Abstract
Background and Objective: Staphylococcus aureus (S. aureus) is a gram-positive bacterium and one of the major nosocomial pathogens. It has the ability to acquire resistance against almost all available classes of antibiotics; Methicillin-Resistant S. aureus (MRSA) is a well-known antibiotic-resistant pathogen. S. aureus is a globally distributed pathogen that needs in-depth epidemiological and genomic level investigation for proper treatment and prevention.
Methods: To explore the genomic epidemiology of S. aureus, in-silico Multi Locus Sequence Typing (MLST) was carried out for 355 complete genomes. Diversity within the species was investigated through pan-genome analysis and a subtractive genomic approach was employed for the identification of the core immunogenic targets.
Results: Epidemiological study identified 62 different sequence types (STs) of S. aureus distributed worldwide, in which ST-8, ST-5, ST-398, ST-239, and ST-30 were the most dominant STs comprising more than 50% of the isolates. The pan-genome of S. aureus is still open with 7,199 genes and there is a major contribution (80%) of MRSA strains in the S. aureus species pangenome. The core genome (2,025 genes) of S. aureus is almost stable (comprising 72% of S. aureus genome size), while accessory and unique genes (28% of S. aureus genome size) are gradually increasing. Screening of 2,025 core genes identified putative vaccine candidates. The best scoring and dominant B-cell and T-cell epitopes were predicted out of the selected potential vaccine candidate proteins with the help of a multi-step screening procedure.
Conclusion: We believe that the current study will provide insight into the genetic epidemiology and diversity of S. aureus, and the predicted epitopes against the pathogen can be tested further for their immunological responses within the host and may provide both humoral and cellular immunity against the disease.
Keywords: Epidemiology, Staphylococcus aureus, MLST, MRSA, pangenome, subtractive proteomics, vaccine candidates.
Graphical Abstract
[http://dx.doi.org/10.1128/CMR.00134-14] [PMID: 26016486]
[http://dx.doi.org/10.1016/j.jgar.2019.09.015] [PMID: 31557564]
[http://dx.doi.org/10.1128/AAC.44.6.1549-1555.2000] [PMID: 10817707]
[http://dx.doi.org/10.1016/j.ajic.2016.05.015] [PMID: 28159067]
bMuellner, P.; Pleydell, E.; Pirie, R.; Baker, M.G.; Campbell, D.; Carter, P.E.; French, N.P. Molecular-based surveillance of campylobacteriosis in New Zealand- from source attribution to genomic epidemiology. Euro Surveill., 2013, 18(3), 20365.
cStruelens, M.J.; Brisse, S. From molecular to genomic epidemiology: transforming surveillance and control of infectious diseases. Euro Surveill., 2013, 18(4), 20386.
[http://dx.doi.org/10.2307/30141781] [PMID: 9758056] [PMID: 23351655] [http://dx.doi.org/10.2807/ese.18.04.20386-en] [PMID: 23369387]
[http://dx.doi.org/10.1128/JB.186.5.1518-1530.2004] [PMID: 14973027]
[http://dx.doi.org/10.1016/j.mib.2008.09.006] [PMID: 19086349]
[http://dx.doi.org/10.1038/nmicrobiol.2017.40] [PMID: 28350002]
[http://dx.doi.org/10.1073/pnas.0506758102] [PMID: 16172379]
bFreschi, L.; Vincent, A.T.; Jeukens, J.; Emond-Rheault, J-G.; Kukavica-Ibrulj, I.; Dupont, M-J.; Charette, S.J.; Boyle, B.; Levesque, R.C. The Pseudomonas aeruginosa pan-genome provides new insights on its population structure, horizontal gene transfer, and pathogenicity. Genome Biol. Evol., 2019, 11(1), 109-120.
[http://dx.doi.org/10.1155/2019/6015730] [http://dx.doi.org/10.1093/gbe/evy259] [PMID: 30496396]
[http://dx.doi.org/10.1016/j.meegid.2018.10.025] [PMID: 30391557]
[http://dx.doi.org/10.3389/fmicb.2017.02485] [PMID: 29312194]
[http://dx.doi.org/10.1126/science.aar4120] [PMID: 29371424]
bZeng, L.; Wang, D.; Hu, N.; Zhu, Q.; Chen, K.; Dong, K.; Zhang, Y.; Yao, Y.; Guo, X.; Chang, Y-F.; Zhu, Y. A novel pan-genome reverse vaccinology approach employing a negative-selection strategy for screening surface-exposed antigens against leptospirosis. Front. Microbiol., 2017, 8, 396.
cBhardwaj, T.; Somvanshi, P. Pan-genome analysis of Clostridium botulinum reveals unique targets for drug development. Gene, 2017, 623, 48-62.
Naz, A.; Obaid, A.; Shahid, F.; Dar, H.A.; Naz, K.; Ullah, N.; Ali, A. Reverse vaccinology and drug target identification through pan-genomics Pan-genomics: Applications, Challenges, and Future Prospects Elsevier, 2020, pp. 317-333.
[http://dx.doi.org/10.1038/s41467-018-06634-y] [PMID: 30333483] [http://dx.doi.org/10.3389/fmicb.2017.00396] [PMID: 28352257] [http://dx.doi.org/10.1016/j.gene.2017.04.019] [PMID: 28450142] [http://dx.doi.org/10.1016/B978-0-12-817076-2.00016-0]
[http://dx.doi.org/10.1016/j.ygeno.2014.05.002] [PMID: 24837790]
[http://dx.doi.org/10.1016/j.meegid.2015.03.027] [PMID: 25818402]
[http://dx.doi.org/10.1016/j.ejps.2017.11.014] [PMID: 29174549]
[http://dx.doi.org/10.1016/j.micres.2018.05.002] [PMID: 29853168]
[http://dx.doi.org/10.1093/nar/gkt1099] [PMID: 24225323]
[http://dx.doi.org/10.1093/bioinformatics/btu153] [PMID: 24642063]
[http://dx.doi.org/10.1093/jac/dks261] [PMID: 22782487]
[http://dx.doi.org/10.1128/JCM.06094-11] [PMID: 22238442]
[http://dx.doi.org/10.1093/molbev/msy096] [PMID: 29722887]
[http://dx.doi.org/10.1093/bioinformatics/btl529] [PMID: 17050570]
[http://dx.doi.org/10.1186/s12859-019-2713-9] [PMID: 30871454]
[http://dx.doi.org/10.1093/molbev/msx148] [PMID: 28460117]
[http://dx.doi.org/10.1007/978-1-59745-535-0_4]
[http://dx.doi.org/10.1093/nar/gkt1131] [PMID: 24243843]
[http://dx.doi.org/10.3390/pathogens5010022] [PMID: 26901227]
[http://dx.doi.org/10.1093/bioinformatics/17.9.849] [PMID: 11590105]
[http://dx.doi.org/10.1093/bioinformatics/btq249] [PMID: 20472543]
[http://dx.doi.org/10.1002/prot.21078] [PMID: 16894596]
[http://dx.doi.org/10.1093/bioinformatics/17.12.1236] [PMID: 11751237]
[http://dx.doi.org/10.1093/bioinformatics/btg108] [PMID: 12761064]
[http://dx.doi.org/10.1371/journal.pone.0099368] [PMID: 24911789]
[http://dx.doi.org/10.1111/1462-2920.14635] [PMID: 31004458]
bSugai, M.; Fujiwara, T.; Ohta, K.; Komatsuzawa, H.; Ohara, M.; Suginaka, H. epr, which encodes glycylglycine endopeptidase resistance, is homologous to femAB and affects serine content of peptidoglycan cross bridges in Staphylococcus capitis and Staphylococcus aureus. J. Bacteriol., 1997, 179(13), 4311-4318.
cOdintsov, S.G.; Sabala, I.; Marcyjaniak, M.; Bochtler, M. Latent LytM at 1.3A resolution. J. Mol. Biol., 2004, 335(3), 775-785.
[http://dx.doi.org/10.1128/JB.179.11.3625-3631.1997] [PMID: 9171409] [http://dx.doi.org/10.1128/JB.179.13.4311-4318.1997] [PMID: 9209049] [http://dx.doi.org/10.1016/j.jmb.2003.11.009] [PMID: 14687573]
[http://dx.doi.org/10.1128/BR.36.4.407-477.1972] [PMID: 4568761]
[http://dx.doi.org/10.1002/pmic.200500764] [PMID: 16826566]
[http://dx.doi.org/10.1111/j.1574-695X.2000.tb01525.x] [PMID: 11064268]
[http://dx.doi.org/10.1038/s41598-018-21724-z] [PMID: 29311619]
[http://dx.doi.org/10.1073/pnas.092569199] [PMID: 11997460]
bFrankel, M.B.; Schneewind, O. Determinants of murein hydrolase targeting to cross-wall of Staphylococcus aureus peptidoglycan. J. Biol. Chem., 2012, 287(13), 10460-10471.
[http://dx.doi.org/10.1111/j.1365-2958.2005.04881.x] [PMID: 16262792] [http://dx.doi.org/10.1074/jbc.M111.336404] [PMID: 22303016]
[http://dx.doi.org/10.1038/s41467-018-03847-z] [PMID: 29643357]