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Abstract

Methicillin-resistant (MRSA) surveillance in regions with mass gatherings presents unique challenges for public health systems. Saudi Arabia, hosting millions of pilgrims annually, provides a distinctive setting for studying how human mobility shapes bacterial populations, yet comprehensive genomic surveillance data from this region remain limited. Here, we present an integrated analysis of isolates collected across seven Saudi Arabian regions, combining whole-genome sequencing with extensive antimicrobial susceptibility testing and standardized metadata following findability, accessibility, interoperability and reusability data principles. Our analysis revealed striking differences between pilgrimage and non-pilgrimage cities. Pilgrimage cities showed significantly higher genetic diversity and antimicrobial resistance rates, harbouring numerous international strains, including recognized clones from diverse geographic origins. Reported lineage dynamics are changing, expanding toward community clones. While genomic prediction of antimicrobial resistance showed high accuracy for some antibiotics, particularly beta-lactams, with varying performance for others, it highlights the necessity for phenotypic testing in clinical settings. Our findings demonstrate how mass gatherings drive bacterial population structures and emphasize the importance of integrated surveillance approaches in regions with significant global connectivity and travel.

Funding
This study was supported by the:
  • King Abdulaziz City for Science and Technology (Award TSC&KACST-KAUST-2018-05-27-01)
    • Principal Award Recipient: RobertHoehndorf
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
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2025-11-12
2025-12-16

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References

  1. Jevons MP. “Celbenin” - resistant Staphylococci. BMJ 1961; 1:124–125 [View Article]
    [Google Scholar]
  2. Katayama Y, Ito T, Hiramatsu K. A new class of genetic element, Staphylococcus cassette chromosome mec, encodes methicillin resistance in Staphylococcus aureus. Antimicrob Agents Chemother 2000; 44:1549–1555 [View Article] [PubMed]
    [Google Scholar]
  3. Hiramatsu K, Cui L, Kuroda M, Ito T. The emergence and evolution of methicillin-resistant Staphylococcus aureus. Trends Microbiol 2001; 9:486–493 [View Article] [PubMed]
    [Google Scholar]
  4. Uehara Y. Current status of Staphylococcal cassette chromosome mec (SCCmec). Antibiotics 2022; 11:86 [View Article]
    [Google Scholar]
  5. Butaye P, Argudín MA, Smith TC. Livestock-associated MRSA and its current evolution. Curr Clin Micro Rpt 2016; 3:19–31 [View Article]
    [Google Scholar]
  6. Lakhundi S, Zhang K. Methicillin-resistant Staphylococcus aureus: molecular characterization, evolution, and epidemiology. Clin Microbiol Rev 2018; 31:00020–18 [View Article] [PubMed]
    [Google Scholar]
  7. Golding GR, Bryden L, Levett PN, McDonald RR, Wong A et al. Whole-genome sequence of livestock-associated ST398 methicillin-resistant Staphylococcus aureus isolated from humans in Canada. J Bacteriol 2012; 194:6627–6628 [View Article] [PubMed]
    [Google Scholar]
  8. Goering RV, Shawar RM, Scangarella NE, O’Hara FP, Amrine-Madsen H et al. Molecular epidemiology of methicillin-resistant and methicillin-susceptible Staphylococcus aureus isolates from global clinical trials. J Clin Microbiol 2008; 46:2842–2847 [View Article] [PubMed]
    [Google Scholar]
  9. Algammal AM, Hetta HF, Elkelish A, Alkhalifah DHH, Hozzein WN et al. Methicillin-resistant Staphylococcus aureus (MRSA): One Health perspective approach to the bacterium epidemiology, virulence factors, antibiotic-resistance, and zoonotic impact. Infect Drug Resist 2020; 13:3255–3265 [View Article] [PubMed]
    [Google Scholar]
  10. Ploug T, Holm S, Gjerris M. The stigmatization dilemma in public health policy--the case of MRSA in Denmark. BMC Public Health 2015; 15:640 [View Article] [PubMed]
    [Google Scholar]
  11. Aljeldah MM. Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, University of Hafr Al Batin, Kingdom of Saudi Arabia Prevalence of methicillin-resistant Staphylococcus aureus (MRSA) in Saudi Arabia: a systematic review. J Pure Appl Microbiol 2020; 14:37–46 [View Article]
    [Google Scholar]
  12. Madani TA, Al-Abdullah NA, Al-Sanousi AA, Ghabrah TM, Afandi SZ et al. Methicillin-resistant Staphylococcus aureus in two tertiary-care centers in Jeddah, Saudi Arabia. Infect Control Hosp Epidemiol 2001; 22:211–216 [View Article] [PubMed]
    [Google Scholar]
  13. Senok A, Ehricht R, Monecke S, Al-Saedan R, Somily A. Molecular characterization of methicillin-resistant Staphylococcus aureus in nosocomial infections in a tertiary-care facility: emergence of new clonal complexes in Saudi Arabia. New Microbes New Infect 2016; 14:13–18 [View Article] [PubMed]
    [Google Scholar]
  14. Al Yousef SA, Taha EM. Methicillin-resistant Staphylococcus aureus in Saudi Arabia: genotypes distribution review. Saudi J Med Med Sci 2016; 4:2–8 [View Article] [PubMed]
    [Google Scholar]
  15. Balkhy HH, Memish ZA, Almuneef MA, Cunningham GC, Francis C et al. Methicillin-resistant Staphylococcus aureus: a 5-year review of surveillance data in a tertiary care hospital in Saudi Arabia. Infect Control Hosp Epidemiol 2007; 28:976–982 [View Article] [PubMed]
    [Google Scholar]
  16. Senok A, Somily A, Slickers P, Raji M, Garaween G. Staphylococcus aureus strain: first description of genome sequencing and molecular characterization of CC15-MRSA. Infect Drug Resist 2017; 10:307–315 [View Article]
    [Google Scholar]
  17. Alkuraythi DM, Alkhulaifi MM, Binjomah AZ, Alarwi M, Aldakhil HM et al. Clonal flux and spread of Staphylococcus aureus isolated from meat and its genetic relatedness to Staphylococcus aureus isolated from patients in Saudi Arabia. Microorganisms 2023; 11:2926 [View Article] [PubMed]
    [Google Scholar]
  18. Ahmed B, Mashat BH. Prevalence of classical enterotoxin genes in Staphylococcus aureus isolated from food handlers in Makkah city kitchens. Asian J Sci Tech 2014; 5:727–731
    [Google Scholar]
  19. Wilkinson MD, Dumontier M, Aalbersberg IJJ, Appleton G, Axton M et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016; 3:160018 [View Article] [PubMed]
    [Google Scholar]
  20. Qian J, Wu Z, Zhu Y, Liu C. One Health: a holistic approach for food safety in livestock. Sci One Health 2022; 1:100015 [View Article] [PubMed]
    [Google Scholar]
  21. Andrews S, Krueger F, Segonds-Pichon A, Biggins L, Virk B et al. Trim Galore Trim galore;
    [Google Scholar]
  22. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20:257 [View Article] [PubMed]
    [Google Scholar]
  23. Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol 2016; 17:132 [View Article] [PubMed]
    [Google Scholar]
  24. Souvorov A, Agarwala R, Lipman DJ. SKESA: strategic k-mer extension for scrupulous assemblies. Genome Biol 2018; 19: [View Article]
    [Google Scholar]
  25. Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 2013; 29:1072–1075 [View Article] [PubMed]
    [Google Scholar]
  26. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014; 30:2068–2069 [View Article]
    [Google Scholar]
  27. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 2015; 31:3691–3693 [View Article] [PubMed]
    [Google Scholar]
  28. Brynildsrud O, Bohlin J, Scheffer L, Eldholm V. Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary. Genome Biol 2016; 17: [View Article]
    [Google Scholar]
  29. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol 2020; 37:1530–1534 [View Article]
    [Google Scholar]
  30. Croucher NJ, Page AJ, Connor TR, Delaney AJ, Keane JA et al. Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res 2015; 43:e15 [View Article] [PubMed]
    [Google Scholar]
  31. Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res 2021; 49:W293–W296 [View Article]
    [Google Scholar]
  32. Torsten S. Abricate; 2020 https://github.com/tseemann/abricate
  33. Jolley KA, Bray JE, Maiden MCJ. Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications. Wellcome Open Res 2018; 3:124 [View Article] [PubMed]
    [Google Scholar]
  34. Nascimento M, Sousa A, Ramirez M, Francisco AP, Carriço JA et al. PHYLOViZ 2.0: providing scalable data integration and visualization for multiple phylogenetic inference methods. Bioinformatics 2017; 33:128–129 [View Article] [PubMed]
    [Google Scholar]
  35. Jia B, Raphenya AR, Alcock B, Waglechner N, Guo P et al. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res 2017; 45:D566–D573 [View Article] [PubMed]
    [Google Scholar]
  36. Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother 2020; 75:3491–3500 [View Article] [PubMed]
    [Google Scholar]
  37. Arango-Argoty G, Garner E, Pruden A, Heath LS, Vikesland P et al. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome 2018; 6:23 [View Article] [PubMed]
    [Google Scholar]
  38. Chen L, Yang J, Yu J, Yao Z, Sun L. VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res 2004; 33:D325–D328 [View Article]
    [Google Scholar]
  39. Petit RA III, Read TD. Bactopia: a flexible pipeline for complete analysis of bacterial genomes. mSystems 2020; 5:10–1128 [View Article]
    [Google Scholar]
  40. Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 2018; 34:3094–3100 [View Article]
    [Google Scholar]
  41. Sanchez-Herrero JM. SpaTyper: Staphylococcal protein A (spa) characterization pipeline; 2020
  42. Crusoe MR, Abeln S, Iosup A, Amstutz P, Chilton J et al. Methods included: standardizing computational reuse and portability with the common workflow language. In Communications of the ACM vol 65 2022 pp 54–63 [View Article]
    [Google Scholar]
  43. Amstutz P, César N, Clegg T, Di Pentima L. The Arvados Authors Arvados.
  44. Klyne G, Carroll JJ. Resource description framework (RDF): concepts and abstract syntax; 2004 http://www.w3.org/TR/2004/REC-rdf-concepts-20040210/ accessed 15 March 2015
  45. Maxat K. Metadata of methicillin resistant Staphylococcus aureus (MRSA) sequences in Saudi Arabia; 2024
  46. Nadig S, Velusamy N, Lalitha P, Kar S, Sharma S et al. Staphylococcus aureus eye infections in two Indian hospitals: emergence of ST772 as a major clone. Clin Ophthalmol 2012; 6:165–173 [View Article] [PubMed]
    [Google Scholar]
  47. Bakthavatchalam YD, Vasudevan K, Rao S, Varughese S, Rupali P et al. Genomic portrait of community-associated methicillin-resistant Staphylococcus aureus ST772-SCCmec V lineage from India. Gene Rep 2021; 24:101235 [View Article]
    [Google Scholar]
  48. Udo EE, O’Brien FG, Al-Sweih N, Noronha B, Matthew B et al. Genetic lineages of community-associated methicillin-resistant Staphylococcus aureus in Kuwait hospitals. J Clin Microbiol 2008; 46:3514–3516 [View Article] [PubMed]
    [Google Scholar]
  49. Boneca IG, Chiosis G. Vancomycin resistance: occurrence, mechanisms and strategies to combat it. Expert Opin Ther Targets 2003; 7:311–328 [View Article] [PubMed]
    [Google Scholar]
  50. Hadfield J, Croucher NJ, Goater RJ, Abudahab K, Aanensen DM et al. Phandango: an interactive viewer for bacterial population genomics. Bioinformatics 2018; 34:292–293 [View Article]
    [Google Scholar]
  51. Authority GS Umrah statistics; 2023
  52. Haseeb A, Saleem Z, Faidah HS, Saati AA, AlQarni A et al. Threat of antimicrobial resistance among pilgrims with infectious diseases during Hajj: lessons learnt from COVID-19 pandemic. Antibiotics 2023; 12:1299 [View Article]
    [Google Scholar]
  53. Senok A, Nassar R, Celiloglu H, Nabi A, Alfaresi M et al. Genotyping of methicillin resistant Staphylococcus aureus from the United Arab Emirates. Sci Rep 2020; 10:18551 [View Article]
    [Google Scholar]
  54. Boswihi SS, Udo EE, Monecke S, Mathew B, Noronha B et al. Emerging variants of methicillin-resistant Staphylococcus aureus genotypes in Kuwait hospitals. PLoS One 2018; 13:e0195933 [View Article] [PubMed]
    [Google Scholar]
  55. Alfouzan W, Udo EE, Modhaffer A, Alosaimi A. Molecular characterization of methicillin- resistant Staphylococcus aureus in a tertiary care hospital in Kuwait. Sci Rep 2019; 9:18527 [View Article] [PubMed]
    [Google Scholar]
  56. Blomfeldt A, Larssen KW, Moghen A, Gabrielsen C, Elstrøm P et al. Emerging multidrug-resistant Bengal Bay clone ST772-MRSA-V in Norway: molecular epidemiology 2004–2014. Eur J Clin Microbiol Infect Dis 2017; 36:1911–1921 [View Article]
    [Google Scholar]
  57. David MZ, Cadilla A, Boyle-Vavra S, Daum RS. Replacement of HA-MRSA by CA-MRSA infections at an academic medical center in the Midwestern United States, 2004-5 to 2008. PLoS One 2014; 9:e92760 [View Article]
    [Google Scholar]
  58. Mediavilla JR, Chen L, Mathema B, Kreiswirth BN. Global epidemiology of community-associated methicillin resistant Staphylococcus aureus (CA-MRSA). Curr Opin Microbiol 2012; 15:588–595 [View Article] [PubMed]
    [Google Scholar]
  59. Senok A, Slickers P, Hotzel H, Boswihi S, Braun SD et al. Characterisation of a novel SCCmec VI element harbouring fusC in an emerging Staphylococcus aureus strain from the Arabian Gulf region. PLoS One 2019; 14:e0223985 [View Article] [PubMed]
    [Google Scholar]
  60. Pang S, Daley DA, Sahibzada S, Mowlaboccus S, Stegger M et al. Genome-wide association studies reveal candidate genes associated to bacteraemia caused by ST93-IV CA-MRSA. BMC Genomics 2021; 22:418 [View Article] [PubMed]
    [Google Scholar]
  61. Bhattacharyya RP, Bandyopadhyay N, Ma P, Son SS, Liu J et al. Simultaneous detection of genotype and phenotype enables rapid and accurate antibiotic susceptibility determination. Nat Med 2019; 25:1858–1864 [View Article] [PubMed]
    [Google Scholar]
  62. Gordillo Altamirano FL, Barr JJ. Phage therapy in the postantibiotic era. Clin Microbiol Rev 2019; 32:00066–18 [View Article] [PubMed]
    [Google Scholar]
  63. Schoch CL, Ciufo S, Domrachev M, Hotton CL, Kannan S et al. NCBI Taxonomy: a comprehensive update on curation, resources and tools. Database 2020; 2020: [View Article]
    [Google Scholar]
  64. Hastings J, Owen G, Dekker A, Ennis M, Kale N et al. ChEBI in 2016: improved services and an expanding collection of metabolites. Nucleic Acids Res 2016; 44:D1214–9 [View Article] [PubMed]
    [Google Scholar]
  65. Golbeck J, Fragoso G, Hartel F, Hendler J, Oberthaler J et al. The National Cancer Institute’s Thésaurus and ontology. J Web Semant 2003; 1:75–80 [View Article]
    [Google Scholar]
  66. Griffiths E, Dooley D, Graham M, Van Domselaar G, Brinkman FSL. Context is everything: harmonization of critical food microbiology descriptors and metadata for improved food safety and surveillance. Front Microbiol 2017; 8: [View Article]
    [Google Scholar]
  67. Gkoutos GV, Schofield PN, Hoehndorf R. The Units Ontology: a tool for integrating units of measurement in science. Database 2012; 2012:bas033 [View Article] [PubMed]
    [Google Scholar]
  68. Tay M, Lee B, Ismail MH, Yam J, Maliki D et al. Usefulness of aircraft and airport wastewater for monitoring multiple pathogens including SARS-CoV-2 variants. J Travel Med 2024; 31:taae074 [View Article] [PubMed]
    [Google Scholar]
  69. Didelot X, Wilson DJ. ClonalFrameML: efficient inference of recombination in whole bacterial genomes. PLoS Comput Biol 2015; 11:e1004041 [View Article] [PubMed]
    [Google Scholar]
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