Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-23T07:50:51.946Z Has data issue: false hasContentIssue false
  • English
  • Français

Parasitism as the main factor shaping peptide vocabularies in current organisms

Published online by Cambridge University Press:  28 February 2017

MICHAELA ZEMKOVÁ Open the ORCID record for MICHAELA ZEMKOVÁ [Opens in a new window] ,
DANIEL ZAHRADNÍK ,
MARTIN MOKREJŠ  and
JAROSLAV FLEGR
MICHAELA ZEMKOVÁ
Affiliation:
Faculty of Science, Department of Philosophy and History of Science, Charles University in Prague, Viničná 7, Prague, CZ-12844, Czech Republic
DANIEL ZAHRADNÍK
Affiliation:
Faculty of Forestry and Wood Sciences, Department of Forest Management, Czech University of Life Sciences Prague, Kamýcká 1176, Prague, CZ-165 21, Czech Republic
MARTIN MOKREJŠ
Affiliation:
Faculty of Science, Department of Philosophy and History of Science, Charles University in Prague, Viničná 7, Prague, CZ-12844, Czech Republic
JAROSLAV FLEGR*
Affiliation:
Faculty of Science, Department of Philosophy and History of Science, Charles University in Prague, Viničná 7, Prague, CZ-12844, Czech Republic
*
*Corresponding author: Division of Biology, Faculty of Science, Charles University in Prague, Vinicna 7, 128 44, Prague, Czech Republic. E-mail:[email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Self/non-self-discrimination by vertebrate immune systems is based on the recognition of the presence of peptides in proteins of a parasite that are not contained in the proteins of a host. Therefore, a reduction of the number of ‘words’ in its own peptide vocabulary could be an efficient evolutionary strategy of parasites for escaping recognition. Here, we compared peptide vocabularies of 30 endoparasitic and 17 free-living unicellular organisms and also eight multicellular parasitic and 16 multicellular free-living organisms. We found that both unicellular and multicellular parasites used a significantly lower number of different pentapeptides than free-living controls. Impoverished pentapeptide vocabularies in parasites were observed across all five clades that contain both the parasitic and free-living species. The effect of parasitism on a number of peptides used in an organism's proteins is larger than effects of all other studied factors, including the size of a proteome, the number of encoded proteins, etc. This decrease of pentapeptide diversity was partly compensated for by an increased number of hexapeptides. Our results support the hypothesis of parasitism-associated reduction of peptide vocabulary and suggest that T-cell receptors mostly recognize the five amino acids-long part of peptides that are presented in the groove of major histocompatibility complex molecules.

Keywords

Peptide vocabulary usageMHC-based recognitionimmune evasion strategyimmunityantigenic presentationproteomics
Type
Research Article
Information
Parasitology , Volume 144 , Issue 7 , June 2017 , pp. 975 - 983
Check for updates
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Adl, S. M., Simpson, A. G. B., Lane, C. E., Lukes, J., Bass, D., Bowser, S. S., Brown, M. W., Burki, F., Dunthorn, M., Hampl, V., Heiss, A., Hoppenrath, M., Lara, E., le Gall, L., Lynn, D. H., McManus, H., Mitchell, E. A. D., Mozley-Stanridge, S. E., Parfrey, L. W., Pawlowski, J., Rueckert, S., Shadwick, L., Schoch, C. L., Smirnov, A. and Spiegel, F. W. (2012). The revised classification of eukaryotes. Journal of Eukaryotic Microbiology, 59, 429–493. doi: 10.1111/j.1550-7408.2012.00644.x.CrossRefGoogle ScholarPubMed
Beckmann, J. S., Brendel, V. and Trifonov, E. N. (1986). Intervening sequences exhibit distinct vocabulary. Journal of Biomolecular Structure and Dynamics 4, 391–400.CrossRefGoogle ScholarPubMed
Bolshoy, A. (2003). DNA sequence analysis linguistic tools: contrast vocabularies, compositional spectra and linguistic complexity. Applied Bioinformatics 2, 103–112.Google ScholarPubMed
Craiu, A., Akoplan, T., Goldberg, A. and Rock, K. L. (1997). Two distinct proteolytic processes in the generation of a major histocompatibility complex class I-presented peptide. Proceedings of the National Academy of Sciences of the United States of America 94, 10850–10855. doi: 10.1073/pnas.94.20.10850.Google Scholar
Diamond, L. S. and Clark, C. G. (1993). A redescription of Entamoeba histolytica Schaudinn, 1903 (Emended Walker, 1911) Separating it from Entamoeba dispar Brumpt, 1925. Journal of Eukaryotic Microbiology 40, 340–344. doi: 10.1111/j.1550-7408.1993.tb04926.x.CrossRefGoogle ScholarPubMed
Elliott, A. M. (1973). Biology of Tetrahymena. Hutchinson & Ross, Dowden.Google Scholar
Eroglu, S. (2014). Language-like behavior of protein length distribution in proteomes. Complexity 20, 12–21. doi: 10.1002/cplx.21498.CrossRefGoogle Scholar
Flegr, J. (2011). Pozor, Toxo! Tajná učebnice praktické metodologie vědy (Watch out for Toxo! The secret guide to practical science). Academia, Prague.Google Scholar
Gatherer, D. (2007). Peptide vocabulary analysis reveals ultraconservation and homonymity in protein sequences. Bioinformatics and Biology Insights 1, 129–137.CrossRefGoogle Scholar
Gimona, M. (2006). Protein linguistics – a grammar for modular protein assembly? Nature Reviews Molecular Cell Biology 7, 68–73. doi: 10.1038/nrm1785.Google Scholar
Hamzah, Z., Petmitr, S., Mungthin, M., Leelayoova, S. and Chavalitshewinkoon-Petmitr, P. (2006). Differential detection of Entamoeba histolytica, Entamoeba dispar, and Entamoeba moshkovskii by a single-round PCR assay. Journal of Clinical Microbiology 44, 3196–3200. doi: 10.1128/Jcm.00778-06.CrossRefGoogle ScholarPubMed
King, R. C., Stansfield, W. D. and Mulligan, P. K. (2006). A Dictionary of Genetics. Oxford University Press, New York.Google Scholar
Lanzavecchia, A. (1985). Antigen-specific interaction between T-cells and B-cells. Nature 314, 537–539.CrossRefGoogle Scholar
Lingelbach, K. and Joiner, K. A. (1998). The parasitophorous vacuole membrane surrounding Plasmodium and Toxoplasma: an unusual compartment in infected cells. Journal of Cell Science 111, 1467–1475.Google Scholar
Motomura, K., Fujita, T., Tsutsumi, M., Kikuzato, S., Nakamura, M. and Otaki, J. M. (2012). Word decoding of protein amino acid sequences with availability analysis: a linguistic approach. PLoS ONE 7, 1–15. doi: ARTN e5003910.1371/journal.pone.0050039.Google Scholar
Neefjes, J. and Ovaa, H. (2013). A peptide's perspective on antigen presentation to the immune system. Nature Chemical Biology 9, 769–775. doi: 10.1038/Nchembio.1391.CrossRefGoogle ScholarPubMed
Orlov, Y. L. and Potapov, V. N. (2004). Complexity: an internet resource for analysis of DNA sequence complexity. Nucleic Acids Research 32, W628–W633. doi: 10.1093/nar/gkh466.Google Scholar
Paz, P., Brouwenstijn, N., Perry, R. and Shastri, N. (1999). Discrete proteolytic intermediates in the MHC class I antigen processing pathway and MHC I-dependent peptide trimming in the ER. Immunity 11, 241–251. doi: 10.1016/S1074-7613(00)80099-0.CrossRefGoogle ScholarPubMed
Pietrokovski, S. and Trifonov, E. N. (1992). Imported sequences in the mitochondrial yeast genome identified by nucleotide linguistics. Gene 122, 129–137. doi: 10.1016/0378-1119(92)90040-V.CrossRefGoogle ScholarPubMed
Popov, O., Segal, D. M. and Trifonov, E. N. (1996). Linguistic complexity of protein sequences as compared to texts of human languages. BioSystens 38, 65–74.Google Scholar
Rencher, A. C. (2002). Methods of Multivariate Analysis, pp. 380–408. Wiley, New York.Google Scholar
Sharon, I., Birkland, A., Chang, K., El-Yaniv, R. and Yona, G. (2005). Correcting BLAST e-values for low-complexity segments. Journal of Computational Biology 12, 980–1003. doi: 10.1089/cmb.2005.12.980.Google Scholar
Sheskin, D. J. (2003). Handbook of Parametric and Nonparametric Statistical Procedures, Chapman & Hall/CRC Press, Boca Raton, FL, USA.Google Scholar
Trombetta, E. S. and Mellman, I. (2005). Cell biology of antigen processing in vitro and in vivo . Annual Review of Immunology 23, 975–1028. doi: 10.1146/annurev.immunol.22.012703.104538.Google Scholar
Tyler, K. M. and Engman, D. M. (2001). The life cycle of Trypanosoma cruzi revisited. International Journal for Parasitology 31, 472–481. doi: 10.1016/S0020-7519(01)00153-9.Google Scholar
Volkovich, Z., Kirzhner, V., Bolshoy, A., Nevo, E. and Korol, A. (2005). The method of N-grams in large-scale clustering of DNA texts. Pattern Recognition 38, 1902–1912. doi: 10.1016/j.patcog.2005.05.002.CrossRefGoogle Scholar
Vyas, J. M., Van der Veen, A. G. and Ploegh, H. L. (2008). The known unknowns of antigen processing and presentation. Nature Reviews Immunology 8, 607–618. doi: 10.1038/nri2368.CrossRefGoogle ScholarPubMed
Wang, X. Y., Chen, W. J., Huang, Y., Sun, J. F., Men, J. T., Liu, H. L., Luo, F., Guo, L., Lv, X. L., Deng, C. H., Zhou, C. H., Fan, Y. X., Li, X. R., Huang, L. S., Hu, Y., Liang, C., Hu, X. C., Xu, J. and Yu, X. B. (2011). The draft genome of the carcinogenic human liver fluke Clonorchis sinensis . Genome Biology 12, 1–14. doi: Artn R10710.1186/Gb-2011-12-10-R107.Google Scholar
Wootton, J. C. and Federhen, S. (1993). Statistics of local complexity in amino-acid-sequences and sequence databases. Computers & Chemistry 17, 149–163. doi: 10.1016/0097-8485(93)85006-X.CrossRefGoogle Scholar
Yang, X. W. and Wang, T. M. (2013). A novel statistical measure for sequence comparison on the basis of k-word counts. Journal of Theoretical Biology 318, 91–100. doi: 10.1016/j.jtbi.2012.10.035.CrossRefGoogle ScholarPubMed
Zemkova, M., Trifonov, E. and Zahradnik, D. (2014). One common structural feature of ‘words’ in protein sequences and human texts. Journal of Biomolecular Structure and Dynamics 32, 1085–1091. doi: 10.1080/07391102.2013.809317.Google Scholar
Supplementary material: PDF

Zemková supplementary material

Tables S1-S3 and Figures S1-S2

Download Zemková supplementary material(PDF)
PDF 3 MB