Genes involved in various Reward Dependence Pathways.
\r\n\t
",isbn:"978-1-83969-561-2",printIsbn:"978-1-83969-560-5",pdfIsbn:"978-1-83969-562-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"65f2a1fef9c804c29b18ef3ac4a35066",bookSignature:"Dr. Luis Loures",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10756.jpg",keywords:"Urban Processes, Urban Patterns, Redevelopment Strategies, Landscape, Land Transformation, Urban Models, Urban Evolution, Urban Organisation, Legislation, Sustainable Development, Green Infrastructure, Regional Planning",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 23rd 2021",dateEndSecondStepPublish:"March 22nd 2021",dateEndThirdStepPublish:"May 21st 2021",dateEndFourthStepPublish:"August 9th 2021",dateEndFifthStepPublish:"October 8th 2021",remainingDaysToSecondStep:"23 days",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Loures has worked on pioneering research on circular planning applied to post-industrial landscape redevelopment. Since he graduated he has published several peer-reviewed papers at the national and international levels and he has been a guest researcher and lecturer both at Michigan State University (USA) and at the University of Toronto (Canada) where he has developed part of his Ph.D. research with the Financial support from the Portuguese Foundation for Science and Technology (Ph.D. grant).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"108118",title:"Dr.",name:"Luis",middleName:null,surname:"Loures",slug:"luis-loures",fullName:"Luis Loures",profilePictureURL:"https://mts.intechopen.com/storage/users/108118/images/system/108118.png",biography:"Luís Loures is a Landscape Architect and Agronomic Engineer, Vice-President of the Polytechnic Institute of Portalegre, who holds a Ph.D. in Planning and a Post-Doc in Agronomy. Since he graduated, he has published several peer reviewed papers at the national and international levels and he has been a guest researcher and lecturer both at Michigan State University (USA), and at University of Toronto (Canada) where he has developed part of his Ph.D. research with the Financial support from the Portuguese Foundation for Science and Technology (Ph.D. grant).\nDuring his academic career he had taught in several courses in different Universities around the world, mainly regarding the fields of landscape architecture, urban and environmental planning and sustainability. Currently, he is a researcher both at VALORIZA - Research Centre for Endogenous Resource Valorization – Polytechnic Institute of Portalegre, and the CinTurs - Research Centre for Tourism, Sustainability and Well-being, University of Algarve where he is a researcher on several financed research projects focusing several different investigation domains such as urban planning, landscape reclamation and urban redevelopment, and the use of urban planning as a tool for achieving sustainable development.",institutionString:"Polytechnic Institute of Portalegre",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"8",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Polytechnic Institute of Portalegre",institutionURL:null,country:{name:"Portugal"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"10",title:"Earth and Planetary Sciences",slug:"earth-and-planetary-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"205697",firstName:"Kristina",lastName:"Kardum Cvitan",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/205697/images/5186_n.jpg",email:"kristina.k@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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This sense of wellbeing results from the interaction of dopamine and neurotransmitters such as serotonin, the opioids, and other brain chemicals. Low serotonin levels are associated with depression. High levels of the opioids (the brain\'s opium) are also associated with a sense of wellbeing. (Blum et al., 2010).
Dopamine has been called the “anti-stress” and/or “pleasure” molecule. When released into the synapse, dopamine stimulates a number of receptors (D1 – D5), which results in increased feelings of wellbeing and stress reduction (Kreek and Koob, 2007). The mesocorticolimbic dopaminergic pathway plays an important role in mediating reinforcement of natural rewards such as food and sex, as well as unnatural rewards such as drugs of abuse (Bruijnzeel et al., 2007). Natural rewards include satisfaction of physiological drives (e.g. hunger and reproduction) and unnatural rewards are learned and involve satisfaction of acquired pleasures such as hedonic sensations derived from alcohol and other drugs, as well as from gambling and other risk-taking behaviors. (Olsen, 2011).
The DRD2 gene is responsible for the synthesis of dopamine D2 receptors (Grady et al., 1989). Further depending on the genotype (allelic form A1 versus A2), the DRD2 gene dictates the number of these receptors at post-junctional sites (Blum et al., 1990; Noble et al. 1991). A low number of D2 receptors leads to hypodopaminergic function. When there is a paucity of dopamine receptors, the person is more prone to seek any substance or behavior that stimulates the dopaminergic system (Conrad et al., 2010). The D2 receptor has been associated with pleasure, and the DRD2 gene has been referred to as a reward gene (Heber & Carpenter, 2011).
Although the DRD2 gene, and especially the Taq1 A1 allele, has been most associated with neuropsychiatric disorders in general and in alcoholism in particular, it is likely involved in other addictions (e.g., carbohydrate). It may also be involved in co-morbid antisocial personality disorder symptoms, especially in children and adults with attention deficit hyperactivity disorder (ADHD), or Tourettes Syndrome, and high novelty seeking behaviors (Noble, 2003).
Reward Deficiency Syndrome was first defined in 1996 as a putative predictor of impulsive and addictive behaviors (Blum et al., 1996). Herein, “Reward Deficiency Syndrome” or “RDS” refers to a group of related addictive behaviors. Dopamine is a major component in the mechanisms related to RDS and brain function. Specifically, RDS involves dopamine resistance, a form of sensory deprivation of the brain\'s reward or pleasure mechanisms ( Bowirrat & Oscar-Berman, 2005). The syndrome can be manifested in relatively mild or severe forms that follow as a consequence of an individual\'s biochemical inability to derive reward from ordinary, everyday activities. The DRD2 A1 genetic variant is also associated with a spectrum of impulsive, compulsive, and addictive behaviors (Gardner, 2011). RDS or anti-reward pathways unite these disorders and explain how certain genetic anomalies give rise to complex aberrant behavior.
In discussing RDS, specific reference is made to an insensitivity and inefficiency in the brain’s reward system. There may be a common neurocircuitry, neuroanatomy, and neurobiology for multiple addictions and for a number of psychiatric disorders. Due to specific genetic antecedents and environmental influences, a deficiency of for example, the D2 receptors may predispose individuals to a high risk for multiple addictive, impulsive, and compulsive behaviors. It is well known that alcohol and other drugs of abuse, as well as most positive reinforcements (e.g., sex, food, gambling, aggressive thrills, etc.), cause activation and neuronal release of brain dopamine, which can decrease negative feelings and satisfy abnormal cravings for substances such as alcohol, cocaine, heroin, and nicotine, which, among others, are linked to low dopamine function (Blum et al., 2008).
In individuals possessing an abnormality in the DRD2 gene, the brain lacks enough dopamine receptor sites to achieve adequate dopamine sensitivity and function from the “normal” dopamine produced in the Reward Center of the brain. Carriers of the A1 DRD2 gene variant may have unhealthy appetites, abuse cocaine, indulge in overeating (which can lead to obesity) or, on the other extreme, be anorexic and/or suffer greater consequences of chronic stress (Bau et al., 2000). In these individuals, their addictive brains lead to generalized craving behavior. In essence, they seek substances including alcohol, opiates, cocaine, nicotine, and/or glucose (all substances known to cause preferential release of dopamine at the Nucleus accumbens (NAc)) to activate dopaminergic pathways in order to offset their low D2 receptors, which are caused by the dopamine D2 receptor gene Taq1 A1 allele antecedents (Sullivan et al., 2011). However, evidence is emerging that anterior cingulate cortex rather than the NAc may be involved in operant effort-based decision making (Walton et al., 2009). In addition, the DRD2 A1 polymorphism is also associated with abnormally aggressive behavior, which also stimulates the brain’s use of dopamine (Chen et al., 2005).
RDS is linked to flawed dopamine metabolism, and especially to low D2 receptor density. Moreover, RDS results from a dysfunction in the mesolimbic system of the brain, which directly links abnormal craving behavior with a defect in the Dopamine D2 Receptor Gene (DRD2) as well as other dopaminergic genes (D1, D3, D4, and D5, DATA1, MAO, COMT), including many genes associated with the brain reward function (Pinto & Ansseau, 2009), as listed in Table 1, below.
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_________________________________________ | ______________________________________ |
_________________________________________ | ______________________________________ |
_________________________________________ | ______________________________________ |
_________________________________________ | ______________________________________ |
_________________________________________ | ______________________________________ |
ALDH1 | |
ALDH2 | |
CAT | |
CYPZE1 | |
ADH1A | |
ADH1B | |
ADH1C | |
ADH4 | |
ADH5 | |
ADH6 | |
ADH6 | |
ADH7 | |
_________________________________________ | ______________________________________ |
BDNF | |
CART | |
CCK | |
CCKAR | |
CLOCK | |
HCRT | |
LEP | |
NR3C1 | |
SLC29A1 | |
TAC |
Genes involved in various Reward Dependence Pathways.
The genesis of all behavior, be it “normal” (socially acceptable) or “abnormal” (socially unacceptable), derives from an individual’s genetic makeup at birth. This genetic predisposition, due to multiple gene combinations and polymorphisms, is expressed differently based on numerous environmental factors including family, friends, educational and socioeconomic status, environmental contaminant exposure, and the availability of psychoactive drugs, including food. The core of predisposition to these behaviors is a set of genes interacting with the environment, which promote a feeling of wellbeing via neurotransmitter interaction at the “reward center” of the brain (located in the meso-limbic system), leading to normal dopamine release (Kendler et al 2011).
Subjects afflicted with RDS carry polymorphic genes in dopaminergic pathways that result in hypo-dopominergic function caused by a reduced number of dopamine D2 receptors, reduced synthesis of dopamine (by dopamine beta –hydroxylase), reduced net release of pre-synaptic dopamine (from, e.g., the dopamine D1 receptor), increased synaptic clearance due to a high number of dopamine transporter sites (dopamine transporter), and low D2 receptor densities (dopamine D2 receptor), making such people more vulnerable to addictive behaviors (Comings and Blum, 2000). The RDS concept involves shared genes and their mRNA expressions and behavioral tendencies, including dependence on alcohol, psycho-stimulants, marijuana, nicotine (smoking), and opiates, altered opiate receptor function, carbohydrate issues (e.g., sugar-binging), obesity, pathological gambling, sex addiction, premeditated aggression, stress, pathological aggression, and certain personality disorders, including novelty-seeking and sex addiction
Since the discovery of the double helix, explorations of brain function in terms of both physiology and behavioral traits have resulted in a plethora of studies linking these activities to neurotransmitter functions having a genetic basis. The mechanisms underlining gene expression and the potential impairments due to polygenic inheritance -- and as such, predisposition to addiction and self-destructive behaviors -- have been studied. Our studies have shown that the prevalence of the DRD2 A1 allele in Cocaine dependent (CD) subjects (n = 53) was 50.9%. It was significantly higher than either the 16.0% prevalence (P < 10(-4)) in non-substance abusing controls (n = 100) or the 30.9% prevalence (P < 10(-2)) in population controls (n = 265) wherein substance abusers were not excluded. Logistic regression analysis of CD subjects identified potent routes of cocaine use and the interaction of early deviant behaviors and parental alcoholism as significant risk factors associated with the DRD2 A1 allele. The cumulative number of these risk factors in CD subjects was positively and significantly (P < 10-3) related to DRD2 A1 allelic prevalence. The data showing a strong association of the minor alleles (A1 and B1) of the DRD2 with CD indicates that a gene, located on the q22-q23 region of chromosome 11, confers susceptibility to this drug disorder (Noble et al.,1993)
Over half a century of dedicated and rigorous scientific research on the mesolimbic system, has provided insight into the addictive brain and the neurogenetic mechanisms involved in man’s quest for happiness (Blum et al., 2009). In brief, the site of the brain where one experiences feelings of wellbeing is the mesolimbic system. This part of the brain has been termed the “reward center”. Chemical messages including serotonin, enkephalins, GABA, and dopamine (DA) work in concert to provide a net release of DA at the nucleus accumbens (NAc), a region in the mesolimbic system. It is well known that genes control the synthesis, vesicular storage, metabolism, receptor formation, and neurotransmitter catabolism. The polymorphic versions of these genes have certain variations that could lead to an impairment of the neurochemical events involved in the neuronal release of DA. The cascade of these neuronal events has been termed “Brain Reward Cascade”. See Figure 1. A breakdown of this cascade ultimately leads to a dysregulation and dysfunction of DA. Since DA has been established as the “pleasure molecule” and the ”anti-stress molecule,” any reduction in its function could lead to reward deficiency and resultant aberrant substance seeking behavior and a lack of wellness (Blum & Kozlowski, 1989).
It is well –known that humans are biologically predisposed to drink, eat, reproduce, and desire pleasurable experiences (Tindell et al., 2006, Peciña et al., 2006). Impairment in the mechanisms involved in these natural processes lead to multiple impulsive, compulsive, and addictive behaviors governed by genetic polymorphisms. While there are a plethora of genetic variations at the level of mesolimbic activity, polymorphisms of the serotonergic-2A receptor (5-HTT2a), serotonergic transporter (5HTTLPR), dopamine D2 receptor (DRD2), dopamine D4 receptor (DRD4), dopamine transporter (DAT1), and Catechol-o-methyl–transferase (COMT), and monoamine–oxidase (MOA) genes, as well as other genes, predispose individuals to excessive cravings and resultant aberrant behaviors.
As stated earlier, an umbrella term to describe the common genetic antecedents of multiple impulsive, compulsive, and addictive behaviors is RDS. Individuals possessing a paucity of serotonergic and/or dopaminergic receptors and an increased rate of synaptic DA catabolism, due to high catabolic genotype of the COMT gene, or high MOA activity are predisposed to self-medicating with any substance or behavior that will activate DA release including alcohol, opiates, psychostimulants, nicotine, glucose, gambling, sex, and even excessive internet gaming, among others. Use of most drugs of abuse, including alcohol, is associated with release of dopamine in the mesocorticolimbic system or “reward pathway” of the brain (Di Chiara, 1995). Activation of this dopaminergic system induces feelings of reward and pleasure. However, reduced activity of the dopamine system (hypodopaminergic functioning) can trigger drug-seeking behavior. Variant alleles can induce hypodopaminergic functioning through reduced dopamine receptor density, blunted response to dopamine, or enhanced dopamine catabolism in the reward pathway. Cessation of chronic drug use induces a hypodopaminergic state that prompts drug-seeking behavior in an attempt to address the withdrawal–induced state (Berridge, 2009).
Acute utilization of these substances can induce a feeling of wellbeing. But, unfortunately sustained and prolonged abuse leads to a toxic pseudo feeling of well being resulting in tolerance and disease or discomfort. Thus, low DA receptors due to carrying the DRD2 A1 allelic genotype results in excessive cravings and consequential behavior, whereas normal or high DA receptors results in low craving induced behavior. In terms of preventing substance abuse, or excessive glucose craving, one goal is to induce a proliferation of DA D2 receptors in genetically prone individuals. Experiments in vitro have shown that constant stimulation of the DA receptor system via a known D2 agonist in low doses results in significant proliferation of D2 receptors in spite of genetic antecedents. In essence (Boundry et al., 1996) D2 receptor stimulation signals negative feedback mechanisms in the mesolimbic system to induce mRNA expression causing proliferation of D2 receptors. This molecular finding serves as the basis to naturally induce DA release to also cause the same induction of D2-directed mRNA and thus proliferation of D2 receptors in the human. This proliferation of D2 receptors in turn, will induce the attenuation of craving behavior. In fact this has been proven with work showing DNA–directed over-expression (a form of gene therapy) of the DRD2 receptors and significant reduction in both alcohol and cocaine craving-induced behavior in animals (Filtz et al.,1994). These observations are the basis for the development of a functional hypothesis of drug–seeking and drug use. The hypothesis is that the presence of a hypodopaminergic state, regardless of the source, is a primary cause of drug–seeking behavior. Thus, genetic polymorphisms that induce hypodopaminergic functioning may be the causal mechanism of a genetic predisposition to chronic drug use and relapse. Finally, utilizing the long term dopaminergic activation approach will ultimately lead to a common safe and effective modality to treat RDS behaviors including Substance Use Disorders (SUD), Attention Deficit Hyperactivity Disorder (ADHD), and obesity among other reward deficient aberrant behaviors (Rothman & Glowa, 1995; Peng et al., 2010).
Support for the impulsive nature of individuals possessing dopaminergic gene variants is derived from a number of important studies illustrating the genetic risk for drug-seeking behaviors based on association and linkage studies implicating these alleles as risk antecedents having impact in the mesocorticolimbic system (see Figure 1).
Schematic of Brain reward Cascade Normal and abnormal representation. (A) represents the normal physiologic state of the neurotransmitter interaction at the mesolimbic region of the brain. Briefly, serotonin in the hypothalamus stimulates neuronal projections of methionine enkephalin in the hypothalamus that, in turn, inhibits the release of GABA in the substania nigra, thereby allowing for the normal amount of Dopamine to be released at the Nucleus Accumbens (NAc; reward site of the brain). (B) Represents hypodopaminergic function of the mesolimbic region of the brain. The hypodopaminergic state is due to gene polymorphisms as well as environmental elements, including both stress and neurotoxicity from aberrant abuse of psychoactive drugs (
In doing association studies that require a representative control sample for a single RDS psychiatric diagnosis or for potential subsets of RDS, one limitation relates to controls poorly screened for multiple RDS behaviors and other related psychiatric disorders (Neiswanger et al.,1995). Missing behaviors that are part of the RDS subset may be the reason for spurious results when genotyping for single subsets of RDS behaviors. For example, in our unpublished study, an individual may not drink alcohol or use drugs but may have other RDS behaviors such as overeating or intensive video-gaming. In support of this, a very strong association of the dopamine D2 receptor A1 allele (100%) was found in one family (Family A) studied over five generations. In addition, every individual in another family, Family B, also had at least one dopaminergic high risk allele (100%) (48% carried the DRD2 A1 allele). Moreover, in Family B only three adult individuals exhibited no addictive behavior. When compared to results in which 55 RDS subjects carried the DRD2 A1 allele at a frequency of 78.2% and the results of a study in which 597 severe alcoholics carried the A1 allele at a frequency of 49.3%, there was a significant difference between these two groups (X2=16.9, p<0.001). This demonstrated that the A1 allele’s prevalence increases with multiple RDS behaviors. The results from these experiments show that multifaceted non-specific RDS behaviors should be considered as the true “reward” phenotype (endophenotype) instead of a single subset RDS behavior such as alcoholism.
Very few behaviors depend upon a single gene. Complexes of genes (polygenic) drive most of heredity-based human actions, and RDS is no exception (Greven et al., 2011). For example, DRD2 and DAT1 gene polymorphisms are significantly associated with reward–dependent traits such as cocaine dependence (Brousse et al., 2010). As a polygenic disorder involving multiple genes and many polymorphisms, RDS likely requires a threshold number of RDS-associated polymorphisms in order to manifest in a particular subject. Unaffected individuals in the population carry some of these alleles. For example, the dopamine D2 receptor gene A1 allele is present in about one-third of unscreened Americans (Noble, 2003).
The GARS test has direct implications for both the diagnosis and targeted treatment of RDS behaviors by analyzing the association of dopaminergic genetic polymorphisms. Without being bound to a particular theory, in keeping with the concept of common neurogenetic mechanisms we believe that RDS is a basic phenotype covering many reward behaviors and psychiatric disorders, including spectrum disorders (Blum & Gold, 2011). The test provides for the first time a polygenic diagnosis of RDS predisposition. Such tests can be used, for example, to determine stratified genetic risk of patients with addictive behaviors upon their entry into a treatment facility, as information about an individual’s genetic predisposition to RDS based on the analysis of a number of RDS-associated alleles will be very useful.
RDS-associated SNPs can be identified by any suitable method, including DNA sequencing of patients diagnosed with one or more RDS behaviors. After validation, newly identified RDS-associated SNPs can be used in the test. As will be appreciated, once identified and validated, the presence, if any, of one or more RDS-associated SNPs in the nucleic acids derived from a biological sample taken from a patient can be determined using any suitable now known or later-developed assay, including those that rely on site-specific hybridization, restriction enzyme analysis, or DNA sequencing. Table 2 below, lists a number of particularly preferred RDS- associated SNPs, whereby, the detection of which can be used for the GARS test.
DRD2 | CTGGACGTCCAGCTGGGCGCCTGCCT[C/T]GACCAGCACTTTGAGGATGGCTGTG |
AGGAACTCCTTGGCCTAGCCCACCCT[G/T]CTGCCTTCTGACGGCCCTGCAATGT | |
CTGCTTCCCACCTCCCTGCCCAGGCC[A/G]GCCAGCCTCACCCTTGCGAACCGTG | |
GTCACTATTATGGTTTTTATTACTAT[G/T]GCTCTTTTTGAGGAATTGGGAAATT | |
CTGACTCTCCCCGACCCGTCCCACCA[C/T]GGTCTCCACAGCACTCCCGACAGCC | |
CCACCAGCTGACTCTCCCCGACCCGT[C/G]CCACCACGGTCTCCACAGCACTCCC | |
ACCCATCTCACTGGCCCCTCCCTTTC[A/C]CCCTCTGAAGACTCCTGCAAACACC | |
TTTTGCTGAGTGACCTTAGGCAAGTT[G/T]CTTACCTTCTATGAGCCTGTTTCCT | |
TGTGATGAATGGGTGCCAAATACACA[A/G]ATACAGAATCTAAGAAAACACATGG | |
CTAGAGGAAGTGATGTTCAACAGACA[A/G]ACAACTGAAGGATGTGTAGGAATTA | |
TGGAAGTCATGTGCTTTGTATGAAAC[A/G]CCTTGGAATGCTGATAAGTTTAATT | |
GTCTAAAGCAAATGGAACCTTTAGGG[A/G]GAGAGATTTGTGTTTGCTGTGTCCC | |
GAGGGGACTGGGGTCAGGCCTCATTC[A/G]GGTTCCCTAGAGTGGAAAGGATTGG | |
GTATCAGACAGATCTAGGCTCAAATA[A/C]CAGCTTCAGTTCTCACCACCTGTGT | |
CCTGAGTGCACAGGATGCTGGAGCCT[C/T]CCAGTTTCTCTGGCTTTCATCTCGT | |
AATCCCCCAACCCCTCCTACCCGTT[-/C]CAGGCCGGGGATCGCCGAGGAGGTA | |
GAGGACCCAGCCTGCAATCACAGCTT[A/G]TTACTCTGGGTGTGGGTGGGAGCGC | |
5HT2A | GCTCAATGGTTGCTCTAGGAAAGCAG[C/T]ATTCTGAAGAGGCTTCTAAAGACAA |
CTGTGAACTCAGGAGCAAGTGCACAC[A/G]TTGCTTATCACTTACCAGAAGCATT | |
CTCTGGTTTTAAGCAAGTCATTTAAT[-/C/T]GGAGTTTTTTTTCTCCCATAAAATG | |
AAATGGTCCTACCATCTATCCAGATA[C/T]ACAGCTTAAAAACTTAGGAGTCTCT | |
TGCTATTTGTAATGCTGCTTATTAGA[G/T]ACATCGCTGATCCTCCTGTCAACTC | |
ATGAACCAAATTGCATGAGCTCTATT[A/G]TGTGCCCCTCTTGTAATATAAAAAT | |
CAGGCAGAATTTCCACAAATGAAATG[C/G]AAATTCAGATATATATCTCTTAATC | |
TCACTCATAACTGAAGATCATTTCAC[C/T]TTTGAATGAGAATTTGTCTCTGAAG | |
TGGGCAGAGGAGGGGAAGGGTCACTG[C/T]ACTCAGGGACAAGAGAAGGGGTGGG | |
ATCAGTGTGGTCACTTCACTGCTTGC[C/G]AAGGATTCCATCTAATTCTGAGGAA | |
AGGCTCTACAGTAATGACTTTAACTC[C/T]GGAGAAGCTAACACTTCTGATGCAT | |
TATGTCCTCGGAGTGCTGTGAGTGTC[C/T]GGCACTTCCATCCAAAGCCAACAGT | |
ANKK1 | GGAGGGGGGTCTTGCCCTCAGCCTCA[C/T]GCAGGTTGGGGTCAGCCTGACGGGA |
GGAGGGGGGTCTTGCCCTCAGCCTCA[C/T]GCAGGTTGGGGTCAGCCTGACGGGA | |
CCTGCAAGCTGTCGCTGCGCCAGCCC[A/G]GGGAGGTGAGTGTGTGGGCTGGGCA | |
ACTTTGCAGCCCAGAATGGGGATGAC[C/G]GCACTGCGCGCCTGCTCCTGGACCA | |
OPRK1 | AATTTCCCAAAAACTACAGTTTTTTT[-/T]TCTTAGCATGCTATTCAGGTAAACA |
TTTGCTAGGTAAGGTTCAGCACCCAT[C/T]TGCTGTGGCCTTCCTATGAAACGTA | |
ATGACTAGTCGTGGAGATGTCTTCGT[A/C]CAGTTCTTCGGGAAGAGAGGAGTTC | |
GAAAACACAAGTGTGATCAAATGCCA[C/T]GGACCCACAGGAAGCTGGTGGCTCT | |
AAGAAATTGTCTGCATATAAACAAAT[A/G]CATCACATTTCCACAAAAGACTTTG | |
TGAGAGCTAATGTTTCAAAGAAACTT[G/T]AAATTCCCAAGATTAAAATTATTGT | |
CCCAGTAAGTGAATTAAATACTTTCA[C/T]AGACACTCTCCATCTAGTAGAACAA | |
TGATAGGCACTGGTTCTACAGTGAGA[C/T]ATATCTCTCCTAAGTCTGGTGACAA | |
ACAGTGGCACGATCTCGGCTCACTGC[A/C]ACCTCCACCTCCCGGGTTTAAGTGA | |
TTACCTGGCTAACAGTTTTCTATCTC[C/T]CACACGAGCCTGGTGGGAGGCAGTG | |
AGCTCTTGTTATCTTACCATTCCCAC[A/G]TTGATTCTCATTTTTATCCCTCTCC | |
TCAAGATAGCTAATTGAGAACAAGCA[C/T]GAGACTCCACTCCTGGTCCCCAAGC | |
CCATTTTCTTTTCTTCTTTGCTTGTC[G/T]TTTTTTTCTGTTTGTTTTTCTTTTC | |
AGAAAATAACTTTTGCTAGATTCACC[A/G]TTGGTTATAGACCTGCATGATCTAA | |
GTGATGTTACCAGCCTGAGGGAAGGA[A/G]GGTTCACAGCCTGATATGTTGGTGA | |
AGTTAGCTCTGGTCAAGGCTAAAAAT[C/G]AATGAGCAAAATGGCAGTATTAACA | |
AATTTTATTAGATTAAACAATTTTTA[A/G]CAGACCTCATGCTTGTTGGAGATAA | |
GGTCCAGGGTACACAACCAAGCAGCC[A/T]TGCTCTAGAGCCCAGCAAGACAGGG | |
ACTGAAGAATAATCATGCTTAACTCA[A/G]GAGAAATGCTCCACCAGACGGGCTG | |
GCACATTTACTGTTTTGTCTAACCTG[C/T]CTAGCCATTTCAGTCAAGCTGATTG | |
GCAATCAGAAAGAAATTCAGTTATTA[C/T]AGTATATGCAAGTCACACTGCAAGC | |
AATGAAACACAAATCATAATCTCTGA[A/G]GCAAATAAGAATGGAAGGACTCCTG | |
TTTAGGGCAAGTCAGAAAGTCCAAAA[A/G]TGCCTCAGATATTCTGTGTGAGTGA | |
AAAAACTGGGCCTGAGCTCAGATGAA[C/T]TGGAGAACTGAACTTTGGCTTAGAA | |
GAGTCATCAGCTCCCAAGGTTTTCTG[C/T]ATGGCTCTGTTTTTATGATTTCTGT | |
GTGTGTACTGCAGTCTGGTCCCATCG[C/T]ATTGCCTTGTGGGATTTGGGAGTAG | |
COMT | GCTCCTACGGTCCCTCAGGCTTGGAG[A/G]GTCACTTTAAACAATAAAAAGCAAC |
TGTGGTTACTTTCTGGAGAGAGCATG[C/T]GGCATGCAGGAGCTGGAGGGGGGGT | |
AAAAGTTACGCTTAATAATGAATGTT[G/T]CAGCACTTTCTTCTCTTCAGGTATT | |
CTGTGAGGCACTGAGGATGCCCTCAC[A/G]CGTGCATCTGCATGTGGCGTGCATG | |
CTGGTTTGTGTATGTTCTTGGTAAAC[C/T]AGCCCTTGGTCTTACACATCATTTC | |
GCTTCCCTGTTCTCTTCTGCTCTGTC[C/T]TCTGGTGCCCTGAGGCTGGCCTCCA | |
GGCATTTCTGAACCTTGCCCCTCTGC[A/G]AACACAAGGGGGCGATGGTGGCACT | |
ATAAGTAACTGTCGAGAAGATTCTCA[C/T]AGGAGACCACGTGGGTTGCCTGAAG | |
SLC6A3 | AATGTCCTCAGCTGGTTCTTCCCCCA[A/G]TGCCCTGATCCTGGGCTCACATGTG |
GAGACGAAGACCCCAGGAAGTCATCC[C/T]GCAATGGGAGAGACACGAACAAACC | |
AAAATCAAGTAATGATTGATTTGTAG[A/G]AGTTTGAGTGAGGCATCGGATCCCC | |
ACCGTGCCCAGCCCTGTGTGGGCATC[A/G]GAGGTGGTTCCCTCTGGTCCTGTCG | |
GTCCAGGCCCCAGGAGCTGCCGCAGC[A/G]GGCAGTGGAAGGAAGGCACGTTCAG | |
CAGCTTCCCCTCCCAACACAGAGGCG[A/C]GGCCCAAGTGCAGGACTCACAACGG | |
AAGACACAGTGACGGTATACTCATGA[C/T]GGAATATGATTCGGCCTTAAAACAA | |
AAGGCGAAGCCGGCGATGGTACGTAC[A/G]TTGGTGACGCAGAACAGGGACAGGA | |
GCCATCGCCACGCTCCCTCTGTCCTC[A/G]GCCTGGGCCGTGGTCTTCTTCATCA | |
TGCTTCCTGCTACCAGCAGGCAGACT[C/T]GGATGGAGGTGGAGGGGACGAGAGT | |
CACGGTAAAAATACAAGGACAGTGTG[A/T]GCAGCAGAATGGCCAGGCAGACCAC | |
AGGGTTATTAGGATGCTGTGGTCATG[C/T]CGTGTGTGGATGAGTCCATGCTGTT | |
GCCAGGCAGGGGCTGGTGGAGGTGCA[C/G]GGCCTGGAGGAACACAGAGCCCAGC | |
AGGAGAGGACGTTTGCGCGATTCTCC[C/G]CAGATCCAGTGTTTCCCGTCAGCCA | |
GGCTCGTGGCCCTGCGGGCGGATCTT[G/T]GGAAGAGCTTGTTCACACTCACCTA | |
TCGAGGCAGGGCCACCGGGGACGTCC[A/G]AGAACATTGGTGATCCCTTCCCAGG | |
AATGCAGGCGTGGGACAAGGCAGCTC[C/T]GAGTCCTGCTCAATGGTTTTGTGAC | |
GAGCTCATCCTTGTCAAGGAGCAGAA[C/T]GGAGTGCAGCTCACCAGCTCCACCC | |
GTGGGGAGGGGTGCAGGGGAAGGAGG[A/G]GCAAACCAGAGTGTCTGTCTTGAGG | |
AAACACGCTGCTGCTGGATCCAAATG[A/C]CAGAAGTCGCCCTGGCTGGGGCGGT | |
CTGCGCGCTGGTGCTCTGGGCAGGGC[G/T]GGGAGGCCGGGCGAGGACTCGCCAG | |
GGAGCCAGGACGCGAGGGCGACCCCG[A/T]CGGCGGGAGGGCGGGGCGGGGCGGA | |
HTR3B | CCTTTACAGCCTTTACCTAAGGCAGT[A/G]CTCTTGCTGACATTCAGGACACTAA |
TTTGGCCTTCTCTCTTGGGCCAAGGA[A/G]TTTCTGCTCTATTGCATGTTCTCAT | |
GAGAGCTCCTTGGAGATGGAATAGGC[C/T]CCAAGGTTAGCCTGTAATTGCCTCC | |
CCTTAGCACCTGTGTGTCTATCATTC[C/T]GGGCAGGAAAACTTGCACAATTAAA | |
PPARG | AAACTCTGGGAGATTCTCCTATTGAC[C/G]CAGAAAGCGATTCCTTCACTGATAC |
AGGATTTTCTTACATTTAAAGCAGAA[C/T]GACACTACTGATACACAAAAGTAAA | |
GAGAAATCTTCGGAGGGCTCACCAGC[A/G]TCACAAGTAGGTAGACCAGAAGAGG | |
GTTTACAGACCTTGTCAGAGTTGGTA[C/G]TAATTCCAGAATATAATCATTTCAA | |
TGGTTGACACAGAGATGCCATTCTGG[C/G]CCACCAACTTTGGGATCAGCTCCGT | |
GATTTATTTAAATCATCTCTAATTCT[C/T]ACAACTCCGAAAAGATAAGAAAACA | |
AGGATTTTCTTACATTTAAAGCAGAA[C/T]GACACTACTGATACACAAAAGTAAA | |
GTTGGGGATCCAGTTGGCCTCATTCT[A/G]AGCTGGCTGTGGATTCACAGAAGAA | |
AAGATACGGGGGAGGAAATTCACTGG[A/G]TTTTACAATATATTTTTCAAGGCAA | |
GAGAAATCTTCGGAGGGCTCACCAGC[A/G]TCACAAGTAGGTAGACCAGAAGAGG | |
ChREBP | GACAAAAAGCAATTGAGGTCCAGGAG[C/G]TGCCGCCCACCCGGCTCCTCCTCTG |
FTO | GCTGTCAGCACCTGGTACAAATACCA[A/G]GATAGGGTTTTTGGGGCCACATTTT |
GCATGCCAGTTGCCCACTGTGGCAAT[A/C]AATATCTGAGCCTGTGGTTTTTGCC | |
AGGTTCCTTGCGACTGCTGTGAATTT[A/T]GTGATGCACTTGGATAGTCTCTGTT | |
GATGACAACATGCAAACTTTATGGCC[A/G]GAAACCAAAGAGTCAGGCAAAATAT | |
AAAAGAAAGTAAACATATTTAAGGTC[A/G]TAAATAAGGCCATGTCTAATAGTGA | |
AGGAATGTTCTGATGGCTTGGCCCAG[G/T]TGGTGACTGTGCAGATAGACTGAAG | |
TCAGCACCCAAGGGACCATCAAAGAG[A/G]CTGTTGTGGAGAGGGAATCCGAAGG | |
TGGAGTGTTTTTCCTTCACCTTTTCC[A/G]GTCTCTGGGTTGCATCGCCAGACTG | |
TGTCTAGCCCTGTGGGTTTACATTAG[A/T]TAGGGTAGGTTATTGCTGCAACGTA | |
CTATCCAGGATGGCTCTAAAGGGACT[C/T]CGCTATAGGTTGGGGCTATGATAGA | |
GCTTATATTCAAAGCTCCAGGTAAAT[A/G]TAAGATGTTGCTATAATTACCTAAG | |
GGTAGGCAGGTGGATCTGAAATCTCA[C/T]ATAGTACCAAGACACGTGACTAGGA | |
TTGATTCTTATACTTTTTTGTTTAGT[C/T]GTTGAAATATGTTGTTTTGGTTGAA | |
AATTAGGAAGATTTGAGTAGCTAAAA[A/G]TTCCAAGAGTGGAATAATAGTTTTA | |
TTTGGTGCACTCCCAATTTACTCTAA[A/T]CTTCTACGGGCTTCCTTGGAGAAAC | |
GACCTGAAAATAGGTGAGCTGTCAAG[G/T]TGTTGGCAGGGAGAGGCTCCTCTGG | |
TGGTTCACTGCATATTCCCAGTAATT[C/G]GAACAATGCCTGACATGAAGTAGAC | |
TTAGAATGTCTGAATTATTATTCTAG[A/G]TTCCTTGCGACTGCTGTGAATTTTG | |
TTGATTTCGGTAGTCATAACACCACC[C/T]TGGAAGGCACCCTAGATAGAGGTCA | |
TTCATTCTACCTGTCTTTAGTATCAT[A/G]GGGGTAGTTACCTCAGCGGGGGTAG | |
TTGCTCAAGGTCACACAGTAACCTTA[A/G]GTAGGCAGGATAAGCTCTGGTTCTG | |
TATGATGGTTAGGTTAGGTTGCAAGT[C/T]TTGGAATATATGCAGAGGAATAACT | |
TTATAAACCTCTAAAATAGTTACTAA[A/G]TAAGTTATTCTTTTAGGTATTTTTC | |
TTTTATTTCCGCAATCACTCCCTAAT[C/G]TTTATTTCTTTTTTGCTTCGCATCA | |
TAGCATTTTTCTGGAGCGTAATTTCA[C/T]AATGTGAATCAGAAGTCTTAATAGT | |
GAGCACAGGTGGAGAGAAAGGGGAGT[A/G]AGAGAAGCAAAGAAGAAAAGCCTTT | |
TAGGGACACAAAAAGGGACATACTAC[A/G]TGAATTACTAATATCTAAGAAAATA | |
ATGAATTACTAATATCTAAGAAAATA[C/T]GATACATTTGAGAACTTAGATGAAG | |
GAAATGTGGTGTAGACGTGACCCAGG[A/G]GGAAATGAGTTTTGTTGGACAGATT | |
CTACATCTCCTACTTAGCCGAGGTCT[C/T]TTCACTCTCTGGGCAAGTCTCCTCA | |
ACACGGCTGAAGAGTCAGGAGTGGGA[C/T]GAAAAATACACTTCATTTGTAGGTG | |
GCACATTTATGCCTTTTATATGCCAC[A/G]TACACACGAAAACTccatatattct | |
AGAGTGAATAAAATTATTTCTAAATT[C/T]ATGCTTCATACCGTGTGTAATTTAG | |
TGTTGCAACAGAGATGATGGCAGTTT[C/T]GGCCACGGTGTAAGAAGCAGAGGTG | |
ACATCTGCCTTCCCAGAGAAAGGAAA[A/G]TCAATGTTTAAAGTCTATTTAAAAA | |
TNF Alpha | GGGAAGCAAAGGAGAAGCTGAGAAGA[C/T]GAAGGAAAAGTCAGGGTCTGGAGGG |
GGAGGCAATAGGTTTTGAGGGGCATG[A/G]GGACGGGGTTCAGCCTCCAGGGTCC | |
TGGCCCAGAAGACCCCCCTCGGAATC[A/G]GAGCAGGGAGGATGGGGAGTGTGAG | |
TCTTTCTGCATCCCCGTCTTTCTCCA[C/T]GTTTTTTTCTCTCCATCCCTCCCTA | |
GTTGAATGCCTGGAAGGTGAATACAC[A/G]GATGAATGGAGAGAGAAAACCAGAC | |
MANEA | CATTTTACAATAGATAAATGCTTGTG[C/T]TACCTAAAGCACTTAGCACACAGTT |
Leptin OB | GCTCTGGGAATGTCTATCCTATGCAA[C/T]GGAGATAAGGACTGAGATACGCCCT |
ATGCAATGGAGATAAGGACTGAGATA[C/T]GCCCTGGTCTCCTGCAGTACCCTCA | |
GGAGCCCCGTAGGAATCGCAGCGCCA[A/G]CGGTTGCAAGGTAAGGCCCCGGCGC | |
AAGTTCCTGACCTCTGAATGAGAGGG[A/G]CTGTGTAAGGCCAATGCCTGGGAGG | |
AATAAAAATAAATGTTCTTCCTTGCA[A/T]TGAAGTTAAATATGTAAATTCTCAA | |
ACTTAGGTATTAGAGGGTGGCCATTA[C/T]TTGAGAGTGACTATGACCACAGTTA | |
TGGGTGAATGTGTTATGCTCTCTCCC[A/G]CCACCATGTCTTTATACCCCCTGAT | |
CTCCCAGTGGGTGGGAGAGAAAGGAC[A/G]TAAGGAAGCAAGTGGTAAAGGCCCT | |
PEMT | ATCCCTTCACCAGAGTGATTTCCTCG[A/C]GGCAGGTGCCTGGGGTAGCCACTGG |
GGACTGCCTGGTTGTGCTTCGGACCC[A/G]GAGGCAGACAGAGGAGGCCTTTGAA | |
MAO-A | CCCACTAGGCAAGCCTCCTAAAAGCA[A/G]TATGGTTGTAGATCACTGGAAAATA |
GTAAACATGCAAACTGAAACATTAGC[A/G]CCCATTTATTCAGCATCTTAGAAGA | |
GAGTGAAGGCCAGGTACAGAGGAAAT[A/G]AAGCATTCCAAATAATGCCAGGTAA | |
CCAAAGTTAACTTGTGAACCCTTCTA[A/G]TAAACTGCTCCAAGATATGACAAAA | |
GTTTGCCATGGATGAACCACCAGGAT[A/G]GTGGGGGAGACAGAAAAGGTTGATG | |
GGAAAATTCCCCTTCCCCTAAGACAT[C/T]CACCCTTCTGGTTTGGGTAATTCCT | |
GCAGAGAGAAACCAGTTAATTCAGCG[G/T]CTTCCAATGGGAGCTGTCATTAAGT | |
GTGCATGATGTATTACAAGGAGGCC[G/T]TCTGGAAGAAGAAGGGTAGGCTGCT | |
AGAGAAGGAAGTGGTGTCCCCACAAA[G/T]GAATTGCTAAGGAGTTCCACAGCCT | |
AAGAGAAAACAAAGCTGAAATGCTGC[A/G]AGTCAATAATATCGTTGCTTTAACA | |
TTTGACAACTATTTCTAGAATTTGCA[C/T]TGAACTCTGCTTTTCCTTTTAAATT | |
GGTCTCGGGAAGGTGACCGAGAAAGA[C/T]ATCTGGGTACAAGAACCTGAATCAA | |
CRH | CTGTCCCACAACATGGGGTCTTACAG[C/T]TCTTTGATGTATCCCCCCACAGGGG |
GCCTCTGGGGTCACCAGGTACATCTT[C/T]GATCTTGGCCACACTGGAGAGTCAA | |
TTTCTAAACACAGAGGACTGGTGTTG[C/T]GTTATGCAAAGAAAAATGCTTCTTA | |
AAGACACTCAGGTGCAGGGACCCTCT[A/C]CATTTTTGCCCAGCAGCAGCCATGC | |
AGGGCCAGGAACCATGAACCAGCGCG[G/T]GTGGGGGCAGCCTCTTCAGGCCTGG | |
GGCACACCAGTCCTTTTGAGCCCCAG[C/T]GTCCCCAGGTTAATAACCTAGAATT | |
TGAACACGGAGGCCACACAAGAGTGG[A/G]TTCCAAGTGAAGGAGTGACCAACTC | |
TCCTTTCCTGGGATCACAGAGGGAAG[C/T]GCGGGGGAGCCTAGAGAGCACCACA | |
TACAGGTGAAGGAAAGTGATTCTTTC[C/T]CCGTTAACTTTGTTTCACGCCAGAT | |
CCCCCAACCAGAGATGATGATGGGGG[A/G]CAGGGGAGGCACCAAACCCTGGGCC | |
AGCAGCATACCCCTAGGGACCTAGGA[A/G]CAGGGAGGGAGAGAGGCAGCCCTGG | |
CAGCTGGCACTGACAGCCTGGGGGGG[-/C/G]CGCTCTCCCCCTGCAGCCGTGCAGG | |
GAGCACAAGAAGGCCAGCCCACTGGG[C/G]CCTGGGGCTGCCCTCGGCAACCGTG | |
CTGCTTCCCACCAATCAGCACAGCTC[A/C]TGCCTGGGGCTGGGACACACTCCCG | |
ADIPOQ | ATCAGAATGTGTGGCTTGCAAGAACC[A/G]GCTCAGATCCTGCCCTTCAAAAACA |
GTTCTACTGCTATTAGCTCTGCCCGG[G/T]CATGACCAGGAAACCACGACTCAAG | |
STS | GATGACAAGCCAGGCAGGGAGGAATG[A/G]ACCTGGATTCCTGGTGAAGGACGTG |
VDR | GTCAGCGATTCTTAATATAAGAAAAA[A/G]TGGTGAAATGTGTTTAGAGTGTGCT |
CCTGGGGTGCAGGACGCCGCGCTGAT[C/T]GAGGCCATCCAGGACCGCCTGTCCA | |
GTTCCTGGGGCCACAGACAGGCCTGC[A/G]CATTCCCAATACTCAGGCTCTGCTC | |
CATAAGACCTACGACCCCACCTACTC[C/T]GACTTCTGCCAGTTCCGGCCTCCAG | |
TGGCCTGCTTGCTGTTCTTACAGGGA[C/T]GGAGGCAATGGCGGCCAGCACTTCC | |
TGTGGGGGTGGGCCAGCCCAGCTTAG[A/G]TTATCTTGGCTCATTGTCCACTAGT | |
DBI | TCTGTCCTCAGGCCAGGGCTTCGCTG[A/C]AGCCCCGGCCACTCCCTAGTGCCTG |
TACGAACTCACTGTAAAACTCACCTT[C/T]GCCATAAGACCTTCTTCAACTAAGT | |
ACAGAGTTTACGAACTCACTGTAAAA[C/T]TCACCTTCGCCATAAGACCTTCTTC | |
GGAGAGAAAACAAAGTCAATGGGGCA[C/T]GTGTGGGAAACCAGCCTGACCTGTG | |
TTACAGGGACTTCCAAGGAAGATGCC[A/G]TGAAAGCTTACATCAACAAAGTAGA | |
GABRA6 | TTGGGAAAGGAGAGTCTGAAGGGACA[A/G]TGCATGGTCGGAGAGCAGTGACAAT |
AAATTGGAAATCTGTAACGCAGCTTC[C/T]GTAAGCATGTGTGGGCAAAAAAGCA | |
TTCTTTCCATCTGGCACCTATTTATT[C/G]ACTATTTATGCATTCGTTGAATTAT | |
CTCTTTCACCATTGACAAATATTTAT[G/T]GACGACTTACTTTCTATGTAAGGTC | |
GABRB3 | CGTTCAGTTTAGTAAGCAAAGGCTTC[C/T]TGGCTTCTCTGGTGATGGGGTTTGT |
AGCTTACCATTTAAGTAGAACTGTTT[A/G]AGATGCTGGACATTCTAATACAATC | |
CCAAATCTGAAATTTACTTGTCACTT[C/T]AGAGTTGTCTTTGAACGGAAAGATT | |
TCTGTTGAGTGATAATCTTTCTCGCA[A/G]ATAACTCACAATATTTAAAAATTGT | |
AAGAACTCTTCCATGATTGAAATGGT[A/C]GCACATGGAATAACATCGATAAGTT | |
ACAGCAGGTTGGAGCACAGGGCCTAA[A/G]TGGGAGGCCAGGGAGGTGGGCAGAG | |
ATTGCTGATTTTCAGGCAAACTATGT[A/T]ACATGGCTTTCAATGGGTGCTTGGC | |
MTHFR | GAAGCAGTTAGTTCTGACACCAACAA[A/G]TGGTGATAAGAGGTTGATAGCCTAG |
GTGGGGGGAGGAGCTGACCAGTGAAG[A/C]AAGTGTCTTTGAAGTCTTTGTTCTT | |
CTTGAAGGAGAAGGTGTCTGCGGGAG[C/T]CGATTTCATCATCACGCAGCTTTTC | |
AGATGTTCCACCCCGGGCCTGGACCC[C/T]GAGCGGCATGAGA | |
MLXIPL | GACAAAAAGCAATTGAGGTCCAGGAG[C/G]TGCCGCCCACCCGGCTCCTCCTCTG |
CAGGTAACTGACCCTTCACACATTTA[C/T]GGTGCCCATCTGACATTCATAGCAT | |
VEGF | GCGCGCGGGCGTGCGAGCAGCGAAAG[C/G]GACAGGGGCAAAGTGAGTGACCTGC |
AGACATGTCCCATTTGTGGGAACTGT[A/G]ACCCTTCCTGTGTGAGCTGGAGGCA | |
AGACATGTCCCATTTGTGGGAACTGT[A/G]ACCCTTCCTGTGTGAGCTGGAGGCA | |
ACATCCTGAGGTGTGTTCTCTTGGGC[C/T]TGGCAGGCATGGAGAGCTCTGGTTC | |
AGCATTCCCGGGCGGGTGACCCAGCA[C/T]GGTCCCTCTTGGAATTGGATTCGCC | |
ATCCTCTTCCTGCTCCCCTTCCTGGG[A/G]TGCAGCCTAAAAGGACCTATGTCCT |
RDS-associated SNPs.
A common class of experiments, known as a multiplexed assay or multiplexed biochemical experiment, comprises of reacting a sample known or suspected to contain one more target analyte species with a set of “probe” molecules. Multiplexing allows two or more, often many more (e.g., 10, 50, 100, 1,000 or more), target analyte species to be probed simultaneously (i.e., in parallel). Genome Wide scans have now been used to identify gene clusters in alcohol and drug addiction (Agrawal et al., 2010). For example, in a gene expression assay, each species of target analyte, usually a nucleic acid (i.e., DNA or RNA) of known nucleotide sequence but whose presence in a particular sample is suspected but is not known with certainty, can be detected using a short probe nucleic acid (e.g., a synthetically produced oligonucleotide) having a nucleotide sequence at least a portion of which is sufficiently complementary to the target sequence in the particular target analyte to allow stable hybridization under the various assay conditions used (including hybridization and stringent washing conditions) so that probe/target hybrids can later be detected using a desired detection scheme. As those in the art appreciate, such assays can involve those wherein target analyte species are labeled (or not) with a detectable label or wherein the various target analyte-specific probe species are labeled (directly or indirectly) with any suitable label that can be detected by the detector used with the particular assay format (e.g., bead-based formats, gene arrays, etc.). Labels include fluorescent molecules.
For example, in many known DNA/genomic bead-based multiplex assays, each probe species includes a DNA molecule of a predetermined nucleotide sequence and length attached to an encoded or otherwise identifiable bead or particle. When a labeled “target” analyte (in this case, a detectably labeled (e.g., fluorescently labeled) DNA molecule containing a target nucleotide sequence) is mixed with the probes, segments of the labeled target analyte selectively bind to complementary segments of the DNA sequence of one of the bead-bound probe species. The probes are then spatially separated and examined for fluorescence. The beads that fluoresce indicate that molecules of the target analyte have attached or hybridized to complementary probe molecules on that bead. The DNA sequence of the target analyte can then be determined, as the complementary nucleotide sequence of the particular probe species hybridized to the labeled target is known, and identification of the encoded bead indicates which probe species was bound to that bead. In addition, in such assays the level of fluorescence is indicative of how many of the target molecules hybridized (or attached) to the probe molecules for a given bead. As is known, similar bead-based assays may be performed with any set of known and unknown molecules, analytes, or ligands.
In such bead-based assays, the bead-bound probes are allowed to mix with samples that may contain the target analytes without any specific spatial position; as such, such assays are commonly called “random bead assays”. In addition, because the bead-bound probes are free to move (usually in a liquid medium), the probe molecules and target analytes have a better opportunity to interact than in other assay techniques, such as in a conventional planar microarray assay format.
There are many bead/substrate types that can be used for tagging or otherwise uniquely identifying individual beads with attached probes. Known methods include using polystyrene latex spheres that are colored or fluorescently labeled. Other methods include using small plastic particles with a conventional bar code applied, or a small container having a solid support material and a radio-frequency (RF) tag. Still other bead-based approaches involve vary small encoded beads, particles, or substrates capable of providing a large number of unique codes (e.g., greater than 1 million codes) are known.
In multiplex assays designed to simultaneously examine 2-25 or so different target analyte species, other assay formats can be adapted for use in the GARS test. Indeed, any format suited for analysis of multiple genes, either simultaneously in one or more parallel reactions or in different reactions carried out in series or at different times, can readily be adapted for use.
The dopamine D2 receptor gene (DRD2) first associated with severe alcoholism is the most widely studied gene in psychiatric genetics (Blum et a l., 1990). The
More recently, the DRD2 haplotypes I-C-G-A2 and I-C-A-A1 have been found to occur with a higher frequency in alcoholics [P=0.026, odds ratio (OR): 1.340; P=0.010, OR: 1.521, respectively]. The rare haplotype I-C-A-A2 occurred less often in alcoholics (P=0.010, OR: 0.507), and was also less often transmitted from parents to their affected children (1 vs. 7). Among the subgroups, I-C-G-A2 and I-C-A-A1 had a higher frequency in Cloninger 1 alcoholics (P=0.083 and 0.001, OR: 1.917, respectively) and in alcoholics with a positive family history (P=0.031, OR: 1.478; P=0.073, respectively). Cloninger 2 alcoholics had a higher frequency of the rare haplotype D-T-A-A2 (P<0.001, OR: 4.614) always compared with controls. In patients with positive family history, haplotype I-C-A-A2 (P=0.004, OR: 0.209) and in Cloninger 1 alcoholics, haplotype I-T-A-A1 (P=0.045 OR: 0.460) was less often present, confirming that haplotypes, which are supposed to induce a low DRD2 expression, are associated with alcohol dependence. Furthermore, supposedly high-expressing haplotypes weakened or neutralized the action of low-expressing haplotypes (Kraschewski et al 2009).
There is evidence that the length of the D4 dopamine receptor (DRD4) exon 3 variable number of tandem repeats (VNTR) affects DRD4 functioning by modulating the expression and efficiency of maturation of the receptor. (Schoots & Van Tol 2003). The 7 repeat (7R) VNTR requires significantly higher amounts of dopamine to produce a response of the same magnitude as other size VNTRs (Oak et al., 2000). This reduced sensitivity or “dopamine resistance” leads to hypodopaminergic functioning. Thus 7R VNTR has been associated with substance–seeking behavior (McGeary et al., 2007). Survival analysis has revealed that by 25 years of age 76% of subjects with a DRD4 7-repeat allele have significantly more persistent ADHD compared with 66% of subjects without the risk allele. In contrast, there were no significant associations between the course of ADHD and the DAT1 10-repeat allele (P=0.94) and 5HTTLPR long allele, suggesting that the DRD4 7-repeat allele is associated with a more persistent course of ADHD (Biederman et al., 2009). This is consistent with the finding of the presence of the 7R DAT genotype in the heroin addict. Moreover, in a study evaluating the role of dopamine D4 receptor (DRD4) exon 3 polymorphisms (48 bp VNTR) in the pathogenesis of alcoholism, significant differences in the short alleles (2-5 VNTR) frequencies were found between controls and patients with a history of delirium tremors and/or alcohol seizures (p = 0.043) (Grzywacz et al., 2009). A trend was also observed in the higher frequency of short alleles amongst individuals with an early age of onset of alcoholism (p = 0.063). These results indicate that inherited short variants of DRD4 alleles (3R) may play a role in pathogenesis of alcohol dependence and carriers of the 4R may have a protective effect for alcoholism risk behaviors. It is of further note that the DRD4 7-repeat allele is significantly over-represented in the opioid-dependent cohort and confers a relative risk of 2.46 (Kotler et al., 1997).
The dopamine transporter protein regulates dopamine –mediated neurotransmission by rapidly accumulating dopamine that has been released into the synapse (Vandenbergh, 1998). The dopamine transporter gene (SLC6A3 or DAT1) is localized to chromosome 5p15.3. Moreover, there is a VNTR polymorphism within the 3’ non-coding region of DAT1 (Michelhaugh et al., 2001). There are two important alleles that may independently increase risk for RDS behaviors. The 9 repeat (9R) VNTR has been shown to influence gene expression and to augment transcription of the dopamine transporter protein, resulting in an enhanced clearance of synaptic dopamine, yielding reduced levels of dopamine to activate postsynaptic neurons. Presence of the 9R VNTR has also been linked to Substance Use Disorder (SUD) (Guindalini et al., 2006). Moreover, in terms of RDS behaviors, tandem repeats of the dopamine transporter gene (DAT) have been associated with high risk for ADHD in children and in adults alike (Vandenbergh et al., 2002; Cook et al., 1995). The 10-repeat allele is significant for hyperactivity-impulsivity (HI) symptoms (Lee et al., 2007).
The catechol-O-methyltransferase (COMT) is an enzyme involved in the metabolism of dopamine, adrenaline and noradrenaline. The Val158Met polymorphism of the COMT gene has been previously associated with a variability of the COMT activity, and alcoholism. Serý (2006) found a relationship between the Val158Met polymorphism of the COMT gene and alcoholism in male subjects. Serý (2006) found the significant difference between male alcoholics and male controls in allele and genotype frequencies (p<0,007; and p<0, 04 respectively. Interestingly in one of the subjects genotyped herein, who battles with heroin as an addiction while carrying the DRD2 A1 allele also carried the low enzyme COMT activity genotype (A/A). This is agreement with the work of Cao et al. (2003) who did not find an association with the high G/G and heroin addiction. No differences in genotype and allele frequencies of 108 val/met polymorphism of COMT gene were observed between heroin-dependent subjects and normal controls (genotype-wise: chi-square=1.67, P=0.43; allele-wise: chi-square=1.23, P=0.27). No differences in genotype and allele frequencies of 900 Ins C/Del C polymorphism of COMT gene were observed between heroin-dependent subjects and normal controls (genotype-wise: chi-square=3.73, P=0.16; allele-wise: chi-square=0.76, P=0.38). While there is still some controversy regarding the COMT association with heroin addiction it was also interesting that the A allele of the val/met polymorphisms (-287 A/G) found by Cao et al. (2003) was found to be much higher in heroin addicts than controls. Faster metabolism results in reduced dopamine availability at the synapse, which reduces postsynaptic activation, inducing hypodopaminergic functioning. Generally Vanderbergh et al. (1997) and Wang et al. (2001) support an association with the Val allele and SUD but others do not (Samochowise et al. 2006).
Monoamine oxidase-A (MAOA) is a mitochondrial enzyme that degrades the neurotransmitters serotonin, norepinephrine, and dopamine. This system is involved with both psychological and physical functioning. The gene that encodes MAOA is found on the X chromosome and contains a polymorphism (MAOA-uVNTR) located 1.2 kb upstream of the MAOA coding sequences (Shih, 1991). In this polymorphism, consisting of a 30-base pair repeated sequence, six allele variants containing either 2-, 3-, 3.5-, 4-, 5-, or 6-repeat copies have been identified (Zhu & Shih, 1997). Functional studies indicate that certain alleles may confer lower transcriptional efficiency than others. The 3-repeat variant conveys lower efficiency, whereas 3.5- and 4-repeat alleles result in higher efficiency (Brummett et al., 2007). The 3- and 4-repeat alleles are the most common, and to date there is less consensus regarding the transcriptional efficiency of the other less commonly occurring alleles (e.g., 2-, 5-, and 6-repeat). The primary role of MAOA in regulating monoamine turnover, and hence ultimately influencing levels of norepinephrine, dopamine, and serotonin, indicates that its gene is a highly plausible candidate for affecting individual differences in the manifestation of psychological traits and psychiatric disorders (Shih et al., 1999). For example, recent evidence indicates that the MAOA gene may be associated with depression (Brummett et al., 2008) and stress (Lee et al., 2009). However, evidence regarding whether higher or lower MAOA gene transcriptional efficiency is positively associated with psychological pathology as been mixed. The low-activity 3-repeat allele of the MAOA-uVNTR polymorphism has been positively related to symptoms of antisocial personality (Ponce et al., 2009) and cluster B personality disorders. Other studies, however, suggest that alleles associated with higher transcriptional efficiency are related to unhealthy psychological characteristics such as trait aggressiveness and impulsivity. High MAO activity and low levels of dopamine are 2 important factors in the development of alcohol dependence. MAO is an important enzyme associated with the metabolism of biogenic amines. Therefore, Huang et al. (2007) investigated whether the association between the dopamine D2 receptor (DRD2) gene and alcoholism is affected by different polymorphisms of the MAO type A (MAOA) gene. The genetic variant of the DRD2 gene was only associated with the anxiety, depression (ANX/DEP) ALC phenotype, and the genetic variant of the MAOA gene was associated with ALC. Subjects carrying the MAOA 3-repeat allele and genotype A1/A1 of the DRD2 were 3.48 times (95% confidence interval = 1.47-8.25) more likely to be ANX/DEP ALC than the subjects carrying the MAOA 3-repeat allele and DRD2 A2/A2 genotype. The MAOA gene may modify the association between the DRD2 gene and ANX/DEP ALC phenotype. Overall, Vanyukov et al (2004) suggested that, although not definitive, variants in MAOA account for a small portion of the variance of SUD risk, possibly mediated by liability to early onset behavioral problems (Vanyukov et al., 2004).
The human serotonin (5-hydroxytryptamine) transporter, encoded by the SLC6A4 gene on chromosome 17q11.1-q12, is the cellular reuptake site for serotonin and a site of action for several drugs with central nervous system effects, including both therapeutic agents (e.g. antidepressants) and drugs of abuse (e.g. cocaine). It is known that the serotonin transporter plays an important role in the metabolic cycle of a broad range of antidepressants, antipsychotics, anxiolytics, antiemetics, and anti-migraine drugs. Salz et al. (2009) found an excess of -1438G and 5-HTTLPR L carriers in alcoholic patients in comparison to the heroin dependent group [OR (95% CI)=1.98 (1.13-3.45) and 1.92 (1.07-3.44), respectively]. The A-1438G and 5-HTTLPR polymorphisms also interacted in distinguishing alcohol from heroin dependent patients (df) =10.21 (4), p=0.037). The association of -1438A/G with alcohol dependence was especially pronounced in the presence of 5-HTTLPR S/S, less evident with 5-HTTLPR L/S and not present with 5-HTTLPR.
Gamma-aminobutyric acid (GABA) receptor genes have also received some attention as candidates for drug use disorders. One reason for this is that the dopamine and GABA systems are functionally interrelated (White, 1996). Research suggests that dopamine neurons projecting from the anterior ventral tegmental area to the NAc are tonically inhibited by GABA through its actions at the GABAA receptor (Ikemoto, Kohl, & McBride, 1997). Moreover, it has been shown that alcohol (Theile, Morikawa, Gonzales, & Morrisett, 2008) or opioid (Johnson & North, 1992) enhancement of GABAergic (through GABAA receptor) transmission inhibits the release of dopamine in the mesocorticolimbic system. Thus, a hyperactive GABA system, by inhibiting dopamine release, could also lead to hypodopaminergic functioning. Because of this, GABA genes are of interest in the search for causes of drug use disorders. A dinucleotide repeat (DNR) polymorphism of the GABA receptor β3 subunit gene (GABRB3) results in either the presence of the 181-bp G1 or 11 other repeats designated as non-G1 (NG1). Research indicates that the NG1 is more prevalent in children of alcoholics (COAs; Namkoong, Cheon, Kim, Jun, & Lee, 2008). Presence of the NG1 has been associated with alcohol dependence (Noble et al., 1998; Song et al., 2003). In addition, other GABA receptor genes have also been associated with this disorder (Edenberg et al., 2004).
There is a need to classify patients at genetic risk for drug seeking behavior prior to or upon entry to residential and or non-residential chemical dependency programs. Instead of continuing to evaluate single gene associations to predict future drug abuse, it seems parsimonious to evaluate multiple genes involved in the brain reward cascade and hypodopaminergic antecedents. As described in this example, such methods employ RDS-associated gene panels to stratify or classify patients entering a treatment facility as having low, moderate, or high genetic predictive risk based on a number of now known and/or later discovered RDS risk alleles. Our laboratory developed a Genetic Addiction Risk Score (GARS) for this purpose. This example describes genetic studies for seven RDS-associated alleles for six candidate genes in a patient population (n=26) of recovering poly-drug abusers (Blum et al., 2010).
To determine RDS risk severity for each of these 26 patients, the percentage of prevalence of the risk alleles was calculated and a severity score based on the percentage of these alleles present in a given patient was developed. Subjects carry the following risk alleles: DRD2=A1; SLC6A3 (DAT) =10R; DRD4=3R or 7R; 5HTTlRP = L or LA; MAO= 3R; and COMT=G. As depicted in Tables 5 and 6, below, Low Severity (LS) = 1-36%, Moderate Severity (MS) =37-50%, and High Severity (HS) = 51-100%, scores were assigned. Two distinct ethnic populations among the 26 patients were studied. Group 1 consisted of 16 male Caucasian psycho-stimulant addicts and Group 2 consisted of 10 Chinese heroin-addicted males. Based on this analysis, the 16 Caucasian 100% of subjects had at least one risk allele (see tables 5 and 6). Therefore, using this approach it was found that 81% of the patients were at moderate to high risk for addictive behavior. Of particular interest was the discovery that 56% of the subjects carried the DRD2 A1 allele (9/16).
Out of the nine Chinese heroin addicts (Group 2), it was found that 11% (1) were HS, 56% (5) were MS, and 33% (3) were LS. These scores were then converted to a fraction and represented as GARS, whereby the average GARS was found to be: 0.28 Low Severity; 0.43 moderate severity; and 0.54 high severity, respectively. Therefore, using GARS it was discovered that 67% of the Group 2 patients were at moderate to high risk for addictive behavior. As with Group 1, 56% of the Group 2 subjects carried the DRD2 A1 allele (5/9). Statistical analysis revealed that the two groups did not differ in terms of overall severity (67% vs. 81%) in these two distinct populations. Combining these two independent study populations reveals that subjects entering a residential treatment facility for poly-drug abuse carry at least one risk allele (100%). Moreover, 74% of the combined 25 subjects who were genotyped by SNP analysis had a moderate-to-high GARS.
The 16 patients of Group 1 were interviewed and evaluated for chemical dependence using a standard battery of diagnostic tests and questionnaires. The tests included the following: a drug history questionnaire; a physical assessment, urine drug tests; a breathalyzer; complete CBC blood test; and a symptom severity questionnaire. The patients were determined to be substance dependent according to Diagnostic and Statistical Manual (DSM-IV) criteria. All patients were residential in-patients enrolled in 30-90 day chemical dependence rehabilitation programs at either of two treatment centers in the U.S.
Table 3 shows the demographics of the 16 patients, including gender, race, age, and length of abstinence. The median age was 29.5 ± 8.8 SD years. The population breakdown was as follows: 87.5 % Caucasian and 12.5% Hispanic. The average number of months abstinent for the entire population was 9.5 ±23.3. There were 3 pure cocaine-only addicts; 4 cocaine crack addicts; and 9 cocaine plus other drugs of abuse (alcohol, opiates and marijuana).
Table 4 includes genotype data from a functional MRI (fMRI) study in China evaluating involving 10 heroin-addicted Chinese males with a median age of 33 ± 7.6 SD years. Diagnosis of heroin dependence was also determined in this group using DSM-1 V criteria and other behavioral instruments. The average number of months abstinent for the entire population was 16 ± 7.9.
Age | 29.5 ±8.80 | (19, 48) | 16 |
Clean time (months) | 9.5 ±23.33 | (2, 101) | 16 |
Race = Caucasian | 14 | ||
Race = Hispanic | 2 | ||
Sex = Male | 16 | ||
Primary Substance = Cocaine only | 3 | ||
Primary Substance = Crack cocaine | 4 | ||
Primary Substance = Cocaine + Other* | 9 |
Demographics of all Caucasian subjects combined.
Age | 33 ± 7.57 | (20, 44) | 10 |
Clean time (months) | 16 ± 7.91 | (1, 24) | 10 |
Race = Chinese | 10 | ||
Sex = Male | 10 | ||
Primary Substance = Heroin only | 10 | ||
Primary Substance = Heroin + other | 0 |
Demographics of all Chinese subjects combined*.
Genotyping (Blum et al., 2010) was performed as follows. Each patient was also genotyped for the following gene polymorphisms: MAOA-VNTR, 5HTTLPR, SLC6A3, DRD4, ANKKI, DRD2 TaqIA (rs1800497), and the COMT val158met SNP (rs4680). Genotypes were scored independently by two investigators.
The dopamine transporter (DAT1, locus symbol SLC6A3, which maps to 5p15.3, contains a 40 base-pair Variable Number Tandem Repeat (VNTR) element consisting of 3-11 copies in the 3\' untranslated region (UTR) of the gene.
The dopamine D4 receptor (DRD4), which maps to 11p15.5, contains a 48 bp VNTR polymorphism in the third exon, which consists of 2-11 repeats.
Monoamine Oxidase A upstream VNTR (MAOA-uVNTR). The MAOA gene, which maps to Xp11.3-11.4, contains a 30 bp VNTR in the 5’ regulatory region of the gene that has been shown to affect expression. A genotype by environment interaction has been reported for this polymorphism.
Serotonin Transporter-Linked Polymorphic region (5HTTLPR). The serotonin transporter (5HTT, Locus Symbol SLC6A4), which maps to 17q11.1-17q12, contains a 43 bp insertion/deletion (ins/del) polymorphism in the 5’ regulatory region of the gene. A SNP (rs25531, A/G) in the Long form of 5HTTLPR has functional significance: The more common LA allele is associated with the reported higher basal activity, whereas the less common LG allele has transcriptional activity no greater than the S. The SNP rs25531 is assayed by incubating the full length PCR product with the restriction endonuclease MspI.
For all of the above VNTR and ins/del polymorphisms, PCR reactions contained approximately 20 ng of DNA, 10% DMSO, 1.8 mM MgCl2, 200 µM deoxynucleotides, with 7’-deaza-2’-deoxyGTP substituted for one half of the dGTP, 400 nM of appropriate forward and reverse amplification primers, and 1 unit of AmpliTaq Gold® polymerase, in a total volume of 20 µl. Amplification was performed using touchdown PCR. After amplification, an aliquot of PCR product was combined with loading buffer containing size standard (Genescan 1200 Liz) and analyzed with an ABI PRISM® 3130 Genetic Analyzer.
DRD2 TaqIA (rs1800497). The gene encoding the dopamine D2 receptor maps to 11q23, and contains a polymorphic TaqI restriction endonuclease site located within exon of the adjacent ANKKI gene that was originally thought to be located in the 3\' untranslated region of the gene. The A1 allele has been reported to reduce the amount of receptor protein. This SNP was assayed using a Taqman (5’Nuclease) assay.
COMT val158met SNP (rs4680). The gene encoding COMT maps to 22q11.21, and codes for both the membrane-bound and soluble forms of the enzyme that metabolizes dopamine to 3-methoxy-4-hydroxyphenylethylamine. An A→G mutation results in a valine to methionine substitution at codons 158/108, respectively. This amino acid substitution has been associated with a four-fold reduction in enzymatic activity. The COMT SNP was assayed with a Taqman method.
In terms of genotyping data it has been determined that when multiple RDS-associated genes are analyzed, such as the genes for serotonergic- 2A receptor (5-HTT2a), serotonergic transportor (5HTTLPR), (dopamine D2 receptor (DRD2), Dopamine D4 receptor (DRD4), Dopamine transporter (DAT1), Catechol-o-methyl –transferase (COMT), and monoamine–oxidase (MOA), 100% of all subjects carried at least one risk allele. To our knowledge this is the first reported attempt to stratify or classify addiction risk by incorporating an algorithm formulation that combines genotyping results for a number of RDS-associated risk alleles by pre-assigning an allele as a risk allele having predictive value for drug use. Previously, Blum et al (1996) using Bayesian statistics it was shown that the DRD2 A1 allele had a predictive value of 74.4% for all Reward Deficiency Syndrome (RDS). Here, the subjects studied in this investigation had multiple drug abuse relapses and presented to in-patient residential treatment programs.
1 | 3R | S/L | S/LG | 9R/10R | 4R/4R | A1/A2 | G/G | POSITIVE | 0.46–MS |
2 | 3R | S/L | S/LA | 10R/10R | 4R/7R | A2/A2 | G/G | POSITIVE | 0.62 –HS |
3 | 3R | L/L | LA /LG | 9R/9R | 3R/4R | A1/A2 | A/G | POSITIVE | 0.57-HS |
4 | 4R | S/L | S/LA | 10R/10R | 3R/7R | A2/A2 | G/G | POSITIVE | 0.46-MS |
5 | 4R | L/L | LA/LA | 10R/10R | 4R/7R | A2/A2 | A/G | POSITIVE | 0.62 –HS |
6 | 3R | S/S | S/S | 9R/10R | 4R/7R | A2/A2 | A/G | POSITIVE | 0.30 –LS |
7 | 4R | S/L | S/LG | 10R/10R | 4R/4R | A1/A1 | A/A | POSITIVE | 0.38 –MS |
8 | 4R | S/L | S/LA | 9R/10R | 3R/4R | A2/A2 | A/A | POSITIVE | 0.23-LS |
9 | 3R | L/L | LA//LA | 9R/9R | 4R/7R | A2/A2 | A/G | POSITIVE | 0.54-HS |
10 | 4R | L/L | LA/LA | 9R/10R | 4R/4R | A2/A2 | G/G | POSITIVE | 0.54 –HS |
11 | 3R | S/L | S/ LA | 9R/10R | 4R/4R | A1/A2 | G/G | POSITIVE | 0.54-HS |
12 | 4R | L/L | LA/LA | 9R/10R | 4R/4R | A1/A2 | A/G | POSITIVE | 0.54-HS |
13 | 4R | S/L | S/ LA | 10R/10R | 4R/4R | A1/A2 | A/G | POSITIVE | 0.46 –MS |
14 | 4R | S/S | S/S | 9R/10R | 4R/4R | A1/A2 | G/G | POSITIVE | 0.30-LS |
15 | 3R | L/L | LA / LA | 10R/10R | 4R/4R | A1/A2 | A/G | POSITIVE | 0.69 –HS |
16 | 4R | S/L | S/LA | 10R/10R | 4R/7R | A1/A2 | A/A | POSITIVE | 0.46-MS |
Group 1 genotyping data for each Caucasian patient. Data taken from Blum et al. 2010.
The finding that 75% of these individuals have moderate-to-high GARS, whereas only 25% had low GARS, indicates that pre-screening patients prior enrolling in a treatment program could be beneficial. Clinically, this will be important for understanding expectations of future success and the need for intensive treatment involving genomic solutions coupled with medical therapies, including bio-holistic therapies. It will also reduce quilt and denial of the entering patient.
1 | 4R | S/L | S/LA | 10R/10R | 4R/4R | A2/A2 | A/A | POSITIVE | 0.30–LS |
2 | 3R | S/S | S/S | 10R/10R | 2R/4R | A1/A2 | GAG | POSITIVE | 0.38–MS |
3 | 4R | S/S | S/S | 10R/10R | 3R/4R | A1/A2 | G/G | POSITIVE | 0.46-MS |
4 | 3R | S/S | S/S | 10R/10R | 4R/6R | A2/A2 | G/G | POSITIVE | 0.38-MS |
5 | 4R | S/S | S/S | 10R/10R | 4R/4R | A1/A2 | A/G | POSITIVE | 0.30–LS |
6 | 3R | L/L | S/ LG | 10R/10R | 4R/4R | A1/A2 | ND | POSITIVE | 0.45 –MS |
7 | 4R | L/L | LA/LG | 10R/10R | 4R/4R | A1/A2 | A/G | POSITIVE | 0.54 –HS |
8 | 4R | S/S | S/S | 10R/10R | 4R/5R | A2/A2 | A/G | POSITIVE | 0.23-LS |
9 | 3R | S/L | S/LA | 10R/10R | 2R/4R | A2/A2 | A/G | POSITIVE | 0.46-MS |
Group 2 genotyping data for each Chinese patient. Data taken from Blum et al 2010.
The GARS study supports the understanding that identifying hypodopaminergic genotypes may be the best predictor of adult and adolescent drug abuse or other SUD behavior. These results are also consistent with a number of functional MRI studies that show that the hypodopaminergic DRD2 A1 genotype leads to blunted responses that can could lead to aberrant drug and/or food seeking behavior (Stice et al., 2008), while the hyperdopaminergic A2 genotype serves as a protective factor against the development of drug disorders (Volkow et al 2006).
A further strength of this study is that only male subjects were used. Males with hypodopaminegic functioning are more likely to abuse drugs that stimulate the mesocorticolimbic system than those with normal dopaminergic functioning. In contrast, females living in a negative environment are at increased risk (possibly not due to their genotypes) for using more drugs and even more types of drug that increase their risk for SUD.
Another strength of this study is that it is in agreement with the work of Conner et al. [2010] confirming the importance of the cumulative effect of multiple genotypes coding for hypodopaminergic functioning, regardless of their genomic location, as a predictive method of drug use in males. Moreover, it extends the current literature, by suggesting for the first time a simple method using genetic testing to classify risk behavior in male patients seeking in-patient residential treatment (see table 7).
Table 7, describes a RDS-associated gene/polymorphism panel that can be used in accordance with the potential development of the GARS test.
Gene | Significance | Comment |
ALDH2 | P= 5 X 10-37 | With alcoholism and alcohol-induced medical diseases |
ADH1B | P= 2 X 10-21 | With alcoholism and alcohol-induced medical diseases |
ADH1C | P = 4 X 10-33 | With alcoholism and alcohol-induced medical diseases |
DRD2 | P =1 X 10-8 | With alcohol and drug abuse |
DRD4 | P= 1 X 10-2 | With alcohol and drug abuse |
SLC6A4 | P= 2 X 10-3 | With alcohol , heroin, cocaine, methamphetamine dependence |
HTRIB | P= 5 X 10-1 | With alcohol and drug abuse |
HTRI2A | P= 5 X 10-1 | With alcohol and drug abuse |
TPH | P = 2 X 10-3 | With alcohol and drug abuse |
MAOA | P = 9 X 10-5 | With alcohol and drug abuse |
OPRD1 | P= 5 X 10-1 | With alcohol and drug abuse |
GABRG2 | P= 5 X 10-4 | With alcohol and drug abuse |
GABRA2 | P= 7 X 10-4 | With alcohol and drug abuse |
GABRA6 | P= 6 X 10-4 | With alcohol and drug abuse |
COMT* | P= 5 X 10-1 | With alcohol and drug abuse in Asians |
DAT1 | P= 5 X 10-1 | With alcohol and drug abuse in Asians |
CNR1 | P= 5 X 10-1 | With alcohol and drug abuse |
CYP2E1* | P =7 X10-2 | With alcohol LIVER DISEASE |
An RDS gene panel. Chen et al. Journal of Psychoactive drugs. June 2011.
The need to genetically test individuals, especially at entry into a residential or even non-residential chemical dependency program, has long been recognized. In the GARS study, a high percentage (75%) of subjects were found to carry a moderate to high GARS, and 100% of individuals tested possessed at least one of the RDS risk alleles tested. It is of some interest that in the Group 2 population only rare DRD4 alleles such as 2R, 5R, and 6R were found. This study supports our understanding that hypodopaminergic state is due to gene polymorphisms, as well as environmental elements including both stress and neurotoxicity from aberrant abuse of psychoactive drugs (e.g., alcohol, heroin, cocaine etc). This study demonstrates that useful genetic variables include serotonergic genes (e.g., serotonergic receptors [5HT2a], serotonin transporter 5HTlPR, etc.), endorphinergic genes (e.g., mu OPRM1 gene, proenkephalin (PENK) [PENK polymorphic 3\' UTR dinucleotide (CA) repeats]), and dopaminergic genes (e.g., ANKKI Taq A; DRD2 C957T, DRD4 7R, COMT Val/met substation, MAO-A uVNTR, and SLC3 9 or 10R). Future studies could add other genes (e.g., GABARB3) and D3 dopamine receptor.
Any of these genetic and/or environmental impairments could result in reduced release of dopamine and or reduced number of dopaminergic receptors. The use of GARS will have prevention and treatment benefits in those patients afflicted with genetic antecedents to RDS-seeking behaviors.
Kenneth Blum, B.William Downs. and Roger Waite owns stock in LifeGen, Inc. the world wide distributors of the GARS test based on US and foreign patents. Frank Fornari is an consultant and owns interest in Dominion Diagnostics Lab the commercial developers of the GARS test. John Giordano is a LifeGen, Inc. partner.
As already mentioned in other chapters, milk whey is a liquid by-product generated after obtaining cottage cheese or curd (proteins coagulated by acid and heat), also known as cheese whey, that for many years has been considered a waste product, and sent to bodies of water, soil, and sewage systems. However, currently it is used due to its multiple nutritional and functional properties [1].
In Mexico, the production of whey in 2016 was estimated at 1,010,000 tons, 47% of which was discharged to soil, drains, and bodies of water. Despite the fact that multiple uses have been found to cheese whey, this has become a serious environmental problem [2]. This by-product is composed of water, lactose, proteins, peptides, fat, and mineral salts [3]. One of the peptides of interest is glycomacropeptide (GMP), which is obtained after the coagulation of milk κ-casein during cheese production and represents 15–20% (w/w) of the total proteins contained in milk whey [4].
GMP is the C-terminal fragment released by the proteolytic action of the endopeptidase chymosin (renin) on κ-casein during the initial stages of cheese making, or by the action of pepsin during the gastric digestion. κ-casein is hydrolyzed at phenylalanine105-methionine106 bond, forming two very different polypeptides. One is called para-κ-casein (residues 1–105), and it is slightly cationic at pH 6.6, hydrophobic and poorly soluble, which remains in cheese curd; and the other is GMP (residues 106–169), that is strongly polar so diffuses into the aqueous phase, being eliminated during the draining with the cheese whey (as reviewed in [5]).
GMP has 64 amino acid residues, with an isoelectric point (pI) between 4 and 5. Fifty percent of GMP is deglycosylated and is known as caseinomacropeptide (CMP) [5]. However, milk GMP can present different types of carbohydrates, such as: sialic acid, galactosyl, and N-acetylgalactosamine, which generate different glycosylated forms of the molecule. GMP is rich in amino acids such as proline, glutamine, serine, and threonine, but deficient in tryptophan, tyrosine, phenylalanine, and cysteine. The absence of aromatic amino acids in its primary structure causes that GMP does not present absorption at the wavelength of 280 nm. However, GMP can be detected at wavelengths between 205 and 226 nm and absorption differences between 210 and 280 nm are used for the characterization of GMP (as reviewed in [5]). The composition of GMP can be variable and depends on the source of serum and the fractionation technology used in its isolation [3] (Figure 1).
Primary structure of bovine GMP variant A and B, where ● indicates its three phosphorylation sites and ▲ the most important glycosylation sites. Modified from Thomä-Worringer et al. [
As reviewed by Neelima [7], the three-dimensional structure of GMP cannot be evaluated due to its crystallization which is not possible, so it can only be seen from a purely theoretical approach. GMP is a peptide that does not possess defined secondary and tertiary structure. However, three-dimensional structure of GMP has been predicted by means of protein modeling and shows that a large part of the peptide has a strong negative charge, whereas there are three small domains with a positive charge at the N-terminal end. At pH 7.0, its mean value of the hydropathy is −0.322, and GMP is more hydrophilic than hydrophobic. The hydropathy value decreases when glycosylation of GMP increases, due to the greater amount of sialic acid residues.
The use of GMP is growing, since it is a bioactive peptide with unique nutritional and nutraceutical properties. Many biological activities of GMP have been reported, highlighting antimicrobial, anticariogenic, gastric acid inhibitory, cholecystokinin (CCK) releasing, prebiotic, and immune modulatory. Of particular interest is GMP’s capacity to modulate the immune response, due to its potential use in treatment or prevention of different immunopathologies.
One of the first antimicrobial effects observed in GMP was due to its ability to bind cholera toxin and
There are several
In association with this antimicrobial effect, an anticariogenic activity to GMP has been demonstrated. Firstly,
Several studies have related GMP with the inhibition of gastric secretion. First ones were mostly developed using dogs by a group of Russian researchers. The first evidence that GMP inhibits gastric secretion was showed by Shlygin and co-workers [18] using gastrin to evoke it. Subsequent works demonstrated similar effect using different gastric secretion stimulants [19]. Some years later, it was proposed that this inhibitory effect was caused by a GMP fragment rather than the whole molecule [20, 21]. Later, injecting dogs with a protein fraction obtained from the gastric content of unweaned rats, it was observed an inhibition in dog gastric secretion to a food stimulus [22]. This inhibitory action was similar to that induced by GMP in dogs. GMP was also demonstrated to inhibit gastric motility after its intravenous injection in dogs [23]. All these experiments point out that at physiological conditions GMP may be playing a crucial role in the preservation of active milk proteins in newborn animal during natural breast feeding. In addition to dogs, other experimental models such as rats, pigs, and calves and also isolated organs were used to demonstrate that GMP induces gastric secretion inhibition in association with a decrease in blood of some regulatory digestive hormones, as gastrin and CCK (as reviewed in [24]). However, variations in used gastric stimuli, GMP dose, and origin, via of administration and experimental approach may be the cause of the differences in the reported intensity to this GMP activity.
Related with the effect of this bioactive peptide on digestive hormones, GMP has also been associated with appetite control. Several
For many years, the prebiotic properties of GMP have been discussed. The first evidence that GMP might possess prebiotic activity arose with the bifidobacterial growth promoting effect of human’s colostrums and milk by
In the last years, several research groups have demonstrated that oral treatment with GMP modifies
GMP has been shown to modulate the immune response in a number of different ways. First, we summarize literature reports about regulatory activity of GMP on immune cells demonstrated by
In relation to the immunomodulatory effects of GMP on immune cells, different
On the other hand, GMP is also able to downregulate dendritic cell response to LPS by inducing a slight but significant decrease in the production of IL-6, IL-1β, and TNF-α, but without changing the production of IL-12 and IL-10 [49]. Strikingly, the regulatory effect of GMP on neutrophils is the opposite, as it improves proliferation and phagocytic activity of the human macrophage like cells U937 [52]. However, the observation that both polypeptide and carbohydrate portions are essential for GMP biological effects is reinforced in this study, as peptides of pepsin-digested GMP and sialic acid-rich GMP fractions significantly enhanced cell proliferation and phagocytic activities stimulated by non-digested or asialo-GMP on U937 cell. Also, an upregulatory effect of GMP on production of IgA by LPS-stimulated splenocytes has been reported, being correlated with an increase in the population of IgA positive cells [53].
There are several studies that analyze the immunomodulatory activity of GMP on immune response when it is orally administered to experimental animals. In the context of splenocytes response to mitogens, two
The effect of orally administered GMP on humoral immunity has also been studied. Mice fed with GMP have shown suppressed levels of specific IgG to dietary and injected antigens, with no change in IgM, IgA, and IgE antibody response [54]. In this regard, a recent study showed that oral administration of GMP to mice resulted in a greater number of IgA positive plasma cells in the intestinal lamina propria [56]. All these results [54, 56] plus
Martínez-Augustin and co-workers [57, 58] have studied the immunomodulatory action of GMP in experimental models of intestinal inflammation. They have demonstrated that GMP administered orally to rats exerts an anti-inflammatory effect in ileitis and colitis induced with trinitrobenzenesulfonic acid (TNBS); said anti-inflammatory effect shows a degree of efficacy similar to that of sulfasalazine, a drug widely used in the treatment of inflammatory bowel disease. GMP was shown to protect rats from TNBS-induced colonic and ileal inflammatory damage, by reducing the damage score and the extent of necrosis, and also by diminishing the increased alkaline phosphatase colonic activity and inducible oxide nitric synthase expression. IL-1β and IL-1ra messenger RNA levels were significantly decreased in colon as a consequence of GMP administration; and myeloperoxidase activity and levels of IL-1β and IL-17 were decreased in ileum. Initially, the authors assumed that the action mechanism of GMP was not related to anti-oxidative activity or to regulatory cell induction, as glutathione or TGF-β levels in colon and Foxp-3 in ileum were not affected [57, 58]. However, when GMP was orally administered to rats, an increase on Foxp3 expression in spleen cells was observed, although secretion of cytokines by
In recent years, a Mexican laboratory led by Salinas [55, 59, 60, 61] has focused on the study of the immunomodulatory activity of GMP in experimental allergy models. They found that oral administration of GMP to rats before and during sensitization with allergen significantly reduces the level of allergen-specific IgE in serum, and also decreases the proliferative response and the production of IL-13 by splenocytes stimulated by the allergen [55]. Treatment of animals with GMP also protected them from systemic anaphylaxis as GMP administration increased survival rates and lessened signs of severity of anaphylactic shock. Moreover, GMP reduced the intensity of urticarial inflammatory reaction when sensitized animals were intradermically challenged with the allergen [55]. With these results, it was demonstrated the immunomodulatory properties of GMP on allergic sensitization and its beneficial effect on clinical signs associated to early-phase allergic reaction. Then, they investigated whether GMP may impact on late-phase and chronic inflammatory allergic reactions, using two experimental models that after repetitive exposure to allergens displayed local recruitment and activation of immune cells with persistent production of inflammatory mediators in affected tissues, together with substantial changes in the extracellular matrix and alterations in structural cells [62]. Specifically, they used experimental models of asthma and atopic dermatitis prophylactically administered with GMP, that is to say, prior to and during pathology establishment. As expected, GMP intake resulted in reduction of IgE titers in serum. In addition to this, in asthma model, GMP substantially decreased blood eosinophilia and suppressed the recruitment of inflammatory cells to the bronchoalveolar compartment. GMP also inhibited eosinophils infiltration, goblet cells hyperplasia, and collagen deposit in lung tissue [59]. Equivalent results were obtained in allergen-induced atopic dermatitis model, where GMP reduced the intensity of cutaneous inflammatory process and edema, abolished pruritus, and reduced eosinophils recruitment and mast cells hyperplasia in dermis [60]. In both models, expression of IL-5 and IL-13 was markedly inhibited in lung and skin, while expression of IL-10 was increased. Their research then turned to the mechanism by which GMP modulates the allergic response. They demonstrated that GMP administration increases the amount of
Finally, there are few studies that analyze the role of GMP on cancer. In a rat model of pharmacological-induced colorectal cancer, oral administration of 100 mg/kg of GMP decreased the number of aberrant crypt foci although no effect was observed at doses of 10 and 50 mg/kg. On the other hand, there was no change in methylation and expression level of p16 and MUC2, two tumor suppressor genes [63]. Additionally, through an
Although more studies are needed in relation to some biological activities, current results propose GMP as a good candidate to be used as a functional ingredient in food industry.
Today, one of the objectives of the food industry is the development of novel food products with beneficial properties for health. For its different health benefits, GMP can be used in therapeutic and dietary foods, or as a functional ingredient in various special products, like oral care products.
It is crucial to demonstrate that GMP is hypoallergenic to be used in food compositions. In this regard, Takahashi and collaborators patented a food composition that contained GMP and a mixture of free amino acids (leucine, lysine, methionine, cysteine, phenylalanine, tyrosine, tryptophan, arginine, histidine, and glycine) [65]. The composition presented good taste, good absorption and digestion properties, and a high nutritional value. They demonstrated that this composition was hypoallergenic, as after repeated injections of the GMP composition together with an adjuvant used to induce experimental allergy in mice, no antibody against GMP was detected in serum by Ouchterlony. Although this method is not very accurate, GMP hypoallergenicity was later corroborated by Milkkelsen and collaborators using ELISA test to show absence of specific antibodies in mice after being sensitized both systemically or orally with GMP [66].
Due to the particular amino acid composition of GMP, devoid of aromatics amino acids (phenylalanine, tryptophan, and tyrosine), it can be used for special diets of people suffering from phenylketonuria (PKU), being an adequate choice as a source of proteins [67]. On the other hand, GMP has low amount of methionine but high amount of branched chain amino acids (valine and isoleucine), which makes this peptide an excellent candidate to be used for the control of liver diseases, as this type of amino acids are good as caloric sources [68]. There is a patent to use of GMP to improve female’s health [69]. The inventors claim that administration of a composition comprising GMP can improve the health of the females. They used murine models fed with GMP composition and showed that females decreased final fat mass and percent body fat, when comparing with females that received a diet based on caseins or free amino acids as source of proteins. In relation to bone characteristics, femur length was larger in GMP administered mice, although only females showed less femoral weakness and greater bone mineral content and density as compared to those fed with amino acids or casein diets, respectively.
As previously mentioned, research results suggest that GMP has an effect on the feeling of fullness but this does not translate into a lower food intake [27, 28]. For an application in food intake regulation and in potentially body weight management, more work is required. Understanding dose, timing, and delivery mode, including food form and composition, in relation to the pattern of release of CCK, is needed for the use of GMP as appetite suppressant [70].
GMP has physicochemical properties that make it attractive for use as an additive in food products. According to studies on the functional properties of GMP, it can act as an emulsifier, foaming, and gelling agent.
GMP as an emulsifier presents stability to pH variations, which is attractive for foods that undergo pH changes during their process, such as the case of fermented milk products [71]. The best emulsifying capacity was obtained at alkaline pH. However, it has been observed that emulsions with GMP as emulsifying agent are not stable during storage when they have received thermal treatment [72]. Besides, GMP modified covalently with disaccharides or fatty acids can present an improved function and even increase its biological activity [73, 74]. Therefore, in order to modify the emulsification activity of GMP, this peptide has been conjugated with other molecules such as lactose [73] and fatty acids [74]. The conjugation of GMP with lactose was carried out through the reaction of Maillard, and this conjugate showed a better emulsifying capacity without significantly reducing the solubility of GMP [73].
Currently, foams have many industrial uses of great importance in the production of beer, soaps, whipped cream, shaving cream, aerosols, etc. The formation of a foam requires the participation of a surfactant capable of diffusing to the air/water interface to lower the surface tension. GMP complies with this property, although the foams formed with GMP are stronger or more stable when combined with other foaming proteins [75, 76]. In order to improve the foam properties of the proteins, synergistic mixtures of biopolymers and pH variations have been made that can modify their charge and, consequently, their foam ability. In relation to this, by combining sodium caseinate with GMP, synergistic interactions take place between these molecules on foaming and on stability at pH 5.5 [77]. Non-glycosylated GMP has better foaming properties than glycosylated GMP [78]. This is due to the glycosidic structures favor a combination of hydrophilic and electrostatic effects, which prevents an orderly adsorption of the glycosylated GMP molecules at the air/water interface; whereas, non-glycosylated GMP forms a very stable network at the interface.
On the other hand, gels are semi-solid systems that consist of a network of solids (three-dimensional network of polymers) with an inside trapped-liquid. They are of great importance in food and pharmaceutical industry as many gelled products are manufactured throughout the world (gummies, gelatins, jelly jams, bakery fillings, and therapeutic or cleaning agents). Generally, gelling agents are proteins and polysaccharides. Gelling properties of GMP has been studied and it is known that its gelation depends on pH and temperature, reporting that even aqueous solutions with low GMP amounts can be gelled at pH below 4 [79]. Besides, GMP can potentiate gellying capacity of other substances. Thus, by fermenting goat milk to which GMP was added, a more ordered and structured gel was obtained, in addition to obtaining a better elasticity in it, as compared to that obtained when whey protein concentrate was added [80]. The influence of GMP on the gelation made by gelatin has also been studied and when these two compounds are mixed, lower concentration of both substances are need to get a gel as compared with the ones need when they are used separately [81]. This synergistic effect in gelation is very important in the food industry for the preparation of desserts and foods based on gels.
Dental caries is one of the chronic diseases that most often affect humans. Due to the anticariogenic and remineralization properties demonstrated to GMP and previously reviewed in biological activities section, nowadays GMP is being incorporated to some oral care products [15, 16, 17].
One of the problems presented by the dairy industry is the adulteration of milk with whey cheese, which is very cheap and not detected by sensorial tests. Cheese whey does not cause harm to health, however, it affects milk-derived products manufacturers financially and can affect the consumers nutritionally, so the addition of cheese whey is considered a fraud. Due to GMP present in cheese whey, the detection of this peptide may indicate the addition of cheese whey to milk. Some of the methods that detect GMP as an indicator of the presence of cheese whey are described below.
High performance liquid chromatography (HPLC) has been widely used to identify GMP as indicative of milk adulteration with cheese whey. In order to carry out the analysis, it is necessary to pre-treat the samples with TCA to precipitate proteins that can interfere (k-casein) and to concentrate GMP [86]. Similarly, a rapid and sensitive HPLC method on a gel permeation column was developed to detect GMP to follow the hydrolysis of k-casein by chymosin in milk [87]. The only pretreatment given to samples was addition of TCA (final concentration 8%) to precipitate the interfering caseins and whey proteins. This method was widely used by several researchers to analyze different samples, such as skimmed milk powder [88]. Cation-exchange chromatography has also been used to detect GMP, previously removing caseins from whey samples by precipitation with HCl at pH 4.6, neutralizing with TCA at 2–8% and analyzing supernatants [89]. On the other hand, a Reversed-Phase HPLC (RP-HPLC) method was developed and validated to separate and quantify GMP and was demonstrated to be precise, sensitive, and reliable [90]. The determinations were performed in the linear range of 15–200 μg/mL and the detection limit was 2 μg/mL. The method was applied to the analysis of rennet and acid whey, whey protein concentrates produced by the dairy industry, and also for the detection of rennet whey in powdered milks.
The European Commission uses two methods to detect the presence of cheese whey in milk: a gel permeation chromatography and subsequently a RP-HPLC as a confirmatory test [91]. However, it has been shown that the sensitivity of this method is affected by the presence of acidified rennet whey, which makes it difficult to detect the addition of whey [92]. Besides, the HPLC methodology used to analyze compounds like GMP in dairy products usually includes extractions with solvents, sample’s preparation require a lot of time and reactives, the equipment is very sophisticated and demands trained personal.
Spectroscopy has also been used to detect GMP. The medium infrared spectroscopy (MIR) was used to analyze milk powder in order to detect GMP as adulteration parameter. Although this method is fast, it is not widely used because derived spectra are not very easy to interpret, in addition to its high cost [93]. On the other hand, by liquid chromatography/electrospray coupled to mass spectrometry, milk products were analyzed and it was able to quantify GMP from concentration of 10 pmol, although the method was not used to detect milk adulteration [94].
Immunoassays are analytical methods of great application in the food area, and have the advantages that they are quick, sensitive, and that the sample to be analyzed requires little or no treatment. Several immunochemical methods have been developed in order to identify and quantify GMP in milk. Firstly, it is necessary to produce antibodies against GMP and later, these antibodies can be used for the development of the different immunochemical methods that detect it. Some of these assays are described below:
In summary, different techniques and methods have been developed and used to detect GMP as an index of adulteration of milk with cheese whey. Some of them can also be used to quantify GMP in food products. The aim of this area of research is to achieve one that bring together being cheap, fast, easy to develop, and to interpret the results, with high sensitivity and a limited sample processing. These characteristics will allow people to use them at the time and place of milk reception.
GMP possesses several nutritional and health promoting properties. Among them, it exerts important modulatory effects on the immune system that are beneficial in a number of different inflammatory conditions. GMP immune response mechanism of action might be mediated by increasing healthy intestinal microbiota, by inhibiting splenocyte proliferation, by promoting both local and systemic regulatory environment, and also by directly modulating immune cell functions. More research is needed to support these findings, as we cannot exclude a possible effect of products derived from GMP digestion on
We appreciate the support given to the Autonomous University of Aguascalientes for the publication of this chapter.
Authors declare that there is no conflict of interest between the authors of the chapter entitled: “Glycomacropeptide: Biological activities and uses.”
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