EGLN1 involvement in high-altitude adaptation revealed through genetic analysis of extreme constitution types defined in Ayurveda

Shilpi Aggarwal, Sapna Negi, Pankaj Jha, Prashant K. Singh, Tsering Stobdan, M. A. Qadar Pasha, Saurabh Ghosh, Anurag Agrawal, Indian Genome Variation Consortium, Bhavana Prasher, and Mitali Mukerji
PNAS November 2, 2010 107 (44) 18961-18966; https://doi.org/10.1073/pnas.1006108107

  1. Edited* by Charles R. Cantor, Sequenom, San Diego, CA, and approved September 20, 2010 (received for review May 6, 2010)

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Abstract

It is being realized that identification of subgroups within normal controls corresponding to contrasting disease susceptibility is likely to lead to more effective predictive marker discovery. We have previously used the Ayurvedic concept of Prakriti, which relates to phenotypic differences in normal individuals, including response to external environment as well as susceptibility to diseases, to explore molecular differences between three contrasting Prakriti types: Vata, Pitta, and Kapha. EGLN1 was one among 251 differentially expressed genes between the Prakriti types. In the present study, we report a link between high-altitude adaptation and common variations rs479200 (C/T) and rs480902 (T/C) in the EGLN1 gene. Furthermore, the TT genotype of rs479200, which was more frequent in Kapha types and correlated with higher expression of EGLN1, was associated with patients suffering from high-altitude pulmonary edema, whereas it was present at a significantly lower frequency in Pitta and nearly absent in natives of high altitude. Analysis of Human Genome Diversity Panel-Centre d’Etude du Polymorphisme Humain (HGDP-CEPH) and Indian Genome Variation Consortium panels showed that disparate genetic lineages at high altitudes share the same ancestral allele (T) of rs480902 that is overrepresented in Pitta and positively correlated with altitude globally (P < 0.001), including in India. Thus, EGLN1 polymorphisms are associated with high-altitude adaptation, and a genotype rare in highlanders but overrepresented in a subgroup of normal lowlanders discernable by Ayurveda may confer increased risk for high-altitude pulmonary edema.

Ayurveda, an ancient system of Indian medicine documented and practiced since 1500 B.C., deals with interindividual variability for personalized and predictive medicine (1). This system of medicine phenotypically classifies individuals into seven broad constitution types termed Prakriti, among which Vata (V), Pitta (P), and Kapha (K), the most contrasting constitutions, exhibit readily recognizable phenotypes, respond differently to diet, drugs, and external environment as well as vary in predisposition to specific diseases (SI Materials and Methods). We have earlier shown differences between the three most contrasting Prakriti types of Indo-European origin in biochemical profiles and genome-wide expression and observed significant overrepresentation of hub and housekeeping genes within the differentially expressed genes (2).

We postulate that the genetic variations that underlie differential expression correlating with Prakriti phenotypes could provide leads for understanding adaptation to external environment and susceptibility to diseases. In this study, we observed significant genetic differences in five of the differentially expressed genes among the Prakriti types. We further studied EGLN1, a key oxygen sensor gene that negatively regulates the activity of hypoxia-inducible factor (HIF-1A). In physiological normoxic conditions, EGLN1 hydroxylates the constitutively expressed HIF at two proline residues, leading to its polyubiquitination by the Von Hippel Lindau (VHL) E-3 ligase complex and subsequent degradation by the proteosomal machinery (3). Hypoxia leads to the inactivation of EGLN1, thereby increasing HIF that induces the expression of genes, which mediates adaptive responses through glycolytic enzymes, hemeoxygenase (cellular level), vascular endothelial growth factor (local), and erythropoietin (systemic level). Because oxygen homeostasis plays a key role in a large number of cellular, physiological, and systemic processes, we hypothesized that interindividual variations in EGLN1 could contribute to differences in hypoxia responsiveness such as in high-altitude conditions. We analyzed the allele frequencies of two common variations (rs479200 and rs480902) in the EGLN1 gene in populations from different altitudes represented in the Indian Genome Variation Consortium (IGVC) and HGDP-CEPH Human Genome Diversity panels (4, 5). We observed these variations not only to be linked to high-altitude adaptation but also to be associated with increased risk of developing high-altitude pulmonary edema (HAPE) in Indo-European sojourners. Thus, our study could establish a link between variations in EGLN1 and high-altitude adaptation as well as susceptibility to HAPE, taking lead from expression and genetic differences in normal individuals identified from three contrasting constitution types described in Ayurveda.

Results

Distribution of Common Variations in Extreme Constitution Types.

We studied the distribution of 141 tag SNPs encompassing 30 genes (Dataset S1) selected from the 251 differentially expressed genes between the V, P, and K from our earlier study in the same cohort (2). The details of recruitment and assessment of Prakriti types are provided in SI Materials and Methods. Ninety-two individuals who were not phenotyped for their constitution types but were from the same ethno-genetic background, namely Indo-European (IE), and large populations (IE-LP) were used as heterogeneous phenotype controls (IE pool).The details of the populations are provided in Materials and Methods below. We observed that 14 SNPs from five genes (AKT3, EGLN1, FAS, FBN2, and RAD51) have significant allele frequency differences between the constitution types, even after correction for multiple testing with false-discovery rate (FDR) threshold set at 5% (Table 1). Although we had selected tag SNPs, majority of the SNPs in AKT3 were in linkage disequilibrium (LD) and were different between P and K. Allele frequencies of rs480902 and rs479200 in EGLN1 were significantly different between P and K. At the FBN2 locus, rs1435514 showed significant allele frequency difference between P and K, and at the RAD51 locus, K differed significantly from V at rs11858338, rs3092982, rs11855560, and rs12593359. At the FAS locus (rs2296603), P differed significantly from V. These differences (with the exception of RAD51) were very striking, because the alleles flip from being less frequent in one group to being more frequent in the other group. The observed genotypic differences also corroborated (except FBN2) with expression differences between the same Prakriti groups. Most importantly, after the constitution types were pooled, these contrasting allele frequencies were averaged out, and the pooled frequencies did not differ significantly from the IE background (Fig. 1). Further comparison of each constitution group with the IE pool revealed significant difference (FDR correction at 5%) between P and IE with respect to AKT3 (rs2220276 and rs2291409) and between V and IE with respect to RAD51 (rs11858338, rs12593359, rs11855560, and rs3092982) and INSR (rs8110533) (Table S1).

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Table 1.

SNPs that show significant difference between the constitution types after FDR correction for multiple testing set at a threshold of significance (P < 0.05)

Fig. 1.
Fig. 1.

Representation of allele frequencies of common variations among extreme constitution types. A representative set of SNPs that shows significant difference between the constitution types Kapha (K), Pitta (P), Vata (V), and differences from the V, P, and K/IE pool are depicted. The gene and SNP with the alleles are given in each panel. IE represents individuals with heterogeneous phenotypes from Indo-European populations, and V, P, and K represent individuals of different constitution types pooled into a single group.

Genetic Variations in EGLN1 Correlate with Altitude in IGVC and HGDP-CEPH Populations.

Because EGLN1 is a key oxygen sensor gene, we reasoned that variations in this gene, if meaningful, might also exhibit differences in allele frequencies across populations of different geographical locations, including those residing at high altitudes. An earlier study by the IGVC had analyzed the extent of genetic relatedness and homogeneity in 55 Indian populations from diverse linguistic and ethnic lineages of different geographical regions using various population genetic measures such as population differentiation by FST, genetic distance by Nei’s DA distance, system structure, and principal component analysis (PCA), and it identified five genetically close, near homogeneous clusters (4, 6). We studied patterns of distribution of EGLN1 variations (rs480902 and rs479200) across a representative set of 24 Indian populations from the clusters described above. There was a significant difference (P = 4.01 × 10−7) in the allele frequencies of rs480902 and rs479200 between Tibeto-Burman (TB) populations (TB-N-IP1 and TB-N-SP1) residing at an altitude 3,500 m above sea level and other members (TB-NE-LP1, IE-N-IP2, and IE-NE-IP1) of the same genetic cluster but residing at low altitude (Fig. 2A and Table 2). At each of these SNPs, the alleles that were more frequent in the high-altitude populations (rs480902, T = 0.71; rs479200, C = 0.71) were also overrepresented in the P group (0.64 for both). The IE populations, which reside in Jammu and Kashmir, also had an overrepresentation of T allele of rs480902 (0.56) (Fig. 2B) and C allele of rs479200 (0.52) that was present in P constitution types. Given the diversity of Indian populations, these observations could also be a consequence of population stratification. We carried out a principal-component analysis of the V, P, and K cohort (Ayur) and the 24 Indian populations on a panel of 2,060 unlinked SNPs to investigate potential inflation of the odds ratio (OR) because of population stratification. ANOVA did not reveal any differences in the Ayur samples with IE populations of North India, and no V, P, or K individuals clustered with high-altitude populations (Fig. S1). The TB populations at high altitude also did not differ from members of the same linguistic background or genetic cluster as revealed by principal component analysis, but they differed significantly at the EGLN1 locus with respect to the altitude (Table 2 and Fig. S1). The T allele of rs480902 associated with high altitude is found at high frequency (0.71) in the outgroup African population (OG-W-IP) residing in India. This population, called Siddi, are descendants from Bantu-speaking parts of East Africa (7). We also observed a conservation of LD between rs479200 and rs480902 across all of the populations (Fig. S2).

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Table 2.

Comparison of allele frequency of EGLN1 SNPs between Indian populations of the same genetic cluster residing at high and low altitudes

Fig. 2.
Fig. 2.

Distribution of T allele frequency of rs480902 in diverse IGVC and HGDP-CEPH populations from different altitudes. (A) Frequency in the 24 IGV populations and their altitude. (B) Spatial frequency map of rs480902 in IGV populations. The color gradient below the map depicts the range of observed frequency of the T allele from minimum to maximum. (C) Frequency distribution in the HGDP-CEPH panel of 52 populations along with their altitudes. Diverse continental populations residing at high altitudes selectively retain the ancestral T allele.

We further analyzed the EGLN1 gene in the HGDP-CEPH Human Genome Diversity Panel that has sampled populations from various geographical locations all around the world (5). We determined the altitudes from Google earth based on the reported latitude and longitude of each population in the HGDP-CEPH panel (Tables S2 and S3). From a genome-wide study on the Illumina 650K array platform (8), the genotype for SNPs of EGLN1 were retrieved for all of the HGDP-CEPH populations (http://hgdp.uchicago.edu/cgi-bin/gbrowse/HGDP/). We observed a significant correlation (P < 0.01) of allele frequencies of four SNPs (rs973252, rs480902, rs2808611, and rs2808614) with altitude, irrespective of genetic relatedness between the populations (Figs. 2C and 3A and Table 3). However, there were two populations, Burusho and Kalash, that, although they reside at very high altitudes, had underrepresentation of the EGLN1 alleles that were associated with other high-altitude populations. These SNPs that were positively correlated with altitude span a region of ∼29 kb in the first intron of the EGLN1 gene and map to the same region that revealed differences in the Indian population with respect to altitude, and they also differed between the constitution types (Fig. S3). The T allele of the SNP rs480902 (also included in the 650K Illumina array) was highly correlated with altitude (Kendall's rank correlation: P < 0.001; τ = 0.2903123). This allele, although associated with high altitude, is found in all HGDP-CEPH populations, and it had higher frequencies predominantly in sub-Saharan Africa (Fig. 3B). In majority of the European populations, the C allele of rs480902 was more frequent (Fig. 3B).

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Table 3.

Correlation of frequency of EGLN1 SNPs with altitude in the HGDP-CEPH Human Genome Diversity Panel

Fig. 3.
Fig. 3.

Allele frequency distribution of rs480902 in HGDP-CEPH populations. (A) Correlation of T allele frequency of rs480902 with increasing altitude (R2 = 0.1056) in HGDP-CEPH populations. (B) Spatial frequency map of rs480902 in the HGDP-CEPH populations retrieved from the HGDP selection browser. Frequencies of the ancestral T allele that is predominantly present in populations residing at high altitude and the derived C allele are represented by dark and light shades, respectively.

Association of Common Variations in the EGLN1 Gene with HAPE.

At the genetic level, K differed significantly from P and V with respect to two SNPs, rs480902 and rs479200, that span an ∼12-kb region in the first intron of the EGLN1 gene. Compared with the TC and CC genotypes at rs479200, the TT genotype that was overrepresented in K also had significantly higher expression of EGLN1 (one-tailed t test; P value = 0.017) (Fig. 4A). Because higher expression of EGLN1 is inversely correlated to HIF activity, we hypothesized that individuals with genotypes associated with high EGLN1 expression may not be able to perform well under hypoxic conditions. To test this hypothesis, we studied a cohort of IE sojourners to high altitudes who suffered from HAPE as well as natives of that high-altitude region. Interestingly, the TT genotype of rs479200 that was associated with higher expression of EGLN1 in the K constitution had a significantly higher frequency (0.44) in HAPE patients compared with natives (0.05) of the high altitude (Fig. 4B). In addition, the frequency of C allele of rs480902 and the T allele of rs479200 (0.63 and 0.64, respectively) in HAPE patients was similar to K type (0.69 and 0.71). The alleles associated with the K constitution were significantly underrepresented in P constitution (0.36 and 0.36) as well as the natives (0.28 and 0.21) of high altitude (Fig. 4C and Table S4). After V, P, and K were pooled, both the SNPs assumed an allele frequency similar to IE population, and their frequency difference from HAPE patients was also not apparent.

Fig. 4.
Fig. 4.

Correlation between EGLN1 genotypes of rs479200 and expression and the association of TT genotype and the T allele with HAPE. (A) Box plot representing ΔCT values of gene expression of EGLN1 by RT-PCR in TT, TC, and CC genotypes of rs479200 in Ayurveda samples. (B) Frequency of TT genotype of rs479200 in different constitution types (K, P, and V), VPK, IE, natives of high altitude, and patients of HAPE. (C) Frequency of T allele of rs479200 in different constitution types (K, P, and V), VPK, IE, natives of high altitude, and patients of HAPE. Fisher's exact test was performed for association analysis of EGLN1 SNP rs479200 between different controls and HAPE. The numbers over each of the bars represent the P values of comparison of each group with HAPE.

Discussion

Interindividual differences in susceptibility to diseases and response to environment and drugs are, to a large extent determined by genomic variations. A large fraction of these variations could be a consequence of population history, drift, or adaptation to spatially varying selective pressures such as diet and climate. However, given the large amount of variations in an individual's genome, linking these to a phenotype is an extremely challenging task. According to Ayurveda, response to external environment (diet, weather, lifestyle, stress, and drugs), susceptibility, and progression of disease are largely determined by an individual's basic constitution (Prakriti), which can be phenotypically analyzed (1, 2). Therefore, classifying normal individuals based on Ayurveda constitution types may also allow us to identify meaningful phenotype to genotype links. Our earlier observations revealed significant differences in biochemical parameters and gene expression between the three contrasting Prakriti types of IE origin identified using phenotyping methods of Ayurveda (2). Here, we show significant differences in allele frequencies of common variations in five genes (FAS, AKT3, FBN2, EGLN1, and RAD51) between the Prakriti groups in the same study population described earlier. After the V, P, and K samples were pooled, these SNPs assumed a frequency similar to the background population. We hypothesized that these variations, which are linked to Prakriti groups that are differently predisposed to diseases, may lead us to identification of predictive markers for differential responsiveness to disease and environment.

As a proof of concept, we studied EGLN1 because it plays a key role in oxygen homeostasis and is also believed to be a target of many pharmacological interventions that aim to stabilize HIF or lower HIF activity (9–11). Although variations in genes of pathways related to hypoxia, such as HIF-1, endothelial function, and vascular remodeling, have been studied (12–17), none of the studies so far have reported polymorphisms linked to EGLN1 in high-altitude adaptation. We analyzed the allele frequencies of EGLN1 polymorphisms that differed between the constitution types in populations residing at different altitudes as well as in subjects who develop HAPE, a condition that normally occurs in unacclimated sojourners at altitudes above 2,500 m and accounts for most of the deaths caused by altitude sickness (18). Analysis of 24 diverse Indian populations revealed that TB populations residing at high altitudes had a significantly higher frequency of T allele of rs480902 and C allele of rs479200 (overrepresented in P constitution types) compared with populations that resided at low altitude but were from the same genetic cluster. Our observation in Indian population was further corroborated with the analysis of the HGDP-CEPH panel, which showed that disparate genetic lineages at high altitude share the same ancestral allele (T) of rs480902. This indicates a selection for retention of an ancestral physiological adaptation at high altitudes, except for in populations Kalash and Burusho, which seem to have acquired adaptation to high altitude through a different mechanism. These populations have inhabited the high altitude much more recently compared with the Tibetan and Andean Highlanders (19). It would be interesting to further explore this finding.

The role of EGLN1 in high-altitude adaptation is further substantiated by the presence of higher frequencies of T allele and TT genotypes of rs479200 in IE sojourners who develop HAPE. The TT genotype, corresponding to higher gene expression of EGLN1, is overrepresented in K and rare in natives and P, which raises the possibility that K may have a higher risk of HAPE and P Prakriti could be more protected. The comparison of sojourners who develop HAPE with healthy individuals of same genetic background (IE pool) did not reveal significant differences. This could be because IE pool is comprised of heterogeneous constitution types, and in the absence of phenotypic stratification, the effect of these variations are masked. Although a cohort of companions of IE sojourners that did not develop HAPE on multiple ascents would have been of much interest, such a cohort was not accessible because of highly sensitive military areas.

Interestingly, Ayurveda assigns Prakriti not only to humans but also to environment and food, and it makes specific mention of adaptation as well as dietary and lifestyle recommendations based on one's Prakriti for achieving healthy balance. An interpretation of our results that P constitution is more protected at high altitudes would be consistent with the Ayurvedic school of thought that considers mountains mainly as K and V dominant regions (SI Materials and Methods), and therefore, there would be higher prevalence of K and V diseases.

EGLN1 gene, owing to its important function as an oxygen sensor, is relevant to the human hypoxic response, both at high altitude in hypoxic conditions or in cellular hypoxia. Furthermore, EGLN1 is being considered as an important pharmacological target. Therefore, it is important to study EGLN1 variations in diseases and drug response, where cellular hypoxia is implicated in pathogenesis. The SNPs that are associated with high-altitude adaptation, both in the Indian study as well as in the global populations, encompass the first intron of the EGLN1 gene. This region is highly conserved and harbors a segmental duplication as well as conserved DNA regulatory elements (Fig. S3). Functional characterization of this region would provide insights into mechanisms of regulation of the EGLN1 gene.

Conclusion

Our study shows that expression and genetic analysis of healthy individuals phenotyped using the principles of Ayurveda could uncover genetic variations that are associated with adaptation to external environment and susceptibility to diseases. We show, through genetic analysis, that two contrasting constitutions within nondiseased normals derived from the same genetic background differ both at the expression and genetic level with respect to the EGLN1 gene, and these differences are linked to high-altitude adaptation and susceptibility to HAPE. Our work further suggests that variations in the hypoxia response pathway are common in most of the world population and could attain different allele frequencies as a consequence of positive selection.

The involvement of the EGLN1 gene in high-altitude adaptation in Tibetan highlanders has been shown by two independent groups using whole-genome approaches while our manuscript was under review (20–22).

Materials and Methods

Study Subjects.

The study was carried out in four different cohorts of samples that are described in detail in SI Materials and Methods. Briefly, the samples comprised of (i) 96 individuals of extreme constitution types V (39), P (29), and K (28) identified from an initial phenotyping of 850 volunteers on the basis of Ayurveda methods and recruited in our earlier study (2) and (ii) 552 samples from 24 diverse Indian populations from the existing panel of IGVC (6). These include 92 heterogeneous phenotype controls (IE pool) from IE North Indian large populations (size > 10 million). Our earlier study on Indian Genome Variation that had sampled diverse populations from different linguistic, geographical, and ethnic background revealed five near homogeneous genetic clusters, where IE large population from Northern India was one of the clusters (4, 6). (iii) Additionally, the samples included 96 unrelated HAPE patients from IE background and (iv) 96 samples of unrelated natives of Leh recruited through Sonam Norboo Memorial (SNM) Hospital, Leh (altitude, ∼3,500 m), Jammu, and Kashmir, India (17).

Genetic and Expression Analysis.

A total of 158 SNPs from 30 genes that exhibited expression differences in our earlier study (SI Materials and Methods and Dataset S1) and 2,060 SNPS that were used for population stratification were genotyped in V, P, and K samples as well as the IGVC panel (http://igvbrowser.igib.res.in) using Illumina Bead Array platform (SI Materials and Methods). Genotyping of rs480902 and rs479200 on HAPE samples and natives was carried out using single base primer extension assay (SNaPSHOT ddNTP Primer extension kit; Applied Biosystems) on an ABI Prism 3100 Genetic Analyzer. The genotype data on EGLN1 SNPs from 52 populations were retrieved from HGDP selection browser (http://hgdp.uchicago.edu/cgi-bin/gbrowse/HGDP/). Relative expression of EGLN1 between the constitution types was measured by real-time quantitative (TaqMan) PCR using two genes, ASAH1 and MAN1A1, as internal control (details in SI Materials and Methods).

Statistical Analysis.

We used Fisher's exact test for estimating allelic frequency differences and testing genotypic and allelic associations. Correction for multiple testing was done using the FDR method. EIGENSTRAT (23) was used for analysis of population stratification in the IGVC panel and Ayurveda samples. Gene expression normalization factor for each sample based on the geometric mean of two internal controls was performed, and differences in expression of EGLN1 with respect to rs479200 genotypes were compared using a one-tailed t test. We used Kendall's rank correlation to study the relation of altitude with different SNPs of EGLN1 in HGDP-CEPH populations.

Details of samples, SNP selection, experiments, and data analysis are described in SI Materials and Methods.

Acknowledgments

We thank Prof. Samir K. Brahmachari for conceiving the idea of integration of the ancient Indian system of personalized and predictive medicine with modern genomics and critical review, Drs. Vani Brahmachari, Ram Niwas Prasher, and Arijit Mukhopadhyay for critical review; Ankita and Amit Mandal for analysis support; and Drs. Ghulam Mohammad and Tashi Thinlas, Sonam Norboo Memorial (SNM) Hospital (Leh, Jammu, and Kashmir, India) for the collection of HAPE samples. Financial support from the Council of Scientific and Industrial Research Senior Research Fellowship (SRF) (to S.A.), Department of Science and Technology (DST) Grant B6.25 (to M.M.), and Council of Scientific and Industrial Research Grants CMM0016 and MLP3601 (to M.M.) is acknowledged.

Footnotes

  • 1To whom correspondence may be addressed. E-mail: mitali{at}igib.res.in or bhavana{at}csir.res.in.

  • Author contributions: M.M. designed research; S.A., S.N., P.J., P.K.S., and B.P. performed research; S.A., S.N., T.S., M.A.Q.P., I.G.V.C., B.P., and M.M. contributed new reagents/analytic tools; S.A., P.J., S.G., A.A., B.P., and M.M. analyzed data; and S.A., P.J., A.A., B.P., and M.M. wrote the paper.

  • Conflict of interest statement: S.A., M.A.Q.P., B.P., and M.M. are the inventors and have filed patent application no. 1336DEL2010 in India. There are no implications of this patent application on the publication of the manuscript, because the provisional patent application has already been filed. S.N., P.J., P.K.S., S.G., A.A., T.S., and The Indian Genome Variation Consortium have been acknowledged for contributing to the invention but do not fulfill the criteria of inventorship.

  • ↵*This Direct Submission article had a prearranged editor.

  • The full list of authors participating in the Indian Genome Variation Consortium can be found at http://igvbrowser.igib.res.in/gbrowse/igvc.html.

  • This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1006108107/-/DCSupplemental.

Freely available online through the PNAS open access option.

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EGLN1 involvement in high-altitude adaptation revealed through genetic analysis of extreme constitution types defined in Ayurveda
Shilpi Aggarwal, Sapna Negi, Pankaj Jha, Prashant K. Singh, Tsering Stobdan, M. A. Qadar Pasha, Saurabh Ghosh, Anurag Agrawal, Indian Genome Variation Consortium, Bhavana Prasher, Mitali Mukerji
Proceedings of the National Academy of Sciences Nov 2010, 107 (44) 18961-18966; DOI: 10.1073/pnas.1006108107
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Effect of glyphosate on honey bee gut
A study suggests that the herbicide glyphosate disrupts bee gut microbiota, increasing bees’ susceptibility to pathogens, and that glyphosate’s effects may contribute to the largely unexplained increase in honey bee colony mortality.
Image courtesy of Vivian Abagiu (The University of Texas at Austin, Austin, TX).
Proceedings of the National Academy of Sciences: 115 (41)
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