- Sacramento
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THE HIGHLY POLYMORPHIC HUMAN CYTOCHROME P450 (CYP) 2A6 GENE:
EXAMINING DIVERSITY AND NICOTINE METABOLISM IN A CENTRAL
AFRICAN POPULATION
A Thesis
Presented to the faculty of the Department of Anthropology
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS in
Anthropology by
Hayley Mann
SUMMER
2013 i
THE HIGHLY POLYMORPHIC HUMAN CYTOCHROME P450 (CYP) 2A6 GENE:
EXAMINING DIVERSITY AND NICOTINE METABOLISM IN A CENTRAL
AFRICAN POPULATION
A Thesis by
Hayley A. Mann
Approved by:
__________________________________, Committee Chair
Roger J. Sullivan, PhD
__________________________________, Second Reader
Samantha M. Hens, PhD
____________________________
Date ii
Student: Hayley A. Mann
I certify that this student has met the requirements for format contained in the University format manual, and that this thesis is suitable for shelving in the Library and credit is to be awarded for the thesis.
__________________________, Graduate Coordinator ___________________
Michael G. Delacorte, PhD Date
Department of Anthropology iii
Abstract of
THE HIGHLY POLYMORPHIC HUMAN CYTOCHROME P450 (CYP) 2A6 GENE:
EXAMINING DIVERSITY AND NICOTINE METABOLISM IN A CENTRAL
AFRICAN POPULATION by
Hayley A. Mann
Human cytochrome P450 (CYP) 2A6 is largely responsible for the catalysis of coumarin and nicotine. In comparison to other human loci, CYP2A6 exhibits a high degree of polymorphism. Some CYP2A6 gene variants have a major effect on phenotypes and presently there are over eighty defined CYP2A6 alleles, which can be divided into either ‘extensive’ or ‘slow’ alleles. Broad individual and inter-ethnic differences in nicotine metabolic rate have been documented. For example, populations of Caucasian and African-descent are commonly cited as extensive metabolizers while Japanese populations are cited as poor metabolizers. Although CYP2A6 has been widely studied by pharmacogeneticists, a consensus regarding the evolutionary mechanisms responsible for generating extensive CYP2A6 diversity is lacking in the literature. The parasite-toxin evolutionary trade-off model explores the possible relationships between nicotine consumption, parasites and CYP2A6 alleles. The Aka are a Central African foraging society that has access to various forms of nicotine, and who are hypothesized in this study to have a higher than expected frequency of slow CYP2A6 alleles in order to help iv
alleviate helminth infection. In order to determine whether or not the Aka can be characterized as a slow or extensive CYP2A6 metabolizing population on average, a genotyping analysis using RFLP and AS-PCR methodologies was performed on 72 blood spots. Alleles assayed include: extensive allele CYP2A6*1B , slow metabolizing alleles
CYP2A6*9 , CYP2A6*17 and CYP2A6*20 , which were found at a frequency of 13.89%,
7.64%, 11.11%, and 0% respectively. Furthermore, the pharmacogenetics and genomics literature was thoroughly reviewed for information interpreting CYPA6 genotyping results, as well as identifying previously CYP2A6 genotyped populations that may be useful for allele frequency comparisons. The genotyping results obtained in this study suggest that the Aka population represent a more extensive metabolizing population, which does not support the study hypothesis. The absence of support for the evolutionary trade-off model and issues that should be addressed for future research are discussed in detail.
_______________________, Committee Chair
Roger J. Sullivan, PhD
_______________________
Date v
ACKNOWLEDGMENTS
Before this great achievement could be made possible, the Department of
Anthropology at Sacramento State had to first accept me as a graduate student. I am incredibly thankful for the faculty recognizing my potential as a Biological
Anthropologist. However, it was my advisor Dr. Roger Sullivan and my committee member Dr. Samantha Hens who believed in me the most. This thesis is a testament to my growth under their guidance and the excellent mentorship they have given me.
I must also thank my mentors at Washington State University. Dr. Edward Hagen gave me the opportunity to contribute to this fascinating project and Dr. Brian Kemp allowed me to complete the laboratory component of my thesis in his stellar molecular anthropology facility. I look forward to future collaborations.
My parents, Richard and Laurie Mann, have invested heavily into ensuring I was successful, but in Darwinian terms this makes sense considering I am their only offspring. Although our genetic lineage may not continue, I promise that our surname will live on in the scientific literature.
Considering all of the help I received from friends, I seriously question inclusive fitness theory. Dr. Anna Ludwig played an exceptionally supportive role throughout the years by providing me with article resources and her valuable expertise in molecular biology. The luxurious duty of emotionally supporting me during graduate school fell upon Joshua Valverde and Christopher Terry—they have been my greatest fans and have never complained once while I rambled on and on about the field I am completely enamored with. vi
TABLE OF CONTENTS
Page
Chapter
Pharmacophagy and Human-Parasite Coevolution .................................................. 3
Acquisition of DNA Samples from Aka Individuals ............................................. 33
CYP2A6 Allele Frequencies of Aka Individuals .................................................... 45
CYP2A6 Genotype and predicted phenotype frequencies of Aka individuals ....... 46
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viii
LIST OF TABLES
Tables Page
1.
2.
CYP2A6 Allele PCR Reagent Concentration ........................................................ 36
3.
4.
Predicted Indivudal CYP2A6 Allele Effect on Metabolic Phenotype ................... 43
5.
Allele Frequencies of CYP2A6 of AKA Population ............................................. 46
6.
CYP2A6 Genotype Frequencies of AKA Population............................................ 47
7.
CYP2A6*1B and CYP2A6 *9 Allele Frequencies Across Populations .................. 54
8.
Sample Population Frequencies of Homozygous Wild Type CYP2A6*1 / CYP2A6*1
Individuals..............................................................................................................58
ix
LIST OF FIGURES
Figures Page
1.
CYP2A6*1B Allele Specific PCR Genotyping ..................................................... 39
2.
3.
CYP2A6*9 Allele -27T>G Mutation Genotyping ................................................ 42
Population Percentage of Extensive, Intermediate and Slow Metabolizing
x
1
CHAPTER 1
BACKGROUND
Subfamily P450 cytochrome 2A6 ( CYP2A6 ) is a member of the P450 superfamily of heme-thiolate monooxygenases, which play a central role in the detoxification of xenobiotics (Zawaira et al., 2008). CYP2A6 is located on chromosome 19q13.2 in the
CYP2ABFGST gene cluster (Hoffman et al., 2001), is approximately 6 kb in length and is comprised of nine exons that encode a total of 494 amino acids (Mwenifumbo and
Tyndale, 2009). Researchers have determined that CYP2A6 is the primary enzymeencoding gene that is responsible for metabolizing nicotine into the alkaloid compound cotinine (COT) in humans (Binnington et al., 2012; Nakajima et al., 1996a). CYP2B6 and
CYP2D6 are also minor contributors in the catalysis of nicotine (Pelkonen et al., 2000;
Yamazaki et al., 1999). After the oxidation of nicotine, cotinine is further hydroxylated into trans-3’-hydroxycotinine (3HC) exclusively by CYP2A6 before being excreted from the body (Binnington et al., 2012; Nakajima et al., 1996b). CYP2A6 is also believed to be the only gene responsible for the catalysis of coumarin; a highly prevalent plant secondary metabolite that is hydroxylated into 7’-hydroxycoumarin (7-OHC) before being excreted from the body (Pelkonen et al., 2000; Yamano et al., 1990). CYP2A6 also plays a role in caffeine metabolism where the caffeine metabolite 1,7-dimethyxanthine
(17X) is hydroxylated into 1,7-dimethyluric acid (17U) prior to excretion (Kimura et al.,
2005; Nowell et al., 2002). In order to study the rate of in vivo nicotine metabolism, the metabolites cotinine, 7-OHC and 17U can be used as biomarkers, and can be detected in urine, saliva and plasma. However, a study by Peamkrasatam et al. (2006) suggests that
2
CYP2A6 metabolic rate results may differ depending on which in vivo biomarker approach is used.
Pharmacogenetics studies determined that there is considerable interindividual and ethnic variability in nicotine metabolic rates, and implicate CYP2A6 alleles
(Mwenifumbo and Tyndale, 2009; Nakajima et al., 2006). CYP2A6 is highly polymorphic and is one of the most genetically diverse genes within the CYP subfamilies
(Solus et al., 2004). Presently, there are approximately 80 defined CYP2A6 alleles
(Ingelman-Sunberg et al., 2010). Furthermore, CYP2A6 alleles are structurally diverse and defined as single nucleotide polymorphisms, nucleotide deletions and insertions, conversions, whole gene duplications, and whole gene deletions (Mwenifumbo &
Tyndale, 2009). A population’s ‘metabolic status’ is defined here as referring to whether a population collectively possesses a more ‘extensive’ or ‘slow’ rate of
CYP2A6 metabolism on average. The two major metabolic status trends that have been documented include Caucasian and African-descent populations who have a high proportion of extensive metabolizers and Asians (especially Japanese) who have an exceptionally high proportion of slow metabolizers (Nakajima et al., 2006). However, the ‘racial categories’ used by pharmacogeneticists in order to describe their populations are problematic in that these terms do not sufficiently describe the sample populations analyzed in these studies. Therefore, given the existence of considerable metabolic rate variation between individual CYP2A6 genotypes, the broad trends cited in the pharmacogenetics literature are likely inadequate in documenting the actual diversity of population-specific CYP2A6 allele distribution and metabolic statuses.
3
One reason for interest in CYP genotyping is that poor metabolism of certain pharmaceuticals can lead to side effects and dosing issues in patients (Andrejak et al.
1998; Hoffman et al., 2001). Furthermore, pharmacogeneticists are also interested in the link between CYP2A6 genotype, and aspects of tobacco use, such as prevalence of smoking, and incidence of tobacco related diseases in different populations (Ariyoshi et al., 2002; Benowitz et al., 2002; Binnginton et al., 2012). Pharmacogenetics studies have provided evidence linking CYP2A6 genotypes to increased tobacco-usage behaviors, and a higher risk for tobacco-specific diseases (Ariyoshi et al., 2002; Binnington et al., 2012).
For example, Ariyoshi et al. (2002) found a link between carriers who are homozygous for the deleted gene CYP2A6 variant *4 and a significantly reduced risk of lung cancer.
An explanation for the prior results is that homozygous deleted-gene individuals consumed nicotine less frequently, which Ariyoshi concludes is the reason why a reduction in lung cancer prevalence was observed. There is therefore good evidence that
CYP2A6 genotypes influence nicotine consumption behaviors in humans.
Pharmacophagy and Human-Parasite Coevolution
The CYP2A6 gene represents a fascinating example of the evolutionary process.
Despite a high degree of polymorphism and an intriguing distribution of CYP2A6 alleles across different human populations, there is little discussion in the literature regarding what selective pressures may be responsible for these patterns (Mann et al., 2012). Given the role of CYP genes in the catalysis of xenobiotics, researchers have discussed the selective pressure of dietary toxins on a few CYP genes (Fuselli et al., 2010). Sullivan et al. (2008) and Hagen at al. (2009) have hypothesized that plant toxins were exploited by
4 human ancestors in order to control infections of helminths and other parasites. Humans and helminthes have a long co-evolutionary history and it is nearly impossible for the immune system to eliminate helminth infections. A host can, however, limit damage— for example, helminth infection is associated with a strong anti-inflammatory response
(Polland, 2008). Another way in which a host may control helminth infection is pharmacophagy—the strategic consumption of plant toxins to kill parasites. Sullivan et al. (2008) and Hagen et al. (2009) suggest that humans have evolved a propensity for recreational drug use as a form of human pharmacophagy. Support for their idea includes that the tobacco plant is commonly used as an antihelmintic. For example, farmers in some underdeveloped regions use nicotine in order to treat livestock for parasite infections (Iqbal et al., 2006; Sullivan et al., 2008). Furthermore, several modern anthelmintics target the same receptor as nicotine, which causes paralysis (Kohler, 2012) followed by expulsion of parasites.
Pharmacophagy is the behavior performed by an herbivore whereby non-nutritive compounds are ingested for the purpose of defense (Smilanich et al., 2011). A review of the literature shows that pharmacophagy is often studied amongst caterpillar species and other insects, as well as livestock, and it is believed that pharmacophagy may explain why different species feed on certain host plants (Boppre, 1986; Provenza and Villalba,
2010; Smilanich et al., 2011; Villalba et al., 2010). For example, the larvae of the
American Monarch butterfly ( Danaus plexippus ) feed on milkweed plants in order to sequester pyrrolizidine alkaloids (PAs) thereby making the larvae toxic to other predators
(Boppre, 1986). In fact, PAs are exploited by several unrelated taxa, which suggest that
5 there is a polyphyletic origin for this type of pharmacophagy behavior (Boppre, 1986).
The tradeoff between the cost associated with plant secondary metabolite consumption and the increase of survival due to a reduction in parasitoid load has also been directly observed (Barbosa et al., 1991; Kohler et al., 2012; Smilanich et al., 2011). The polyphagous caterpillar Grammia incorrupta sequesters PAs, which was shown to decrease its fly and wasp parasitoid burden (Smilanich et al., 2011). In addition, it was found that G. incorrupta exhibited different host plant specificity depending on the parasitoid taxon present and the stage of infection (Smilanich et al., 2011). It is also known that nicotine alkaloid consumption inhibits growth of several bacterial and fungal pathogens (Pavia et al., 2000) and studies have documented enhanced survival of parasitized organisms that feed on tobacco plants (Barbosa et al., 1991). Kohler et al.
(2012) investigated the function of plant secondary metabolites in nectar by conducting a honey bee preference test experiment where nectar nicotine dosage varied. Although honey bees were not tested directly for parasites, Kohler et al. (2012) found that in comparison to sugar-only controls, nicotine increased the survival of “weaker colonies” that were feeding on higher doses of nicotine, thus suggesting that dietary nicotine may have health benefits. An earlier study by Barbosa et al. (1991) examined the hornworm
( Manduca sexta ), which only feeds on the tobacco plant. Three plant secondary metabolites found in the tobacco plant were analyzed independently in order to determine their effect on the horn worm and its parasitoids. Barbosa et al. (1991) found that only nicotine had a pronounced effect on pathogens and different concentrations of nicotine did not affect horn worm mortality. While studies of pharmacophagy in insects provide
6 good evidence for the exploitation of plant secondary metabolites for pathogen burden reduction, this phenomenon has yet to be thoroughly investigated amongst other animal phyla.
It is estimated that humans are hosts for over three hundred species of helminth worms (Cox, 2002). Parasites that infect humans include those inherited by our African primate ancestors and also those that we have acquired from other animals throughout our evolution, migration and agricultural practices (Cox, 2002). The host organismal struggle against pathogens however, represents a significantly longer time span. A parasite can be defined as having a negative impact on a host’s fitness (Penn, 2001). In order to avoid infection, hosts possess various defenses and arguably, the most impressive is the highly complex vertebrate immune system (Penn, 2001). In turn, parasites have evolved counter-adaptations to host defenses (Penn, 2001). The coevolution between hosts and parasites is commonly described as an evolutionary ‘arms race’ and the ‘Red Queen Hypothesis’ (Van Valen, 1973) is often cited when discussing the evolution of sexual reproduction (Morran et al., 2011). Due to co-evolving parasites that genetically ‘track’ their hosts, asexual host lineages are more vulnerable to extinction
(Jaenike, 1978). On the other hand, sexually reproducing hosts are more difficult to track, which is why many biologists believe the costly behavior of sexual reproduction has evolved (Ebert and Hamilton, 1996; Tooby, 1982). Moreover, pathogens have an incredibly high rate of evolution and sexual reproduction can therefore be considered a counter-adaptation in that it increases the rate of evolution in host species (Ebert and
Hamilton, 1996). Scholars have also combined ideas on the evolution of sexual
7 reproduction with theories on the evolution of virulence. Pathogens evolve levels of virulence that are optimal for their respective life histories and it is postulated that hosts continually evolve counter-adaptations specifically to reduce pathogen virulence (Ebert and Hamilton, 1996). Host genetic diversity is therefore crucial for preventing pathogens from achieving optimal virulence, and thus, virulence may be the most influential force in host-parasite coevolution (Ebert and Hamilton, 1996). An additional consideration when discussing human host-pathogen coevolution is physical environment and cultural practices, which are largely responsible for the spread and current distribution of most parasites (Cox, 2002; Ewald, 1993). For example, human sedentary lifestyles increased the transmission of parasites between humans, and the establishment of trade routes in turn led to greater spread of parasitic infections (Cox, 2002). Paul Ewald (1993) has provided several examples of the dynamics affecting human-parasite coevolution, with the evolution of antibiotic resistance being an obvious contemporary example. In summary, the human genome has been heavily shaped by selection from pathogens, and human behavior and culture has also played a role in population differences in pathogen burden.
Evolution and CYP2A6 Variation
Although a case can be made that populations can be meaningfully described as proportions of CYP2A6 extensive or slow metabolizers, pinpointing the precise selective pressures responsible for CYP2A6 allele distributions will be a greater challenge. As previously stated, the xenobiotic substrates for CYP genes could well have played a selective role. While the evolutionary history of CYP2A6 has not been formally
8 discussed, another CYP gene, CYP2D6 has been discussed in regards to human dietary selection regimes. Fuselli et al. (2010) attributes population-specific genetic variation of
CYP2D6 to the transition from hunter-gathering to agricultural food procurement strategies. CYP2D6 sequences from six hunting and gathering African populations were compared to four Asian and European food production societies. The authors concluded that the transition to agriculture (a less toxic diet) ultimately led to an increase of slow rate CYP2D6 alleles in food producing societies (Fuselli et al., 2010). However, in the case of CYP2D6 , as well as, other CYP genes, it would be difficult to identify what substrate (or substrates) in particular was responsible for slow CYP2D6 allele selection
(Fuselli et al., 2010). Further complicating this analysis is that xenobiotic selection pressures have also not remained constant in human populations throughout time (Fuselli et al., 2010). For instance, some of the alleles that are found in high frequencies are nonfunctional alleles, which suggest a lack of selective pressure by specific xenobiotics
(Sullivan et al., 2008) and/or other selective agents. For example, Mwenifumbo and
Tyndale (2009) point out that individuals possessing the homozygous deleted gene variant CYP2A6*4/CYP2A6*4 genotype suggests that the CYP2A6 gene is non-essential in some environments. Deciphering what specific selective agent (or agents) might be responsible for population-specific CYP allele distributions would require an examination of all known CYP xenobiotic substrates dietary consumption across different human populations. Although the prior approach would require great effort, Luca et al. (2010) explains that such an endeavor would be worthwhile considering studying dietary-related
9 genes not only provide insight into human evolutionary history, but also into the metabolic pathways that are associated with certain diseases.
Determining the type of evolutionary processes (e.g.—positive selection, negative selection, genetic drift, etc…) that have affected different CYP genes could help identify selective agents and contribute to a more comprehensive understanding of human dietary adaption. Given the high degree of polymorphism in CYP2A6 , it is possible that neutral processes (drift without selection) may be partially responsible for extant allele distribution. Geographic clines have been used to explain human biological variation patterns for traits that are influenced by natural selection (e.g.—skin color), traits that appear to be neutral (e.g.—craniometrics), as well as, certain presumably neutral genetic polymorphisms (Relethford, 2009). The slow CYP2A6 alleles prevalent in Eastern populations may be explained by a clinal distribution. However, given the broad ethnic categories used in pharmacogenetics and the lack of genotyped populations, describing a
CYP2A6 geographic cline in satisfactory detail is not possible at present. In summary, determining the type of evolutionary processes that have enacted on CYP genes would be useful; if positive selection is detected, then neutral processes can be ruled out, which would support efforts to identify selective agents.
Investigating the effects of evolutionary processes on genes falls within the realm of genomics. There are a few CYP gene studies in the literature that have conducted genomic statistical analyses in order to determine the specific type of evolutionary processes that are currently (or have operated on) certain CYP family members. Solus et al. (2004) analyzed selection in numerous CYP families and subfamilies.
10
Nonsynonymous substitutions can affect the fitness of an organism, therefore, when adaptive molecular evolution occurs, the nonsynonymous substitution rate (dN) will be higher than the synonymous substitution rate (dS) and the ω ratio (dN/dS) will be greater than 1 (Yang et al., 2000). In comparison to other CYP genes analyzed in the study,
Solus et al. (2004) determined that CYP2A6 possesses the highest degree of polymorphic diversity. Despite the high degree of polymorphism amongst CYP loci, Solus et al.
(2004) proposed that negative selection was occurring in almost all of the CYP genes, and furthermore, CYP2A6 had one of the lowest ω values (i.e.—evidence of strong purifying selection). Solus et al. (2004) interpreted the high degree of genetic diversity and the presence of negative selection as environmental diversity coupled with conservation of functional alleles.
Solus et al.’s conclusion differs from that of other genomics researchers. Given the high degree of polymorphism and variable metabolic responses to food and drugs,
CYP genes are attractive candidates for testing for positive selection (Zawaira et al.,
2008). Specifically, genes associated with dietary adaptation are predicted to be positively selected (Vallender and Lahn, 2004). In another CYP genomics study, Fonseca et al. (2007) used the maximum likelihood (ML) method which, unlike traditional ω ratio estimates, accounts for the transition/transversion mutational rate bias, as well as the codon-usage bias (Yang and Bielawski, 2000). Although the traditional codon-based analysis (ω) in theory has less statistical power than ML, both methods are flawed in that they assume that all amino acid changes are advantageous (Yang & Bielawski, 2000)— thus ignoring the possibility of neutral processes. One way to avoid the assumption that
11 all amino acid substitution changes are beneficial is to distinguish between codon sites that are under positive selection from sites that are highly conserved (McClellan et al.,
2004). Therefore, most of the studies concerned with determining what evolutionary processes are operating on CYP genes include only substrate recognition site (SRS) analyses (Fonseca et al., 2007; Gotoh, 1992; Thomas, 2007; Zawaira et al., 2008).
Because SRSs (the protein-encoding domains of the enzyme) actively bind foreign chemicals, they are assumed to be the units under selection in the CYP gene, whereas other genetic regions have remained more conserved over time. Using the ML approach,
Fonseca et al. (2007) calculated ω ratios for SRSs and detected positive selection in subfamily member CYP2C . Their finding contradicts Solus et al. (2004) who obtained ω
< 1 values for all CYP2 subfamily members analyzed ( CYP2C19 =0.06, CYP2C9 =0.59, and CYP2C9 =0.56). Considering that Fonseca et al. (2007) detected positive selection in the CYP2C family, Solus et al.’s detection of purifying selection on CYP2A6 should be questioned. On the other hand, the different analytical methodologies used by Solus et al.
(2004) and Fonesca et al. (2007) suggest two very different stories for the evolution of
CYP allele variation.
Because CYP genes are numerous and metabolize a diverse array of substrates, the possibility exists that different types of selection are occurring on different CYP s, and a review of the literature indicates that this is probably the case (Fuselli et al., 2010;
Gotoh, 1992; Wooding et al., 2002; Voight et al., 2006; Zawaira et al., 2008).
Distinguishing between positive selection and genetic drift is often a challenge for genomics researchers (e.g. Galtier et al., 2008; Kostka et al., 2011). For example, relaxed
12 constraints (weak selection) can also cause an excess of nonsynonymous mutations, thus a signature of positive selection may be detected (Carroll, 2003; Luca et al., 2010). In a
‘nearly-neutral’ model of selection, relaxed constraints can result in an accumulation of deleterious mutations, which may not be immediately selected against and removed from a gene pool (Ohta, 1992). Challenging a nearly-neutral model of CYP gene evolution,
Gotoh (1992) analyzed the substitution patterns of subfamily members CYP2A , 2B , 2C and 2D
, and found that SRSs consistently had larger ω values (0.74±0.35) in comparison to the rest of the (presumably conserved) protein sequence (0.30±0.08). However, Gotoh
(1992) could still not rule out neutral evolution because although ω was consistently larger in SRS domains, ω values were too insignificant to conclude positive selection.
Despite this, Gotoh (1992) still hypothesized that the diversification of drug-metabolizing
CYP genes occurs in SRS regions in order to adapt to a wide range foreign chemicals over time. Because Gotoh analyzed macro-subfamily CYP2A (which includes various subfamily members) it cannot be determined whether or not subfamily CYP2A6 specifically exhibits positive selection.
In 2008, Zawaira et al. expanded on the work done by Gotoh (1992) by finding statistically significant signatures of adaptive evolution in the human CYP2C and CYP2D
SRSs. CYP2A appears to have been omitted in Zawaira et al.’s (2008) study, but their conclusion supports Gotoh’s notion that CYP SRSs are undergoing adaptive evolution and diversity is selected for in order to expand the breadth of substrates for cytochrome
P450 genes. Furthermore, Zawaira et al. (2008) suggests CYP s have evolved in a cooperative manner where various cytochrome P450 genes ‘work together’ in order to
13 metabolize a greater breadth of xenobiotics (Gotoh, 1992; Zawaira et al. 2008).
However, nonfunctional CYP alleles still represent a curious case in that they reduce the breadth of dietary adaptation which suggests a reduced or complete absence of a xenobiotic substrate, and/or another benefit that outweighs the cost of not metabolizing a xenobiotic. Ziwaira et al.’s study also detected different selection processes operating on
CYP s indicating that cytochrome P450 genes should be analyzed independently in order to detect potential differences in evolutionary history.
Examples of other CYP genes that have been discussed in evolutionary terms include CYP3A4 and CYP3A5 , which are important in pharmacogenetics studies because they metabolize over fifty-percent of pharmaceutical drugs (Thompson et al., 2004).
CYP3A5 polymorphisms have been linked to salt and water retention (Givens et al.,
2003). African Americans have a higher prevalence of hypertension, which is believed to be partially influenced by genetic differences in salt regulation (Thompson et al., 2004).
The CYP3A5*1 and CYP3A5*3 alleles reduce CYP3A5 expression in the liver, which in turn causes a decrease in salt and water absorption. Thus, CYP3A5*1/*3 may be considered as ‘protective’ alleles against hypertension. Researchers that have discussed
CYP3A5 variation from an evolutionary perspective have proposed that polymorphisms leading to an increase in salt and water retention could be beneficial in populations that frequently experience water shortages (Kuehl et al., 2001). Thompson et al. (2004) confirmed that African Americans have a lower percentage of the CYP3A5*3 allele
(~27%-50%) in comparison to non-African American populations (~60-95%). In addition, the authors found that there is a higher prevalence of CYP3A5*3 alleles in
14 populations further away from the equator. Thompson et al. (2004) therefore conclude that greater salt and water retention is disadvantageous in populations further away from the equator, explaining selection for the “normal” allele ( CYP3A5*3 ) in high latitudes.
Finally, given the various substrates metabolized by the CYP3A locus, it has undoubtedly been exposed to multiple types of selective pressures (Chen et al., 2009; Thompson et al.,
2004). In fact, several studies have discovered signatures of positive selection at the
CYP3A locus (Chen et al., 2009; Zawaira et al., 2008). The high degree of polymorphism and phenotypic variability of cytochrome P450 genes is therefore likely due to a complex pattern of natural selection.
In order to explain the high prevalence of alcohol non-metabolizers in East Asian populations, a hypothesis that links environmental toxins and pathogens to positivelyselected genotypes has been proposed. Alcohol-dehydrogenase (ADH) is responsible for the oxidation of ethanol into acetaldehyde, which is further metabolized by aldehyde dehydrogenase (ALDH). The ADH2*2 ( ADH1B*47His ) variant is prevalent in East
Asian populations (~59%) and in comparison to the wild type allele, ADH2*2 accelerates the rate of ethanol oxidation (Osier et al., 2002). The aldehyde-dehydrogenase deficiency variant ( ALDH2*2 ) is also found at a high frequency in East Asian populations (~30%) and it causes acetaldehyde to remain in the blood stream for a longer period of time, thus causing a characteristic facial flushing (Oota et al., 2004; Peterson et al., 1999). In their research on alcohol metabolism population genetics, Oota et al. (2004) concludes that genetic drift alone cannot account for the high prevalence of these two independent loci in East Asian populations. Considering that high levels of acetaldehyde are toxic, it has
15 been proposed that ALDH2 deficiency was selected for as a protective effect against parasites or alcoholism (Goldman and Enoch, 1990). Other researchers have also discussed the beneficial effects of acetaldehyde on systemic pathogens. For example,
Chinese Han have a high prevalence of ALDH2*2 alleles and hepatitis B viral infection
(Lin and Cheng, 2002). Individuals with hepatitis B have a greater risk for liver cirrhosis and cancer, and alcoholics have an even greater risk of developing these liver pathologies. Therefore, the high incidence of hepatitis B may have led to a greater frequency of ALDH2*2 alleles (Lin and Cheng, 2002). Another interesting observation is that nitroimidazole antibiotics inhibit ALDH, which suggests that higher levels of acetaldehyde is beneficial for preventing the growth of various anaerobes and microaerophiles (Goldman and Enoch, 1990; Oota et al., 2004). Although these ALDH2 evolutionary hypotheses are plausible, none have been tested.
Human Parasite Morbidity
Gastrointestinal helminth infections are one of the most prevalent human parasitic infections in developing countries (Brooker, 2010). The WHO (2013) states that more than 1.5 billion (~24% of the world’s population) are infected with soil-transmitted helminths. Infection rates are highest in children residing in sub-Saharan Africa, followed by Asia, Latin America, and the Caribbean (Harhay et al., 2010). It is estimated that approximately a quarter of the population residing in sub-Saharan Africa are infected with at least one or more species of helminths (Harhay et al., 2010). However, a review by Brooker (2010) explains that precise estimates of population morbidity and mortality are difficult to determine for various reasons. National health statistics are likely
16 unreliable considering symptoms of infection are typically non-specific, which suggests that infection rates will be significantly underreported (Bundy et al., 2004). Chan (1997) estimates that one to five deaths each year may be attributable to severe ascariasis
(parasitic roundworm) complications and the WHO (1992) estimates hookworms are responsible for approximately 65,000 deaths per year. However, given the non-specific health consequences of helminth infection, mortality estimates (which are predominantly derived from hospital data) are also unreliable (Brooker, 2010). Given the low helminthassociated mortality rates, morbidity estimates are therefore more important in determining helminth infection prevalence and health impact. Intestinal helminth burden is typically measured by counting the number of eggs per gram (EPGs) of feces (Hotez et l., 2008). Based on EPGs and how certain thresholds relate to morbidity, infected individuals are classified into light, moderate, and heavy infection categories as defined by the WHO (Brooker 2010; Hotez et al., 2008). However, Brooker (2010) points out that studies have found drastically different burden estimates. One explanation may be differences in burden evaluation methodology, including data which is not adequately described (Brooker, 2010). Also, the relationship between EPGs and morbidity is not well understood (Brooker, 2010). In addition to non-specific clinical symptoms and inconsistencies in burden diagnostics, there is also a lack of epidemiological data in countries where helminth burden is known to be most prevalent. For example, although national surveys have recently been conducted in a number of African countries (Angola,
Burkina Faso, Mali, Malawai, Mozambique, Niger, Sierra Leone, and Uganda), the
Central African Republic and the Democratic Republic of the Congo have little to no
17 survey data (Brooker, 2010). Overall, estimating the global distribution and disease burden of helminth is an “inexact science” and will “never be known” (Brooker,
2010:1142-43). Brooker concludes by stating that accurate information is necessary in order to estimate costs and to design effective local intervention programs.
Although approximately 341 helminth species have been found to infect human
(Coombs and Crompton, 1991) leading researchers consider only 25 of these species to be of public health concern (Brooker, 2010; Stoll, 1947). The most globally prevalent helminth species include the soil-transmitted helminths Ascaris lumbricoides , Trichuris trichuira and the hook worms, Necator americanus and Ancyclostoma duodenale
—all of which are known to infect the Aka (Brooker, 2010; Mann et al., 2012; Stoll, 1947). Soil transmitted gastrointestinal helminths are found in environments characterized by warm temperatures, greater humidity, poor sanitation, and substandard/crowded housing
(Harhay et al., 2010). As stated previously, defining specific morbidity is difficult because few clinical symptoms are specific to helminth infection, especially in cases of light and moderate infections (Bundy et al., 2004). What is known, however, is that health risk is strongly associated with the number of worms infecting individuals
(Brooker, 2010). Pathological effects are mostly due to immunological reactions to eggs that become deposited in different tissues (Cox, 2002). For example, although T. trichuira infections are typically asymptomatic, a heavy infection arises when worms spread to the colon and in turn cause abdominal pain, hemorrhages, symptoms of dysentery, and rectal prolapse (Bundy and Cooper, 1989). In the case of A. lumbricoides , pulmonary migration can occur, which causes asthma-like symptoms (Loeffler’s
18 syndrome). Also, migrating A. lumbricoides can cause liver bile duct constriction
(Brooker, 2010). Finally, heavy A. lumbricoides intestinal infection can result in the malabsorption of nutrients, thus causing stunted growth in children (O’Lorcain and
Holland, 2000). Hookworm larva skin penetration can result in intense itching and burning, known as ‘ground itch’ (Brooker, 2010). The serious clinical pathology associated with hook worms is intestinal blood loss, which may result in anemia (Brooker et al., 2008). There is also evidence that moderate and heavy helminth infections in children may affect cognitive functions (Jukes et al., 2008). In addition, Guyatt (2000) discusses the loss of productivity and wage-earning later in adult life due to childhood infections. Overall, health consequences of helminth-causing diseases are related to the reduction of host nutritional status and development, which is difficult to clinically assess
(Hotez et al., 2008). Furthermore, because children suffer most from intestinal helminth infection and they are predominantly responsible for transmission of helminths (poor hygiene practices, etc…), they are predominantly the targets of health intervention programs (Harhay, 2010). Studies relating to health consequences of helminth infection represent less than 1% of global research funds (Hotez, 2008) thus, helminth-related diseases have been categorized as ‘neglected tropical diseases.’
Study Population
The focus of this study is the Aka (also called BaAka, Biaka and Bayaka), a foraging society residing in the western Congo Basin; a tropical forest region encompassing the southwestern Central African Republic (CAR) and the northern part of the Democratic Republic of the Congo (DRC) (Hewlett, 1991). There are an estimated
19
30,000 Aka, and most Aka live in small camps of ~20 to 35 individuals throughout the
Congo-Basin (Hewlett, 1996). The majority of Aka are transitional foragers who spend most of the year hunting and gathering (Hewlett, 1991, 1996). As observed in other hunter-gatherer populations the Aka have high fertility and mortality, and the mean age of the population is young (Hewlett 1991). The Aka also maintain a symbiotic relationship with their Bantu farming neighbors for important social and economic purposes (Bahuchet and Guillaume, 1982; Mann et al., 2012). The Aka speak a dialect of
Bantu language referred to as diaka , which further distinguishes them from their farming neighbors (Hewlett ,1996). Genetic studies have determined that the Aka possess ancient
African mitochondrial DNA (mtDNA) lineages thus making them one of the oldest human populations (Chen et al., 2000). An autosomal microsatellite loci analysis performed by Verdu et al. (2009) suggests that an ancient divergence time of approximately 89,675-53,975 YBP between African hunter-gatherer populations (Aka,
Bezan, Koya, Kola, and Bongo) and other African populations.
The Aka smoke a variety of substances including: cannabis, ‘motunga’
( Polyalthia suaveolens ), cigarettes, and locally grown tobacco (Mann et al., 2012). An unfortunate reality however, is that parasitic diseases are one of the main causes of death for individuals of all ages in Aka communities (Hewlett et al,. 1986). Sub-Saharan
Africa accounts for approximately 90% of world-wide schistosomiasis cases (Steinmann et al., 2006) and the DRC is within the top three countries in reported helminth
(hookworm and ascariasis) burden (Hotez et al., 2009). The Aka however, suffer from an even higher incidence of parasitic worm infection due to exceptionally grim living
20 conditions (Lilly, 2005). While the Aka spend the majority of the year in the tropical forest hunting and gathering, they live near farming villages three to four months each year in order to trade forest products and labor for commercial and agricultural goods
(Bahuchet and Guillaume, 1982; Mann et al., 2012). Because Aka territory is heavily exploited by logging agencies, natural resources utilized by the Aka are diminishing and greater numbers of Aka individuals are becoming more sedentary, exacerbating parasite morbidity (Mogba and Freudeberger, 1998; Lilly et al., 2002). Aka villages (which reside next to logging roads) have little medical care and little-to-no access to commercial antihelmintics (Lilly, 2005; Mann et al., 2012). Lilly et al. (2002) reported that over 90% of the Aka tested positive for helminth infection and in a related study (Lilly, 2005), almost 100% of Aka individuals tested positive for intestinal helminthes. It is not known, however, if the Aka consumed a nicotine-containing plant prior to the introduction of tobacco in West Africa a few centuries ago. Nicotine is produced by a number of Old
World plants, so such consumption is not out of the question. It is also possible that there is another CYP2A6 substrate in West African plants with anti-parasite properties that was consumed by Aka.
Issues in CYP2A6 Research
Data for population CYP2A6 allele frequencies started to appear in the literature after the completion of the Human Genome Project (1995) and pharmacogeneticists have since dominated CYP2A6 research. Prior to1995 single CYP genes were difficult to isolate and amplify via polymerase chain reaction (PCR) due to sequence conservation amongst CYP loci (Hoffman et al., 2001). Evolutionary processes enacting on CYP
21 family tandem arrays (repetitive DNA motifs that normally expand and contract over time) resulted in the retention of extra genes/pseudogenes with extensive DNA sequence homology (Hoffman and Hu, 2007). The publication of improved CYP2A6 allele protocols followed the availability of higher resolution human sequences (Fernandez-
Salguero et al., 1995; Oscarson et al., 1998; Oscarson, 2001; Nakajima et al., 2004;
Paschke et al., 2001). One of the biggest challenges in developing CYP2A6 allele PCR amplification assays is that, without well-designed PCR primers, co-amplification of the similarly conserved genes CYP2A7 and CYP2A13 may occur ultimately convoluting
CYP2A6 genotyping results (Fernandez-Salguero et al., 1995; Hoffman et al., 2001).
Although PCR-based assays for the detection of CYP2A6 alleles are now available, using different PCR methodologies can still yield inconsistent allele frequency estimates
(Hoffman et al., 2001). Another genotyping consideration is that because the CYP2A6 gene is approximately 6,000 DNA base pairs (bp) in length, a standard PCR reaction
(typically up to 2,000 bp) cannot amplify the entire gene. Therefore, more expensive sequencing approaches must be used in order to screen samples for all known CYP2A6 alleles, as well as, to discover new CYP2A6 alleles.
The most common approaches for genotyping samples includes screening a population for a few phenotypically significant (i.e.—extensive or slow) major variant
CYP2A6 alleles by using restriction fragment length polymorphism (RFLP) analysis, or by using a two-step allele-specific PCR (AS-PCR) protocol (Djordjevic et al., 2010;
Gyamfi et al., 2005; Peamkrasatam et al., 2006; Vasconcelos et al., 2005). While RFLPs are relatively standard, CYP2A6 alleles that require a two-step PCR procedure imply that
22 a single amplification of this allele (PCR I step) would result in the co-amplification of undesired alleles. Thus, in order to ensure greater accuracy of genotyping, an additional
PCR amplification (PCR II) is required (essentially, significantly ‘enriching’ the amount of the desired product by increasing PCR II reaction specificity by using PCR I amplicon as a starting DNA template). Furthermore, AS-PCR assays (a PCR II step procedure in this study) involve the amplification of a genetic region under stringent PCR conditions.
However, achieving a successful AS-PCR amplification can be challenging and time consuming (Latorra et al., 2003). In summary, a review of CYP2A6 literature provides the insight that the high degree of sequence conservation amongst CYP genes causes difficulties in genotyping accuracy if a researcher does not take into account the high probability of CYP allele co-amplification in their assay designs. An additional option explored in this study is performing a sequencing analysis in order to detect CYP2A6 allele SNPs, however, a limitation is that subjecting all DNA samples to sequencing analysis requires greater cost.
One of the challenges in conducting CYP2A6 research is that there is a lack of comprehensive CYP2A6 allele frequency studies. In the context of this study, Aka allele frequencies can be compared to other genotyped populations that have been phenotypically characterized for CYP2A6 metabolic status. The assumption is that comparable allele frequencies will imply a comparable metabolic status (i.e.—a higher proportion of extensive or slow alleles). However, due to the lack of comprehensive
CYP2A6 allele frequency studies and an inconsistency in regard to the alleles assayed, direct comparison of several allele frequencies across different sample populations is not
23 entirely possible. If a research group is unable to conduct whole-gene CYP2A6 sequencing but their aim is still to characterize a population’s metabolic status, then choosing what CYP2A6 alleles to assay for should be strategic—widely distributed alleles that have previously been found in the population of interest should be included in their analysis. For example, the extensive allele CYP2A6*1B , as well as slow alleles
CYP2A6*4 , and CYP2A6*9 , are typically included in CYP2A6 genotyping studies because they have been detected in all sample populations tested to date (e.g.—Gyamfi et al., 2005; Nakajima et al., 2006). Therefore, assaying for CYP2A6*1B , *4 and *9 would be useful for the purpose of allele frequency comparison across different studies.
Furthermore, certain CYP2A6 alleles have been associated with specific populations. For example, slow alleles CYP2A6*17 and CYP2A6*20 have only been detected in Africandescent populations and their frequencies were 10.5% and 1.7% respectively (Nakajima et al., 2006). While CYP2A6*17 and *20 are not included in every study, therefore, they represent major variant alleles in African-descent populations. The only African population that has been CYP2A6 genotyped (Gyamfi et al., 2005) did not include
CYP2A6*17 and *20 , however, these alleles (especially CYP2A6*17 ) should be assayed for in the Aka population because of shared African ancestry between the Aka and previously genotyped African-descent populations.
Another consideration regarding CYP2A6 allele study design and frequency analysis is how to interpret the CYP2A6 wild type categories ( CYP2A6*1 and
CYP2A6*1A ). Although pharmacogenetics studies do not explicitly state how sample alleles are determined to be wild type, a closer examination of research methodologies
24 shows that if a sample does not test positive for a specific CYP2A6 allele, researchers will assign the unknown allele as ‘
CYP2A6*1A
’ or ‘
CYP2A6*1
’ (the later includes both
*1A and *1B alleles). In pharmacokinetic studies (i.e. measuring the individual metabolic rate of nicotine to cotinine conversion), the wild type CYP2A6*1 allele category is given an associated metabolic rate, such as, 100% (Mwenifumbo et al., 2008a). Wild type alleles are treated as ‘extensive’ metabolizing, therefore, even though an allele given the wild type designation may be an undetected slow allele. The point here is that if only a few phenotypically significant CYP2A6 alleles are tested for, then there will be a higher frequency of CYP2A6*1 alleles, giving the impression that there is a greater proportion of extensive metabolizers in a given population. The most comprehensive CYP2A6 publication at present is Nakajima et al. (2006) who screened four different ethnicities for twenty-nine different alleles. However, despite the large number of alleles tested,
Nakajima’s sample populations still had a high frequency wild type alleles—84.5%
(Caucasian), 74.4% (African-descent), 53.3% (Korean), and 45.1% (Japanese). Because
Nakajima’s study assayed for a large number of alleles, it is possible that the prior wild type allele percentages are relatively accurate. The individuals assigned to the homozygous wild type category ( CYP2A6*1/CYP2A6*1 ) exhibited a high degree of metabolic rate variation between populations. Nakajima et al. (2006) explain that wild type rate differences between populations may be due to non-discovered/uncharacterized alleles, individual dietary differences, or possibly an unknown mechanism of epigenetic regulation.
25
As discussed, in a comprehensive allele study individuals assigned as
CYP2A6*1/CYP2A6*1 will collectively represent the mean wild type metabolic rate of that sample population. If multiple sample populations are compared, they can therefore be characterized relative to one another—in other words; a population with a significantly lower rate of metabolism in relation to other populations may be characterized as having a high proportion of slower metabolizers on average. According to Nakajima et al.’s
(2006) data on CYP2A6*1/CYP2A6*1 rate of nicotine clearance, Japanese have the slowest mean metabolic rate, which agrees with the documented trend that the Japanese population have a higher proportion of slow metabolizing alleles overall. The prior observation implies that because the Japanese population has a higher frequency of known slow alleles, it is therefore likely that a higher prevalence of unknown/uncharacterized slow alleles are included in the CYP2A6*1/CYP2A6*1 category. For the purpose of this study, the most significant observation is that Nakajima et al.’s (2006) slower metabolizing populations also have a lower percentage of wild type alleles (e.g. African-descent; 74.4%, versus, Japanese; 45.1%). Despite uncharacterized alleles being assigned to wild type, such frequencies are still informative about a population’s metabolic status if major variant alleles are assayed for. An additional way in which the wild type designation can be useful to researchers is that when pharmacokinetic data is collected, new alleles may be discovered by identifying and whole-gene sequencing individuals who display non-predictable genotype-phenotype relationships. For example, using the aforementioned approach, Mwenifumbo et al.
(2010) discovered novel slow CYP2A6 alleles in an African-descent population. The
26 point here is that even if a comprehensive allele study is conducted, the wild type category is still likely to represent the highest allele frequency percentage; researchers should therefore understand that we are still in the era of CYP2A6 genotype-phenotype discovery and the wild type category should be carefully interpreted. In reference to the
Aka, the acquired frequencies of known alleles and the percentage of alleles assigned as wild type should strongly relate to Aka metabolic status.
In addition to comparing individual allele and wild type category frequencies, a third method to characterize the metabolic status of the Aka population includes analyzing the proportion of metabolic phenotypes via genotype-phenotype prediction.
The homozygous CYP2A6*1 wild type category can be assigned a metabolic rate of
100% (Mwenifumbo et al., 2008a) therefore, the degree to which other alleles may affect overall metabolic phenotype can be quantified. Based on pharmacokinetics data,
Mwenifumbo et al. (2008a) divided genotypes into three phenotypic categories which include extensive CYP2A6 metabolizers (having ≥100% activity), intermediate metabolizers (≥50% activity), and slow metabolizers (<50% activity). Using these phenotypic categories, CYP2A6 alleles can be assigned a metabolic status of ‘extensive’ or ‘slow’, thus metabolic phenotypes can be predicted based on the contribution of each allele belonging to a certain genotype. Using these metabolic categories, three genotypepredicted metabolic statuses can be constructed: extensive metabolizers (EM), which possess two copies of fast alleles; intermediate metabolizers (IM), which are heterozygous for one copy of a fast and a slow allele; and slow metabolizers (SM), which possess two copies of a slow allele. By applying the genotype-phenotype prediction
27 approach in Nakajima’s data, Aka population proportions of EM, IM and SM individuals can be compared. In other words, the CYP2A6 phenotype profile in Nakajima’s study that Aka data most closely resembles, should elude to whether or not the Aka represent a slower or more extensive metabolizing population.
Nakajima et al. (2006) is the most comprehensive CYP2A6 genotyping study at present and the sample populations included in their analysis are metabolically wellcharacterized. Therefore, the argument can be made that whatever population the Aka is more similar to in terms of CYP2A6 allele, genotype and phenotype distribution, will determine whether or not the Aka broadly represent a more extensive or slow metabolizing population. This study predicts that the Aka will have a higher than expected proportion of slow metabolizers, however, the process of determining ‘higher than expected’ slow allele frequencies should include as many relevant populations as possible. Aside from Nakajima’s populations, a few other study populations exist in the literature that can be useful for comparing Aka CYP2A6 allele frequencies and include another African population (Gyamfi et al., 2005), and a Thai population (Peamkrasatam et al., 2006). Gyamfi et al. (2005) is the only study that has CYP2A6 -genotyped an
African population. In comparison to other populations sampled to date, the Ghana population is presumably more genetically related to the Aka, so if a higher frequency of slow CYP2A6 alleles in the Aka is observed, then it may provide evidence for a difference in environmental selective pressure between the two populations. However, given the lack of description of the Ghana sample population, it is difficult to determine how comparable it is to the Aka population in terms of nicotine consumption, helminth
28 burden and environmental settings. In fact, Ghanaian living conditions are highly variable and a recent study suggests that helminth risk is predominantly dependent upon access to piped water, toilet facilities and the type of floor households possess
(Magalhaes et al., 2011). The population of Thailand has been reported to have a high prevalence of helminth infection (Satarug et al., 1996). Interestingly, Satarug et al.
(1996) explains that the inflammation response caused by liver flukes results in the upregulation of CYP2A6 in the human liver, which has been hypothesized to increase the risk of liver cancer. Cancer associated with liver fluke (cholangiocarcinoma) is rare, however, in Northeast Thailand, 30-40% of the population is infected with helminthes and a 2-3-fold increase of cholangiocarcinoma is observed (Satarug et al., 1996).
Therefore, a similar hypothesis to the parasite-toxin evolutionary trade-off model could be formulated in regard to liver flukes selecting for slow CYP2A6 non-metabolizers. In fact, the Thai have a high prevalence of slow metabolizing alleles with CYP2A6*4 and
CYP2A6*9 detected at 20% and 14% respectively (Peamkrasatam et al., 2006), which is comparable to Japanese allele frequencies (Nakajima et al., 2006). Thailand may therefore represent a population that has undergone selection for slow CYP2A6 alleles due to pathogens. Although not enough information about the samples are provided in either of these studies, the Ghana and Thai population may potentially aid in explaining
CYP2A6 allele distribution.
Summary
The parasite-toxin evolutionary trade-off model explores the possible relationships between nicotine consumption, parasites and CYP2A6 alleles. Due to poor
29 living conditions and a high degree of nicotine consumption, the Aka may have a higher than expected frequency of slow CYP2A6 alleles in order to help alleviate helminth infection. The pharmacogenetics literature was thoroughly reviewed for information about genotyping methodologies, and in determining which previously CYP2A6 genotyped populations may serve as a useful comparison for allele frequencies. In order to determine with confidence whether or not the Aka population represents a more extensive or slower metabolizing population, three different cross-study comparative approaches are performed: an individual allele frequency comparison, a wild type percentage comparison, and also a phenotype proportion analysis. Although deviation from predicted allele frequencies can be detected in the Aka, pinpointing the precise selective pressures responsible for CYP2A6 allele distribution across different populations will be a greater challenge. Given the lack of evolutionary-related studies for cytochrome P450 genes, more studies that directly investigate dietary and recreational xenobiotic consumption in relation to genotypes, and parasiteCYP genotype research are necessary in order to support or rule out the relationships proposed in the evolutionary trade-off model. A useful area of research includes genomics, which has the potential to determine the direction of evolutionary selection on CYP s. Detecting positive selection would support the idea of various selective agents being responsible for differences in
CYP2A6 allele distribution across different populations. However, although the few genomics studies of CYP s suggest that the high degree of polymorphism can be explained by evolutionary mechanisms selecting for diversity, CYP2A6 has not been individually analyzed with the latest advances in genomic techniques.
30
Statement of the Problem
Subfamily P450 cytochrome 2A6 ( CYP2A6 ) is a member of the P450 superfamily of heme-thiolate monooxygenases that play a central role in the detoxification of xenobiotics (Zawaira et al., 2012). In vivo pharmacogenetics studies have determined that there is considerable interindividual and ethnic variability of nicotine metabolic rates, and different CYP2A6 alleles contribute to this variation (Mwenifumo and Tyndale, 2009;
Nakajima et al., 2006). Findings indicate that Caucasians and African-descent individuals have the highest proportion of extensive metabolizers, whereas Asians
(especially Japanese) have the highest proportion of slow metabolizers (Nakajima et al.,
2006). Despite the high degree of polymorphism and intriguing distribution of CYP2A6 alleles across different human populations, there is little discussion in the literature regarding the selective agents that might be responsible for these patterns (Mann et al.,
2012). However, identifying exact selective environmental agents is challenging for several reasons including that xenobiotic metabolizing genes often have several known and unknown substrates. Also, xenobiotic pressures have not remained constant in human populations throughout time (Fuselli et al., 2010). Finally, one review (Luca et al., 2012) discusses the difficulty in dissecting the specific impact of selective dietary pressures considering other variables unrelated to diet may have also influenced certain loci including: climate, subsistence strategy, and pathogens.
The present study contributes to Sullivan and Hagen’s work (Hagen et al., 2009;
Sullivan et al., 2008) by investigating how CYP2A6 genotypes may have been influenced by human exposure to parasites. Because different CYP2A6 alleles contribute to different
31 rates of nicotine metabolism (and perhaps to differential metabolism of other plant secondary compounds with anti-parasitic effects), it is reasonable to hypothesize that the prevalence of environmental CYP2A6 substrates and parasites in a given environment may ultimately explain extant variation of CYP2A6 alleles across different human populations. The prior statement describes an evolutionary trade-off between protection against toxins and protection against parasites—slow metabolizing alleles reduce parasite load by maintaining a high blood concentration of plant toxins, but are constrained by toxic damage to the organism, whereas, fast metabolizing alleles protect the body from toxins, but leave the organism vulnerable to a higher parasite load (Mann et al., 2012). In modern populations with gene flow, these opposing selection pressures will leave the average genotypes most common. In stable environments with or without endemic parasites, we may see more common slow and fast genotypes respectively (Mann et al.,
2012). Simply stated, in the context of CYP2A6 , the parasite-toxin evolutionary trade-off model proposes a relationship between CYP2A6 alleles, parasites, and consumption of nicotine (or other naturally occurring CYP2A6 substrate with antiparasite properties).
One way to provide evidence for the relationships proposed in the trade-off model includes a comparison of CYP2A6 allele frequencies between different populations who have differential exposure to CYP2A6 substrates and parasites.
In order to investigate the relationship between CYP2A6 allele frequencies, pathogens and nicotine, Aka individuals were genotyped for slow and extensive CYP2A6 alleles. The Aka live with many environmental challenges that were also faced by early human ancestors in terms of extracting calories from wild plants and animals. Aka
32 foraging subsistence practices are also distinct, therefore, distinguishing them from other agricultural peoples who may have similar helminth exposure risks. Because the Aka are a tropical foraging population, use nicotine extensively in various forms, and have a high exposure to parasites, it is hypothesized here that Aka CYP2A6 allele frequencies may be different relative to previously reported CYP2A6 allele frequencies for populations living in more developed settings. Specifically, the trade-off model suggests that Aka CYP2A6 metabolic status may be comparatively slower in order to reduce the severity of parasitic infection.
33
CHAPTER 2
MATERIALS AND METHODS
The Aka are a tropical foraging population that reside in the Western Central
African Republic/Northwestern Congo. As the Aka population has been shown to use nicotine extensively in various forms, and to have a high exposure to parasites (Lilly,
2005; Mann et al., 2012), this study set out to genotype four major CYP2A6 single nucleotide polymorphism (SNP) alleles associated with the fast or slow catalysis of nicotine. Genotyping of CYP2A6 gene was performed on 72 Aka individuals, which represents a sample population of 144 alleles. For each Aka individual, four metabolically characterized alleles were tested for— CYP2A6 variants *1B , *9 , * 17 , and
*20 . Allele CYP2A6*1B was tested for by using a two-step allele-specific PCR method
(Djordevic et al., 2010; Oscarson et al., 1999b). The alleles CYP2A6*9 and CYP2A6*20 were tested for by PCR amplification followed by reverse primer sequencing analysis.
Finally, CYP2A6*17 was tested for by PCR amplification and RFLP digestion analysis
(Fukami et al., 2004).
Acquisition of DNA Samples from Aka Individuals
Buccal and blood samples were previously collected from Aka individuals from three populations in the Lobaye region, Central African Republic. The cohort consists of
72 samples consisting of 55 male and 17 female individuals between the ages of 21 and
55.
34
DNA Samples and Extraction
Genomic DNA was extracted from 34 buccal swabs and 38 blood spot samples.
The FTA card buccal swabs were collected in the summer of 2009, kept at room temperature in the field for approximately two months and then stored at -20 o
C until they were extracted in June of 2011. The blood spot samples on Whatman protein saver cards were collected in the summer of 2010 and were stored at -20 o
C at fieldsite in a solar powered freezer for approximately two months in zip lock bags with desiccant. The
Whatman cards were shipped at room temperature for twenty-four hours and then stored at -20 o
C until they were extracted in June of 2011. Genomic DNA was extracted from
FTA and Whatman samples using Qiagen (Valencia, California) QIAamp DNA Mini Kit reagents and materials. The Qiagen manual QIAamp DNA Mini Kit DNA purification from dried blood spots protocol was used, however, one-half FTA circle and two
Whatman circles were used as the starting DNA material. Also, Qiagen ATL, proteinase
K and AL reagents were doubled in volume for the first part of the extraction protocol.
Genotyping of CYP2A6 Alleles
Each DNA sample (n=72) was assayed for four different CYP2A6 alleles:
CYP2A6*1B, CYP2A6*9, CYP2A6*17, CYP2A6*20 . CYP2A6*1B is a fast metabolizing allele whereas CYP2A6*9 is a slow metabolizing allele, and both alleles have been detected in all populations sampled to date (e.g. Nakajima at al., 2006), which is useful for cross-study comparison. The alleles CYP2A6*17 and CYP2A6*20 are slow metabolizing alleles that have only been found in African-descent populations and are therefore likely to be found in the Aka population. Amplifying the deleted gene variant
35
CYP2A6*4 (a slow metabolizing allele also found in all sample populations to date) would have been useful for detecting a potentially higher prevalence of slow metabolizers in the Aka, however, all attempts to replicate previous protocols (Djordevic et al., 2010;
Fukami et al., 2006; Goodz and Tyndale, 2002; Oscarson et al., 1999a) were unsuccessful in this study. Sample alleles that did not test positive for the prior successfully amplified alleles were given the wild type designation CYP2A6*1A . PCR primer sequences are listed in Table 1. PCR reagent concentrations for each allele PCR are listed in Table 2 and cycling conditions for each allele are listed in Table 3. Each reaction was performed in a final volume of 15 µl. Platinum
Taq DNA polymerase was purchased from
Invitrogen Life Technologies (Grand Island, NY). Restriction enzyme TaiI ( MaeIII ) and dNTPs were purchased from Fermentas, Inc (Burnie, MD). Primers were ordered from
Sigma-Aldrich Corporation (The Woodlands, TX). Sequencing of PCR products was completed by Elim Biopharmaceuticals, Inc (Hayward, CA).
Table 1:
CYP2A6 Allele Primers
Primer Name
2A6ex8F2
Sequence
5’-CTCCAACCCCCAGGACTTCAA-3’
2A61Bwt
5’-ACTGGGGGCAGGATGGC-3’
2A61Bmut
2A6E6F
5’-AATGGGGGGAAGATGCG-3’
5’-TTGAAAAACCTGGTGTGAC-3’
2A6INT7AS 5’-CTGAGATTTCTGTCCCTAT-3’
2A6INT1AS 5’-TCCTGTCTTTCTGATGCTGA-3’
Reference
Djordevic et al., 2010
Djordevic et al., 2010
Djordevic et al., 2010
Fukami et al., 2004
Fukami et al., 2004
Fukami et al., 2004
2A6INT4R2
5’-CTTGGAGACAGGGTATTGGA-3’
2A6R5
2A6-9F
2A6-20F
Fukami et al., 2005
5’-GCACTTATGTTTTGTGAGACATC-3’ Oscarson et al.,1999
5’-TCCCTCTTTTTCAGGCAGTA-3’
(This study)
5’-CGCTTTGACTATAAGGACA-3’
(This study)
Table 2:
CYP2A6 Allele PCR Reagent Concentration
DNA
(µl)
10X buffer
MgCl 2+
(mM)
[Reagent] dNTPs
(mM)
F Primer
(mM)
R Primer
(mM)
Taq
(U)
CYP2A6*1B
(PCR I)
CYP2A6*1B
(PCR II)
CYP2A6*9
CYP2A6*17
CYP2A6*20
CYP2A6*20
CYP2A6*1B
(PCR I)
CYP2A6*1B
(PCR II)
CYP2A6*9
CYP2A6*17
1.5 1X
1.0
PCR I 1X
1.5 1X
1.5
1.5
1X
1X
1.20
1.20
1.50
1.50
0.80
0.40
1.60
0.80
0.25
0.25
0.40
0.40
0.25
0.25
0.40
0.40
0.5
0.5
0.5
0.5
1.50
Table 3:
0.80 0.25 0.25 0.5
CYP2A6 Allele PCR Cycling Conditions
Initial
Denaturation
94 °C
3 min
94 °C
3 min
94 °C
3 min
94 °C
3 min
94 °C
3 min
Denaturation
94 °C
20 s
94 °C
20 s
94 °C
20 s
94 °C
30 s
94 °C
20 s
Anneal Extension Cycles
60 °C
20 s
54 °C
15 s
63 °C
25 s
54 °C
25 s
67 °C
25 s
72 °C
2 min
72 °C
45 s
72 °C
30 s
72 °C
1 min
72 °C
30 s
35x
15x
35x
40x
35x
Final
Extension
72 °C
7 min
72 °C
5 min
72 °C
5 min
72 °C
5 min
72 °C
5 min
36
37
In 2012, the NCBI reference sequence entitled, “Homo sapiens cytochrome P450, family 2, subfamily A, polypeptide 6 ( CYP2A6
), RefSeqGene on chromosome 19” was updated from version NG_000008.7 to version NG_008377.1. Therefore, the diagnostic
SNP allele positions previously reported in other studies, as well as stated on the CYP2A6 allele nomenclature webpage (Ingelman-Sunberg et al., 2010), are from the earlier version of CYP2A6 reference sequence. The two reference versions do not differ in base pair sequence, however, base pair positions within the CYP2A6 reference sequence have shifted. Samples for this study were genotyped using the new reference sequence
(NG_008377.1); however, in order to limit confusion between this study and past studies, diagnostic SNP positions from the previous reference sequence are included in parentheses (see Results Table 5) for each of the alleles assayed in this study.
A two-step allele-specific PCR method was used to test for CYP2A6*1B
(Djordevic et al., 2010; Oscarson et al., 1999b). Due to the high degree of conservation amongst CYP loci (Hoffman et al., 2001), a two-step PCR method was necessary for some CYP2A6 alleles, which ensured greater specificity and accuracy of PCR products.
In a two-step PCR assay, a larger DNA product was amplified during PCR I and in order to increase specificity, PCR I product was used as a template for a second, nested PCR
(II), which yields a smaller sized DNA amplicon. For PCR I of the CYP2A6*1B allele, a
1300 bp region was amplified by using the 2A6ex8F2 forward primer and the 2A6R5 reverse primer. In order to perform an allele-specific PCR II, a 300 bp region was amplified by using the 2A61Bwt forward primer and the 2A61Bmut forward primer. In an AS-PCR assay (Figure 1), the PCR will only proceed if the specific allele (wild type
38 or mutant) that the forward primer is designed for is present. Therefore, each sample was subjected to two different PCRs where both PCRs had the same reverse primer in common, but differ in the forward primer. The CYP2A6*1B allele is a gene conversion located at the 3’ flanking region of the
CYP2A6 gene (Ingelman-Sunberg et al., 2010).
Therefore, the forward primer was designed to detect SNPs located in the gene conversion region (Djordevic et al., 2010). Specifically, diagnostic positions 6741C>A
(6720C>A), 6748C>G (6727C>G), 6750G>A (6729G>A), 6755G>C (6734G>C), and
6756C>G (6735C>G) were tested for using the forward 2A61Bmut primer. If a sample has two wild type alleles, then the mutant PCR will not amplify (Figure 1). Accordingly, if a sample is heterozygous ( CYP2A6*1A / CYP2A6*1B ), then both PCRs will amplify.
Finally, in the case of a homozygote mutant ( CYP2A6*1B / CYP2A6*1B ), the wild type
PCR will fail to amplify. In order to visualize sample genotypes, PCR II products were run out on a 2% agarose gel.
A single PCR amplification followed by a restriction fragment length polymorphism (RFLP) analysis was used to test for CYP2A6*17 (Fukami et al., 2004).
The initial PCR was performed by using forward primer 2A6E6F and reverse primer
2A6INT7AS, which yields a 1,065 bp fragment. PCR products were then digested by using 0.65X R buffer and 10 U of TaiI followed by incubation at 65 o
C for 3 hours.
RFLP fragments were then visualized on a 2% agarose gel (Figure 2). The recognition sequence for endonuclease restriction enzyme TaiI is 5’-ACGT-3’, which occurs twice in the wild type allele and once in the mutant CYP2A6*17 allele. In the wild type allele,
TaiI cleaves the 1,065 bp fragment at positions 4596T (4575T) and 5087T (5066T),
39 which creates three fragments with the approximate sizes of 215 bp, 491 bp, and 383 bp
(Figure 2). A primary diagnostic SNP for CYP2A6*17 is the 5086G>A (5065G>A) mutation site, which results in the removal of the second TaiI restriction site. Therefore, in the mutant CYP2A6*17 allele, TaiI cleaves the 1,065 bp fragment only at position
4596T (4575T), which creates two approximately sized 215 bp and 874 bp fragments. In the case of CYP2A6*1A / CYP2A6*17 heterozygotes, 215 bp, 874 bp, 491 bp, and 383 bp
RFLP fragments will be present.
Figure 1:
CYP2A6*1B Allele Specific PCR Genotpying
Sample 1 is a wild type CYP2A6*1A/CYP2A6*1A individual, as indicated by AS-PCR amplification of a single wild type band (W) and lack of amplification via the mutant primer (M) at the 300bp gel position. Sample 2 is a heterozygous
CYP2A6*1A/CYP2A6*1B individual, as indicated by the amplification of both the wild type and mutant primer PCRs. Sample 3 is a homozygous CYP2A6*1B/CYP2A6*1B individual, as indicated by the amplification of the mutant primer PCR.
40
Figure 2:
CYP2A6*17 RFLP Genotyping
Lane 5 is a DNA ladder with 1000 bp and 500 bp reference bands. Lane 1 is a 1,065 bp
PCR product undigested control. Lane 2 is a wild type CYP2A6*1/CYP2A6*1 individual as indicated by the presence of two TaiI cleavage sites, which produced 491, 383, and
215 bp fragments. Lane 4 is a homozygous CYP2A6*17/CYP2A6*17 individual as indicated by the presence of a single TaiI cleavage site which produced 874 and 215 bp fragments. Lane 3 is a heterozygous CYP2A6*1A/CYP2A6*17 individual as indicated by
874, 491, 383, and 215 bp fragments.
A single PCR amplification followed by capillary sequencing using the reverse primer was used to test for CYP2A6*9 (Figure 3) (Fukami et al., 2004). The forward primer used for the CYP2A6*9 allele assay is 2A6-9F and the reverse primer used is
2A6INT1AS, which yields a 385 bp fragment. CYP2A6*9 represents a TATA box mutation that precedes the start of the CYP2A6 gene sequence (Ingelman-Sunberg et al.,
2010). The forward primer is a 20 bp in length and the diagnostic SNP, -27T>G (-
48T>G), directly follows the 3’ end of the last base of the forward primer. Therefore,
41 because the diagnostic CYP2A6*9 SNP is located at the 5’ end of the amplicon, the 385 bp PCR product was sequenced via the reverse primer 2A6INT1AS. With this approach, a sample with the -27T>G SNP, will be observed at the 5’ end of the amplicon via a reverse primer sequencing reaction (hence, 3’ ïƒ 5’ sequencing). In the sequencing electropherogram, herterozygous samples CYP2A6*1A / CYP2A6*9 exhibited two overlapping peaks at the -27T>G SNP position (Figure 3). Samples that were homozygous wild type or homozygous for the mutant allele ( CYP2A6*9 / CYP2A6*9 ) only exhibited a single T or G peak.
A single PCR amplification followed by capillary sequencing using the reverse primer was used to test for CYP2A6*20 .
The forward primer used for the CYP2A6*20 allele assay was 2A6-20F and the reverse primer used was 2A6INT4R2, which yields either a 274 bp or 272 bp fragment. CYP2A6*20 is a frameshift mutation of the CYP2A6 gene sequence, which results in a two base pair deletion and thus a 272 bp fragment when
PCR amplified (Ingelman-Sunberg et al., 2010). The forward primer is 19bp in length and the diagnostic base pair deletions 2162_2163delAA (2141_2142delAA) directly follow the 3’ end of the last base of the forward primer. Therefore, because the deleted
CYP2A6*20
SNPs are located at the 5’ end of the amplicon, the PCR product was sequenced via the reverse primer 2A6INT4R2. With a reverse sequencing approach, if a sample has the 2162_2163delAA mutation, then it can be observed near the 5’ end of the amplicon. In the electropherogram, if samples that were heterozygous
CYP2A6*1A / CYP2A6*20 would exhibit two overlapping peaks (A/G) starting at the
42
Figure 3:
CYP2A6*9 Allele -27T>G Mutation Genotyping
Because the reverse primer was used for sequencing, the compliment base pair -27A>C is visualized. Top: Single A peak; indicating a homozygous wild type individual. Middle:
Overlapping A and C peaks; indicating a heterozygous individual. Bottom: Single C peak; indicating a homozygous mutant individual.
43
2163delA SNP position, and over lapping peaks would have continued throughout the rest of the sequence. Samples that were homozygous wild type or homozygous mutant
( CYP2A6*20 / CYP2A6*20 ) will exhibit a single peak.
Phenotyping of CYP2A6 Alleles
Because no physiological data were included in this study, each CYP2A6 allele was assigned a metabolic status of either “extensive” or “slow” (Table 4). Phenotypic information from the CYP2A6 Allele Consortium page (Ingelman-Sunberg et al., 2010), as well as, several other studies (Nakajima et al., 2006; Mwenifumbo and Tyndale, 2009;
Tiong et al., 2010) was used in order to characterize each allele for its effect on overall metabolic rate of nicotine. Individuals whose genotype is homozygous extensive were assigned an “extensive” phenotype status, while individuals who possess homozygous slow alleles were assigned “slow” phenotype status. Also, individuals who are heterozygous extensive/slow were assigned the metabolic status of “intermediate.”
Table 4:
Predicted Individual CYP2A6 Allele Effect on Metabolic Phenotype
Allele
CYP2A6*1A
†
CYP2A6*1B
CYP2A6*1X2
CYP2A6*8
CYP2A6*14
CYP2A6*16
CYP2A6*18
CYP2A6*2
CYP2A6*4
CYP2A6*5
CYP2A6*6
CYP2A6*7
CYP2A6*9
Effect
Extensive
Extensive
Extensive
Extensive
Extensive
Extensive
Extensive
Slow
Slow
Slow
Slow
Slow
Slow
44
CYP2A6*10
CYP2A6*11
CYP2A6*12
CYP2A6*13
CYP2A6*15
CYP2A6*17
CYP2A6*19
CYP2A6*20
CYP2A6*21
CYP2A6*22
CYP2A6*26
CYP2A6*27
CYP2A6*28
Slow
Slow
Slow
Slow
Slow
Slow
Slow
Slow
Slow
Slow
Slow
Slow
Slow
†
The CYP2A6*1A ‘wild type’ designation represents the alleles in the population that did not test positive for the other alleles. While most studies classify this allele as ‘extensive’
(e.g. Vasconcelos et al., 2005) true metabolic phenotype is unknown unless additional physiological metabolic tests are performed on each individual.
In this study, unclassified alleles that are given the CYP2A6*1A wild type designation have an unknown effect on individual metabolic phenotype, however, the
CYP2A6*1A wild type allele is still treated as extensive (Mwenifumbo et al., 2008b). In
Nakajima et al.’s (2006) study, individuals with a CYP2A6*1A/*1A genotype collectively represent the average rate of metabolism, or rather, the ‘wild type’ rate of metabolism for a given population. Therefore, an extensive phenotypic status implies that the rate of metabolism is equal to or greater than the wild type rate of metabolism, while a slow phenotypic status implies that the rate metabolism is well below the wild type rate.
Finally, an intermediate phenotypic status implies that the rate of metabolism falls between the fastest and slowest metabolizers in a population.
45
CHAPTER 3
RESULTS
Although frequencies of CYP2A6 alleles differ across human populations, the selective environmental agents responsible for these patterns are not known (Mann et al.,
2012). One hypothesis is that humans have evolved seeking behavior for nicotine and other plant secondary compounds in order to control parasitic infections (Hagen et al.
2009; Sullivan et al. 2008). Therefore, parasites may have influenced extant CYP2A6 allele distributions (Mann et al., 2012). Genotyping the CYP2A6 alleles in populations with differing degrees of exposure to nicotine or similar plant toxins, and parasites, may reveal the relationships between these potential selective agents.
CYP2A6 Allele Frequencies of Aka Individuals
Individual allele frequencies of the Aka sample population are presented in Table
5. Sample population alleles that did not test positive for any specific CYP2A6 allele were assigned to the unclassified CYP2A6*1A wild type category, which represented
67.38% (n=97) of assayed alleles. Slow allele CYP2A6*20 was not detected in the Aka.
The extensive metabolizing allele CYP2A6*1B , which has been associated with a more rapid metabolic clearance of nicotine in comparison to wild type alleles (Mwenifumbo and Tyndale, 2009), was found at a frequency of 13.89% (n=20). Slow alleles
CYP2A6*9 and CYP2A6*17 were detected at a frequency of 7.64% (n=11) and 11.11%
(n=16) respectively.
46
Table 5:
Allele Frequencies of CYP2A6 of Aka Population
Allele Mutation(s)
†
CYP2A6*1A
††
CYP2A6*1B
Wild type
6741C>A (6720C>A),
6748C>G (6727C>G),
6750G>A (6729G>A),
6755G>C (6734G>C),
6756C>G (6735C>G)
CYP2A6*9 -27T>G (-48T>G)
Metabolizer
Status
Extensive
Extensive
Aka CYP2A6
Allele Counts n=97 n=20
Allele
Frequency
67.36%
13.89%
Slow
Slow n=11 n=16
7.64%
11.11%
CYP2A6*17 5086G>A (5065G>A)
CYP2A6*20
2162_2163delAA
(2141_2142delAA)
Slow n=0 0%
Total number of alleles n=144
†
SNP allele positions for latest reference sequence version NG_008377.1, while the previous version NG_00008.7 SNP positions are notated in parentheses.
††
The CYP2A6*1A ‘wild type’ designation represents the alleles in the population that did not test positive for the other alleles in this allele. While some studies classify this allele as ‘extensive’ (e.g. Vasconcelos et al., 2005) true metabolic phenotype is unknown unless additional physiological metabolic tests are performed on each individual.
CYP2A6 Genotype and predicted phenotype frequencies of Aka individuals
Genotyping of the CYP2A6 gene was performed on 72 Aka individuals, who were assayed for four different metabolically characterized alleles. Genotype frequencies of the Aka sample population are presented in Table 6. This is not to be confused with the frequencies of individual alleles, mentioned above, but instead is the combination of the two CYP2A6 alleles carried by each individual, or his/her genotype. Alleles that did not test positive for CYP2A6*1B , *9 , * 17 , or *20 were assigned to the unclassified
CYP2A6*1A wild type category. The percentage of unclassified genotypes
47
( CYP2A6*1A/*1A ) in the population is 48.61% (n=35) therefore, almost half of the individuals assayed have an unknown genotype. However, because the wild type category can represent mean metabolic rate of a population (Nakajima et al., 2006), the wild type category is treated as extensive in this study. All metabolic phenotype predictions were made by using Methods Table 4 as a guide.
Table 6:
CYP2A6 Genotype Frequencies of Aka Population
CYP2A6 Genotype
Predicted
Phenotype
†
CYP2A6*1A/*1A
††
Extensive
CYP2A6*1A/*1B
CYP2A6*1B/*1B
Extensive
Extensive
CYP2A6*1A/*9
CYP2A6*1A/*17
CYP2A6*1A/*20
CYP2A6*1B/*9
CYP2A6*1B/*17
CYP2A6*1B/*20
CYP2A6*9/*9
Intermediate
Intermediate
Intermediate
Intermediate
Intermediate
Intermediate
Slow
Aka Genotype
Counts (n=72) n=35 n=10
n=2
n=6 n=11
n=0
n=3
n=3
n=0
n=1
Genotype
Frequency
48.61%
13.89%
2.78%
8.33%
15.28%
0%
4.17%
4.17%
0%
1.39%
CYP2A6*9/*17
CYP2A6*9/*20
Slow
Slow
n=0
n=0
0%
0%
CYP2A6*17/*17
CYP2A6*17/*20
Slow
Slow
n=1
n=0
1.39%
0%
CYP2A6*20/*20 Slow n=0 0%
†
The CYP2A6*1A ‘wild type’ designation represents the alleles in the population that did not test positive for the other alleles in this allele.
††
Phenotype categories were assigned via predicted effect of each individual allele on overall metabolic rate (see Table 4).
The extensive metabolizing allele CYP2A6*1B and poor metabolizing alleles
CYP2A6*9 , * 17 , and *20 have been characterized by other studies (Ingelman-Sunberg et al., 2010) therefore, in contrast to wild type alleles, metabolic phenotype categories can be assigned to individuals with greater confidence. The CYP2A6*20 allele was not
48 detected in this study population. Only two individuals in this study possessed homozygous slow alleles ( CYP2A6*9/*9 and CYP2A6*17/*17 ), indicating a very low capacity to metabolize nicotine (Fukami et al., 2004; Nakajima et al., 2006). Conversely, one individual in the sample population possessed a definitive homozygous extensive genotype ( CYP2A6*1B/*1B ), which has been associated with a more rapid metabolic clearance of nicotine in comparison to wild type metabolic rates (Mwenifumbo et al.,
2008b). Individuals that were heterozygous for a classified extensive and slow allele
( CYP2A6*1B/*9 and CYP2A6*1B/*17 ) were designated in this study as having an intermediate metabolizer status (8.33%, n=6), and were predicted to have a rate of metabolism that falls between the fastest and slowest metabolizers in the population. A large percentage of the sample population (23.6%, n=17) possessed both the unclassified wild type allele and classified poor allele ( CYP2A6*1A/*9 , CYP2A6*1A/17 ). Although true phenotype status for unclassified/classified heterozygous individuals is unknown in this study, these genotypes have been designated as intermediate metabolizers.
Furthermore, a significant portion of the samples were heterozygous CYP2A6*1A/*1B
(n=10, 13.89%), which is predicted to be an extensive metabolizer phenotype.
In conjunction with genotype frequencies, corresponding metabolic categories suggest that only a very small fraction of the Aka population are slow nicotine metabolizers (2.78%, n=2). Heterozygous slow and extensive genotype individuals with an intermediate metabolizing status represent 31.9% (n=23) of the sample population.
Finally, 65.28% (n=47) of the sample population are extensive metabolizers, which
49 suggests that in comparison to slow metabolizing individuals, the majority of Aka individuals are fast nicotine metabolizers.
50
CHAPTER 4
DISCUSSION
Pharmacogenetics studies have determined that different CYP2A6 alleles strongly contribute to interindividual and between — population variability of metabolic rates
(Mwenifumo and Tyndale, 2009; Nakajima et al., 2006). However, despite the high degree of polymorphism and intriguing distribution of CYP2A6 alleles across different human populations, there is little discussion in the literature regarding the selective environmental agents that might be responsible for these patterns (Mann et al., 2012).
Although environmental xenobiotics have undoubtedly influenced the evolution of CYP genes, identifying the xenobiotic compounds are responsible is challenging given that
CYP genes often have several known and unknown substrates (Fuselli et al., 2010).
Furthermore, xenobiotic pressures have not remained constant in human populations throughout time (Fuselli et al., 2010). The CYP2A6 gene, however, has been wellstudied, and it is known that CYP2A6 is primarily responsible for the catalysis of nicotine and is believed to be the only gene responsible for the catalysis of the plant secondary metabolite coumarin (Binnington et al., 2012; Nakajima et al., 1996a; Pelkonen et al.,
2000; Yamano et al., 1990). A scan of the literature shows that there has been some speculation regarding a link between drug metabolism alleles, xenobiotic compounds and parasites (Luca et al., 2012); however, models proposing such an association have not been formally investigated. Sullivan et al. (2008) and Hagen et al. (2009) suggest that humans have evolved a propensity for recreational drug use for the purpose of alleviating parasitic infections. Because nicotine is a highly effective anti-helminthic (Hammond et
51 al., 1997), it is reasonable to hypothesize that both CYP2A6 substrates and the prevalence of parasites in a given environment may ultimately explain variation of CYP2A6 alleles across different human populations. This statement suggests an evolutionary trade-off between protection against toxins and protection against parasites—extensive metabolizing alleles remove toxins quickly, whereas slow metabolizing alleles allow for toxins to remain in the system longer, thus potentially increasing the likelihood of killing more parasites. In order to investigate the relationship between parasites and CYP2A6 alleles, populations with differing degrees of exposure to nicotine and parasites can be analyzed. The Aka are a tropical foraging population that reside in the Western Central
African Republic/Northwestern Congo. The Aka use nicotine extensively in various forms, and have a high exposure to parasites (Lilly, 2005; Mann et al., 2012). Aka
CYP2A6 allele frequencies may therefore be significantly different in comparison to previously reported CYP2A6 allele frequencies from populations in other ecological contexts. The parasite-toxin evolutionary trade-off model predicts that in comparison to other populations with less exposure to parasites, the Aka should have a higher frequency of slow metabolizing CYP2A6 alleles and genotypes.
A higher than expected frequency of slow alleles detected in the Aka would support the trade-off model. Previously studied populations which may be useful for comparing Aka CYP2A6 allele frequencies include: Caucasian/European (Djordjevic et al., 2010; Schoedal et al., 2005), African-descent (Schoedal et al., 2005), Japanese
(Fujieda et al., 2004; Gyamfi et al., 2005), Ghana (Gyamfi et al. 2005), and Thai samples
(Peamkrasatam et al., 2006). Caucasian and African-descent populations have been
52 consistently cited in the pharmacogenetics literature as having a higher proportion of extensive metabolizers (Ariyoshi et al., 2002; Fujieda et al., 2004; Gyamfi et al., 2005;
Nakajima et al., 2006), and the higher rate of CYP2A6 metabolism in these populations is likely due to a lower frequency of slow alleles. By including the Ghana population
CYP2A6 allele study (Gyamfi et al., 2005), a difference in allele distribution within
African populations may be observed. If a higher prevalence of slow alleles in the Aka is found in comparison to the Ghana CYP2A6 allele study, then a stronger argument can be made for local environmental factors as being responsible for allele distribution differences between the Aka and other populations. This prediction assumes that the Aka and Ghana sample population have experienced different environmental conditions such as differential exposure to nicotine and helminthes. However, neutral processes could also explain differences in allele frequencies (Relethford, 2009). Finally, the Thai population has a high prevalence of slow alleles (Peamkrasatam et al., 2006), and a high prevalence of helminthes (Satarug et al., 1996), which may represent an existing example of the trade-off model.
In order to compare Aka CYP2A6 allele frequencies to previously studied populations, genotyping of the CYP2A6 gene was performed on blood spots from 72 Aka individuals. Four previously metabolically characterized alleles were tested for—
CYP2A6*1B , *9 , * 17 , and *20 . Individual allele results for the Aka sample allele population (n=144) include: the extensive metabolizing allele CYP2A6*1B was found at
13.89% (n=20), while slow metabolizing alleles CYP2A6*9 , CYP2A6*17 , and
CYP2A6*20 were found at 7.64% (n=11), 11.11% (n=16) and 0% respectively (Results
53
Table 5). Major variant alleles CYP2A6*1B and CYP2A6*9 have been observed in all
CYP2A6 genotyped populations; including the only study to have sampled an African population (Gyamfi et al., 2005). However, CYP2A6*17 has only been previously observed in a few other African-descent population studies (Fukami et al., 2004;
Nakajima et al., 2006), therefore, this study is the first to identify the CYP2A6*17 allele in an African population. The frequency of CYP2A6*17 in the Aka (11.11%, n=16) is comparable to Nakajima et al.’s (2006) African-descent population (10.5%, n=37).
However, while Nakajima et al. (2006) observed slow metabolizing allele CYP2A6*20 in their African-descent population (1.7%, n=6), the CYP2A6*20 allele was not observed in the Aka. There are two immediate explanations for why CYP2A6*20 was not found; the
Aka sample size was too small to be detected (n=72 Aka individuals, versus n=172 sample size for Nakajima et al.), or, that the CYP2A6*20 allele is absent in the Aka population.
As previously stated, the presence of a higher proportion of slow metabolizing alleles in the Aka population may provide some support for the CYP2A6 -parasite model.
Aka allele frequencies of major variant alleles CYP2A6*9 and CYP2A6*1B can be compared to the majority of other study populations in which the allele are common
(deleted gene variant CYP2A6*4 is also typically included, however it was omitted from this study due to technical difficulties). Table 7 suggests that slow allele CYP2A6*9 is found in similar frequencies (5.7-8.2%) amongst Caucasian, Serbian, African-descent,
Ghanaian, and Aka populations. In contrast, Thai and Japanese populations have a significantly higher CYP2A6*9 allele frequency (~20%). As for extensive allele
54
CYP2A6*1B , the Aka (13.9%) have a comparable frequency to the African-descent population (13.0%) and a slightly higher frequency in comparison to the Ghana population (11.9%). Therefore, both the slow allele CYP2A6*9 and extensive allele
CYP2A6*1B frequency in the Aka is comparable to populations that are cited as being extensive metabolizers (Ariyoshi et al., 2002; Fujieda et al., 2004; Gyamfi et al., 2005;
Nakajima et al., 2006). Japanese and Thai populations are documented in the literature as having a slow rate of nicotine metabolism (Peamkrasatam et al., 2006; Nakajima et al.,
2006). Another observation from Table 7, however, includes that although these populations have a high prevalence of the slow metabolizing allele CYP2A6*9 , their extensive allele CYP2A6*1B frequencies (25.6% and 27.0%, respectively) are comparable to the Caucasian (33.5%) population (Djordjevic et al., 2010; Schoedal et al.,
2005). The high frequency of the extensive allele in these slow metabolizing populations is therefore, not a useful distinguishing characteristic. The frequency of slow alleles may be a better predictor for characterizing a metabolic status of a population.
Table 7:
CYP2A6*1B and CYP2A6*9 Allele Frequencies Across Populations
Population
Allele
Caucasian a Serbian b Thai c Japanese d,e Aka
Africandescent a Ghanaian d
CYP2A6*1B 33.5% 30.7% 27.0% 25.6% 13.9% 13.0% 11.9%
5.7% CYP2A6*9 7.1% 8.2% 20.0% 20.7% 7.6% 7.1% a
Schoedal et al., 2010 b
Djordjevic et al., 2010 c
Peamkrasatam et al., 2006 d Gyamfi et al., 2005 e
Fujieda et al., 2004
55
One drawback of the current study is that it does not include individual nicotine metabolism physiological data, which is a measurement of the conversion rate of nicotine to its metabolite cotinine ( i.e.
—the cotinine/nicotine ratio in plasma or urine over time).
Therefore, comparing the Aka to previously studied populations in terms of physiological
CYP2A6 metabolic status cannot be performed. However, because CYP2A6 metabolic rate is strongly influenced by genotype (Mwenifumbo and Tyndale, 2009; Nakajima et al., 2006), the metabolic status of populations can be determined by analyzing genotypes and then predicting CYP2A6 metabolic phenotypes. As previously mentioned, Nakajima et al. (2006) found that in comparison to their Japanese sample population, Caucasian and
African-descent populations have higher mean metabolic ratios, which suggests that the these populations can be classified as extensive metabolizers on average. Because
Nakajima et al. (2006) performed a comprehensive assay for several alleles, and includes corresponding physiological data, analyzing Aka CYP2A6 genotype frequencies and making phenotype predictions should provide some insight as to whether or not the Aka represent an extensive or slow metabolizing population on average. There are at least two issues in the literature that makes analyzing genotype frequencies across different populations, and characterizing a population’s metabolic status relatively difficult. First, although the alleles CYP2A6*1B and CYP2A6*9 are typically included in most CYP2A6 genotyping studies, there is great variability in terms of what alleles are assayed. The second issue pertains to the prevalence of wild type alleles in a sample population—
CYP2A6*1A and CYP2A6*1 (includes both CYP2A6*1A and CYP2A6*1B alleles) represent the wild type CYP2A6 allele variant, which is treated as an extensive
56 metabolizing allele in pharmacogenetics studies (Mwenifumbo et al., 2008a).
Essentially, when an allele does not test positive for any of the other CYP2A6 alleles assayed, the unknown allele is given the wild type allele designation. If a study only tests for a few CYP2A6 alleles in a population, then there will be a higher frequency of
CYP2A6*1 alleles, which in turn, gives the impression that there is a greater proportion of extensive metabolizing alleles in a given population. Therefore, the wild type designation is problematic in that the percentage of actual CYP2A6 wild type alleles is not accurate.
However, although there are undetected slow alleles (both known and undiscovered) in a wild type cohort, the homozygous CYP2A6*1 / CYP2A6*1 category along with physiological data is still useful for comparing the average ‘wild type’ rate of nicotine metabolism across different populations. To elaborate; when multiple sample populations are compared, individuals within those populations that are assigned to the homozygous CYP2A6*1 / CYP2A6*1 category will collectively represent the mean metabolic rate. Furthermore, if there is a high prevalence of slow alleles in the population, then this population represents a slow metabolizing population and the mean metabolic rate will be less in comparison to the other sampled extensive populations
(Nakajima et al., 2006). Similarly, if at least some slow alleles are tested for in sample populations, then a higher frequency of wild type alleles suggests that the average rate of
CYP2A6 metabolism will be higher (Gyamfi et al., 2005; Nakajima et al., 2006).
Ultimately, the percentage of individuals assigned to the wild type category is dependent upon how many alleles are assayed for in a given study. Nakajima et al. (2006) is the
57 most comprehensive CYP2A6 allele study to date—four different ethnic populations were assayed for twenty-four different alleles. Nakajima’s homozygous wild type extensive population percentage results include: 71.5% of Caucasian (n=126), 56.0% of Africandescent (n=90) and 23.9% of Japanese (n=22) were assigned as CYP2A6*1/CYP2A6*1 .
The allele CYP2A6*1 designation includes both CYP2A6*1A and CYP2A6*1B alleles— unfortunately, Nakajima et al., (2006) did not assay for CYP2A6*1B in all of their samples. In order compare Aka data to Nakajima’s homozygous CYP2A6*1 wild type category, therefore, it is necessary to combine the frequency of Aka individuals assigned to CYP2A6*1A/CYP2A6*1A, CYP2A6*1B/CYP2A6*1B, and CYP2A6*1A/CYP2A6*1B genotypes. Thus, 65.3% of the Aka sample population are CYP2A6*1/CYP2A6*1; which suggests a 6.2% lower frequency of homozygous wild type individuals than Nakajima’s
Caucasian population, and a 9.3% higher frequency of wild type alleles in comparison to
Nakajima’s African-descent population (Table 8). If we assume that the Aka are more closely related to the African-descent population, then combining Nakajima’s Africandescent frequencies of genotypes not tested for in this study accounts for less than 1%— in other words, variant alleles not tested for in this study can explain only 1% of the difference (9.3%) in the frequency of CYP2A6*1/CYP2A6*1 genotypes. As observed in the Aka, the high prevalence of wild type alleles and a low prevalence of variant alleles is consistent with what has been previously observed in African-descent and Caucasian populations (Peamkrasatam et al., 2006). In summary, the prior analysis (Table 8) suggests that due to a higher frequency of homozygous wild type individuals, the Aka are likely to represent an extensive metabolizing group on average.
58
Table 8:
Sample Population Frequencies of Homozygous Wild Type CYP2A6*1/CYP2A6*1
Individuals
Population
African-
Genotype Caucasian a
†
CYP2A6*1/CYP2A6*1 71.5%
Aka
65.3% descent
56.0% a
Japanese
23.9% a a
Nakajima et al., 2006
Population
Genotype Ghana b
†
CYP2A6*1/CYP2A6*1 89.7%
Aka
86.12% b
Gyamfi et al., 2005
†
In order to compare genotype frequencies more accurately, the percentage of
CYP2A6*1/CYP2A6*1 individuals in each sample population was adjusted to
Reflect (roughly) as though each study assayed for the same CYP2A6 alleles.
When the percentage of Aka CYP2A6*1/CYP2A6*1 genotypes is adjusted so that
Aka data can be compared to the Ghana study, 86.1% of Aka and 89.7% of Ghana individuals are CYP2A6*1/CYP2A6*1 (Table 8)—thus, there is a 3.6% difference, which is likely to be insignificant in terms of discovering a significant CYP2A6 allele distribution difference within African populations (as noted earlier, Table 7 suggests the same conclusion). Another observation is that Gyamfi et al.’s (2005) Ghana CYP2A6 allele study illustrates the second issue discussed previously; without assaying for a known population-specific major variant allele, the impression of a higher proportion of extensive metabolizers is given. Gyamfi et al. (2005) concluded that there is a the high prevalence of wild type alleles in Ghana, which implies that Ghanaian populations have a significantly higher proportion of extensive metabolizers in comparison to Nakajima et al.’s (2006) sample populations. However, in order to compare Aka genotype data to the
59
Ghana study, the slow allele CYP2A6*17 had to be re-classified as CYP2A6*1 , which in the Aka samples, accounts for ~20.8% of genotypes. Therefore, assuming the Ghana population is comparable to Aka and African-descent populations, the higher frequency of CYP2A6*1/CYP2A6*1 observed in the Ghana study (21-34%) may be solely due to not assaying for the slow allele CYP2A6*17 . Overall, the data presented in Table 8 suggests that the Aka and Ghana population appear to be relatively comparable in terms of wild type CYP2A6 genotype frequency. However, if future studies conduct a multiallele genotyping analysis on an African population, they should consider including the slow allele CYP2A6*17 in their analysis.
A final allele frequency analysis includes a metabolic phenotype breakdown of sample populations. Figure 4 is a visual representation of metabolic phenotypes which includes Aka individuals and Nakajima et al.’s (2006) population data. Taking individual genotypes into account, the different sample populations have been characterized for the percentage of extensive metabolizers (EM), intermediate metabolizers (IM) and slow metabolizers (SM). In order to construct Figure 4, the alleles in each sample population were assigned a metabolic status of either “extensive” or “slow” (Methods Table 4), where EM individuals possess homozygous extensive alleles, IM individuals possess heterozygous extensive and slow alleles, and SM individuals possess homozygous slow alleles. When sample populations are ordered from least proportion of slow metabolizers to greatest proportion of slow metabolizers, the Aka (EM=65.2%; IM=31.9%; SM=2.78) fall in between Nakajima’s Caucasian (EM=78.98%; IM=18.75%%; SM=2.28%) and
African-descent (EM=60.64%; IM=32.52%; SM=6.27%) populations. Although
60 assigning only three metabolic phenotypes for a population greatly underestimates the true diversity of CYP2A6 metabolic rate, Figure 4 still supports the basic trends that have been documented in various pharmacokinetics studies—Caucasian and African-descent populations have a higher rate of nicotine metabolism, while Asian populations typically have a slower rate of nicotine metabolism (Nakajima et al., 2006). Therefore, when metabolic status is determined by the contribution of each allele to overall phenotype, the
Aka appear to be more similar to extensive metabolizing populations.
100
90
80
70
60
50
40
30
20
10
0
EM
IM
SM
Caucasian Aka African-descent
Figure 4:
Korean Japanese
Population† Percentage of Extensive, Intermediate and Slow Metabolizing
Genotypes
††
†
Caucasian, African-descent, Korean, and Japanese population data obtained from
Nakajima et al. (2006).
††
Genotypes were assigned a metabolic status of extensive (EM), intermediate (IM), or slow (SM) based upon the possession of either an extensive or slow allele. Each allele’s individual contributing effect to overall metabolic status was determined via the CYP2A6
Allele Consortium Committee website (Ingelman-Sunberg et al., 2010), as well as, other studies (Nakajima et al., 2006; Mwenifumbo and Tyndale, 2009; Tiong et al., 2010).
61
The allele frequency comparison analyses in this study all suggest that due to a low percentage of the slow allele CYP2A6*9 (Table 7), a high percentage of wild type alleles (Table 8) and a CYP2A6 metabolic profile comparable to populations that are characterized as being extensive metabolizing on average (Figure 4), the Aka are likely to have a CYP2A6 allele distribution comparable to other extensive metabolizing populations. Therefore, the hypothesis that the Aka have a higher than expected percentage of slow alleles is not supported. However, the study data may not disprove the existence of the relationships proposed by the parasite-toxin evolutionary trade-off model. There are several reasons as to why this study may have failed to provide evidence for the evolutionary trade-off model, including that current CYP2A6 genotyping and physiological data from pharmacogenetics studies has insufficient resolution and that predictions of selective agents on CYP2A6 allele frequencies cannot be observed.
Another reason is that allele/genotype frequencies are not significantly affected by exposure to pathogens. Finally, an investigation of the other relationships proposed by the evolutionary trade-off model may be more significant in describing extant CYP2A6 allele distribution.
Other pertinent issues arise from these data (Figure 4). The most obvious is that the geographically “Asian” data appears qualitatively distinct from the “non-Asian” populations in regard to CYP2A6 metabolism. Exposure to different environmental agents may have influenced CYP2A6 metabolism in Asian populations. For example, one hypothesis is that Asian populations have a different evolutionary life history of exposure to nicotine-like substrates. If “Asian” and “non-Asian” populations have qualitatively
62 distinct CYP2A6 evolutionary and cultural “causes” for their different CYP2A6 metabolism characteristics, then the most appropriate comparison populations for the Aka to are other “non-Asian” populations. Finally, as mentioned previously, the labels
“Extensive metabolizer” (EM) and “Slow metabolizer” (SM) are relatively arbitrary and were coined in the medical literature in order to describe the metabolism of mainly commercial drugs. In a sense, “extensive metabolism” is “normal” metabolic function, rather than the implied elevated metabolic function. When characterized in this way, the picture of the data shift to a category of relatively “normal” function (EM), and another of somewhat less-than-normal function (IM and SM). Taken together, if we consider a comparison between “non-Asians” and re-conceptualizing EM as “normal” CYP2A6 function, then we observe a large difference in average genotype between “Caucasian” and the “African” samples, the African samples showing lower
CYP2A6 metabolism on average.
The idea that the current published data is insufficient to detect the relationships proposed by the evolutionary trade-off model is at least partially true. The concept of personalized medicine is apparently the ultimate goal of pharmacogenetics/genomics studies (Burke and Pstay, 2007; Guttmacher et al., 2007; Sadee and Dai, 2005) and according to the pharmacogenetics literature, studying CYP2A6 alleles is necessary because major pharmaceutical drugs are metabolized by CYP2A6 and certain genotypes may cause increased risk of side-effects (Andrejak et al. 1998; Hoffman et al., 2001). If comprehensively describing CYP2A6 metabolism is in fact important to pharmacogeneticists, then several key issues in the literature should be addressed. In
63 order to more accurately characterize CYP2A6 genotypes, it should be recognized that different sampling techniques yield different metabolic rate results (Malaiyandi et al.,
2006; Peamkrasatam et al., 2006). For example, Peamkrasatam et al. (2006) found a difference between using coumarin versus nicotine as a probe drug in predicting the metabolic capacity of slow CYP2A6 genotypes. If medical relevance is to be achieved, then congruent sampling methods and controls should be considered within the field so that genotypes can be more accurately characterized and compared. Furthermore, considering a good majority of alleles remain unclassified and therefore designated as
‘wild type,’ researchers should include physiological data so that wild type outliers can be identified as candidates for wholeCYP2A6 gene sequencing and new allele discovery.
Although existing data for known alleles may be of some use for evolutionary studies of
CYP2A6 , the medical utility of studies who simply publish frequencies for a few alleles is questionable.
Another glaring problem in the pharmacogenetics literature, is that a majority of studies on CYP2A6 metabolism use the concept of biological ‘race’ in order to separate and characterize certain demographics and populations (e.g.—Benowitz et al., 2002;
Emamghoreishi et al., 2008; Menifumbo et al., 2008b; Nakajima et al., 2006). From an anthropological viewpoint, labeling sample populations as, ‘Caucasian’, ‘black,’ ‘Asian’,
‘Oriental,’ ‘Latino,’ ‘intermediate,’ (etc…) is highly problematic for several reasons, including; typifying populations into such broad racial categories completely undermines the diverse ancestry/migratory histories of the individuals who are obviously assigned to these arbitrary categories via physical appearance. Therefore, the existing CYP2A6
64 metabolic characterizations about certain ‘races’ in the literature may be inaccurate in regards to adequately describing the metabolic diversity between different populations.
While genotyping individual drug metabolism in a health care setting may possibly be a
‘luxury’ option for a select few, adequately characterizing certain populations would be useful for public health-related purposes. Therefore, future pharmacogenetics/genomics studies should describe their sample populations in greater detail. Additionally, cultural variables for human evolutionary studies should be carefully considered. For example, in highly stratified societies, individuals belonging to different classes experience different environmental selective pressures, such as varying exposure to parasites. By addressing all of these issues, not only will it benefit evolutionary studies of CYP2A6 alleles, but it would also increase the medical relevance and applications of pharmacogenetics findings.
To the author’s knowledge, selective agents that may have influenced CYP2A6 allele distribution has not been discussed in the literature. It would be worth investigating whether or not a clinal relationship exists between the abundance of coumarins in an environment and the prevalence of slow alleles in a population.
However, coumarins are a highly prevalent plant secondary metabolite (Lacy and
O’Kennedy, 2004) and its geographic distribution may not significantly differ.
Regardless, a prediction can still be made; populations residing in high coumarin plant areas will have a lower frequency of slow CYP2A6 alleles. Assuming coumarins do differ significantly in their distribution, several populations would need to be strategically selected and CYP2A6 geno- and phenotyped. A potentially more useful analysis would be studies on population-specific dietary consumption of coumarins. Again, the
65 prediction is that populations who consume more dietary coumarins may have a lower prevalence of slow CYP2A6 alleles.
In order to more directly investigate the parasite-toxin evolutionary trade-off model, a study of coumarin consumption could also coincide with studies of nicotine usage and systemic parasites, such as helminthes. However, instead of hypothesizing that slow alleles are selected for due to parasites, another prediction can be made; in high coumarin environments and/or populations with high nicotine consumption, extensive genotypes will still be selected for regardless of parasites. In this view, removing toxins takes precedent, as opposed to the selection for slow alleles (which are hypothesized to aid in systemic parasite reduction). In other words, if the consumption of nicotine is high enough to reduce parasites, then parasite selective pressure for slower CYP2A6 genotypes is redundant. Thus, while environmental and/or dietary coumarins influence CYP2A6 genotypes, individuals may consume the ‘necessary’ amount of nicotine to reduce parasites, which is ultimately influenced by their CYP2A6 genotype. Correlations between the amount of nicotine consumption and CYP2A6 genotypes have been documented by pharmacogeneticists; individuals with extensive metabolizer genotypes smoke more than slow metabolizer genotypes (Liu et al., 2011; Perez-Stable et al., 1998;
Sellers 1998; Vasconcelos et al., 2005). Therefore, if there is a relationship between nicotine usage and parasite load, individuals who are extensive metabolizers and are infected with helminthes will have the highest rate of nicotine usage (Mann et al., 2012).
However, detecting such a straightforward relationship may be difficult; demographic and cultural variables would require complex controls in study design. For example, in
66
Aka society, nicotine usage is associated with maturity in males and female usage of nicotine is less (Mann et al., 2012). Overall, a more complex study that analyzes individual CYP2A6 genotype, nicotine consumption and parasite load could directly test all of the relationships proposed by the parasite-toxin evolutionary trade-off model.
The idea that none of these relationships exist should at least be entertained. If no environmental agent is responsible for extant CYP2A6 distribution, then it implies that genetic drift is responsible for slow allele frequency differences between extensive and slow metabolizing populations. However, although genetic drift seems unlikely given the role that CYP genes play in dietary adaptation (Fuselli et al., 2010), the deleted gene variant CYP2A6*4 might suggest a lack of selective pressure/s in Asian populations where it is found in high frequency. Furthermore, CYP2A6 is highly polymorphic, which could be explained by positive selection, balancing selection, or genetic drift (Fuselli et al., 2010; Solus et al., 2004; Zawaira et al., 2008). The genomics studies of CYP genes cited here, however, are insufficient in that some of their results may be due to differences in methodologies (Yang and Bielawski, 2000). Therefore, in order to confirm what type of selection has or is occurring, more genomic studies of CYP genes would be informative and should be pursued—not just for the purpose of detecting selective pressures, but also because CYP genes are incredibly diverse and studying them would enrich our understanding of human dietary adaptation and disease (Fuselli et al., 2010).
Furthermore, considering the immense xenobiotic substrate diversity of CYP enzymes and that the amount of environmental xenobiotics may have changed throughout a
67 population’s history,
CYP subfamily genes should be analyzed independently in order to take into account the potentially different evolutionary histories of various CYP genes.
More CYP2A6 genotyping data from diverse populations is necessary for formulating more precise evolutionary explanations. Researchers in this field of research must be aware that due to the conserved nature of the cytochrome P450 gene family
(Hoffman et al., 2001), genotyping analysis for CYP2A6 requires careful method design.
For example, CYP2A6 population frequency data should be interpreted with the understanding that the selected alleles assayed for in a study greatly impact conclusions about the metabolic status of a given population. This current study contributes to pharmacogenetics literature in that it provides allele frequencies for an African population. Furthermore, an attempt was made to explain extant CYP2A6 population distributions via the toxin-parasite evolutionary trade-off model. However, the alleles assayed indicate that the Aka have similar frequencies to those previously described for
African-descent populations, and are associated with an extensive metabolic status.
Although a strong case can be made regarding whether or not the Aka can be characterized as an extensive or slow metabolizing population, pinpointing the precise selective pressures responsible for CYP2A6 allele distributions will be a greater challenge. More studies that investigate dietary and recreational xenobiotic consumption in relation to genotypes and parasite-genotype analyses, are necessary in order to support or rule out the relationships proposed in the evolutionary trade-off model. Another promising area of research includes investigating the direction of evolutionary selection on CYP2A6— detecting positive selection would support the idea and help identify
68 selective agents responsible for differences in cytochrome P450 allele distribution across different populations. Another issue is that the pharmacogenetics literature that does exist seemingly lacks significance in terms of medical utility. Populations under study should include better documentation of ancestral background and socioeconomic variables considering variability of nicotine metabolism impacts clinical outcomes
(Nakajima et al., 2006). Nicotine related illness and death is a world-wide concern, and public health interventions are likely to be more effective if an evolutionary perspective is incorporated into disease etiology.
69
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