Differential genotypic signatures of Toll-like receptor polymorphisms among dengue-chikungunya mono- and co-infected Eastern Indian patients


Dengue (DENV) and chikungunya (CHIKV) viral infections trigger high patient morbidity and mortality. Mono-/co-infection of these viruses activates innate immune response, triggering Toll-like receptor (TLR) pathways. The present study investigated the differential role of TLR3, 7 and 8 single-nucleotide polymorphisms (SNPs) between mono- and co-infected Eastern Indian patients. Interaction of TLR polymorphic variants with signal peptidase complex (SPC18) was explored which might affect immune signalling against DENV/CHIKV infections. Out of 550 febrile symptomatic patients, 128 DENV-CHIKV co-infected samples were genotyped for eight SNPs of TLR3 (rs3775290-chr4:186083063), TLR7 (rs179008-chrX:12885540, rs5741880-chrX:12869297, rs179010-chrX:12884766, rs3853839-chrX:12889539) and TLR8 (rs5744080-chrX:12919685, rs3764879-chrX:12906578, rs3764880-chrX:12906707) by PCR-RFLP along with 157 healthy individuals. Statistical analysis established genotypic association of TLR SNPs with DENV-CHIKV co-infection, and difference between mono- and co-infected patients and their role in determining high viral load (HVL) during competitive viral replication among co-infected patients. In silico protein-protein docking evaluated interactive effect of TLR variants with SPC18. The findings revealed patients with CC genotypes of TLR7 and 8 SNPs were significantly susceptible towards co-infection, whereas specific genotypes of TLR7 and 8 imparted protection against co-infection. Differential analysis between mono-/co-infected patients revealed distinct genotypic distribution of TLR3, 7 and 8 SNPs. Co-infected patients with TT-rs179010 exhibited DENV-HVL, whereas CHIKV-HVL was detected among patients with other genotypes. Molecular docking of TLR7-rs179008 Q variant and TLR8-rs3764880 V variant with SPC18 generated better free binding energy. This study underlined the importance of TLR7 and 8 SNPs towards mono-/co-infection of DENV/CHIKV, with certain genotypes associated with co-infection susceptibility. Moreover, it suggested a probable role of specific genotypes of TLR7 and 8 polymorphisms imparting high dengue/chikungunya viral load among co-infected patients.

This is a preview of subscription content, access via your institution.

Fig. 1

Data availability

All data generated and analysed during this study are included in this article and supplementary file.


  1. 1.

    Murray NE, Quam MB, Wilder-Smith A (2013) Epidemiology of dengue: past, present and future prospects. Clin Epidemiol 5:299–309. https://doi.org/10.2147/CLEP.S34440

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Taraphdar D, Sarkar A, Chatterjee S Mass scale screening of common arboviral infections by an affordable, cost effective RT-PCR method. (2012). Asian Pac J Trop Biomed 2(2):97–101. https://doi.org/10.1016/S2221-1691(11)60200-1

  3. 3.

    Sengupta S, Mukherjee S, Haldar SK, Bhattacharya N, Tripathi A (2020) Re-emergence of chikungunya virus infection in Eastern India. Braz J Microbiol 51(1):177–182. https://doi.org/10.1007/s42770-019-00212-0

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Mukherjee S, Dutta SK, Sengupta S, Tripathi A (2017) Evidence of dengue and chikungunya virus co-infection and circulation of multiple dengue serotypes in a recent Indian outbreak. Eur J Clin Microbiol Infect Dis 36(11):2273–2279. https://doi.org/10.1007/s10096-017-3061-1

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Rezza G (2014) Dengue and chikungunya: long-distance spread and outbreaks in naïve areas. Pathog Glob Health 108(8):349–355. https://doi.org/10.1179/2047773214Y.0000000163

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    World Health Organization, Regional Office for South-East Asia. Comprehensive guideline for prevention and control of dengue and dengue haemorrhagic fever. New Delhi: World Health Organization Regional Office for South-East Asia; 2011

  7. 7.

    Kaur M, Singh K, Sidhu SK, Devi P, Kaur M, Soneja S, Singh N (2018) Coinfection of chikungunya and dengue viruses: a serological study from North Western region of Punjab, India. J Lab Physicians 10(4):443–447. https://doi.org/10.4103/JLP.JLP_13_18

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Bell JI (2002) Single nucleotide polymorphisms and disease gene mapping. Arthritis Res 4(Suppl 3(Suppl 3)):S273–S278. https://doi.org/10.1186/ar555

    Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Kwok PY, Chen X (2003) Detection of single nucleotide polymorphisms. Curr Issues Mol Biol 5(2):43–60

    CAS  PubMed  Google Scholar 

  10. 10.

    Dutta SK, Tripathi A (2017) Association of toll-like receptor polymorphisms with susceptibility to chikungunya virus infection. Virology 511:207–213. https://doi.org/10.1016/j.virol.2017.08.009

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Alagarasu K, Bachal RV, Memane RS, Shah PS, Cecilia D (2015) Polymorphisms in RNA sensing toll like receptor genes and its association with clinical outcomes of dengue virus infection. Immunobiology. 220(1):164–168

    CAS  Article  Google Scholar 

  12. 12.

    Auclair SM, Bhanu MK, Kendall DA (2012) Signal peptidase I: cleaving the way to mature proteins. Protein Sci 21(1):13–25. https://doi.org/10.1002/pro.757

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Oue N, Naito Y, Hayashi T et al (2014) Signal peptidase complex 18, encoded by SEC11A, contributes to progression via TGF-α secretion in gastric cancer. Oncogene 33:3918–3926. https://doi.org/10.1038/onc.2013.364

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Snapp EL, McCaul N, Quandte M, Cabartova Z, Bontjer I, Källgren C, Nilsson I, Land A, von Heijne G, Sanders RW, Braakman I (2017) Structure and topology around the cleavage site regulate post-translational cleavage of the HIV-1 gp160 signal peptide. eLife 6:e26067. https://doi.org/10.7554/eLife.26067

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    World Health Organization & Special Programme for Research and Training in Tropical Diseases. Dengue guidelines for diagnosis, treatment, prevention and control. WHO (2009). http://apps.who.int/iris/bitstream/10665/44188/1/9789241547871_eng.pdf

  16. 16.

    Mukherjee S, Tripathi A (2019) Contribution of Toll like receptor polymorphisms to dengue susceptibility and clinical outcome among eastern Indian patients. Immunobiology. 224(6):774–785. https://doi.org/10.1016/j.imbio.2019.08.009

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21:263–265

    CAS  Article  Google Scholar 

  18. 18.

    Almagro Armenteros JJ, Tsirigos KD, Sønderby CK et al (2019) SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol 37(4):420–423. https://doi.org/10.1038/s41587-019-0036-z

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Parthiban V, Gromiha MM, Schomburg D (2006) CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Res 34:W239–W242

    CAS  Article  Google Scholar 

  20. 20.

    Venselaar H, Te Beek TA, Kuipers RK, Hekkelman ML, Vriend G (2010) Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist friendly interfaces. BMC Bioinformatics 11:548. https://doi.org/10.1186/1471-2105-11-548

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Sim N-L, Kumar P, Hu J, Henikoff S, Schneider G, Ng PC (2012) SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. https://doi.org/10.1093/nar/gks539

  22. 22.

    López-Ferrando V, Gazzo A, de la Cruz X, Orozco M, Gelpí JL (2017) PMut: a web-based tool for the annotation of pathological variants on proteins, update. Nucleic Acids Res. https://doi.org/10.1093/nar/gkx313

  23. 23.

    Adzhubei IA, Schmidt S, Peshkin L et al (2010) A method and server for predicting damaging missense mutations. Nat Methods 7(4):248–249. https://doi.org/10.1038/nmeth0410-248

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Schwarz J, Cooper D, Schuelke M et al (2014) MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods 11:361–362. https://doi.org/10.1038/nmeth.2890

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Anoosha P, Sakthivel R, Gromiha MM (2015) Prediction of protein disorder on amino acid substitutions. Anal Biochem 491:18–22. https://doi.org/10.1016/j.ab

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Mi H, Muruganujan A, Ebert D, Huang X, Thomas PD (2019) PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. https://doi.org/10.1093/nar/gky1038

  27. 27.

    Xu D, Zhang Y (2012) Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins 80:1715–1735

    CAS  Article  Google Scholar 

  28. 28.

    Xu D, Zhang Y (2013) Toward optimal fragment generations for ab initio protein structure assembly. Proteins 81:229–239

    CAS  Article  Google Scholar 

  29. 29.

    Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, Beglov D, Vajda S (2017) The ClusPro web server for protein-protein docking. Nat Protoc 12(2):255–278

    CAS  Article  Google Scholar 

  30. 30.

    Mosaad YM, Metwally SS, Farag RE, Lotfy ZF, AbdelTwab HE (2019) Association between Toll-like receptor 3 (TLR3) rs3775290, TLR7 rs179008, TLR9 rs352140 and chronic HCV. Immunol Investig 48(3):321–332. https://doi.org/10.1080/08820139.2018.1527851

    CAS  Article  Google Scholar 

  31. 31.

    Askar E, Ramadori G, Mihm S (2010) Toll-like receptor 7 rs179008/Gln11Leu gene variants in chronic hepatitis C virus infection. J Med Virol 82(11):1859–1868. https://doi.org/10.1002/jmv.21893

    CAS  Article  PubMed  Google Scholar 

  32. 32.

    Shrinet J, Shastri JS, Gaind R, Bhavesh NS, Sunil S (2016) Serum metabolomics analysis of patients with chikungunya and dengue mono/co-infections reveals distinct metabolite signatures in the three disease conditions. Sci Rep 6:36833. https://doi.org/10.1038/srep36833

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Byers NM, Fleshman AC, Perera R, Molins CR (2019) Metabolomic insights into human arboviral infections: dengue, chikungunya, and zika viruses. Viruses 11(3):225. https://doi.org/10.3390/v11030225

    CAS  Article  PubMed Central  Google Scholar 

  34. 34.

    Shrinet J, Srivastava P, Kumar A et al (2018) Differential proteome analysis of chikungunya virus and dengue virus coinfection in Aedes mosquitoes. J Proteome Res 17(10):3348–3359. https://doi.org/10.1021/acs.jproteome.8b00211

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Caron M, Paupy C, Grard G et al (2012) Recent introduction and rapid dissemination of Chikungunya virus and Dengue virus serotype 2 associated with human and mosquito coinfections in Gabon, central Africa. Clin Infect Dis 55(6):e45–e53. https://doi.org/10.1093/cid/cis530

    Article  PubMed  Google Scholar 

Download references


The authors are extremely grateful to the Director, Calcutta School of Tropical Medicine, Kolkata, India, for his support, inspiration and providing of necessary facilities for this study. The authors are grateful to Indian Council of Medical Research, India, for granting fellowship to the first author [5/3/8/6/ITR-F/2018-ITR]. The authors also acknowledge Mr. Advaita Chakraborty, Lab Manager, Department of Biomedical Sciences, College of Health Sciences, Marquette University, Milwaukee, WI, for providing his email ID for using QUARK server and ClusPro 2.0 server.

Author information




Siddhartha Sengupta: Experimental work, formal analysis and investigation, original draft preparation, review and editing. Saikat Mukherjee: Dengue detection methodology, review and editing. Nemai Bhattacharya: Sample resources. Anusri Tripathi: Conceptualization, formal analysis and investigation, review and editing, consumable and chemical resources and supervision

Corresponding author

Correspondence to Anusri Tripathi.

Ethics declarations

Ethical approval

All procedures performed in this study involving collection of blood from human participants as well as healthy controls were in accordance with ethical standards of Clinical Research Ethical Committee of Calcutta School of Tropical Medicine (CREC-STM/53 dated 26 September 2013).

Consent to participate

Written consent was received from patients and healthy control individuals prior to participation in the study.

Consent to publish

Not applicable.

Competing interest

The authors declare that they have no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information


(JPG 952 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sengupta, S., Mukherjee, S., Bhattacharya, N. et al. Differential genotypic signatures of Toll-like receptor polymorphisms among dengue-chikungunya mono- and co-infected Eastern Indian patients. Eur J Clin Microbiol Infect Dis (2021). https://doi.org/10.1007/s10096-020-04125-x

Download citation


  • Dengue
  • Chikungunya
  • Co-infection
  • TLR
  • Single-nucleotide polymorphism
  • Signal peptide
  • Docking