Publication Date


Document Type

Doctoral Dissertation

Academic Program



Neurobiology; Gardner Lab

First Thesis Advisor

Paul D. Gardner


Nicotine, Withdrawal, Addiction, Anxiety, mRNA, miRNA, sequencing, Profilin, pfn2, ventral tegmental area, nucleus accumbens, interpeduncular nucleus, medial habenula


Nicotine dependence is responsible for perpetuating the adverse health effects due to tobacco use, the leading cause of preventable death worldwide. Nicotine is an agonist for nicotinic acetylcholine receptors, which are enriched in the mesocorticolimbic and habenulo-interpeduncular circuitries, underlying nicotine reward and withdrawal, respectively. Drugs of abuse, including nicotine, induce stable neuroadaptations, requiring protein synthesis through regulation of transcription factors, epigenetic mechanisms, and non-coding RNAs. It also been shown that miRNAs in brain are regulated by nicotine and that miRNA dysregulation contributes to brain dysfunction, including drug addiction. While much is known about the neurocircuitry responsible for the behaviors associated with nicotine reward or withdrawal, the underlying molecular mechanisms of how these changes in behavior are induced are less clear.

Using miRNA-/mRNA-Seq, we demonstrate that there are widespread changes in both miRNA and mRNA expression in brain regions comprising the mesocorticolimbic circuit after chronic nicotine treatment, and the habenulo-interpeduncular circuit during acute nicotine withdrawal. Conserved, differentially expressed miRNAs were predicted to target inversely regulated mRNAs. We determined that expression of miR-106b-5p is up-regulated and Profilin 2 (Pfn2), an actin-binding protein enriched in the brain, is down-regulated in the interpeduncular nucleus (IPN) during acute nicotine withdrawal. Further we show that miR-106b-5p represses Pfn2 expression. We demonstrate that knockdown of Pfn2 in the IPN is sufficient to induce anxiety, a symptom of withdrawal. This novel role of Pfn2 in nicotine withdrawal-associated anxiety is a prime example of this dataset’s utility, allowing for the identification of a multitude of miRNAs/mRNA which may participate in the molecular mechanisms underlying the neuroadaptations of nicotine dependence.



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