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Comprehensive blueprint of Salmonella genomic plasticity identifies hotspots for pathogenicity genes [1]

['Simran Krishnakant Kushwaha', 'Department Of Biological Sciences', 'Birla Institute Of Technology', 'Science', 'Bits', 'Pilani', 'Rajasthan', 'School Of Biological Sciences', 'University Of Southampton', 'Southampton']

Date: 2024-08

Understanding the dynamic evolution of Salmonella is vital for effective bacterial infection management. This study explores the role of the flexible genome, organised in regions of genomic plasticity (RGP), in shaping the pathogenicity of Salmonella lineages. Through comprehensive genomic analysis of 12,244 Salmonella spp. genomes covering 2 species, 6 subspecies, and 46 serovars, we uncover distinct integration patterns of pathogenicity-related gene clusters into RGP, challenging traditional views of gene distribution. These RGP exhibit distinct preferences for specific genomic spots, and the presence or absence of such spots across Salmonella lineages profoundly shapes strain pathogenicity. RGP preferences are guided by conserved flanking genes surrounding integration spots, implicating their involvement in regulatory networks and functional synergies with integrated gene clusters. Additionally, we emphasise the multifaceted contributions of plasmids and prophages to the pathogenicity of diverse Salmonella lineages. Overall, this study provides a comprehensive blueprint of the pathogenicity potential of Salmonella. This unique insight identifies genomic spots in nonpathogenic lineages that hold the potential for harbouring pathogenicity genes, providing a foundation for predicting future adaptations and developing targeted strategies against emerging human pathogenic strains.

Funding: This work was supported by British Council Newton Bhabha Fund [grant number 654669088] to S.K.K and Wessex Medical Trust [grant number AB03] to F.L.N. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability: All original code has been deposited at GitHub https://simrankushwaha.github.io/Genome-Plasticity-in-Salmonella/ and Zenodo, https://doi.org/10.5281/zenodo.12667378 . An interactive visualisation of the gene content of the spots is also available at GitHub. The metadata of the isolates, phylogenetic analysis and the country of isolation can be visualised on Microreact, https://microreact.org/project/pRbGPKfTYKTHJfDipWbZze-project1-genomic-plasticity-is-a-blueprint-of-diversity-in-salmonella-lineages and https://microreact.org/project/rxxw1HJL7CGqSRqifU6q3W-project2-genomic-plasticity-is-a-blueprint-of-diversity-in-salmonella-lineages .

Copyright: © 2024 Krishnakant Kushwaha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

To gain further understanding of the structural and functional features of RGP in Salmonella, we carried out a comprehensive analysis of 12,244 Salmonella spp. genomes. Our findings revealed that gene clusters associated with virulence, stress resistance, antibiotic resistance, and anti-phage defence exhibit specific preferences for RGP integrated into distinct genomic spots. These preferences seem to be influenced by neighbouring genes that likely share regulatory and functional coordination. The irregular distribution of these genomic spots across diverse Salmonella lineages establishes a blueprint for pathogenicity and survival strategies. Deciphering the complex interplay between pathogenicity-related gene clusters and RGP not only improves our understanding of Salmonella evolution, but also enables us to uncover novel pathogenicity genes, anticipate future adaptations, and identify targets for disease prevention, management, and therapeutic interventions.

Salmonella offers an excellent model for studying these variable genomic features. Its diverse spectrum of species, subspecies, and serovars showcases the inherent flexibility in its genome, a pivotal factor in shaping both the phylogeny and pathogenic potential of Salmonella [ 10 – 12 ]. Consequently, exploring the genomic plasticity of Salmonella becomes a key avenue for gaining insights into its evolution as a pathogen.

The interplay between conserved and variable features in bacterial genomes plays a crucial role in shaping the diversity and adaptability of different species [ 1 ]. Within a species, the core genome, comprising genes universally present, handles essential cellular functions. In contrast, the flexible genome consists of genes that vary between individual strains, allowing bacteria to adapt to specific environments and acquire pathogenic traits [ 2 – 4 ]. These variable genes are often organised into regions of genomic plasticity (RGP) [ 5 ], which are regions of a genome structurally absent in other related genomes and typically associated with frequent rearrangements, such as those mediated by mobile genetic elements (MGEs). These elements serve as potent facilitators for acquiring genes related to virulence, antibiotic and stress resistance, and anti-phage immunity, contributing to the dynamic evolution of the bacterial genome [ 6 – 9 ]. Exploring this genomic plasticity is crucial for understanding bacterial evolution, phylogeny, and pathogenic potential.

Results

Antibiotic resistance determinants are primarily located in the chromosome Antibiotic resistance (ABR) determinants were predominantly found in the chromosome (84%) (S7 and S10 Tables). This high prevalence is primarily driven by widespread resistance to aminocoumarin, aminoglycoside, carbapenem, cephalosporin, cephamycin, fluoroquinolone, glycylcycline, macrolide, monobactam, nitroimidazole, penam, penem, peptide, phenicol, rifamycin, tetracycline, and triclosan antibiotics across all Salmonella subspecies and serovars (S1 Fig). These resistances are largely attributed to mutations in conserved genes, such as cpr, cpxA, and hns, which regulate multidrug efflux pump expression [35–38] (S10 Table). Additionally, 16% of ABR determinants were located in plasmid contigs. These plasmids serve as the primary reservoir for resistance against β-lactam, diaminopyrimidine, gentamycin, kanamycin, streptomycin, sulphonamide, and trimethoprim antibiotics (S1 Fig). Among the plasmid schemes, all carried ABR, with IncN and the pBSSB1-family being most frequently associated with these determinants (Fig 2D and S9 Table). Less than 1% of ABR determinants were associated with prophages, mostly from the Eganvirus, Punavirus, Peduovirus, and Lambdavirus genera (Fig 2C). Importantly, resistance to colistin, an antibiotic of last resort, was detected in 2.4% (288) of strains belonging to S. enterica subsp. enterica, with a predominant occurrence in serovars Saintpaul, Cholerasuis, and Paratyphi B (S1 Fig). In summary, our results indicate that chromosomal mutations in various Salmonella genes confer ABR, reinforce the role of plasmids in influencing ABR patterns, highlighting plasmids of all schemes are drivers of ABR dissemination in Salmonella.

Stress resistance genes are primarily located on plasmids and chromosomal regions The presence of acid, biocide, and heavy metal resistance genes is closely linked to the maintenance and spread of antimicrobial resistance [39–41]. Interestingly, we observed that 2 multidrug-resistant serovars, Indiana and Rissen, exhibit the highest prevalence of qac genes, which are small multidrug resistance efflux proteins associated with increased tolerance to quaternary ammonium compounds (QACs) and other cationic biocides [42] (S1 Fig). qac genes are generally found in MGEs, particularly plasmids; here, they were found on IncA/C plasmids in over 25% of the cases (S9 Table). Curiously, most strains in our dataset do not carry any heat-resistant genes (hde, hsp, kef, psi, shs, trx, and yfd), except for a small percentage (<20%) of strains from serovars Montevideo, Senftenberg, and Worthington, and the majority of these genes are located on IncA/C plasmids. On the other hand, Salmonella strains commonly exhibited resistance to heavy metals, with approximately 80% of the strains carrying genes conferring resistance to gold (S1 Fig). The only exceptions are S. enterica subsp. houtenae and S. enterica subsp. enterica serovars Typhi and Paratyphi A, which do not carry the gol cluster responsible for gold resistance. Serovars Heidelberg and Infantis show a high incidence (>95%) of arsenic resistance genes (ars), while serovars Tennessee, Rissen, Schwarzengrund, Worthington, and Senftenberg exhibit frequent (>80%) copper (pco) and silver (sil) resistance (S1 Fig and S7 Table). Genes that confer resistance to heavy metals such as gold and arsenic are predominantly located in chromosomal regions, while those associated with mercury and tellurite resistance are commonly found on plasmids (IncA/C and Shigella flexneri [43] plasmid schemes for mercury, and IncHI1 and IncHI2 for tellurite) (S1 Fig and S9 Table). Among the different plasmid schemes, Shigella flexneri (pINV virulence plasmids) and IncHI2 plasmids are those most frequently associated with stress resistance genes, but IncA/C plasmids, due to their abundance, are responsible for the movement of most stress resistance genes (Fig 2D and S9 Table). In summary, our findings reveal that genes associated with resistance to heat and heavy metals such as mercury and tellurite are primarily found on plasmids, while resistance to gold and arsenic is commonly found within chromosomal regions.

Anti-phage defence systems are more prevalent in chromosomal regions Anti-phage defence systems were found to be prevalent among Salmonella strains, with an average of 8 defence systems per strain. This is higher than the average found in Escherichia coli (6) [44] or Pseudomonas aeruginosa (7) [45] in previous studies. However, there is considerable variation in the number of defence systems carried by different subspecies and serovars. For example, serovars Typhimurium [17], Saintpaul [15], Panama [15], and Indiana [14] exhibit the highest prevalence of defence systems, while serovars Berta, Javiana, and Johannesburg have the lowest [4] (Fig 2A and S7 and S8 Tables). Among the 90 defence system subtypes identified in Salmonella strains, the most prevalent were the restriction-modification (RM) and type I-E CRISPR-Cas systems, which are present in almost all subspecies and serovars (S1 Fig). However, the CRISPR-Cas system is absent in serovars Brandenburg, Lubbock, and Worthington. We noted significant variation in the prevalence of other defence systems across the Salmonella genus (S1 Fig). Each serovar appears to have a distinct profile of defence systems, suggesting selection of the most beneficial systems in specific environments or host interactions, as previously observed for distinct E. coli phylogroups [44]. For example, in serovars Typhi and Paratyphi A, the 3HP and Druantia type III systems are highly abundant. On the other hand, in Typhimurium, we observed a predominance of the BREX type I, Mokosh type II, PARIS types I and II, and Retron II-A defence systems. Strains of Enteritidis exhibit an enrichment in CBASS type I, while Gallinarium and Pullorum frequently harbour Mokosh type I in addition to CBASS type I. Additionally, we found specific defence systems enriched in particular species and subspecies. For example, dCTP deaminase is more prevalent in S. bongori, Septu type I in S. enterica subsp. indica and salamae, and Gabija in S. enterica subsp. arizonae (S1 Fig). In general, defence systems, including the abundant RM and CRISPR-Cas systems, are more frequently found within chromosomal regions (94%, Fig 2B) compared to prophages or plasmids. However, prophages of the Aguilavirus, Elveevirus, Felsduovirus, Quadragintavirus, Uetakevirus, and Wadgaonvirus show a clear preference for carrying defence systems over other types of pathogenicity-related genes (Fig 2C), and the defence systems 3HP, AbiL, BstA, Kiwa, Retron types I-A, I-C, and VI are predominantly found on prophages (S1 and S2A Figs). Other defence systems, such as AbiQ, Bunzi, Gao_19, Lit, PifA, ppl, retron type V, SoFic, and tmn are frequently associated with plasmids. When located on plasmids, the system Gao_19 (34%) is primarily linked to IncA/C plasmids, while PifA is mostly found (34%) on IncI1 plasmids. Lit (33%), Bunzi (50%), and tmn (37%) are often identified on IncHI1 plasmids, and ppl (30%) and SoFic (31%) are mostly associated with IncF plasmids (S2B Fig and S9 Table). Interestingly, although plasmids of all types often accommodate a greater abundance of other pathogenicity-related elements (Fig 2D), it is noteworthy that the IncHI1 plasmids demonstrate a higher inclination toward carrying defence systems compared to other plasmid schemes. In summary, anti-phage defence systems are widespread in Salmonella, with a notable prevalence of the R-M and the CRISPR-Cas systems. The significant variation in defence system repertoire across Salmonella species and serovars observed here highlights the significance of these systems in the evolution and adaptation of this pathogenic bacterium.

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[1] Url: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002746

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