| 31. |
- Bernardi, R. E., et al.
(författare)
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A gene-by-sex interaction for nicotine reward: evidence from humanized mice and epidemiology
- 2016
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Ingår i: Translational Psychiatry. - : NATURE PUBLISHING GROUP. - 2158-3188. ; 6:e861
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Tidskriftsartikel (refereegranskat)abstract
- It has been proposed that vulnerability to nicotine addiction is moderated by variation at the mu-opioid receptor locus (OPRM1), but results from human studies vary and prospective studies based on genotype are lacking. We have developed a humanized mouse model of the most common functional OPRM1 polymorphism rs1799971_A4G (A118G). Here we use this model system together with a cohort of German youth to examine the role of the OPRM1 A118G variation on nicotine reward. Nicotine reinforcement was examined in the humanized mouse model using i.v. self-administration. Male (n = 17) and female (n = 26) mice homozygous either for the major human A allele (AA) or the minor G allele (GG) underwent eight daily 2 h sessions of nicotine self-administration. Furthermore, male (n = 104) and female (n = 118) subjects homozygous for the A allele or carrying the G allele from the Mannheim Study of Children at Risk were evaluated for pleasurable and unpleasant experiences during their initial smoking experience. A significant sex-by-genotype effect was observed for nicotine self-administration. Male 118GG mice demonstrated higher nicotine intake than male 118AA mice, suggesting increased nicotine reinforcement. In contrast, there was no genotype effect in female mice. Human male G allele carriers reported increased pleasurable effects from their first smoking experience, as compared to male homozygous A, female G and female homozygous A allele carriers. The 118G allele appears to confer greater sensitivity to nicotine reinforcement in males, but not females.
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| 32. |
- Oskolkov, Nikolay, et al.
(författare)
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High-throughput muscle fiber typing from RNA sequencing data
- 2022
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Ingår i: Skeletal Muscle. - : Springer Science and Business Media LLC. - 2044-5040. ; 12, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- Background: Skeletal muscle fiber type distribution has implications for human health, muscle function, and performance. This knowledge has been gathered using labor-intensive and costly methodology that limited these studies. Here, we present a method based on muscle tissue RNA sequencing data (totRNAseq) to estimate the distribution of skeletal muscle fiber types from frozen human samples, allowing for a larger number of individuals to be tested. Methods: By using single-nuclei RNA sequencing (snRNAseq) data as a reference, cluster expression signatures were produced by averaging gene expression of cluster gene markers and then applying these to totRNAseq data and inferring muscle fiber nuclei type via linear matrix decomposition. This estimate was then compared with fiber type distribution measured by ATPase staining or myosin heavy chain protein isoform distribution of 62 muscle samples in two independent cohorts (n = 39 and 22). Results: The correlation between the sequencing-based method and the other two were rATPas = 0.44 [0.13–0.67], [95% CI], and rmyosin = 0.83 [0.61–0.93], with p = 5.70 × 10–3 and 2.00 × 10–6, respectively. The deconvolution inference of fiber type composition was accurate even for very low totRNAseq sequencing depths, i.e., down to an average of ~ 10,000 paired-end reads. Conclusions: This new method (https://github.com/OlaHanssonLab/PredictFiberType) consequently allows for measurement of fiber type distribution of a larger number of samples using totRNAseq in a cost and labor-efficient way. It is now feasible to study the association between fiber type distribution and e.g. health outcomes in large well-powered studies.
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