Investigating the influence of perinatal nicotine and alcohol exposure on the genetic profiles of dopaminergic neurons in the VTA using miRNA–mRNA analysis

Kazemi, Tina; Huang, Shuyan; Avci, Naze G.; Waits, Charlotte Mae K.; Akay, Yasemin M.; Akay, Metin

doi:10.1038/s41598-020-71875-1

Cited by 18 publications

(15 citation statements)

References 137 publications

(157 reference statements)

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“…We identified several similarities across cigarette smoking and alcohol use traits. For example, neuronal processes such as neuronal migration and apoptosis were associated with cigarette smoking and alcohol use, which is in line with studies that have pinpointed the disruption of neuronal migration and neurotransmission in response to substance use 29–31 . We also observed myelination and gliogenesis to be associated with DPW and CPD, respectively, hinting at the role of neuron-glia interactions in substance use traits 32, 33 .…”

Section: Resultssupporting

confidence: 84%

Chromatin architecture in addiction circuitry elucidates biological mechanisms underlying cigarette smoking and alcohol use traits

Won

Akbarian

Hancock

et al. 2021

Preprint

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Cigarette smoking and alcohol use are among the most prevalent substances used worldwide and account for a substantial proportion of preventable morbidity and mortality, underscoring the public health significance of understanding their etiology. Genome-wide association studies (GWAS) have successfully identified genetic variants associated with cigarette smoking and alcohol use traits. However, the vast majority of risk variants reside in non-coding regions of the genome, and their target genes and neurobiological mechanisms are unknown. Chromosomal conformation mappings can address this knowledge gap by charting the interaction profiles of risk-associated regulatory variants with target genes. To investigate the functional impact of common variants associated with cigarette smoking and alcohol use traits, we applied Hi-C coupled MAGMA (H-MAGMA) built upon cortical and midbrain dopaminergic neuronal Hi-C datasets to GWAS summary statistics of nicotine dependence, cigarettes per day, problematic alcohol use, and drinks per week. The identified risk genes mapped to key pathways associated with cigarette smoking and alcohol use traits, including drug metabolic processes and neuronal apoptosis. Risk genes were highly expressed in cortical glutamatergic, midbrain dopaminergic, GABAergic, and serotonergic neurons, suggesting them as relevant cell types in understanding the mechanisms by which genetic risk factors influence cigarette smoking and alcohol use. Lastly, we identified pleiotropic genes between cigarette smoking and alcohol use traits under the assumption that they may reveal substance-agnostic, shared neurobiological mechanisms of addiction. The number of pleiotropic genes was ∼26-fold higher in dopaminergic neurons than in cortical neurons, emphasizing the critical role of ascending dopaminergic pathways in mediating general addiction phenotypes. Collectively, brain region- and neuronal subtype-specific 3D genome architecture refines neurobiological hypotheses for smoking, alcohol, and general addiction phenotypes by linking genetic risk factors to their target genes.

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Section: Resultssupporting

confidence: 84%

Chromatin architecture in addiction circuitry elucidates biological mechanisms underlying cigarette smoking and alcohol use traits

Won

Akbarian

Hancock

et al. 2021

Preprint

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“…As shown in Figure 1b, the running time of calculating the feature importance using the Random Forest model increases linearly against the feature size. The short running time facilities the generation of stable feature The classification accuracy of the algorithm (i.e., correctly predicting all three labels) was 68 ± 15 %, which was similar to other classification accuracy of other samples from our studies [29,31]. This method is rather stable.…”

Section: B Selecting Important Features Efficientlysupporting

confidence: 72%

“…All experiments were performed in accordance with the protocols approved by the Institutional Animal Care and Use Committee (IACUC) and the University of Houston Animal Care Operations (ACO). The detailed protocol has been published previously [29,31,32]. Briefly, pregnant Sprague-Dawley (SD) rats (Charles River, Wilmington, MA, USA) were maintained at standard conditions and were given an ad libitum diet.…”

Section: A Animal Experimentsmentioning

confidence: 99%

“…These DEGs and DEmiRs were identified from microarray data which compared gene and miRNA expression profiles of VTA DA neurons between treatment groups and their respective controls. This approach has been previously described in more detail [29,32]. Briefly, this method is based on a qvalue <0.001 (adjusted p-value using Benjamini-Hochberg (BH) correction) and absolute log2 fold change >1 (≥1 for upregulation, ≤−1 for downregulation) [32].…”

Section: A Data Multi-labelingmentioning

confidence: 99%

“…In this paper, we performed feature selection using the supervised Random Forest classifier over a collection of expression data (differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs)) obtained from 13 microarrays. These data were previously generated by our group to investigate perinatal exposure to alcohol, nicotine, and both nicotine and alcohol during rat gestational development [29,30]. This final dataset for this study consisted of 5523 genes and miRNAs for the alcohol exposure, 7863 for nicotine, and 5613 for co-exposure (i.e., nicotine and alcohol) dataset.…”

Section: Introductionmentioning

confidence: 99%

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Selecting Relevant Genes From Microarray Datasets Using a Random Forest Model

Xia

Akay

2021

IEEE Access

Self Cite

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Recent studies have demonstrated microarray expression data can be used to identify gene regulatory pathways. However, one of the major challenges is to utilize the large microarray data (genes and micro-RNAs) to have an efficient computational model. Therefore, there is an urgent need to reduce the dimensionality of these large sets using machine learning methods without compromising the accuracy. This requires an appropriate machine learning algorithm to select the significant features from these large datasets. Therefore, in this study, we use a supervised method based on a Random Forest to identify significant features from three microarray datasets from prenatal nicotine, alcohol, and nicotine and alcohol exposure groups in two different cell types (dopamine and non-dopamine neurons). Our approach was computationally efficient to reduce the dimensionality of extremely large microarray datasets. Furthermore, our results indicated that using only the top 20% of features was sufficient to confirm the genetic pathways previously identified when using all of the features in the model.

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Chronic nicotine exposure alters sperm small RNA content in C57BL/6J mouse model

Zeid

Gould

2023

Developmental Psychobiology

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Multigenerational inheritance is a nongenomic form of heritability characterized by altered phenotypes in the first generation born from the exposed parent. Multigenerational factors may account for inconsistencies and gaps in heritable nicotine addiction vulnerability. Our lab previously found that F1 offspring of male C57BL/6J mice chronically exposed to nicotine exhibited altered hippocampus functioning and related learning, nicotine-seeking, nicotine metabolism, and basal stress hormones. In an effort to identify germline mechanisms underlying these multigenerational phenotypes, the current study sequenced small RNA extracted from sperm of males chronically administered nicotine using our previously established exposure model. We identified 16 miRNAs whose expression in sperm was dysregulated by nicotine exposure. A literature review of previous research on these transcripts suggested an enrichment for regulation of psychological stress and learning. mRNAs predicted to be regulated by differentially expressed sperm small RNAs were further analyzed using exploratory enrichment analysis, which suggested potential modulation of pathways related to learning, estrogen signaling, and hepatic disease, among other findings. Overall, our findings point to links between nicotine-exposed F0 sperm miRNA and altered F1 phenotypes in this multigenerational inheritance model, particularly F1 memory, stress, and nicotine metabolism. These findings provide a valuable foundation for future functional validation of these hypotheses and characterization of mechanisms underlying male-line multigenerational inheritance.

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Investigating the influence of perinatal nicotine and alcohol exposure on the genetic profiles of dopaminergic neurons in the VTA using miRNA–mRNA analysis

Cited by 18 publications

References 137 publications

Chromatin architecture in addiction circuitry elucidates biological mechanisms underlying cigarette smoking and alcohol use traits

Chromatin architecture in addiction circuitry elucidates biological mechanisms underlying cigarette smoking and alcohol use traits

Selecting Relevant Genes From Microarray Datasets Using a Random Forest Model

Chronic nicotine exposure alters sperm small RNA content in C57BL/6J mouse model

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