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Throughout the millennia of human history, our species has undergone a remarkable journey of adaptation and evolution, responding to the challenges posed by our ever-changing environment. Central to this process is the remarkable molecule known as DNA, which carries the genetic information that defines who we are as individuals. Over time, this genetic code has evolved and diversified, resulting in the phenomenon we refer to as genetic variation, the differences in DNA sequences among individuals within a population (Armstrong, 2017).
Genetic variation manifests itself in numerous ways, influencing traits such as our ability to metabolize caffeine or digest lactose-containing foods.
For instance, some individuals can enjoy a cup of coffee without experiencing sleep disturbances, while others may suffer from gastrointestinal discomfort after consuming milk. These differences are a product of our genetic makeup, prompting us to explore the origins of such variations. Charles Darwin's theory of natural selection posits that heritable traits vary among individuals within a species, and those traits conferring a higher likelihood of survival and reproduction are favored (Leicht, 2018).
One striking example of genetic variation in humans is the ability to digest cow's milk.
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Individuals who possess this ability have a distinct advantage, as milk provides a valuable source of nutrients. The digestion of lactose, the primary carbohydrate in mammalian milk, depends on the presence of an enzyme in the intestine that hydrolyzes lactose into glucose and galactose (Mattar, 2012). The LCT enhancer region, situated on chromosome 2, contains two alleles, C and T. As humans are diploid organisms with sexual reproduction, they inherit one allele from each parent, resulting in three possible genotypic combinations: CC, CT, and TT.
Similarly, the CYP1A2 gene, located on chromosome 15, encodes the Cytochrome enzyme responsible for metabolizing caffeine and theophylline, among other substances (Sachse, 1999). The CYP1A2 locus harbors two alleles, C and A, leading to the genotypes AA, AC, or CC.
In this experiment, we utilized DNA samples obtained from students enrolled in Biology 1411 to investigate the tolerance to lactose and the metabolism of caffeine based on their genetic profiles. Our objective is to ascertain whether the genetic variation observed at the CYP1A2 and LCT loci reflects the outcome of evolutionary processes. To explore this question, we will apply the Hardy-Weinberg Principle, a fundamental concept in population genetics that describes a non-evolving population. This principle asserts that allele frequencies within a population will remain constant from one generation to the next in the absence of disruptive factors (Andrew, 2010).
We employed the polymerase chain reaction (PCR) technique to amplify and analyze the LCT and CYP1A2 loci. Cheek cells were collected as the source of template DNA, and PCR allowed us to amplify the target DNA selectively within a test tube. In contrast to normal DNA replication, where the entire DNA molecule is copied, PCR specifically targets and duplicates a defined DNA sequence (Leicht, 2018). Think of it as a search engine, where you input keywords (PCR primer sequences) into a specific database (template DNA), and it precisely replicates the DNA sequence of interest.
For the CYP1A2 locus, the primer sequences used were as follows: CYP-F (5'-GAGAGCGATGGGGAGGGC-3') and CYP-R (5'-CCCTTGAGACCCAGAATACC-3'). The amplification process involved a total of forty-two cycles. In the case of the LCT locus, the primer sequences were LCT-F (5'-GTTGAATGCTCACGACCATG-3') and LCT-R (5'-TGCTTTGGTTGAAGCGAAGATG-3'). Similarly, a total of forty-two cycles were employed, but with different temperature settings (Leicht, 2018).
To distinguish between different allele types, we utilized restriction enzyme digestion. A 10μl cocktail labeled "A" was employed for the CYP1A2 locus at 25°C, while a 10μl cocktail labeled "B" was used for the LCT locus at 65°C. We prepared agarose gels using a 1.6% agarose solution (0.64g in 40ml), heating the loci before loading. To facilitate later visualization, samples were stained with 3μl of 10X Loading Dye after a water bath incubation. The gel electrophoresis setup involved loading the gel with DNA size markers, uncut samples, and samples digested with C and L enzymes, following a carefully sequenced process. The electrophoresis system ran for half an hour at the HIGH setting, and each gel was subsequently photographed. Later in the analysis, phenotypic distinctions were made to differentiate between the types. To assess the validity of our hypothesis, we utilized the Hardy-Weinberg Principle to calculate the P-value, determining whether the data supported or rejected our hypothesis. Since each locus has two alleles, three genotypes can be identified, and three equations were employed to determine:
Our results are presented through two sets of gel photographs that clearly distinguish the phenotypes of each student, including their caffeine metabolization rate and lactose tolerance or intolerance. Due to the complexity of the data, we provide backup gel photographs for further analysis.
In the LCT experiment, as previously mentioned, we observed three distinct genotypes. The presence of the C allele indicates the absence of the BsmF1 site, signifying lactose intolerance in adulthood. On the other hand, the T allele, which carries the BsmF1 site, represents lactose tolerance in adulthood. The gel image below illustrates the genotypes, with 'M' denoting the base pair bands indicator, 'U' representing uncut samples, 'S1' corresponding to CT genotypes, 'S2' indicating TT genotypes, 'S3' representing CC genotypes, 'S4' as CT genotypes, and the last one being uncut.
In the CYP1A2 experiment, we also observed three genotypes. The presence of the 'A' allele indicates the absence of the ApaI site, while the 'C' allele confirms the presence of the ApaI site, which is dominant. Consequently, 'AA' genotypes signify fast caffeine metabolizers, while 'AC' or 'CC' genotypes indicate slow caffeine metabolism. The gel image below illustrates the genotypes, with 'M' serving as the base pair bands indicator, 'U' representing uncut samples, 'S1' as CC genotypes, 'S2' indicating AA genotypes, 'S3' representing AC genotypes, 'S4' as AA genotypes, and the last one being uncut.
Looking at the pooled genotype data for LCT tolerance, we analyzed a total of 266 samples, with 77 being TT, 100 being CT, and 89 being CC. The frequency for the 'T' allele is calculated as [2(77) + 100] / 532 = 0.47, resulting in a 'C' allele frequency of 1 - 0.47 = 0.53. In terms of caffeine metabolism, we examined 235 samples, with 107 being AA, 81 being AC, and 47 being CC. The frequency for the 'A' allele is determined as [2(107) + 81] / 470 = 0.63, while the 'C' allele frequency is 0.37.
| TT | CT | CC | TOTAL |
|---|---|---|---|
| 77 | 100 | 89 | 266 |
| AA | AC | CC | TOTAL |
|---|---|---|---|
| 107 | 81 | 47 | 266 |
Examining the self-reported data for LCT tolerance, we collected a total of 425 samples. Out of these, 360 individuals reported being lactose tolerant, 41 reported lactose intolerance, while 24 remained uncertain, summing up to 401 responses. The estimated frequency for the 'C' allele is 0.35, with the remaining frequency attributed to 'T' at 0.65. Regarding caffeine metabolism, we gathered data from 422 samples, where 198 reported fast caffeine metabolism, 137 reported slow metabolism, and 87 were uncertain, resulting in a total of 335 responses. The estimated frequency for the 'A' allele is 0.77, with the complementary frequency being 'C' at 0.23.
In order to perform a Chi-square analysis, it is essential to pool the data to determine the observed number of each genotype. The table below provides the expected genotype frequencies, the number of individuals expected out of the total sample, the number of individuals observed out of the total sample, and the calculated Chi-Square values for both the LCT and CYP1A2 loci:
| Locus Name | Expected Genotype freq. | # of Individuals Expected out of Total | # of Individuals Observed out of Total | Chi-Square |
|---|---|---|---|---|
| CC | 0.2809 | 74.7194 | 89 | 2.73 |
| CT | 0.4982 | 132.5212 | 100 | 7.98 |
| TT | 0.2209 | 58.7594 | 77 | 5.66 |
| Total 1 | Total 1 | Total 266 | Total 266 | Total 16.37 |
| Locus Name | Expected Genotype freq. | # of Individuals Expected out of Total | # of Individuals Observed out of Total | Chi-Square |
|---|---|---|---|---|
| CC | 0.1369 | 32.1715 | 47 | 6.83 |
| AC | 0.4662 | 109.557 | 81 | 7.44 |
| AA | 0.3969 | 93.2715 | 107 | 2.02 |
| Total 1 | Total 1 | Total 235 | Total 235 | Total 16.29 |
The P-Value for both Chi-square scores at degrees of freedom (DF) = 1, under the significance level of 0.05, is extremely small, < 0.05. Therefore, we reject the null hypothesis in favor of the alternative hypothesis.
The results of our study indicate that the genetic makeup of the Biology 1411 class adheres to the Hardy-Weinberg (H-W) expectations for genotype frequencies at both the CYP1A2 and LCT loci, as evidenced by similar Chi-Square results and statistically significant, small P-values. However, it is important to acknowledge that several factors may have influenced these results. Our classes consist of individuals from diverse geographic backgrounds, implying the presence of gene flow and the potential for genetic exchange between populations. While the chance of spontaneous mutations is relatively low, we meet the random mating requirements described by the H-W principle. Nevertheless, natural selection may be at play, favoring fast caffeine metabolism, which allows us to consume more caffeine, and lactose tolerance, which offers an alternative source of nutrients. Both traits contribute to increased survival and align with the principles of evolution.
It is worth noting that the size of our sampled population is not particularly large, and there were some procedural errors during the experiments that may have led to inaccuracies or, in some cases, the absence of outcomes. Despite these limitations, we can still make some insightful observations. Based on our data, it is reasonable to expect that genetic variation in humans can result in faster metabolism and increased lactose tolerance. However, it is also plausible that metabolism speed may decrease in modern humans who no longer face the same immediate threats as our ancestors did, requiring quick reactions and alertness.
In line with our findings, Segurel and Bon (2017) suggest that the ability to broaden the dietary repertoire and derive glucose from milk has been strongly favored in certain populations. This emphasizes the significant role of lactose tolerance in modern society. It's noteworthy that the diagnosis of lactose intolerance remains a complex matter, as Sachse et al. (1999) pointed out. The genetic basis of lactose intolerance may not be straightforward, and personal experiences, such as your own, may deviate from genetic predictions. This could be due to a variety of factors, including the specific dietary habits of your ancestors. Our understanding of lactose intolerance may benefit from further refinement and improved testing methods.
Looking forward, the field of genetics holds the potential for exciting developments. It is conceivable that in the future, we may have the ability to manipulate genes to enhance our metabolism or eliminate discomfort associated with caffeine consumption. Such advancements could revolutionize the way we interact with our genetic makeup, offering new possibilities for optimizing our health and well-being.
Genetic Variation in Humans among Loci CYP1A2 and LCT. (2024, Jan 23). Retrieved from https://studymoose.com/document/genetic-variation-in-humans-among-loci-cyp1a2-and-lct
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