Indian Journal of Animal Research

  • Chief EditorK.M.L. Pathak

  • Print ISSN 0367-6722

  • Online ISSN 0976-0555

  • NAAS Rating 6.50

  • SJR 0.263

  • Impact Factor 0.4 (2024)

Frequency :
Monthly (January, February, March, April, May, June, July, August, September, October, November and December)
Indexing Services :
Science Citation Index Expanded, BIOSIS Preview, ISI Citation Index, Biological Abstracts, Scopus, AGRICOLA, Google Scholar, CrossRef, CAB Abstracting Journals, Chemical Abstracts, Indian Science Abstracts, EBSCO Indexing Services, Index Copernicus
Submit

Research Article

Indian Journal of Animal Research, volume 56 issue 12 (december 2022) : 1442-1447

Analysis of Genetic Polymorphism and the Identification of the mi-RNA Binding Sites of the Hypoxia Related Genes in Indian Breeds of Cattle

Sanjeev Singh1,*, Shivam Bhardwaj1, Indrajit Ganguly1, A.K. Bhatia1, S.P. Dixit1
1Animal Genetics Division, ICAR-National Bureau of Animal Genetic Resources, Karnal-132 001, Haryana, India.
Cite article:- Singh Sanjeev, Bhardwaj Shivam, Ganguly Indrajit, Bhatia A.K., Dixit S.P. (2022). Analysis of Genetic Polymorphism and the Identification of the mi-RNA Binding Sites of the Hypoxia Related Genes in Indian Breeds of Cattle . Indian Journal of Animal Research. 56(12): 1442-1447. doi: 10.18805/IJAR.B-4335.

ABSTRACT

Background: There are certain cattle breeds which are adaptable to the temperate type environmental conditions of high altitude regions of Himalayas. The genome of such cattle have signature of adaptability in the genes related to the hypoxia. Therefore, the current study was undertaken on three hypoxia related genes (EGLN2, EGLN3 and EPAS1) in four breeds of cattle adapted to the diverse agro-climatic conditions of high (Ladakhi and Siri) and low (Hallikar and Kankrej) altitude.

Methods: The genotyping of the samples was carried out by using 777 K BovineHD BeadChip (Illumina) at Agri genome Pvt. Ltd (Kerala) and the in-silico analysis of the samples was carried out at ICAR-NBAGR during 2019-2020. The SNPs underlying the genes were evaluated for the gene and genotypic frequencies at nine SNP loci residing in these three candidate genes (EGLN2, EGLN3 and EPAS1). The diversity parameters for these SNPs were assessed by GenAlEx 6.2 software and Minor Allele Frequency (MAF) differences among the breeds were calculated by Duncan’s Multiple Range Test (DMRT) using SAS software. The mi-RNA binding sites in the 3’UTR region of genes were identified by TargetScan software.

Result: Higher level of polymorphism was obtained in the Ladakhi and Siri breeds of cattle of high altitude/ cold adapted region than Hallikar and Kankrej of plain and hot arid/ semi-arid region. Several mi-RNA binding sites were obtained in the 3’UTR region of these 3 genes by Target Scan software. The polymorphism obtained in these candidate genes can be utilized in the markers assisted selection of the animals more adapted to the extreme cold and high altitude region for their genetic improvement as well as designing tools for the therapy of the diseases prevalent at high altitude.

INTRODUCTION

The environment of India comprises of some of the world’s most bio-diverse eco-zones. These diverse environmental conditions act as agents for the natural selection of animals to make them locally more adaptable. The indigenous breeds of cattle possess various unique characteristics, which makes them well adapted to the prevailing tropical climate of India. However, there are certain breeds which are also adaptable to the temperate type environmental conditions of high altitude regions of Himalayas like Ladakhi and Siri cattle. They thrive there under low oxygen concentration due to their adaptation in the hypoxic climate. The genome of such cattle have signature of adaptability in the genes related to the hypoxia (Bhardwaj, 2020). Hypoxia Inducible Factor (HIF) is the main transcriptional regulator of the hypoxic response in metazoans (Giaccia et al., 2003; Wenger et al., 2005; Lendahl et al., 2009). It is a heterodimer consisting of one β subunit (HIF-β) and one of three α subunits (HIF-1α, HIF-2α and HIF-3α). The principal means by which HIF activity is controlled in response to oxygen concentration is through site-specific prolyl hydroxylation of the HIF-α subunit (Ivan et al., 2001; Jaakkola et al., 2001; Yu et al., 2001). In normal oxygen concentration, prolyl hydroxylase domain proteins (PHDs 1-3) hydroxylate HIF-α in its oxygen dependent degradation domain (Ivan et al., 2002). Under hypoxia this action is inhibited causing the stabilization of HIF-α followed by its dimerization with HIF-β and activation of several genes involved in systemic and cellular adaptation to hypoxia (Mole et al., 2009; Xia et al., 2009; Schodel et al., 2011). Two important HIF pathway genes which showed the strong positive directional selection include HIF2A and PHD2 in Tibetian population residing in high altitude (Bigham and Lee 2014). Identification of the high altitude adapted genetic variants/alleles in these genes in cattle adapted to high altitude (Ladakhi and Siri) will be helpful in the genetic introgression of the selected advantageous adapted variants into the low land cattle which are high producers, for their better survival and adaptability in high land areas. This will further be helpful in designing of the new therapies for diseases like chronic hypoxia, ischemia and high altitude pulmonary hypertension prevailing in the animals of low land. Therefore, the study was carried out for the genetic diversity analysis in genes involved in the HIF pathways (EPAS1/HIF2A, EGLN2/PHD1 and EGLN3/PHD3) in four cattle breeds (2 high land viz. Ladakhi and Siri and 2 low land viz. Hallikar and Kankrej) so as to identify the selectively advantageous genes of high altitude cattle.

MATERIALS AND METHODS

Animal samples and SNP genotyping
 
To investigate the genetic basis of local adaptation, a total of 46 samples were genotyped using 777 K BovineHD BeadChip (Illumina) from four native cattle breeds belonging to contrasting landscape and climatic conditions; Siri (9) and Ladakhi (11) from cold hilly high altitude region and Kankrej (14) and Hallikar (12) from hot arid and semi-arid regions of low land, respectively. The SNP genotyping of the samples were carried out by Agri-Genome Labs Pvt. Ltd. Company (Kerala) and in-silico experiments were designed at ICAR-NBAGR during 2019-2020. 9 SNP loci underlying the hypoxia related genes were identified and used in the diversity analysis.
 
Analysis of the genetic diversity
 
The genetic diversity parameters were estimated by genAlEx software 6.2 for the calculation of the within and between breed diversity. The unbiased heterozygosity as well as observed and effective number of alleles for each locus was also calculated. Log-likelihood method was used for assigning the animals belonging to each breed.
 
Principal component analysis (PCA)
 
PCA, which is based on the variance standardized relationship matrix, was carried out in genAlEx software 6.2 which extracted the top 20 principal components. PC1 versus PC2 plot is of most significance as it explains most of the variability of the data. PCA graphs are frequently used to provide a low-dimensional visualization to display and discover patterns in SNP data from humans, animals, plants and microbes specially to elucidate population structure.  Principal components analysis on genotype data infers continuous axes of genetic variation. Intuitively, the axes of variation reduce the data to a small number of dimensions, describing as much variability as possible; they are defined as the top eigenvectors of a covariance matrix between samples (Price et al., 2006).
 
Breed differentiation and statistical analysis
 
The differentiation of the breed was carried out by Duncan’s multiple range test (DMRT) test of the minor allele frequency (MAF) using one way ANOVA of Statistical Analysis software (SAS). The details of the MAF and SNP variant as per ARS-UCD1.2 release 150 is given in Table 3. Test of K proportions was also carried out by XLSTAT using 3 tests viz Chi square, Monte- Carlo method and Marascuilo procedure for testing the significant difference (P<0.05) between the breeds for MAF at each loci.
 
Analysis of the effect of the SNPs for mi-RNA prediction site
 
Target Scan software (Agarwal et al., 2015) was used for the prediction of mi-RNA binding sites residing in the 3'UTR region.

RESULTS AND DISCUSSION

Various workers have studied the polymorphism in the genome of livestock species by different molecular techniques like microsatellite markers, PCR-RFLP, sanger based direct sequencing etc (Rajput et al., 2013; Singh et al., 2014; Kumar et al., 2017 and Mishra et al., 2018). However, the SNP genotyping technique used in the present investigation is more accurate than the previous techniques due to more stringent quality control. The percentage of polymorphic loci in Kankrej (KN) and Hallikar (HK) breeds ranged from 22.22% to 55.56%; whereas in Ladakhi (LC) and Siri (SR) all the loci were polymorphic (100%). Thus, more polymorphism was observed in Ladakhi and Siri animals in comparision to Hallikar and Kankrej. The average % of polymorphic loci for all the breeds was 69.44±18.91. The genetic distance between KN and HK was highest (0.135) whereas; it was lowest between Ladakhi and Siri (0.066) (Table 1). The unbiased expected heterozygosity (He) varied from 0.086±0.057 to 0.423±0.035 among Kankrej and Ladakhi, indicating highest genetic diversity in Ladakhi and least diversity in Kankrej breed (Table 2a). Global FST value indicated that 14.3% variation was among these breeds (Table 2b). Locus 1, 2, 8 and 9 had more variation than other loci (26.5 to 15.7%). The FIS values were highest in Siri (0.240±0.144) and lowest in Ladakhi (-0.398±0.045) breed. Principal coordinate analysis revealed 82.16% variation due to PCA1 and PCA2 (Fig 1). Various workers also reported that the majority of the variation can be accounted by PCA1 and PCA2 (Mishra et al., 2017). Breed differentiation by minor allele frequencies was studied by DMRT test using SAS software. It was observed that there were significant breed differences among these four breed at each loci (P<0.05). Further, group wise MAF were also different between High altitude and low altitude breeds (Table 3). On the basis of log-likelihood estimates 63% of the animals assigned to their self-population by these 9 SNP loci (Table 4).
 

Table 1: Pair wise breeds FST values (below diagonal) between four breeds of cattle.


 

Table 2a: Sample size (N), no. of alleles (Na), no. of effective alleles (Ne), information index (I), observed heterozygosity (Ho), expected (He) and unbiased expected heterozygosity (UHe) and fixation index (F) obtained in the four breeds of cattle.


 

Table 2b: Locus wise fixation index and mean for all the breeds pooled together.


 

Table 3: Locus wise minor allele frequency (MAF) and mean value in four breeds of cattle (KN: Kankrej, HK: Hallikar, LC: Ladakhi and SR: Siri).



Table 4: Breed assignment by log-likelihood method in all the four breeds of cattle.


 

Fig 1: Principal coordinate analysis of the 4 different breeds of cattle (PC1 V/S PC2).


       
EGLN2 (PHD1) is egl-9 family hypoxia inducible factor 2. It is one of the central HIF pathway genes. The hypoxia inducible factor (HIF) is a transcriptional complex that is involved in oxygen homeostasis. At normal oxygen levels, the subunit of HIF is targeted for degradation by prolyl hydroxylation (Ivan et al., 2001). This gene encodes an enzyme responsible for this post-translational modification. Diseases associated with EGLN2 in humans include Hypoxia and Familial Isolated Hypoparathyroidism, Chronic obstructive pulmonary disease (COPD), cancer, cardio vascular disease etc. (Ding et al., 2015; Cioffi et al., 2003 and Zhang et al., 2019).
       
Higher FST values (0.221 to 0.265) were obtained among the breeds for SNP (loci1 and loci 2) lying in this genes. Moreover, minor allele Frequency (MAF) differences were lower in Ladakhi and Siri (Table 3) breeds of high altitude region in comparison to hot/low lying versus cold/high altitude breeds groups. Similar results were also obtained by other workers in which lower level of population differentiation was observed between Yak and Tibetan cattle and high level of population differentiation between Tibetan cattle and Zebu having strong signal of gene introgression on chromosome 18 (Wu et al., 2018). This indicates that the favorable adaptive allele for EGLN2 (PHD1) genes might have accumulated in the breeds of high altitude region to overcome the stressful hypoxic conditions prevailing in this region. For EGLN3 (PHD3), FST value for the loci 3-7 was about 0.94 and MAF was highest (0.318) for Ladakhi and least for Kankrej.
       
For EPAS1 (HIF2A) FST varied from 0.15-0.177 (Loci 8-9) and MAF was highest for Ladakhi and least for Hallikar. This again indicates that the hypoxia related genes showed population differentiation on the basis of high altitude versus low land region breeds. Huerta-Sánchez et al., (2014) also observed that selected haplotypes in EPAS1 genes are only found in Denisovan and Tibetans population lying in high altitude region and not in other populations.
       
The genetics variants identified in the EPAS1 gene have been found to be associated with high-altitude pulmonary hypertension (HAPH) in cattle which leads to brisket disease (Newman et al., 2015) as its expression is extremely higher in lung than other tissues (Wu et al., 2015). Moreover, some variants are also associated with the differences in hemoglobin concentrations in Tibetans (Homo Sepiens), Yak (Bos grunniens) and Tibetian mastiffs’ dog (Canis familiaris) residing at high altitude (Beall et al., 2010; Wu et al., 2015; Wen et al., 1998).
       
MicroRNAs (miRNAs) are about 22-nt RNAs that mediate post-transcriptional gene repression (Bartel, 2004). Hypoxia stimulates a distinct change in a specific group of miRNAs, termed hypoxamirs (Nallamshetty et al., 2013). The hypoxamiRs are considered as potential candidates for diagnostic markers or therapeutic targets for better adaptation at high altitude. They bind with an Argonaute protein to form a silencing complex. miRNAs function as sequence-specific guides, directing the silencing complex to transcripts, primarily through Watson-Crick pairing between the miRNA seed (miRNA nucleotides 2-7) and complementary sites within the 3’ untranslated regions (3’UTRs) of target RNAs (Lewis et al., 2005; Bartel 2009). 3’UTR regions are considered as the potential target for the binding of mi-RNAs Therefore, the hypoxia genes under investigation were analysed for their mi-RNA binding in the 3’UTR region by TargetScan software. The 3’UTR region of EGLN2 gene of cattle consists of 592 bp. At 433-440 positions, binding site for 2 mi-RNAs (bta-miR-23a and bta-miR-23b-3p) were observed and at 561-567 positions, 9 binding sites (bta-let-7d/g/i/f/c/b/e, 7a-5p, bta-miR-98) were observed which are broadly conserved among the vertebrates. This indicates that this gene might have been influenced by atleast 11 mi-RNAs for its expression. The 3'UTR region of EGLN3 gene consists of 1655 nt. For EGLN3, 5 binding sites (bta-miR-9-5p, bta-miR-142-5p, bta-miR-17-5p/20/93/106, bta-miR 122, bta-miR-218) broadly conserved among the vertebrates were observed. Moreover, this gene has two transcripts (ENST00000250457.3 of 1655 nt long 3’UTR and ENST00000547327.2 of 1972 nt long 3’UTR) due to the alternate splicing. ENST00000547327.2, transcript had no miRNA binding site which could be broadly conserved in the vertebrates. The presence of 5 intronic variations (loci 3-7) in this gene observed in our study might have affected the splicing process during the post transcriptional modifications. Similarly, 12 mi-RNA binding sites (Cow EPAS1: ENST00000263734.3; 3’UTR length: 2029) were observed in EPAS1 gene 3’UTR. Thus it is quite possible that the expression of these genes might have been influenced by the identified SNPs which could be responsible for the variation in the phenotypes related to hypoxia among the studied breeds. Altitude affects the biochemical profile of the cross-bred cattle (Kumar and Kumar 2000). In comparision with the high altitude cross-bred cattle, haematological profile of native Ladakhi cattle is within the range of plain area cross bred cattle and Ladakhi cattle is well adapted to the prevalent high altitude stress conditions (Kumari et al., 2020). The selection of favorable alleles of hypoxia related genes and their optimum expression in the specific tissues might be responsible for keeping their levels within the range in spite of stressful hypoxic conditions of high altitude.

CONCLUSION

Higher level of polymorphism was obtained in high altitude cattle of the Ladakhi and Siri breeds. The polymorphism and mi-RNA binding sites observed in these hypoxia related candidate genes may be utilized in the development of markers for markers assisted selection of the animals more adapted to the extreme cold and high altitude region. In addition, in the design of diagnostic markers or therapeutic targets for better adaptation at high altitude, expression studies of identified hypoxamirs along with the hypoxia genes in different breeds of cattle will be helpful.

ACKNOWLEDGEMENT

The authors are thankful to the Director, ICAR-NBAGR, Incharges of Various Livestock Farms and Veterinarians of the Animal Husbandry Department for providing the facilities of the blood collection and research work.

REFERENCES


  1. Agarwal, V., Bell, G.W., Nam, J.W. and Bartel, D.P. (2015). Predicting effective microRNA target sites in mammalian mRNAs. https://doi.org/10.7554/eLife.05005.

  2. Bartel, D.P. (2004). MicroRNAs. Cell. 116(2): 281-297. https://doi. org/10.1016/S0092-8674(04)00045-5.

  3. Bartel, D.P. (2009). MicroRNAs: Target Recognition and Regulatory Functions. Cell 136(2): 215-233. https://doi.org/10.1016/j.cell.2009.01.002.

  4. Beall, C.M., Cavalleri, G.L., Deng, L., Elston, R.C., Gao, Y., et al. (2010). Natural selection on EPAS1 (HIF2) associated with low hemoglobin concentration in Tibetan highlanders. Proceedings of the National Academy of Sciences. 107(25): 11459-11464.https://doi.org/10.1073/pnas.1002443107

  5. Bhardwaj, S. (2020). Genome wide association studies on indigenous cattle for cold adaptation. M.V. Sc. Thesis submitted to Deemed University, ICAR-NDRI, Karnal, India.

  6. Bigham, A.W. and Lee, F.S. (2014). Human high-altitude adaptation: Forward genetics meets the HIF pathway. Genes and Development. 28(20): 2189-2204. https://doi.org/10.1101/gad.250167.114.

  7. Cioffi, C.L., Qin Liu, X., Kosinski, P.A., Garay, M. and Bowen, B.R. (2003). Differential regulation of HIF-1á prolyl-4-hydroxylase genes by hypoxia in human cardiovascular cells. Biochemical and Biophysical Research Communications. 303(3): 947-953. https://doi.org/10.1016/S0006-291X(03)00453-4.

  8. Ding, Y., Niu, H., Yang, H., Sun, P., Chen, Y., Duan, M., Xu, D., Xu, J. and Jin, T. (2015). EGLN2 and RNF150 genetic variants are associated with chronic obstructive pulmonary disease risk in the Chinese population. International Journal of Chronic Obstructive Pulmonary Disease. 10(1): 145-151. https://doi.org/10.2147/COPD.S73031.

  9. Giaccia, A., Siim, B.G. and Johnson, R.S. (2003). HIF-1 as a target for drug development. Nature Reviews Drug Discovery. 2(10): 803-811. https://doi.org/10.1038/nrd1199.

  10. Huerta-Sánchez, E., Jin, X., Asan, Bianba, Z., Peter, B.M., Vinckenbosch, N., Liang, Y., Yi, X., He, M., Somel, M., Ni, P., Wang, B., Ou, X., Huasang, Luosang, J., Cuo, Z.X.P., Li, K., Gao, G., Yin, Y., Nielsen, R. (2014). Altitude adaptation in Tibetans caused by introgression of Denisovan-like DNA. Nature. 512(7513): 194-197. https://doi.org/10.1038/nature13408.

  11. Ivan, M., Haberberger, T., Gervasi, D.C., Michelson, K.S., Gunzler, V., Kondo, K., Yang, H., Sorokina, I., Conaway, R.C., Conaway, J.W. and Kaelin, W.G. (2002). Biochemical purification and pharmacological inhibition of a mammalian prolyl hydroxylase acting on hypoxia-inducible factor. Proceedings of the National Academy of Sciences. 99(21): 13459-13464. https://doi.org/10.1073/pnas.192342099.

  12. Ivan, M., Kondo, K., Yang, H., Kim, W., Valiando, J., Ohh, M., Salic, A., Asara, J. M., Lane, W. S. and Kaelin, W.G. (2001). HIF alpha Targeted for VHL-Mediated Destruction by Proline Hydroxylation: Implications for O2 Sensing. Science. 292(5516): 464-468.https://doi.org/10.1126/science.1059817.

  13. Jaakkola, P., Mole, D.R., Tian, Y.M., Wilson, M.I., Gielbert, J., et al. (2001). Targeting of HIF-alpha to the von Hippel-Lindau Ubiquitylation Complex by O2-Regulated Prolyl Hydroxylation. Science. 292(5516): 468-472. https://doi.org/10.1126/science.1059796.

  14. Kumar, B. and Kumar, N. (2000). Effect of altitude on some blood biochemical profile of 15 months old non-pregnant crossbred heifers. Indian Journal of Animal Research. 34(2): 108-111.

  15. Kumar, S., Ganguly, I., Singh, S., Sodhi, M. and Bhakat, M. (2017). Y-chromosome variation in Indian native cattle breeds and crossbred population. Indian Journal of Animal Research. 51(1): 1-7.

  16. Kumari, P., Bharti, V.K., Kumar, D., Mukesh, M. and Sharma, I. (2020). Haematological and biochemical profiling of Ladakhi cow: A native cattle of high altitude Leh-Ladakh, India. Indian Journal of Animal Sciences. 90(4): 599-602.

  17. Lendahl, U., Lee, K.L., Yang, H. and Poellinger, L. (2009). Generating specificity and diversity in the transcriptional response to hypoxia. Nature Reviews Genetics. 10(12): 821-832. https://doi.org/10.1038/nrg2665.

  18. Lewis, B.P., Burge, C.B. and Bartel, D.P. (2005). Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets. Cell. 120(1): 15-20. https://doi.org/10.1016/j.cell.2004.12.035.

  19. Mishra, A.K., Vohra, V., Raja, K.N., Singh, S. and Singh, Y. (2017). Principal component analysis of biometric traits to explain body conformation in Kajali sheep of Punjab, India. Indian Journal of Animal Sciences. 87(1): 939.

  20. Mishra, S.K., Mishra, A.K., Raja, K.N., Vohra, V., Singh, S., Singh, Y., Ahlawat, S. and Kataria, R.S. (2018). Polymorphism analysis at FecB locus in Kajali sheep of India. Indian Journal of Animal Research. 52 (3): 474-476.

  21. Mole, D.R., Blancher, C., Copley, R.R., Pollard, P.J., Gleadle, J.M., Ragoussis, J. and Ratcliffe, P.J. (2009). Genome-wide Association of Hypoxia-inducible Factor (HIF)-1α and HIF-2α DNA Binding with Expression Profiling of Hypoxia-inducible Transcripts. Journal of Biological Chemistry. 284(25): 16767-16775. https://doi.org/10.1074/jbc.M 901790200.

  22. Nallamshetty, S., Chan, S.Y. and Loscalzo, J. (2013). Hypoxia: A master regulator of microRNA biogenesis and activity. Free Radical Biology and Medicine. 64: 20-30. https://doi.org/10.1016/j.freeradbiomed.2013.05.022.

  23. Newman, J.H., Holt, T.N., Cogan, J.D., Womack, B., Phillips, J.A., Li, C., Kendall, Z., Stenmark, K.R., Thomas, M.G., Brown, R.D., Riddle, S R., West, J.D. and Hamid, R. (2015). Increased prevalence of EPAS1 variant in cattle with high-altitude pulmonary hypertension. Nature Communications. 6(1): 6863. https://doi.org/10.1038/ncomms7863.

  24. Price, A.L., Patterson, N.J., Plenge, R.M., Weinblatt, M.E., Shadick, N.A. and Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics. 38(8): 904-909. https://doi.org/10.1038/ng1847.

  25. Rajput, N., Shrivastav, A.B., Parmar, S.N.S., Ranjan, R., Singh, S. and Joseph, E. (2013). Characterization of 12S rRNA gene for meat identification of common wild and domestic small herbivores as an aid to wildlife forensic. Veterinary World. 6(5): 254-259. 

  26. Schödel, J., Oikonomopoulos, S., Ragoussis, J., Pugh, C.W., Ratcliffe, P.J. and Mole, D.R. (2011). High-resolution genome-wide mapping of HIF-binding sites by ChIP-seq. Blood. 117(23): e207-e217. https://doi.org/10.1182/blood-2010-10-314427.

  27. Singh, S., Kankoriya, S., Jain, S.K., Jain, H. and Singh, S.P. (2014). Genetic diversity analysis of Sahiwal and Tharparker breeds of cattle (Bos indicus) by microsatellite markers. Indian Journal of Animal Research 48(5): 418-421.

  28. Wen, J. and Yuan, N. (1998). Analysis on the parameters of blood in healthy Tibetan Mastiffs. Journal of Qinghai Medical College. 20: 7-9.

  29. Wenger, R. H., Stiehl, D.P. and Camenisch, G. (2005). Integration of Oxygen Signaling at the Consensus HRE. Science Signaling. 306: re12. https://doi.org/10.1126/stke. 3062005re12.

  30. Wu, D.D., Ding, X.D., Wang, S., Wójcik, J.M., Zhang, Y., Tokarska, M., Li, Y., Wang, M.S., Faruque, O., Nielsen, R., Zhang, Q. and Zhang, Y.P. (2018). Pervasive introgression facilitated domestication and adaptation in the Bos species complex. Nature Ecology and Evolution. 2(7): 1139-1145. https://doi.org/10.1038/s41559-018-0562-y.

  31. Wu, X., Ding, X., Chu, M., Guo, X., Bao, P., Liang, C. and Yan, P. (2015). Novel SNP of EPAS1 gene associated with higher hemoglobin concentration revealed the hypoxia adaptation of yak (Bos grunniens). Journal of Integrative Agriculture. 14(4): 741-748. https://doi.org/10.1016/S2095-3119(14) 60854-6.

  32. Xia, X., Lemieux, M.E., Li, W., Carroll, J.S., Brown, M., Liu, X.S. and Kung, A.L. (2009). Integrative analysis of HIF binding and transactivation reveals its role in maintaining histone methylation homeostasis. Proceedings of the National Academy of Sciences. 106(11): 4260-4265. https:// doi.org/10.1073/pnas.0810067106.

  33. Yu, F., White, S.B., Zhao, Q. and Lee, F.S. (2001). HIF-1 binding to VHL is regulated by stimulus-sensitive proline hydroxylation. Proceedings of the National Academy of Sciences. 98(17): 9630-9635. https://doi.org/10.1073/pnas.181341498.

  34. Zhang, S., Zhu, K., Zhang, Z., Wang, H. and Wang, X. (2019). Association between an indel polymorphism within the distal promoter of EGLN2 and cancer risk: An updated meta analysis. Molecular Genetics and Genomic Medicine. 7(10): e00936. https://doi.org/10.1002/mgg3.936. 
Reviewed By

Download Article
In this Article
    Contact us
    Follow us

    Editorial Board

    View all (0)