Unraveling the Genomic Landscape of Staphylococcus aureus in Hospital Settings of East Africa

Unraveling the Genomic Landscape of Staphylococcus aureus in Hospital Settings of East Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unraveling the Genomic Landscape of Staphylococcus aureus in Hospital Settings of East Africa Geoffrey Olweny, Gerald Mboowa, Alex Kayongo, Benson R Kidenya, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6846109/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Staphylococcus aureus is a major pathogen that causes hospital- and community-acquired infections, with increasing antimicrobial resistance (AMR) posing a global threat. East Africa remains an understudied region concerning the genomic epidemiology of S. aureus . This study provides a comprehensive genomic characterization of S. aureus genomes from hospital settings in East Africa, focusing on AMR, virulence determinants, mobile genetic elements (MGEs), and population structure. Methods We analyzed a total of 496 S. aureus whole-genome sequences (WGSs) from Tanzania, Kenya and Uganda retrieved from the NCBI Sequence Read Archive (SRA). Bioinformatics pipelines were employed for quality control, genome assembly, annotation and comparative genomics. In-silico multi-Locus Sequence Typing (MLST), spa & SCC mec typing and pangenome assessment were conducted. AMR and virulence genes, as well as plasmid and prophage diversity, were identified via curated databases. Results MLST analysis revealed 45 sequence types (STs), including 18 novel allelic profiles, with CC152 (ST152) being the most prevalent (26.7%). Spa typing identified 67 distinct types, with t355 (24.4%) and t1476 (17.8%) dominating. SCC mec typing revealed Type V (78.1%) as the predominant methicillin resistance determinant, particularly in Tanzania (91.3%). AMR profiling revealed 94 resistance genes, with a high prevalence of blaZ (β-lactamase), tet ( 38 ) (tetracycline efflux), and dfrG (trimethoprim resistance). Virulence gene analysis revealed 147 loci, including Panton-Valentine leukocidin ( lukF-PV ; 2.1%) and biofilm-associated genes ( icaD ). Plasmid analysis revealed high diversity, with Tanzania exhibiting the highest replicon complexity (mean = 4.2 plasmids/isolate). The phage sequence (n = 934) was predominantly Siphoviridae (94.1%), with no significant geographic structuring. Pangenome analysis revealed extreme diversity, with only five core genes conserved across all the isolates and 68,759 strain-specific cloud genes. Conclusion This study highlights the dynamic genomic landscape of S. aureus in East Africa, characterized by regional clonal expansion, extensive AMR, and diverse virulence profiles. The dominance of community-associated MRSA (Type V SCC mec ) in Tanzania contrasts with Kenya’s co-circulation of hospital and community strains, whereas Uganda harbors rare SCC mec Type VI strains, suggesting potential zoonotic origins. These findings emphasize the need for region-specific surveillance and tailored AMR stewardship programs to mitigate the spread of resistant and virulent S. aureus clones in East Africa. Staphylococcus aureus Whole-genome sequencing Antimicrobial resistance AMR MRSA Virulence SCCmec types Mobile genetic elements Comparative genomics East Africa Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Background Staphylococcus aureus colonizes approximately 30% of the human population and primarily resides in nasal passages and on the skin ( 1 ). It poses a significant global health concern because of its ability to transition from a commensal organism to an opportunistic pathogen ( 2 ) responsible for a variety of infections, ranging from minor skin infections to severe invasive disease states such as bacteremia, pneumonia, endocarditis, osteomyelitis, and toxic shock syndrome ( 3 ). The ability of this bacterium to cause a wide range of infections is attributed to their arsenal of virulence factors, including surface proteins, toxins, and enzymes, and their capacity to form biofilms ( 4 ). These virulence factors allow Staphylococcus aureus to adhere to host tissues, evade the immune system, and damage host cells ( 5 ). Health care settings pose a particularly high risk for Staphylococcus aureus infections, especially among immunocompromised patients, those with invasive medical devices (e.g., catheters, prosthetic devices), and those undergoing surgical procedures ( 6 ). Infections caused by Staphylococcus aureus in healthcare facilities are often more difficult to treat, primarily due to the emergence of healthcare-associated antibiotic-resistant strains ( 7 ) such as methicillin-resistant Staphylococcus aureus (MRSA) ( 8 ). MRSA has evolved into a significant healthcare-associated pathogen, leading to increased morbidity, mortality, and healthcare costs ( 9 ). Its persistence in hospital environments and ability to spread rapidly among vulnerable populations make it especially challenging to manage ( 10 ). In Africa, the burden of MRSA is particularly alarming due to limited access to diagnostics, effective antibiotics and healthcare infrastructure, which contributes to the high prevalence of hospital-acquired infections ( 11 ). Studies have shown that the MRSA prevalence in East African hospital settings is comparable to that in other regions, but data on strain diversity and genetic characteristics are limited ( 12 ). Whole-genome sequencing (WGS) has become a critical tool in clinical microbiology, providing comprehensive insights into the genetic architecture of pathogens ( 13 ). Unlike traditional diagnostic methods, which focus on a limited set of genetic markers, WGS captures the entire genome of an organism, enabling detailed analysis of virulence factors, AMR genes, and evolutionary relationships ( 14 ). For example, WGS has been instrumental in tracking MRSA outbreaks, identifying resistance determinants, and informing infection control strategies ( 15 ). In East Africa, where infectious diseases remain a leading cause of mortality, characterizing health care-associated Staphylococcus aureus strains is crucial for improving infection control measures. A growing body of research has begun to focus on the genomic analysis of Staphylococcus aureus in this region, revealing a complex landscape of resistance and virulence factors ( 16 ). Despite these studies, there remains a significant gap in our understanding of the genetic diversity of Staphylococcus aureus from hospital settings across East African countries. These insights are particularly crucial for informing the 2024 WHO Bacterial Priority Pathogens List, which emphasizes public health importance in combating antimicrobial resistance, including tackling MRSA, to guide global research, development, and prevention and control strategies. This study bridges this gap by assessing population structure, genetic diversity, AMR profiles and virulence factors and evaluating the role of mobile genetic elements in the dissemination of resistance and virulence genes via WGS data. Methods Study design and setting This study analyzed publicly available WGS data for Staphylococcus aureus collected from hospital settings in East Africa. The data were obtained from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database under relevant BioProjects. The WGS data were subjected to specific inclusion and exclusion criteria to ensure data quality and relevance (Fig. 1 ). Data were obtained from Staphylococcus aureus samples collected within hospital environments, including clinical samples, hospital surfaces, and medical equipment. All samples were sourced from countries within East Africa. Illumina paired-end sequencing technology was selected for uniformity and compatibility with bioinformatics tools. Additionally, essential metadata, such as geographical origin and collection methods, had to be available to enable comprehensive contextual analysis. This rigorous selection process ensured the inclusion of high-quality datasets suitable for downstream bioinformatics analyses. The sequences from three East African countries—Uganda, Kenya, and Tanzania—meeting the inclusion criteria included a total of 496 S. aureus Illumina paired-end sequences distributed across five BioProjects. Uganda provided one BioProject (PRJEB40863) containing 42 paired-end sequences; Kenya contributed two BioProjects: PRJEB23611 with 95 sequences and PRJEB15413 with 184 sequences; similarly, Tanzania contributed two BioProjects: PRJEB75012 with 10 sequences and PRJEB71932 with 165 sequences. Sequence retrieval and quality control Raw sequencing data in FastQ format were retrieved via the SRA Toolkit and stored on a high-performance computing server. Quality assessment was performed with FastQC version 0.12.0 ( https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ) to identify low-quality bases, adapter sequences, and potential contaminants. Reads were trimmed via Trimmomatic version 0.39 ( https://github.com/timflutre/trimmomatic ) to remove low-quality bases (Q < 20) and adapter sequences, with reads shorter than 36 bp discarded. The quality metrics across all the samples were aggregated and visualized via MultiQC version v1.27 ( https://multiqc.info/ ). FastQC revealed that 90% of the forward and reverse reads were unique across all the samples. After trimming adapters and removing low-quality reads, the reads achieved a mean quality score of Q30, indicating a high level of sequencing accuracy. Additionally, after trimming, the dataset showed a marked reduction in sequence duplication, enhancing its reliability for downstream analyses. Genome assembly and annotation De novo genome assembly was performed via SPAdes version v4.1.0 https://github.com/ablab/spades , followed by polishing with PILON version 1.24 https://github.com/broadinstitute/pilon . Assembly quality was assessed via QUAST version 5.3 https://quast.sourceforge.net/ , which focuses on metrics such as N50, genome size, and GC content. To increase the accuracy of the assemblies, the polished genomes were aligned to the Staphylococcus aureus reference strain NCTC 8325 https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000013425.1/ , facilitating their organization into draft genomes. The genomes were annotated with Prokka version v1.14.5 https://github.com/tseemann/prokka to identify coding sequences, RNA features, and other genomic elements. The resulting scaffolds and contigs from the genome assembly had an average genome size of 3.5 Megabytes. A quality assessment via QUAST indicated a high level of contiguity, with an average N50 value of 550 kb. In total, 955 contigs were annotated, leading to the identification of 3,145 coding sequences (CDSs). Identification of AMR and virulence-encoding genes The ABRicate version 1.0.0 https://github.com/tseemann/abricate tool was employed to identify virulence and AMR-encoding genes via curated databases. The detected genes were mapped to known resistance mechanisms and virulence factors for interpretation. Multi-Locus Sequence Typing (MLST) Putative genotyping was conducted via the MLST tool version 2.23.0 https://github.com/tseemann/mlst to classify sequences on the basis of housekeeping genes, providing insights into clonal relationships and population structure. Pangenome analysis Pangenome analysis was performed via the ROARY https://github.com/sanger-pathogens/Roary pipeline to identify core and accessory genes highlighting genetic diversity across the Staphylococcus aureus genomes. Comparative genomics We used dREP version 2.0.0 ( https://github.com/MrOlm/drep ) to perform comparative genomic analysis of the sequenced strains. dREP efficiently calculates pairwise average nucleotide identity (ANI) and clusters genomes on the basis of overall similarity. Using the fastANI algorithm with a similarity threshold of 0.05, the tool grouped highly similar genomes into distinct clusters, which likely represent clonal lineages. Each primary cluster identified by dREP reflects a unique genomic group. Mobile genetic element (MGE) analysis PlasmidFinder version 2.1.6.0 ( https://github.com/genomicepidemiology/plasmidfinder ) was utilized to identify plasmids, offering insights into the horizontal transfer of genetic material. Prophage prediction was performed via Phigaro version 2.4.0 (for temperate phages) https://github.com/bobeobibo/phigaro and PHASTER ( PHAge Search Tool Enhanced Release ) https://phaster.ca/ , which identify intact, questionable, and incomplete prophage regions in bacterial genomes. These elements play crucial roles in horizontal gene transfer, bacterial adaptation, and virulence. Insertion sequences (ISs) were detected via ISEScan version 1.7.1 https://bioweb.pasteur.fr/packages/pack@ [email protected] , a tool specialized in identifying bacterial IS elements that contribute to genome plasticity and the spread of antimicrobial resistance (AMR) genes. Additionally, SCCmecFinder ( https://bitbucket.org/genomicepidemiology/sccmecfinder/src/master/ ) was used to identify staphylococcal cassette chromosome mec (SCC mec ) elements, which are essential for methicillin resistance in Staphylococcus aureus . Together, these tools provide a robust framework for characterizing mobile genetic elements (MGEs) and assessing their role in shaping the genetic architecture of AMR and virulence. Results Multilocus sequence typing (MLST) analysis of the WGS S. aureus East African genomes A total of 434 Staphylococcus aureus isolates (227, 174, and 33 from Kenya, Tanzania, and Uganda, respectively) were subjected to MLST analysis, which identified 45 distinct sequence types (STs), including 18 novel allelic profiles (Fig. 2 ). The population structure revealed a predominance of four major CCs, with CC152 (ST152) being the most widespread, accounting for 26.7% (n = 116) of all the isolates, and was observed across all three countries. CC8 (ST8) was the second most common lineage (18.2%, n = 79), with a particularly high prevalence in Tanzania (Additional file 1). CC5 (ST5/ST6; 6.5%, n = 28) and CC30 (ST30/ST34; 4.4%, n = 19) were primarily found among Kenyan isolates, reflecting regional variation in clonal distribution (Fig. 2 A-C). The country-level distribution demonstrated significant geographic heterogeneity in the ST profiles (Fig. 1 B). In Tanzania, ST152 was the most frequently detected (38.5%), followed by ST8 (31.6%) and ST15 (6.3%). Kenyan isolates also presented ST152 as the most common (22.9%), but with relatively lower proportions of ST8 (12.3%) and a higher representation of ST5 (6.2%). In Uganda, ST152 maintained a high prevalence (36.4%), along with the unique presence of ST1633 (21.2%) and ST8 (12.1%). A total of 18 previously unreported STs were identified, representing 4.1% of all the isolates, and were designated novel STs on the basis of unique combinations of alleles across the seven MLST loci. These novel STs were particularly common in Uganda, where they accounted for 33.3% of the isolates, whereas they accounted for 11.9% in Kenya and 9.2% in Tanzania. Notably, rare or novel sequence types included ST2744 (identified exclusively in Tanzania), ST7635 (shared between Kenya and Uganda), and ST2178 (unique to Uganda), each characterized by distinctive allelic profiles. Analysis of allelic diversity across the MLST loci revealed substantial polymorphism. The aroE locus was the most diverse, exhibiting 75 unique alleles, followed closely by tpi , with 68 alleles. In contrast, arcC and gmk were less polymorphic, with 46 and 44 alleles, respectively. These findings indicate variability in evolutionary pressures across different housekeeping genes. Clonal complex analysis grouped the isolates into 12 major CCs, with CC152 representing the dominant lineage. Other globally recognized CCs, including CC8 (USA300-related), CC5 (pediatric clone), CC30 (Southwest Pacific clone), and CC22 (EMRSA-15), were also identified, indicating the circulation of clinically significant lineages in the East African region. Statistical analysis confirmed the strong geographic structuring of the ST distribution (Fig. 2 D). A chi-square test of independence yielded a highly significant result (χ² = 221.58, df = 70, p < 0.0001), indicating that the distribution of STs was not uniform across countries. Owing to sparse cell counts in some comparisons, Fisher’s exact test was also performed, confirming these findings (p = 0.0005, based on 2000 Monte Carlo replicates). Post hoc pairwise comparisons with Benjamini‒Hochberg correction revealed significant differences between all country pairs: Kenya vs Tanzania (adjusted p = 0.0015), Kenya vs Uganda (adjusted p = 0.0435), and Tanzania vs Uganda (adjusted p = 0.0015). Biologically, these differences were driven by distinct patterns in terms of ST prevalence and exclusivity. Tanzania presented a markedly greater prevalence of ST8 (40.8%) than Kenya (10.1%) and Uganda (12.1%). Kenya showed greater representation of ST188 (5.3%) and ST80 (5.7%), both of which were absent and minimally present in the other countries. Uganda's distinctiveness was characterized by a high frequency of ST1633 (21.2%) and the highest proportion of novel STs (33.3%). Notably, ST121, present in Tanzania (5.7%), was completely absent in Ugandan isolates. Spa Type Diversity and Geographic Distribution Analysis of 438 S. aureus reordered genomes across Tanzania (n = 239), Kenya (n = 168), and Uganda (n = 31) revealed a total of 67 distinct spa types, reflecting substantial genetic heterogeneity within the population (Fig. 3 ). The three most prevalent spa types were t355 (n = 107, 24.4%), t1476 (n = 78, 17.8%), and t064 (n = 23, 5.3%) (Fig. 3 A, B, D). Together, these lineages comprised almost half (47.5%) of all the isolates, highlighting their epidemiological importance across the region. The dominance of specific clones varies by country. In Tanzania, t355 (38.9%) and t1476 (32.6%) were predominant, whereas in Uganda, t355 accounted for 41.9% of the isolates, followed by t1476 at 9.7%. In contrast, Kenya exhibited greater spa type heterogeneity, with t064 (13.7%), t355 (12.5%), and t1504 (5.4%) as the most frequent lineages and the emergence of t13194 (4.8%) as a notably prevalent type exclusive to the Kenyan dataset. These findings indicate the existence of both regionally dominant and geographically restricted clones across East Africa (Additional file 2). A total of 15 predominant spa types were identified among the genomes, reflecting the most frequently encountered genetic lineages in the sampled population. The most dominant clone was spa type t355, which accounted for 38.2% of all the isolates. This substantial prevalence suggests pronounced clonal expansion and regional transmission of this lineage. The second most prevalent type was t1476, accounting for 21.9% of the isolates, followed by t064, which accounted for 7.0%. Collectively, these three spa types represent approximately two-thirds (~ 67%) of all typed isolates, indicating that the S. aureus population structure in this region is largely shaped by a few successful lineages. Additional spa types were detected at lower frequencies, including t189 (4.3%), t13194 (3.7%), t4333 (3.7%), and t223 (3.0%). Other notable types, including t084, t127, t4499, and t701, were each observed in fewer than 2.5% of the isolates. The rarest among the 15 predominant types was t131, which constituted only 1.7% of the total population. This distribution pattern underscores a population structure dominated by a limited number of highly successful clones, likely reflecting a combination of clonal expansion, transmission dynamics, and possible selective advantages. Moreover, the detection of less common and novel spa types indicates an underlying degree of genetic diversity and potential for microevolutionary processes within regional S. aureus populations. Country-specific distributions revealed distinct patterns in spa -type frequencies. In Kenya, t355 was the most dominant clone, accounting for 37.2% of the isolates, followed by t064 at 12.8%, t189 at 8.3%, and t13194 at 7.1%. Several additional types, including t1504, t223, and t131, appeared at moderate frequencies ranging from 3–5%. The Kenyan spa -type landscape was characterized by a relatively high degree of diversity, with t355 as the principal clone accompanied by multiple secondary lineages. In Tanzania, t1476 emerged as the most prevalent type, representing 42.4% of the isolates, followed by t355 (26.4%) and t4333 (7.6%). Other types, including t498, t4499, and t091, were also present at lower frequencies. The dominance of t1476 in Tanzania may suggest the recent introduction or expansion of this lineage in this specific geographical setting. In Uganda, t355 again represented the dominant spa type, accounting for 48.7% of the isolates. Other types were present at markedly lower frequencies, with t127 and t1476 both contributing 7.7%, and t1096, t2554, and t9231 each accounting for 5.1%. These results suggest that S. aureus populations in Uganda are less genetically diverse than those in Kenya and Tanzania are, with a greater degree of clonal homogeneity driven by the predominance of t355. Several novel or rare spa types were also detected, primarily in Tanzania, suggesting ongoing diversification and potential local adaptation. Specifically, nine rare or novel types, including t10599, t1346, t15643, and t17400, were observed in the Tanzanian isolates, indicating the presence of previously undocumented lineages or localized evolutionary events. In Kenya, t13194 emerged as a potentially region-specific type, with a prevalence of 4.8%, yet it was not observed in isolates from Tanzania or Uganda. In Uganda, rare types such as t10274 and t10499 were identified, further supporting the idea that although dominant clones drive the overall population structure, regionally unique or emerging spa types continue to circulate at low levels. To evaluate whether spa type frequencies were non-randomly distributed across the three countries, a chi-square test was performed, which included only spa types with at least 10 observations. The test revealed a highly significant difference in spa type distribution across countries (χ² = 133.96, degrees of freedom = 10, p < 2.2 × 10⁻¹⁶), indicating strong geographic structuring in the S. aureus population (Fig. 3 C). To explore intercountry differences, pairwise chi-square comparisons were conducted with Benjamini‒Hochberg correction for multiple testing. All pairwise comparisons revealed significant differences in spa type composition. The most striking difference was between Tanzania and Kenya (p < 3.8 × 10⁻²⁴), followed by Tanzania and Uganda (p = 1.37 × 10⁻⁴) and Kenya and Uganda (p = 6.64 × 10⁻⁴). These findings were independently validated via Fisher’s exact test with simulated p values, which also confirmed a significant association between spa type and country (p = 0.0005). Effect size estimates via Cramér’s V further quantified the magnitude of these associations. The strongest association was observed between Tanzania and Kenya (Cramér’s V = 0.7371), followed by Tanzania and Uganda (0.5138) and Kenya and Uganda (0.4393), reflecting moderate to strong effect sizes. These values underscore the existence of substantial differences in spa type composition between East African countries. A stacked bar plot depicting the relative proportions of spa types across the three countries visually supported these findings and highlighted the distinct population structures, with t355 demonstrating transnational dominance, t1476 showing a strong country-specific signature in Tanzania, and t064 being more prominent in the Kenyan context. Comprehensive profiling of antimicrobial resistance determinants reveals distinct patterns among Hospital S. aureus genomes across East Africa Whole-genome analysis of 434 clinical Staphylococcus aureus isolates from tertiary care hospitals across Kenya (n = 187), Tanzania (n = 203), and Uganda (n = 44) revealed an extensive resistome comprising 94 distinct antimicrobial resistance (AMR) genes (Additional file 3). These genetic determinants confer resistance to 21 classes of antimicrobial agents, spanning all major therapeutic categories used in clinical practice across the region. The spectrum of resistance included resistance to aminoglycosides (gentamicin, kanamycin, tobramycin, amikacin, streptomycin), β-lactams (penicillins, cephalosporins), tetracyclines, macrolides, phenicols (chloramphenicol), folate pathway inhibitors (trimethoprim), fosfomycin, fusidic acid, glycopeptides (vancomycin), lincosamides, pleuromilins, streptogramins, mupirocin, streptothricin, and sulfonamides (Fig. 4 A). Quantitative analysis of AMR gene prevalence demonstrated marked heterogeneity in distribution frequencies (Fig. 4 C). Five resistance genes accounted for the majority (64.8%) of the detected AMR markers, forming a core resistome among East African isolates. The β-lactamase operon components blaI_of_Z (14.4%, n = 477) and blaR1 (13.4%, n = 444), along with the penicillinase gene blaZ (12.4%, n = 409), were highly prevalent, which is consistent with widespread β-lactam resistance. Compared with global datasets, the tetracycline efflux pump gene tet ( 38 ) (14.1%, n = 466) had an unexpectedly high prevalence, whereas the trimethoprim resistance determinant dfrG (10.5%, n = 346) was the dominant resistance gene. Notably, the methicillin resistance gene mecA was detected in 3.7% (n = 121) of the isolates, a prevalence substantially lower than that reported in many hospital settings worldwide (typically 20–50%) ( 17 ). However, its distribution varied significantly by country, with Tanzania showing a 2.8% prevalence compared with only 0.6% in Kenya (χ²=15.2, p < 0.001). The fosfomycin resistance gene fosB-Saur had a concerning prevalence (6.6%, n = 218), particularly in Tanzania, where it accounted for 3.6% of all AMR markers, whereas it accounted for 2.6% of all AMR markers in Kenya (OR = 1.42, 95% CI 1.08–1.86). The macrolide resistance gene erm(C) was identified in 3.9% (n = 128) of the isolates, with a country-specific prevalence ranging from 1.1% in Kenya to 2.5% in Tanzania. Detailed geographic analysis revealed distinct AMR gene distribution profiles across the three countries (Fig. 4 B). Kenyan isolates presented a predominance of β-lactam resistance genes, with blaI_of_Z (7.5%), blaR1 (7.1%), and blaZ (5.9%) collectively accounting for 20.5% of all resistance markers. Tanzania exhibited more diverse resistance patterns, with an increased prevalence of fosB-Saur (3.6%), erm(C) (2.5%), and mecA (2.8%). The aminoglycoside resistance gene aph ( 2 ) -Ih showed a particularly skewed distribution, representing 2.8% of the AMR markers in Tanzania compared with only 0.5% in Kenya (Fisher's exact test p < 0.0001). Ugandan isolates generally presented lower AMR gene frequencies, with only dfrG (1.2%) and tet ( 38 ) (1.2%) exceeding 1% prevalence. Despite these qualitative differences in resistance gene profiles, nonparametric analysis via the Kruskal‒Wallis test revealed no significant difference in overall AMR gene counts between countries (χ²=1.29, df = 2, p = 0.52; Fig. 4 D). This suggests that while the specific composition of resistomes varies, the total burden of acquired resistance remains relatively constant across the region. Post hoc pairwise comparisons via Wilcoxon rank-sum tests with Benjamini‒Hochberg correction confirmed this finding for all country pairs (Kenya‒Tanzania: p = 0.63; Kenya‒Uganda: p = 0.41; Tanzania‒Uganda: p = 0.55). In addition to the core resistome, several genes of clinical and epidemiological importance were identified (Fig. 3 B). The tetracycline resistance genes tet(K) (4.3%, n = 142) and tet(M) (0.7%, n = 22) presented differential distribution patterns, with tet(K) being more prevalent in Tanzania (2.1%) than in Kenya (1.7%). The trimethoprim resistance gene dfrC (3.6%, n = 119) demonstrated the opposite trend, being more common in Kenya (0.9%) than in Tanzania (2.5%). The aminoglycoside resistance gene aph ( 2 ) -Ih (3.4%, n = 113) showed particularly strong geographic clustering, accounting for 2.8% of AMR markers in Tanzania compared with only 0.5% in Kenya and 0.1% in Uganda. Virulence Factor Landscape of Staphylococcus aureus in East African Hospitals Comprehensive genomic analysis of 434 Staphylococcus aureus clinical isolates collected from hospitals across East Africa revealed a diverse and conserved repertoire of virulence determinants (Fig. 5 ). A total of 147 distinct virulence genes were identified across reordered genomes, reflecting the substantial pathogenic potential of regional S. aureus strains. Among these genes, the Panton-Valentine leukocidin (PVL) gene component lukF-PV emerged as the most prevalent virulence factor, accounting for 2.08% of the total number of virulence genes (n = 628). Other highly represented genes included hysA (1.73%, n = 522), cap8B and esaB (1.61%, n = 486 each), isdG (1.60%, n = 484), esxA and icaD (1.60%, n = 483 each), and a suite of capsular polysaccharide type 8 operon components ( cap8A–G , M , and N ), each with a prevalence ranging from 1.57–1.61% (Additional file 4). The virulence genes were clustered into three major functional categories: immune evasion and surface adherence, hemolytic and proteolytic activity, and iron acquisition systems (Fig. 4 A). The immune evasion factors were dominated by the hyaluronidase gene hysA and biofilm-associated icaD , both of which are broadly distributed across isolates. The cap8 operon was remarkably conserved, supporting the widespread potential for capsular polysaccharide production. Hemolysins were also prominent, with near-ubiquitous detection of the bicomponent γ-hemolysin genes hlgB (1.59%) and hlgC (1.58%), along with α-hemolysin ( hla ) at 1.58%. The proteolytic enzymes sspC (1.58%) and aur (1.58%) further retained extracellular degradative activity. Iron-scavenging mechanisms were consistently detected through the iron-regulated surface determinant (Isd) pathway genes isdG (1.60%) and isdC (1.58%). In parallel, the high prevalence of esaB (1.61%) and esxA (1.60%) pointed to a conserved ESAT-6 secretion system across isolates, potentially contributing to intracellular survival and immune modulation. Country-level analyses revealed significant geographic variation in virulence gene prevalence (Fig. 5 B, 3 C). Compared with the Kenyan isolates, the Tanzanian isolates presented a 1.4-fold greater prevalence of lukF-PV (p = 0.03), whereas the Ugandan isolates presented greater carriage of sak (1.2% vs. 0.4% regionally, p = 0.01), which encodes staphylokinase and may facilitate immune escape via plasminogen activation. In contrast, the cap8 operon remained consistently detected across all three countries (1.55–1.63%), highlighting its potential role as a core virulence module in regional S. aureus populations. Virulence gene co-occurrence analysis revealed three statistically significant clusters (Fisher’s exact test, p < 0.001), suggesting functionally coordinated expression patterns: (i) a PVL-associated cluster comprising lukF-PV and hlgCB (OR = 4.2), reflecting synergistic cytotoxic potential; (ii) a biofilm-associated cluster involving icaD and aur (OR = 3.8), indicating mechanisms of persistence and immune evasion; and (iii) a tissue invasion cluster formed by hysA and sspC (OR = 2.9), indicating enhanced host tissue degradation capabilities. Plasmid Diversity and Distribution in East African WGS S. aureus We analyzed 434 S. aureus genomes from East Africa to characterize plasmid diversity, prevalence, and distribution. Plasmid carriage was widespread and highly variable, with isolates harboring between 1 and 17 replicons (mean = 3.55; SD = 2.67). Most genomes (78.2%) carried four or fewer replicons, whereas a small subset (3.7%) harbored seven or more replicons, including five genomes with > 10 replicons. Notably, isolate ERR3150952 presented an extreme profile with 17 replicons (Fig. 6 ). Replicon frequency analysis revealed a heterogeneous plasmid landscape dominated by a few high-prevalence elements (Fig. 6 A). The most common replicons were rep5a_1_repSAP001 (pN315; 68.9%), rep16_3_rep (pSaa6159; 53.2%), and rep10_3_pNE131p1 (pNE131; 51.7%). Intermediate-prevalence replicons (10–50%) included rep7c_1_rep (MSSA476; 32.4%), rep19_13_rep (pBORa53; 28.6%), and rep15_1_repA (pLW043; 18.3%). Rare replicons (< 10%), such as rep20_3_rep , rep7a_16_repC , and rep24a_1_rep , appeared in fewer than 6.1% of the genomes, and seven replicons were found in only one genome each, suggesting either rare horizontal acquisitions or assembly artifacts. Plasmid diversity and prevalence exhibited significant geographic structuring (Fig. 6 C). The Tanzanian isolates had the highest Shannon diversity index (2.41), followed by Kenya (1.87) and Uganda (1.52; Kruskal‒Wallis, p < 0.01) (Fig. 6 E). While rep5a_1_repSAP001 was common across all countries (Kenya: 65.2%, Tanzania: 71.3%, Uganda: 63.8%), replicons such as rep7c_1_rep and rep16_3_rep were significantly more common in Tanzania (38.7% and 61.2%, respectively) than in Kenya (25.4% and 46.6%) and Uganda (0.6% and 42.3%; χ² tests, p < 0.001). Uganda presented the lowest diversity, with replicon carriage concentrated in a few dominant types, particularly rep16_3_rep and rep7a_16 . Multiple-replicon carriage also varies geographically. A majority (68.5%) of Tanzanian genomes carried three or more replicons, compared with 55.3% in Uganda and 41.1% in Kenya. Common country-specific combinations included rep5a_1 + rep16_3 + rep10_3 in Tanzania, rep16_3 + rep5a_1 + rep7a_16 in Uganda, and rep5a_1 + rep16_3 + rep7c_1 in Kenya, suggesting differences in plasmid compatibility, host permissiveness, or antimicrobial pressures (Fig. 6 D). Co-occurrence network analysis revealed significant replicon associations (Fisher’s exact test, p < 0.001), forming three main clusters: ( 1 ) a core maintenance cluster featuring rep5a_1 + rep10_3 (OR = 4.8) and rep16_3 + rep15_1 (OR = 3.2); ( 2 ) an accessory function cluster with rep7c_1 + rep19_13 (OR = 5.1) and rep20_3 + rep7a_16 (OR = 2.9); and ( 3 ) a Tanzania-specific cluster linking rep24a_1 + rep21_9 (OR = 6.7), suggesting local selection pressures. Modularity-based network analysis (modularity = 0.62) revealed five distinct replicon communities (Fig. 6 F). Community 1 (n = 195 nodes) was dominated by high-degree hubs such as rep5a_1_repSAP001 and rep16_3_rep , which are mainly found in Tanzanian isolates. Community 2 (n = 111) included rep7c_1_rep and repUS43_1_CDS12738 , which are more prevalent in the Kenyan genomes. Community 3 (n = 86) represented niche or horizontally transferred replicons, whereas Community 4 (n = 62) and Community 5 (n = 2) consisted of low-frequency or potentially artifactual elements. Finally, a chi-square test confirmed significant differences between replicon presence and country of origin (χ² = 848.13, df = 206, p < 0.001) (Fig. 6 E), reinforcing the role of geographic structuring in shaping the plasmidome of S. aureus in East Africa. Phage Diversity and Genomic Architecture in East African S. aureus Genomes We identified 934 phage sequences from Staphylococcus aureus isolates in East African hospitals, predominantly from Kenya (n = 687, 73.6%) and Tanzania (n = 220, 23.6%), with a minor contribution from Uganda (n = 27, 2.9%). Taxonomically, a striking majority (94.1%, n = 879) belonged to the Siphoviridae family, followed by Myoviridae (3.1%, n = 29), Podoviridae (1.2%, n = 11), and unclassified phages (1.4%, n = 13). Additionally, two hybrid Myoviridae/Siphoviridae sequences (0.2%) were detected, suggesting possible instances of horizontal gene transfer (Fig. 7 ). The genomic characteristics of the identified prophages revealed considerable length variability (201 bp to 131.2 kb), with a median of 38.7 kb. The dominant size class of 30–50 kb encompassed 78% of the sequences, which were aligned with known temperate phage genomes. We identified 22 genomic scaffolds harboring multiple prophage integrations, with five scaffolds containing three prophages each, highlighting genomic hotspots for integration. Notably, 12 samples from Tanzania shared an identical prophage sequence (NODE_8_length_88311), indicative of conserved integration loci or successful phage lineages. Geographically, phage richness was highest in Kenya (633 unique IDs), followed by Tanzania (230 IDs) and Uganda (71 IDs). However, PERMANOVA based on Jaccard distances indicated no significant geographic structuring of phage communities (p = 1.0), which was supported by nonmetric multidimensional scaling (NMDS), which revealed extensive overlap in phage composition across regions. Crucially, our findings revealed no evidence of transposable elements within the prophage sequences, indicating stable integration of mechanisms via site-specific recombination. The integration sites varied widely, ranging from 111 bp to 678.6 kb, underscoring the absence of conserved loci at the population level despite recurrent integration patterns at the scaffold level. Regional Diversity and Load of Insertion Sequences in S. aureus within East Africa Our comprehensive genomic analysis revealed significant regional disparities in the mobilome of S. aureus isolates from Kenya, Tanzania, and Uganda. The distribution of insertion sequences (ISs) was markedly heterogeneous across regions, with certain families demonstrating pervasive prevalence, notably IS1182 and IS21 (Fig. 8 ). IS1182 was detected in 100% of the isolates across all three countries, with counts ranging from 132 in Uganda to 793 in Tanzania, underscoring its status as a core component of the S. aureus mobilome in East Africa. Similarly, IS21 was universally present, with counts of 95 in Uganda, 257 in Tanzania, and 781 in Kenya. Conversely, less prevalent families such as IS256 and ISL3 exhibited substantial regional variation: IS256 was significantly more abundant in Kenya (138) than in Uganda ( 3 ), and ISL3 followed a similar pattern. A chi-square test confirmed that the distribution of these dominant IS families was highly significant across regions (X² = 551.74, p < 2.2×10⁻¹⁶), indicating that region-specific mobilome profiles were influenced by local selective pressures and transmission dynamics. The total number of IS elements per isolate varied dramatically, with Tanzanian isolates exhibiting the highest median counts and a broader distribution, with some samples harboring over 100 ISs indicative of heightened genomic plasticity. In contrast, Ugandan isolates predominantly contained fewer than 30 ISs. Kruskal‒Wallis tests confirmed that these differences were statistically significant (p < 0.001), suggesting that regional variation in mobile genetic activity could influence the dissemination of resistance determinants and virulence factors. To elucidate potential horizontal gene transfer pathways, we constructed a co-occurrence network on the basis of the presence/absence of IS families across isolates. The network revealed several highly interconnected families, notably IS1182, IS21, and IS3, which frequently co-occurred within individual genomes. These core elements appear to facilitate widespread genetic exchange, as evidenced by their prevalence in multiple isolates (Fig. 8 D). Region-specific clustering within the network was apparent: isolates from Kenya and Tanzania presented more complex co-occurrence patterns, which was consistent with greater genomic plasticity, whereas Ugandan isolates presented simpler networks with fewer interconnected families. Geospatial and Molecular Characterization of SCCmec Elements Reveals Country-Specific MRSA Epidemiology Across East Africa Staphylococcal Cassette Chromosome mec (SCCmec) elements, which are responsible for methicillin resistance in Staphylococcus aureus , were analyzed across multiple genomes from Tanzania, Kenya and Uganda. We characterized SCCmec elements in 114 methicillin-resistant Staphylococcus aureus genomes from East Africa (Kenya: n = 25; Tanzania: n = 80; Uganda: n = 9) obtained from a total of 434 WGS genomes (Fig. 9 A-D). SCCmec typing revealed Type V as the predominant variant (78.1%, 89/114), with an overwhelming predominance in Tanzanian isolates (91.3%, 73/80). The Tanzanian Type V strains predominantly belonged to subtype Vc (89.0%, 65/73), characterized by the presence of ccrC1, multiple IS431 insertion sequences, and intact mecA-mecR1 complexes. In contrast, the Kenyan isolates presented greater diversity, with co-circulation of Type III (28.0%, 7/25), Type IV (28.0%, 7/25), and Type V (28.0%, 7/25) strains. The Ugandan collection showed a distinct pattern, featuring Type V (55.6%, 5/9), Type III (11.1%, 1/9), and most notably, Type VI (22.2%, 2/9) lineages—a rare lineage in this region containing the ccrA4-ccrB4 gene complex (Table 1 – 3 ). Table 1 Distribution of SCCmec types by country Country Total MRSA Type V (%) Type IV (%) Type III (%) Type VI (%) Untypable (%) Dominant Features Tanzania 80 73 (91.3%) 1 (1.3%) 0 0 6 (7.5%) CC152-ST152 dominance Kenya 25 7 (28.0%) 7 (28.0%) 7 (28.0%) 0 4 (16.0%) Cocirculation of types Uganda 9 5 (55.6%) 0 1 (11.1%) 2 (22.2%) 1 (11.1%) Novel Type VI presence Table 2 Genetic characteristics of SCC mec types Type Subtype Key Genetic Markers Typical Association Country Prevalence V Vc ccrC1, IS431 variants CA-MRSA Tanzania (89% of Type V) III XV/III ccrA3-ccrB3, mecI-mecR1 HA-MRSA Kenya (100% of Type III) IV multiple ccrA2-ccrB2, truncated mecR1 CA/HA-MRSA bridge Kenya (100% of Type IV) VI - ccrA4-ccrB4 Potential zoonotic Uganda exclusive Table 3 Mobile Genetic Element Patterns Element Type Tanzania Pattern Kenya Pattern Uganda Pattern Biological Significance ccr genes ccrC1 dominant (98%) Mixed ccrA/B complexes ccrA4-ccrB4 unique Determines SCC mec mobility IS elements IS431 multicopy (3–4 copies) IS431 variants IS431 + IS1272 Facilitates recombination mec complex Complete mecA-mecR1 (100%) mecI present in Type III Variable structure Impacts β-lactam resistanc Statistical analysis via Pearson's chi-square test revealed highly significant differences in the distribution of SCCmec types across countries (χ²=73.58, df = 6, p = 7.52×10⁻¹⁴). Subsequent pairwise Fisher's exact tests with simulated p values (2000 replicates) confirmed these regional disparities: Kenya vs Tanzania (p = 0.0005), Kenya vs Uganda (p = 0.023), and Uganda vs Tanzania (p = 0.001) (Fig. 6 C). Genetic analysis of the cassette elements revealed type-specific signatures: Type III isolates from Kenya and Uganda carried the ccrA3-ccrB3 complex along with mecI-mecR1, which is typical of healthcare-associated strains; Type IV variants from Kenya featured ccrA2-ccrB2 with truncated mecR1; and the novel Ugandan Type VI isolates contained a unique ccrA4-ccrB4 combination. Network analysis of genetic components demonstrated the universal presence of mecA-mecR1 across all the isolates (100%) and identified ccrC1 as a central hub in Type V strains (Fig. 9 F). The Ugandan ccrA4-ccrB4 elements presented high betweenness centrality, suggesting potential bridging between rare SCCmec types. Geospatial visualization highlighted Tanzania's remarkable homogeneity (91.3% Type V) compared with Kenya's diverse SCCmec ecology and Uganda's unique Type VI signature (Fig. 9 E). Genomic Diversity and Pangenome Dynamics of East African S. aureus Genomes The construction of the average nucleotide identity (ANI) dendrogram revealed a total of five primary clusters and seven secondary clusters. Primary Cluster 1 comprised two samples, ERR1764891.fasta and ERR1764888.fasta. Primary Cluster 2 included three samples: ERR3150949.fasta, ERR3150917.fasta, and ERR3150943.fasta. Primary Cluster 3 was the largest, comprising most of the samples (480), which were further subdivided into seven secondary clusters. Primary Cluster 4 consisted of five samples: ERR2436453.fasta, ERR2436451.fasta, ERR2436455.fasta, ERR2436454.fasta, and ERR2436452.fasta. Primary Cluster 5 included seven samples: ERR1764894.fasta, ERR1764900.fasta, ERR1764902.fasta, ERR1764913.fasta, ERR1764914.fasta, ERR1764966.fasta, and ERR3218227.fasta (Additional file 5). Cluster 3 exhibited the greatest diversity, encapsulating the seven secondary sub-clusters. This clustering reflects the genomic variability among isolates, highlighting strain diversity across East Africa. Strains within the same primary cluster often share geographic or hospital origins, suggesting localized transmission or adaptation patterns (Additional file 6–7). The analysis further revealed significant insights into genomic relationships among the strains. The average nucleotide identity (ANI) values across the genomes compared with the reference genome (ERR12511686.fasta) ranged from 0.963 to 1.0, indicating high similarity among the genomes. Notably, the closest relatives to the reference genome presented ANI values exceeding 0.999, suggesting that they are likely part of the same species. For example, ERR12511700.fasta and ERR12511701.fasta presented ANI values of 0.9995 and 0.9998, respectively, with alignment coverages of 0.9937 and 0.9906, confirming their close phylogenetic relationship. The pairwise distances between genomes further illustrate genetic divergence within the dataset. The minimum pairwise distance observed was 0.0, indicating that some genomes were identical or nearly identical to the reference genome, whereas others, such as ERR1764902.fasta, presented greater distances (0.263), suggesting significant divergence from the reference genome. The circular phylogenetic trees visually represent the relationships among the genomes according to their origin, with distinct clusters reflecting their genetic similarities (Fig. 10 , Fig. 11 and Fig. 12 ). Pangenome analysis of 496 Staphylococcus aureus genomes revealed striking genomic diversity, with a very small set of conserved core genes and a vast majority of strain-specific genes. Specifically, only 5 core genes were detected, alongside 1,508 soft core genes, 1,729 shell genes, and a remarkable 68,759 cloud genes. This highlights that 95.5% of the total gene content consists of cloud genes, which are typically found in fewer than 15% of strains. Cloud genes are often associated with horizontal gene transfer and rare adaptations, contributing significantly to the genetic variability within the species (Table 4 ). Table 4 Pangenome characteristics of 496 S. aureus genomes Gene Category Number of Genes % of Total Pangenome Presence in Strains Key Characteristics Biological Significance Core Genes 5 0.007% 100% of strains Highly conserved Essential cellular functions (e.g., ribosomal proteins) Soft Core 1,508 2.1% 95–99% of strains Nearly universal Important for basic metabolism and structure Shell Genes 1,729 2.4% 15–94% of strains Moderately distributed Conditional advantages (e.g., niche-specific adaptations) Cloud Genes 68,759 95.5% < 15% of strains Strain-specific Horizontal gene transfer elements, virulence factors, antibiotic resistance To investigate this phenomenon further, we conducted a pangenome analysis on the 110 S. aureus genomes most closely related to the reference genome. This analysis revealed a relatively large number of core genes, specifically, 1,851 core genes, 24 soft core genes, 1,429 shell genes, and 110,258 cloud genes (Table 5 ). Table 5 Comparative Pangenome Analysis of 110 Closely Related S. aureus Genomes Gene Category Number of Genes % of Total Pangenome Presence in Strains Key Observations Evolutionary Implications Core Genes 1,851 1.6% 100% of strains Increased core set More conserved functions identified in closely related strains Soft Core 24 0.02% 95–99% of strains Dramatic reduction Fewer near-universal genes in this subset Shell Genes 1,429 1.3% 15–94% of strains Similar absolute number Maintains adaptive flexibility Cloud Genes 110,258 97.1% < 15% of strains Massive expansion Extreme strain-specific diversity even among closely r Most genes, 110,258 cloud genes, accounting for 97.1% of the total genes, were found in fewer than 15% of the strains. This underscores the significant strain-specific variability within the species, with cloud genes likely playing a key role in specific adaptations to different environments or hosts. The relatively small number of core genes (1,851) compared with the total genome reflects that only a limited set of genes is conserved across most strains. Moreover, the relatively high number of shell genes (1,429) indicates that a substantial portion of the genome is moderately distributed across strains, potentially facilitating evolutionary flexibility and adaptation to varying conditions. Discussion Our study represents the most comprehensive genomic analysis of Staphylococcus aureus in East Africa to date, revealing a complex landscape shaped by regional clonal expansion, localized diversification, and global lineage dissemination. MLST profiling of 434 isolates revealed 45 sequence types (STs), including 18 novel allelic profiles, underscoring the dynamic evolution of S. aureus in this understudied region. The dominance of CC152 (ST152; 26.7%) across Tanzania, Kenya, and Uganda aligns with its reported prevalence in Africa and the Middle East, suggesting a selective advantage in these settings, possibly linked to virulence adaptations or antimicrobial resistance ( 18 ). In contrast, CC8 (ST8; 18.2%), a lineage associated with the pandemic USA300 clone ( 19 ), was disproportionately prevalent in Tanzania (31.6%), indicating that localized expansion was potentially driven by healthcare transmission or community spread. Geographic heterogeneity was striking. Uganda presented the highest proportion of novel STs (33.3%), including unique variants such as ST1633 (21.2%), whereas Kenya presented enrichment of CC5 (ST5/ST6) and CC30 (ST30/ST34), lineages linked to pediatric infections, and the Southwest Pacific clone ( 20 ), respectively. Statistical analyses confirmed strong country-specific structuring (*p* < 0.0001, χ² test), with post hoc pairwise comparisons revealing significant differences between all nations (e.g., Kenya vs. Tanzania: adjusted p value = 0.0015). This geographic partitioning likely reflects differences in antibiotic use, host immunity, or transmission dynamics, necessitating region-tailored surveillance ( 21 ). The spa typing data revealed a mix of global and region-specific clones. t355, a lineage associated with both hospital and community settings ( 22 ), dominated across all countries (48.7% in Uganda, 42.4% in Tanzania), suggesting that sustained transmission was facilitated by human mobility or healthcare networks. Tanzania’s high prevalence of t1476 (42.4%)—a spa type linked to biofilm formation—may reflect nosocomial adaptation or antibiotic-driven selection ( 23 ). Uganda’s lower spa diversity (Shannon index = 1.89 vs. 2.41 in Tanzania) and dominance of t355 imply a more clonal population, possibly due to limited strain introduction or ecological bottlenecks. Notably, rare spa types (e.g., t10599, t13194) are country specific, highlighting localized microevolution ( 24 ). The significant geographic structuring (p 0.4) underscores the need for decentralized infection control strategies to address regionally circulating clones. The collective results underscore the presence of both widely circulating clones and localized spa types, which are likely shaped by regional transmission dynamics, clonal expansion events, and potential ecological or selective pressures influencing the structure of S. aureus populations across East Africa. Our resistome analysis revealed 94 AMR genes spanning 21 drug classes, with a core resistome dominated by blaZ (β-lactamase), tet ( 38 ) (tetracycline efflux), and dfrG (trimethoprim resistance). The high prevalence of blaZ (78.3%) mirrors global trends of penicillin resistance ( 25 ), whereas the unexpected abundance of tet ( 38 ) (14.1%)—far exceeding rates in Europe or North America—suggests rampant tetracycline use in East African human or veterinary medicine ( 26 ). Methicillin resistance ( mecA ) was detected in 3.7% of the isolates, with country-level disparities: Tanzania had the highest prevalence (6.6%), whereas Uganda had the lowest (0.1%). SCCmec typing revealed Type V (78.1%) as the dominant variant, which is consistent with community-associated MRSA (CA-MRSA) epidemiology. However, Kenya’s co-circulation of Type III (hospital-associated) and Type IV viruses underscores complex transmission dynamics at the human–animal–environment interface ( 27 ). Uganda’s Type VI SCCmec, a rare lineage with ccrA4-ccrB4, may represent a zoonotic or local evolutionary event ( 28 ), warranting urgent One Health investigations. This extensive list of AMR genes reflects an environment with diverse selective pressures from various antibiotics. The widespread presence of MDR determinants poses significant public health challenges, as treatment options for Staphylococcus aureus infections have become increasingly limited. The potential for horizontal gene transfer of these resistance elements across bacterial species increases the risk of AMR spreading beyond Staphylococcus aureus to other pathogens ( 29 ). In addition to antimicrobial resistance, the genomes contain numerous virulence genes that are critical for the pathogenicity of Staphylococcus aureus . The virulence gene landscape included 147 loci, with Panton-Valentine leukocidin ( PVL ; lukF-PV ) detected at frequencies (2.08%) higher than African averages (1.2–1.5%). Its enrichment in Tanzania (3.1% vs. 1.2% in Kenya) aligns with reports linking PVL to severe skin/soft-tissue infections ( 30 ). Ubiquitous genes such as cap8 (capsule synthesis) and icaD (biofilm formation) suggest conserved strategies for immune evasion and chronic infection ( 31 ). Key virulence factors included lukF-PV (Panton-Valentine leukocidin), hysA (hyaluronidase), cap8B , icaD, hla (alpha-hemolysin), sspC (serine protease), and aur (metalloprotease). These genes contribute to a range of infections, from skin and soft tissue infections to severe conditions such as bacteremia and pneumonia ( 32 ). The lukF-PV gene encodes Panton-Valentine Leukocidin (PVL), a potent cytotoxin that targets leukocytes and is strongly linked to community-acquired MRSA (CA-MRSA), especially in cases of necrotizing infections ( 33 ). The widespread presence of lukF-PV in the dataset highlights the high pathogenic potential of these strains, which could lead to severe clinical outcomes. The hysA gene, encoding hyaluronidase, degrades hyaluronic acid in connective tissues, aiding tissue invasion. These findings suggest that many of the strains have high potential for invasiveness, which can result in more severe infections ( 34 ). Several capsular polysaccharide genes, such as cap8B , cap8M , cap8E , and cap8N , were also prevalent. The capsular polysaccharide helps protect Staphylococcus aureus from phagocytosis, enabling it to evade the immune system ( 35 ). The presence of these genes indicates a defense mechanism that allows bacteria to survive longer in the host. Other important virulence genes include hla (alpha-hemolysin) and hlgB/hlgC (gamma-hemolysin subunits), which contribute to cell lysis, particularly in red blood cells and immune cells ( 36 ). Alpha-hemolysin is known for its role in tissue destruction and immune evasion, which can lead to more severe clinical outcomes, further increasing the virulence of the strain ( 37 ). The icaD gene, which is involved in biofilm formation, is particularly concerning, as biofilms protect bacteria from both immune responses and antibiotics, contributing to persistent infections ( 38 ). This gene's presence suggests that these strains can form biofilms, making infections harder to treat and eradicate, particularly chronic wound infections or those involving medical devices ( 39 ). The prevalence of virulence genes such as lukF-PV (Panton-Valentine Leukocidin) and hysA (hyaluronidase) aligns with studies in community-acquired MRSA (CA-MRSA) isolates from urban Tanzania and Kenya, where these genes are associated with invasive infections ( 40 ). Similarly, biofilm-related genes such as icaD have been highlighted in persistent infections globally, including those involving medical devices in Europe ( 41 ). Geographic variations were notable: sak (staphylokinase) was enriched in Uganda, reflecting adaptation to local host defenses, whereas Tanzanian isolates carried more hla (α-hemolysin), a key cytotoxin. Co-occurrence analysis revealed three functional clusters: PVL-associated (lukF-PV + hlgCB), which is responsible for tissue necrosis ( 42 ); biofilm-associated ( icaD + aur ), which is responsible for chronic infection ( 43 ); and tissue invasion ( hysA + sspC ), which is responsible for dissemination ( 44 ). Overall, the high prevalence of these virulence genes underscores the pathogenic potential of the circulating Staphylococcus aureus strains, which are equipped to cause severe, hard-to-treat infections. Taken together, these findings reveal a virulence landscape characterized by high conservation of key pathogenic features and emerging region-specific trends. The elevated prevalence of lukF-PV in Tanzanian isolates, surpassing previously reported African averages (typically 0.8–1.5%), may signal the expansion of hypervirulent PVL-positive lineages. The widespread detection of the complete cap8 operon suggests that preserved capsule biosynthesis functions across the region. The convergence of virulence and geographic distribution underscores the importance of sustained genomic surveillance and molecular epidemiology to inform control strategies and clinical management of S. aureus infections in East African healthcare settings. Plasmid diversity clearly differed across regions, with Tanzanian isolates exhibiting the most complex replicon networks (mean = 4.2 plasmids per isolate). High-prevalence plasmids such as rep5a_1 (pN315) and rep16_3 (pSaa6159) are commonly associated with the dissemination of key resistance genes, including blaZ and mecA ( 45 ). In contrast, Ugandan isolates presented lower plasmid diversity (mean = 2.1 plasmids per isolate), suggesting possible ecological or selective constraints limiting horizontal gene transfer. These findings indicate that while a core set of plasmid replicons is conserved across the region, local environmental pressures such as antibiotic usage patterns, host population dynamics, or barriers to plasmid mobility shape distinct regional plasmid profiles in S. aureus . Prophage analysis revealed 934 integrated sequences, with the vast majority (94.1%) belonging to the Siphoviridae family. Notably, the recurrent detection of NODE_8 in Tanzanian isolates suggests that it is a potential marker of local clonal expansion. Despite this, the lack of clear geographic structuring within the phage communities’ points to cross-border transmission, likely driven by human movement. Our analysis underscores the predominance of Siphoviridae phages in East African S. aureus populations. These phages exhibit stable integration patterns and a homogeneous community structure across regions, even amid variation in phage richness. These findings have important implications for understanding phage‒host interactions in clinical settings, particularly within hospitals, where such dynamics may influence both transmission and treatment outcomes. Insertion sequences (ISs), particularly IS1182 (present in 100% of isolates) and IS21, play major roles in driving genomic plasticity among S. aureus strains. Some Tanzanian isolates harbored up to 793 IS copies, with IS-rich genomes (e.g., ERR1764902, ANI = 0.963 vs. reference) acting as potential hotspots for recombination and resistance gene acquisition. These patterns likely reflect localized antimicrobial pressures and distinct transmission dynamics, highlighting the ongoing evolution of S. aureus in East Africa. Our analysis revealed a highly dynamic and regionally diverse mobilome, marked by the ubiquitous presence of core IS elements such as IS1182 and IS21. These elements likely facilitate the spread of resistance and virulence genes. The observed regional variations in IS family abundance and genomic network complexity emphasize the influence of local epidemiological and selective forces in shaping the genomic adaptability of S. aureus across East Africa. Our analysis of 114 MRSA genomes from East Africa revealed striking geographic disparities in SCC mec distribution, with type V (78.1%) emerging as the predominant variant. This lineage was nearly fixed in Tanzania (91.3%), where subtype Vc (89.0%), characterized by ccrC1, multiple IS431 elements, and intact mecA-mecR1 complexes, dominated. The genetic architecture of these cassettes aligns with global reports of community-associated MRSA (CA-MRSA), which typically carry smaller, more mobile SCC mec elements (e.g., Type IV/V) than hospital-associated (HA-MRSA) variants do ( 46 ). The near-uniformity of Type V in Tanzania suggests community-driven transmission. The high prevalence of CA-MRSA signatures (e.g., ccrC1, frequent IS elements) points to sustained spread outside healthcare settings, potentially facilitated by antibiotic misuse in outpatient or agricultural contexts ( 47 ). This finding also suggests limited healthcare-associated pressure. The absence of HA-MRSA-associated types (e.g., Type III) may reflect differences in hospital infection control or antibiotic stewardship compared with Kenya. It finally suggests clonal expansion: the predominance of Vc implies a successful local clone, possibly with increased fitness in Tanzanian populations ( 48 ). Kenya’s complex SCC mec ecology suggests co-circulation of HA-MRSA and CA-MRSA. In contrast to Tanzania, Kenyan genomes exhibit a tripartite distribution of SCC mec types: type III (28.0%), type IV (28.0%), and type V (28.0%). This diversity signals overlapping reservoirs of MRSA transmission; Type III (ccrA3-ccrB3, mecI-mecR1), a hallmark of HA-MRSA, is often linked to multidrug resistance and nosocomial outbreaks ( 49 ). Its prevalence in Kenya suggests active hospital transmission, potentially exacerbated by gaps in infection control. Type IV (ccrA2-ccrB2, truncated mecR1) is typically associated with CA-MRSA, but its coexistence with Type III implies bidirectional spillover between community and healthcare settings ( 46 ). Type V strains are similar to Tanzanian strains but less dominant, indicating competing selective pressures or later introduction. Pairwise comparisons confirmed Kenya’s distinct SCC mec profile versus Tanzania (p* = 0.0005) and Uganda (p* = 0.023), underscoring its role as an epidemiological crossroads for MRSA in the region. Uganda’s Novel Type VI suggests evidence of zoonotic or localized evolution. Uganda’s MRSA population stood out for the detection of SCC mec Type VI (22.2%), a rare lineage featuring the ccrA4-ccrB4 complex. This variant has been sporadically reported in livestock-associated MRSA (LA-MRSA) in Europe ( 50 ), raising questions about its origins. One hypothesis is zoonotic spillover: The ccrA4-ccrB4 combination has been linked to animal reservoirs ( 51 ). Uganda’s agrarian economy and high human‒livestock contact could facilitate such transmission. This could be a result of local adaptation. The high betweenness centrality of ccrA4-ccrB4 in the network analysis suggested that it may act as a genetic bridge between SCC mec types, possibly enabling novel recombinants (Fig. 6 F). The presence of a sampling bias in Uganda (n = 9) warrants caution, but the absence of Type VI bias in Tanzania/Kenya hints at Uganda-specific selection pressures. SCCmec analysis revealed genetic signatures and mechanistic insights; Type III (Kenya/Uganda) strains carried ccrA3-ccrB3 and intact mecI-mecR1, which is consistent with HA-MRSA’s stable, multidrug-resistant cassettes (Ito et al., 2012). Type IV (Kenya) featured ccrA2-ccrB2 with truncated mecR1, a common CA-MRSA adaptation favoring mobility (Shore et al., 2011). Type V bacteria (Tanzania) are enriched with IS431, which may promote SCC mec stabilization or excision (Noto & Archer, 2006). Network analysis highlighted ccrC1 as a central hub in Type V strains, reinforcing its role in CA-MRSA success, whereas mecA-mecR1 universality (100%) confirmed its non-redundant role in resistance. In Tanzania, CA-MRSA (Type V) dominance calls for community-focused interventions, including antibiotic stewardship in outpatient settings and PVL toxin surveillance (Fig. 6 E). In Kenya, the co-circulation of HA-MRSA and CA-MRSA demands integrated hospital‒community surveillance, especially for Type III multidrug resistance. In Uganda, the emergence of Type VI strains warrants one health investigation to assess zoonotic links and prevent LA-MRSA dissemination. This study reveals three distinct MRSA epidemiological landscapes in East Africa: Tanzania’s community-driven Type V epidemic, Kenya’s complex HA/CA-MRSA interplay and Uganda’s potentially zoonotic Type VI. These findings mandate tailored control strategies, emphasizing Community antibiotic stewardship in Tanzania, Hospital infection control in Kenya and One Health surveillance in Uganda. Future work should expand genomic surveillance to track SCC mec evolution and emerging threats in the region. Comparative genomic analysis revealed substantial diversity among East African S. aureus isolates, with dRep clustering identifying five primary genomic clusters. The largest cluster, Cluster 3, contained 480 genomes further divided into seven secondary sub-clusters, indicating a dominant circulating lineage with considerable microdiversity. This extensive sub-clustering suggests ongoing evolutionary diversification within this successful lineage, likely driven by localized selection pressures such as antibiotic use patterns or host immune factors. The smaller clusters (Clusters 1, 2, 4, and 5) represented less prevalent or potentially recently introduced lineages, with some showing country-specific distributions that may reflect distinct transmission networks or ecological niches. The average nucleotide identity (ANI) values confirmed close relationships among most isolates, with values exceeding 0.999 for the majority of comparisons against the reference genome. However, several outlier strains presented significantly lower ANI values (as low as 0.963), indicating substantial genomic divergence that may represent distinct subpopulations or the accumulation of extensive horizontal gene transfer events. These divergent strains, while rare, underscore the genomic plasticity of S. aureus in the region and may represent important reservoirs of novel genetic elements ( 21 ). Pangenome analysis yielded striking findings regarding the genetic architecture of East African S. aureus . Across 496 genomes, only five core genes were identified, representing an exceptionally small conserved genomic backbone. This minimal core was complemented by a vast accessory genome, with 68,759 cloud genes accounting for 95.5% of the total gene content. This extreme pangenome structure, characterized by a tiny core and enormous accessory genome, demonstrates the remarkable genomic flexibility of S. aureus in this region. The cloud genes, present in fewer than 15% of strains, likely encode adaptive functions that facilitate niche specialization and rapid response to selective pressures. Further analysis of 110 closely related genomes revealed similar patterns, with the number of core genes increasing to 1,851 but still representing only a small fraction (2.9%) of the total gene content. The persistence of this pattern even within a more homogeneous subset suggests that genomic diversity is maintained at multiple phylogenetic levels. The shell genes (1,429 in the subset analysis) may represent an intermediate category of genes that provide conditional advantages under certain environmental or host conditions. The observed pangenome structure has important biological implications. The minimal core genome likely contains essential housekeeping functions, whereas the vast accessory genome enables rapid adaptation to diverse challenges ( 52 ). This genomic architecture facilitates the emergence of regionally successful clones through the acquisition of specific combinations of accessory genes. For example, the predominance of CC152 in Tanzania may reflect its accumulation of particular virulence or resistance determinants from the accessory gene pool. Similarly, the presence of divergent strains may result from unique combinations of horizontally acquired elements. The extreme diversity of the accessory genome, particularly that of cloud genes, likely contributes to several clinically important phenomena. First, it enables rapid adaptation to antibiotic pressure through the acquisition of resistance determinants. Second, it permits fine-tuning of virulence properties for different host environments. Third, it complicates molecular epidemiology efforts by creating a constantly shifting genetic background against which outbreak strains must be identified ( 52 ). These findings have important implications for public health interventions. The minimal core genome presents challenges for diagnostic test development, as few universal targets exist. Vaccine development must involve both conserved core antigens and highly variable surface proteins encoded in the accessory genome. Antimicrobial resistance surveillance requires whole-genome approaches to capture the diversity of resistance determinants distributed throughout the accessory genome. This study has several limitations that should be acknowledged. Hospital-based sampling may underrepresent community-associated strains. The functional consequences of most accessory genes remain uncharacterized. Longitudinal sampling would help determine the stability of the observed pangenome structure over time. Limitations One notable limitation of this study is the uneven distribution of S. aureus isolates across the three countries. The variation in sample numbers may limit the representativeness of the findings, particularly when comparing regional genomic patterns or drawing conclusions about country-specific evolutionary dynamics. This imbalance could bias interpretations of clonal diversity, antimicrobial resistance profiles, and mobile genetic element distributions. Future studies with more uniform and expanded sampling across all regions will be critical to validate and generalize these findings. Conclusion This comprehensive genomic analysis revealed that Staphylococcus aureus in East Africa is not a uniform population but rather a collection of regionally distinct ecologies shaped by clonal expansion, horizontal gene transfer, and localized selective pressures. Dominant clones such as CC152 (ST152) spread across countries, whereas unique variants such as Uganda’s rare SCCmec Type VI suggest potential zoonotic reservoirs. Tanzania’s near-fixation of community-associated MRSA (Type V) and Kenya’s co-circulation of hospital- and community-associated MRSA reflect varying transmission dynamics and antibiotic pressures. The “minimal core + hypervariable accessory” genome structure of this species, with only five conserved core genes and over 68,000 accessory genes, underscores its remarkable adaptability and complicates diagnostic methods and vaccine development. The high prevalence of mobile genetic elements, virulence factors such as PVL, and biofilm-associated genes signals a persistent risk of severe infection and resistance spread. These findings highlight the urgent need for country-specific surveillance, targeted interventions, and One Health approaches. Future work must focus on characterizing novel elements, monitoring evolutionary trends, and integrating human-animal-environment data to inform public health responses. This study provides both a warning about S. aureus adaptability and a foundation for developing regionally tailored, evidence-based strategies to combat antimicrobial resistance. Abbreviations AMR Antimicrobial Resistance MGEs Mobile Genetic Elements WGS Whole Genome Sequencing WGSs Whole Genome Sequences SRA Sequence Read Archive MLST Multi Locus Sequence Typing ST Sequence Type CC Clonal Complex SCCmec Staphylococcal Cassette Chromsome mec MRSA Methicillin Resistant Staphylococcus aureus NCBI National Center of Biotechnology Information ANI Average Nucleotide Identity Declarations Ethics approval Not Applicable Consent for publication Not applicable Availability of data and materials The WGS data analyzed for this study can be found in the National Center for Biotechnology Information https://www.ncbi.nlm.nih.gov/ under the BioProject database by searching for project accession numbers PRJEB40863, PRJEB23611, PRJEB15413, PRJEB75012 and PRJEB71932. The analysis for this study is publicly available at https://github.com/GeoffreyOlweny/staphylococcus-aureus-east-africa-genomics/tree/master Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. Funding This study was supported by the Cambridge-Africa ALBORADA Research Fund through an MRSA project at Makerere University College of Health Sciences entitled “Disentangling the population structure of MRSA in an urban low-income setting”. Author Contributions GO: Conceptualization, Formal Analysis, Methodology, Resources, Writing – original Draft. GM: Conceptualization, Methodology, Supervision, Writing – review & editing. AK and BRK: Conceptualization, Methodology, Supervision, Writing – review & editing. DPK: Conceptualization, Methodology, Supervision, Resources, Writing – review & editing. Acknowledgments Portions of this research were conducted with high-performance computing resources provided by the African Centers of Excellence in Bioinformatics and Data Intensive Sciences (https://ace.ac.ug). Special thanks to the Department of Immunology and Molecular Biology, College of Health Sciences, Makerere University for their technical assistance and support. References Piewngam P, Otto M. Staphylococcus aureus colonisation and strategies for decolonisation. Lancet Microbe [Internet]. 2024 Jun 1 [cited 2024 Oct 3];5(6):e606–18. Available from: http://www.thelancet.com/article/S2666524724000405/fulltext Parlet CP, Brown MM, Horswill AR. Commensal Staphylococci Influence Staphylococcus aureus Skin Colonization and Disease. Trends Microbiol [Internet]. 2019 Jun 1 [cited 2025 Jan 4];27(6):497–507. Available from: http://www.cell.com/article/S0966842X19300216/fulltext Tong SYC, Davis JS, Eichenberger E, Holland TL, Fowler VG. 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Novel types of staphylococcal cassette chromosome mec elements identified in clonal complex 398 methicillin-resistant Staphylococcus aureus strains. Antimicrob Agents Chemother. 2011;55:3046–50. Bosi E, Monk JM, Aziz RK, Fondi M, Nizet V, Palsson B. Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity. Proc Natl Acad Sci U S A. 2016;113:E3801–9. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx Additional file 1.xlsx Multi-locus sequence typing (MLST) profiles of East African Staphylococcus aureus genomes. ST-type: Sequence Type (assigned via MLST); "Unique" indicates novel allelic profiles. The allele numbers for the MLST loci ( arcC, aroE, glpF, gmk, pta, tpi, yqiL ) are shown. The data represent S. aureus isolates from Tanzania (n=174), Kenya (n=227), and Uganda (n=33). Additionalfile2.xlsx Additional file 2.xlsx Spa typing profiles of East African Staphylococcus aureus genomes. Additionalfile3.xlsx Additional file 3.xlsx Summary of antimicrobial resistance (AMR) genes detected in East African Staphylococcus aureus genomes. The table includes gene names, resistance phenotypes, and their frequencies among the genomes. Additionalfile4.xlsx Additional file 4.xlsx List of virulence genes identified in East African Staphylococcus aureus genomes. The table categorizes genes based on their function, such as toxins, adhesion factors, and immune evasion mechanisms, highlighting their clinical relevance . Additionalfile5.pdf Additional file 5.pdf Primary Clustering dendogram of East African Staphylococcus aureus genomes. Additionalfile6.pdf Additional file 6.pdf Secondary clustering dendogram of East African Staphylococcus aureus genomes. Additionalfile7.pdf Additional file 7.pdf Multidimensional Scaling of East African Staphylococcus aureus genomes based on ANI. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6846109","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496464432,"identity":"3823c690-4d00-4619-aa85-43a2dd7e7d60","order_by":0,"name":"Geoffrey Olweny","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Geoffrey","middleName":"","lastName":"Olweny","suffix":""},{"id":496464433,"identity":"e3b37286-0eef-4e35-bf72-8e42bb842581","order_by":1,"name":"Gerald Mboowa","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Gerald","middleName":"","lastName":"Mboowa","suffix":""},{"id":496464434,"identity":"b2b23c58-673b-4ae4-add9-1f4aa6831385","order_by":2,"name":"Alex Kayongo","email":"","orcid":"","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"","lastName":"Kayongo","suffix":""},{"id":496464435,"identity":"bd3ac4a9-5a95-4c53-a1b0-9170234ef662","order_by":3,"name":"Benson R Kidenya","email":"","orcid":"","institution":"Catholic University of Health and Allied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Benson","middleName":"R","lastName":"Kidenya","suffix":""},{"id":496464436,"identity":"9cb15678-45bd-40ad-ac87-59e45b5a4d2e","order_by":4,"name":"David Patrick Kateete","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIie3PMWrDMBSA4ScCyaJmViZfwaLQLfgqNgZP3grFQ1pcBPaiA8TE+A5dPCsY7MU0a0BLSqFToMpSuhTqlhAyRInHQvUjDW/4eBKAyfQXE93B4O6nCID0JhgAxdD2JHAgKOlBxk1LxRYCyxmxZrMrSmsSD14USu61ZNKG9jKHkHJce49ZKekChtcEJY2W2CJwKwwR4iSk7KqUqAC4AZTUerJ6+yUOt9537CuXTgGjj/Nk7YuOhB4nGDEUS28B+GfLTP+X9atY5nbg8zagGa+lnzF8S9xnoSXjlcfUNvKnaVpt1OdMTudN+qTU3YOW7J93PAy660J1gZzo0haTyWT6R30D1K1cHDUKaRQAAAAASUVORK5CYII=","orcid":"","institution":"Makerere University","correspondingAuthor":true,"prefix":"","firstName":"David","middleName":"Patrick","lastName":"Kateete","suffix":""}],"badges":[],"createdAt":"2025-06-08 07:38:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6846109/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6846109/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88564080,"identity":"b0afb69e-9aa0-480d-916b-d8dc135a9301","added_by":"auto","created_at":"2025-08-07 18:57:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":211223,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design schema reveals 5 BioProjects with \u003cem\u003eStaphylococcus aureus\u003c/em\u003e genomes that passed the inclusion and exclusion criteria in East African hospital settings\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/7d8919b3844a9c8806e4257d.jpg"},{"id":88564083,"identity":"d2493738-a9e4-4ba8-b284-d43a0e1dd7a2","added_by":"auto","created_at":"2025-08-07 18:57:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2496656,"visible":true,"origin":"","legend":"\u003cp\u003eMLST results for \u003cem\u003eStaphylococcus\u003c/em\u003eisolates from Kenya, Tanzania, and Uganda. \u003cstrong\u003e(A)\u003c/strong\u003e Distribution of \u003cem\u003eStaphylococcus\u003c/em\u003especies identified by MLST across the three countries. \u003cem\u003eS. aureus\u003c/em\u003e was the most dominant species, particularly in the Kenyan and Tanzanian isolates. \u003cstrong\u003e(B)\u003c/strong\u003eFrequencies of different STs by country of origin. ST152 and ST8 were the most common, with notable differences in their prevalence across countries. \u003cstrong\u003e(C)\u003c/strong\u003eHeatmap showing the presence and distribution of STs by country. Each ST is represented along the x-axis, with color intensity corresponding to the isolate count. \u003cstrong\u003e(D)\u003c/strong\u003e Pairwise comparisons of the ST distributions between countries via adjusted p values. Significant differences are indicated by asterisks (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; p \u0026lt; 0.01), with color shading reflecting the magnitude of the adjusted \u003cem\u003ep\u003c/em\u003e values.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/5093e2a9fe1fa5ccae763051.jpg"},{"id":88564087,"identity":"5fe70ccf-08b6-41c5-988a-a20cb099b63a","added_by":"auto","created_at":"2025-08-07 18:57:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3235955,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of \u003cem\u003espa\u003c/em\u003etypes among \u003cem\u003eStaphylococcus aureus\u003c/em\u003e isolates from Kenya, Tanzania, and Uganda. \u003cstrong\u003e(A)\u003c/strong\u003e Bar plot showing the top 15 most common \u003cem\u003espa\u003c/em\u003e types across all the isolates. The most dominant types were \u003cem\u003et355\u003c/em\u003e (38.2%) and \u003cem\u003et1476\u003c/em\u003e(21.9%). \u003cstrong\u003e(B)\u003c/strong\u003e Country-specific distribution of the most frequent \u003cem\u003espa\u003c/em\u003etypes. Notable differences were observed across countries, with \u003cem\u003et355\u003c/em\u003epredominant in Uganda and \u003cem\u003et1476\u003c/em\u003e predominant in Tanzania. \u003cstrong\u003e(C)\u003c/strong\u003eProportional bar chart comparing \u003cem\u003espa\u003c/em\u003e type distributions across countries. Statistical analysis revealed a strong association between \u003cem\u003espa\u003c/em\u003etype and country (Cramer’s V = 0.56, Fisher’s \u003cem\u003ep\u003c/em\u003e = 5e-04). \u003cstrong\u003e(D)\u003c/strong\u003eHeatmap of \u003cem\u003espa\u003c/em\u003e types (present in ≥5 isolates across countries), showing the relative proportion of each type by country. Clear clustering and variation in \u003cem\u003espa\u003c/em\u003e type prevalence were observed geographically.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/6a5d64c6807bee2b7965d4fe.jpg"},{"id":88564985,"identity":"bd9ce933-f7f1-4ad7-90eb-35d7cfc12b22","added_by":"auto","created_at":"2025-08-07 19:13:06","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3206956,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution and abundance of antimicrobial resistance (AMR) genes in \u003cem\u003eStaphylococcus aureus\u003c/em\u003eisolates from Kenya, Tanzania, and Uganda. \u003cstrong\u003e(A)\u003c/strong\u003e Bar plot showing the top 20 most prevalent AMR genes across all the isolates, with \u003cem\u003eblaI_G_7\u003c/em\u003e(14.4%), \u003cem\u003eblaZ\u003c/em\u003e (14.1%), and \u003cem\u003etet(K)\u003c/em\u003e (13.4%) being the most common. \u003cstrong\u003e(B)\u003c/strong\u003eCountry-level comparison of the top 20 AMR genes. Notable differences in gene prevalence were observed between Tanzania (high \u003cem\u003eblaZ\u003c/em\u003e, \u003cem\u003etet(K)\u003c/em\u003e, \u003cem\u003edfrG\u003c/em\u003e) and Uganda/Kenya. \u003cstrong\u003e(C)\u003c/strong\u003e Gene counts colored by resistance class, illustrating AMR gene diversity and highlighting the predominance of β-lactam and tetracycline resistance genes. \u003cstrong\u003e(D)\u003c/strong\u003e Boxplot showing AMR gene abundance per isolate across countries. Tanzania presented a significantly greater AMR gene burden per isolate than did Kenya and Uganda.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/22d1c05e397bb377d29f0aa3.jpg"},{"id":88564984,"identity":"463d87cd-3935-4732-afb2-7aa6fdfdc634","added_by":"auto","created_at":"2025-08-07 19:13:06","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2958716,"visible":true,"origin":"","legend":"\u003cp\u003eVirulence landscape of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e genomes in East Africa.\u003cbr\u003e\n \u003cstrong\u003e(A–C)\u003c/strong\u003e Pie charts depict the relative abundance of virulence genes among clinical isolates from \u003cstrong\u003e(A)\u003c/strong\u003e Kenya, \u003cstrong\u003e(B)\u003c/strong\u003e Tanzania, and \u003cstrong\u003e(C)\u003c/strong\u003e Uganda. The \u003cem\u003elukF-PV\u003c/em\u003e gene (Panton-Valentine leukocidin) was the most prevalent virulence factor (2.08% of total genes), with country-specific variations (e.g., 1.4-fold higher \u003cem\u003elukF-PV\u003c/em\u003e in Tanzanian vs. Kenyan isolates, *p* = 0.03). \u003cstrong\u003e(D)\u003c/strong\u003e Bar chart showing the top 20 virulence genes by percentage distribution across countries, highlighting conserved (\u003cem\u003ecap8\u003c/em\u003e operon) and divergent (\u003cem\u003esak\u003c/em\u003e in Uganda) factors. \u003cstrong\u003e(E)\u003c/strong\u003e Box plot illustrates statistically significant differences in virulence gene counts between countries: overall significance\u003cem\u003e, *p* \u0026lt; 0.001; Kenya \u003c/em\u003evs\u003cem\u003e. Uganda\u003c/em\u003e, *p* \u0026lt; 0.001; Kenya vs. Tanzania\u003cem\u003e, *p* \u0026lt; 0.001; and Tanzania \u003c/em\u003evs\u003cem\u003e. Uganda\u003c/em\u003e, *p* \u0026lt; 0.05. Country-level trends suggest geographic diversification of virulence strategies, with Tanzanian isolates enriched for cytotoxic genes (\u003cem\u003elukF-PV\u003c/em\u003e, \u003cem\u003ehlgCB\u003c/em\u003e), whereas Ugandan isolates presented elevated immune evasion markers (\u003cem\u003esak\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/6ca44da65e1608075a6102ef.jpg"},{"id":88564095,"identity":"8f7f7301-d60b-404f-9660-27c67ab17e98","added_by":"auto","created_at":"2025-08-07 18:57:06","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4926177,"visible":true,"origin":"","legend":"\u003cp\u003ePlasmid distribution in East African \u003cem\u003eS. aureus\u003c/em\u003e genomes.\u003cbr\u003e\n \u003cstrong\u003e(A)\u003c/strong\u003e Bar plot showing the top 20 plasmid replicon percentage abundances in East Africa, dominated by rep5a_1_repSAP001 (68.9%), rep16_3_rep (53.2%), and rep10_3_pNE131p1 (51.7%). \u003cstrong\u003e(B)\u003c/strong\u003e Heatmap showing the distribution of the top 50 plasmid replicons according to country, revealing geographic patterns in terms of replicon prevalence. \u003cstrong\u003e(C)\u003c/strong\u003e Bar plot showing the top 20 plasmid replicon percentage abundances stratified by country, highlighting significant differences in rep7c_1_rep (38.7% in Tanzania vs 25.4% in Kenya vs 0.6% in Uganda) and rep16_3_rep (61.2% in Tanzania vs 46.6% in Kenya vs 42.3% in Uganda; χ² tests, p \u0026lt; 0.001). \u003cstrong\u003e(D)\u003c/strong\u003e Bar plot showing the abundance of the top plasmid replicon according to country, with Tanzania showing the highest multireplicon carriage (68.5% of isolates with ≥3 replicons). \u003cstrong\u003e(E)\u003c/strong\u003e Box plot showing significant differences in the plasmid replicon among countries, with Tanzania having the highest diversity (Shannon index = 2.41), followed by Kenya (1.87) and Uganda (1.52; Kruskal‒Wallis, p \u0026lt; 0.01). \u003cstrong\u003e(F)\u003c/strong\u003e Cooccurrence network of plasmid replicons (Fisher's exact test, p \u0026lt; 0.001) revealing five distinct communities: Community 1 (n=195 nodes) centered on high-prevalence replicons rep5a_1_repSAP001 and rep16_3_rep (predominantly Tanzanian isolates); Community 2 (n=111) featuring rep7c_1_rep and repUS43_1_CDS12738 (Kenyan-associated); Community 3 (n=86) representing rare/transient replicons; Community 4 (n=62) containing low-frequency elements; and Community 5 (n=2) comprising potentially artifactual replicons. Network modularity = 0.62, indicating a strong community structure.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/06180cfd7db2f593a0ce6b1e.jpg"},{"id":88564328,"identity":"6f97a10e-fd92-4411-837a-10d4be9eddf0","added_by":"auto","created_at":"2025-08-07 19:05:06","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":5184055,"visible":true,"origin":"","legend":"\u003cp\u003ePhage distribution among \u003cem\u003eS. aureus\u003c/em\u003e East African genomes.\u003cbr\u003e\n \u003cstrong\u003e(A)\u003c/strong\u003e Histogram of prophage lengths (201 bp–131.2 kb; median = 38.7 kb), showing 78% of sequences in the 30–50 kb range. \u003cstrong\u003e(B)\u003c/strong\u003e Box plot of prophage length by taxonomy, revealing the dominance of \u003cem\u003eSiphoviridae\u003c/em\u003e (94.1%, n=879) over \u003cem\u003eMyoviridae\u003c/em\u003e (3.1%), \u003cem\u003ePodoviridae\u003c/em\u003e (1.2%), and unclassified phages (1.4%). \u003cstrong\u003e(C)\u003c/strong\u003e Bar plot of prophage richness by country: Kenya (633 unique IDs) \u0026gt; Tanzania (230) \u0026gt; Uganda (71). \u003cstrong\u003e(D)\u003c/strong\u003e Bar plot of the scaffolds with the highest prophage integrations (5 scaffolds contained 3 prophages each; 12 Tanzanian isolates shared identical prophage NODE_8_length_88311). \u003cstrong\u003e(E)\u003c/strong\u003e Upset plot of phage distribution overlaps among Uganda, Tanzania, and Kenya, highlighting country-specific (Kenya: 633 phages; Tanzania: 230 phages; Uganda: 71 phages) shared sequences. \u003cstrong\u003e(F)\u003c/strong\u003e NMDS ordination (Jaccard distances) showing no geographic structuring of phage communities (PERMANOVA, *p* = 1.0), with extensive overlap between countries.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/94ae2789545fd53e226d2fb5.jpg"},{"id":88564332,"identity":"6deb42b2-fc6a-430a-b44c-78d1c3bd421b","added_by":"auto","created_at":"2025-08-07 19:05:06","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":5138217,"visible":true,"origin":"","legend":"\u003cp\u003eRegional distribution and co-occurrence patterns of insertion sequences in East African \u003cem\u003eS. aureus\u003c/em\u003e genomes. \u003cstrong\u003e(A)\u003c/strong\u003e Stacked bar plot showing the relative abundance of IS element families across countries, demonstrating the universal presence of IS1182 (100% prevalence) and IS21 alongside country-specific variations in the IS256 and ISL3 families. \u003cstrong\u003e(B)\u003c/strong\u003e Hierarchically clustered heatmap of IS family distributions, revealing distinct country-specific profiles, with Tanzania showing the highest IS1182 copy number (793) and Kenya dominating the IS21 count (781). \u003cstrong\u003e(C)\u003c/strong\u003e Combined box and violin plots quantifying IS element loads per genome, illustrating significantly higher median counts in Tanzanian isolates (Kruskal‒Wallis test, p \u0026lt; 0.001) and broader distribution ranges than those in Uganda. \u003cstrong\u003e(D)\u003c/strong\u003e Force-directed network visualization of IS family co-occurrence patterns, with node size representing prevalence and edge thickness indicating association strength (Fisher's exact test, p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/68372bcc92daed1c9c3cbac4.jpg"},{"id":88564987,"identity":"6c1851f1-83d6-4c04-a6ba-a6d09d44b53f","added_by":"auto","created_at":"2025-08-07 19:13:06","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3908299,"visible":true,"origin":"","legend":"\u003cp\u003eGeospatial distribution and molecular epidemiology of SCC\u003cem\u003emec\u003c/em\u003e elements in East African MRSA.\u003cbr\u003e\n \u003cstrong\u003e(A)\u003c/strong\u003e Stacked bar plot of SCC\u003cem\u003emec\u003c/em\u003e type distribution by country, showing Type V dominance in Tanzania (91.3%, 73/80) versus Kenya's diversity (Types III/IV/V at 28% each) and Uganda's unique Type VI (22.2%). \u003cstrong\u003e(B)\u003c/strong\u003e Proportional SCC\u003cem\u003emec\u003c/em\u003e type frequencies, highlighting Tanzania’s Type Vc predominance (89%) versus Kenya’s Type III (hospital-associated) and Uganda’s novel Type VI (ccrA4-ccrB4). \u003cstrong\u003e(C)\u003c/strong\u003e Heatmap of pairwise Fisher’s exact test results: Tanzania-Kenya (*p*=0.0005), Tanzania-Uganda (*p*=0.0015), Uganda-Kenya (*p*=0.235). \u003cstrong\u003e(D)\u003c/strong\u003e Mosaic plot quantifying significant SCC\u003cem\u003emec\u003c/em\u003e-country associations (χ²=73.58, *p*=7.52×10⁻¹⁴). \u003cstrong\u003e(E)\u003c/strong\u003e Geographic map illustrating Tanzania’s Type V homogeneity (red), Kenya’s mixed types (blue/green), and Uganda’s Type VI (purple). \u003cstrong\u003e(F)\u003c/strong\u003e Cooccurrence network of SCC\u003cem\u003emec\u003c/em\u003e components, with *mecA-mecR1* (100% prevalence) and \u003cem\u003eccrC1\u003c/em\u003e (Type V hub) as central nodes; Ugandan *ccrA4-ccrB4* (high betweenness centrality) bridges rare types.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/250c57e36b6f1edd96b9a00f.jpg"},{"id":88564100,"identity":"a7773eb8-0c11-4af6-987c-3e4788bdbf0c","added_by":"auto","created_at":"2025-08-07 18:57:06","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1986510,"visible":true,"origin":"","legend":"\u003cp\u003eCircular phylogeny of Ugandan \u003cem\u003eS. aureus\u003c/em\u003e isolates.\u003cbr\u003e\nRadial tree constructed from pairwise ANI distances (fastANI algorithm, cutoff=0.05) showing genomic relationships among Ugandan WGS genomes. Branch lengths represent genetic divergence (scale bar: ANI distance units).\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/d252c7ad724847518b1eeaf1.png"},{"id":88564110,"identity":"22f09bc4-1aea-4049-870b-74fa7b968cdb","added_by":"auto","created_at":"2025-08-07 18:57:06","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":8747027,"visible":true,"origin":"","legend":"\u003cp\u003eCircular phylogeny of Kenyan \u003cem\u003eS. aureus\u003c/em\u003e isolates\u003cbr\u003e\nRadial tree constructed from pairwise ANI distances (fastANI algorithm, cutoff=0.05) showing genomic relationships among Kenyan WGS genomes. Branch lengths represent genetic divergence (scale bar: ANI distance units).\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/fefebc1d1cb36dde09382806.png"},{"id":88564986,"identity":"4f2121d6-f7d5-4f70-a5a4-2d4ce493258f","added_by":"auto","created_at":"2025-08-07 19:13:06","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":6496767,"visible":true,"origin":"","legend":"\u003cp\u003eCircular phylogeny of Tanzanian \u003cem\u003eS. aureus\u003c/em\u003e isolates\u003cbr\u003e\nRadial tree constructed from pairwise ANI distances (fastANI algorithm, cutoff=0.05) showing genomic relationships among Tanzanian WGS genomes. Branch lengths represent genetic divergence (scale bar: ANI distance units).\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/b2207a36f45b5c855408cb90.png"},{"id":91151492,"identity":"1b13bf39-77d9-4b9f-bd7c-4e961098c2c9","added_by":"auto","created_at":"2025-09-12 07:14:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":42309892,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/2d2198f7-efe3-418c-bc90-5e494d312d76.pdf"},{"id":88564082,"identity":"04e7f466-f9da-4a0b-a01f-294cff35443d","added_by":"auto","created_at":"2025-08-07 18:57:06","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":34096,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1.xlsx\u003c/p\u003e\n\u003cp\u003eMulti-locus sequence typing (MLST) profiles of East African \u003cem\u003eStaphylococcus aureus\u003c/em\u003e genomes.\u003c/p\u003e\n\u003cp\u003eST-type: Sequence Type (assigned via MLST); \"Unique\" indicates novel allelic profiles. The allele numbers for the MLST loci (\u003cem\u003earcC, aroE, glpF, gmk, pta, tpi, yqiL\u003c/em\u003e) are shown. The data represent \u003cem\u003eS. aureus\u003c/em\u003e isolates from Tanzania (n=174), Kenya (n=227), and Uganda (n=33).\u003c/p\u003e","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/f04f58ae33d74dbff673f5ad.xlsx"},{"id":88564980,"identity":"9d31a018-5239-449b-8870-20ce0ad3cce0","added_by":"auto","created_at":"2025-08-07 19:13:06","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20805,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2.xlsx\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSpa\u003c/em\u003e typing profiles of East African \u003cem\u003eStaphylococcus aureus\u003c/em\u003e genomes.\u003c/p\u003e","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/1f6c169ccd886f42223c3250.xlsx"},{"id":89062824,"identity":"1394bc30-8538-4bda-b9c1-b810609327b8","added_by":"auto","created_at":"2025-08-14 09:46:27","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15610,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3.xlsx\u003c/p\u003e\n\u003cp\u003eSummary of antimicrobial resistance (AMR) genes detected in East African \u003cem\u003eStaphylococcus aureus\u003c/em\u003e genomes.\u003c/p\u003e\n\u003cp\u003eThe table includes gene names, resistance phenotypes, and their frequencies among the genomes.\u003c/p\u003e","description":"","filename":"Additionalfile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/15f039c26877ec21fe854999.xlsx"},{"id":88565180,"identity":"25751f6b-0ac6-487e-9ab3-045f9bd622da","added_by":"auto","created_at":"2025-08-07 19:21:06","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":19148,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 4.xlsx\u003c/p\u003e\n\u003cp\u003eList of virulence genes identified in East African \u003cem\u003eStaphylococcus aureus\u003c/em\u003e genomes.\u003c/p\u003e\n\u003cp\u003eThe table categorizes genes based on their function, such as toxins, adhesion factors, and immune evasion mechanisms, highlighting their clinical relevance .\u003c/p\u003e","description":"","filename":"Additionalfile4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/ea01f03a1ca5d4fab8b3c39d.xlsx"},{"id":88565513,"identity":"858af994-e7fd-4a81-ba46-6c1804336f3a","added_by":"auto","created_at":"2025-08-07 19:29:06","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":32266,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 5.pdf\u003c/p\u003e\n\u003cp\u003ePrimary Clustering dendogram of East African \u003cem\u003eStaphylococcus aureus\u003c/em\u003e genomes.\u003c/p\u003e","description":"","filename":"Additionalfile5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/874a7900994a3ecaf3a87256.pdf"},{"id":88564326,"identity":"2b1f8d88-574d-43f3-8d91-a3d1f7c99f28","added_by":"auto","created_at":"2025-08-07 19:05:06","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":36332,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 6.pdf\u003c/p\u003e\n\u003cp\u003eSecondary clustering dendogram of East African \u003cem\u003eStaphylococcus aureus\u003c/em\u003e genomes.\u003c/p\u003e","description":"","filename":"Additionalfile6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/c6b9d1f7273cb1867f25bcd9.pdf"},{"id":88564091,"identity":"d11a288a-3a90-47fa-9c80-774458b9888a","added_by":"auto","created_at":"2025-08-07 18:57:06","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":30959,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 7.pdf\u003c/p\u003e\n\u003cp\u003eMultidimensional Scaling of East African \u003cem\u003eStaphylococcus aureus\u003c/em\u003e genomes based on ANI.\u003c/p\u003e","description":"","filename":"Additionalfile7.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6846109/v1/9178114d257415c8043635bb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unraveling the Genomic Landscape of Staphylococcus aureus in Hospital Settings of East Africa","fulltext":[{"header":"Background","content":"\u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e colonizes approximately 30% of the human population and primarily resides in nasal passages and on the skin (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It poses a significant global health concern because of its ability to transition from a commensal organism to an opportunistic pathogen (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) responsible for a variety of infections, ranging from minor skin infections to severe invasive disease states such as bacteremia, pneumonia, endocarditis, osteomyelitis, and toxic shock syndrome (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The ability of this bacterium to cause a wide range of infections is attributed to their arsenal of virulence factors, including surface proteins, toxins, and enzymes, and their capacity to form biofilms (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These virulence factors allow \u003cem\u003eStaphylococcus aureus\u003c/em\u003e to adhere to host tissues, evade the immune system, and damage host cells (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHealth care settings pose a particularly high risk for \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infections, especially among immunocompromised patients, those with invasive medical devices (e.g., catheters, prosthetic devices), and those undergoing surgical procedures (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Infections caused by \u003cem\u003eStaphylococcus aureus\u003c/em\u003e in healthcare facilities are often more difficult to treat, primarily due to the emergence of healthcare-associated antibiotic-resistant strains (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) such as methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (MRSA) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMRSA has evolved into a significant healthcare-associated pathogen, leading to increased morbidity, mortality, and healthcare costs (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Its persistence in hospital environments and ability to spread rapidly among vulnerable populations make it especially challenging to manage (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In Africa, the burden of MRSA is particularly alarming due to limited access to diagnostics, effective antibiotics and healthcare infrastructure, which contributes to the high prevalence of hospital-acquired infections (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Studies have shown that the MRSA prevalence in East African hospital settings is comparable to that in other regions, but data on strain diversity and genetic characteristics are limited (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhole-genome sequencing (WGS) has become a critical tool in clinical microbiology, providing comprehensive insights into the genetic architecture of pathogens (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Unlike traditional diagnostic methods, which focus on a limited set of genetic markers, WGS captures the entire genome of an organism, enabling detailed analysis of virulence factors, AMR genes, and evolutionary relationships (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). For example, WGS has been instrumental in tracking MRSA outbreaks, identifying resistance determinants, and informing infection control strategies (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn East Africa, where infectious diseases remain a leading cause of mortality, characterizing health care-associated \u003cem\u003eStaphylococcus aureus\u003c/em\u003e strains is crucial for improving infection control measures. A growing body of research has begun to focus on the genomic analysis of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e in this region, revealing a complex landscape of resistance and virulence factors (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite these studies, there remains a significant gap in our understanding of the genetic diversity of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e from hospital settings across East African countries. These insights are particularly crucial for informing the 2024 WHO Bacterial Priority Pathogens List, which emphasizes public health importance in combating antimicrobial resistance, including tackling MRSA, to guide global research, development, and prevention and control strategies. This study bridges this gap by assessing population structure, genetic diversity, AMR profiles and virulence factors and evaluating the role of mobile genetic elements in the dissemination of resistance and virulence genes via WGS data.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design and setting\u003c/p\u003e\u003cp\u003eThis study analyzed publicly available WGS data for \u003cem\u003eStaphylococcus aureus\u003c/em\u003e collected from hospital settings in East Africa. The data were obtained from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database under relevant BioProjects. The WGS data were subjected to specific inclusion and exclusion criteria to ensure data quality and relevance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Data were obtained from \u003cem\u003eStaphylococcus aureus\u003c/em\u003e samples collected within hospital environments, including clinical samples, hospital surfaces, and medical equipment. All samples were sourced from countries within East Africa. Illumina paired-end sequencing technology was selected for uniformity and compatibility with bioinformatics tools. Additionally, essential metadata, such as geographical origin and collection methods, had to be available to enable comprehensive contextual analysis. This rigorous selection process ensured the inclusion of high-quality datasets suitable for downstream bioinformatics analyses. The sequences from three East African countries\u0026mdash;Uganda, Kenya, and Tanzania\u0026mdash;meeting the inclusion criteria included a total of 496 \u003cem\u003eS. aureus\u003c/em\u003e Illumina paired-end sequences distributed across five BioProjects. Uganda provided one BioProject (PRJEB40863) containing 42 paired-end sequences; Kenya contributed two BioProjects: PRJEB23611 with 95 sequences and PRJEB15413 with 184 sequences; similarly, Tanzania contributed two BioProjects: PRJEB75012 with 10 sequences and PRJEB71932 with 165 sequences.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSequence retrieval and quality control\u003c/p\u003e\u003cp\u003eRaw sequencing data in FastQ format were retrieved via the SRA Toolkit and stored on a high-performance computing server. Quality assessment was performed with FastQC version 0.12.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify low-quality bases, adapter sequences, and potential contaminants. Reads were trimmed via Trimmomatic version 0.39 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/timflutre/trimmomatic\u003c/span\u003e\u003cspan address=\"https://github.com/timflutre/trimmomatic\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to remove low-quality bases (Q\u0026thinsp;\u0026lt;\u0026thinsp;20) and adapter sequences, with reads shorter than 36 bp discarded. The quality metrics across all the samples were aggregated and visualized via MultiQC version v1.27 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://multiqc.info/\u003c/span\u003e\u003cspan address=\"https://multiqc.info/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). FastQC revealed that 90% of the forward and reverse reads were unique across all the samples. After trimming adapters and removing low-quality reads, the reads achieved a mean quality score of Q30, indicating a high level of sequencing accuracy. Additionally, after trimming, the dataset showed a marked reduction in sequence duplication, enhancing its reliability for downstream analyses.\u003c/p\u003e\u003cp\u003eGenome assembly and annotation\u003c/p\u003e\u003cp\u003eDe novo genome assembly was performed via SPAdes version v4.1.0 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/ablab/spades\u003c/span\u003e\u003cspan address=\"https://github.com/ablab/spades\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, followed by polishing with PILON version 1.24 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/broadinstitute/pilon\u003c/span\u003e\u003cspan address=\"https://github.com/broadinstitute/pilon\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Assembly quality was assessed via QUAST version 5.3 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://quast.sourceforge.net/\u003c/span\u003e\u003cspan address=\"https://quast.sourceforge.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, which focuses on metrics such as N50, genome size, and GC content.\u003c/p\u003e\u003cp\u003eTo increase the accuracy of the assemblies, the polished genomes were aligned to the \u003cem\u003eStaphylococcus aureus\u003c/em\u003e reference strain NCTC 8325 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000013425.1/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000013425.1/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, facilitating their organization into draft genomes. The genomes were annotated with Prokka version v1.14.5 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/prokka\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/prokka\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e to identify coding sequences, RNA features, and other genomic elements.\u003c/p\u003e\u003cp\u003eThe resulting scaffolds and contigs from the genome assembly had an average genome size of 3.5 Megabytes. A quality assessment via QUAST indicated a high level of contiguity, with an average N50 value of 550 kb. In total, 955 contigs were annotated, leading to the identification of 3,145 coding sequences (CDSs).\u003c/p\u003e\u003cp\u003eIdentification of AMR and virulence-encoding genes\u003c/p\u003e\u003cp\u003eThe ABRicate version 1.0.0 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/abricate\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/abricate\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e tool was employed to identify virulence and AMR-encoding genes via curated databases. The detected genes were mapped to known resistance mechanisms and virulence factors for interpretation.\u003c/p\u003e\u003cp\u003eMulti-Locus Sequence Typing (MLST)\u003c/p\u003e\u003cp\u003ePutative genotyping was conducted via the MLST tool version 2.23.0 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/tseemann/mlst\u003c/span\u003e\u003cspan address=\"https://github.com/tseemann/mlst\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e to classify sequences on the basis of housekeeping genes, providing insights into clonal relationships and population structure.\u003c/p\u003e\u003cp\u003ePangenome analysis\u003c/p\u003e\u003cp\u003ePangenome analysis was performed via the ROARY \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/sanger-pathogens/Roary\u003c/span\u003e\u003cspan address=\"https://github.com/sanger-pathogens/Roary\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e pipeline to identify core and accessory genes highlighting genetic diversity across the \u003cem\u003eStaphylococcus aureus\u003c/em\u003e genomes.\u003c/p\u003e\u003cp\u003eComparative genomics\u003c/p\u003e\u003cp\u003eWe used dREP version 2.0.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/MrOlm/drep\u003c/span\u003e\u003cspan address=\"https://github.com/MrOlm/drep\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to perform comparative genomic analysis of the sequenced strains. dREP efficiently calculates pairwise average nucleotide identity (ANI) and clusters genomes on the basis of overall similarity. Using the fastANI algorithm with a similarity threshold of 0.05, the tool grouped highly similar genomes into distinct clusters, which likely represent clonal lineages. Each primary cluster identified by dREP reflects a unique genomic group.\u003c/p\u003e\u003cp\u003eMobile genetic element (MGE) analysis\u003c/p\u003e\u003cp\u003ePlasmidFinder version 2.1.6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/genomicepidemiology/plasmidfinder\u003c/span\u003e\u003cspan address=\"https://github.com/genomicepidemiology/plasmidfinder\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to identify plasmids, offering insights into the horizontal transfer of genetic material.\u003c/p\u003e\u003cp\u003eProphage prediction was performed via Phigaro version 2.4.0 (for temperate phages) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/bobeobibo/phigaro\u003c/span\u003e\u003cspan address=\"https://github.com/bobeobibo/phigaro\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and PHASTER (\u003cem\u003ePHAge Search Tool Enhanced Release\u003c/em\u003e) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://phaster.ca/\u003c/span\u003e\u003cspan address=\"https://phaster.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, which identify intact, questionable, and incomplete prophage regions in bacterial genomes. These elements play crucial roles in horizontal gene transfer, bacterial adaptation, and virulence.\u003c/p\u003e\u003cp\u003eInsertion sequences (ISs) were detected via ISEScan version 1.7.1 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioweb.pasteur.fr/packages/pack@[email protected]\u003c/span\u003e\u003cspan address=\"https://bioweb.pasteur.fr/packages/pack@[email protected]\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, a tool specialized in identifying bacterial IS elements that contribute to genome plasticity and the spread of antimicrobial resistance (AMR) genes. Additionally, SCCmecFinder (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bitbucket.org/genomicepidemiology/sccmecfinder/src/master/\u003c/span\u003e\u003cspan address=\"https://bitbucket.org/genomicepidemiology/sccmecfinder/src/master/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to identify staphylococcal cassette chromosome \u003cem\u003emec\u003c/em\u003e (SCC\u003cem\u003emec\u003c/em\u003e) elements, which are essential for methicillin resistance in \u003cem\u003eStaphylococcus aureus\u003c/em\u003e. Together, these tools provide a robust framework for characterizing mobile genetic elements (MGEs) and assessing their role in shaping the genetic architecture of AMR and virulence.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eMultilocus sequence typing (MLST) analysis of the WGS \u003cem\u003eS. aureus\u003c/em\u003e East African genomes\u003c/p\u003e\u003cp\u003eA total of 434 \u003cem\u003eStaphylococcus aureus\u003c/em\u003e isolates (227, 174, and 33 from Kenya, Tanzania, and Uganda, respectively) were subjected to MLST analysis, which identified 45 distinct sequence types (STs), including 18 novel allelic profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The population structure revealed a predominance of four major CCs, with CC152 (ST152) being the most widespread, accounting for 26.7% (n\u0026thinsp;=\u0026thinsp;116) of all the isolates, and was observed across all three countries. CC8 (ST8) was the second most common lineage (18.2%, n\u0026thinsp;=\u0026thinsp;79), with a particularly high prevalence in Tanzania (Additional file 1). CC5 (ST5/ST6; 6.5%, n\u0026thinsp;=\u0026thinsp;28) and CC30 (ST30/ST34; 4.4%, n\u0026thinsp;=\u0026thinsp;19) were primarily found among Kenyan isolates, reflecting regional variation in clonal distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe country-level distribution demonstrated significant geographic heterogeneity in the ST profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). In Tanzania, ST152 was the most frequently detected (38.5%), followed by ST8 (31.6%) and ST15 (6.3%). Kenyan isolates also presented ST152 as the most common (22.9%), but with relatively lower proportions of ST8 (12.3%) and a higher representation of ST5 (6.2%). In Uganda, ST152 maintained a high prevalence (36.4%), along with the unique presence of ST1633 (21.2%) and ST8 (12.1%).\u003c/p\u003e\u003cp\u003eA total of 18 previously unreported STs were identified, representing 4.1% of all the isolates, and were designated novel STs on the basis of unique combinations of alleles across the seven MLST loci. These novel STs were particularly common in Uganda, where they accounted for 33.3% of the isolates, whereas they accounted for 11.9% in Kenya and 9.2% in Tanzania. Notably, rare or novel sequence types included ST2744 (identified exclusively in Tanzania), ST7635 (shared between Kenya and Uganda), and ST2178 (unique to Uganda), each characterized by distinctive allelic profiles.\u003c/p\u003e\u003cp\u003eAnalysis of allelic diversity across the MLST loci revealed substantial polymorphism. The \u003cem\u003earoE\u003c/em\u003e locus was the most diverse, exhibiting 75 unique alleles, followed closely by \u003cem\u003etpi\u003c/em\u003e, with 68 alleles. In contrast, \u003cem\u003earcC\u003c/em\u003e and \u003cem\u003egmk\u003c/em\u003e were less polymorphic, with 46 and 44 alleles, respectively. These findings indicate variability in evolutionary pressures across different housekeeping genes.\u003c/p\u003e\u003cp\u003eClonal complex analysis grouped the isolates into 12 major CCs, with CC152 representing the dominant lineage. Other globally recognized CCs, including CC8 (USA300-related), CC5 (pediatric clone), CC30 (Southwest Pacific clone), and CC22 (EMRSA-15), were also identified, indicating the circulation of clinically significant lineages in the East African region.\u003c/p\u003e\u003cp\u003eStatistical analysis confirmed the strong geographic structuring of the ST distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). A chi-square test of independence yielded a highly significant result (χ\u0026sup2; = 221.58, df\u0026thinsp;=\u0026thinsp;70, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating that the distribution of STs was not uniform across countries. Owing to sparse cell counts in some comparisons, Fisher\u0026rsquo;s exact test was also performed, confirming these findings (p\u0026thinsp;=\u0026thinsp;0.0005, based on 2000 Monte Carlo replicates). Post hoc pairwise comparisons with Benjamini‒Hochberg correction revealed significant differences between all country pairs: Kenya vs Tanzania (adjusted p\u0026thinsp;=\u0026thinsp;0.0015), Kenya vs Uganda (adjusted p\u0026thinsp;=\u0026thinsp;0.0435), and Tanzania vs Uganda (adjusted p\u0026thinsp;=\u0026thinsp;0.0015).\u003c/p\u003e\u003cp\u003eBiologically, these differences were driven by distinct patterns in terms of ST prevalence and exclusivity. Tanzania presented a markedly greater prevalence of ST8 (40.8%) than Kenya (10.1%) and Uganda (12.1%). Kenya showed greater representation of ST188 (5.3%) and ST80 (5.7%), both of which were absent and minimally present in the other countries. Uganda's distinctiveness was characterized by a high frequency of ST1633 (21.2%) and the highest proportion of novel STs (33.3%). Notably, ST121, present in Tanzania (5.7%), was completely absent in Ugandan isolates.\u003c/p\u003e\n\u003ch3\u003eSpa Type Diversity and Geographic Distribution\u003c/h3\u003e\n\u003cp\u003eAnalysis of 438 \u003cem\u003eS. aureus\u003c/em\u003e reordered genomes across Tanzania (n\u0026thinsp;=\u0026thinsp;239), Kenya (n\u0026thinsp;=\u0026thinsp;168), and Uganda (n\u0026thinsp;=\u0026thinsp;31) revealed a total of 67 distinct \u003cem\u003espa\u003c/em\u003e types, reflecting substantial genetic heterogeneity within the population (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The three most prevalent \u003cem\u003espa\u003c/em\u003e types were t355 (n\u0026thinsp;=\u0026thinsp;107, 24.4%), t1476 (n\u0026thinsp;=\u0026thinsp;78, 17.8%), and t064 (n\u0026thinsp;=\u0026thinsp;23, 5.3%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B, D). Together, these lineages comprised almost half (47.5%) of all the isolates, highlighting their epidemiological importance across the region. The dominance of specific clones varies by country. In Tanzania, t355 (38.9%) and t1476 (32.6%) were predominant, whereas in Uganda, t355 accounted for 41.9% of the isolates, followed by t1476 at 9.7%. In contrast, Kenya exhibited greater \u003cem\u003espa\u003c/em\u003e type heterogeneity, with t064 (13.7%), t355 (12.5%), and t1504 (5.4%) as the most frequent lineages and the emergence of t13194 (4.8%) as a notably prevalent type exclusive to the Kenyan dataset. These findings indicate the existence of both regionally dominant and geographically restricted clones across East Africa (Additional file 2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eA total of 15 predominant \u003cem\u003espa\u003c/em\u003e types were identified among the genomes, reflecting the most frequently encountered genetic lineages in the sampled population. The most dominant clone was \u003cem\u003espa\u003c/em\u003e type t355, which accounted for 38.2% of all the isolates. This substantial prevalence suggests pronounced clonal expansion and regional transmission of this lineage. The second most prevalent type was t1476, accounting for 21.9% of the isolates, followed by t064, which accounted for 7.0%. Collectively, these three \u003cem\u003espa\u003c/em\u003e types represent approximately two-thirds (~\u0026thinsp;67%) of all typed isolates, indicating that the \u003cem\u003eS. aureus\u003c/em\u003e population structure in this region is largely shaped by a few successful lineages. Additional \u003cem\u003espa\u003c/em\u003e types were detected at lower frequencies, including t189 (4.3%), t13194 (3.7%), t4333 (3.7%), and t223 (3.0%). Other notable types, including t084, t127, t4499, and t701, were each observed in fewer than 2.5% of the isolates. The rarest among the 15 predominant types was t131, which constituted only 1.7% of the total population. This distribution pattern underscores a population structure dominated by a limited number of highly successful clones, likely reflecting a combination of clonal expansion, transmission dynamics, and possible selective advantages. Moreover, the detection of less common and novel \u003cem\u003espa\u003c/em\u003e types indicates an underlying degree of genetic diversity and potential for microevolutionary processes within regional \u003cem\u003eS. aureus\u003c/em\u003e populations.\u003c/p\u003e\u003cp\u003eCountry-specific distributions revealed distinct patterns in \u003cem\u003espa\u003c/em\u003e-type frequencies. In Kenya, t355 was the most dominant clone, accounting for 37.2% of the isolates, followed by t064 at 12.8%, t189 at 8.3%, and t13194 at 7.1%. Several additional types, including t1504, t223, and t131, appeared at moderate frequencies ranging from 3\u0026ndash;5%. The Kenyan \u003cem\u003espa\u003c/em\u003e-type landscape was characterized by a relatively high degree of diversity, with t355 as the principal clone accompanied by multiple secondary lineages. In Tanzania, t1476 emerged as the most prevalent type, representing 42.4% of the isolates, followed by t355 (26.4%) and t4333 (7.6%). Other types, including t498, t4499, and t091, were also present at lower frequencies. The dominance of t1476 in Tanzania may suggest the recent introduction or expansion of this lineage in this specific geographical setting. In Uganda, t355 again represented the dominant \u003cem\u003espa\u003c/em\u003e type, accounting for 48.7% of the isolates. Other types were present at markedly lower frequencies, with t127 and t1476 both contributing 7.7%, and t1096, t2554, and t9231 each accounting for 5.1%. These results suggest that \u003cem\u003eS. aureus\u003c/em\u003e populations in Uganda are less genetically diverse than those in Kenya and Tanzania are, with a greater degree of clonal homogeneity driven by the predominance of t355.\u003c/p\u003e\u003cp\u003eSeveral novel or rare \u003cem\u003espa\u003c/em\u003e types were also detected, primarily in Tanzania, suggesting ongoing diversification and potential local adaptation. Specifically, nine rare or novel types, including t10599, t1346, t15643, and t17400, were observed in the Tanzanian isolates, indicating the presence of previously undocumented lineages or localized evolutionary events. In Kenya, t13194 emerged as a potentially region-specific type, with a prevalence of 4.8%, yet it was not observed in isolates from Tanzania or Uganda. In Uganda, rare types such as t10274 and t10499 were identified, further supporting the idea that although dominant clones drive the overall population structure, regionally unique or emerging \u003cem\u003espa\u003c/em\u003e types continue to circulate at low levels.\u003c/p\u003e\u003cp\u003eTo evaluate whether \u003cem\u003espa\u003c/em\u003e type frequencies were non-randomly distributed across the three countries, a chi-square test was performed, which included only \u003cem\u003espa\u003c/em\u003e types with at least 10 observations. The test revealed a highly significant difference in \u003cem\u003espa\u003c/em\u003e type distribution across countries (χ\u0026sup2; = 133.96, degrees of freedom\u0026thinsp;=\u0026thinsp;10, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2 \u0026times; 10⁻\u0026sup1;⁶), indicating strong geographic structuring in the \u003cem\u003eS. aureus\u003c/em\u003e population (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). To explore intercountry differences, pairwise chi-square comparisons were conducted with Benjamini‒Hochberg correction for multiple testing. All pairwise comparisons revealed significant differences in \u003cem\u003espa\u003c/em\u003e type composition. The most striking difference was between Tanzania and Kenya (p\u0026thinsp;\u0026lt;\u0026thinsp;3.8 \u0026times; 10⁻\u0026sup2;⁴), followed by Tanzania and Uganda (p\u0026thinsp;=\u0026thinsp;1.37 \u0026times; 10⁻⁴) and Kenya and Uganda (p\u0026thinsp;=\u0026thinsp;6.64 \u0026times; 10⁻⁴). These findings were independently validated via Fisher\u0026rsquo;s exact test with simulated p values, which also confirmed a significant association between \u003cem\u003espa\u003c/em\u003e type and country (p\u0026thinsp;=\u0026thinsp;0.0005).\u003c/p\u003e\u003cp\u003eEffect size estimates via Cram\u0026eacute;r\u0026rsquo;s V further quantified the magnitude of these associations. The strongest association was observed between Tanzania and Kenya (Cram\u0026eacute;r\u0026rsquo;s V\u0026thinsp;=\u0026thinsp;0.7371), followed by Tanzania and Uganda (0.5138) and Kenya and Uganda (0.4393), reflecting moderate to strong effect sizes. These values underscore the existence of substantial differences in \u003cem\u003espa\u003c/em\u003e type composition between East African countries. A stacked bar plot depicting the relative proportions of \u003cem\u003espa\u003c/em\u003e types across the three countries visually supported these findings and highlighted the distinct population structures, with t355 demonstrating transnational dominance, t1476 showing a strong country-specific signature in Tanzania, and t064 being more prominent in the Kenyan context.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComprehensive profiling of antimicrobial resistance determinants reveals distinct patterns among Hospital\u003c/b\u003e \u003cb\u003eS. aureus\u003c/b\u003e \u003cb\u003egenomes across East Africa\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhole-genome analysis of 434 clinical \u003cem\u003eStaphylococcus aureus\u003c/em\u003e isolates from tertiary care hospitals across Kenya (n\u0026thinsp;=\u0026thinsp;187), Tanzania (n\u0026thinsp;=\u0026thinsp;203), and Uganda (n\u0026thinsp;=\u0026thinsp;44) revealed an extensive resistome comprising 94 distinct antimicrobial resistance (AMR) genes (Additional file 3). These genetic determinants confer resistance to 21 classes of antimicrobial agents, spanning all major therapeutic categories used in clinical practice across the region. The spectrum of resistance included resistance to aminoglycosides (gentamicin, kanamycin, tobramycin, amikacin, streptomycin), β-lactams (penicillins, cephalosporins), tetracyclines, macrolides, phenicols (chloramphenicol), folate pathway inhibitors (trimethoprim), fosfomycin, fusidic acid, glycopeptides (vancomycin), lincosamides, pleuromilins, streptogramins, mupirocin, streptothricin, and sulfonamides (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eQuantitative analysis of AMR gene prevalence demonstrated marked heterogeneity in distribution frequencies (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Five resistance genes accounted for the majority (64.8%) of the detected AMR markers, forming a core resistome among East African isolates. The β-lactamase operon components \u003cem\u003eblaI_of_Z\u003c/em\u003e (14.4%, n\u0026thinsp;=\u0026thinsp;477) and \u003cem\u003eblaR1\u003c/em\u003e (13.4%, n\u0026thinsp;=\u0026thinsp;444), along with the penicillinase gene \u003cem\u003eblaZ\u003c/em\u003e (12.4%, n\u0026thinsp;=\u0026thinsp;409), were highly prevalent, which is consistent with widespread β-lactam resistance. Compared with global datasets, the tetracycline efflux pump gene \u003cem\u003etet\u003c/em\u003e(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) (14.1%, n\u0026thinsp;=\u0026thinsp;466) had an unexpectedly high prevalence, whereas the trimethoprim resistance determinant \u003cem\u003edfrG\u003c/em\u003e (10.5%, n\u0026thinsp;=\u0026thinsp;346) was the dominant resistance gene.\u003c/p\u003e\u003cp\u003eNotably, the methicillin resistance gene \u003cem\u003emecA\u003c/em\u003e was detected in 3.7% (n\u0026thinsp;=\u0026thinsp;121) of the isolates, a prevalence substantially lower than that reported in many hospital settings worldwide (typically 20\u0026ndash;50%) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). However, its distribution varied significantly by country, with Tanzania showing a 2.8% prevalence compared with only 0.6% in Kenya (χ\u0026sup2;=15.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The fosfomycin resistance gene \u003cem\u003efosB-Saur\u003c/em\u003e had a concerning prevalence (6.6%, n\u0026thinsp;=\u0026thinsp;218), particularly in Tanzania, where it accounted for 3.6% of all AMR markers, whereas it accounted for 2.6% of all AMR markers in Kenya (OR\u0026thinsp;=\u0026thinsp;1.42, 95% CI 1.08\u0026ndash;1.86). The macrolide resistance gene \u003cem\u003eerm(C)\u003c/em\u003e was identified in 3.9% (n\u0026thinsp;=\u0026thinsp;128) of the isolates, with a country-specific prevalence ranging from 1.1% in Kenya to 2.5% in Tanzania.\u003c/p\u003e\u003cp\u003eDetailed geographic analysis revealed distinct AMR gene distribution profiles across the three countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Kenyan isolates presented a predominance of β-lactam resistance genes, with \u003cem\u003eblaI_of_Z\u003c/em\u003e (7.5%), \u003cem\u003eblaR1\u003c/em\u003e (7.1%), and \u003cem\u003eblaZ\u003c/em\u003e (5.9%) collectively accounting for 20.5% of all resistance markers. Tanzania exhibited more diverse resistance patterns, with an increased prevalence of \u003cem\u003efosB-Saur\u003c/em\u003e (3.6%), \u003cem\u003eerm(C)\u003c/em\u003e (2.5%), and \u003cem\u003emecA\u003c/em\u003e (2.8%). The aminoglycoside resistance gene \u003cem\u003eaph\u003c/em\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003cem\u003e-Ih\u003c/em\u003e showed a particularly skewed distribution, representing 2.8% of the AMR markers in Tanzania compared with only 0.5% in Kenya (Fisher's exact test p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Ugandan isolates generally presented lower AMR gene frequencies, with only \u003cem\u003edfrG\u003c/em\u003e (1.2%) and \u003cem\u003etet\u003c/em\u003e(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) (1.2%) exceeding 1% prevalence.\u003c/p\u003e\u003cp\u003eDespite these qualitative differences in resistance gene profiles, nonparametric analysis via the Kruskal‒Wallis test revealed no significant difference in overall AMR gene counts between countries (χ\u0026sup2;=1.29, df\u0026thinsp;=\u0026thinsp;2, p\u0026thinsp;=\u0026thinsp;0.52; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). This suggests that while the specific composition of resistomes varies, the total burden of acquired resistance remains relatively constant across the region. Post hoc pairwise comparisons via Wilcoxon rank-sum tests with Benjamini‒Hochberg correction confirmed this finding for all country pairs (Kenya‒Tanzania: p\u0026thinsp;=\u0026thinsp;0.63; Kenya‒Uganda: p\u0026thinsp;=\u0026thinsp;0.41; Tanzania‒Uganda: p\u0026thinsp;=\u0026thinsp;0.55).\u003c/p\u003e\u003cp\u003eIn addition to the core resistome, several genes of clinical and epidemiological importance were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The tetracycline resistance genes \u003cem\u003etet(K)\u003c/em\u003e (4.3%, n\u0026thinsp;=\u0026thinsp;142) and \u003cem\u003etet(M)\u003c/em\u003e (0.7%, n\u0026thinsp;=\u0026thinsp;22) presented differential distribution patterns, with \u003cem\u003etet(K)\u003c/em\u003e being more prevalent in Tanzania (2.1%) than in Kenya (1.7%). The trimethoprim resistance gene \u003cem\u003edfrC\u003c/em\u003e (3.6%, n\u0026thinsp;=\u0026thinsp;119) demonstrated the opposite trend, being more common in Kenya (0.9%) than in Tanzania (2.5%). The aminoglycoside resistance gene \u003cem\u003eaph\u003c/em\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003cem\u003e-Ih\u003c/em\u003e (3.4%, n\u0026thinsp;=\u0026thinsp;113) showed particularly strong geographic clustering, accounting for 2.8% of AMR markers in Tanzania compared with only 0.5% in Kenya and 0.1% in Uganda.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVirulence Factor Landscape of\u003c/b\u003e \u003cb\u003eStaphylococcus aureus\u003c/b\u003e \u003cb\u003ein East African Hospitals\u003c/b\u003e\u003c/p\u003e\u003cp\u003eComprehensive genomic analysis of 434 \u003cem\u003eStaphylococcus aureus\u003c/em\u003e clinical isolates collected from hospitals across East Africa revealed a diverse and conserved repertoire of virulence determinants (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). A total of 147 distinct virulence genes were identified across reordered genomes, reflecting the substantial pathogenic potential of regional \u003cem\u003eS. aureus\u003c/em\u003e strains. Among these genes, the Panton-Valentine leukocidin (PVL) gene component \u003cem\u003elukF-PV\u003c/em\u003e emerged as the most prevalent virulence factor, accounting for 2.08% of the total number of virulence genes (n\u0026thinsp;=\u0026thinsp;628). Other highly represented genes included \u003cem\u003ehysA\u003c/em\u003e (1.73%, n\u0026thinsp;=\u0026thinsp;522), \u003cem\u003ecap8B\u003c/em\u003e and \u003cem\u003eesaB\u003c/em\u003e (1.61%, n\u0026thinsp;=\u0026thinsp;486 each), \u003cem\u003eisdG\u003c/em\u003e (1.60%, n\u0026thinsp;=\u0026thinsp;484), \u003cem\u003eesxA\u003c/em\u003e and \u003cem\u003eicaD\u003c/em\u003e (1.60%, n\u0026thinsp;=\u0026thinsp;483 each), and a suite of capsular polysaccharide type 8 operon components (\u003cem\u003ecap8A\u0026ndash;G\u003c/em\u003e, \u003cem\u003eM\u003c/em\u003e, and \u003cem\u003eN\u003c/em\u003e), each with a prevalence ranging from 1.57\u0026ndash;1.61% (Additional file 4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe virulence genes were clustered into three major functional categories: immune evasion and surface adherence, hemolytic and proteolytic activity, and iron acquisition systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The immune evasion factors were dominated by the hyaluronidase gene \u003cem\u003ehysA\u003c/em\u003e and biofilm-associated \u003cem\u003eicaD\u003c/em\u003e, both of which are broadly distributed across isolates. The \u003cem\u003ecap8\u003c/em\u003e operon was remarkably conserved, supporting the widespread potential for capsular polysaccharide production. Hemolysins were also prominent, with near-ubiquitous detection of the bicomponent γ-hemolysin genes \u003cem\u003ehlgB\u003c/em\u003e (1.59%) and \u003cem\u003ehlgC\u003c/em\u003e (1.58%), along with α-hemolysin (\u003cem\u003ehla\u003c/em\u003e) at 1.58%. The proteolytic enzymes \u003cem\u003esspC\u003c/em\u003e (1.58%) and \u003cem\u003eaur\u003c/em\u003e (1.58%) further retained extracellular degradative activity. Iron-scavenging mechanisms were consistently detected through the iron-regulated surface determinant (Isd) pathway genes \u003cem\u003eisdG\u003c/em\u003e (1.60%) and \u003cem\u003eisdC\u003c/em\u003e (1.58%). In parallel, the high prevalence of \u003cem\u003eesaB\u003c/em\u003e (1.61%) and \u003cem\u003eesxA\u003c/em\u003e (1.60%) pointed to a conserved ESAT-6 secretion system across isolates, potentially contributing to intracellular survival and immune modulation.\u003c/p\u003e\u003cp\u003eCountry-level analyses revealed significant geographic variation in virulence gene prevalence (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Compared with the Kenyan isolates, the Tanzanian isolates presented a 1.4-fold greater prevalence of \u003cem\u003elukF-PV\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.03), whereas the Ugandan isolates presented greater carriage of \u003cem\u003esak\u003c/em\u003e (1.2% vs. 0.4% regionally, p\u0026thinsp;=\u0026thinsp;0.01), which encodes staphylokinase and may facilitate immune escape via plasminogen activation. In contrast, the \u003cem\u003ecap8\u003c/em\u003e operon remained consistently detected across all three countries (1.55\u0026ndash;1.63%), highlighting its potential role as a core virulence module in regional \u003cem\u003eS. aureus\u003c/em\u003e populations.\u003c/p\u003e\u003cp\u003eVirulence gene co-occurrence analysis revealed three statistically significant clusters (Fisher\u0026rsquo;s exact test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting functionally coordinated expression patterns: (i) a PVL-associated cluster comprising \u003cem\u003elukF-PV\u003c/em\u003e and \u003cem\u003ehlgCB\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;4.2), reflecting synergistic cytotoxic potential; (ii) a biofilm-associated cluster involving \u003cem\u003eicaD\u003c/em\u003e and \u003cem\u003eaur\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;3.8), indicating mechanisms of persistence and immune evasion; and (iii) a tissue invasion cluster formed by \u003cem\u003ehysA\u003c/em\u003e and \u003cem\u003esspC\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;2.9), indicating enhanced host tissue degradation capabilities.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePlasmid Diversity and Distribution in East African WGS\u003c/b\u003e \u003cb\u003eS. aureus\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe analyzed 434 \u003cem\u003eS. aureus\u003c/em\u003e genomes from East Africa to characterize plasmid diversity, prevalence, and distribution. Plasmid carriage was widespread and highly variable, with isolates harboring between 1 and 17 replicons (mean\u0026thinsp;=\u0026thinsp;3.55; SD\u0026thinsp;=\u0026thinsp;2.67). Most genomes (78.2%) carried four or fewer replicons, whereas a small subset (3.7%) harbored seven or more replicons, including five genomes with \u0026gt;\u0026thinsp;10 replicons. Notably, isolate ERR3150952 presented an extreme profile with 17 replicons (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eReplicon frequency analysis revealed a heterogeneous plasmid landscape dominated by a few high-prevalence elements (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The most common replicons were \u003cem\u003erep5a_1_repSAP001\u003c/em\u003e (pN315; 68.9%), \u003cem\u003erep16_3_rep\u003c/em\u003e (pSaa6159; 53.2%), and \u003cem\u003erep10_3_pNE131p1\u003c/em\u003e (pNE131; 51.7%). Intermediate-prevalence replicons (10\u0026ndash;50%) included \u003cem\u003erep7c_1_rep\u003c/em\u003e (MSSA476; 32.4%), \u003cem\u003erep19_13_rep\u003c/em\u003e (pBORa53; 28.6%), and \u003cem\u003erep15_1_repA\u003c/em\u003e (pLW043; 18.3%). Rare replicons (\u0026lt;\u0026thinsp;10%), such as \u003cem\u003erep20_3_rep\u003c/em\u003e, \u003cem\u003erep7a_16_repC\u003c/em\u003e, and \u003cem\u003erep24a_1_rep\u003c/em\u003e, appeared in fewer than 6.1% of the genomes, and seven replicons were found in only one genome each, suggesting either rare horizontal acquisitions or assembly artifacts.\u003c/p\u003e\u003cp\u003ePlasmid diversity and prevalence exhibited significant geographic structuring (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The Tanzanian isolates had the highest Shannon diversity index (2.41), followed by Kenya (1.87) and Uganda (1.52; Kruskal‒Wallis, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). While \u003cem\u003erep5a_1_repSAP001\u003c/em\u003e was common across all countries (Kenya: 65.2%, Tanzania: 71.3%, Uganda: 63.8%), replicons such as \u003cem\u003erep7c_1_rep\u003c/em\u003e and \u003cem\u003erep16_3_rep\u003c/em\u003e were significantly more common in Tanzania (38.7% and 61.2%, respectively) than in Kenya (25.4% and 46.6%) and Uganda (0.6% and 42.3%; χ\u0026sup2; tests, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Uganda presented the lowest diversity, with replicon carriage concentrated in a few dominant types, particularly \u003cem\u003erep16_3_rep\u003c/em\u003e and \u003cem\u003erep7a_16\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eMultiple-replicon carriage also varies geographically. A majority (68.5%) of Tanzanian genomes carried three or more replicons, compared with 55.3% in Uganda and 41.1% in Kenya. Common country-specific combinations included \u003cem\u003erep5a_1\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003erep16_3\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003erep10_3\u003c/em\u003e in Tanzania, \u003cem\u003erep16_3\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003erep5a_1\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003erep7a_16\u003c/em\u003e in Uganda, and \u003cem\u003erep5a_1\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003erep16_3\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003erep7c_1\u003c/em\u003e in Kenya, suggesting differences in plasmid compatibility, host permissiveness, or antimicrobial pressures (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eCo-occurrence network analysis revealed significant replicon associations (Fisher\u0026rsquo;s exact test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), forming three main clusters: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) a core maintenance cluster featuring \u003cem\u003erep5a_1\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003erep10_3\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;4.8) and \u003cem\u003erep16_3\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003erep15_1\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;3.2); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) an accessory function cluster with \u003cem\u003erep7c_1\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003erep19_13\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;5.1) and \u003cem\u003erep20_3\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003erep7a_16\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;2.9); and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) a Tanzania-specific cluster linking \u003cem\u003erep24a_1\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003erep21_9\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;6.7), suggesting local selection pressures.\u003c/p\u003e\u003cp\u003eModularity-based network analysis (modularity\u0026thinsp;=\u0026thinsp;0.62) revealed five distinct replicon communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Community 1 (n\u0026thinsp;=\u0026thinsp;195 nodes) was dominated by high-degree hubs such as \u003cem\u003erep5a_1_repSAP001\u003c/em\u003e and \u003cem\u003erep16_3_rep\u003c/em\u003e, which are mainly found in Tanzanian isolates. Community 2 (n\u0026thinsp;=\u0026thinsp;111) included \u003cem\u003erep7c_1_rep\u003c/em\u003e and \u003cem\u003erepUS43_1_CDS12738\u003c/em\u003e, which are more prevalent in the Kenyan genomes. Community 3 (n\u0026thinsp;=\u0026thinsp;86) represented niche or horizontally transferred replicons, whereas Community 4 (n\u0026thinsp;=\u0026thinsp;62) and Community 5 (n\u0026thinsp;=\u0026thinsp;2) consisted of low-frequency or potentially artifactual elements.\u003c/p\u003e\u003cp\u003eFinally, a chi-square test confirmed significant differences between replicon presence and country of origin (χ\u0026sup2; = 848.13, df\u0026thinsp;=\u0026thinsp;206, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE), reinforcing the role of geographic structuring in shaping the plasmidome of \u003cem\u003eS. aureus\u003c/em\u003e in East Africa.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhage Diversity and Genomic Architecture in East African\u003c/b\u003e \u003cb\u003eS. aureus\u003c/b\u003e \u003cb\u003eGenomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe identified 934 phage sequences from \u003cem\u003eStaphylococcus aureus\u003c/em\u003e isolates in East African hospitals, predominantly from Kenya (n\u0026thinsp;=\u0026thinsp;687, 73.6%) and Tanzania (n\u0026thinsp;=\u0026thinsp;220, 23.6%), with a minor contribution from Uganda (n\u0026thinsp;=\u0026thinsp;27, 2.9%). Taxonomically, a striking majority (94.1%, n\u0026thinsp;=\u0026thinsp;879) belonged to the Siphoviridae family, followed by Myoviridae (3.1%, n\u0026thinsp;=\u0026thinsp;29), Podoviridae (1.2%, n\u0026thinsp;=\u0026thinsp;11), and unclassified phages (1.4%, n\u0026thinsp;=\u0026thinsp;13). Additionally, two hybrid Myoviridae/Siphoviridae sequences (0.2%) were detected, suggesting possible instances of horizontal gene transfer (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe genomic characteristics of the identified prophages revealed considerable length variability (201 bp to 131.2 kb), with a median of 38.7 kb. The dominant size class of 30\u0026ndash;50 kb encompassed 78% of the sequences, which were aligned with known temperate phage genomes. We identified 22 genomic scaffolds harboring multiple prophage integrations, with five scaffolds containing three prophages each, highlighting genomic hotspots for integration. Notably, 12 samples from Tanzania shared an identical prophage sequence (NODE_8_length_88311), indicative of conserved integration loci or successful phage lineages.\u003c/p\u003e\u003cp\u003eGeographically, phage richness was highest in Kenya (633 unique IDs), followed by Tanzania (230 IDs) and Uganda (71 IDs). However, PERMANOVA based on Jaccard distances indicated no significant geographic structuring of phage communities (p\u0026thinsp;=\u0026thinsp;1.0), which was supported by nonmetric multidimensional scaling (NMDS), which revealed extensive overlap in phage composition across regions.\u003c/p\u003e\u003cp\u003eCrucially, our findings revealed no evidence of transposable elements within the prophage sequences, indicating stable integration of mechanisms via site-specific recombination. The integration sites varied widely, ranging from 111 bp to 678.6 kb, underscoring the absence of conserved loci at the population level despite recurrent integration patterns at the scaffold level.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRegional Diversity and Load of Insertion Sequences in\u003c/b\u003e \u003cb\u003eS. aureus\u003c/b\u003e \u003cb\u003ewithin East Africa\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur comprehensive genomic analysis revealed significant regional disparities in the mobilome of \u003cem\u003eS. aureus\u003c/em\u003e isolates from Kenya, Tanzania, and Uganda. The distribution of insertion sequences (ISs) was markedly heterogeneous across regions, with certain families demonstrating pervasive prevalence, notably IS1182 and IS21 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIS1182 was detected in 100% of the isolates across all three countries, with counts ranging from 132 in Uganda to 793 in Tanzania, underscoring its status as a core component of the \u003cem\u003eS. aureus\u003c/em\u003e mobilome in East Africa. Similarly, IS21 was universally present, with counts of 95 in Uganda, 257 in Tanzania, and 781 in Kenya. Conversely, less prevalent families such as IS256 and ISL3 exhibited substantial regional variation: IS256 was significantly more abundant in Kenya (138) than in Uganda (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), and ISL3 followed a similar pattern. A chi-square test confirmed that the distribution of these dominant IS families was highly significant across regions (X\u0026sup2; = 551.74, p\u0026thinsp;\u0026lt;\u0026thinsp;2.2\u0026times;10⁻\u0026sup1;⁶), indicating that region-specific mobilome profiles were influenced by local selective pressures and transmission dynamics.\u003c/p\u003e\u003cp\u003eThe total number of IS elements per isolate varied dramatically, with Tanzanian isolates exhibiting the highest median counts and a broader distribution, with some samples harboring over 100 ISs indicative of heightened genomic plasticity. In contrast, Ugandan isolates predominantly contained fewer than 30 ISs. Kruskal‒Wallis tests confirmed that these differences were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that regional variation in mobile genetic activity could influence the dissemination of resistance determinants and virulence factors.\u003c/p\u003e\u003cp\u003eTo elucidate potential horizontal gene transfer pathways, we constructed a co-occurrence network on the basis of the presence/absence of IS families across isolates. The network revealed several highly interconnected families, notably IS1182, IS21, and IS3, which frequently co-occurred within individual genomes. These core elements appear to facilitate widespread genetic exchange, as evidenced by their prevalence in multiple isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eRegion-specific clustering within the network was apparent: isolates from Kenya and Tanzania presented more complex co-occurrence patterns, which was consistent with greater genomic plasticity, whereas Ugandan isolates presented simpler networks with fewer interconnected families.\u003c/p\u003e\n\u003ch3\u003eGeospatial and Molecular Characterization of SCCmec Elements Reveals Country-Specific MRSA Epidemiology Across East Africa\u003c/h3\u003e\n\u003cp\u003eStaphylococcal Cassette Chromosome mec (SCCmec) elements, which are responsible for methicillin resistance in \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, were analyzed across multiple genomes from Tanzania, Kenya and Uganda. We characterized SCCmec elements in 114 methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e genomes from East Africa (Kenya: n\u0026thinsp;=\u0026thinsp;25; Tanzania: n\u0026thinsp;=\u0026thinsp;80; Uganda: n\u0026thinsp;=\u0026thinsp;9) obtained from a total of 434 WGS genomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-D). SCCmec typing revealed Type V as the predominant variant (78.1%, 89/114), with an overwhelming predominance in Tanzanian isolates (91.3%, 73/80). The Tanzanian Type V strains predominantly belonged to subtype Vc (89.0%, 65/73), characterized by the presence of ccrC1, multiple IS431 insertion sequences, and intact mecA-mecR1 complexes. In contrast, the Kenyan isolates presented greater diversity, with co-circulation of Type III (28.0%, 7/25), Type IV (28.0%, 7/25), and Type V (28.0%, 7/25) strains. The Ugandan collection showed a distinct pattern, featuring Type V (55.6%, 5/9), Type III (11.1%, 1/9), and most notably, Type VI (22.2%, 2/9) lineages\u0026mdash;a rare lineage in this region containing the ccrA4-ccrB4 gene complex (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of SCCmec types by country\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal MRSA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eType V (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eType IV (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eType III (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eType VI (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eUntypable (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDominant Features\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTanzania\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73 (91.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6 (7.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCC152-ST152 dominance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKenya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (28.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (28.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (28.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4 (16.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCocirculation of types\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUganda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (55.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2 (22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1 (11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNovel Type VI presence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGenetic characteristics of SCC\u003cem\u003emec\u003c/em\u003e types\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubtype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKey Genetic Markers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTypical Association\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCountry Prevalence\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eccrC1, IS431 variants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCA-MRSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTanzania (89% of Type V)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eXV/III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eccrA3-ccrB3, mecI-mecR1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHA-MRSA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKenya (100% of Type III)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emultiple\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eccrA2-ccrB2, truncated mecR1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCA/HA-MRSA bridge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKenya (100% of Type IV)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eccrA4-ccrB4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePotential zoonotic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUganda exclusive\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMobile Genetic Element Patterns\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElement Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTanzania Pattern\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKenya Pattern\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUganda Pattern\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBiological Significance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eccr genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eccrC1 dominant (98%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMixed ccrA/B complexes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eccrA4-ccrB4 unique\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDetermines SCC\u003cem\u003emec\u003c/em\u003e\u0026nbsp;mobility\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIS elements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIS431 multicopy (3\u0026ndash;4 copies)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIS431 variants\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIS431\u0026thinsp;+\u0026thinsp;IS1272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFacilitates recombination\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emec complex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComplete mecA-mecR1 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emecI present in Type III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVariable structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eImpacts β-lactam resistanc\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eStatistical analysis via Pearson's chi-square test revealed highly significant differences in the distribution of SCCmec types across countries (χ\u0026sup2;=73.58, df\u0026thinsp;=\u0026thinsp;6, p\u0026thinsp;=\u0026thinsp;7.52\u0026times;10⁻\u0026sup1;⁴). Subsequent pairwise Fisher's exact tests with simulated p values (2000 replicates) confirmed these regional disparities: Kenya vs Tanzania (p\u0026thinsp;=\u0026thinsp;0.0005), Kenya vs Uganda (p\u0026thinsp;=\u0026thinsp;0.023), and Uganda vs Tanzania (p\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Genetic analysis of the cassette elements revealed type-specific signatures: Type III isolates from Kenya and Uganda carried the ccrA3-ccrB3 complex along with mecI-mecR1, which is typical of healthcare-associated strains; Type IV variants from Kenya featured ccrA2-ccrB2 with truncated mecR1; and the novel Ugandan Type VI isolates contained a unique ccrA4-ccrB4 combination. Network analysis of genetic components demonstrated the universal presence of mecA-mecR1 across all the isolates (100%) and identified ccrC1 as a central hub in Type V strains (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF). The Ugandan ccrA4-ccrB4 elements presented high betweenness centrality, suggesting potential bridging between rare SCCmec types. Geospatial visualization highlighted Tanzania's remarkable homogeneity (91.3% Type V) compared with Kenya's diverse SCCmec ecology and Uganda's unique Type VI signature (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenomic Diversity and Pangenome Dynamics of East African\u003c/b\u003e \u003cb\u003eS. aureus\u003c/b\u003e \u003cb\u003eGenomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe construction of the average nucleotide identity (ANI) dendrogram revealed a total of five primary clusters and seven secondary clusters. Primary Cluster 1 comprised two samples, ERR1764891.fasta and ERR1764888.fasta. Primary Cluster 2 included three samples: ERR3150949.fasta, ERR3150917.fasta, and ERR3150943.fasta. Primary Cluster 3 was the largest, comprising most of the samples (480), which were further subdivided into seven secondary clusters. Primary Cluster 4 consisted of five samples: ERR2436453.fasta, ERR2436451.fasta, ERR2436455.fasta, ERR2436454.fasta, and ERR2436452.fasta. Primary Cluster 5 included seven samples: ERR1764894.fasta, ERR1764900.fasta, ERR1764902.fasta, ERR1764913.fasta, ERR1764914.fasta, ERR1764966.fasta, and ERR3218227.fasta (Additional file 5).\u003c/p\u003e\u003cp\u003eCluster 3 exhibited the greatest diversity, encapsulating the seven secondary sub-clusters. This clustering reflects the genomic variability among isolates, highlighting strain diversity across East Africa. Strains within the same primary cluster often share geographic or hospital origins, suggesting localized transmission or adaptation patterns (Additional file 6\u0026ndash;7).\u003c/p\u003e\u003cp\u003eThe analysis further revealed significant insights into genomic relationships among the strains. The average nucleotide identity (ANI) values across the genomes compared with the reference genome (ERR12511686.fasta) ranged from 0.963 to 1.0, indicating high similarity among the genomes. Notably, the closest relatives to the reference genome presented ANI values exceeding 0.999, suggesting that they are likely part of the same species. For example, ERR12511700.fasta and ERR12511701.fasta presented ANI values of 0.9995 and 0.9998, respectively, with alignment coverages of 0.9937 and 0.9906, confirming their close phylogenetic relationship.\u003c/p\u003e\u003cp\u003eThe pairwise distances between genomes further illustrate genetic divergence within the dataset. The minimum pairwise distance observed was 0.0, indicating that some genomes were identical or nearly identical to the reference genome, whereas others, such as ERR1764902.fasta, presented greater distances (0.263), suggesting significant divergence from the reference genome.\u003c/p\u003e\u003cp\u003eThe circular phylogenetic trees visually represent the relationships among the genomes according to their origin, with distinct clusters reflecting their genetic similarities (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePangenome analysis of 496 \u003cem\u003eStaphylococcus aureus\u003c/em\u003e genomes revealed striking genomic diversity, with a very small set of conserved core genes and a vast majority of strain-specific genes. Specifically, only 5 core genes were detected, alongside 1,508 soft core genes, 1,729 shell genes, and a remarkable 68,759 cloud genes. This highlights that 95.5% of the total gene content consists of cloud genes, which are typically found in fewer than 15% of strains. Cloud genes are often associated with horizontal gene transfer and rare adaptations, contributing significantly to the genetic variability within the species (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePangenome characteristics of 496 \u003cem\u003eS. aureus\u003c/em\u003e genomes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of Genes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% of Total Pangenome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePresence in Strains\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKey Characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBiological Significance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCore Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.007%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100% of strains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHighly conserved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEssential cellular functions (e.g., ribosomal proteins)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoft Core\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95\u0026ndash;99% of strains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNearly universal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eImportant for basic metabolism and structure\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShell Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u0026ndash;94% of strains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerately distributed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eConditional advantages (e.g., niche-specific adaptations)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCloud Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68,759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e95.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;15% of strains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStrain-specific\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHorizontal gene transfer elements, virulence factors, antibiotic resistance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo investigate this phenomenon further, we conducted a pangenome analysis on the 110 \u003cem\u003eS. aureus\u003c/em\u003e genomes most closely related to the reference genome. This analysis revealed a relatively large number of core genes, specifically, 1,851 core genes, 24 soft core genes, 1,429 shell genes, and 110,258 cloud genes (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative Pangenome Analysis of 110 Closely Related \u003cem\u003eS. aureus\u003c/em\u003e Genomes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of Genes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% of Total Pangenome\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePresence in Strains\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKey Observations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEvolutionary Implications\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCore Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100% of strains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncreased core set\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMore conserved functions identified in closely related strains\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoft Core\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95\u0026ndash;99% of strains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDramatic reduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFewer near-universal genes in this subset\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShell Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u0026ndash;94% of strains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSimilar absolute number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMaintains adaptive flexibility\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCloud Genes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e110,258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;15% of strains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMassive expansion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eExtreme strain-specific diversity even among closely r\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMost genes, 110,258 cloud genes, accounting for 97.1% of the total genes, were found in fewer than 15% of the strains. This underscores the significant strain-specific variability within the species, with cloud genes likely playing a key role in specific adaptations to different environments or hosts.\u003c/p\u003e\u003cp\u003eThe relatively small number of core genes (1,851) compared with the total genome reflects that only a limited set of genes is conserved across most strains. Moreover, the relatively high number of shell genes (1,429) indicates that a substantial portion of the genome is moderately distributed across strains, potentially facilitating evolutionary flexibility and adaptation to varying conditions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study represents the most comprehensive genomic analysis of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e in East Africa to date, revealing a complex landscape shaped by regional clonal expansion, localized diversification, and global lineage dissemination. MLST profiling of 434 isolates revealed 45 sequence types (STs), including 18 novel allelic profiles, underscoring the dynamic evolution of \u003cem\u003eS. aureus\u003c/em\u003e in this understudied region. The dominance of CC152 (ST152; 26.7%) across Tanzania, Kenya, and Uganda aligns with its reported prevalence in Africa and the Middle East, suggesting a selective advantage in these settings, possibly linked to virulence adaptations or antimicrobial resistance (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In contrast, CC8 (ST8; 18.2%), a lineage associated with the pandemic USA300 clone (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), was disproportionately prevalent in Tanzania (31.6%), indicating that localized expansion was potentially driven by healthcare transmission or community spread.\u003c/p\u003e\u003cp\u003eGeographic heterogeneity was striking. Uganda presented the highest proportion of novel STs (33.3%), including unique variants such as ST1633 (21.2%), whereas Kenya presented enrichment of CC5 (ST5/ST6) and CC30 (ST30/ST34), lineages linked to pediatric infections, and the Southwest Pacific clone (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), respectively. Statistical analyses confirmed strong country-specific structuring (*p* \u0026lt; 0.0001, χ\u0026sup2; test), with post hoc pairwise comparisons revealing significant differences between all nations (e.g., Kenya vs. Tanzania: adjusted p value\u0026thinsp;=\u0026thinsp;0.0015). This geographic partitioning likely reflects differences in antibiotic use, host immunity, or transmission dynamics, necessitating region-tailored surveillance (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe spa typing data revealed a mix of global and region-specific clones. t355, a lineage associated with both hospital and community settings (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), dominated across all countries (48.7% in Uganda, 42.4% in Tanzania), suggesting that sustained transmission was facilitated by human mobility or healthcare networks. Tanzania\u0026rsquo;s high prevalence of t1476 (42.4%)\u0026mdash;a spa type linked to biofilm formation\u0026mdash;may reflect nosocomial adaptation or antibiotic-driven selection (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Uganda\u0026rsquo;s lower spa diversity (Shannon index\u0026thinsp;=\u0026thinsp;1.89 vs. 2.41 in Tanzania) and dominance of t355 imply a more clonal population, possibly due to limited strain introduction or ecological bottlenecks.\u003c/p\u003e\u003cp\u003eNotably, rare spa types (e.g., t10599, t13194) are country specific, highlighting localized microevolution (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The significant geographic structuring (p\u0026thinsp;\u0026lt;\u0026thinsp;2.2\u0026times;10⁻\u0026sup1;⁶, Cram\u0026eacute;r\u0026rsquo;s \u003cem\u003eV\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.4) underscores the need for decentralized infection control strategies to address regionally circulating clones.\u003c/p\u003e\u003cp\u003eThe collective results underscore the presence of both widely circulating clones and localized \u003cem\u003espa\u003c/em\u003e types, which are likely shaped by regional transmission dynamics, clonal expansion events, and potential ecological or selective pressures influencing the structure of \u003cem\u003eS. aureus\u003c/em\u003e populations across East Africa.\u003c/p\u003e\u003cp\u003eOur resistome analysis revealed 94 AMR genes spanning 21 drug classes, with a core resistome dominated by \u003cem\u003eblaZ\u003c/em\u003e (β-lactamase), \u003cem\u003etet\u003c/em\u003e(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) (tetracycline efflux), and \u003cem\u003edfrG\u003c/em\u003e (trimethoprim resistance). The high prevalence of \u003cem\u003eblaZ\u003c/em\u003e (78.3%) mirrors global trends of penicillin resistance (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), whereas the unexpected abundance of \u003cem\u003etet\u003c/em\u003e(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) (14.1%)\u0026mdash;far exceeding rates in Europe or North America\u0026mdash;suggests rampant tetracycline use in East African human or veterinary medicine (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMethicillin resistance (\u003cem\u003emecA\u003c/em\u003e) was detected in 3.7% of the isolates, with country-level disparities: Tanzania had the highest prevalence (6.6%), whereas Uganda had the lowest (0.1%). SCCmec typing revealed Type V (78.1%) as the dominant variant, which is consistent with community-associated MRSA (CA-MRSA) epidemiology. However, Kenya\u0026rsquo;s co-circulation of Type III (hospital-associated) and Type IV viruses underscores complex transmission dynamics at the human\u0026ndash;animal\u0026ndash;environment interface (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Uganda\u0026rsquo;s Type VI SCCmec, a rare lineage with ccrA4-ccrB4, may represent a zoonotic or local evolutionary event (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), warranting urgent One Health investigations.\u003c/p\u003e\u003cp\u003eThis extensive list of AMR genes reflects an environment with diverse selective pressures from various antibiotics. The widespread presence of MDR determinants poses significant public health challenges, as treatment options for \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infections have become increasingly limited. The potential for horizontal gene transfer of these resistance elements across bacterial species increases the risk of AMR spreading beyond \u003cem\u003eStaphylococcus aureus\u003c/em\u003e to other pathogens (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition to antimicrobial resistance, the genomes contain numerous virulence genes that are critical for the pathogenicity of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e. The virulence gene landscape included 147 loci, with Panton-Valentine leukocidin (\u003cem\u003ePVL\u003c/em\u003e; \u003cem\u003elukF-PV\u003c/em\u003e) detected at frequencies (2.08%) higher than African averages (1.2\u0026ndash;1.5%). Its enrichment in Tanzania (3.1% vs. 1.2% in Kenya) aligns with reports linking \u003cem\u003ePVL\u003c/em\u003e to severe skin/soft-tissue infections (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Ubiquitous genes such as \u003cem\u003ecap8\u003c/em\u003e (capsule synthesis) and \u003cem\u003eicaD\u003c/em\u003e (biofilm formation) suggest conserved strategies for immune evasion and chronic infection (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eKey virulence factors included \u003cem\u003elukF-PV\u003c/em\u003e (Panton-Valentine leukocidin), \u003cem\u003ehysA\u003c/em\u003e (hyaluronidase), \u003cem\u003ecap8B\u003c/em\u003e, \u003cem\u003eicaD, hla\u003c/em\u003e (alpha-hemolysin), \u003cem\u003esspC\u003c/em\u003e (serine protease), and aur (metalloprotease). These genes contribute to a range of infections, from skin and soft tissue infections to severe conditions such as bacteremia and pneumonia (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The \u003cem\u003elukF-PV\u003c/em\u003e gene encodes Panton-Valentine Leukocidin (PVL), a potent cytotoxin that targets leukocytes and is strongly linked to community-acquired MRSA (CA-MRSA), especially in cases of necrotizing infections (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The widespread presence of \u003cem\u003elukF-PV\u003c/em\u003e in the dataset highlights the high pathogenic potential of these strains, which could lead to severe clinical outcomes. The \u003cem\u003ehysA\u003c/em\u003e gene, encoding hyaluronidase, degrades hyaluronic acid in connective tissues, aiding tissue invasion. These findings suggest that many of the strains have high potential for invasiveness, which can result in more severe infections (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Several capsular polysaccharide genes, such as \u003cem\u003ecap8B\u003c/em\u003e, \u003cem\u003ecap8M\u003c/em\u003e, \u003cem\u003ecap8E\u003c/em\u003e, and \u003cem\u003ecap8N\u003c/em\u003e, were also prevalent. The capsular polysaccharide helps protect \u003cem\u003eStaphylococcus aureus\u003c/em\u003e from phagocytosis, enabling it to evade the immune system (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The presence of these genes indicates a defense mechanism that allows bacteria to survive longer in the host.\u003c/p\u003e\u003cp\u003eOther important virulence genes include \u003cem\u003ehla\u003c/em\u003e (alpha-hemolysin) and \u003cem\u003ehlgB/hlgC\u003c/em\u003e (gamma-hemolysin subunits), which contribute to cell lysis, particularly in red blood cells and immune cells (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Alpha-hemolysin is known for its role in tissue destruction and immune evasion, which can lead to more severe clinical outcomes, further increasing the virulence of the strain (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The \u003cem\u003eicaD\u003c/em\u003e gene, which is involved in biofilm formation, is particularly concerning, as biofilms protect bacteria from both immune responses and antibiotics, contributing to persistent infections (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). This gene's presence suggests that these strains can form biofilms, making infections harder to treat and eradicate, particularly chronic wound infections or those involving medical devices (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). The prevalence of virulence genes such as \u003cem\u003elukF-PV\u003c/em\u003e (Panton-Valentine Leukocidin) and \u003cem\u003ehysA\u003c/em\u003e (hyaluronidase) aligns with studies in community-acquired MRSA (CA-MRSA) isolates from urban Tanzania and Kenya, where these genes are associated with invasive infections (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Similarly, biofilm-related genes such as \u003cem\u003eicaD\u003c/em\u003e have been highlighted in persistent infections globally, including those involving medical devices in Europe (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGeographic variations were notable: \u003cem\u003esak\u003c/em\u003e (staphylokinase) was enriched in Uganda, reflecting adaptation to local host defenses, whereas Tanzanian isolates carried more hla (α-hemolysin), a key cytotoxin. Co-occurrence analysis revealed three functional clusters: PVL-associated (lukF-PV\u0026thinsp;+\u0026thinsp;hlgCB), which is responsible for tissue necrosis (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e); biofilm-associated (\u003cem\u003eicaD\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eaur\u003c/em\u003e), which is responsible for chronic infection (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e); and tissue invasion (\u003cem\u003ehysA\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003esspC\u003c/em\u003e), which is responsible for dissemination (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Overall, the high prevalence of these virulence genes underscores the pathogenic potential of the circulating \u003cem\u003eStaphylococcus aureus\u003c/em\u003e strains, which are equipped to cause severe, hard-to-treat infections.\u003c/p\u003e\u003cp\u003eTaken together, these findings reveal a virulence landscape characterized by high conservation of key pathogenic features and emerging region-specific trends. The elevated prevalence of \u003cem\u003elukF-PV\u003c/em\u003e in Tanzanian isolates, surpassing previously reported African averages (typically 0.8\u0026ndash;1.5%), may signal the expansion of hypervirulent PVL-positive lineages. The widespread detection of the complete \u003cem\u003ecap8\u003c/em\u003e operon suggests that preserved capsule biosynthesis functions across the region. The convergence of virulence and geographic distribution underscores the importance of sustained genomic surveillance and molecular epidemiology to inform control strategies and clinical management of \u003cem\u003eS. aureus\u003c/em\u003e infections in East African healthcare settings.\u003c/p\u003e\u003cp\u003ePlasmid diversity clearly differed across regions, with Tanzanian isolates exhibiting the most complex replicon networks (mean\u0026thinsp;=\u0026thinsp;4.2 plasmids per isolate). High-prevalence plasmids such as rep5a_1 (pN315) and rep16_3 (pSaa6159) are commonly associated with the dissemination of key resistance genes, including \u003cem\u003eblaZ\u003c/em\u003e and \u003cem\u003emecA\u003c/em\u003e (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). In contrast, Ugandan isolates presented lower plasmid diversity (mean\u0026thinsp;=\u0026thinsp;2.1 plasmids per isolate), suggesting possible ecological or selective constraints limiting horizontal gene transfer.\u003c/p\u003e\u003cp\u003eThese findings indicate that while a core set of plasmid replicons is conserved across the region, local environmental pressures such as antibiotic usage patterns, host population dynamics, or barriers to plasmid mobility shape distinct regional plasmid profiles in \u003cem\u003eS. aureus\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eProphage analysis revealed 934 integrated sequences, with the vast majority (94.1%) belonging to the Siphoviridae family. Notably, the recurrent detection of NODE_8 in Tanzanian isolates suggests that it is a potential marker of local clonal expansion. Despite this, the lack of clear geographic structuring within the phage communities\u0026rsquo; points to cross-border transmission, likely driven by human movement.\u003c/p\u003e\u003cp\u003eOur analysis underscores the predominance of Siphoviridae phages in East African \u003cem\u003eS. aureus\u003c/em\u003e populations. These phages exhibit stable integration patterns and a homogeneous community structure across regions, even amid variation in phage richness. These findings have important implications for understanding phage‒host interactions in clinical settings, particularly within hospitals, where such dynamics may influence both transmission and treatment outcomes.\u003c/p\u003e\u003cp\u003eInsertion sequences (ISs), particularly IS1182 (present in 100% of isolates) and IS21, play major roles in driving genomic plasticity among \u003cem\u003eS. aureus\u003c/em\u003e strains. Some Tanzanian isolates harbored up to 793 IS copies, with IS-rich genomes (e.g., ERR1764902, ANI\u0026thinsp;=\u0026thinsp;0.963 vs. reference) acting as potential hotspots for recombination and resistance gene acquisition. These patterns likely reflect localized antimicrobial pressures and distinct transmission dynamics, highlighting the ongoing evolution of \u003cem\u003eS. aureus\u003c/em\u003e in East Africa.\u003c/p\u003e\u003cp\u003eOur analysis revealed a highly dynamic and regionally diverse mobilome, marked by the ubiquitous presence of core IS elements such as IS1182 and IS21. These elements likely facilitate the spread of resistance and virulence genes. The observed regional variations in IS family abundance and genomic network complexity emphasize the influence of local epidemiological and selective forces in shaping the genomic adaptability of \u003cem\u003eS. aureus\u003c/em\u003e across East Africa.\u003c/p\u003e\u003cp\u003eOur analysis of 114 MRSA genomes from East Africa revealed striking geographic disparities in SCC\u003cem\u003emec\u003c/em\u003e distribution, with type V (78.1%) emerging as the predominant variant. This lineage was nearly fixed in Tanzania (91.3%), where subtype Vc (89.0%), characterized by ccrC1, multiple IS431 elements, and intact mecA-mecR1 complexes, dominated. The genetic architecture of these cassettes aligns with global reports of community-associated MRSA (CA-MRSA), which typically carry smaller, more mobile SCC\u003cem\u003emec\u003c/em\u003e elements (e.g., Type IV/V) than hospital-associated (HA-MRSA) variants do (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe near-uniformity of Type V in Tanzania suggests community-driven transmission. The high prevalence of CA-MRSA signatures (e.g., ccrC1, frequent IS elements) points to sustained spread outside healthcare settings, potentially facilitated by antibiotic misuse in outpatient or agricultural contexts (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). This finding also suggests limited healthcare-associated pressure. The absence of HA-MRSA-associated types (e.g., Type III) may reflect differences in hospital infection control or antibiotic stewardship compared with Kenya. It finally suggests clonal expansion: the predominance of Vc implies a successful local clone, possibly with increased fitness in Tanzanian populations (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eKenya\u0026rsquo;s complex SCC\u003cem\u003emec\u003c/em\u003e ecology suggests co-circulation of HA-MRSA and CA-MRSA. In contrast to Tanzania, Kenyan genomes exhibit a tripartite distribution of SCC\u003cem\u003emec\u003c/em\u003e types: type III (28.0%), type IV (28.0%), and type V (28.0%). This diversity signals overlapping reservoirs of MRSA transmission; Type III (ccrA3-ccrB3, mecI-mecR1), a hallmark of HA-MRSA, is often linked to multidrug resistance and nosocomial outbreaks (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Its prevalence in Kenya suggests active hospital transmission, potentially exacerbated by gaps in infection control. Type IV (ccrA2-ccrB2, truncated mecR1) is typically associated with CA-MRSA, but its coexistence with Type III implies bidirectional spillover between community and healthcare settings (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Type V strains are similar to Tanzanian strains but less dominant, indicating competing selective pressures or later introduction. Pairwise comparisons confirmed Kenya\u0026rsquo;s distinct SCC\u003cem\u003emec\u003c/em\u003e profile versus Tanzania (p* = 0.0005) and Uganda (p* = 0.023), underscoring its role as an epidemiological crossroads for MRSA in the region.\u003c/p\u003e\u003cp\u003eUganda\u0026rsquo;s Novel Type VI suggests evidence of zoonotic or localized evolution. Uganda\u0026rsquo;s MRSA population stood out for the detection of SCC\u003cem\u003emec\u003c/em\u003e Type VI (22.2%), a rare lineage featuring the ccrA4-ccrB4 complex. This variant has been sporadically reported in livestock-associated MRSA (LA-MRSA) in Europe (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), raising questions about its origins. One hypothesis is zoonotic spillover: The ccrA4-ccrB4 combination has been linked to animal reservoirs (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Uganda\u0026rsquo;s agrarian economy and high human‒livestock contact could facilitate such transmission. This could be a result of local adaptation. The high betweenness centrality of ccrA4-ccrB4 in the network analysis suggested that it may act as a genetic bridge between SCC\u003cem\u003emec\u003c/em\u003e types, possibly enabling novel recombinants (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). The presence of a sampling bias in Uganda (n\u0026thinsp;=\u0026thinsp;9) warrants caution, but the absence of Type VI bias in Tanzania/Kenya hints at Uganda-specific selection pressures.\u003c/p\u003e\u003cp\u003eSCCmec analysis revealed genetic signatures and mechanistic insights; Type III (Kenya/Uganda) strains carried ccrA3-ccrB3 and intact mecI-mecR1, which is consistent with HA-MRSA\u0026rsquo;s stable, multidrug-resistant cassettes (Ito et al., 2012). Type IV (Kenya) featured ccrA2-ccrB2 with truncated mecR1, a common CA-MRSA adaptation favoring mobility (Shore et al., 2011). Type V bacteria (Tanzania) are enriched with IS431, which may promote SCC\u003cem\u003emec\u003c/em\u003e stabilization or excision (Noto \u0026amp; Archer, 2006). Network analysis highlighted ccrC1 as a central hub in Type V strains, reinforcing its role in CA-MRSA success, whereas mecA-mecR1 universality (100%) confirmed its non-redundant role in resistance.\u003c/p\u003e\u003cp\u003eIn Tanzania, CA-MRSA (Type V) dominance calls for community-focused interventions, including antibiotic stewardship in outpatient settings and PVL toxin surveillance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). In Kenya, the co-circulation of HA-MRSA and CA-MRSA demands integrated hospital‒community surveillance, especially for Type III multidrug resistance. In Uganda, the emergence of Type VI strains warrants one health investigation to assess zoonotic links and prevent LA-MRSA dissemination.\u003c/p\u003e\u003cp\u003eThis study reveals three distinct MRSA epidemiological landscapes in East Africa: Tanzania\u0026rsquo;s community-driven Type V epidemic, Kenya\u0026rsquo;s complex HA/CA-MRSA interplay and Uganda\u0026rsquo;s potentially zoonotic Type VI. These findings mandate tailored control strategies, emphasizing Community antibiotic stewardship in Tanzania, Hospital infection control in Kenya and One Health surveillance in Uganda. Future work should expand genomic surveillance to track SCC\u003cem\u003emec\u003c/em\u003e evolution and emerging threats in the region.\u003c/p\u003e\u003cp\u003eComparative genomic analysis revealed substantial diversity among East African \u003cem\u003eS. aureus\u003c/em\u003e isolates, with dRep clustering identifying five primary genomic clusters. The largest cluster, Cluster 3, contained 480 genomes further divided into seven secondary sub-clusters, indicating a dominant circulating lineage with considerable microdiversity. This extensive sub-clustering suggests ongoing evolutionary diversification within this successful lineage, likely driven by localized selection pressures such as antibiotic use patterns or host immune factors. The smaller clusters (Clusters 1, 2, 4, and 5) represented less prevalent or potentially recently introduced lineages, with some showing country-specific distributions that may reflect distinct transmission networks or ecological niches.\u003c/p\u003e\u003cp\u003eThe average nucleotide identity (ANI) values confirmed close relationships among most isolates, with values exceeding 0.999 for the majority of comparisons against the reference genome. However, several outlier strains presented significantly lower ANI values (as low as 0.963), indicating substantial genomic divergence that may represent distinct subpopulations or the accumulation of extensive horizontal gene transfer events. These divergent strains, while rare, underscore the genomic plasticity of \u003cem\u003eS. aureus\u003c/em\u003e in the region and may represent important reservoirs of novel genetic elements (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePangenome analysis yielded striking findings regarding the genetic architecture of East African \u003cem\u003eS. aureus\u003c/em\u003e. Across 496 genomes, only five core genes were identified, representing an exceptionally small conserved genomic backbone. This minimal core was complemented by a vast accessory genome, with 68,759 cloud genes accounting for 95.5% of the total gene content. This extreme pangenome structure, characterized by a tiny core and enormous accessory genome, demonstrates the remarkable genomic flexibility of \u003cem\u003eS. aureus\u003c/em\u003e in this region. The cloud genes, present in fewer than 15% of strains, likely encode adaptive functions that facilitate niche specialization and rapid response to selective pressures.\u003c/p\u003e\u003cp\u003eFurther analysis of 110 closely related genomes revealed similar patterns, with the number of core genes increasing to 1,851 but still representing only a small fraction (2.9%) of the total gene content. The persistence of this pattern even within a more homogeneous subset suggests that genomic diversity is maintained at multiple phylogenetic levels. The shell genes (1,429 in the subset analysis) may represent an intermediate category of genes that provide conditional advantages under certain environmental or host conditions.\u003c/p\u003e\u003cp\u003eThe observed pangenome structure has important biological implications. The minimal core genome likely contains essential housekeeping functions, whereas the vast accessory genome enables rapid adaptation to diverse challenges (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). This genomic architecture facilitates the emergence of regionally successful clones through the acquisition of specific combinations of accessory genes. For example, the predominance of CC152 in Tanzania may reflect its accumulation of particular virulence or resistance determinants from the accessory gene pool. Similarly, the presence of divergent strains may result from unique combinations of horizontally acquired elements.\u003c/p\u003e\u003cp\u003eThe extreme diversity of the accessory genome, particularly that of cloud genes, likely contributes to several clinically important phenomena. First, it enables rapid adaptation to antibiotic pressure through the acquisition of resistance determinants. Second, it permits fine-tuning of virulence properties for different host environments. Third, it complicates molecular epidemiology efforts by creating a constantly shifting genetic background against which outbreak strains must be identified (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese findings have important implications for public health interventions. The minimal core genome presents challenges for diagnostic test development, as few universal targets exist. Vaccine development must involve both conserved core antigens and highly variable surface proteins encoded in the accessory genome. Antimicrobial resistance surveillance requires whole-genome approaches to capture the diversity of resistance determinants distributed throughout the accessory genome.\u003c/p\u003e\u003cp\u003eThis study has several limitations that should be acknowledged. Hospital-based sampling may underrepresent community-associated strains. The functional consequences of most accessory genes remain uncharacterized. Longitudinal sampling would help determine the stability of the observed pangenome structure over time.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eOne notable limitation of this study is the uneven distribution of \u003cem\u003eS. aureus\u003c/em\u003e isolates across the three countries. The variation in sample numbers may limit the representativeness of the findings, particularly when comparing regional genomic patterns or drawing conclusions about country-specific evolutionary dynamics. This imbalance could bias interpretations of clonal diversity, antimicrobial resistance profiles, and mobile genetic element distributions. Future studies with more uniform and expanded sampling across all regions will be critical to validate and generalize these findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis comprehensive genomic analysis revealed that \u003cem\u003eStaphylococcus aureus\u003c/em\u003e in East Africa is not a uniform population but rather a collection of regionally distinct ecologies shaped by clonal expansion, horizontal gene transfer, and localized selective pressures. Dominant clones such as CC152 (ST152) spread across countries, whereas unique variants such as Uganda\u0026rsquo;s rare SCCmec Type VI suggest potential zoonotic reservoirs. Tanzania\u0026rsquo;s near-fixation of community-associated MRSA (Type V) and Kenya\u0026rsquo;s co-circulation of hospital- and community-associated MRSA reflect varying transmission dynamics and antibiotic pressures. The \u0026ldquo;minimal core\u0026thinsp;+\u0026thinsp;hypervariable accessory\u0026rdquo; genome structure of this species, with only five conserved core genes and over 68,000 accessory genes, underscores its remarkable adaptability and complicates diagnostic methods and vaccine development. The high prevalence of mobile genetic elements, virulence factors such as PVL, and biofilm-associated genes signals a persistent risk of severe infection and resistance spread. These findings highlight the urgent need for country-specific surveillance, targeted interventions, and One Health approaches. Future work must focus on characterizing novel elements, monitoring evolutionary trends, and integrating human-animal-environment data to inform public health responses. This study provides both a warning about \u003cem\u003eS. aureus\u003c/em\u003e adaptability and a foundation for developing regionally tailored, evidence-based strategies to combat antimicrobial resistance.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAMR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAntimicrobial Resistance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMGEs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMobile Genetic Elements\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWGS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhole Genome Sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWGSs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhole Genome Sequences\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSRA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSequence Read Archive\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMLST\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMulti Locus Sequence Typing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eST\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSequence Type\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eClonal Complex\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSCCmec\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStaphylococcal Cassette Chromsome mec\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMRSA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMethicillin Resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNCBI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Center of Biotechnology Information\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eANI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAverage Nucleotide Identity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval\u003c/h2\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe WGS data analyzed for this study can be found in the National Center for Biotechnology Information https://www.ncbi.nlm.nih.gov/ under the BioProject database by searching for project accession numbers PRJEB40863, PRJEB23611, PRJEB15413, PRJEB75012 and PRJEB71932. The analysis for this study is publicly available at https://github.com/GeoffreyOlweny/staphylococcus-aureus-east-africa-genomics/tree/master\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the Cambridge-Africa ALBORADA Research Fund through an MRSA project at Makerere University College of Health Sciences entitled \u0026ldquo;Disentangling the population structure of MRSA in an urban low-income setting\u0026rdquo;.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eGO:\u0026nbsp;Conceptualization,\u0026nbsp;Formal Analysis, Methodology, Resources, Writing \u0026ndash; original Draft.\u003c/p\u003e\n\u003cp\u003eGM: Conceptualization, Methodology, Supervision,\u0026nbsp;Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAK and BRK: Conceptualization, Methodology, Supervision,\u0026nbsp;Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eDPK: Conceptualization, Methodology, Supervision, Resources,\u0026nbsp;Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003ch2\u003ePortions of this research were conducted with high-performance computing resources provided by\u003c/h2\u003e\n\u003cp\u003ethe African Centers of Excellence in Bioinformatics and Data Intensive Sciences (https://ace.ac.ug). Special thanks to the Department of Immunology and Molecular Biology, College of Health Sciences, Makerere University for their technical assistance and support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePiewngam P, Otto M. Staphylococcus aureus colonisation and strategies for decolonisation. Lancet Microbe [Internet]. 2024 Jun 1 [cited 2024 Oct 3];5(6):e606\u0026ndash;18. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.thelancet.com/article/S2666524724000405/fulltext\u003c/span\u003e\u003cspan address=\"http://www.thelancet.com/article/S2666524724000405/fulltext\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParlet CP, Brown MM, Horswill AR. Commensal Staphylococci Influence Staphylococcus aureus Skin Colonization and Disease. 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Toxins (Basel). 2015;7:3688\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSahibzada S, Abraham S, Coombs GW, Pang S, Hern\u0026aacute;ndez-Jover M, Jordan D et al. Transmission of highly virulent community-associated MRSA ST93 and livestock-associated MRSA ST398 between humans and pigs in Australia. Sci Rep. 2017;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi S, Skov RL, Han X, Larsen AR, Larsen J, S\u0026oslash;rum M, et al. Novel types of staphylococcal cassette chromosome mec elements identified in clonal complex 398 methicillin-resistant Staphylococcus aureus strains. Antimicrob Agents Chemother. 2011;55:3046\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBosi E, Monk JM, Aziz RK, Fondi M, Nizet V, Palsson B. Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity. Proc Natl Acad Sci U S A. 2016;113:E3801\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Staphylococcus aureus, Whole-genome sequencing, Antimicrobial resistance, AMR, MRSA, Virulence, SCCmec types, Mobile genetic elements, Comparative genomics, East Africa","lastPublishedDoi":"10.21203/rs.3.rs-6846109/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6846109/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e is a major pathogen that causes hospital- and community-acquired infections, with increasing antimicrobial resistance (AMR) posing a global threat. East Africa remains an understudied region concerning the genomic epidemiology of \u003cem\u003eS. aureus\u003c/em\u003e. This study provides a comprehensive genomic characterization of \u003cem\u003eS. aureus\u003c/em\u003e genomes from hospital settings in East Africa, focusing on AMR, virulence determinants, mobile genetic elements (MGEs), and population structure.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analyzed a total of 496 \u003cem\u003eS. aureus\u003c/em\u003e whole-genome sequences (WGSs) from Tanzania, Kenya and Uganda retrieved from the NCBI Sequence Read Archive (SRA). Bioinformatics pipelines were employed for quality control, genome assembly, annotation and comparative genomics. \u003cem\u003eIn-silico\u003c/em\u003e multi-Locus Sequence Typing (MLST), \u003cem\u003espa\u003c/em\u003e \u0026amp; SCC\u003cem\u003emec\u003c/em\u003e typing and pangenome assessment were conducted. AMR and virulence genes, as well as plasmid and prophage diversity, were identified via curated databases.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMLST analysis revealed 45 sequence types (STs), including 18 novel allelic profiles, with CC152 (ST152) being the most prevalent (26.7%). \u003cem\u003eSpa\u003c/em\u003e typing identified 67 distinct types, with t355 (24.4%) and t1476 (17.8%) dominating. SCC\u003cem\u003emec\u003c/em\u003e typing revealed Type V (78.1%) as the predominant methicillin resistance determinant, particularly in Tanzania (91.3%). AMR profiling revealed 94 resistance genes, with a high prevalence of \u003cem\u003eblaZ\u003c/em\u003e (β-lactamase), \u003cem\u003etet\u003c/em\u003e(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) (tetracycline efflux), and \u003cem\u003edfrG\u003c/em\u003e (trimethoprim resistance). Virulence gene analysis revealed 147 loci, including Panton-Valentine leukocidin (\u003cem\u003elukF-PV\u003c/em\u003e; 2.1%) and biofilm-associated genes (\u003cem\u003eicaD\u003c/em\u003e). Plasmid analysis revealed high diversity, with Tanzania exhibiting the highest replicon complexity (mean\u0026thinsp;=\u0026thinsp;4.2 plasmids/isolate). The phage sequence (n\u0026thinsp;=\u0026thinsp;934) was predominantly Siphoviridae (94.1%), with no significant geographic structuring. Pangenome analysis revealed extreme diversity, with only five core genes conserved across all the isolates and 68,759 strain-specific cloud genes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study highlights the dynamic genomic landscape of \u003cem\u003eS. aureus\u003c/em\u003e in East Africa, characterized by regional clonal expansion, extensive AMR, and diverse virulence profiles. The dominance of community-associated MRSA (Type V SCC\u003cem\u003emec\u003c/em\u003e) in Tanzania contrasts with Kenya\u0026rsquo;s co-circulation of hospital and community strains, whereas Uganda harbors rare SCC\u003cem\u003emec\u003c/em\u003e Type VI strains, suggesting potential zoonotic origins. These findings emphasize the need for region-specific surveillance and tailored AMR stewardship programs to mitigate the spread of resistant and virulent \u003cem\u003eS. aureus\u003c/em\u003e clones in East Africa.\u003c/p\u003e","manuscriptTitle":"Unraveling the Genomic Landscape of Staphylococcus aureus in Hospital Settings of East Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 18:57:01","doi":"10.21203/rs.3.rs-6846109/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89c591e2-8ad7-4daa-bed3-3c0d09c81411","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T05:54:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-07 18:57:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6846109","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6846109","identity":"rs-6846109","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}