Structure classification of the proteins from Salmonella enterica pangenome revealed novel potential pathogenicity islands.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
28 May 2024
28 May 2024
Historique:
received:
20
02
2024
accepted:
30
04
2024
medline:
29
5
2024
pubmed:
29
5
2024
entrez:
28
5
2024
Statut:
epublish
Résumé
Salmonella enterica is a pathogenic bacterium known for causing severe typhoid fever in humans, making it important to study due to its potential health risks and significant impact on public health. This study provides evolutionary classification of proteins from Salmonella enterica pangenome. We classified 17,238 domains from 13,147 proteins from 79,758 Salmonella enterica strains and studied in detail domains of 272 proteins from 14 characterized Salmonella pathogenicity islands (SPIs). Among SPIs-related proteins, 90 proteins function in the secretion machinery. 41% domains of SPI proteins have no previous sequence annotation. By comparing clinical and environmental isolates, we identified 3682 proteins that are overrepresented in clinical group that we consider as potentially pathogenic. Among domains of potentially pathogenic proteins only 50% domains were annotated by sequence methods previously. Moreover, 36% (1330 out of 3682) of potentially pathogenic proteins cannot be classified into Evolutionary Classification of Protein Domains database (ECOD). Among classified domains of potentially pathogenic proteins the most populated homology groups include helix-turn-helix (HTH), Immunoglobulin-related, and P-loop domains-related. Functional analysis revealed overrepresentation of these protein in biological processes related to viral entry into host cell, antibiotic biosynthesis, DNA metabolism and conformation change, and underrepresentation in translational processes. Analysis of the potentially pathogenic proteins indicates that they form 119 clusters or novel potential pathogenicity islands (NPPIs) within the Salmonella genome, suggesting their potential contribution to the bacterium's virulence. One of the NPPIs revealed significant overrepresentation of potentially pathogenic proteins. Overall, our analysis revealed that identified potentially pathogenic proteins are poorly studied.
Identifiants
pubmed: 38806511
doi: 10.1038/s41598-024-60991-x
pii: 10.1038/s41598-024-60991-x
doi:
Substances chimiques
Bacterial Proteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
12260Subventions
Organisme : NIGMS NIH HHS
ID : GM147367
Pays : United States
Organisme : NIGMS NIH HHS
ID : GM127390
Pays : United States
Organisme : Welch Foundation
ID : I-2095-20220331
Organisme : Welch Foundation
ID : I-1505
Organisme : National Science Foundation
ID : DBI 2224128
Informations de copyright
© 2024. The Author(s).
Références
Tindall, B. J., Grimont, P. A. D., Garrity, G. M. & Euzeby, J. P. Nomenclature and taxonomy of the genus Salmonella. Int. J. Syst. Evol. Microbiol. 55, 521–524. https://doi.org/10.1099/ijs.0.63580-0 (2005).
doi: 10.1099/ijs.0.63580-0
pubmed: 15653930
LeLievre, V., Besnard, A., Schlusselhuber, M., Desmasures, N. & Dalmasso, M. Phages for biocontrol in foods: What opportunities for Salmonella sp. control along the dairy food chain?. Food Microbiol. 78, 89–98. https://doi.org/10.1016/j.fm.2018.10.009 (2019).
doi: 10.1016/j.fm.2018.10.009
pubmed: 30497612
Ferrari, R. G. et al. Worldwide epidemiology of salmonella serovars in animal-based foods: A meta-analysis. Appl. Environ. Microbiol. 85, 14. https://doi.org/10.1128/AEM.00591-19 (2019).
doi: 10.1128/AEM.00591-19
Gal-Mor, O., Boyle, E. C. & Grassl, G. A. Same species, different diseases: How and why typhoidal and non-typhoidal Salmonella enterica serovars differ. Front. Microbiol. 5, 391. https://doi.org/10.3389/fmicb.2014.00391 (2014).
doi: 10.3389/fmicb.2014.00391
pubmed: 25136336
pmcid: 4120697
Johnson, R. et al. Comparison of Salmonella enterica serovars typhi and typhimurium reveals typhoidal serovar-specific responses to bile. Infect. Immun. 86, 3. https://doi.org/10.1128/IAI.00490-17 (2018).
doi: 10.1128/IAI.00490-17
Newell, D. G. et al. Food-borne diseases—the challenges of 20 years ago still persist while new ones continue to emerge. Int. J. Food Microbiol. 139(Suppl 1), S3-15. https://doi.org/10.1016/j.ijfoodmicro.2010.01.021 (2010).
doi: 10.1016/j.ijfoodmicro.2010.01.021
pubmed: 20153070
pmcid: 7132498
Wain, J., Hendriksen, R. S., Mikoleit, M. L., Keddy, K. H. & Ochiai, R. L. Typhoid fever. Lancet 385, 1136–1145. https://doi.org/10.1016/S0140-6736(13)62708-7 (2015).
doi: 10.1016/S0140-6736(13)62708-7
pubmed: 25458731
Upadhayay, A., Pal, D. & Kumar, A. Salmonella typhi induced oncogenesis in gallbladder cancer: Co-relation and progression. Adv. Cancer Biol. Metastasis 4, 100032. https://doi.org/10.1016/j.adcanc.2022.100032 (2022).
doi: 10.1016/j.adcanc.2022.100032
Costa, T. R. et al. Secretion systems in Gram-negative bacteria: structural and mechanistic insights. Nat. Rev. Microbiol. 13, 343–359. https://doi.org/10.1038/nrmicro3456 (2015).
doi: 10.1038/nrmicro3456
pubmed: 25978706
Pallen, M. J. & Wren, B. W. Bacterial pathogenomics. Nature 449, 835–842. https://doi.org/10.1038/nature06248 (2007).
doi: 10.1038/nature06248
pubmed: 17943120
Juhas, M. et al. Genomic islands: Tools of bacterial horizontal gene transfer and evolution. FEMS Microbiol. Rev. 33, 376–393. https://doi.org/10.1111/j.1574-6976.2008.00136.x (2009).
doi: 10.1111/j.1574-6976.2008.00136.x
pubmed: 19178566
Vernikos, G. S. & Parkhill, J. Interpolated variable order motifs for identification of horizontally acquired DNA: Revisiting the Salmonella pathogenicity islands. Bioinformatics 22, 2196–2203. https://doi.org/10.1093/bioinformatics/btl369 (2006).
doi: 10.1093/bioinformatics/btl369
pubmed: 16837528
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589. https://doi.org/10.1038/s41586-021-03819-2 (2021).
doi: 10.1038/s41586-021-03819-2
pubmed: 34265844
pmcid: 8371605
Medvedev, K. E., Schaeffer, R. D., Chen, K. S. & Grishin, N. V. Pan-cancer structurome reveals overrepresentation of beta sandwiches and underrepresentation of alpha helical domains. Sci. Rep. 13, 11988. https://doi.org/10.1038/s41598-023-39273-5 (2023).
doi: 10.1038/s41598-023-39273-5
pubmed: 37491511
pmcid: 10368619
Schaeffer, R. D. et al. ECOD domain classification of 48 whole proteomes from AlphaFold Structure Database using DPAM2. PLoS Comput. Biol. 20, e1011586. https://doi.org/10.1371/journal.pcbi.1011586 (2024).
doi: 10.1371/journal.pcbi.1011586
pubmed: 38416793
pmcid: 10927120
Cheng, H. et al. ECOD: An evolutionary classification of protein domains. PLoS Comput. Biol. 10, e1003926. https://doi.org/10.1371/journal.pcbi.1003926 (2014).
doi: 10.1371/journal.pcbi.1003926
pubmed: 25474468
pmcid: 4256011
Schaeffer, R. D. et al. ECOD: identification of distant homology among multidomain and transmembrane domain proteins. BMC Mol. Cell Biol. 20, 18. https://doi.org/10.1186/s12860-019-0204-5 (2019).
doi: 10.1186/s12860-019-0204-5
pubmed: 31226926
pmcid: 6588880
Zhang, J., Schaeffer, R. D., Durham, J., Cong, Q. & Grishin, N. V. DPAM: A domain parser for AlphaFold models. Protein Sci. 32, e4548. https://doi.org/10.1002/pro.4548 (2023).
doi: 10.1002/pro.4548
pubmed: 36539305
pmcid: 9850437
Medvedev, K. E., Kinch, L. N., Schaeffer, R. D. & Grishin, N. V. Functional analysis of Rossmann-like domains reveals convergent evolution of topology and reaction pathways. PLoS Comput. Biol. 15, e1007569. https://doi.org/10.1371/journal.pcbi.1007569 (2019).
doi: 10.1371/journal.pcbi.1007569
pubmed: 31869345
pmcid: 6957218
Medvedev, K. E., Kinch, L. N., Dustin Schaeffer, R., Pei, J. & Grishin, N. V. A fifth of the protein world: Rossmann-like proteins as an evolutionarily successful structural unit. J. Mol. Biol. 433, 166788. https://doi.org/10.1016/j.jmb.2020.166788 (2021).
doi: 10.1016/j.jmb.2020.166788
pubmed: 33387532
Kato, J., Lefebre, M. & Galan, J. E. Structural features reminiscent of ATP-driven protein translocases are essential for the function of a type III secretion-associated ATPase. J. Bacteriol. 197, 3007–3014. https://doi.org/10.1128/JB.00434-15 (2015).
doi: 10.1128/JB.00434-15
pubmed: 26170413
pmcid: 4542171
Browning, D. F., Butala, M. & Busby, S. J. W. Bacterial transcription factors: Regulation by pick “N” Mix. J. Mol. Biol. 431, 4067–4077. https://doi.org/10.1016/j.jmb.2019.04.011 (2019).
doi: 10.1016/j.jmb.2019.04.011
pubmed: 30998934
Owji, H., Nezafat, N., Negahdaripour, M., Hajiebrahimi, A. & Ghasemi, Y. A comprehensive review of signal peptides: Structure, roles, and applications. Eur. J. Cell Biol. 97, 422–441. https://doi.org/10.1016/j.ejcb.2018.06.003 (2018).
doi: 10.1016/j.ejcb.2018.06.003
pubmed: 29958716
Berman, H. M. et al. The protein data bank. Nucleic Acids Res. 28, 235–242. https://doi.org/10.1093/nar/28.1.235 (2000).
doi: 10.1093/nar/28.1.235
pubmed: 10592235
pmcid: 102472
Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinf. 10, 421. https://doi.org/10.1186/1471-2105-10-421 (2009).
doi: 10.1186/1471-2105-10-421
Madan-Babu, M. & Teichmann, S. A. Evolution of transcription factors and the gene regulatory network in Escherichia coli. Nucleic Acids Res. 31, 1234–1244. https://doi.org/10.1093/nar/gkg210 (2003).
doi: 10.1093/nar/gkg210
pubmed: 12582243
pmcid: 150228
Hansen-Wester, I. & Hensel, M. Salmonella pathogenicity islands encoding type III secretion systems. Microbes Infect. 3, 549–559. https://doi.org/10.1016/s1286-4579(01)01411-3 (2001).
doi: 10.1016/s1286-4579(01)01411-3
pubmed: 11418329
Lou, L., Zhang, P., Piao, R. & Wang, Y. Salmonella pathogenicity Island 1 (SPI-1) and its complex regulatory network. Front. Cell Infect. Microbiol. 9, 270. https://doi.org/10.3389/fcimb.2019.00270 (2019).
doi: 10.3389/fcimb.2019.00270
pubmed: 31428589
pmcid: 6689963
Miletic, S. et al. Substrate-engaged type III secretion system structures reveal gating mechanism for unfolded protein translocation. Nat. Commun. 12, 1546. https://doi.org/10.1038/s41467-021-21143-1 (2021).
doi: 10.1038/s41467-021-21143-1
pubmed: 33750771
pmcid: 7943601
Lara-Tejero, M., Kato, J., Wagner, S., Liu, X. & Galan, J. E. A sorting platform determines the order of protein secretion in bacterial type III systems. Science 331, 1188–1191. https://doi.org/10.1126/science.1201476 (2011).
doi: 10.1126/science.1201476
pubmed: 21292939
Notti, R. Q., Bhattacharya, S., Lilic, M. & Stebbins, C. E. A common assembly module in injectisome and flagellar type III secretion sorting platforms. Nat. Commun. 6, 7125. https://doi.org/10.1038/ncomms8125 (2015).
doi: 10.1038/ncomms8125
pubmed: 25994170
Bonemann, G., Pietrosiuk, A. & Mogk, A. Tubules and donuts: A type VI secretion story. Mol. Microbiol. 76, 815–821. https://doi.org/10.1111/j.1365-2958.2010.07171.x (2010).
doi: 10.1111/j.1365-2958.2010.07171.x
pubmed: 20444095
Blondel, C. J., Jimenez, J. C., Contreras, I. & Santiviago, C. A. Comparative genomic analysis uncovers 3 novel loci encoding type six secretion systems differentially distributed in Salmonella serotypes. BMC Genom. 10, 354. https://doi.org/10.1186/1471-2164-10-354 (2009).
doi: 10.1186/1471-2164-10-354
Mistry, J. et al. Pfam: The protein families database in 2021. Nucleic Acids Res. 49, D412–D419. https://doi.org/10.1093/nar/gkaa913 (2021).
doi: 10.1093/nar/gkaa913
pubmed: 33125078
Gabler, F. et al. Protein sequence analysis using the MPI bioinformatics toolkit. Curr. Protoc. Bioinf. 72, e108. https://doi.org/10.1002/cpbi.108 (2020).
doi: 10.1002/cpbi.108
van Kempen, M. et al. Fast and accurate protein structure search with Foldseek. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01773-0 (2023).
doi: 10.1038/s41587-023-01773-0
pubmed: 37156916
pmcid: 10869269
Zhou, J. et al. Structural evidence for a [4Fe-5S] intermediate in the non-redox desulfuration of thiouracil. Angew. Chem. Int. Ed. Engl. 60, 424–431. https://doi.org/10.1002/anie.202011211 (2021).
doi: 10.1002/anie.202011211
pubmed: 32929873
Liu, B., Zheng, D., Zhou, S., Chen, L. & Yang, J. VFDB 2022: A general classification scheme for bacterial virulence factors. Nucleic Acids Res. 50, D912–D917. https://doi.org/10.1093/nar/gkab1107 (2022).
doi: 10.1093/nar/gkab1107
pubmed: 34850947
Paysan-Lafosse, T. et al. InterPro in 2022. Nucleic Acids Res. 51, D418–D427. https://doi.org/10.1093/nar/gkac993 (2023).
doi: 10.1093/nar/gkac993
pubmed: 36350672
Rao, D. N., Dryden, D. T. & Bheemanaik, S. Type III restriction-modification enzymes: A historical perspective. Nucleic Acids Res. 42, 45–55. https://doi.org/10.1093/nar/gkt616 (2014).
doi: 10.1093/nar/gkt616
pubmed: 23863841
Wong, S. G. & Dessen, A. Structure of a bacterial alpha2-macroglobulin reveals mimicry of eukaryotic innate immunity. Nat. Commun. 5, 4917. https://doi.org/10.1038/ncomms5917 (2014).
doi: 10.1038/ncomms5917
pubmed: 25221932
Robb, C. S., Assmus, M., Nano, F. E. & Boraston, A. B. Structure of the T6SS lipoprotein TssJ1 from Pseudomonas aeruginosa. Acta Crystallogr. Sect. F Struct. Biol. Cryst. Commun. 69, 607–610. https://doi.org/10.1107/S1744309113012220 (2013).
doi: 10.1107/S1744309113012220
pubmed: 23722835
pmcid: 3668576
Edwards, R. A., Schifferli, D. M. & Maloy, S. R. A role for Salmonella fimbriae in intraperitoneal infections. Proc. Natl. Acad. Sci. U. S. A. 97, 1258–1262. https://doi.org/10.1073/pnas.97.3.1258 (2000).
doi: 10.1073/pnas.97.3.1258
pubmed: 10655518
pmcid: 15588
Fenwick, M. K., Philmus, B., Begley, T. P. & Ealick, S. E. Toxoflavin lyase requires a novel 1-His-2-carboxylate facial triad. Biochemistry 50, 1091–1100. https://doi.org/10.1021/bi101741v (2011).
doi: 10.1021/bi101741v
pubmed: 21166463
Peat, T. S., Newman, J., Waldo, G. S., Berendzen, J. & Terwilliger, T. C. Structure of translation initiation factor 5A from Pyrobaculum aerophilum at 1.75 A resolution. Structure 6, 1207–1214. https://doi.org/10.1016/s0969-2126(98)00120-8 (1998).
doi: 10.1016/s0969-2126(98)00120-8
pubmed: 9753699
Diard, M. et al. Antibiotic treatment selects for cooperative virulence of Salmonella typhimurium. Curr. Biol. 24, 2000–2005. https://doi.org/10.1016/j.cub.2014.07.028 (2014).
doi: 10.1016/j.cub.2014.07.028
pubmed: 25131673
Darby, E. M. et al. Molecular mechanisms of antibiotic resistance revisited. Nat. Rev. Microbiol. 21, 280–295. https://doi.org/10.1038/s41579-022-00820-y (2023).
doi: 10.1038/s41579-022-00820-y
pubmed: 36411397
Varadi, M. et al. AlphaFold protein structure database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444. https://doi.org/10.1093/nar/gkab1061 (2022).
doi: 10.1093/nar/gkab1061
pubmed: 34791371
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60. https://doi.org/10.1038/nmeth.3176 (2015).
doi: 10.1038/nmeth.3176
pubmed: 25402007
Steinegger, M. et al. HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinf. 20, 473. https://doi.org/10.1186/s12859-019-3019-7 (2019).
doi: 10.1186/s12859-019-3019-7
Schaeffer, R. D. et al. Classification of domains in predicted structures of the human proteome. Proc. Natl. Acad. Sci. U. S. A. 120, e2214069120. https://doi.org/10.1073/pnas.2214069120 (2023).
doi: 10.1073/pnas.2214069120
pubmed: 36917664
pmcid: 10041065
Holm, L. & Sander, C. Protein structure comparison by alignment of distance matrices. J. Mol. Biol. 233, 123–138. https://doi.org/10.1006/jmbi.1993.1489 (1993).
doi: 10.1006/jmbi.1993.1489
pubmed: 8377180
Kabsch, W. & Sander, C. Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577–2637. https://doi.org/10.1002/bip.360221211 (1983).
doi: 10.1002/bip.360221211
pubmed: 6667333
Amavisit, P., Lightfoot, D., Browning, G. F. & Markham, P. F. Variation between pathogenic serovars within Salmonella pathogenicity islands. J. Bacteriol. 185, 3624–3635. https://doi.org/10.1128/JB.185.12.3624-3635.2003 (2003).
doi: 10.1128/JB.185.12.3624-3635.2003
pubmed: 12775700
pmcid: 156220
Gerlach, R. G. et al. Salmonella pathogenicity Island 4 encodes a giant non-fimbrial adhesin and the cognate type 1 secretion system. Cell Microbiol. 9, 1834–1850. https://doi.org/10.1111/j.1462-5822.2007.00919.x (2007).
doi: 10.1111/j.1462-5822.2007.00919.x
pubmed: 17388786
Knodler, L. A. et al. Salmonella effectors within a single pathogenicity island are differentially expressed and translocated by separate type III secretion systems. Mol. Microbiol. 43, 1089–1103. https://doi.org/10.1046/j.1365-2958.2002.02820.x (2002).
doi: 10.1046/j.1365-2958.2002.02820.x
pubmed: 11918798
Pickard, D. et al. Composition, acquisition, and distribution of the Vi exopolysaccharide-encoding Salmonella enterica pathogenicity island SPI-7. J. Bacteriol. 185, 5055–5065. https://doi.org/10.1128/JB.185.17.5055-5065.2003 (2003).
doi: 10.1128/JB.185.17.5055-5065.2003
pubmed: 12923078
pmcid: 180996
Espinoza, R. A. et al. Differential roles for pathogenicity islands SPI-13 and SPI-8 in the interaction of Salmonella Enteritidis and Salmonella Typhi with murine and human macrophages. Biol. Res. 50, 5. https://doi.org/10.1186/s40659-017-0109-8 (2017).
doi: 10.1186/s40659-017-0109-8
pubmed: 28202086
pmcid: 5311848
Velasquez, J. C. et al. SPI-9 of Salmonella enterica serovar Typhi is constituted by an operon positively regulated by RpoS and contributes to adherence to epithelial cells in culture. Microbiol. (Read.) 162, 1367–1378. https://doi.org/10.1099/mic.0.000319 (2016).
doi: 10.1099/mic.0.000319
Bishop, A. L. et al. Analysis of the hypervariable region of the Salmonella enterica genome associated with tRNA(leuX). J. Bacteriol. 187, 2469–2482. https://doi.org/10.1128/JB.187.7.2469-2482.2005 (2005).
doi: 10.1128/JB.187.7.2469-2482.2005
pubmed: 15774890
pmcid: 1065210
Chiu, C. H. et al. The genome sequence of Salmonella enterica serovar Choleraesuis, a highly invasive and resistant zoonotic pathogen. Nucleic Acids Res. 33, 1690–1698. https://doi.org/10.1093/nar/gki297 (2005).
doi: 10.1093/nar/gki297
pubmed: 15781495
pmcid: 1069006
Tomljenovic-Berube, A. M. et al. Mapping and regulation of genes within Salmonella pathogenicity island 12 that contribute to in vivo fitness of Salmonella enterica Serovar Typhimurium. Infect. Immun. 81, 2394–2404. https://doi.org/10.1128/IAI.00067-13 (2013).
doi: 10.1128/IAI.00067-13
pubmed: 23630960
pmcid: 3697593
Shah, D. H. et al. Identification of Salmonella gallinarum virulence genes in a chicken infection model using PCR-based signature-tagged mutagenesis. Microbiol. (Read.) 151, 3957–3968. https://doi.org/10.1099/mic.0.28126-0 (2005).
doi: 10.1099/mic.0.28126-0
Ashburner, M. et al. Gene ontology: Tool for the unification of biology, The Gene Ontology Consortium. Nat. Genet. 25, 25–29. https://doi.org/10.1038/75556 (2000).
doi: 10.1038/75556
pubmed: 10802651
pmcid: 3037419
UniProt, C. UniProt: The Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 51, D523–D531. https://doi.org/10.1093/nar/gkac1052 (2023).
doi: 10.1093/nar/gkac1052
Kruskal, J. B. & Wish, M. Multidimensional Scaling (SAGE Publications Inc., 1978).
doi: 10.4135/9781412985130