In Silico Analysis of the Structural and Functional Consequences of Polymorphic Amino Acid Substitutions in the Cattle HSF1 Protein

Heat stress causes a decrease in the productivity of livestock by negatively aff ecting some important economic features such as fertility, growth and milk production. Th e heat shock transcription factor 1 (HSF1) gene plays a key role in the regulation of the stress response. Th erefore, the present study aimed to predict the most deleterious non-synonymous single nucleotide polymorphisms (nsSNP) on the cattle HSF1 gene via in silico analyses. Out of 170 nsSNPs in the HSF1 gene, 14 SNPs were predicted as deleterious by all the nine servers (PredictSNP, Mapp, PhDSNP, PolyPhen-1, PolyPhen-2, Sift , Snap, nsSNPAnalyzer, and Panther). Consurf analysis determined that the vast majority of SNPs predicted to be deleterious were evolutionary conserved. Protein structural analyses were performed I-Mutant, Mupro, Hope Project server, RaptorX and Swiss Model server. Th e 12 amino acid substitutions (V15G, F18L, L19R, K21M, I35T, V46E, V56G, F61L, A67D, Y76D, V81G, L112P) in the DNA binding region of the cattle HSF1 protein were predicted to be highly deleterious. Th e P112 variant was predicted to disrupt an α-helix structure. It was determined that the two amino acid changes (K21M, Y76D) on the surface of the protein were diff erent in terms of hydrophobicity, charge, and size. Th ese variants (M21, D76) might hamper the protein’s interaction with the heat shock elements.

PDF

___

1. Mishra SR: Significance of molecular chaperones and micro RNAs in acquisition of thermo-tolerance in dairy cattle. Anim Biotechnol, 1-11, 2020. DOI: 10.1080/10495398.2020.1830788
2. Strandén I, Kantanen J, Russo IRM, Orozco-terWengel P, Bruford MW, the Climgen Consortium: Genomic selection strategies for breeding adaptation and production in dairy cattle under climate change. Heredity, 123 (3): 307-317, 2019. DOI: 10.1038/s41437-019-0207-1
3. Tesema Z, Taye M, Ayichew D: The role of phenotypic and genetic basis of livestock selection for climate change adaptation and mitigation: A review. J Appl Adv Res, 4 (2): 66-77, 2019. DOI: 10.21839/jaar.2019.v4i2.251
4. Rocha RFB, Baena MM, de Cássia Estopa A, Gervásio IC, Ibelli AMG, Gionbelli TRS, Gionbelli MP, de Freitas RTF, Meirelles SLC: Differential expression of HSF1 and HSPA6 genes and physiological responses in Angus and Simmental cattle breeds. J Therm Biol, 84, 92-98, 2019. DOI: 10.1016/j. jtherbio.2019.06.002
5. Rong Y, Zeng M, Guan X, Qu K, Liu J, Zhang J, Chen H, Huang B, Lei C: Association of HSF1 genetic variation with heat tolerance in Chinese cattle. Animals, 9 (12): 1027, 2019. DOI: 10.3390/ani9121027
6. Bagath M, Krishnan G, Devaraj C, Rashamol VP, Pragna P, Lees AM, Sejian V: The impact of heat stress on the immune system in dairy cattle: A review. Res Vet Sci, 126, 94-102, 2019. DOI: 10.1016/j.rvsc.2019.08.011
7. Scharf B, Carroll JA, Riley DG, Chase Jr CC, Coleman SW, Keisler DH, Weaber RL, Spiers DE: Evaluation of physiological and blood serum differences in heat-tolerant (Romosinuano) and heat-susceptible (Angus) Bos taurus cattle during controlled heat challenge. J Anim Sci, 88 (7): 2321- 2336, 2010. DOI: 10.2527/jas.2009-2551
8. Balasubramaniam B, Balamurugan K: Role of Heat Shock Factors in Diseases and Immunity. In, Asea AAA, Kaur P (Eds): Heat Shock Proteins in Human Diseases. 197-210, Springer, Cham, 2020. DOI: 10.1007/ 7515_2020_21
9. Zeng L, Qu K, Zhang J, Huang B, Lei C: Genes related to heat tolerance in cattle-A review. Anim Biotechnol, 1-9, 2022. DOI: 10.1080/ 10495398.2022.2047995
10. Onasanya GO, Thiruvenkadan AK, Sreekumar C, Okpeku M, Tirumurugaan GK, Msalya GM, Sanni MT, Olowofeso O, Fafiolu AO, Decampos JS, Obadire FO, Abdullahi AA, Ikeobi CON: Molecular characterization of hsp 70 gene using single nucleotide polymorphism in nigerian breeds of zebu cattle. Fuw Trends Sci Technol J, 4, 714-720, 2019.
11. Jaiswal L, De S, Singh RK, Baithalu RK: Molecular characterization and protein structure prediction of heat shock transcriptional factors in goat (Capra hircus) and sheep (Ovis aries). Anim Biotechnol, 31 (5): 432-439, 2020. DOI: 10.1080/10495398.2019.1615497
12. Barna J, Csermely P, Vellai T: Roles of heat shock factor 1 beyond the heat shock response. Cell Mol Life Sci, 75 (16): 2897-2916, 2018. DOI: 10.1007/s00018-018-2836-6
13. Kovács D, Sigmond T, Hotzi B, Bohár B, Fazekas D, Deák V, Vellai T, Barna J: HSF1Base: A comprehensive database of HSF1 (heat shock factor 1) target genes. Int J Mol Sci, 20 (22): 5815, 2019. DOI: 10.3390/ijms20225815
14. Howe KL, Achuthan P, Allen J, Allen J, Alvarez-Jarreta J, Amode MR, Armean IM, Azov AG, Bennett R, Bhai J, Billis K, Boddu S, Charkhchi M, Cummins C, Fioretto LDR, Davidson C, Dodiya K, El Houdaigui B, Fatima R, Gall A, Giron CG, Grego T, Guijarro-Clarke C, Haggerty L, Hemrom A, Hourlier T, Izuogu OG, Juettemann T, Kaikala V, Kay M, Lavidas I, Le T, Lemos D, Martinez JG, Marugán JC, Maurel T, McMahon AC, Mohanan S, Moore B, Muffato M, Oheh DN, Paraschas D, Parker A, Parton A, Prosovetskaia I, Sakthivel MP, Abdul Salam AI, Schmitt BM, Schuilenburg H, Sheppard D, Steed E, Szpak M, Szuba M, Taylor K, Thormann A, Threadgold G, Walts B, Winterbottom A, Chakiachvili M, Chaubal A, De Silva N, Flint B, Frankish A, Hunt SE, IIsley GR, Langridge N, Loveland JE, Martin FJ, Mudge JM, Morales J, Perry E, Ruffier M, Tate J, Thybert D, Trevanion SJ, Cunningham F, Yates AD, Zerbino DR, Flicek P: Ensembl 2021. Nucleic Acids Res, 49 (D1): D884-D891, 2021. DOI: 10.1093/nar/gkaa942
15. Anckar J, Sistonen L: Regulation of HSF1 function in the heat stress response: Implications in aging and disease. Annu Rev Biochem, 80, 1089- 1115, 2011. DOI: 10.1146/annurev-biochem-060809-095203
16. Sharma C, Seo YH: Small molecule inhibitors of HSF1-activated pathways as potential next-generation anticancer therapeutics. Molecules, 23 (11): 2757, 2018. DOI: 10.3390/molecules23112757
17. Gomez-Pastor R, Burchfiel ET, Thiele DJ: Regulation of heat shock transcription factors and their roles in physiology and disease. Nat Rev Mol Cell Biol, 19 (1): 4-19, 2018. DOI: 10.1038/nrm.2017.73
18. Hentze N, Le Breton L, Wiesner J, Kempf G, Mayer MP: Molecular mechanism of thermosensory function of human heat shock transcription factor Hsf1. eLife, 5:e11576, 2016. DOI: 10.7554/eLife.11576
19. Badri TM, Chen KL, Alsiddig MA, Li L, Cai Y, Wang GL: Genetic polymorphism in Hsp90AA1 gene is associated with the thermotolerance in Chinese Holstein cows. Cell Stress Chaperones, 23 (4): 639-651, 2018. DOI:10.1007/s12192-017-0873-y
20. Malheiros JM, Enríquez-Valencia CE, da Silva Duran BO, de Paula TG, Curi RA, de Vasconcelos Silva JAII, Dal-Pai-Silva M, de Oliveira HN, Chardulo LAL: Association of CAST2, HSP90AA1, DNAJA1 and HSPB1 genes with meat tenderness in Nellore cattle. Meat Sci, 138, 49-52, 2018. DOI: 10.1016/j.meatsci.2018.01.003
21. Shergojry SA, Ganayi BA, Ramesha KP, Rengarajan K, Srihari GV, Das DN, Kataktalware MA: Association of single nucleotide polymorphisms (SNPS) of HSP90AA1 gene with reproductive traits in Deoni cattle. Int J Livest Res, 1 (1): 17-29, 2011. DOI: 10.5455/ijlr.20120204082821
22. Bendl J, Stourac J, Salanda O, Pavelka A, Wieben ED, Zendulka J, Brezovsky J, Damborsky J: PredictSNP: Robust and accurate consensus classifier for prediction of disease-related mutations. Plos Comp Biol, 10 (1):e1003440, 2014. DOI: 10.1371/journal.pcbi.1003440
23. Mueller SC, Backes C, Haas J, the INHERITANCE Study Group, Katus HA, Meder B, Meese E, Keller A: Pathogenicity prediction of nonsynonymous single nucleotide variants in dilated cardiomyopathy. Brief Bioinform, 16 (5): 769-779, 2015. DOI: 10.1093/bib/bbu054
24. Capriotti E, Fariselli P, Casadio R: I-Mutant2. 0: Predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res, 33 (Suppl. 2): W306-W310, 2005. DOI: 10.1093/nar/gki375
25. Cheng J, Randall A, Baldi P: Prediction of protein stability changes for single‐site mutations using support vector machines. Proteins Struct Funct Bioinf, 62 (4): 1125-1132, 2006. DOI: 10.1002/prot.20810
26. Ashkenazy H, Erez E, Martz E, Pupko T, Ben-Tal N: ConSurf 2010: Calculating evolutionary conservation in sequence and structure of proteins and nucleic acids. Nucleic Acids Res, 38 (Suppl. 2): W529-W533, 2010. DOI: 10.1093/nar/gkq399
27. Venselaar H, Te Beek TAH, Kuipers RKP, Hekkelman ML, Vriend G: Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces. BMC Bioinform, 11 (1): 1-10, 2010. DOI: 10.1186/1471-2105-11-548
28. Wang S, Li W, Liu S, Xu J: RaptorX-Property: A web server for protein structure property prediction. Nucleic Acids Res, 44 (W1): W430-W435, 2016. DOI: 10.1093/nar/gkw306
29. Guex N, Peitsch MC: SWISS‐MODEL and the Swiss‐Pdb Viewer: An environment for comparative protein modeling. Electrophoresis, 18 (15): 2714-2723, 1997. DOI: 10.1002/elps.1150181505
30. Mohapatra S, Kundu AK, Mishra SR, Senapati S, Jyotiranjan T, Panda G: HSF1 and GM-CSF expression, its association with cardiac health, and assessment of organ function during heat stress in crossbred Jersey cattle. Res Vet Sci, 139, 200-210, 2021. DOI: 10.1016/j.rvsc.2021.07.018
31. Wankhede NL, Kale MB, Upaganlawar AB, Taksande BG, Umekar MJ, Behl T, Abdellatif AAH, Bhaskaran PM, Dachani SR, Sehgal A, Singh S, Sharma N, Makeen HA, Albratty M, Dailah HG, Bhatia S, Al-Harrasi A, Bungau S: Involvement of molecular chaperone in protein-misfolding brain diseases. Biomed Pharmacother, 147:112647, 2022. DOI: 10.1016/j. biopha.2022.112647
32. Höhfeld J, Benzing T, Bloch W, Fürst DO, Gehlert S, Hesse M, Hoffmann B, Hoppe T, Huesgen PF, Köhn M, Kolanus W, Merkel R, Niessen CM, Pokrzywa W, Rinschen MM, Wachten D, Warscheid B: Maintaining proteostasis under mechanical stress. Embo Rep, 22 (8):e52507, 2021. DOI: 10.15252/embr.202152507
33. Li QL, Ju ZH, Huang JM, Li JB, Li RL, Hou MH, Wang CF, Zhong JF: Two novel SNPs in HSF1 gene are associated with thermal tolerance traits in Chinese Holstein cattle. DNA Cell Biol, 30 (4): 247-254, 2011. DOI: 10.1089/ dna.2010.1133
34. Lee YS, Won K, Shin D, Oh JD: Risk prediction and marker selection in nonsynonymous single nucleotide polymorphisms using whole genome sequencing data. Anim Cells Syst, 24 (6): 321-328, 2020. DOI: 10.1080/ 19768354.2020.1860125
35. Kumar A, Rajendran V, Sethumadhavan R, Shukla P, Tiwari S, Purohit R: Computational SNP analysis: Current approaches and future prospects. Cell Biochem Biophys, 68 (2): 233-239, 2014. DOI: 10.1007/ s12013-013-9705-6
36. Domené S, Scaglia PA, Gutiérrez ML, Domené HM: Applying bioinformatic platforms, in vitro, and in vivo functional assays in the characterization of genetic variants in the GH/IGF pathway affecting growth and development. Cells, 10 (8): 2063, 2021. DOI: 10.3390/cells10082063
37. Oliver GR, Zimmerman MT, Klee EW, Urrutia RA: “The molecule’s the thing:” The promise of molecular modeling and dynamic simulations in aiding the prioritization and interpretation of genomic testing results. F1000Res, 5:766, 2016. DOI: 10.12688/f1000research.8600.3
38. Gassoum A, Abdelraheem NE, Elsadig N: Comprehensive analysis of rsSNPs associated with hypertension using in-silico bioinformatics tools. Open Access Lib J, 3 (7): 1, 2016. DOI: 10.4236/oalib.1102839
39. Feng N, Feng H, Wang S, Punekar AS, Ladenstein R, Wang DC, Zhang Q, Ding J, Liu W: Structures of heat shock factor trimers bound to DNA. iScience, 24 (9):102951, 2021. DOI: 10.1016/j.isci.2021.102951
40. Alshehri MA, Manee MM, Al-Fageeh MB, Al-Shomrani BM: Genomic analysis of intrinsically disordered proteins in the genus Camelus. Int J Mol Sci, 21 (11): 4010, 2020. DOI: 10.3390/ijms21114010
41. Bridges TM, Scheraga RG, Tulapurkar ME, Suffredini D, Liggett SB, Ramarathnam A, Potla R, Singh IS, Hasday JD: Polymorphisms in human heat shock factor-1 and analysis of potential biological consequences. Cell Stress Chaperones, 20 (1): 47-59, 2015. DOI: 10.1007/s12192-014-0524-5
42. Wang Z, Huang C, Lv H, Zhang M, Li X: In silico analysis and high-risk pathogenic phenotype predictions of non-synonymous single nucleotide polymorphisms in human Crystallin beta A4 gene associated with congenital cataract. Plos One, 15 (1):e0227859, 2020. DOI: 10.1371/journal.pone .0227859
43. Ghani U, Ullah R, Anjum T, Ali Y, Hussain S, Shah S, Zulfiqar I, Sultan T, Tanveer MA, Sabar MF: Identification of most lethal nonsynonymous SNPs in tollip gene-An in-silico analysis. 2021. DOI: 10.21203/ rs.3.rs-488110/v1
44. Mirzaie M: Hydrophobic residues can identify native protein structures. Proteins Struct Funct Genet, 86 (4): 467-474, 2018. DOI: 10.1002/prot.25466
45. Kumar A, Biswas P: Effect of site‐directed point mutations on protein misfolding: A simulation study. Proteins Struct Funct Genet, 87 (9): 760-773, 2019. DOI: 10.1002/prot.25702
46. Sudarshan S, Kumar M, Kaur P, Kumar A, Sethuraman G, Sapra S, Gulati S, Gupta N, Kabra M, Roy Chowdhury M: Decoding of novel missense TSC2 gene variants using in-silico methods. BMC Med Genet, 20 (1): 1-9, 2019. DOI: 10.1186/s12881-019-0891-y
47. Agrahari AK, Kumar A, Siva R, Zayed H, Doss CGP: Substitution impact of highly conserved arginine residue at position 75 in GJB1 gene in association with X-linked Charcot–Marie-tooth disease: A computational study. J Theor Biol, 437, 305-317, 2018. DOI: 10.1016/j.jtbi.2017.10.028
48. Elnasri HA, Al Bkrye AM, Khaier MAM: In silico analysis of non synonymous SNPs in DHCR7 gene. Am J Bioinform Res, 8 (1): 12-18, 2018.
49. Sedaghat A, Zamani M, Jahanshahi A, Ghaderian SB, Shariati G, Saberi A, Hamid M, Aminzadeh M, Galehdari H: Frequent novel mutations are causative for maple syrup urine disease from Southwest Iran. Meta Gene, 16, 96-104, 2018. DOI: 10.1016/j.mgene.2018.01.010
50. Abdelhalim DM, Albkrye AM, Salih MA, Abodlaa AEE, Elnasri HA, Khaier MAM: In silico analysis of a single nucleotide polymorphism (SNPS) in human GSTM1 gene associated with cancer development. European J Biomed Pharm Sci, 7 (1): 514-521, 2020.