In Silico Analysis of nsSNPs of Carp TLR22 Gene Affecting its Binding Ability with Poly I:C

  • Vemulawada Chakrapani
  • Kiran D. Rasal
  • Sunil Kumar
  • Shibani D. Mohapatra
  • Jitendra K. Sundaray
  • Pallipuram Jayasankar
  • Hirak K. BarmanEmail author
Original Research Article


Immune response mediated by toll-like receptor 22 (TLR22), only found in teleost/amphibians, is triggered by double-stranded RNA binding to its LRR (leucine-rich repeats) ecto-domain. Accumulated evidences suggested that missense mutations in TLR genes affect its function. However, information on mutation linked pathogen recognition for TLR22 was lacking. The present study was commenced for predicting the effect of non-synonymous single-nucleotide polymorphisms (nsSNPs) on the pathogen recognizable LRR domain of TLR22 of farmed carp, Labeo rohita. The sequence-based algorithms (SIFT, PROVEAN and I-Mutant2.0) indicated that three SNPs (out of 27) such as p.L159F (rs76759876) and p.L529P (rs749355507) of LRR, and p.I836M (rs750758397) of intracellular motifs could potentially disrupt protein function. The 3D structure was generated using MODELLER 9.13 and further validated by SAVEs server. The simulated molecular docking of native TLR22 and mutants with poly I:C ligand indicated that mutations positioned at p.L159F and p.L529P of the LRR region affects the binding affinity significantly. This is the first kind of study of predicting nsSNPs of teleost TLR22 with disturbed ligand binding affinity with its extra-cellular LRR domain and thereby likely hindrance in subsequent signal transduction. This study serves as a guide for in vivo evaluation of impact of mutation on immune response mediated by teleost TLR22 gene.


TLR22 nsSNPs Protein modelling Docking simulation 



Sorting intolerant from tolerant


Single-nucleotide polymorphism


Non-synonymous single-nucleotide polymorphisms


Amino acid substitutions


Protein variation effect analyzer


Protein data bank


Structural analysis and verification server



This work was supported by a grant from the National Agricultural Science Fund (NASF), Indian Council of Agricultural Research, Union Ministry of Agriculture, Government of India. Thanks to the Director of this Institute for providing required facilities to carry out this research.


  1. 1.
    Krishnan J, Selvarajoo K, Tsuchiya M, Lee G, Choi S (2007) Toll-like receptor signal transduction. Exp Mol Med 39(4):421–438. doi: 10.1038/emm.2007.47 CrossRefPubMedGoogle Scholar
  2. 2.
    Rastogi A, Murik O, Bowler C, Tirichine L (2016) PhytoCRISP-Ex: a web-based and stand-alone application to find specific target sequences for CRISPR/CAS editing. BMC Bioinform 17(1):261. doi: 10.1186/s12859-016-1143-1 CrossRefGoogle Scholar
  3. 3.
    Panda RP, Chakrapani V, Patra SK, Saha JN, Jayasankar P, Kar B, Sahoo PK, Barman HK (2014) First evidence of comparative responses of toll-like receptor 22 (TLR22) to relatively resistant and susceptible Indian farmed carps to Argulus siamensis infection. Dev Comp Immunol 47(1):25–35. doi: 10.1016/j.dci.2014.06.016 CrossRefPubMedGoogle Scholar
  4. 4.
    Medzhitov R (2001) Toll-like receptors and innate immunity. Nat Rev Immunol 1(2):135–145. doi: 10.1038/35100529 CrossRefPubMedGoogle Scholar
  5. 5.
    Byadgi O, Puteri D, Lee YH, Lee JW, Cheng TC (2014) Identification and expression analysis of cobia (Rachycentron canadum) toll-like receptor 9 gene. Fish Shellfish Immunol 36(2):417–427. doi: 10.1016/j.fsi.2013.12.017 CrossRefPubMedGoogle Scholar
  6. 6.
    Roach JC, Glusman G, Rowen L, Kaur A, Purcell MK, Smith KD, Hood LE, Aderem A (2005) The evolution of vertebrate toll-like receptors. Proc Natl Acad Sci USA 102(27):9577–9582. doi: 10.1073/pnas.0502272102 CrossRefPubMedGoogle Scholar
  7. 7.
    Rebl A, Siegl E, Kollner B, Fischer U, Seyfert HM (2007) Characterization of twin toll-like receptors from rainbow trout (Oncorhynchus mykiss): evolutionary relationship and induced expression by Aeromonas salmonicida salmonicida. Dev Comp Immunol 31(5):499–510. doi: 10.1016/j.dci.2006.08.007 CrossRefPubMedGoogle Scholar
  8. 8.
    Hawn TR, Verbon A, Lettinga KD, Zhao LP, Li SS, Laws RJ, Skerrett SJ, Beutler B, Schroeder L, Nachman A, Ozinsky A, Smith KD, Aderem A (2003) A common dominant TLR5 stop codon polymorphism abolishes flagellin signaling and is associated with susceptibility to legionnaires’ disease. J Exp Med 198(10):1563–1572. doi: 10.1084/jem.20031220 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Liu ZJ, Cordesb JF (2004) DNA marker technologies and their applications in aquaculture genetics. Aquaculture 242(1–4):735–736. doi: 10.1016/j.aquaculture.2004.05.027 CrossRefGoogle Scholar
  10. 10.
    Rasal KD, Chakrapani V, Patra SK, Jena S, Mohapatra SD, Nayak S, Sundaray JK, Jayasankar P, Barman HK (2015) Identification and prediction of consequences of non-synonymous SNP in glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene of zebrafish, Danio rerio. Turk J Biol. doi: 10.3906/biy-1501-11 CrossRefGoogle Scholar
  11. 11.
    Prado-Montes de Oca E, Velarde-Felix JS, Rios-Tostado JJ, Picos-Cardenas VJ, Figuera LE (2009) SNP 668C (-44) alters a NF-kappaB1 putative binding site in non-coding strand of human beta-defensin 1 (DEFB1) and is associated with lepromatous leprosy. Infect Genet Evol 9(4):617–625. doi: 10.1016/j.meegid.2009.03.006 CrossRefPubMedGoogle Scholar
  12. 12.
    Wurfel MM, Gordon AC, Holden TD, Radella F, Strout J, Kajikawa O, Ruzinski JT, Rona G, Black RA, Stratton S, Jarvik GP, Hajjar AM, Nickerson DA, Rieder M, Sevransky J, Maloney JP, Moss M, Martin G, Shanholtz C, Garcia JG, Gao L, Brower R, Barnes KC, Walley KR, Russell JA, Martin TR (2008) Toll-like receptor 1 polymorphisms affect innate immune responses and outcomes in sepsis. Am J Respir Crit Care Med 178(7):710–720. doi: 10.1164/rccm.200803-462OC CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Oshiumi H, Tsujita T, Shida K, Matsumoto M, Ikeo K, Seya T (2003) Prediction of the prototype of the human toll-like receptor gene family from the pufferfish, Fugu rubripes, genome. Immunogenetics 54(11):791–800. doi: 10.1007/s00251-002-0519-8 CrossRefPubMedGoogle Scholar
  14. 14.
    Tenga MJ, Lazar IM (2011) Impact of peptide modifications on the isobaric tags for relative and absolute quantitation method accuracy. Anal Chem 83(3):701–707. doi: 10.1021/ac100775s CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Sagar M, Pandey N, Qamar N, Singh B, Shukla A (2015) Domain analysis of 3 Keto Acyl-CoA synthase for structural variations in Vitis vinifera and Oryza brachyantha using comparative modelling. Interdiscip Sci Comput Life Sci 7(1):7–20. doi: 10.1007/s12539-013-0017-8 CrossRefGoogle Scholar
  16. 16.
    Gfeller D, Ernst A, Jarvik N, Sidhu SS, Bader GD (2014) Prediction and experimental characterization of nsSNPs altering human PDZ-binding motifs. PLoS One 9(4):e94507. doi: 10.1371/journal.pone.0094507 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    de Alencar SA, Lopes JC (2010) A comprehensive in silico analysis of the functional and structural impact of SNPs in the IGF1R gene. J Biomed Biotechnol. doi: 10.1155/2010/715139 CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    George Priya Doss C, Rajasekaran R, Sudandiradoss C, Ramanathan K, Purohit R, Sethumadhavan R (2008) A novel computational and structural analysis of nsSNPs in CFTR gene. Genomic Med 2(1–2):23–32. doi: 10.1007/s11568-008-9019-8 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Joshi BB, Koringa PG, Mistry KN, Patel AK, Gang S, Joshi CG (2015) In silico analysis of functional nsSNPs in human TRPC6 gene associated with steroid resistant nephrotic syndrome. Gene 572(1):8–16. doi: 10.1016/j.gene.2015.06.069 CrossRefPubMedGoogle Scholar
  20. 20.
    AbdulAzeez S, Borgio JF (2016) In-silico computing of the most deleterious nsSNPs in HBA1 gene. PLoS One 11(1):e0147702. doi: 10.1371/journal.pone.0147702 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Borgio JF, Al-Madan MS, AbdulAzeez S (2016) Mutation near the binding interfaces at alpha-hemoglobin stabilizing protein is highly pathogenic. Am J Transl Res 8(10):4224–4232PubMedPubMedCentralGoogle Scholar
  22. 22.
    Li Y-G, Siripanyaphinyo U, Tumkosit U, Noranate N, A-nuegoonpipat A, Pan Y, Kameoka M, Kurosu T, Ikuta K, Takeda N, Anantapreecha S (2012) Poly (I:C), an agonist of toll-like receptor-3, inhibits replication of the Chikungunya virus in BEAS-2B cells. Virol J 9(1):1–8. doi: 10.1186/1743-422x-9-114 CrossRefGoogle Scholar
  23. 23.
    Hwang SD, Ohtani M, Hikima J, Jung TS, Kondo H, Hirono I, Aoki T (2012) Molecular cloning and characterization of toll-like receptor 3 in Japanese flounder, Paralichthys olivaceus. Dev Comp Immunol 37(1):87–96. doi: 10.1016/j.dci.2011.12.004 CrossRefPubMedGoogle Scholar
  24. 24.
    Sahoo BR, Basu M, Swain B, Maharana J, Dikhit MR, Jayasankar P, Samanta M (2012) Structural insights of rohu TLR3, its binding site analysis with fish reovirus dsRNA, poly I: C and zebrafish TRIF. Int J Biol Macromol 51(4):531–543. doi: 10.1016/j.ijbiomac.2012.06.005 CrossRefPubMedGoogle Scholar
  25. 25.
    Chiou PP, Lin CM, Bols NC, Chen TT (2007) Characterization of virus/double-stranded RNA-dependent induction of antimicrobial peptide hepcidin in trout macrophages. Dev Comp Immunol 31(12):1297–1309. doi: 10.1016/j.dci.2007.03.009 CrossRefPubMedGoogle Scholar
  26. 26.
    Phelan PE, Mellon MT, Kim CH (2005) Functional characterization of full-length TLR3, IRAK-4, and TRAF6 in zebrafish (Danio rerio). Mol Immunol 42(9):1057–1071. doi: 10.1016/j.molimm.2004.11.005 CrossRefPubMedGoogle Scholar
  27. 27.
    Rodriguez MF, Wiens GD, Purcell MK, Palti Y (2005) Characterization of toll-like receptor 3 gene in rainbow trout (Oncorhynchus mykiss). Immunogenetics 57(7):510–519. doi: 10.1007/s00251-005-0013-1 CrossRefPubMedGoogle Scholar
  28. 28.
    Palti Y, Rodriguez MF, Gahr SA, Purcell MK, Rexroad CE 3rd, Wiens GD (2010) Identification, characterization and genetic mapping of TLR1 loci in rainbow trout (Oncorhynchus mykiss). Fish Shellfish Immunol 28(5–6):918–926. doi: 10.1016/j.fsi.2010.02.002 CrossRefPubMedGoogle Scholar
  29. 29.
    Matsuo A, Oshiumi H, Tsujita T, Mitani H, Kasai H, Yoshimizu M, Matsumoto M, Seya T (2008) Teleost TLR22 recognizes RNA duplex to induce IFN and protect cells from birnaviruses. J Immunol 181(5):3474–3485CrossRefGoogle Scholar
  30. 30.
    Chen A, Li C, Hu W, Lau MY, Lin H, Rockwell NC, Martin SS, Jernstedt JA, Lagarias JC, Dubcovsky J (2014) Phytochrome C plays a major role in the acceleration of wheat flowering under long-day photoperiod. Proc Natl Acad Sci USA 111(28):10037–10044. doi: 10.1073/pnas.1409795111 CrossRefPubMedGoogle Scholar
  31. 31.
    Mohapatra C, Barman HK, Panda RP, Kumar S, Das V, Mohanta R, Mohapatra SD, Jayasankar P (2010) Cloning of cDNA and prediction of peptide structure of Plzf expressed in the spermatogonial cells of Labeo rohita. Mar Genomics 3(3–4):157–163. doi: 10.1016/j.margen.2010.09.002 CrossRefPubMedGoogle Scholar
  32. 32.
    Barman HK, Mohanta R, Patra SK, Chakrapani V, Panda RP, Nayak S, Jena S, Jayasankar P, Nandanpawar P (2015) The beta-actin gene promoter of rohu carp (Labeo rohita) drives reporter gene expressions in transgenic rohu and various cell lines, including spermatogonial stem cells. Cell Mol Biol Lett 20(2):237–247. doi: 10.1515/cmble-2015-0010 CrossRefPubMedGoogle Scholar
  33. 33.
    Mohanta R, Jayasankar P, Das Mahapatra K, Saha JN, Barman HK (2014) Molecular cloning, characterization and functional assessment of the myosin light polypeptide chain 2 (mylz2) promoter of farmed carp, Labeo rohita. Transgenic Res 23(4):601–607. doi: 10.1007/s11248-014-9798-8 CrossRefPubMedGoogle Scholar
  34. 34.
    Mohapatra C, Barman HK (2014) Identification of promoter within the first intron of Plzf gene expressed in carp spermatogonial stem cells. Mol Biol Rep 41(10):6433–6440. doi: 10.1007/s11033-014-3525-7 CrossRefPubMedGoogle Scholar
  35. 35.
    Rasal KD, Chakrapani V, Patra SK, Mohapatra SD, Nayak S, Jena S, Sundaray JK, Jayasankar P, Barman HK (2016) Identification of deleterious mutations in myostatin gene of rohu carp (Labeo rohita) using modeling and molecular dynamic simulation approaches. Biomed Res Int. doi: 10.1155/2016/7562368 CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Kumar P, Henikoff S, Ng PC (2009) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 4(7):1073–1081. doi: 10.1038/nprot.2009.86 CrossRefPubMedGoogle Scholar
  37. 37.
    Ng PC, Henikoff S (2003) SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res 31(13):3812–3814CrossRefGoogle Scholar
  38. 38.
    Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K, Muruganujan A, Narechania A (2003) PANTHER: a library of protein families and subfamilies indexed by function. Genome Res 13(9):2129–2141. doi: 10.1101/gr.772403 CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Thomas PD, Kejariwal A (2004) Coding single-nucleotide polymorphisms associated with complex vs. mendelian disease: evolutionary evidence for differences in molecular effects. Proc Natl Acad Sci USA 101(43):15398–15403. doi: 10.1073/pnas.0404380101 CrossRefPubMedGoogle Scholar
  40. 40.
    Capriotti E, Fariselli P, Casadio R (2005) I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 33:306–310. doi: 10.1093/nar/gki375 CrossRefGoogle Scholar
  41. 41.
    Choi Y, Sims GE, Murphy S, Miller JR, Chan AP (2012) Predicting the functional effect of amino acid substitutions and indels. PLoS One 7(10):e46688. doi: 10.1371/journal.pone.0046688 CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410. doi: 10.1016/S0022-2836(05)80360-2 CrossRefPubMedGoogle Scholar
  43. 43.
    Shankaracharya Das S, Vidyarthi AS (2011) Homology modeling and function prediction of hABH1, involving in repair of alkylation damaged DNA. Interdiscip Sci Comput Life Sci 3(3):175. doi: 10.1007/s12539-011-0087-4 CrossRefGoogle Scholar
  44. 44.
    Manivannan P, Muralitharan G (2014) Molecular modeling of abc transporter system—permease proteins from Microcoleus chthonoplastes PCC 7420 for effective binding against secreted aspartyl proteinases in Candida albicans—a therapeutic intervention. Interdiscip Sci Comput Life Sci 6(1):63–70. doi: 10.1007/s12539-014-0189-x CrossRefGoogle Scholar
  45. 45.
    Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22(22):4673–4680CrossRefGoogle Scholar
  46. 46.
    Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234(3):779–815. doi: 10.1006/jmbi.1993.1626 CrossRefPubMedGoogle Scholar
  47. 47.
    Tabassum A, Rajeshwari T, Soni N, Raju DS, Yadav M, Nayarisseri A, Jahan P (2014) Structural characterization and mutational assessment of podocin—a novel drug target to nephrotic syndrome—an in silico approach. Interdiscip Sci Comput Life Sci 6(1):32–39. doi: 10.1007/s12539-014-0190-4 CrossRefGoogle Scholar
  48. 48.
    Jones RT, Chahal SP (1997) The use of radiolabelling techniques to measure substantivity to, and penetration into, hair of protein hydrolysates. Int J Cosmet Sci 19(5):215–226. doi: 10.1046/j.1467-2494.1997.171717.x CrossRefPubMedGoogle Scholar
  49. 49.
    Wang R, Lai L, Wang S (2002) Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des 16(1):11–26CrossRefGoogle Scholar
  50. 50.
    George Priya Doss C, Rajith B (2012) Computational refinement of functional single nucleotide polymorphisms associated with ATM gene. PLoS One 7(4):e34573. doi: 10.1371/journal.pone.0034573 CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Gautam B, Singh G, Wadhwa G, Farmer R, Singh S, Singh AK, Jain PA, Yadav PK (2012) Metabolic pathway analysis and molecular docking analysis for identification of putative drug targets in Toxoplasma gondii: novel approach. Bioinformation 8(3):134–141CrossRefGoogle Scholar
  52. 52.
    Sahoo BR, Dikhit MR, Bhoi GK, Maharana J, Lenka SK, Dubey PK, Tiwari DK (2015) Understanding the distinguishable structural and functional features in zebrafish TLR3 and TLR22, and their binding modes with fish dsRNA viruses: an exploratory structural model analysis. Amino Acids 47(2):381–400. doi: 10.1007/s00726-014-1872-2 CrossRefPubMedGoogle Scholar
  53. 53.
    Ng PC, Henikoff S (2006) Predicting the effects of amino acid substitutions on protein function. Annu Rev Genomics Hum Genet 7:61–80. doi: 10.1146/annurev.genom.7.080505.115630 CrossRefPubMedGoogle Scholar
  54. 54.
    Auer TO, Duroure K, De Cian A, Concordet JP, Del Bene F (2014) Highly efficient CRISPR/Cas9-mediated knock-in in zebrafish by homology-independent DNA repair. Genome Res 24(1):142–153. doi: 10.1101/gr.161638.113 CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Chakrapani V, Patra SK, Panda RP, Rasal KD, Jayasankar P, Barman HK (2016) Establishing targeted carp TLR22 gene disruption via homologous recombination using CRISPR/Cas9. Dev Comp Immunol 61:242–247. doi: 10.1016/j.dci.2016.04.009 CrossRefPubMedGoogle Scholar
  56. 56.
    Miyaoka Y, Chan AH, Judge LM, Yoo J, Huang M, Nguyen TD, Lizarraga PP, So PL, Conklin BR (2014) Isolation of single-base genome-edited human iPS cells without antibiotic selection. Nat Methods 11(3):291–293. doi: 10.1038/nmeth.2840 CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Wang F, Shi Z, Cui Y, Guo X, Shi YB, Chen Y (2015) Targeted gene disruption in Xenopus laevis using CRISPR/Cas9. Cell Biosci. doi: 10.1186/s13578-015-0006-1 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Vemulawada Chakrapani
    • 1
  • Kiran D. Rasal
    • 1
  • Sunil Kumar
    • 2
  • Shibani D. Mohapatra
    • 1
  • Jitendra K. Sundaray
    • 1
  • Pallipuram Jayasankar
    • 1
  • Hirak K. Barman
    • 1
    Email author
  1. 1.Fish Genetics and Biotechnology DivisionICAR, Central Institute of Freshwater AquacultureBhubaneswarIndia
  2. 2.ICAR, National Bureau of Agriculturally Important MicroorganismsMauIndia

Personalised recommendations