A systematic bioinformatics approach for large-scale identification and characterization of host-pathogen shared sequences.
Bioinformatics
Cross-reactivity
Crossreactome
Dengue virus
Flaviviridae
Flavivirus
Hepacivirus
Hepatitis C virus
Host-pathogen
Large-scale
Methodology
Pegivirus
Peptide overlap
Peptide sharing
Pestivirus
Share-ome
Shared sequences
West Nile virus
and Molecular mimicry.
Journal
BMC genomics
ISSN: 1471-2164
Titre abrégé: BMC Genomics
Pays: England
ID NLM: 100965258
Informations de publication
Date de publication:
28 Sep 2021
28 Sep 2021
Historique:
received:
14
03
2021
accepted:
28
04
2021
entrez:
29
9
2021
pubmed:
30
9
2021
medline:
1
10
2021
Statut:
epublish
Résumé
Biology has entered the era of big data with the advent of high-throughput omics technologies. Biological databases provide public access to petabytes of data and information facilitating knowledge discovery. Over the years, sequence data of pathogens has seen a large increase in the number of records, given the relatively small genome size and their important role as infectious and symbiotic agents. Humans are host to numerous pathogenic diseases, such as that by viruses, many of which are responsible for high mortality and morbidity. The interaction between pathogens and humans over the evolutionary history has resulted in sharing of sequences, with important biological and evolutionary implications. This study describes a large-scale, systematic bioinformatics approach for identification and characterization of shared sequences between the host and pathogen. An application of the approach is demonstrated through identification and characterization of the Flaviviridae-human share-ome. A total of 2430 nonamers represented the Flaviviridae-human share-ome with 100% identity. Although the share-ome represented a small fraction of the repertoire of Flaviviridae (~ 0.12%) and human (~ 0.013%) non-redundant nonamers, the 2430 shared nonamers mapped to 16,946 Flaviviridae and 7506 human non-redundant protein sequences. The shared nonamer sequences mapped to 125 species of Flaviviridae, including several with unclassified genus. The majority (~ 68%) of the shared sequences mapped to Hepacivirus C species; West Nile, dengue and Zika viruses of the Flavivirus genus accounted for ~ 11%, ~ 7%, and ~ 3%, respectively, of the Flaviviridae protein sequences (16,946) mapped by the share-ome. Further characterization of the share-ome provided important structural-functional insights to Flaviviridae-human interactions. Mapping of the host-pathogen share-ome has important implications for the design of vaccines and drugs, diagnostics, disease surveillance and the discovery of unknown, potential host-pathogen interactions. The generic workflow presented herein is potentially applicable to a variety of pathogens, such as of viral, bacterial or parasitic origin.
Sections du résumé
BACKGROUND
BACKGROUND
Biology has entered the era of big data with the advent of high-throughput omics technologies. Biological databases provide public access to petabytes of data and information facilitating knowledge discovery. Over the years, sequence data of pathogens has seen a large increase in the number of records, given the relatively small genome size and their important role as infectious and symbiotic agents. Humans are host to numerous pathogenic diseases, such as that by viruses, many of which are responsible for high mortality and morbidity. The interaction between pathogens and humans over the evolutionary history has resulted in sharing of sequences, with important biological and evolutionary implications.
RESULTS
RESULTS
This study describes a large-scale, systematic bioinformatics approach for identification and characterization of shared sequences between the host and pathogen. An application of the approach is demonstrated through identification and characterization of the Flaviviridae-human share-ome. A total of 2430 nonamers represented the Flaviviridae-human share-ome with 100% identity. Although the share-ome represented a small fraction of the repertoire of Flaviviridae (~ 0.12%) and human (~ 0.013%) non-redundant nonamers, the 2430 shared nonamers mapped to 16,946 Flaviviridae and 7506 human non-redundant protein sequences. The shared nonamer sequences mapped to 125 species of Flaviviridae, including several with unclassified genus. The majority (~ 68%) of the shared sequences mapped to Hepacivirus C species; West Nile, dengue and Zika viruses of the Flavivirus genus accounted for ~ 11%, ~ 7%, and ~ 3%, respectively, of the Flaviviridae protein sequences (16,946) mapped by the share-ome. Further characterization of the share-ome provided important structural-functional insights to Flaviviridae-human interactions.
CONCLUSION
CONCLUSIONS
Mapping of the host-pathogen share-ome has important implications for the design of vaccines and drugs, diagnostics, disease surveillance and the discovery of unknown, potential host-pathogen interactions. The generic workflow presented herein is potentially applicable to a variety of pathogens, such as of viral, bacterial or parasitic origin.
Identifiants
pubmed: 34583643
doi: 10.1186/s12864-021-07657-4
pii: 10.1186/s12864-021-07657-4
pmc: PMC8477458
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
700Informations de copyright
© 2021. The Author(s).
Références
Tagini F, Greub G. Bacterial genome sequencing in clinical microbiology: a pathogen-oriented review. Eur J Clin Microbiol Infect Dis. 2017;36(11):2007–20. https://doi.org/10.1007/s10096-017-3024-6 .
doi: 10.1007/s10096-017-3024-6
pubmed: 28639162
pmcid: 5653721
Warrenfeltz S, Basenko EY, Crouch K, Harb OS, Kissinger JC, Roos DS, et al. EuPathDB: the eukaryotic pathogen genomics database resource. Methods Mol Biol. 2018;1757:69–113. https://doi.org/10.1007/978-1-4939-7737-6_5 .
doi: 10.1007/978-1-4939-7737-6_5
pubmed: 29761457
pmcid: 7124890
Van Goethem N, Descamps T, Devleesschauwer B, Roosens NHC, Boon NAM, Van Oyen H, et al. Status and potential of bacterial genomics for public health practice: a scoping review. Implement Sci. 2019;14(1):79. https://doi.org/10.1186/s13012-019-0930-2 .
doi: 10.1186/s13012-019-0930-2
pubmed: 31409417
pmcid: 6692930
Pickett BE, Sadat EL, Zhang Y, Noronha JM, Squires RB, Hunt V, et al. ViPR: an open bioinformatics database and analysis resource for virology research. Nucleic Acids Res. 2012;40(D1):593–8. https://doi.org/10.1093/nar/gkr859 .
doi: 10.1093/nar/gkr859
Elbe S, Buckland-Merrett G. Data, disease and diplomacy: GISAID’s innovative contribution to global health. Global Chall. 2017;1(1):33–46. https://doi.org/10.1002/gch2.1018 .
doi: 10.1002/gch2.1018
Stephens ZD, Lee SY, Faghri F, Campbell RH, Zhai C, Efron MJ, et al. Big Data: Astronomical or Genomical? PLoS Biol. 2015;13(7):e1002195. https://doi.org/10.1371/journal.pbio.1002195 .
doi: 10.1371/journal.pbio.1002195
pubmed: 26151137
pmcid: 4494865
Rigden DJ, Fernandez XM. The 2018 nucleic acids research database issue and the online molecular biology database collection. Nucleic Acids Res. 2018;46(D1):1–7. https://doi.org/10.1093/nar/gkx1235 .
doi: 10.1093/nar/gkx1235
Sintchenko V, Holmes EC. The role of pathogen genomics in assessing disease transmission. BMJ. 2015;350:1–13.
doi: 10.1136/bmj.h1314
Bansal S, Chowell G, Simonsen L, Vespignani A, Viboud C. Big data for infectious disease surveillance and modeling. J Infect Dis. 2016;214:375–84. https://doi.org/10.1093/infdis/jiw400 .
Dye C. After 2015: infectious diseases in a new era of health and development. Philos Trans R Soc B Biol Sci. 2014;369(1645):1–9. https://doi.org/10.1098/rstb.2013.0426 .
doi: 10.1098/rstb.2013.0426
Sarmah P, Dan MM, Adapa D, Sarangi Tk. A review on common pathogenic microorganisms and their impact on human health. Electron J Biol. 2018;14(1):50–8. https://ejbio.imedpub.com/a-review-on-common-pathogenic-microorganisms-and-their-impact-on-human-health.php?aid=22368 . Accessed 21 Dec 2020.
Campbell EM, Hope TJ. HIV-1 capsid: the multifaceted key player in HIV-1 infection. Nat Rev Microbiol. 2015;13(8):471–83. https://doi.org/10.1038/nrmicro3503 .
doi: 10.1038/nrmicro3503
pubmed: 26179359
pmcid: 4876022
Feschotte C, Gilbert C. Endogenous viruses: insights into viral evolution and impact on host biology. Nat Rev Genet. 2012;13(4):283–96. https://doi.org/10.1038/nrg3199 .
doi: 10.1038/nrg3199
pubmed: 22421730
Grow EJ, Flynn RA, Chavez SL, Bayless NL, Wossidlo M, Wesche DJ, et al. Intrinsic retroviral reactivation in human preimplantation embryos and pluripotent cells. Nature. 2015;522(7555):221–5. https://doi.org/10.1038/nature14308 .
doi: 10.1038/nature14308
pubmed: 25896322
pmcid: 4503379
Stoye JP. Studies of endogenous retroviruses reveal a continuing evolutionary saga. Nat Rev Microbiol. 2012;10(6):395–406. https://doi.org/10.1038/nrmicro2783 .
doi: 10.1038/nrmicro2783
pubmed: 22565131
Lucchese G, Capone G, Kanduc D. Peptide sharing between influenza a H1N1 hemagglutinin and human axon guidance proteins. Schizophr Bull. 2014;40(2):362–75. https://doi.org/10.1093/schbul/sbs197 .
doi: 10.1093/schbul/sbs197
pubmed: 23378012
Kanduc D, Stufano A, Lucchese G, Kusalik A. Massive peptide sharing between viral and human proteomes. Peptides. 2008;29(10):1755–66. https://doi.org/10.1016/j.peptides.2008.05.022 .
doi: 10.1016/j.peptides.2008.05.022
pubmed: 18582510
pmcid: 7115663
Davey NE, Travé G, Gibson TJ. How viruses hijack cell regulation. Trends Biochem Sci. 2011;36(3):159–69. https://doi.org/10.1016/j.tibs.2010.10.002 .
doi: 10.1016/j.tibs.2010.10.002
pubmed: 21146412
Taylor DJ, Leach RW, Bruenn J. Filoviruses are ancient and integrated into mammalian genomes. BMC Evol Biol. 2010;10(1):193. https://doi.org/10.1186/1471-2148-10-193 .
doi: 10.1186/1471-2148-10-193
pubmed: 20569424
pmcid: 2906475
Gouw M, Michael S, Sámano-Sánchez H, Kumar M, Zeke A, Lang B, et al. The eukaryotic linear motif resource – 2018 update. Nucleic Acids Res. 2018;46(D1):D428–34. https://doi.org/10.1093/nar/gkx1077 .
doi: 10.1093/nar/gkx1077
pubmed: 29136216
Lucchese G, Stufano A, Calabro M, Kanduc D. Charting the peptide crossreactome between HIV-1 and the human proteome. Front Biosci (Elite Ed). 2011;3:1385–400. https://doi.org/10.2741/e341 .
doi: 10.2741/e341
Capone G, Pagoni M, Delfino AP, Kanduc D. Evidence for a vast peptide overlap between West Nile virus and human proteomes. J Basic Microbiol. 2012;52:1–8. https://doi.org/10.1002/jobm.201200204 .
doi: 10.1002/jobm.201200204
Capone G, Calabrò M, Lucchese G, Fasano C, Girardi B, Polimeno L, et al. Peptide matching between Epstein-Barr virus and human proteins. Pathog Dis. 2013;69(3):205–12. https://doi.org/10.1111/2049-632X.12066 .
doi: 10.1111/2049-632X.12066
pubmed: 23873730
Carrillo-Bustamante P, Keşmir C, de Boer RJ. Virus encoded MHC-like decoys diversify the inhibitory KIR repertoire. PLoS Comput Biol. 2013;9:1–13.
doi: 10.1371/journal.pcbi.1003264
Capone G, Novello G, Bavaro SL, Fasano C, Polito AN, Kanduc D. A quantitative description of the peptide sharing between poliovirus and Homo sapiens. Immunopharmacol Immunotoxicol. 2012;34(5):779–85. https://doi.org/10.3109/08923973.2012.654610 .
doi: 10.3109/08923973.2012.654610
pubmed: 22303874
Kanduc D. Measles virus hemagglutinin epitopes are potential hotspots for crossreactions with immunodeficiency-related proteins. Future Microbiol. 2015;10(4):503–15. https://doi.org/10.2217/fmb.14.137 .
doi: 10.2217/fmb.14.137
pubmed: 25865190
Trost B, Kusalik A, Lucchese G, Kanduc D. Bacterial peptides are intensively present throughout the human proteome. Self Nonself. 2010;1(1):71–4. https://doi.org/10.4161/self.1.1.9588 .
doi: 10.4161/self.1.1.9588
pubmed: 21559180
pmcid: 3091599
Trost B, Lucchese G, Stufano A, Bickis M, Kusalik A, Kanduc D. No human protein is exempt from bacterial motifs, not even one. Self Nonself. 2010;1(4):328–34. https://doi.org/10.4161/self.1.4.13315 .
doi: 10.4161/self.1.4.13315
pubmed: 21487508
pmcid: 3062388
Sheng Tao Z, Rui L, Xia Z, CanHua H, YuQuan W. Viral proteomics: the emerging cutting-edge of virus research. Sci China Life Sci. 2011;54:502–12.
doi: 10.1007/s11427-011-4177-7
Maxwell KL, Frappier L. Viral proteomics. Microbiol Mol Biol Rev. 2007;71(2):398–411. https://doi.org/10.1128/MMBR.00042-06 .
doi: 10.1128/MMBR.00042-06
pubmed: 17554050
pmcid: 1899879
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10(1):421. https://doi.org/10.1186/1471-2105-10-421 .
doi: 10.1186/1471-2105-10-421
pubmed: 20003500
pmcid: 2803857
Pearson WR. An introduction to sequence similarity (“homology”) searching. Curr Protoc Bioinformatics. 2013;Chapter 3:Unit3.1. https://doi.org/10.1002/0471250953.bi0301s42 .
doi: 10.1002/0471250953.bi0301s42
pubmed: 23749753
Agarwala R, Barrett T, Beck J, Benson DA, Bollin C, Bolton E, et al. Database resources of the National Center for biotechnology information. Nucleic Acids Res. 2016;44:D7–19.
doi: 10.1093/nar/gkv1290
Chen Q, Zobel J, Verspoor K. Duplicates, redundancies and inconsistencies in the primary nucleotide databases: a descriptive study. Database. 2017;2017:1–16. https://doi.org/10.1093/database/baw163 .
doi: 10.1093/database/baw163
Subramaniy V, Pandian SC. A complete survey of duplicate record detection using data mining techniques. Inf Technol J. 2012;11(8):941–5. https://doi.org/10.3923/itj.2012.941.945 .
doi: 10.3923/itj.2012.941.945
Koh J, Lee M, Khan A, Tan PT, Brusic V. Duplicate detection in biological data using association rule mining. In European Workshop on Data Mining and Text Mining in Bioinformatics. 2004. https://www.semanticscholar.org/paper/Duplicate-Detection-in-Biological-Data-using-Rule-Koh-Lee/ebf0d6c78174c2bfe69efa74369645bc90e7835d . Accessed 21 Dec 2020.
Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22(13):1658–9. https://doi.org/10.1093/bioinformatics/btl158 .
doi: 10.1093/bioinformatics/btl158
pubmed: 16731699
Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28(23):3150–2. https://doi.org/10.1093/bioinformatics/bts565 .
doi: 10.1093/bioinformatics/bts565
pubmed: 23060610
pmcid: 3516142
Khan AM, Miotto O, Nascimento EJM, Srinivasan KN, Heiny AT, Zhang GL, et al. Conservation and variability of dengue virus proteins: implications for vaccine design. PLoS Negl Trop Dis. 2008;2(8):1–7. https://doi.org/10.1371/journal.pntd.0000272 .
doi: 10.1371/journal.pntd.0000272
Anvar SY, Khachatryan L, Vermaat M, Galen MV, Pulyakhina I, Ariyurek Y, et al. Determining the quality and complexity of next-generation sequencing data without a reference genome. Genome Biol. 2014;15(12):555. https://doi.org/10.1186/s13059-014-0555-3 .
doi: 10.1186/s13059-014-0555-3
pubmed: 25514851
pmcid: 4298064
Jiang J, Wang N, Chen P, Zheng C, Wang B. Prediction of protein hotspots from whole protein sequences by a random projection ensemble system. Int J Mol Sci. 2017;18(7):1–13. https://doi.org/10.3390/ijms18071543 .
doi: 10.3390/ijms18071543
Yang L, Orenstein Y, Jolma A, Yin Y, Taipale J, Shamir R, et al. Transcription factor family-specific DNA shape readout revealed by quantitative specificity models. Mol Syst Biol. 2017;13:1–14. https://doi.org/10.15252/msb.20167238 .
doi: 10.15252/msb.20167238
Altschul SF. BLAST Algorithm. eLS; 2014. https://doi.org/10.1002/9780470015902.a0005253.pub2 .
doi: 10.1002/9780470015902.a0005253.pub2
Stauss HJ. Peptides feeling groovy. Curr Biol. 1991;1(5):328–30. https://doi.org/10.1016/0960-9822(91)90102-3 .
doi: 10.1016/0960-9822(91)90102-3
pubmed: 15336114
Parham P. Oh to be twenty seven again. Nature. 1991;351(6327):523. https://doi.org/10.1038/351523a0 .
doi: 10.1038/351523a0
pubmed: 2046762
Doytchinova IA, Flower DR. In Silico identification of Supertypes for class II MHCs. J Immunol. 2005;174(11):7085–95. https://doi.org/10.4049/jimmunol.174.11.7085 .
doi: 10.4049/jimmunol.174.11.7085
pubmed: 15905552
Okonechnikov K, Golosova O, Fursov M, Varlamov A, Vaskin Y, Efremov I, et al. Unipro UGENE: a unified bioinformatics toolkit. Bioinformatics. 2012;28(8):1166–7. https://doi.org/10.1093/bioinformatics/bts091 .
doi: 10.1093/bioinformatics/bts091
pubmed: 22368248
Luo H, Nijveen H. Understanding and identifying amino acid repeats. Brief Bioinform. 2014;15(4):582–91. https://doi.org/10.1093/bib/bbt003 .
doi: 10.1093/bib/bbt003
pubmed: 23418055
pmcid: 4103538
Barik S. Amino acid repeats avert mRNA folding through conservative substitutions and synonymous codons, regardless of codon bias. Heliyon. 2017;3(12):e00492. https://doi.org/10.1016/j.heliyon.2017.e00492 .
doi: 10.1016/j.heliyon.2017.e00492
pubmed: 29387823
pmcid: 5772840
Kumar AS, Sowpati DT, Mishra RK. Single amino acid repeats in the proteome world: structural, functional, and evolutionary insights. PLoS One. 2016;11(11):1–19. https://doi.org/10.1371/journal.pone.0166854 .
doi: 10.1371/journal.pone.0166854
Wallqvist A, Memišević V, Zavaljevski N, Pieper R, Rajagopala SV, Kwon K, et al. Using host-pathogen protein interactions to identify and characterize Francisella tularensis virulence factors. BMC Genomics. 2015;16(1):1–18. https://doi.org/10.1186/s12864-015-2351-1 .
doi: 10.1186/s12864-015-2351-1
Forterre P, Krupovic M, Prangishvili D. Cellular domains and viral lineages. Trends Microbiol. 2014;22(10):554–8. https://doi.org/10.1016/j.tim.2014.07.004 .
doi: 10.1016/j.tim.2014.07.004
pubmed: 25129822
Durzyńska J, Goździcka-Józefiak A. Viruses and cells intertwined since the dawn of evolution. Virol J. 2015;12(1):169. https://doi.org/10.1186/s12985-015-0400-7 .
doi: 10.1186/s12985-015-0400-7
pubmed: 26475454
pmcid: 4609113
Kim H-I, Kim J-H, Park Y-J. Transcriptome and gene ontology (GO) enrichment analysis reveals genes involved in biotin metabolism that affect l-lysine production in Corynebacterium glutamicum. Int J Mol Sci. 2016;17(3):353. https://doi.org/10.3390/ijms17030353 .
doi: 10.3390/ijms17030353
pubmed: 27005618
pmcid: 4813214
Khan AM, Miotto O, Heiny AT, Salmon J, Srinivasan KN, Nascimento E, et al. A systematic bioinformatics approach for selection of epitope-based vaccine targets. Cell Immunol. 2007;244(2):141–7. https://doi.org/10.1016/j.cellimm.2007.02.005 .
doi: 10.1016/j.cellimm.2007.02.005
pmcid: 2041846
Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 2016;44(D1):D279–85. https://doi.org/10.1093/nar/gkv1344 .
doi: 10.1093/nar/gkv1344
pubmed: 26673716
Mulder NJ, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, et al. InterPro: an integrated documentation resource for protein families, domains and functional sites. Brief Bioinform. 2002;3(3):225–35. https://doi.org/10.1093/bib/3.3.225 .
doi: 10.1093/bib/3.3.225
pubmed: 12230031
Marchler-Bauer A, Zheng C, Chitsaz F, Derbyshire MK, Geer LY, Geer RC, et al. CDD: conserved domains and protein three-dimensional structure. Nucleic Acids Res. 2013;41:348–52.
doi: 10.1093/nar/gks1243
Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics. 2009;10:1–7.
doi: 10.1186/1471-2105-10-48
Soria-Guerra RE, Nieto-Gomez R, Govea-Alonso DO, Rosales-Mendoza S. An overview of bioinformatics tools for epitope prediction: implications on vaccine development. J Biomed Inform. 2015;53:405–14. https://doi.org/10.1016/j.jbi.2014.11.003 .
doi: 10.1016/j.jbi.2014.11.003
pubmed: 25464113
Sun P, Ju H, Liu Z, Ning Q, Zhang J, Zhao X, et al. Bioinformatics resources and tools for conformational B-cell epitope prediction. Comput Math Methods Med. 2013;2013:1–11. https://doi.org/10.1155/2013/943636 .
doi: 10.1155/2013/943636
Raval A, Piana S, Eastwood MP, Dror RO, Shaw DE. Refinement of protein structure homology models via long, all-atom molecular dynamics simulations. Proteins. 2012;80(8):2071–9. https://doi.org/10.1002/prot.24098 .
doi: 10.1002/prot.24098
pubmed: 22513870
Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. J Mol Graph. 1996;14(1):33–8. https://doi.org/10.1016/0263-7855(96)00018-5 .
doi: 10.1016/0263-7855(96)00018-5
pubmed: 8744570
Klausen MS, Jespersen MC, Nielsen H, Jensen KK, Jurtz VI, Sønderby CK, et al. NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning. Proteins Struct Funct Bioinforma. 2019;87:520–7. https://doi.org/10.1002/prot.25674 .
doi: 10.1002/prot.25674
Krissinel E, Henrick K. Inference of macromolecular assemblies from crystalline state. J Mol Biol. 2007;372(3):774–97. https://doi.org/10.1016/j.jmb.2007.05.022 .
doi: 10.1016/j.jmb.2007.05.022
pubmed: 17681537
Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers. 1983;22(12):2577–637. https://doi.org/10.1002/bip.360221211 .
doi: 10.1002/bip.360221211
pubmed: 6667333
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem. 2009;30(16):2785–91. https://doi.org/10.1002/jcc.21256 .
doi: 10.1002/jcc.21256
pubmed: 19399780
pmcid: 2760638
Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001 .
doi: 10.1016/j.softx.2015.06.001
Simmonds P, Becher P, Bukh J, Gould EA, Meyers G, Monath T, et al. ICTV virus taxonomy profile: Flaviviridae. J Gen Virol. 2017;98(1):2–3. https://doi.org/10.1099/jgv.0.000672 .
doi: 10.1099/jgv.0.000672
pubmed: 28218572
pmcid: 5370391
Murray NEA, Quam MB, Wilder-Smith A. Epidemiology of dengue: past, present and future prospects. Clin Epidemiol. 2013;5:299–309. https://doi.org/10.2147/CLEP.S34440 .
Koo QY, Khan AM, Jung K-O, Ramdas S, Miotto O, Tan TW, et al. Conservation and variability of West Nile virus proteins. PLoS One. 2009;4(4):e5352. https://doi.org/10.1371/journal.pone.0005352 .
doi: 10.1371/journal.pone.0005352
pubmed: 19401763
pmcid: 2670515
Hu Z-L, Bao J, Reecy JM. CateGOrizer: a web-based program to batch analyzegene ontology classification categories. Online J Bioinform. 2008b;9(2):108–12.
Le Breton M, Meyniel-Schicklin L, Deloire A, Coutard B, Canard B, de Lamballerie X, et al. Flavivirus NS3 and NS5 proteins interaction network: a high-throughput yeast two-hybrid screen. BMC Microbiol. 2011;11(1):234. https://doi.org/10.1186/1471-2180-11-234 .
doi: 10.1186/1471-2180-11-234
pubmed: 22014111
pmcid: 3215679
Wiborg O, Pedersen MS, Wind A, Berglund LE, Marcker KA, Vuust J. The human ubiquitin multigene family: some genes contain multiple directly repeated ubiquitin coding sequences. EMBO J. 1985;4(3):755–9. https://doi.org/10.1002/j.1460-2075.1985.tb03693.x .
doi: 10.1002/j.1460-2075.1985.tb03693.x
pubmed: 2988935
pmcid: 554252
Radici L, Bianchi M, Crinelli R, Magnani M. Ubiquitin C gene: structure, function, and transcriptional regulation. Adv Biosci Biotechnol. 2013;04(12):1057–62. https://doi.org/10.4236/abb.2013.412141 .
doi: 10.4236/abb.2013.412141
Cook HV, Doncheva NT, Szklarczyk D, von Mering C, Jensen LJ. Viruses.STRING: a virus-host protein-protein interaction database. Viruses. 2018;10:1–11.
doi: 10.3390/v10100519
Ghosh A, Stewart D, Matlashewski G. Regulation of human p53 activity and cell localization by alternative splicing. Mol Cell Biol. 2004;24(18):7987–97. https://doi.org/10.1128/MCB.24.18.7987-7997.2004 .
doi: 10.1128/MCB.24.18.7987-7997.2004
pubmed: 15340061
pmcid: 515058
Majumder M, Ghosh AK, Steele R, Ray R, Ray RB. Hepatitis C virus NS5A physically associates with p53 and regulates p21/waf1 gene expression in a p53-dependent manner. J Virol. 2001;75(3):1401–7. https://doi.org/10.1128/JVI.75.3.1401-1407.2001 .
doi: 10.1128/JVI.75.3.1401-1407.2001
pubmed: 11152513
pmcid: 114046
Agis-Juárez RA, Galván I, Medina F, Daikoku T, Padmanabhan R, Ludert JE, et al. Polypyrimidine tract-binding protein is relocated to the cytoplasm and is required during dengue virus infection in Vero cells. J Gen Virol. 2009;90(12):2893–901. https://doi.org/10.1099/vir.0.013433-0 .
doi: 10.1099/vir.0.013433-0
pubmed: 19692542
Lucchese G, Kanduc D. Zika virus and autoimmunity: From microcephaly to Guillain-Barré syndrome, and beyond. Autoimmun Rev. 2016;15(8):801–8. https://doi.org/10.1016/j.autrev.2016.03.020 .
Tan WH, Eichler FS, Hoda S, Lee MS, Baris H, Hanley CA, et al. Isolated sulfite oxidase deficiency: a case report with a novel mutation and review of the literature. Pediatrics. 2005;116(3):757–66. https://doi.org/10.1542/peds.2004-1897 .
doi: 10.1542/peds.2004-1897
pubmed: 16140720
Lucchese G, Kanduc D. Minimal immune determinants connect Zika virus, human Cytomegalovirus, and toxoplasma gondii to microcephaly-related human proteins. Am J Reprod Immunol. 2017;77(2):e12608. https://doi.org/10.1111/aji.12608 .
doi: 10.1111/aji.12608
Kanduc D. Proteome-wide epstein-barr virus analysis of peptide sharing with human systemic lupus erythematosus autoantigens. Isr Med Assoc J. 2019;21(7):444–8.
pubmed: 31507118
Kanduc D. The comparative biochemistry of viruses and humans: an evolutionary path towards autoimmunity. Biol Chem. 2019;400(5):629–38. https://doi.org/10.1515/hsz-2018-0271 .
doi: 10.1515/hsz-2018-0271
pubmed: 30504522
Welch MD. Why should cell biologists study microbial pathogens? Mol Biol Cell. 2015;26(24):4295–301. https://doi.org/10.1091/mbc.e15-03-0144 .
doi: 10.1091/mbc.e15-03-0144
pubmed: 26628749
pmcid: 4666125
Chen C, Li Z, Huang H, Suzek BE, Wu CH. A fast peptide match service for UniProt knowledgebase. Bioinformatics. 2013;29(21):2808–9. https://doi.org/10.1093/bioinformatics/btt484 .
doi: 10.1093/bioinformatics/btt484
pubmed: 23958731
pmcid: 3799477
Bavaro SL, Calabrò M, Kanduc D. Pentapeptide sharing between Corynebacterium diphtheria toxin and the human neural protein network. Immunopharmacol Immunotoxicol. 2011;33(2):360–72. https://doi.org/10.3109/08923973.2010.518618 .
doi: 10.3109/08923973.2010.518618
pubmed: 20874613
Amela I, Cedano J, Querol E. Pathogen proteins eliciting antibodies do not share epitopes with host proteins: a bioinformatics approach. PLoS One. 2007;2(6):e512. https://doi.org/10.1371/journal.pone.0000512 .
doi: 10.1371/journal.pone.0000512
pubmed: 17551592
pmcid: 1885212
Kohm AP, Fuller KG, Miller SD. Mimicking the way to autoimmunity: an evolving theory of sequence and structural homology. Trends Microbiol. 2003;11(3):101–5. https://doi.org/10.1016/S0966-842X(03)00006-4 .
doi: 10.1016/S0966-842X(03)00006-4
pubmed: 12648936
Karlsen AE, Dyrberg T. Molecular mimicry between non-self, modified self and self in autoimmunity. Semin Immunol. 1998;10(1):25–34. https://doi.org/10.1006/smim.1997.0102 .
doi: 10.1006/smim.1997.0102
pubmed: 9529653
Hurford A, Day T. Immune evasion and the evolution of molecular mimicry in parasites. Evolution. 2013;67(10):2889–29904. https://doi.org/10.1111/evo.12171 .
doi: 10.1111/evo.12171
pubmed: 24094341
Lucchese G, Kanduc D. Cytomegalovirus infection: the neurodevelopmental peptide signatures. Curr Drug Discov Technol. 2018;15(3):251–62. https://doi.org/10.2174/1570163814666170829152100 .
doi: 10.2174/1570163814666170829152100
pubmed: 28875854
Levison ME, Levison JH. Pharmacokinetics and pharmacodynamics of antibacterial agents. Infect Dis Clin N Am. 2009;23(4):791–815. https://doi.org/10.1016/j.idc.2009.06.008 .
doi: 10.1016/j.idc.2009.06.008
Shrivastava SR, Shrivastava PS, Ramasamy J. World health organization releases global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics. J Med Soc. 2018;32(1):76–7. https://doi.org/10.4103/jms.jms_25_17 .
NIAID. Emerging Infectious Diseases/Pathogens. 2018. https://www.niaid.nih.gov/research/emerging-infectious-diseases-pathogens . Accessed 21 Dec 2020.