Membrane Proteins and Proteomics of Cronobacter sakazakii Cells: Reliable Method for Identification and Subcellular Localization
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
35435719
PubMed Central
PMC9088360
DOI
10.1128/aem.02508-21
Knihovny.cz E-zdroje
- Klíčová slova
- Cronobacter, cell subfractionation, food pathogen, membrane proteomics,
- MeSH
- Cronobacter sakazakii * MeSH
- Cronobacter * MeSH
- faktory virulence metabolismus MeSH
- kojenec MeSH
- lidé MeSH
- náhražky mateřského mléka mikrobiologie MeSH
- novorozenec MeSH
- potravinářská mikrobiologie MeSH
- proteiny vnější bakteriální membrány metabolismus MeSH
- proteom metabolismus MeSH
- proteomika MeSH
- Check Tag
- kojenec MeSH
- lidé MeSH
- novorozenec MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- faktory virulence MeSH
- proteiny vnější bakteriální membrány MeSH
- proteom MeSH
Members of the genus Cronobacter are responsible for severe infections in infants and immunosuppressed individuals. Although several virulence factors have been described, many proteins involved in the pathogenesis of such infections have not yet been mapped. This study is the first to fractionate Cronobacter sakazakii cells into outer membrane, inner membrane, periplasmic, and cytosolic fractions as the basis for improved proteome mapping. A novel method was designed to prepare the fractionated samples for protein identification. The identification was performed via one-dimensional electrophoresis-liquid chromatography electrospray ionization tandem mass spectrometry. To determine the subcellular localization of the identified proteins, we developed a novel Python-based script (Subcelloc) that combines three web-based tools, PSORTb 3.0.2, CELLO 2.5, and UniProtKB. Applying this approach enabled us to identify 1,243 C. sakazakii proteins, which constitutes 28% of all predicted proteins and 49% of all theoretically expressed outer membrane proteins. These results represent a significant improvement on previous attempts to map the C. sakazakii proteome and could provide a major step forward in the identification of Cronobacter virulence factors. IMPORTANCECronobacter spp. are opportunistic pathogens that can cause rare and, in many cases, life-threatening infections, such as meningitis, necrotizing enterocolitis, and sepsis. Such infections are mainly linked to the consumption of contaminated powdered infant formula, with Cronobacter sakazakii clonal complex 4 considered the most frequent agent of serious neonatal infection. However, the pathogenesis of diseases caused by these bacteria remains unclear; in particular, the proteins involved throughout the process have not yet been mapped. To help address this, we present an improved method for proteome mapping that emphasizes the isolation and identification of membrane proteins. Specific focus was placed on the identification of the outer membrane proteins, which, being exposed to the surface of the bacterium, directly participate in host-pathogen interaction.
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Iversen C, Mullane N, McCardell B, Tall BD, Lehner A, Fanning S, Stephan R, Joosten H. 2008. Cronobacter gen. nov., a new genus to accommodate the biogroups of Enterobacter sakazakii, and proposal of Cronobacter sakazakii gen. nov., comb. nov., Cronobacter malonaticus sp. nov., Cronobacter turicensis sp. nov., Cronobacter muytjensii sp. nov., Cronobacter dublinensis sp. nov., Cronobacter genomospecies 1, and of three subspecies, Cronobacter dublinensis subsp. dublinensis subsp. nov., Cronobacter dublinensis subsp. lausannensis subsp. nov. and Cronobacter dublinensis subsp. lactaridi subsp. nov. Int J Syst Evol Microbiol 58:1442–1447. 10.1099/ijs.0.65577-0. PubMed DOI
Joseph S, Cetinkaya E, Drahovska H, Levican A, Figueras MJ, Forsythe SJ. 2012. Cronobacter condimenti sp. nov., isolated from spiced meat, and Cronobacter universalis sp. nov., a species designation for Cronobacter sp. genomospecies 1, recovered from a leg infection, water and food ingredients. Int J Syst Evol Microbiol 62:1277–1283. 10.1099/ijs.0.032292-0. PubMed DOI
FAO/WHO. 2008. Enterobacter sakazakii (Cronobacter spp.) in powdered follow-up formulae. Microbiological risk assessment series no. 15. FAO, Rome, Italy.
Forsythe SJ. 2015. Chapter 13. New insights into the emergent bacterial pathogen Cronobacter. Academic Press, San Diego, CA. 10.1016/B978-0-12-800245-2.00013-7. DOI
Ogrodzki P, Forsythe SJ. 2017. DNA-sequence based typing of the Cronobacter genus using MLST, CRISPR-cas array and capsular profiling. Front Microbiol 8:1875. 10.3389/fmicb.2017.01875. PubMed DOI PMC
Malmstrom L, Malmstrom J, Aebersold R. 2011. Quantitative proteomics of microbes: principles and applications to virulence. Proteomics 11:2947–2956. 10.1002/pmic.201100088. PubMed DOI
Poetsch A, Wolters D. 2008. Bacterial membrane proteomics. Proteomics 8:4100–4122. 10.1002/pmic.200800273. PubMed DOI
Tan S, Tan HT, Chung MC. 2008. Membrane proteins and membrane proteomics. Proteomics 8:3924–3932. 10.1002/pmic.200800597. PubMed DOI
Wallin E, von Heijne G. 1998. Genome-wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms. Protein Sci 7:1029–1038. 10.1002/pro.5560070420. PubMed DOI PMC
Rawlings AE. 2016. Membrane proteins: always an insoluble problem? Biochem Soc Trans 44:790–795. 10.1042/BST20160025. PubMed DOI PMC
Riedel K, Lehner A. 2007. Identification of proteins involved in osmotic stress response in Enterobacter sakazakii by proteomics. Proteomics 7:1217–1231. 10.1002/pmic.200600536. PubMed DOI
Hobb RI, Fields JA, Burns CM, Thompson SA. 2009. Evaluation of procedures for outer membrane isolation from Campylobacter jejuni. Microbiology (Reading) 155:979–988. 10.1099/mic.0.024539-0. PubMed DOI PMC
Santoni V, Molloy M, Rabilloud T. 2000. Membrane proteins and proteomics: un amour impossible? Electrophoresis 21:1054–1070. 10.1002/(SICI)1522-2683(20000401)21:6<1054::AID-ELPS1054>3.0.CO;2-8. PubMed DOI
Hynek R, Svensson B, Jensen ON, Barkholt V, Finnie C. 2006. Enrichment and identification of integral membrane proteins from barley aleurone layers by reversed-phase chromatography, SDS-PAGE, and LC-MS/MS. J Proteome Res 5:3105–3113. 10.1021/pr0602850. PubMed DOI
Carranza P, Hartmann I, Lehner A, Stephan R, Gehrig P, Grossmann J, Barkow-Oesterreicher S, Roschitzki B, Eberl L, Riedel K. 2009. Proteomic profiling of Cronobacter turicensis 3032, a food-borne opportunistic pathogen. Proteomics 9:3564–3579. 10.1002/pmic.200900016. PubMed DOI
Ye Y, Li H, Ling N, Han Y, Wu Q, Xu X, Jiao R, Gao J. 2016. Identification of potential virulence factors of Cronobacter sakazakii isolates by comparative proteomic analysis. Int J Food Microbiol 217:182–188. 10.1016/j.ijfoodmicro.2015.08.025. PubMed DOI
Du XJ, Han R, Li P, Wang S. 2015. Comparative proteomic analysis of Cronobacter sakazakii isolates with different virulences. J Proteomics 128:344–351. 10.1016/j.jprot.2015.08.013. PubMed DOI
Ye Y, Gao J, Jiao R, Li H, Wu Q, Zhang J, Zhong X. 2015. The membrane proteins involved in virulence of Cronobacter sakazakii virulent G362 and attenuated L3101 isolates. Front Microbiol 6:1238. 10.3389/fmicb.2015.01238. PubMed DOI PMC
Jaradat ZW, Rashdan AM, Ababneh QO, Jaradat SA, Bhunia AK. 2011. Characterization of surface proteins of Cronobacter muytjensii using monoclonal antibodies and MALDI-TOF mass spectrometry. BMC Microbiol 11:148–153. 10.1186/1471-2180-11-148. PubMed DOI PMC
Aldubyan MA, Almami IS, Benslimane FM, Alsonosi AM, Forsythe SJ. 2017. Comparative outer membrane protein analysis of high and low-invasive strains of Cronobacter malonaticus. Front Microbiol 8:2268. 10.3389/fmicb.2017.02268. PubMed DOI PMC
Loo RR, Dales N, Andrews PC. 1994. Surfactant effects on protein structure examined by electrospray ionization mass spectrometry. Protein Sci 3:1975–1983. 10.1002/pro.5560031109. PubMed DOI PMC
Moumène A, Marcelino I, Ventosa M, Gros O, Lefrancois T, Vachiery N, Meyer DF, Coelho AV. 2015. Proteomic profiling of the outer membrane fraction of the obligate intracellular bacterial pathogen Ehrlichia ruminantium. PLoS One 10:e0116758. 10.1371/journal.pone.0116758. PubMed DOI PMC
Rahman MA, Noore MS, Hasan MA, Ullah MR, Rahman MH, Hossain MA, Ali Y, Islam MS. 2014. Identification of potential drug targets by subtractive genome analysis of Bacillus anthracis A0248: an in silico approach. Comput Biol Chem 52:66–72. 10.1016/j.compbiolchem.2014.09.005. PubMed DOI
Hasan MA, Khan MA, Sharmin T, Mazumder MHH, Chowdhury AS. 2016. Identification of putative drug targets in vancomycin-resistant Staphylococcus aureus (VRSA) using computer aided protein data analysis. Gene 575:132–143. 10.1016/j.gene.2015.08.044. PubMed DOI
Koleiev IM, Starosyla SA, Protopopov MV, Volynets GP, Sapelkin VM, Pletnova LV, Syniugin AR, Kachaput NO, Matiushok VI, Bdzhola VG, Yarmoluk SM. 2020. Identification of membrane proteins as potential drug targets in Escherichia coli ATCC 25922 using in silico approaches. Biopolym Cell 36:348–357. 10.7124/bc.000A38. DOI
King BR, Vural S, Pandey S, Barteau A, Guda C. 2012. ngLOC: software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes. BMC Res Notes 5:351. 10.1186/1756-0500-5-351. PubMed DOI PMC
Magnus M, Pawlowski M, Bujnicki JM. 2012. MetaLocGramN: a meta-predictor of protein subcellular localization for Gram-negative bacteria. Biochim Biophys Acta 1824:1425–1433. 10.1016/j.bbapap.2012.05.018. PubMed DOI
Yu CS, Lin CJ, Hwang JK. 2004. Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci 13:1402–1406. 10.1110/ps.03479604. PubMed DOI PMC
Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, Dao P, Sahinalp SC, Ester M, Foster LJ, Brinkman FSL. 2010. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics 26:1608–1615. 10.1093/bioinformatics/btq249. PubMed DOI PMC
Koebnik R, Locher KP, Van Gelder P. 2000. Structure and function of bacterial outer membrane proteins: barrels in a nutshell. Mol Microbiol 37:239–253. 10.1046/j.1365-2958.2000.01983.x. PubMed DOI
Juncker AS, Willenbrock H, Von Heijne G, Brunak S, Nielsen H, Krogh A. 2003. Prediction of lipoprotein signal peptides in Gram‐negative bacteria. Protein Sci 12:1652–1662. 10.1110/ps.0303703. PubMed DOI PMC
Armenteros JJA, Tsirigos KD, Sønderby CK, Petersen TN, Winther O, Brunak S, von Heijne G, Nielsen H. 2019. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol 37:420–423. 10.1038/s41587-019-0036-z. PubMed DOI
Krogh A, Larsson B, Von Heijne G, Sonnhammer EL. 2001. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 305:567–580. 10.1006/jmbi.2000.4315. PubMed DOI
Berven FS, Flikka K, Jensen HB, Eidhammer I. 2004. BOMP: a program to predict integral β-barrel outer membrane proteins encoded within genomes of Gram-negative bacteria. Nucleic Acids Res 32:W394–W399. 10.1093/nar/gkh351. PubMed DOI PMC
Wimley WC. 2003. The versatile beta-barrel membrane protein. Curr Opin Struct Biol 13:404–411. 10.1016/s0959-440x(03)00099-x. PubMed DOI
Chaturvedi D, Mahalakshmi R. 2017. Transmembrane beta-barrels: evolution, folding and energetics. Biochim Biophys Acta Biomembr 1859:2467–2482. 10.1016/j.bbamem.2017.09.020. PubMed DOI PMC
Thein M, Sauer G, Paramasivam N, Grin I, Linke D. 2010. Efficient subfractionation of gram-negative bacteria for proteomics studies. J Proteome Res 9:6135–6147. 10.1021/pr1002438. PubMed DOI
Gygi SP, Aebersold R. 2000. Mass spectrometry and proteomics. Curr Opin Chem Biol 4:489–494. 10.1016/s1367-5931(00)00121-6. PubMed DOI
Baggerman G, Vierstraete E, De Loof A, Schoofs L. 2005. Gel-based versus gel-free proteomics: a review. Comb Chem High Throughput Screen 8:669–677. 10.2174/138620705774962490. PubMed DOI
Lin X-m, Li H, Wang C, Peng X-X. 2008. Proteomic analysis of nalidixic acid resistance in Escherichia coli: identification and functional characterization of OM proteins. J Proteome Res 7:2399–2405. 10.1021/pr800073c. PubMed DOI
Heerklotz H, Seelig J. 2000. Correlation of membrane/water partition coefficients of detergents with the critical micelle concentration. Biophysical J 78:2435–2440. 10.1016/S0006-3495(00)76787-7. PubMed DOI PMC
Baik SC, Kim KM, Song SM, Kim DS, Jun JS, Lee SG, Song JY, Park JU, Kang HL, Lee WK, Cho MJ, Youn HS, Ko GH, Rhee KH. 2004. Proteomic analysis of the sarcosine-insoluble outer membrane fraction of Helicobacter pylori strain 26695. J Bacteriol 186:949–955. 10.1128/JB.186.4.949-955.2004. PubMed DOI PMC
Huang CZ, Lin XM, Wu LN, Zhang DF, Liu D, Wang SY, Peng XX. 2006. Systematic identification of the subproteome of Escherichia coli cell envelope reveals the interaction network of membrane proteins and membrane-associated peripheral proteins. J Proteome Res 5:3268–3276. 10.1021/pr060257h. PubMed DOI
Papadioti A, Markoutsa S, Vranakis I, Tselentis Y, Karas M, Psaroulaki A, Tsiotis G. 2011. A proteomic approach to investigate the differential antigenic profile of two Coxiella burnetii strains. J Proteomics 74:1150–1159. 10.1016/j.jprot.2011.04.016. PubMed DOI
Vit O, Man P, Kadek A, Hausner J, Sklenar J, Harant K, Novak P, Scigelova M, Woffendin G, Petrak J. 2016. Large-scale identification of membrane proteins based on analysis of trypsin-protected transmembrane segments. J Proteomics 149:15–22. 10.1016/j.jprot.2016.03.016. PubMed DOI
Budzik JM, Poor CB, Faull KF, Whitelegge JP, He C, Schneewind O. 2009. Intramolecular amide bonds stabilize pili on the surface of bacilli. Proc Natl Acad Sci USA 106:19992–19997. 10.1073/pnas.0910887106. PubMed DOI PMC
Tabb DL, Vega-Montoto L, Rudnick PA, Variyath AM, Ham AJ, Bunk DM, Kilpatrick LE, Billheimer DD, Blackman RK, Cardasis HL, Carr SA, Clauser KR, Jaffe JD, Kowalski KA, Neubert TA, Regnier FE, Schilling B, Tegeler TJ, Wang M, Wang P, Whiteaker JR, Zimmerman LJ, Fisher SJ, Gibson BW, Kinsinger CR, Mesri M, Rodriguez H, Stein SE, Tempst P, Paulovich AG, Liebler DC, Spiegelman C. 2010. Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry. J Proteome Res 9:761–776. 10.1021/pr9006365. PubMed DOI PMC
Nilsson T, Mann M, Aebersold R, Yates JR, Bairoch A, Bergeron JJ. 2010. Mass spectrometry in high-throughput proteomics: ready for the big time. Nat Methods 7:681–685. 10.1038/nmeth0910-681. PubMed DOI
Law HCH, Kong RPW, Szeto SSW, Zhao Y, Zhang Z, Wang Y, Li G, Quan Q, Lee SMY, Lam HC, Chu IK. 2015. A versatile reversed phase-strong cation exchange-reversed phase (RP–SCX–RP) multidimensional liquid chromatography platform for qualitative and quantitative shotgun proteomics. Analyst 140:1237–1252. 10.1039/c4an01893a. PubMed DOI
Stejskal K, Potěšil D, Zdráhal Z. 2013. Suppression of peptide sample losses in autosampler vials. J Proteome Res 12:3057–3062. 10.1021/pr400183v. PubMed DOI
Blažková M, Javůrková B, Vlach J, Göselová S, Karamonová L, Ogrodzki P, Forsythe S, Fukal L. 2015. Diversity of O antigens within the genus Cronobacter: from disorder to order. Appl Environ Microbiol 81:5574–5582. 10.1128/AEM.00277-15. PubMed DOI PMC
Vlach J, Javůrková B, Karamonová L, Blažková M, Fukal L. 2017. Novel PCR-RFLP system based on rpoB gene for differentiation of Cronobacter species. Food Microbiol 62:1–8. 10.1016/j.fm.2016.08.004. PubMed DOI
Hariri S, Joseph S, Forsythe SJ. 2013. Cronobacter sakazakii ST4 strains and neonatal meningitis, United States. Emerg Infect Dis 19:175–177. 10.3201/eid1901.120649. PubMed DOI PMC
Kosová K, Chrpová J, Šantrůček J, Hynek R, Štěrbová L, Vítámvás P, Bradová J, Prášil IT. 2017. The effect of Fusarium culmorum infection and deoxynivalenol (DON) application on proteome response in barley cultivars Chevron and Pedant. J Proteomics 169:112–124. 10.1016/j.jprot.2017.07.005. PubMed DOI