ACSL6 Is Associated with the Number of Cigarettes Smoked and Its Expression Is Altered by Chronic Nicotine Exposure

Individuals with schizophrenia tend to be heavy smokers and are at high risk for tobacco dependence. However, the nature of the comorbidity is not entirely clear. We previously reported evidence for association of schizophrenia with SNPs and SNP haplotypes in a region of chromosome 5q containing the SPEC2, PDZ-GEF2 and ACSL6 genes. In this current study, analysis of the control subjects of the Molecular Genetics of Schizophrenia (MGS) sample showed similar pattern of association with number of cigarettes smoked per day (numCIG) for the same region. To further test if this locus is associated with tobacco smoking as measured by numCIG and FTND, we conducted replication and meta-analysis in 12 independent samples (n>16,000) for two markers in ACSL6 reported in our previous schizophrenia study. In the meta-analysis of the replication samples, we found that rs667437 and rs477084 were significantly associated with numCIG (p = 0.00038 and 0.00136 respectively) but not with FTND scores. We then used in vitro and in vivo techniques to test if nicotine exposure influences the expression of ACSL6 in brain. Primary cortical culture studies showed that chronic (5-day) exposure to nicotine stimulated ACSL6 mRNA expression. Fourteen days of nicotine administration via osmotic mini pump also increased ACSL6 protein levels in the prefrontal cortex and hippocampus of mice. These increases were suppressed by injection of the nicotinic receptor antagonist mecamylamine, suggesting that elevated expression of ACSL6 requires nicotinic receptor activation. These findings suggest that variations in the ACSL6 gene may contribute to the quantity of cigarettes smoked. The independent associations of this locus with schizophrenia and with numCIG in non-schizophrenic subjects suggest that this locus may be a common liability to both conditions

Fast and Accurate Resonance Assignment of Small-to-Large Proteins by Combining Automated and Manual Approaches

The process of resonance assignment is fundamental to most NMR studies of protein structure and dynamics. Unfortunately, the manual assignment of residues is tedious and time-consuming, and can represent a significant bottleneck for further characterization. Furthermore, while automated approaches have been developed, they are often limited in their accuracy, particularly for larger proteins. Here, we address this by introducing the software COMPASS, which, by combining automated resonance assignment with manual intervention, is able to achieve accuracy approaching that from manual assignments at greatly accelerated speeds. Moreover, by including the option to compensate for isotope shift effects in deuterated proteins, COMPASS is far more accurate for larger proteins than existing automated methods. COMPASS is an open-source project licensed under GNU General Public License and is available for download from http://www.liu.se/forskning/foass/tidigare-foass/patrik-lundstrom/software?l=en. Source code and binaries for Linux, Mac OS X and Microsoft Windows are available.Funding Agencies|Swedish Research Council [Dnr. 2012-5136]</p

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns

Evidence for similar structural brain anomalies in youth and adult attention-deficit/hyperactivity disorder: a machine learning analysis

Attention-deficit/hyperactivity disorder (ADHD) affects 5% of children world-wide. Of these, two-thirds continue to have impairing symptoms of ADHD into adulthood. Although a large literature implicates structural brain differences of the disorder, it is not clear if adults with ADHD have similar neuroanatomical differences as those seen in children with recent reports from the large ENIGMA-ADHD consortium finding structural differences for children but not for adults. This paper uses deep learning neural network classification models to determine if there are neuroanatomical changes in the brains of children with ADHD that are also observed for adult ADHD, and vice versa. We found that structural MRI data can significantly separate ADHD from control participants for both children and adults. Consistent with the prior reports from ENIGMA-ADHD, prediction performance and effect sizes were better for the child than the adult samples. The model trained on adult samples significantly predicted ADHD in the child sample, suggesting that our model learned anatomical features that are common to ADHD in childhood and adulthood. These results support the continuity of ADHD’s brain differences from childhood to adulthood. In addition, our work demonstrates a novel use of neural network classification models to test hypotheses about developmental continuity