular regulatory mechanisms and demonstrate how our deconvolution algorithm can be applied to produce a more realistic picture of temporal regulation in a cell

Comprehensive Structural and Substrate Specificity Classification of the Saccharomyces cerevisiae Methyltransferome

Methylation is one of the most common chemical modifications of biologically active molecules and it occurs in all life forms. Its functional role is very diverse and involves many essential cellular processes, such as signal transduction, transcriptional control, biosynthesis, and metabolism. Here, we provide further insight into the enzymatic methylation in S. cerevisiae by conducting a comprehensive structural and functional survey of all the methyltransferases encoded in its genome. Using distant homology detection and fold recognition, we found that the S. cerevisiae methyltransferome comprises 86 MTases (53 well-known and 33 putative with unknown substrate specificity). Structural classification of their catalytic domains shows that these enzymes may adopt nine different folds, the most common being the Rossmann-like. We also analyzed the domain architecture of these proteins and identified several new domain contexts. Interestingly, we found that the majority of MTase genes are periodically expressed during yeast metabolic cycle. This finding, together with calculated isoelectric point, fold assignment and cellular localization, was used to develop a novel approach for predicting substrate specificity. Using this approach, we predicted the general substrates for 24 of 33 putative MTases and confirmed these predictions experimentally in both cases tested. Finally, we show that, in S. cerevisiae, methylation is carried out by 34 RNA MTases, 32 protein MTases, eight small molecule MTases, three lipid MTases, and nine MTases with still unknown substrate specificity