Classification Tree Analysis as a Method for Uncovering Relations Between CHRNA5A3B4 and CHRNB3A6 in Predicting Smoking Progression in Adolescent Smokers

Sex- and genotype-dependent nicotine plus cue-primed reinstatement is enhanced in adolescent Sprague Dawley rats containing the human CHRNA6 3'-UTR polymorphism (rs2304297)

Rationale

Large-scale human candidate gene studies have indicated that a genetic variant (rs2304297) in the alpha(α)6 nicotinic acetylcholine receptor (nAChR) subunit, encoded by the CHRNA6 gene, may play a key role in adolescent nicotine addictive behavior. We hypothesized that the polymorphism selectively enhances nicotine + cue-primed reinstatement, but not nicotine- or cue-reinstatement in α6 GG (risk) vs. α6 CC (non-risk) allele carriers, without having baseline effects on natural rewards.

Methods

Using CRISPR-Cas9 genomic engineering, we developed a humanized rat line with the human gene variant of the CHRNA6 3'-UTR C 123 G polymorphism in Sprague-Dawley rats. Genetically modified adolescent male and female rats were food trained under a fixed-ratio (FR)1 schedule of reinforcement and progressively increased to FR5. Animals were implanted with catheters and began nicotine self-administration (15 μg/kg/infusion) at FR5. Upon reaching stable responding, reinforced behavior was extinguished by removal of drug and cues. Reinstatement testing began for cue only, nicotine only, and nicotine + cue in a Latin Square Design. Animals were returned to extinction conditions for 2 days minimum between testing.

Results

For natural food rewards, nicotine self-administration, progressive ratio, and extinction, adolescent male and female (α6 GG and α6 CC ) rats exhibited equivalent behaviors. Male α6 GG rats show enhanced nicotine + cue-primed reinstatement when compared with male α6 CC rats. This genotype effect on reinstatement was not seen in female rats.

Conclusion

Our findings support the in vivo functional role of the human CHRNA6 3'-UTR SNP genetic variant in sex-dependently enhancing nicotine seeking behavior in adolescent rats. Overall, the findings support clinical and preclinical data highlighting a role of α6 nAChRs mediating sex heterogeneity in substance use and related phenotypes.

Sex- and Genotype-Dependent Nicotine-Induced Behaviors in Adolescent Rats with a Human Polymorphism (rs2304297) in the 3'-UTR of the CHRNA6 Gene

In human adolescents, a single nucleotide polymorphism (SNP), rs2304297, in the 3′-UTR of the nicotinic receptor subunit gene, CHRNA6, has been associated with increased smoking. To study the effects of the human CHRNA6 3′-UTR SNP, our lab generated knock-in rodent lines with either C or G SNP alleles. The objective of this study was to determine if the CHRNA6 3′-UTR SNP is functional in the knock-in rat lines. We hypothesized that the human CHRNA6 3′-UTR SNP knock-in does not impact baseline but enhances nicotine-induced behaviors. For baseline behaviors, rats underwent food self-administration at escalating schedules of reinforcement followed by a locomotor assay and a series of anxiety tests (postnatal day (PN) 25-39). In separate cohorts, adolescent rats underwent 1- or 4-day nicotine pretreatment (2×, 30 μg/kg/0.1 mL, i.v.). After the last nicotine injection (PN 31), animals were assessed behaviorally in an open-field chamber, and brain tissue was collected. We show the human CHRNA6 3′-UTR SNP knock-in does not affect food reinforcement, locomotor activity, or anxiety. Further, 4-day, but not 1-day, nicotine exposure enhances locomotion and anxiolytic behavior in a genotype- and sex-specific manner. These findings demonstrate that the human CHRNA6 3′-UTR SNP is functional in our in vivo model.

The role of nicotinic receptor genes (CHRN) in the pathways of prenatal tobacco exposure on smoking behavior among young adult light smokers

BACKGROUND

Prenatal tobacco exposure (PTE) is associated with more frequent smoking among young, light smokers. Little is known about how nicotinic acetylcholine receptor (CHRN) genes may contribute to this relationship.

METHODS

Data were drawn from a longitudinal cohort of young light smokers of European ancestry (N = 511). Three single nucleotide polymorphisms (SNPs) among offspring, rs16969968 and rs6495308 in CHRNA5A3B4 and rs2304297 in CHRNB3A6, were analyzed with respect to whether they 1) predict PTE status; 2) confound the previously-reported effects of PTE on future smoking; 3) have effects on youth smoking frequency that are mediated through PTE; and 4) have effects that are moderated by PTE.

RESULTS

rs2304297 and rs6495308 were associated with increased likelihood and severity of PTE, respectively. In a path analysis, rs16969968 directly predicted more frequent smoking in young adulthood (B = 1.50, p = .044); this association was independent of, and not mediated by, PTE. The risk of rs16969968 (IRR = 1.07, p = .015) and the protective effect of rs2304297 (IRR = 0.84, p < .001) on smoking frequency were not moderated by PTE. PTE moderated the effect of rs6495308, such that these alleles were protective against later smoking frequency only among non-exposed youth (IRR = 0.85, p < .001).

CONCLUSIONS

The association between offspring CHRNB3A6 and PTE is a novel finding. The risk of rs16969968 on youth smoking is independent and unrelated to that of PTE among young, light smokers. PTE moderates the protective effect of rs6495308 on youth smoking frequency. However, PTE's pathway to youth smoking behavior was not explained by these genetic factors, leaving its mechanism(s) of action unclear.

Big Data's Role in Precision Public Health

Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.