Genomics & Informatics

  • 제20권4호
  • /
  • Pages.41.1-41.9
  • /
  • 2022
  • /
  • 1598-866X(pISSN)
  • /
  • 2234-0742(eISSN)
한국유전체학회 (Korea Genome Organization)
권호 표지
DOI QR코드

DOI QR Code

In silico detection and characterization of novel virulence proteins of the emerging poultry pathogen Gallibacterium anatis

  • L. G. T. G. Rajapaksha (Veterinary Medical Center and College of Veterinary Medicine, Jeonbuk National University) ;
  • C. W. R. Gunasekara (College of Fisheries Science, Pukyong National University) ;
  • P. S. de Alwis (College of Fisheries Science, Pukyong National University)
  • 투고 : 2022.01.17
  • 심사 : 2022.10.14
  • 발행 : 2022.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The pathogen Gallibacterium anatis has caused heavy economic losses for commercial poultry farms around the world. However, despite its importance, the functions of its hypothetical proteins (HPs) have been poorly characterized. The present study analyzed the functions and structures of HPs obtained from Gallibacterium anatis (NCTC11413) using various bioinformatics tools. Initially, all the functions of HPs were predicted using the VICMpred tool, and the physicochemical properties of the identified virulence proteins were then analyzed using Expasy's ProtParam server. A virulence protein (WP_013745346.1) that can act as a potential drug target was further analyzed for its secondary structure, followed by homology modeling and three-dimensional (3D) structure determination using the Swiss-Model and Phyre2 servers. The quality assessment and validation of the 3D model were conducted using ERRAT, Verify3D, and PROCHECK programs. The functional and phylogenetic analysis was conducted using ProFunc, STRING, KEGG servers, and MEGA software. The bioinformatics analysis revealed 201 HPs related to cellular processes (n = 119), metabolism (n = 61), virulence (n = 11), and information/storage molecules (n = 10). Among the virulence proteins, three were detected as drug targets and six as vaccine targets. The characterized virulence protein WP_013745346.1 is proven to be stable, a drug target, and an enzyme related to the citrate cycle in the present pathogen. This enzyme was also found to facilitate other metabolic pathways, the biosynthesis of secondary metabolites, and the biosynthesis of amino acids.

키워드

참고문헌

  1. Singh SV, Singh BR, Sinha DK, Kumar VO, Vadhana PA, Bhardwaj M, et al. Gallibacterium anatis: an emerging pathogen of poultry birds and domiciled birds. J Vet Sci Technol 2016;7:3.
  2. Bisgaard M. Ecology and significance of Pasteurellaceae in animals. Zentralbl Bakteriol 1993;279:7-26. https://doi.org/10.1016/S0934-8840(11)80487-1
  3. Mushin R, Weisman Y, Singer N. Pasteurella haemolytica found in the respiratory tract of fowl. Avian Dis 1980;24:162-168. https://doi.org/10.2307/1589775
  4. Bisgaard M. Incidence of Pasteurella haemolytica in the respiratory tract of apparently healthy chickens and chickens with infectious bronchitis. characterisation of 213 strains. Avian Pathol 1977;6:285-292. https://doi.org/10.1080/03079457708418238
  5. Bojesen AM, Nielsen SS, Bisgaard M. Prevalence and transmission of haemolytic Gallibacterium species in chicken production systems with different biosecurity levels. Avian Pathol 2003;32:503-510. https://doi.org/10.1080/0307945031000154107
  6. Narasinakuppe Krishnegowda D, Dhama K, Kumar Mariappan A, Munuswamy P, Iqbal Yatoo M, Tiwari R, et al. Etiology, epidemiology, pathology, and advances in diagnosis, vaccine development, and treatment of Gallibacterium anatis infection in poultry: a review. Vet Q 2020;40:16-34. https://doi.org/10.1080/01652176.2020.1712495
  7. Persson G, Bojesen AM. Bacterial determinants of importance in the virulence of Gallibacterium anatis in poultry. Vet Res 2015;46:57.
  8. Bashir Z, Rizwan M, Mushtaq K, Munir A, Ali I. In silico structural and functional prediction of phaseolus vulgaris hypothetical protein PHA VU_004G136400g. J Proteomics Bioinform 2017;10:207-211.
  9. Khan A, Ahmed H, Jahan N, Ali SR, Amin A, Morshed MN. An in silico approach for structural and functional annotation of Salmonella enterica serovar typhimurium hypothetical protein R_27. Int J Biautomation 2016;20:31-42.
  10. Loewenstein Y, Raimondo D, Redfern OC, Watson J, Frishman D, Linial M, et al. Protein function annotation by homology-based inference. Genome Biol 2009;10:207.
  11. Saha S, Raghava GP. VICMpred: an SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition. Genomics Proteomics Bioinformatics 2006;4:42-47. https://doi.org/10.1016/S1672-0229(06)60015-6
  12. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, et al. Protein identification and analysis tools on the ExPASy server. In: The Proteomics Protocols Handbook (Walker JM, ed.). Totowa: Springer, 2005. pp. 571-607.
  13. Almagro Armenteros JJ, Tsirigos KD, Sonderby CK, Petersen TN, Winther O, Brunak S, et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol 2019;37:420-423.
  14. Bhasin M, Garg A, Raghava GP. PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 2005;21: 2522-2524. https://doi.org/10.1093/bioinformatics/bti309
  15. Emanuelsson O. Predicting protein subcellular localisation from amino acid sequence information. Brief Bioinform 2002;3:361-376. https://doi.org/10.1093/bib/3.4.361
  16. Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA, Sonnhammer EL, et al. Pfam: the protein families database in 2021. Nucleic Acids Res 2021;49:D412-D419. https://doi.org/10.1093/nar/gkaa913
  17. Finn RD, Clements J, Eddy SR. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res 2011;39:W29-W37. https://doi.org/10.1093/nar/gkr367
  18. de Castro E, Sigrist CJ, Gattiker A, Bulliard V, Langendijk-Genevaux PS, Gasteiger E, et al. ScanProsite: detection of PROSITE signature matches and ProRule-associated functional and structural residues in proteins. Nucleic Acids Res 2006;34: W362-W365. https://doi.org/10.1093/nar/gkl124
  19. Zdobnov EM, Apweiler R. InterProScan: an integration platform for the signature-recognition methods in InterPro. Bioinformatics 2001;17:847-848. https://doi.org/10.1093/bioinformatics/17.9.847
  20. Schultz J, Copley RR, Doerks T, Ponting CP, Bork P. SMART: a web-based tool for the study of genetically mobile domains. Nucleic Acids Res 2000;28:231-234. https://doi.org/10.1093/nar/28.1.231
  21. Laskowski RA, Watson JD, Thornton JM. ProFunc: a server for predicting protein function from 3D structure. Nucleic Acids Res 2005;33:W89-W93. https://doi.org/10.1093/nar/gki414
  22. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/ measurement sets. Nucleic Acids Res 2021;49:D605-D612. https://doi.org/10.1093/nar/gkaa1074
  23. Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol 2016;33:1870-1874. https://doi.org/10.1093/molbev/msw054
  24. Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 2016;44:D457-D462. https://doi.org/10.1093/nar/gkv1070
  25. Geourjon C, Deleage G. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput Appl Biosci 1995;11:681-684.
  26. Buchan DW, Jones DT. The PSIPRED Protein Analysis Workbench: 20 years on. Nucleic Acids Res 2019;47:W402-W407. https://doi.org/10.1093/nar/gkz297
  27. Waterfield N, Kamita SG, Hammock BD, ffrench-Constant R. The Photorhabdus Pir toxins are similar to a developmentally regulated insect protein but show no juvenile hormone esterase activity. FEMS Microbiol Lett 2005;245:47-52. https://doi.org/10.1016/j.femsle.2005.02.018
  28. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr 1993;26:283-291. https://doi.org/10.1107/S0021889892009944
  29. Imam N, Alam A, Ali R, Siddiqui MF, Ali S, Malik MZ, et al. In silico characterization of hypothetical proteins from Orientia tsutsugamushi str. Karp uncovers virulence genes. Heliyon 2019;5:e02734.
  30. Fatoba AJ, Okpeku M, Adeleke MA. Subtractive genomics approach for identification of novel therapeutic drug targets in Mycoplasma genitalium. Pathogens 2021;10:921.
  31. Lima RM, Freitas ES, Silva LD, Ribeiro JF, Neves BJ, Brock M, et al. A structure-based approach for the discovery of inhibitors against methylcitrate synthase of Paracoccidioides lutzii. J Biomol Struct Dyn 2022;40:9361-9373.
  32. Munoz-Elias EJ, Upton AM, Cherian J, McKinney JD. Role of the methylcitrate cycle in Mycobacterium tuberculosis metabolism, intracellular growth, and virulence. Mol Microbiol 2006;60:1109-1122. https://doi.org/10.1111/j.1365-2958.2006.05155.x
  33. Schlachter CR, Klapper V, Radford T, Chruszcz M. Comparative studies of Aspergillus fumigatus 2-methylcitrate synthase and human citrate synthase. Biol Chem 2019;400:1567-1581. https://doi.org/10.1515/hsz-2019-0106
  34. Oany AR, Ahmad SA, Siddikey MA, Hossain MU, Ferdoushi A. Computational structure analysis and function prediction of an uncharacterized protein (I6U7D0) of Pyrococcus furiosus COM1. Austin J Comput Biol Bioinform 2014;1:5.