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Published in final edited form as: Ann Hum Genet. 2014 Apr 26;78(4):290–298. doi: 10.1111/ahg.12064

Drug Addiction and Stress-Response Genetic Variability: Association Study in African Americans

Orna Levran 1,*, Matthew Randesi 1, Yi Li 2, John Rotrosen 3, Jurg Ott 4,5, Miriam Adelson 1,6, Mary Jeanne Kreek 1
PMCID: PMC4065216  NIHMSID: NIHMS579196  PMID: 24766650

Summary

Stress is a significant risk factor in the development of drug addictions and in addiction relapse susceptibility. This hypothesis-driven study was designed to determine if specific SNPs in genes related to stress response are associated with heroin and/or cocaine addiction in African Americans. The analysis included 27 genes (124 SNPs) and was performed independently for each addiction. The sample consisted of former heroin addicts in methadone maintenance treatment (n = 314), cocaine addicts (n = 281), and controls (n = 208). Fourteen SNPs showed nominally significant association with heroin addiction (p < 0.05), including the African-specific, missense SNP rs5376 (Asn334Ser) in the galanin receptor type 1 gene (GALR1) and the functional FKBP5 intronic SNP rs1360780. Thirteen SNPs showed association with cocaine addiction, including the synonymous SNPs rs237902, in the oxytocin receptor gene (OXTR), and rs5374 in GALR1. No signal remained significant after correction for multiple testing. Four additional SNPs (GALR1 rs2717162, AVP rs2282018, CRHBP rs1875999, and NR3C2 rs1040288) were associated with both addictions and may indicate common liability. The study provides preliminary evidence for novel association of variants in several stress related genes with heroin and/or cocaine addictions and may enhance the understanding of the interaction between stress and addictions.

Keywords: heroin addiction, cocaine addiction, African Americans, stress, HPA axis, GALR1, FKBP5, NR3C2, OXTR, AVP

INTRODUCTION

Addiction to heroin or cocaine, as well as illicit abuse of prescription opioids causes high personal, social and economic burdens. Drug addiction is a chronic disease affected by a combination of genetic, environmental and drug-induced factors (Kreek et al., 2012; Kreek et al., 2005). Stress is one of the factors that may predispose individuals to drug addiction and perpetuate the cycle of addiction, including initiation, maintenance and relapse (Koob and Kreek 2007; Kreek and Koob 1998; Sinha 2008). Similar neural circuitry is essential for stress response and drug reward (Koob et al., 2013). Cocaine stimulates many systems that are activated by stress and cocaine dependent subjects display abnormal patterns of HPA axis activity (Sinha et al., 2006). Stress exposure as well as drugs of abuse activates the hypothalamus-pituitary-adrenal (HPA) axis. Consequently, corticotropin-releasing-hormone (CRH, CRF) and arginine-vasopressin (AVP) are released from the hypothalamus paraventricular nucleus (PVN) and stimulate adrenocorticotrophic hormone (ACTH) secretion, which in turn stimulates glucocorticoids (GCs) synthesis and release from the adrenal cortex. GCs regulate the activity of the HPA axis through negative feedback.

Despite the many similarities between cocaine and heroin addiction, there are differences in the behavioral and neurobiological mechanisms underlying cocaine and heroin addiction (Badiani et al., 2011; Peters et al., 2013). A twin study demonstrated that heroin addiction has the largest unique genetic influences compared with other drug groups (Tsuang et al., 1998). In this study, we have analyzed cocaine addiction (CA) and heroin addiction (HA) separately, in order to identify SNPs that may have a selective drug-specific effect. Nevertheless, this approach also allows the identification of SNPs that have general (non-drug-specific) addiction disease liability.

Except for our previous report (Levran et al., 2009), we are not aware of reports of association between polymorphisms in stress-related genes and specific drug addictions in populations of African ancestry. There are several reports of such associations in cohorts of European ancestry, including the AVPR1A SNP and non-specific drug use disorders (Maher et al., 2011), CRHBP in alcohol use disorder (Enoch et al., 2008; Ray 2011), CRHR1 SNPs and alcoholism (Enoch 2011; Kranzler et al., 2011; Ray et al., 2013), GAL SNPs and opioid addiction (Beer et al., 2013; Levran et al., 2008) and SNPs of NPY1R and NPY2R with nicotine, amphetamine, alcohol, and cocaine dependence (Okahisa et al., 2009; Wei et al., 2012; Wetherill et al., 2008). In another study from our laboratory, an MC2R SNP was associated with heroin addiction in Hispanics (Proudnikov et al., 2008).

Here, we report the results of a case-control hypothesis-driven association study of 124 SNPs from 27 genes related to the stress response with HA and/or CA, in a sample of 803 American subjects of predominantly African ancestry. The study is an expansion of our previous study of HA (Levran et al., 2009) to which a second addiction (cocaine), 481 samples, and 12 stress-related genes were added. This study also employed more stringent inclusion criteria for ancestry, based on biographic ancestry scores obtained by STRUCTURE analysis of 155 ancestry informative markers (AIMs).

MATERIALS AND METHODS

Subjects

The study sample (n = 803, 41% females) is part of a larger cohort recruited by the Kreek Laboratory at the Rockefeller University for the study of the genetics of specific drug addictions. The subjects were selected based on phenotype (history of severe heroin addiction, cocaine addiction or normal controls) and African ancestry. Ancestry was verified by family history questionnaire and STRUCTURE analysis (see below) and specific inclusion criteria were employed to obtain relative homogeneity and to limit population stratification. To be included in the study, an individual had to show > 50% African ancestry contribution by STRUCTURE analysis of AIMs, except for self-identified Hispanics that were not included even if they had African ancestry >0.5.

The study was divided into three cohorts: controls (normal volunteers, n = 208), subjects with heroin addiction (HA, n = 314) and subjects with cocaine addiction (CA, n = 281). The HA study is an expansion of our previous study (Levran et al., 2009). The current study included 178 HA subjects and 144 controls from the original study and the rest of the 481 subjects were new.

Ascertainment was made by extensive personal interview, using several instruments: the Addiction Severity Index (McLellan et al., 1992), the Kreek-McHugh-Schluger-Kellogg Scale (KMSK)(Kellogg et al., 2003) and the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV). The following exclusion criteria from the healthy control category were used: (1) at least one instance of drinking to intoxication or any illicit drug use in the previous 30 days; (2) a history of alcohol drinking to intoxication or illicit drug use, more than twice a week, for more than 6 consecutive months and (3) cannabis use for more than 12 days in the previous 30 days or past cannabis use for more than twice a week for more than 4 years. The HA subjects were former heroin addicts with a history of at least 1 year of daily multiple uses of heroin, treated at a methadone maintenance treatment program at the time of recruitment. Of the subjects with HA as a major dependency, 56% also had CA but cocaine was not their preferred drug. According to our classification, none of the CA subjects also had HA.

Subjects for the HA group were recruited at the Rockefeller University Hospital, the Manhattan Campus of the VA NY Harbor Health Care System and the Dr. Miriam and Sheldon G. Adelson Clinic for Drug Abuse Treatment and Research in Las Vegas. Subjects for the CA group were recruited at the Rockefeller University Hospital. The Institutional Review Boards of the Rockefeller University Hospital and the VA New York Harbor Healthcare System approved the study. The Rockefeller University IRB also reviews the Adelson Clinic, LV. All subjects signed informed consent for genetic studies.

Genes/SNPs selection and array design

Twenty-seven genes were selected based on their known function in the response to stress (Table 1, Table S1). In addition to the genes included on the original hypothesis-driven “addiction” array (GS0007064-OPA; Illumina, San Diego, CA, USA)(Hodgkinson et al., 2008) that was used in our original association studies (Levran et al., 2008; Levran et al., 2009), we have added 12 stress-related genes to the current array (GS0013101-OPA). Of the 68 new SNPs from stress-related genes that were selected for the new array, based on previous reports of potential functionality and/or association with stress response, 13 SNPs were not technically suitable for this platform based on the Illumina Assay Design Tool, and 55 SNPs were added to the new array. A total of 143 SNPs from these stress-related genes were included in the array, of which 88 SNPs were from the original “addiction” array (Table S1).

Table 1.

Stress-related genes list

Gene symbol Gene name
AVP arginine vasopressin
AVPR1A arginine vasopressin receptor 1A
AVPR1B arginine vasopressin receptor 1B
CARTPT CART prepropeptide
CCK cholecystokinin
CRH corticotropin releasing hormone
CRHBP corticotropin releasing hormone binding protein
CRHR1 corticotropin releasing hormone receptor 1
CRHR2 corticotropin releasing hormone receptor 2
FKBP5 FK506-binding protein 51
GAL galanin
GALR1 galanin receptor 1
GLRA1 glycine receptor, alpha 1
HCRT hypocretin neuropeptide precursor
HCRTR1 hypocretin receptor 1
HCRTR2 hypocretin receptor 2
MC2R melanocortin 2 receptor
NPY neuropeptide Y
NPY1R neuropeptide Y receptor Y1
NPY2R neuropeptide Y receptor Y2
NPY5R neuropeptide Y receptor Y5
NR3C1 nuclear receptor subfamily 3, group C, member 1
NR3C2 nuclear receptor subfamily 3, group C, member 2
OXT oxytocin
OXTR oxytocin receptor
PITX1 paired-like homeodomain transcription factor 1
SERPINA6 corticosteroid binding globulin
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Genes are sorted by alphabetical order

Genotyping

Blood samples were taken and DNA was extracted and quantified using standard methods. DNA (700 ng) was precipitated as described (Levran et al., 2008). Genotyping was performed using an Illumina GoldenGate Custom Panel of 1536 SNPs at the Rockefeller University Genomics Resource Center according to the manufacturer’s protocol. Random samples were genotyped in duplicate. Analysis was performed with BeadStudio software v2.3.43 (Illumina). The genotype data for all SNPs were visually inspected to verify and correct automatic calling. Genotype data were filtered based on SNP call rates (>90%). Of the 143 SNPs genotyped, 14 SNPs were excluded from analysis based on low quality (Table S1).

Assessment of percentage of African Ancestry Using AIMs

Of the original 186 AIMs from the GS0007064-OPA panel (Hodgkinson et al., 2008), 171 SNPs with adequate quality were included in the new panel, and 155 AIMs with high quality score were used for analysis. Biographic Ancestry Scores (e.g., fractions of genetic affiliation of the individual in each cluster) were estimated using the program STRUCTURE 2.2 (Pritchard et al., 2000) with seven populations (k). Each subject was anchored against genotypes of 1051 samples from 51 worldwide populations represented in the Human Genome Diversity Cell Line Panel (http://www.cephb.fr/HGDP-CEPH-Panel), as described (Ducci et al., 2009).

Statistical analysis

Quality control for the SNP genotypes was carried out as follows. Exact tests for deviation from Hardy-Weinberg equilibrium (HWE) were performed with the PLINK program (Purcell et al., 2007), with SNPs to be rejected based on the recommended threshold of p ≤ 0.001 in control individuals. Pairwise linkage disequilibrium (LD) values were estimated using Haploview 4.2 (Barrett et al., 2005). Association analysis was performed for each SNP separately by logistic regression in the PLINK program, with genotype and sex as covariates, where in different analyses the genotype was coded as a linear allelic effect (genotypes AA, AB, and BB were given numerical values 0, 1, and 2, respectively), and as two groups reflecting dominant or recessive inheritance model. The multiple testing issue was addressed by assuming 105 independent tests (there are 19 highly correlated SNPs.) An uncorrected p = 0.0005 was chosen as the threshold for significance.

RESULTS

A total of 803 African American subjects (314 HA, 281 CA, and 208 controls) were included in the two independent case-control association analyses for HA and CA. All subjects had at least 50% African ancestry based on STRUCTURE analysis of 155 AIMs, and were non-Hispanics based on self-report. From the original sample of self-identified African Americans (AA), 31 subjects were excluded because they did not meet the inclusion criteria of >50% African ancestry contribution and 15 subjects were excluded based on major conflict between their self-identified ethnicity (AA) and STRUCTURE results. Fifty-eight subjects with ambiguous self-identified ancestry and 14 subjects who self-identified as Caribbean African (non-Hispanic), European, or Native American were included based on >50% African ancestry contribution. Cases and controls had similar combinations of African and other ancestries with an average of 81% African ancestry and 6% European ancestry (Fig. 1).

Figure 1.

Figure 1

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Schematic representation of the individual admixture estimates based on STRUCTURE analysis using k=7. a. controls; b. Heroin addicts; c. cocaine addicts. Each vertical line (X axis) represents one individual, and subjects are displayed according to their predominant cluster contribution (see Methods). The Y axis represents percentage of ancestry. The clusters correspond to the geographical regions based on the HGDP sample. Color code: Africa (blue), Europe (red), Middle East (green), Central Asia (purple), Far East Asia (cyan), Oceania (amber), and America (light blue).

Quality control resulted in the exclusion of 14 SNPs with low quality and four SNPs based on minor allele frequency (MAF) < 0.05 (Table S1). No SNP showed deviation from HWE in controls (p < 0.001). LD analysis of all SNPs, in the control sample, revealed that 20 SNP pairs are in moderate to strong LD (r2 > 0.5), including two AVPR1A SNPs that are in complete LD (Table S2). One of these AVPR1A SNPs was excluded from analyses.

Genotypes of 124 SNPs from 27 stress-related genes were analyzed for association with HA or CA (Table 1, Table S1). Fourteen SNPs, in ten genes, showed nominally significant association of genotype with HA (p < 0.05) (Table 2A), of which the two FKBP5 signals are not independent, based on LD analysis (Fig. S1). One SNP (GALR1 rs5376) is non-synonymous, two SNPs are from the 3' UTR and the rest are intronic or intergenic. Thirteen SNPs in 11 genes showed nominally significant association of genotype with CA (p < 0.05) (Table 2B). Two SNPs (OXTR rs237902 and GALR1 rs5374) are synonymous, one SNP is from the 3' UTR, and the rest are intronic or intergenic. Four SNPs (AVP rs2282018, CRHBP rs1875999, GALR1 rs2717162, and NR3C2 rs1040288) were associated with the two addictions. The strongest association was observed at GALR1 SNP rs5374 for CD (p = 0.001) but no signal remained significant after correction for multiple testing.

Table 2.

Significant SNP associationsof opioid or cocaine addiction (p< 0.05)

A. Opioid addiction
Gene SNP Chr. Position Location Alleles1 P OR Test
AVP rs2282018 20 3064949 intronic C/T2 0.03 NA Geno
rs2740204 20 3062467 3' intergenic3 G/T 0.04 1.47 Dom
CRHBP rs7728378 5 76259350 intronic C/T2 0.02 1.54 Dom
rs1875999 5 76264982 3' UTR T/C 0.04 0.67 Dom
CRHR1 rs81189 17 43894798 intronic G/C 0.04 1.88 Rec
FKBP5 rs3800373 6 35542476 3' UTR A/C 0.03 0.65 Dom
rs1360780 6 35607571 intronic C/T 0.04 0.67 Dom
GAL rs3136541 11 68457943 intronic C/T2 0.04 1.66 Rec
GALR1 rs5376 18 74980809 Asn334Ser A/G4 0.02 1.49 Dom
rs2717162 18 74968327 intronic T/C 0.04 NA Geno
MC2R rs1893219 18 13916387 5' intergenic G/A 0.03 0.52 Rec
NPY5R rs6536721 4 164496347 3' intergenic5 A/G 0.03 0.66 Dom
NR3C1 rs864082 5 142663939 intronic T/G 0.03 0.62 Add
NR3C2 rs1040288 4 149048117 intronic G/C 0.04 0.60 Rec
B. Cocaine addiction
Gene SNP Chr. Position Location Alleles1 P OR Test
AVP rs2282018 20 3064949 intronic C/T2 0.027 0.59 Rec
CCK rs754635 3 42305131 intronic G/C 0.040 1.47 Dom
rs2029127 3 42311742 5' intergenic G/A4 0.049 1.47 Dom
CRHBP rs1875999 5 76264982 3' UTR T/C 0.038 0.76 Add
CRHR1 rs8072451 17 43893716 intronic C/T6 0.045 0.59 Dom
CRHR2 rs3779250 7 30694260 intronic G/A6 0.022 0.09 Rec
GALR1 rs5374 18 74962645 Gly47Gly T/C2 0.001 1.53 Add
rs2717162 18 74968327 intronic T/C 0.03 0.66 Dom
GLRA1 rs1428157 5 151306610 5' intergenic G/T 0.046 0.51 Rec
NPY5R rs4632602 4 164267129 intronic T/C 0.015 2.74 Rec
NR3C1 rs10482672 5 142692533 intronic C/T 0.035 0.70 Add
NR3C2 rs1040288 4 149048117 intronic G/C 0.038 0.76 Add
OXTR rs237902 3 8809184 Asn230Asn G/A 0.037 1.36 Add
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Chr, chromosome; Geno, genotype; Dom, dominant; Rec, recessive; Add, additive. SNPs are sorted alphabetically by gene symbol. SNPs in bold were associated with HA and CA.

1

Major/minor in populations with African ancestry (forward strand).

2

The minor allele in African populations is the major allele in European populations.

3

This SNP is located in the intergenicregionbetween OXT and AVP and is listed as OXT SNP in some studies.ASNP listed under each gene may influence the other gene as well.

4

The minor allele is absent in European populations.

5

NPY1R and NPY5R are located 8 kb apart such that SNP listed under each gene may influence the other gene as well.

6

This SNP is very rare in YRI.

The MAFs of the identified SNPs were compared among the control sample of this study, Americans of African ancestry in Southwest USA (HapMap ASW), and Africans from Yoruba, Nigeria (HapMap YRI)(Table S3). For the most part, the MAF data in the control sample is in concordance with that of ASW.

DISCUSSION

The goal of this study was to identify variations in genes related to the stress response that contribute to vulnerability for heroin and/or cocaine addiction in African Americans. Except for our previous report (Levran et al., 2009), we are not aware of previous reports of association between polymorphisms in stress related genes and specific drug addictions in populations of African ancestry. This study is an expansion of our previous study of heroin addiction in AA (Levran et al., 2009). In the original study we analyzed 130 genes, including 15 stress-related genes. In the current study, we have limited the analysis to 27 stress-related genes, of which 12 were not part of the original array. We have also added several SNPs in genes that were included in the original array. From the top results of the original study, only one SNP belonged to a stress-related gene (AVPR1A, rs3759292), a result that was not supported by the current study.

An important finding of this study is that variants in the genes encoding galanin (GAL) and galanin receptor 1 (GALR1) are associated with HA and CA, including a GALR1 SNP (rs2717162) that is associated with both addictions. Galanin and its receptors were shown to be involved in the behavioral and neurochemical effects of opiates and stress response and were shown to counteract opiate reward and withdrawal (Picciotto 2010). Galanin is considered a candidate for a protective factor against opiate addiction, and galanin receptor agonists have been suggested as therapeutic targets. Studies suggest that the effects of galanin result from its ability to reduce stress response through the HPA axis (Picciotto et al., 2010). Recent studies in mice showed that chronic stress increased nucleus accumbens mRNA expression of galanin and that fluoxetine reversed this elevation (Zhao et al., 2013).

From the SNPs indicated in this study, GALR1 SNP rs2717162 was previously associated with craving for tobacco (Lori et al., 2011), and with quitting success in bupropion smoking cessation trials (Gold et al., 2012). GAL haplotype (including rs3136541) was associated with alcoholism (Belfer et al., 2006). We have previously reported an association of GAL SNP rs694066 and HA in subjects of European ancestry (Levran et al., 2008).

One of the GALR1 SNPs indicated, rs5376, is a missense SNP (Asn334Ser) located at the C-terminus of the galanin receptor. It is African-specific with high MAF of the G allele (43%). This is the first report of association of this SNP with any phenotype. The second GALR1 SNP indicated, rs5374, is synonymous and is in moderate LD (r2 ~ 0.4, HapMap YRI) with several SNPs from the 5' region that may be functional. GAL SNP rs3136541 indicated in this study was also indicated in a recent study of HA in subjects of European ancestry (Levran et al. submitted) suggesting an effect that is not population-specific.

Cocaine and heroin addictions may be heterogeneous due to differences in chemical classes, primary targets, routes of administration, metabolic pathways, and psychopharmacologic effects, but may share some biological mechanisms (Badiani et al., 2011; Kendler et al., 2003; Peters et al., 2013; Tsuang et al., 1998; Vanyukov et al., 2003). A twin study demonstrated that heroin addiction has the largest unique genetic influences compared with other drug groups (Tsuang et al., 1998). Association studies of mixed addictions may be limited to the identification of general effects, and specific drug effects could be missed. We have analyzed CA and HA separately in order to identify SNPs that may have specific effects. Nevertheless, this approach also allows the identification of variants with general effects. Four SNPs (AVP rs2282018, CRHBP rs1875999, GALR1 rs2717162, and NR3C2 rs1040288) were associated with both addictions in this study and may indicate a common liability.

Although none of the associations identified in this study survived multiple testing corrections, additional support for the results is provided by the results of previous association studies of affective disorders and personality traits. These include CRHBP SNPs rs7728378 and rs1875999 (Claes et al., 2003; De Luca et al., 2010; Enoch et al., 2008; Roy et al., 2012), CRHR2 SNP rs3779250 (Ishitobi et al., 2012), OXTR SNP rs237902 (Montag et al., 2013; Stankova et al., 2012), CCK SNP rs754635 (Koefoed et al., 2010; Maron et al., 2005), NPY5R SNP rs4632602 (Wetherill et al., 2008), and FKBP5 SNPs rs1360780 and rs3800373 (Binder et al., 2004).

FKBP5 SNP rs1360780 as well as CRHBP SNPs rs7728378 and rs1875999 were shown to interact with childhood trauma to predict depression, suicidality, aggression, or PTSD (Appel et al., 2011; Bevilacqua et al., 2012; Binder et al., 2008; Roy et al., 2010; Xie et al., 2010; Zimmermann et al., 2011). The FKBP5 SNP rs1360780 was shown to alter chromatin conformations and transcription in response to childhood trauma (Klengel et al., 2013).

Although the risk to develop heroin or cocaine addiction is similar among all ethnic groups, LD and allele frequency differences between populations of European and African ancestry may indicate that each may have some distinct genetic risk factors for heroin and/or cocaine addiction. Nevertheless, they may share some of the risk factors. Our current and previous studies (Levran et al., 2008; Levran et al., 2009) (Levran et al. submitted) support this assumption. Most of the findings were distinct between the samples of European and African ancestry, but several SNPs were associated with heroin and/or cocaine addiction in both samples (AVP rs2282018, FKBP5 rs3800373 and rs1360780, GAL rs3136541, GLRA1 rs1428157, and NR3C2 rs1040288). Notably, AVP rs2282018 and NR3C2 rs1040288 were also identified as risk factors for both cocaine and heroin addiction and may represent non drug-specific and non-population-specific liability.

The SNPs identified by this study showed only nominally significant association that may not reflect true association. However, testing a set of candidate genes increases the probability of detecting true associations and may not require as stringent a threshold for significance as a hypothesis-free study. Nevertheless, an independent study is warranted to further corroborate these findings.

In summary, the study provides novel preliminary evidence for the association of several variants in stress-related genes with heroin and/or cocaine addictions and may enhance the understanding of the interaction between stress and drug addictions. It may assist the development of pharmacological treatments targeting stress mechanisms. The study supports the presence of both shared and distinct genetic liability for heroin and/or cocaine addictions.

Supplementary Material

Supp TableS1
Supp TableS2
Supp TableS3
supp FigureS1

Acknowledgements

We thank all the clinical staff who enrolled and assessed subjects for this study, including S. Linzy, P. Casadonte, E. Ducat and B. Ray. We are grateful to P-H. Shen and D. Goldman, from the NIH/NIAAA, for STRUCTURE analysis, and C. Zhao and B. Zhang, from the Rockefeller Genomic Resource Center, for their excellent assistance in genotyping. This work was supported by the Adelson Medical Research Foundation and the Shanxi Scholarship Council of China (LY).

Footnotes

Supporting Information

Additional supporting information may be found in the online version of this article:

Table S1: SNPs list.

Table S2: Highest LD scores in the control group.

Table S3: Comparison of minor allele frequencies (MAF).

Figure S1: Pairwise LD between the FKBP5 SNPs.

As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer-reviewed and may be re-organised for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.

Conflict of interest

The authors declare no conflict of interest

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