Parasites, Hosts and Diseases

  • 제62권3호
  • /
  • Pages.351-364
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  • 2024
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  • 2982-5164(pISSN)
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  • 1738-0006(eISSN)
대한기생충학·열대의학회 (The Korea Society for Parasitology and Tropical Medicine)
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Alterations in immunized antigens of Anisakis pegreffii by ampicillin-induced gut microbiome changes in mice

  • Myungjun Kim (Department of Tropical Medicine, Institute of Tropical Medicine, and Arthropods of Medical Importance Resource Bank, Yonsei University College of Medicine) ;
  • Jun Ho Choi (Department of Tropical Medicine, Institute of Tropical Medicine, and Arthropods of Medical Importance Resource Bank, Yonsei University College of Medicine) ;
  • Myung-hee Yi (Department of Tropical Medicine, Institute of Tropical Medicine, and Arthropods of Medical Importance Resource Bank, Yonsei University College of Medicine) ;
  • Singeun Oh (Department of Tropical Medicine, Institute of Tropical Medicine, and Arthropods of Medical Importance Resource Bank, Yonsei University College of Medicine) ;
  • Tai-Soon Yong (Department of Tropical Medicine, Institute of Tropical Medicine, and Arthropods of Medical Importance Resource Bank, Yonsei University College of Medicine) ;
  • Ju Yeong Kim (Department of Tropical Medicine, Institute of Tropical Medicine, and Arthropods of Medical Importance Resource Bank, Yonsei University College of Medicine)
  • 투고 : 2023.11.13
  • 심사 : 2024.06.11
  • 발행 : 2024.08.31
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초록

The gut microbiome plays an essential role in host immune responses, including allergic reactions. However, commensal gut microbiota is extremely sensitive to antibiotics and excessive usage can cause microbial dysbiosis. Herein, we investigated how changes in the gut microbiome induced by ampicillin affected the production of IgG1 and IgG2a antibodies in mice subsequently exposed to Anisakis pegreffii antigens. Ampicillin treatment caused a notable change in the gut microbiome as shown by changes in both alpha and beta diversity indexes. In a 1-dimensional immunoblot using Anisakis-specific anti-mouse IgG1, a 56-kDa band corresponding to an unnamed Anisakis protein was detected using mass spectrometry analysis only in ampicillin-treated mice. In the Anisakis-specific anti-mouse IgG2a-probed immunoblot, a 70-kDa band corresponding to heat shock protein 70 (HSP70) was only detected in ampicillin-treated and Anisakis-immunized mice. A 2-dimensional immunoblot against Anisakis extract with immunized mouse sera demonstrated altered spot patterns in both groups. Our results showed that ampicillin treatment altered the gut microbiome composition in mice, changing the immunization response to antigens from A. pegreffii. This research could serve as a basis for developing vaccines or allergy immunotherapies against parasitic infections.

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과제정보

This research was supported by Bumsuk Academic Research Fund in 2022.

참고문헌

  1. Sohn WM, Chai JY. Anisakiosis (Anisakidosis). In Palmer SR, Soulsby L, Torgerson PR, Brown WG, eds, Oxford Textbook of Zoonoses-Biology, Clinical Practice, and Public Health Control. 2nd ed. Oxford University Press. Oxford, UK. 2011, pp 774-786. 
  2. Bao M, Pierce GJ, Pascual S, Gonzalez-Munoz M, Mattiucci S, et al. Assessing the risk of an emerging zoonosis of worldwide concern: anisakiasis. Sci Rep 2017;7(1):43699. https://doi.org/10.1038/srep43699 
  3. Kim JY, Yi MH, Yong TS. Allergen-like molecules from parasites. Curr Protein Pept Sci 2020;21(2):186-202. https://doi.org/10.2174/1389203720666190708154300 
  4. Purello-D'Ambrosio F, Pastorello E, Gangemi S, Lombardo G, Ricciardi L, et al. Incidence of sensitivity to Anisakis simplex in a risk population of fishermen/fishmongers. Ann Allergy Asthma Immunol 2000;84(4):439-444. https://doi.org/10.1016/S1081-1206(10)62278-8 
  5. Pravettoni V, Primavesi L, Piantanida M. Anisakis simplex: current knowledge. Eur Ann Allergy Clin Immunol 2012;44(4):150-156. 
  6. Ciabattini A, Olivieri R, Lazzeri E, Medaglini D. Role of the microbiota in the modulation of vaccine immune responses. Front Microbiol 2019;10:1305. https://doi.org/10.3389/fmicb.2019.01305 
  7. Qian LJ, Kang SM, Xie JL, Huang L, Wen Q, et al. Early-life gut microbial colonization shapes Th1/Th2 balance in asthma model in BALB/c mice. BMC Microbiol 2017;17(1):135. https://doi.org/10.1186/s12866-017-1044-0 
  8. Watts AM, West NP, Zhang P, Smith PK, Cripps AW, et al. The gut microbiome of adults with allergic rhinitis is characterised by reduced diversity and an altered abundance of key microbial taxa compared to controls. Int Arch Allergy Immunol 2021;182(2):94-105. https://doi.org/10.1159/000510536 
  9. Duong QA, Pittet LF, Curtis N, Zimmermann P. Antibiotic exposure and adverse long-term health outcomes in children: a systematic review and meta-analysis. J Infect 2022;85(3):213-300. https://doi.org/10.1016/j.jinf.2022.01.005 
  10. Lee N, Kim WU. Microbiota in T-cell homeostasis and inflammatory diseases. Exp Mol Med 2017;49(5):e340. https://doi.org/10.1038/emm.2017.36 
  11. Baird FJ, Su X, Aibinu I, Nolan M, Sugiyama H. The Anisakis transcriptome provides a resource for fundamental and applied studies on allergy-causing parasites. PLoS Negl Trop Dis 2016;10(7):e0004845. https://doi.org/10.1371/journal.pntd.0004845 
  12. Choi JH, Kim JY, Yi MH, Kim M, Yong TS. Anisakis pegreffii extract induces airway inflammation with airway remodeling in a murine model system. Biomed Res Int 2021;2021:2522305. https://doi.org/10.1155/2021/2522305 
  13. Ceylani T, Jakubowska-Dogru E, Gurbanov R, Teker HT, Gozen AG. The effects of repeated antibiotic administration to juvenile BALB/c mice on the microbiota status and animal behavior at the adult age. Heliyon 2018;4(6):e00644. https://doi.org/10.1016/j.heliyon.2018.e00644 
  14. Baeza ML, Conejero L, Higaki Y, Martin E, Perez C, et al. Anisakis simplex allergy: a murine model of anaphylaxis induced by parasitic proteins displays a mixed Th1/Th2 pattern. Clin Exp Immunol 2005;142(3):433-440. https://doi.org/10.1111/j.1365-2249.2005.02952.x 
  15. Bahk YY, Kim SA, Kim JS, Euh HJ, Bai GH, et al. Antigens secreted from Mycobacterium tuberculosis: identification by proteomics approach and test for diagnostic marker. Proteomics 2004;4(11):3299-3307. https://doi.org/10.1002/pmic.200400980 
  16. Gobom J, Nordhoff E, Mirgorodskaya E, Ekman R, Roepstorff P. Sample purification and preparation technique based on nano-scale reversed-phase columns for the sensitive analysis of complex peptide mixtures by matrix-assisted laser desorption/ionization mass spectrometry. J Mass Spectrom 1999;34(2):105-116. https://doi.org/10.1002/(SICI)1096-9888(199902)34:2<105::AID-JMS768>3.0.CO;2-4 
  17. Koenig T, Menze BH, Kirchner M, Monigatti F, Parker KC, et al. Robust prediction of the MASCOT score for an improved quality assessment in mass spectrometric proteomics. J Proteome Res 2008;7(9):3708-3717. https://doi.org/10.1021/pr700859x 
  18. Kim JY, Kim EM, Yi MH, Lee J, Lee S, et al. Chinese liver fluke Clonorchis sinensis infection changes the gut microbiome and increases probiotic Lactobacillus in mice. Parasitol Res 2019;118(2):693-699. https://doi.org/10.1007/s00436-018-6179-x 
  19. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30(15):2114-2120. https://doi.org/10.1093/bioinformatics/btu170 
  20. Masella AP, Bartram AK, Truszkowski JM, Brown DG, Neufeld JD. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics 2012;13:31. https://doi.org/10.1186/1471-2105-13-31 
  21. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 2009;75(23):7537-7541. https://doi.org/10.1128/AEM.01541-09 
  22. Yoon SH, Ha SM, Kwon S, Lim J, Kim Y, et al. Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int J Syst Evol Microbiol 2017;67(5):1613-1617. https://doi.org/10.1099/ijsem.0.001755 
  23. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990;215(3):403-410. https://doi.org/10.1016/S0022-2836(05)80360-2 
  24. Myers EW, Miller W. Optimal alignments in linear space. Comput Appl Biosci 1988;4(1):11-17. https://doi.org/10.1093/bioinformatics/4.1.11 
  25. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010;26(19):2460-2461. https://doi.org/10.1093/bioinformatics/btq461 
  26. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 2011;27(16):2194-2200. https://doi.org/10.1093/bioinformatics/btr381 
  27. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 2012;28(23):3150-3152. https://doi.org/10.1093/bioinformatics/bts565 
  28. Shannon CE. A mathematical theory of communication. Bell Syst Tech J 1948;27:379-423. 
  29. Gower JC. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 1966;53(3-4):325-338. https://doi.org/10.2307/2333639 
  30. Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 2005;71(12):8228-8235. https://doi.org/10.1128/AEM.71.12.8228-8235.2005 
  31. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, et al. Metagenomic biomarker discovery and explanation. Genome Biol 2011;12:R60. https://doi.org/10.1186/gb-2011-12-6-r60 
  32. Lee KH, Song Y, Wu W, Yu K, Zhang G. The gut microbiota, environmental factors, and links to the development of food allergy. Clin Mol Allergy 2020;18:5. https://doi.org/10.1186/s12948-020-00120-x 
  33. Zhou C, Chen LL, Lu RQ, Ma WW, Xiao R. Alteration of intestinal microbiota composition in oral sensitized C3H/HeJ Mice is associated with changes in dendritic cells and T Cells in mesenteric lymph nodes. Front Immunol 2021;12:631494. https://doi.org/10.3389/fimmu.2021.631494 
  34. Firacative C, Gressler AE, Schubert K, Schulze B, Muller U, et al. Identification of T helper (Th) 1-and Th2-associated antigens of Cryptococcus neoformans in a murine model of pulmonary infection. Sci Rep 2018;8(1):2681. https://doi.org/10.1038/s41598-018-21039-z 
  35. Kochanowski M, Rozycki M, Dabrowska J, Belcik A, Karamon J, et al. Proteomic and bioinformatic investigations of heat-treated Anisakis simplex third-stage larvae. Biomolecules 2020;10(7):1066. https://doi.org/10.3390/biom10071066 
  36. Chuang JG, Su SN, Chiang BL, Lee HJ, Chow LP. Proteome mining for novel IgE-binding proteins from the German cockroach (Blattella germanica) and allergen profiling of patients. Proteomics 2010;10(21):3854-3867. https://doi.org/10.1002/pmic.201000348 
  37. Gonzalez-Fernandez J, Daschner A, Nieuwenhuizen NE, Lopata AL, Frutos CD, et al. Haemoglobin, a new major allergen of Anisakis simplex. Int J Parasitol 2015;45(6):399-407. https://doi.org/10.1016/j.ijpara.2015.01.002 
  38. Song H, Jung BK, Cho J, Chang T, Huh S, et al. Molecular identification of Anisakis larvae extracted by gastrointestinal endoscopy from health check-up patients in Korea. Korean J Parasitol 2019;57(2):207-211. https://doi.org/10.3347/kjp.2019.57.2.207 
  39. Quiazon KM, Zenke K, Yoshinaga T. Molecular characterization and comparison of four Anisakis allergens between Anisakis simplex sensu stricto and Anisakis pegreffii from Japan. Mol Biochem Parasitol 2013;190(1):23-26. https://doi.org/10.1016/j.molbiopara.2013.05.006