Genetic Variation Analysis: CYP1A2 and TAS2R38 Loci Study

Introduction

In the world, no two individuals are exactly alike, owing to the intricate interplay of genes that underlies the diversity of life and fuels the process of evolution. The vast array of living organisms on Earth can attribute its existence to various factors, with mutations, natural selection, and heritable traits being chief among them. These factors contribute significantly to the unique attributes of each individual, resulting in a rich tapestry of life forms. Measuring the extent of genetic variation among individuals can provide valuable insights into the evolution and temporal changes of such variations.

Typically, this genetic variation is expressed as a percentage of a specific phenotype within a defined population.

Among the myriad of traits that distinguish one person from another, two specific loci were examined in this experiment: the CYP1A2 and TAS2R38 genes. The study aimed to predict whether individuals were "tasters" and to determine the speed at which they metabolized caffeine. These traits fall under the category of discrete characteristics, as individuals can either be tasters or non-tasters, slow or fast caffeine metabolizers, with no intermediate options.

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This is in contrast to quantitative traits, like height, where a range of values is possible.

The rate at which our bodies metabolize caffeine is primarily determined by the CYP1A2 gene, located on chromosome 15. This gene encodes the enzyme Cytochrome P450 1A2, which initiates the caffeine metabolism process (Leicht and McAllister, 2018).

It is worth noting that CYP1A2 plays a significant role in metabolizing various commonly used drugs, including clozapine, imipramine, caffeine, paracetamol, phenacetin, theophylline, and tacrine, among others (Sachse et al.

, 1999). The CYP1A2 gene possesses two alleles: A and C. Individuals with the AA genotype are fast caffeine metabolizers, while those with the AC or CC genotype are slow metabolizers. These alleles are located within intron 1 and represent a single nucleotide polymorphism (SNP), denoted as A or C (Leicht and McAllister, 2018). This SNP leads to the production of a restriction fragment length polymorphism (RFLP).

The second gene examined in this project was the TAS2R38 gene, responsible for encoding the protein that enables individuals to taste the bitter chemical phenylthiocarbamide (Leicht and McAllister, 2018). This gene is situated on human chromosome number seven. The two alleles investigated in this study are the PAV allele and the AVI allele, with PAV being dominant over AVI. For simplicity, these alleles can be represented as "T" for PAV and "t" for AVI, based on the dominance of PAV. Tasters possess either the TT or Tt genotype, while non-tasters have the tt genotype.

Our group formulated hypotheses based on whether we believed that both loci, only one, or neither would conform to Hardy-Weinberg Equilibrium. To meet the criteria for Hardy-Weinberg Equilibrium, five conditions must be satisfied: no mutations, an infinite population size, random mating, no gene flow, and no natural selection. Fulfilling these conditions results in a non-evolving gene pool (Leicht and McAllister, 2018). Given the complexity of meeting all these criteria, we predicted that neither of the loci would conform to Hardy-Weinberg Equilibrium.

Our experimental approach encompassed several steps, beginning with the extraction of individual DNA samples, followed by DNA amplification to isolate the target loci. Subsequently, gel electrophoresis was employed to separate DNA fragments of different lengths.

Materials and Methods

DNA Extraction

To examine the two target loci, we initiated the process by identifying tasters and non-tasters within the class through self-examination. Each class member used a strip of PTC paper and placed it on their tongues. Those who could taste the paper were categorized as "tasters," while those who tasted nothing were categorized as "non-tasters." These initial predictions were subject to error due to the inherent variability of self-examination. Additionally, each classmate recorded their belief about whether they were fast or slow caffeine metabolizers.

Following these preliminary predictions, we commenced the DNA extraction procedure to verify our initial assumptions. The extraction process began with each individual using a cotton swab to collect DNA from the epithelial cells on the inside of their cheek. Each person swabbed their cheek for approximately 20 seconds, after which the swab was placed into a tube containing DNA extraction solution. The tube, now holding combined DNA and extraction solution, was securely closed and vortexed for 10 seconds. The tubes were then incubated at 65°C. Subsequently, we vortexed the tubes for an additional 15 seconds and incubated them for two minutes at 98°C. The tubes were vortexed for 15 seconds once more, and throughout this process, they were kept in a cup of ice to maintain the integrity of the DNA.

PCR Amplification

For the third part of the experiment, we set up polymerase chain reactions (PCR) to amplify the target loci. As a group, we obtained two 0.2 ml PCR tubes containing the PCR Master Mix, along with tubes containing the primer mixes. The CYP1A2 locus used two primers: a forward primer with the sequence 5'GAGAGCGATGGGGAGGGC3' and a reverse primer with the sequence 5'CCCTTGAGACCCAGAATACC3'. The recognition site for the C allele was identified as 5'GGGCCC3' for Apa 1, while the A allele lacked this recognition site, allowing us to distinguish between slow and fast metabolism.

For the TAS2R38 locus, we used two primers as well. The forward primer, TAS2R38F, had the sequence 5'AACTGGCAGATTAAAGATCTCAATTTAT3', while the reverse primer, TAS2R38R, had the sequence 5'AACACAAACCATCACCCCTATTTT3' (Leicht and McAllister, 2018). Each forward and reverse primer mix was prepared with a concentration of 500 nanometers.

Next, we pipetted 20 microliters of each primer mix into the respective PCR tubes. Additionally, 5 microliters of the cheek cell DNA extract were added to each PCR tube. The tubes were thoroughly mixed, and after a brief centrifugation (approximately five seconds), they were loaded into the thermocycler. The thermocyclers had been pre-programmed with specific temperatures and durations. The cycling process began with an initial denaturation step at 94°C for 5 minutes, followed by 35 cycles of denaturation at 94°C for 30 seconds, annealing at 58°C for 30 seconds, and extension at 68°C for one minute. Finally, the thermocycler completed the process by a single cycle of polymerase extension at 68°C for 5 minutes.

Following PCR amplification, we subjected our DNA to treatment with restriction enzymes using restriction fragment length polymorphism (RFLP) to isolate the specific alleles we were interested in. We combined the required amounts of PCR DNA, sterile water, and restriction enzyme cocktail. The C tube was incubated at 65°C, while the T tube was incubated at 37°C. The tubes were allowed to incubate for approximately 60 minutes.

Gel Electrophoresis

While our PCR tubes were incubating, we set up the gel electrophoresis apparatus. These gels, composed of agarose, were poured into molds with wells at one end to accommodate the DNA samples. The percentage of agarose (ranging from 0.7% to 2%) was selected based on the size of the DNA molecules in the mixture, with lower percentages being suitable for larger molecules and higher percentages for smaller fragments. Our group calculated that 0.64 grams of agarose in 40 ml of 1.6% agarose solution was needed. We carefully measured out the agarose, placed it in an Erlenmeyer flask, added 40 ml of 1X TBE buffer, and swirled the flask to mix the contents. The solution was microwaved for one minute and then allowed to cool. Ethidium Bromide, used to stain the DNA fragments during migration, was added by our TA while the agarose was still warm.

Once the mixture was ready and the agarose had cooled in the mold, we carefully removed the comb, which had created wells for loading the DNA samples. 1X TBE buffer was added to the chamber to fully immerse the gel, ensuring that it did not overflow.

Loading and Running the Gel

With our gel electrophoresis setup and chambers ready, we proceeded to add 3 microliters of 10X loading dye to each group member's digested DNA samples. Simultaneously, we collected tubes containing the leftover undigested DNA and added 6 microliters of 10X loading dye to each of these samples. Once all our samples were prepared, we pipetted 10 microliters from each tube into the designated wells on the gel. To provide reference points for our DNA samples, we included undigested DNA on both sides of our target DNA samples.

Following the loading of all DNA samples into their respective wells, we applied an electrical current set to high for a duration of 30-40 minutes (Leicht and McAllister, 2018). This current induced the movement of negatively charged DNA molecules towards the positive electrode and positively charged molecules towards the negative electrode. Smaller DNA molecules moved through the gel at a faster rate than larger ones. After the electrophoresis process was complete, we captured photographs of each gel and labeled the images to correspond with the specific loci present in each gel.

Genotype Frequency Calculations

The final step in our experiment involved assessing whether the pooled data from our Foundations of Biology class conformed to Hardy-Weinberg equilibrium. To accomplish this, we employed the Hardy-Weinberg equation to calculate allele and genotype frequencies. Our first task was to determine allele frequencies.

The initial part of the Hardy-Weinberg equation is represented as (p + q = 1), where "p" denotes the frequency of one allele, typically the dominant allele, and "q" represents the frequency of the other allele, generally the recessive allele. The sum of "p" and "q" always equals 1. By knowing the frequency of one allele, we can derive the frequency of the other.

The second segment of the Hardy-Weinberg equation is (p^2 + 2pq + q^2 = 1), which pertains to genotype frequencies, indicating the percentage of individuals in a population with a specific phenotype. Here, "p^2" corresponds to the squared allele frequency from the previous equation, as does "q^2" with the recessive allele. "2pq" represents the heterozygous genotype, while "p" and "q" denote the homozygous dominant and homozygous recessive genotypes, respectively.

To determine whether our initial null hypothesis should be rejected or not, we used the Chi-square goodness-of-fit test to compare our calculated values from the Hardy-Weinberg equation with the observed values from our class data. The Chi-square goodness-of-fit test equation is χ^2 = Σ [ (Observed - Expected)^2 / Expected ]. This equation yields a p-value that, along with one degree of freedom, helped us determine whether to accept or reject our initial null hypothesis.

Results

Our gel electrophoresis results revealed distinct banding patterns that corresponded to the different genotypes of the CYP1A2 and TAS2R38 genes. By comparing the observed bands with our expected outcomes, we were able to calculate allele and genotype frequencies. These frequencies were subsequently utilized in the Chi-square goodness-of-fit test to assess whether our data conformed to Hardy-Weinberg equilibrium.

Discussion

Our experiment aimed to determine whether the genetic variations at the CYP1A2 and TAS2R38 loci in our class population conformed to Hardy-Weinberg equilibrium. We began by conducting self-examinations to classify individuals as tasters or non-tasters of PTC and to identify their perceived caffeine metabolization rates. While this initial categorization provided a preliminary assessment, it had inherent limitations due to subjectivity and individual perception.

The subsequent steps of DNA extraction, PCR amplification, and gel electrophoresis allowed us to examine the genetic variations at the molecular level. Gel electrophoresis enabled us to distinguish between different alleles and genotypes based on the migration patterns of DNA fragments through the agarose gel. This separation provided us with the necessary data to calculate allele and genotype frequencies.

Our calculations were crucial for determining whether our class data adhered to Hardy-Weinberg equilibrium, as genetic variations over generations are governed by this principle. The Chi-square goodness-of-fit test allowed us to compare the observed and expected values, yielding a p-value that helped us assess the equilibrium. In the context of our experiment, the null hypothesis was that the genetic variations at the CYP1A2 and TAS2R38 loci in our class population were in Hardy-Weinberg equilibrium.

Ultimately, the p-value obtained from the Chi-square test, when compared with a significance level (usually 0.05), informed us whether to reject or fail to reject the null hypothesis. If the p-value was less than 0.05, we would reject the null hypothesis, indicating that the genetic variations deviated from Hardy-Weinberg equilibrium. Conversely, if the p-value exceeded 0.05, we would fail to reject the null hypothesis, suggesting that the genetic variations in our class population were in equilibrium.

Conclusion

In conclusion, our experiment explored genetic variations at the CYP1A2 and TAS2R38 loci within our class population. We began with subjective self-examinations to categorize individuals as tasters or non-tasters of PTC and estimate their caffeine metabolization rates. Subsequently, we performed DNA extraction, PCR amplification, and gel electrophoresis to analyze genetic variations at the molecular level. Through careful calculations and statistical analysis, we evaluated whether our data adhered to Hardy-Weinberg equilibrium.

Our findings provide insights into the genetic diversity within our class population and shed light on the principles of genetic variation and equilibrium. By applying the Chi-square goodness-of-fit test, we were able to make informed conclusions regarding the genetic variations at the studied loci. The results of our experiment contribute to our understanding of the complex interplay between genetic factors that shape individual traits and characteristics.