alexa SNPs, Linkage Disequilibrium and Transcriptional Factor Binding Sites Associated with Acute Mountain Sickness among Han Chinese at the Qinghai-Tibetan Plateau | Open Access Journals
ISSN: 2472-128X
Journal of Clinical & Medical Genomics
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on
Medical, Pharma, Engineering, Science, Technology and Business

SNPs, Linkage Disequilibrium and Transcriptional Factor Binding Sites Associated with Acute Mountain Sickness among Han Chinese at the Qinghai-Tibetan Plateau

Norman E Buroker1*, Xue-Han Ning1,2, Kui Li3, Zhao-Nian Zhou4, Wei-Jun Cen3, Xiu-Feng Wu4, Wei-Zhong Zhu5, C Ronald Scott1 and Shi-Han Chen1
1Department of Pediatrics, University of Washington, Seattle, WA USA
2Division of Cardiology, Seattle Children’s Hospital Institute Foundation, Seattle, WA USA
3Lhasa People Hospital, Tibet
4Laboratory of Hypoxia Physiology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China
5Center for Cardiovascular Biology and Regenerative Medicine, University of Washington, Seattle, WA USA
Corresponding Author : Norman E. Buroker
Department of Pediatrics
University of Washington, Seattle, WA USA
Tel: 206 616 0472
E-mail: [email protected]
Received: March 17, 2015 Accepted: April 04, 2015 Published: April 12, 2015
Citation: Buroker NE, Ning XH, Li K, Zhou ZN, Cen WJ, et al, (2015) SNPs, Linkage Disequilibrium and Transcriptional Factor Binding Sites Associated with Acute Mountain Sickness among Han Chinese at the Qinghai-Tibetan Plateau J Clin Med Genomics 3:120. doi: 10.4172/2472-128X.1000120
Copyright: © 2015 Buroker NE, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Related article at Pubmed, Scholar Google

Visit for more related articles at Journal of Clinical & Medical Genomics

Abstract

Acute mountain sickness (AMS) occurs in up to 50% of individuals ascending to high altitudes greater than 2600 meters. An AMS Han Chinese and a normal Han group were compared for 17 single nucleotide polymorphisms (SNPs) within 9 genes that have been associated with AMS. The SNPs were analyzed with respect to linkage disequilibrium (LD) between intra- and intergenic SNP alleles and alterations in transcriptional factor binding sites (TFBS). Included in the study was the angiotensin-converting enzyme (ACE) (rs4340), the angiotensinogen (AGT) (rs699) and the angotensin II type 1 receptor (AGTR1) (rs5186) SNPs from the renin-angiotension system (RAS) as well as the GNB3 (rs2071057) SNP from G-protein signaling and a LDL apolipoprotein B (APOB) (rs693) SNP. The endothetal Per-Arnt-Sim (PAS) domain protein 1 (EPAS1) SNP and two egl nine homolog 1 (EGLN1) SNPS (rs480902 and rs516651) from the hypoxia-inducible factor (HIF) oxygen signaling pathway were included. SNPs analyzed in the vascular endothelial growth factor (VEGF) signaling pathway are the v-akt murine thymoma viral oncogene homolog 3 (AKT3) (rs4590656 and rs2291409), the endothelial cell nitric oxide synthase 3 (eNOS3) (rs1007311 and rs1799983) and the (VEGFA) (rs79469752, rs13207351, rs28357093, rs1570360 and rs3025039). These SNP alleles alter the TFBS for TF binding. Pair-wise LD was computed between SNPs. An increase in LD occurred in 32 pair-wise comparisons while a decrease was found in 22 pair-wise comparisons between the AMS and controls. Increases and decreases in LD pairs were found within and between signaling pathways and systems indicating the interaction of SNP alleles or potential TFBS from different areas of the genome. The most drastic change in TFBS occurs with ACE (I/D) SNP (rs4340) where the ACE-I allele generates 84 potential TFBS while the ACE-D allele generates only four binding sites. The alteration in TFBS generated by the 17 SNPs is discussed with respect to AMS.

Keywords
Acute mountain sickness; LD; SNPs; TFBS
Introduction
Acute mountain sickness (AMS) is very common among some individuals who ascend to altitudes greater than 2600 m. The illness is characterized by headache, lightheadedness, breathlessness, fatigue, insomnia, anorexia, and nausea [1,2]. The symptoms of the illness begin 2-3 hours after ascent to the higher altitude. The medical condition is generally self-limiting in the individual where most symptoms disappear after two or three days, although insomnia can persist longer [3]. Individuals with the sickness will enter the hospital to be treated under emergency conditions. The illness will resolve itself if no additional altitude is attempted; however, in some cases the descent to a lower altitude is necessary in order to reverse the condition. The precise pathogenesis of AMS is not well understood, but hypoxia is considered to be the major factor [4-7], which raises the question of why some individuals are susceptible to the sickness while others are not, under the same high altitude environment.In an effort to provide in cite into this question, we have previously published on known genetic associations of single nucleotide polymorphisms (SNPs) with high altitude sickness [8-11]. In the present study, we examine the interaction of seventeen SNPs in nine genes [8-11] with regard to AMS and the effect these SNPs have on potential changes in transcriptional factor binding sites (TFBS).
Included in the study is the renin-angiotensin system (RAS) and G protein signaling which are related to1 hypertension [2] as well as polymorphisms affecting blood levels of low-density lipoprotein (LDL) and triglyceride [13,14]. The RAS plays a major role in the regulation of systemic arterial blood pressure and is also involved in the regulation of pulmonary vascular tone. We included three RAS gene polymorphisms, the angiotensin-converting enzyme (ACE) insertion/deletion (rs4340), the angiotensinogen (AGT) M268T (rs699) and the angotensin II type 1 receptor (AGTR1) A1166C (rs5186) variants. The GNB3 gene encodes the Gβ3 subunit of heterotrimeric G proteins (α, β, γ), which are key components of intracellular signal transduction present in all cells of the body [15] and has been associated with hypertension [16]. We included the GNB3, A(-350)G (rs2071057) SNP in the promoter region of the gene, which is in linkage disequilibrium with two other GNB3 SNPs within the gene, C825T (exon 10) and C1429T (exon 11) [17]. Since LDL cholesterol and triglyceride concentrations are strongly influenced by the genetic constitution of each individual and physical activity has a role in determining an individual’s lipid profiles, we included a LDL apolipoprotein B (APOB) (rs693) SNP in the coding region of the gene [8].
Two genes from the hypoxia-inducible factor (HIF) oxygen signaling pathwayare included in the study [10]. The endothetal Per-Arnt- Sim (PAS) domain protein 1 (EPAS1) gene, which encodes hypoxiainducible- factor-2 alpha (HIF2A) a transcription factor that responses to hypoxia conditions. The EPAS1 gene has a SNP [ch2: 46441523 (hg18)] in intron five located five base pairs from the beginning of exon six that has a 78% frequency difference between Tibetan and Han Chinese [18]. The egl nine homolog 1 (EGLN1) gene acts as a key oxygen sensor which negatively regulates the activity of the hypoxia-inducible factor-1 alpha (HIF-1A). Hypoxia causes an inactivation of the EGLN1 gene thereby increasing HIF activity that induces the expression of genes which mediates the adaptive responses through glycolytic enzymes, hemeoxygenase, vascular endothelial growth factor and erythropoietin [19]. The two EGLN1 SNPs (rs480902 and rs516651) included in the study are located in intron 2 of EGLN1 have been associated with high altitude adaptation in human populations [10,19-22].
We also included SNPs from three genes in the vascular endothelial growth factor (VEGF) signaling pathway[9] where the VEGFA protein is a growth factor activator for angiogenesis, vasculogenesis and endothelial cell growth. Four VEGFA SNPs (rs79469752, rs13207351, rs28357093 and rs1570360) are found in the promoter region while a fifth VEGFA SNP (rs3025039) is located in the 3’UTR region. Also in this pathway is the v-akt murine thymoma viral oncogene (homolog 3) (AKT3) gene whose protein is a serine/threonine kinase that plays a key role in regulating cell survival, insulin signaling, angiogenesis and tumor formation. The two AKT3 SNPs (rs4590656 and rs2291409) used in the study are located in introns. A third gene in the pathway is the endothelial cell nitric oxide synthase 3 (eNOS3) which produces nitric oxide (NO) and is implicated in vascular smooth muscle relaxation. NO mediates VEGF-induced angiogenesis in coronary vessels and promotes blood clotting through the activation of platelets. The two eNOS3 SNPs (rs1007311 and rs1799983) used in the study are located in an intron and an exon, respectively.
In genetics, linkage disequilibrium (LD) is defined as the nonrandom association in a given population between the alleles of two or more loci [23]. LD between SNPs in the regulatory region of a gene can be used as a method of identifying associations of certain haplotypes that lead to sickness or disease in a population [9,11].This can be achieved when levels of LD between SNPs within haplotypes are seen to change substantially in a disease or sickness group when compared to the normal baseline population. In such cases, the relationship between LD, SNPs and TFBS can be used to identify potential binding changes for TFs responsible for gene regulation. Such TFBS changes could result in disease or sickness [11,24-29]. In this report, LD is considered to be the non-random association of SNP alleles within and between genes. LD was computed among the 17 SNPs and compared between the AMS and control Han Chinese group. Since these SNPs are located in potential TFBS, any changes in LD between the groups is discussed with relation to the elimination, change or addition of a punitive TFBS created by each SNP.
Materials and Methods
Study Groups
The Han Chinese who are considered upward migrants from low altitudes were used as our study source. All AMS patients in this study had been hospitalized and diagnosed at the Lhasa People Hospital (Tibet, China at 3,670 M above sea level) between 2002 and 2008. AMS was diagnosed by using the current consensus of mountain sickness in Tibet (Diagnosis and Therapeutics for Mountain Sickness, Xizang Autonomous Region), which is in accord with the Lake Louise scoring system [30]. We sampled Han AMS patients from the hospital with symptoms of acute pulmonary edema as diagnosed by a cough accompanied with pink frothy sputum. Moist or bubbling rales in the lungs was suggestive of high altitude pulmonary edema (HAPE), showing a characteristic shadow on chest X-rays. In addition to the characteristic symptoms of severe acute mountain response, acute high altitude cerebral edema (HACE) was diagnosed by ataxia, disturbance of consciousness or coma, abnormal plantar reflexes and papilledema. The AMS Han patients had recently arrived from the low land and acquired the illness within two days after reaching the high altitude of Tibet. Patients with other diseases having similar clinical manifestations were excluded. Healthy Han Chinese from the Lhasa area was randomly selected to serve as control subjects. All patients and controls sampled in the study signed an informed consent approved by the Human Ethics Committee of the Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences.
Sampling
Buccal brush samples were collected from 85 Han Chinese with AMS during the high occurrence period (spring and winter) at the Lhasa People Hospital. Samples for controls were also collected via buccal brush from unaffected Han individuals determined to be in good health upon physical examination by doctors at the Lhasa People Hospital. The controls consisted of 79 Han lowlanders who had travelled to the high altitude of Tibet. The people from the control group had lived at the 3600 M altitude for at least six months prior to being sampled. These sampling procedures have been previously described [8-10,31].
Genotyping
Genomic DNA was extracted from the buccal brushes using the PureGene DNA method from Gentra Systems, Inc., Minneapolis, MN. The DNA yield in this study ranged from 0.5 to 7.6 μg per buccal brush. We found the yield adequate for all PCR reactions conduced in the study. The Vector NTI Advance 11 computer program from Invitrogen, Carlsbad, CA, was used to develop the primers for genotyping each SNP. The genotyping methods and genotyping for each SNP have been previously outlined and reported [8-10,31]. The SNPs and genes used in the present study are found in (Table 1).
Statistical Analysis
All statistical methods used to genotype samples have been previously discussed [8-10,31]. Linkage disequilibrium (LD) [32] was computed between SNPs for the AMS and Han Chinese control groups (Figure1). The degree of genetic linkage between the 17 SNPs in each study group was estimated as Lewontin’s coefficient |D’|, where no color (|D’|=0) indicates that LD is weak or non-existent and the dark red (|D’|=1) indicates that there exists strong pairwise linkage disequilibrium between SNPs (Figure 1). The change in LD between control and AMS groups among all SNPs is tabulated in (Table 2).
Transcriptional factor binding sites
The JASPAR CORE database [33,34] and ConSite [35] were used to identify the TFBS in this study. JASPAR is a collection of transcription factor DNA-binding preferencesused for scanning genomic sequences where ConSite is a web-based tool for finding cis-regulatory elements in genomic sequences. The TFBS and SNP location within the binding site are listed in (Table 3). TFBS that are in bold lettering are unique to the given allele while those with normal lettering occur with both SNP alleles. The minor allele frequencies (MAF) for the 17 SNPs were compiled from our previous studies [8-10,32] (Table 3).
Results
SNPs and punitive TFBS in the genes from renin-angiotension system (ACE, AGT, AGTR1), G protein signaling (GNB3) and LDL apolipoprotein B (APOB) were evaluated as well as the HIF oxygen signaling pathway (EPAS1&EGLN1) and the VEGF signaling pathway (AKT3, eNOS3&VEGFA) genes (Tables 1,3), (Figures 1,2). In pairwise LD estimates the ACE withAGTR1, EGLN1 andGNB3 SNP pairs exhibit an increase in LD for the AMS study group compared to the control group and a decrease in ACE with EPAS1, eNOS-17, VEGFA-79, VEGFA-28 and VEGFA-30 pairs (Figure 1 and Table 2). In pair-wise LD estimates the AGTR1 with AKT3-45, AKT3-22, EGLN1-48, EGLN1-51, GNB3, eNOS3-10, VEGFA-13 and VEGFA-15 pairs exhibit an increase in LD for the AMS study group compared to the control group and a decrease in AGTR1 with only APOB (Figure 1 and Table 2). Similar results can be seen for the other pair-wise LD estimates for all the SNPs (Figure 1 and Table 2) including the association of SNPs between these signaling pathways and systems (Figure 2). The relationship between LD and SNPs can result from changes in alleles within punitive TFBS caused by the SNPs (Table 3) which sometimes results in different TFBS for each SNP allele. For example, the SNP (rs480902) EGLN1-T allele generates two unique (FOXL1 and NFE2L1::MafG) TFBS (Supplement) while the remaining potential TFBS are generated with both the EGLN1 T and C alleles (Table 3). These changes may result in unfavorable allele or punitive TFBS combinations within haplotypes or between genes which in turn may result in disease or sickness [36]. Taken together, the changes in LD observed between these pair-wise SNP studies indicates the strong interaction of some of these SNPs with each other as a result of diversity of punitive TFBS generated by the SNP alleles (Table 3).
The ACE-I/D is probably the most extreme example of changes in potential TFBS and disease (Table 3). The ACE-D allele generates four TFBS of which only the MYB site is unique while the ACE-I allele generates 84 punitive TFBS with many unique TFBS including one for hypoxia TF binding (HIF1A::ARNT) and others for steroid hormone binding (PPAR, RAR, ROR and THR) (Table 3, Supplement). The increase in pair-wise LD between the ACE and AGTR1 SNPs for the AMS group compared to the control group (Figure 1, Table 2) could result from the generation of the potential GATA3 TFBS created by the minor AGTR1-C allele since GATA3 does not occur with either of the ACE I/D SNP alleles (Table 3). The increase in pair-wise LD between ACE and EGLN1-51 SNPs for the AMS group compared to the normal control group could result from the potential HNF4A, HNF4G, NR2C2, NG2F1 and TAL1:GATA1 TFBS created by the common EGLN1-51 allele since these TFBS are not found to be associated with either of the ACE I/D SNP alleles. The increase in pair-wise LD between ACE and GNB3 SNPs for the AMS group compared to the normal control group could result from the potential INSM1, NFYB and RREB1 TFBS created by the minor GNB3 allele since these TFBS are not found to be associated with the ACE- I/D SNP alleles. Since the AGTR1 SNP generates only two unique potential (HOXA5 and GATA3) TFBS between the common and minor alleles, respectively (Table 3), could be the reason that the AGTR1 SNP exhibits an increase in pair-wise LD with nine other SNPs (AKT3-45, AKT3-22, EGLN1-48, EGLN- 51, GNB3, eNOS3-10, VEGFA-13, VEGFA-15, and VEGFA-30) whose combined alleles generate many more potential TFBS (Table 3). The SNPs of the AKT3, eNOS3 and VEGFA genes in the VEGF signally pathway (a growth factor activator for angiogenesis) and the SNPs of the EGLN1 and EPAS1 genes in the HIF oxygen signaling pathway exhibit an increase in pair-wise LD for the AMS group compared to the control Han Chinese groups (Figure 1, Table 2) both within and between pathways (Figure 2). For the twelve SNPs in these two pathways there are unique potential TFBS generated between alleles in each SNP and also unique potential TFBS between all twelve SNPs (Table 3). Beside the SNP (rs4340) ACE-I allele, there are only two other SNPs (rs4590656 and rs3025039) AKT3-Cand VEGFA-C alleles, respectively, that generate a potential unique hypoxia TF binding (HIF1A::ARNT) sites (Table 3).
A decrease in LD between SNPs for the AMS group compared to the Han Chinese normal group could indicate that some genes are responding to different attributes of the AMS sickness. As an example, the VEGFA SNPs (rs79469752, rs13207351, rs28357093 and rs1570360) are tightly linked within about 50bp of each other in the promoter. Consequently, they should be acting as a single linkage group as indicated in Figure 1A with the normal control group; however, in the AMS group the VEGFA SNPs (rs13207351 and rs1570360) are acting as though they are totally unlinked as indicated in Figure 2B. We have previously shown that these two VEGFA SNPs are in strong LD [9]. From an examination of Table 3, it can be seen that these two SNPs generate potential TFBS for TFs that are involved in the machinery of gene regulation (i.e. HINFP, NFIC, KLF4, KLF5, NRF1, PAX5, SP2, SPIB and ZNF354C) with no overlap in potential TFBS between the two SNPs. The decrease in LD could result from unfavorable TFBS combinations created by the alternate alleles of the VEGFA SNPs (rs132077351 and rs1570360) resulting in a break down in the gene regulation machinery function. An increase in LD was found in 32 pairwise comparisons while a decrease was found 22 pair-wise comparisons between the AMS and control groups (Figure 1, Table 2).
Discussion
GWAS over the last decade have identified nearly 6,500 disease or trait-predisposing SNPs where only 7% of these are located in proteincoding regions of the genome [36,37] and the remaining 93% are located within non-coding areas [38,39] such as regulatory or intergenic regions. SNPs which occur in the putative regulatory region of a gene where a single base change in the DNA sequence of a potential TFBS may affect the process of gene expression are drawing more attention [40-42]. A SNP in a TFBS can have multiple consequences. Often the SNP does not change the TFBS interaction nor does it alter gene expression since a transcriptional factor (TF) will usually recognize a number of different binding sites in the gene. In some cases the SNP may increase or decrease the TF binding which results in allele-specific gene expression. In other cases, a SNP may eliminate the natural binding site or generate a new binding site. In which cases the gene is no longer regulated by the original TF. Therefore, functional regulatory(r) SNPs in TFBS may result in differences in gene expression, phenotypes and susceptibility to environmental exposure [42]. Examples of rSNPs associated with disease susceptibility are numerous and several reviews have been published [42-45]. rSNPs which occur in the non-coding regions of these genes have been found to be associated with human diseases or sicknesses. These non-coding regions host the binding sites for the transcription factors that regulate gene expression [36].
We have previously reported on SNPs, potential TFBS and high altitude sickness (HAS) [31]. In this report we include the association of SNPs and LD with potential TFBS inAMS. AMS occurs in up to 50% of individuals ascending to high altitude[46] and may progress to life-threatening pulmonary and cerebral edema in a minority of cases [47]. One of the most well studied gene polymorphisms in association with HAS is the angiotensin-converting enzyme (ACE) insertion/ deletion (rs4340) from the RAS system [48-50]. The ACE-I allele has been associated with superior performance benefit for mountaineers ascending to extreme altitude compared to the ACE-D allele [51,52], while the ACE -I/I genotype has been found to maintain higher arterial oxygen saturation at rest and during exercise at high altitude [53]. Perhaps the genetic reason for these findings is that the 288bp ACE-I allele generates at least 84 potential TFBS compared to the ACE-D allele which generates four TFBS (Table 3). Of these 84 punitive TFBS for the ACE-I allele, there is a hypoxia TF binding (HIF1A::ARNT) site and other TFBS for steroid hormone binding proteins (PPAR, RAR, ROR and THR). From a scan of the entire ACE gene with the VNTI program we find that the HIF1A::ARNT binding site occurs only once in the ACE-I allele and the ROR site occurs one other time in intron 14, while the PPAR, RAR and THR binding sites occur multiple times. The HIF1A::ARNT binding site would be beneficial to enhanced arterial oxygen saturation of red blood cells and superior performance in exercise at high altitudes. From our previous study, we found that there is a much higher incidence of ACE-D allele (0.42) in AMS patients than the normal Han Chinese control roup (0.29) [8] (Table 3) suggesting that the ACE-I allele does better in high altitude environments than the ACE-D allele.Also in the RAS system, the angiotensinogen (AGT) M268T polymorphism (rs699) has been reported to be significantly associated with HAPE in a Chinese population [54] and more recently with an Indian population [55]. The AGT-T allele (M268) generates two unique potential TFBS (EBF1 and MZF1_5-13) while the AGT-C allele (268T) generates five unique potential TFBS (E2F1, E2F4, E2F6, INSM1 and NFE2L1::MafG) (Table 3). Only the dimer MFE2L1::MafG TFs are involved with the activation of α and β globin and erythrocyte development (Supplement) which would be important in high altitude (HA) environments and consequently should benefit people carrying the AGT-C allele (268T). Another gene in the RAS system is the AGTR1 whose polymorphism A1166C (rs5186) has been associated with many human diseases [56-59]. The AGTR1-A allele generates one unique potential TFBS for the HOXA5 TF while the AGTR1-C allele generates a different unique potential TBFS for the GATA3 TF (Table 3). HOXA5 is part of a developmental regulatory system and GATA3 plays an important role in endothelial cell biology (Supplement).
Although the APOB polymorphism (rs693) has not been linked to HAS, it been found to be associated with other human diseases such as dyslipidemia and higher LDL levels [60] and has been shown to influence plasma levels [61, 62]. The APOB-C allele generates three unique potential TFBS for the HLTF, STAT4 and STAT5a::STAT5b TFs while the APOB-T allele generates one unique potential TFBS for the PAX2 TF (Supplement). For the GNB3 polymorphism (rs2071057), we have reported a significantly high incidence of the G-protein (GNB3) (-350)A allele in the AMS patients [8]. The GNB3-G allele generates no unique potential TFBS but the GNB3-A allele generates eight unique TFBS for the E2F1, E2F6, INSM1, MZF1_5-13, NFIC, NFYB, RREB1 and THAP1 TFs. The RREB1 TF is involved with repressing the angiotensinogen gene which regulates blood pressure and fluid balance (Supplement). In the study, we found no GNB3-A alleles in the control Han Chinese study group but did find a 5% occurrence of this allele in the AMS Han Chinese group [8].
The EPAS1 gene from the HIF oxygen signaling pathway has a SNP whose EPAS1-C allele generates one unique potential TFBS for the RUNX1 TF while the EPAS1-T allele generates three unique potential TFBS for the ELK1, NRF1 and TP53 TFs. The NRF1 TF actives nuclear genes required for respiration, heme biosynthesis, and mitochondrial DNA transcription and replication while the TP53 TF is involved with response to cellular stress (such as DNA damage, hypoxia, spindle damage). Both of which would affect individuals in HA environments. The EGLN1 gene is also part of the HIF oxygen signaling pathway. The EGLN1-T allele of the rs480902 polymorphism generates two unique potential TFBS for the FOXL1 and NFE2L1: MafG TFs while the EGLN1-C allele does not generate any unique TFBS; however, the two rs480902 SNP alleles generate seven common TFBS (Table 3). The FOXL1 TF is a target gene of the hedgehog signaling pathway (Supplement) which is a key regulatory of vertebrate organogenesis and the TF is involved with proper proliferation and differentiation in the gastrointestinal epithelium. The dimer MFE2L1::MafG TFs is involved with the activation of α and β globin and erythrocyte development (Supplement) which would be important in HA environments and consequently should benefit people carrying the EGLN1-T allele.The EGLN1-C allele of the rs516651 polymorphism generates seven unique potential TFBS for the ESRRA, HNF4A, NR2C2, NR2F1, NR4A2, RORA_1 and TAL1::GATA1 TFs while the EGLN1-T allele does not generate any unique TFBS; however, the two rs516651 SNP alleles generate five common TFBS (Table 3). Of the seven unique TFBS for the EGLN1-C allele, the ESRRA, NR2C2, NR2F1 and RORA_1 TFs are involved with steroid hormone activity including estrogen receptor (ER), PPAR and THR at this binding site (Supplement). The interaction between EGLN1 and PPAR has been well documented in HA environments [21,63-65].
The AKT3 gene from the VEGF signaling pathway has two SNPs (rs4590656 and rs2291409) whose alleles generate unique punitive TFBS. The AKT3-C allele from the rs4590656 polymorphism generates two unique potential TBFS for the FOXD1 and HIF1A::ARNT TFs while the AKT3-T allele generates six unique potential TFBS for the HNF4G, HOXA5, RUNX2, SOX10, SPIB and TCF7L2 TFs.The FOXD1 TF is involved with positional identity in the developing retina while the dimer HIF1A::ARNT TF plays an essential role in cellular and systemic responses to hypoxia (Supplement). The HNF4G TF is involved in steroid hormone receptor activity and sequence-specific DNA binding transcription factor activity while the TCF7L2 TF has been implicated in blood glucose homeostasis. The AKT3-G allele from the rs2291409 polymorphism generates one unique potential TFBS for the RORA_2 TF which regulates a number of genes involved with lipid metabolism, in cerebellum and photoreceptor development and skeletal muscle development. The AKT3-A allele from this polymorphism generates two unique potential TFBS for the FOXC1 and NR1A2 TFs. While the FOXC1 TF is an important regulator of cell viability and resistance to oxidative stress in the eye, the NR4A2 TF is a regulatory for differentiation and maintenance of meso-diencephalic dopaminergic neurons during development (Supplement).
The eNOS3 gene also from the VEGF signaling pathway has two SNPs (rs1007311 and rs1799983) whose alleles generate unique TFBS. The eNOS3-A allele from the rs1007311 polymorphism generates two unique potential TFBS for the BRCA1 and MZF1_1-4 TFs while the eNOS3-G allele generates three unique potential TFBS for the EBF1, MZF1_5-13 and NFE2L1::MafG TFs. The two alleles of the rs1007311 SNP also generate five common potential TFBS for the INSM1, RFX5, RUNX1, TFAP2A and TFAP2C TFs (Table 3). The BRCA1 TF plays a role in maintaining genomic stability while the MZF1_1-4 TF functions as a transcription regulator (Supplement). The EBF1 TF is involved with transcription machinery while the MZF1_5-13 TF is a regulator of transcriptional events during hemopoietic development and the NFE2L1::MafG TF coordinates the up-regulation of cytoprotective genes. The eNOS3-G allele from the rs1799983 polymorphism generates five unique potential TFBS for the FOXL2, GATA3, RUNX1, THAP1 and ZEB1 TFs while the eNOS3-T allele generates seven unique potential TFBS for the MZF1_1-4, MZF1_5-13, TAL1::TCF3, USF1 & 2, ZNF263 and ZNF354C TFs (Table 3). The two alleles of the rs1799983 polymorphism generate four common potential TFBS for the EBF1, INSM1, NFE2L1::MafG and TAL1::GATA1 TFs. Perhaps the TF with the most effect on AMS would be RUNX1 which is involved in the development of normal hematopoiesis. The potential TFBS (TTCTGGGGGCT) for RUNX1 is generated by the rs1799983 eNOS3-G allele whose frequency is 0.772 in the AMS group compared to 0.898 in the normal Han control group (Table 3).Since the RUNX1 binding site (TTCTGGGGCTG) commonly occurs among Han Chinese with the rs1007311 polymorphism, might explain why an increase in LD is seen between the two eNOS3 SNPs (Figure 1, Table 2). The potential RUNX1 TFBS (TTCTGGGGCTG) at the rs1007311 polymorphism is the only motifthat occurs in the gene from a scan of eNOS3 with the Vector NTI Advance 11 computer program.
The five VEGFA SNPs included in this study are rs79469742, rs13207351, rs28357093, rs1570360 and rs3025039 whose allele frequencies and unique potential TFBS for each SNP are found in Table 3. The VEGFA-C allele from the rs79469742 polymorphism generates three unique potential TFBS for the E2F1, NFIC and TFAP2C TFs while the VEGFA-T allele generates six unique potential TFBS for the ESR2, NFE2L1::MafG, NR2C2, NR2F1, RUNX1 and THAP1 TFs. The two alleles of this SNP generate one common potential TFBS for the PAX5 TF. Perhaps the most interesting unique potential TFBS generated by the VEGFA-T allele are for the ESR2 and NR2C2 TFs which involve the estrogen nuclear receptor and regulation of the nuclear receptor signaling pathways for steroid hormone. Another unique potential TFBS created by this allele is for the RUNX1 TF which is involved in the development of normal hematopoiesis (Table 3).The VEGFA-G allele from the rs13207351 polymorphism generates one unique potential TFBS for the NFIC TF while the VEGFA-A allele generates two unique potential TFBS for the HINFP and PAX5 TFs. These TFs are involved with transcription machinery (Table 3, Supplement). The two alleles of this SNP generate one common potential TFBS for the NRF1 TF which is involved with respiration, heme biosynthesis, and mitochondrial DNA transcription and replication. The VEGFA-A allele of the rs28357093 polymorphism creates two unique potential TFBS for the EBF1 and E2F3 TSs while the VEGFA-C allele generates two unique potential TFBS for the RFX5 and THAP1 TFs. These TFs are also involved with transcription machinery (Table 3, Supplement). The two alleles of this SNP also generate one common potential TFBS for the NRF1 TF those function is mentioned above.
The VEGFA-G allele of the rs1570360 polymorphism generates four unique potential TFBS for the EGR1, KLF4, MZF1_5-13 and SP2 TFs while the VEGFA-A allele generates six unique potential TFBS for the EGR2, EHF, FOXH1, MAFK, SPIBand THAP1TFs. The two alleles of this SNP also generate five common potential TFBS for the KLF5, SP1, SREBF1, TFAP2C and ZNF354C TFs. The potential TFBS created by this SNP are all involved with transcriptional regulation (Table 3, Supplement).The VEGFA-C allele of the rs3025039 polymorphism generates four unique potential TFBS for the BRCA1, ESR2, HIF1A::ARNT and NFE2L1::MafG TFs while the VEGFA-T allele generates three unique potential TFBS for the NFE2::MAF, RFX5 and YY1 TFs. The two alleles of this SNP also generate two common potential TFBS for the BATF::JUN and E2F6 TFs. Of all these potential TFBS, perhaps the TF with the most effect on AMS would be HIF1A::ARNT which plays an essential role in cellular and systemic responses to hypoxia which is generated by the VEGFA-C allele whose frequency is 0.845 in the AMS group compared to 0.875 in the normal Han control group (Table 3).
In conclusion, since nearly all of the SNPs used in this study have previously been found to be associated with AMS, it may not be only one SNP that alters the TFBS for a TF to bind that causes the sickness but more likely a combination of SNP changes in TFBS that lead to the illness. Perhaps, the ACE (I/D) rs4340 SNP would be the largest contributor for AMS because the ACE-I allele creates 84 punitive TFBS compared to the ACE-D allele that creates only four TFBS. The interaction of all SNPs from different areas of the genome (Table 1) as examined by LD analysis (Figure 1, Table 2) indicates that certain TFBS associations throughout the genomeare involved in AMS (Table 2). SNPs that alter the TFBS are not only found in the promoter regions but in the introns, exons and the UTRs of a gene (Table 3). The nucleus of the cell is where epigenetic alterations and TFs operate to convert chromosomes into single stranded DNA for mRNA transcription while it is the cytoplasm where mRNA is processed by separating exons and introns for protein translation. Consequently, it doesn’t matter where TFs bind the DNA in the nucleus because it is only there that TFs function. The SNPs outlined in this report should be considered as rSNPs since they change the DNA landscape for TF binding and have been associated with AMS.
Dedication
This manuscript is dedicated to the work and memory of Xue-Han Ning.
Appendix 1. The ACE-I allele creates 84 potential TFBS.
image
Supplement. Transcriptional factors (TF), protein name and their description or function.
image
image
image
image
image
image
References
  1. Hackett PH, Roach RC (2001) High-altitude illness. N Engl J Med 345: 107-114.

  2. Bartsch P, Bailey DM, Berger MM, Knauth M, Baumgartner RW (2004) Acute mountain sickness: controversies and advances. High Alt Med Biol 5: 110-124.

  3. Ning XH, Li SP (2006) Plateau Tin Road Health Line (Universal Self-Care Reader). Shanghai Science and Technology Publishing House, Shanghai, China,66-8.

  4. West JB; American College of Physicians; American Physiological Society (2004) The physiologic basis of high-altitude diseases. Ann Intern Med 141: 789-800.

  5. Schoene RB (2008) Illnesses at high altitude. Chest 134: 402-416.

  6. Strohl KP (2008) Lessons in hypoxic adaptation from high-altitude populations. Sleep Breath 12: 115-121.

  7. Wilson MH, Newman S, Imray CH (2009) The cerebral effects of ascent to high altitudes. Lancet Neurol 8: 175-191.

  8. Buroker NE, Ning XH, Zhou ZN, Li K, Cen WJ, et al. (2010) Genetic associations with mountain sickness in Han and Tibetan residents at the Qinghai-Tibetan Plateau. Clin Chim Acta 411: 1466-1473.

  9. Buroker NE, Ning XH, Zhou ZN, Li K, Cen WJ, et al. (2012) AKT, ANGPTL, eNOS, and VEGFA associations with high altitude sickness in Han and Tibetan Chinese at the Qinghai-Tibetan Plateau. Int J Hematol 96: 200-213.

  10. Buroker NE, Ning XH, Zhou ZN, Li K, Cen WJ, et al. (2012) EPAS1 and EGLN1 associations with high altitude sickness in Han and Tibetan Chinese at the Qinghai-Tibetan Plateau. Blood cells molecules & diseases 49: 67-73.

  11. Buroker NE, Ning XH, Zhou ZN, Li K, Cen WJ, et al. (2013) VEGFA SNPs and transcriptional factor binding sites associated with high altitude sickness in Han and Tibetan Chinese at the Qinghai-Tibetan Plateau. J Physiol Sci 63: 183-93.

  12. Naber CK, Siffert W (2004) Genetics of human arterial hypertension. Minerva Med 95: 347-356.

  13. Kathiresan S, Melander O, Guiducci C, Surti A, Burtt NP, et al. (2008) Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet 40: 189-197.

  14. Willer CJ, Sanna S, Jackson AU, Scuteri A, Bonnycastle LL, et al. (2008) Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat Genet 40: 161-9.

  15. Hamm HE (1998) The many faces of G protein signaling. J Biol Chem 273: 669-672.

  16. Siffert W, Rosskopf D, Siffert G, Busch S, Moritz A, et al. (1998) Association of a human G-protein beta3 subunit variant with hypertension. Nat Genet 18: 45-48.

  17. Rosskopf D, Manthey I, Siffert W (2002) Identification and ethnic distribution of major haplotypes in the gene GNB3 encoding the G-protein beta3 subunit. Pharmacogenetics 12: 209-220.

  18. Yi X, Liang Y, Huerta-Sanchez E, Jin X, Cuo ZX, et al. (2010) Sequencing of 50 human exomes reveals adaptation to high altitude. Science 329: 75-8.

  19. Aggarwal S, Negi S, Jha P, Singh PK, Stobdan T, et al. (2010) EGLN1 involvement in high-altitude adaptation revealed through genetic analysis of extreme constitution types defined in Ayurveda. Proceedings of the National Academy of Sciences of the United States of America 107: 18961-6.

  20. Bigham A, Bauchet M, Pinto D, Mao X, Akey JM, et al. (2010)  Identifying signatures of natural selection in Tibetan and Andean populations using dense genome scan data. PLoS genetics 6.

  21. Simonson TS, Yang Y, Huff CD, Yun H, Qin G, et al. (2010) Genetic evidence for high-altitude adaptation in Tibet. Science 329: 72-75.

  22. Xu S, Li S, Yang Y, Tan J, Lou H, et al. (2011) A genome-wide search for signals of high-altitude adaptation in Tibetans. Mol Biol Evol 28: 1003-1011.

  23. Lewontin RC, Kojima K (1960) The Evolutionary Dynamics of Complex Polymorphisms. Evolution 14: 458-72.

  24. Buroker NE, Ning XH, Zhou ZN, Li K, Cen WJ, et al.(2013) VEGFA SNPs and transcriptional factor binding sites associated with high altitude sickness in Han and Tibetan Chinese at the Qinghai-Tibetan Plateau. Journal of Physiological Sciences 63: 183-93.

  25. Buroker NE, Ning XH, Zhou ZN, Li K, Cen WJ, et al. (2012) AKT3, ANGPTL4, eNOS3, and VEGFA associations with high altitude sickness in Han and Tibetan Chinese at the Qinghai-Tibetan Plateau. International journal of hematology 96:200-13

  26. Buroker NE (2014) VEGFA rSNPs, transcriptional factor binding sites and human disease. J Physiol Sci 64: 73-76.

  27. Buroker NE (2013) AKT3 rSNPs, Transcritional Factor Binding Sites and Human Disease. Open Journal of Blood Diseases 3: 116-29.

  28. Buroker NE (2013) ATF3 rSNPs, transcriptional factor binding sites and human etiology. Open Journal of Genetics 3: 253-61.

  29. Buroker NE (2013) ADRBK1 (GRK2) rSNPs, Transcriptional Factor Binding Sites and Cardiovascular Disease in the Black Population. Journal of Cardiovascular Disease 2.

  30. Buroker NE (2014) TBXA2R rSNPs, Transcriptional Factor Binding Sites and Asthma in Asians. Open Journal of Pediatrics 4: 148-61.

  31. Buroker NE (2014) ADRBD1 (GRK2), TBXA2R and VEGFA rSNPs in KLF4 and SP1 TFBS Exhibit Linkage Disequilibrium. Open Journal of Genetics 4.

  32. Buroker NE, Ning XH, Zhou ZN, Li K, Cen WJ, et al. (2013) VEGFA SNPs and transcriptional factor binding sites associated with high altitude sickness in Han and Tibetan Chinese at the Qinghai-Tibetan Plateau. J Physiol Sci 63: 183-193.

  33. Hackett Phao (1992) The diagnosis accord with the Lake Louise scoring system. In: JR Sutton GC, and CS Houston (eds) Hypoxia and Mountainsickness Pergamon Press, New York, 327-30.

  34. Buroker NE, Ning XH, Zhou ZN, Li K, Cen WJ, et al. (2013) VEGFA SNPs and transcriptional factor binding sites associated with high altitude sickness in Han and Tibetan Chinese at the Qinghai-Tibetan Plateau. J Physiol Sci63:183-93

  35. Ding K, Zhou K, He F, Shen Y (2003) LDA--a java-based linkage disequilibrium analyzer. Bioinformatics 19: 2147-2148.

  36. Bryne JC, Valen E, Tang MH, Marstrand T, Winther O, et al. (2008) JASPAR, the open access database of transcription factor-binding profiles: new content and tools in the 2008 update. Nucleic Acids Res 36: D102-106.

  37. Sandelin A, Alkema W, Engström P, Wasserman WW, Lenhard B (2004) JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res 32: D91-94.

  38. Sandelin A, Wasserman WW, Lenhard B (2004) ConSite: web-based prediction of regulatory elements using cross-species comparison. Nucleic Acids Res 32: W249-252.

  39. Buroker NE (2014) Regulatory SNPs and transcriptional factor binding sites in ADRBK1, AKT3, ATF3, DIO2, TBXA2R and VEGFA. Transcription 5: e964559.

  40. Pennisi E (2011) The Biology of Genomes. Disease risk links to gene regulation.  Science 332: 1031.

  41. Kumar V, Wijmenga C, Withoff S (2012) From genome-wide association studies to disease mechanisms: celiac disease as a model for autoimmune diseases.  Semin Immunopathol 34: 567-580.

  42. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, et al. (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106: 9362-9367.

  43. Kumar V, Westra HJ, Karjalainen J, Zhernakova DV, Esko T, et al. (2013) Human disease-associated genetic variation impacts large intergenic non-coding RNA expression. PLoS Genet 9: e1003201.

  44. Knight JC (2003) Functional implications of genetic variation in non-coding DNA for disease susceptibility and gene regulation. Clin Sci (Lond) 104: 493-501.

  45. Wang X, Tomso DJ, Liu X, Bell DA (2005) Single nucleotide polymorphism in transcriptional regulatory regions and expression of environmentally responsive genes. Toxicol Appl Pharmacol 207: 84-90.

  46. Chorley BN, Wang X, Campbell MR, Pittman GS, Noureddine MA, et al. (2008) Discovery and verification of functional single nucleotide polymorphisms in regulatory genomic regions: current and developing technologies. Mutat Res 659: 147-57.

  47. Prokunina L, Alarcón-Riquelme ME (2004) Regulatory SNPs in complex diseases: their identification and functional validation. Expert Rev Mol Med 6: 1-15.

  48. Buckland PR (2006) The importance and identification of regulatory polymorphisms and their mechanisms of action. Biochim Biophys Acta 1762: 17-28.

  49. Sadee W, Wang D, Papp AC, Pinsonneault JK, Smith RM, et al. (2011) Pharmacogenomics of the RNA world: structural RNA polymorphisms in drug therapy. Clin Pharmacol Ther 89: 355-365.

  50. Maggiorini M, Bühler B, Walter M, Oelz O (1990) Prevalence of acute mountain sickness in the Swiss Alps.  BMJ 301: 853-855.

  51. Roach RC, Hackett PH (2001) Frontiers of hypoxia research: acute mountain sickness. J Exp Biol 204: 3161-3170.

  52. Costerousse O, Allegrini J, Lopez M, Alhenc-Gelas F (1993) Angiotensin I-converting enzyme in human circulating mononuclear cells: genetic polymorphism of expression in T-lymphocytes. Biochem J 290: 33-40.

  53. Danser AH, Schalekamp MA, Bax WA, van den Brink AM, Saxena PR, et al. (1995) Angiotensin-converting enzyme in the human heart. Effect of the deletion/insertion polymorphism. Circulation 92: 1387-1388.

  54. Zhu X, Bouzekri N, Southam L, Cooper RS, Adeyemo A, et al. (2001) Linkage and association analysis of angiotensin I-converting enzyme (ACE)-gene polymorphisms with ACE concentration and blood pressure.  Am J Hum Genet 68: 1139-1148.

  55. Montgomery HE, Marshall R, Hemingway H, Myerson S, Clarkson P, et al. (1998) Human gene for physical performance. Nature 393: 221-222.

  56. Tsianos G, Eleftheriou KI, Hawe E, Woolrich L, Watt M, et al. (2005) Performance at altitude and angiotensin I-converting enzyme genotype. Eur J Appl Physiol 93: 630-633.

  57. Woods DR, Pollard AJ, Collier DJ, Jamshidi Y, Vassiliou V, et al. (2002) Insertion/deletion polymorphism of the angiotensin I-converting enzyme gene and arterial oxygen saturation at high altitude. Am J Respir Crit Care Med 166: 362-366.

  58. Qi Y, Niu W, Zhu T, Zhou W, Qiu C (2008) Synergistic effect of the genetic plymorphisms of the renin-angiotensin-aldosterone system on high-altitude pulmonary edema: a study from Qinghai-Tibet altitude.  Eur J Epidemiol 23: 143-152.

  59. Stobdan T, Ali Z, Khan AP, Nejatizadeh A, Ram R, et al. (2011) Polymorphisms of renin--angiotensin system genes as a risk factor for high-altitude pulmonary oedema. J Renin Angiotensin Aldosterone Syst 12: 93-101.

  60. Liu Y, Zhuoma C, Shan G, Cui C, Hou S, et al. (2002) A1166C polymorphism of the angiotensin II type 1 receptor gene and essential hypertension in Han, Tibetan and Yi populations. Hypertens Res 25: 515-21.

  61. Jones A, Dhamrait SS, Payne JR, Hawe E, Li P, et al. (2003) Genetic variants of angiotensin II receptors and cardiovascular risk in hypertension. Hypertension 42: 500-506.

  62. Yan C, Zhan J, Feng W (2005) Gene polymorphisms of angiotensin II type 1 receptor and angiotensin-converting enzyme in two ethnic groups living in Zhejiang Province, China. J Renin Angiotensin Aldosterone Syst 6: 132-137.

  63. Wu CK, Tsai CT, Chang YC, Luo JL, Wang YC, et al. (2009) Genetic polymorphisms of the angiotensin II type 1 receptor gene and diastolic heart failure. Journal of hypertension 27: 502-7.

  64. Rodrigues AC, Sobrino B, Genvigir FD, Willrich MA, Arazi SS, et al. (2013 ) Genetic variants in genes related to lipid metabolism and atherosclerosis, dyslipidemia and atorvastatin response. Clin Chim Acta 417: 8-11.

  65. Benn M, Stene MC, Nordestgaard BG, Jensen GB, Steffensen R, et al. (2008) Common and rare alleles in apolipoprotein B contribute to plasma levels of low-density lipoprotein cholesterol in the general population. J Clin Endocrinol Metab 93: 1038-1045.

  66. Zhu H, Tucker HM, Grear KE, Simpson JF, Manning AK, et al. (2007) A common polymorphism decreases low-density lipoprotein receptor exon 12 splicing efficiency and associates with increased cholesterol. Hum Mol Genet 16: 1765-1772.

  67. Storz JF (2010) Evolution. Genes for high altitudes. Science 329: 40-41.

  68. Simonson TS, McClain DA, Jorde LB, Prchal JT (2012) Genetic determinants of Tibetan high-altitude adaptation. Hum Genet 131: 527-533.

  69. Ge RL, Simonson TS, Cooksey RC, Tanna U, Qin G, et al. (2012) Metabolic insight into mechanisms of high-altitude adaptation in Tibetans. Mol Genet Metab 106: 244-247.

Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Relevant Topics

Recommended Conferences

Article Usage

  • Total views: 11739
  • [From(publication date):
    September-2015 - Aug 20, 2017]
  • Breakdown by view type
  • HTML page views : 7930
  • PDF downloads :3809
 

Post your comment

captcha   Reload  Can't read the image? click here to refresh

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords