Phage‐mediated horizontal transfer of Salmonella enterica virulence genes with regulatory feedback from the host

Tianjing She; Demeng Tan; Jose Luis Balcazar; Ville‐Petri Friman; Danrui Wang; Dong Zhu; Mao Ye; Mingming Sun; Shujian Yuan; Feng Hu

doi:10.1002/imt2.70042

. 2025 May 20;4(4):e70042. doi: 10.1002/imt2.70042

Phage‐mediated horizontal transfer of Salmonella enterica virulence genes with regulatory feedback from the host

Tianjing She ¹, Demeng Tan ², Jose Luis Balcazar ^3,^✉, Ville‐Petri Friman ⁴, Danrui Wang ¹, Dong Zhu ⁵, Mao Ye ⁶, Mingming Sun ^1,^✉, Shujian Yuan ¹, Feng Hu ¹

PMCID: PMC12371270 PMID: 40860440

Abstract

Phage‐mediated horizontal transfer of virulence genes can enhance the transmission and pathogenicity of Salmonella enterica (S. enterica), a process potentially regulated by its regulatory mechanisms. In this study, we explored the global dynamics of phage‐mediated horizontal transfer in S. enterica and investigated the role of its regulatory mechanisms in transduction. A total of 5178 viral sequences encoding 12 S. enterica virulence genes were retrieved from the Integrated Microbial Genomes and Virome (IMG/VR) database, alongside 466,136 S. enterica genomes from EnteroBase. Virulence genes, including iacP (acyl carrier protein), mgtB (P‐type Mg²⁺ transporter), misL (autotransporter porin), and fliC (flagellar filament protein), were widely distributed in phages and S. enterica across North America, Europe, and Asia. Phylogenetic analysis revealed close genetic affinity between phage‐ and bacterial‐encoded virulence genes, suggesting shared ancestry and historical horizontal gene transfer events. The global regulator carbon storage regulator A (csrA) was highly conserved and ubiquitous in S. enterica. Overexpression of csrA inhibited prophage cyclization and release by upregulating the prophage cI repressor during horizontal gene transfer. Overall, these findings enhance our understanding of phage‐mediated horizontal transfer of virulence genes, explore new areas of bacterial regulators that inhibit gene exchange and evolution by affecting phage life cycles, and offer a novel approach to controlling the transmission of phage‐mediated S. enterica virulence genes.

Keywords: horizontal gene transfer, phages, regulators, Salmonella enterica, virulence genes

In this study, we found evidence of phage‐mediated horizontal transfer of virulence genes in Salmonella enterica in global habitats from a molecular evolutionary perspective. Notably, csrA, a global regulator in Salmonella enterica, curbed horizontal gene transfer by inhibiting the cyclization and release of prophage. This is a novel finding in phage–bacteria interactions that provides new ideas for controlling pathogenic bacteria in the environment.

graphic file with name IMT2-4-e70042-g001.jpg

Highlights

Phages carrying Salmonella enterica virulence genes are globally distributed across multiple habitats.
Horizontal transfer of virulence genes occurs between phages and Salmonella enterica.
The global regulator csrA is ubiquitous and genetically conserved in Salmonella enterica.
csrA limits horizontal transfer of Salmonella virulence genes by inhibiting prophage cyclization and release in Salmonella enterica.

INTRODUCTION

Infections caused by Salmonella enterica (S. enterica) can lead to typhoid and paratyphoid fevers, diarrheal illness, and, less commonly, blood infection. As a major cause of foodborne illness, S. enterica infections emerged from industrialized poultry farming in the 1980s in North America, South America, and Europe, and subsequently spread to various habitats around the world [1]. Decades after the initial salmonellosis outbreak, an estimated 17.8 million cases of typhoid fever caused by the pathogen occur annually in low‐ and middle‐income countries, making these infections an ongoing global health issue [2, 3]. Centralized sourcing and international trade of poultry breeding have primarily contributed to the global spread of Salmonella Enteritidis [1]. However, the global distribution pattern of S. enterica across different habitats remains largely unexplored.

The presence of virulence factors is an important prerequisite for pathogenicity, meaning that the outcome of S. enterica infection largely depends on whether the host bacterium carries these virulence factors. “Classical” virulence factors of S. enterica include virulence plasmids, toxins, pili, and flagella. For instance, the P‐type Mg²⁺ transporter encoded by mgtB helps S. enterica survive in phagocytes [4, 5], whereas the flagellar protein encoded by fliC is associated with bacterial motility [6, 7]. In addition, the autotransporter family porin encoded by misL and the acyl carrier protein encoded by iacP play roles in bacterial colonization and invasion, respectively [8, 9]. Horizontal gene transfer (HGT) is a process in which genetic material is transferred between different organisms, rather than being passed down vertically from parent to offspring. Phage‐mediated HGT involves the packaging of DNA fragments from host bacteria into phage particles, followed by the transfer of these genes to other bacteria during infection. Phage‐mediated horizontal transfer of virulence genes is a crucial mechanism through which S. enterica acquires pathogenicity and adapts to ever‐changing environments [10, 11, 12]. For example, prophages can confer virulence to Corynebacterium diphtheriae and Corynebacterium ulcerans after lysogenization [13]. In S. enterica, phage P22 undergoes lateral transduction (LT), which facilitates high‐frequency transfer of host DNA and contributes to host evolution [14]. Patterns of virulence genes located close to phage integration sites (attB) and in the direction of packaging suggest that their transfer may be facilitated via LT [15]. However, there is currently no direct evidence of phage‐mediated horizontal transfer of S. enterica virulence genes.

Pathogenic bacteria, including S. enterica, encode a variety of regulators to modulate the expression of virulence genes. Major regulators include the carbon storage regulator A (csrA) that regulates S. enterica flagella expression, biofilm formation, Salmonella Pathogenicity Island 1 (SPI‐1) virulence genes, and carbon‐related genes [16, 17, 18, 19]; two‐component system BarA/SirA that affects SPI‐1 gene expression [20]; the histone‐like nucleoid structuring protein (H‐NS) that silences the expression of exogenous genes [21]; and the CRISPR‐Cas system that plays an important role in blocking the acquisition of exogenous genetic material and defending against phages [22]. As a result, these regulators are likely to influence the transmission of virulence genes, either promoting or repressing the expression of S. enterica virulence genes upon entry into host cells [23, 24]. However, the role of these regulators in HGT has not yet been explored, which is a crucial step in understanding the complex influence of phages on the horizontal transfer of virulence genes and in identifying potential strategies for its control.

In this study, we hypothesize that S. enterica virulence genes can be encoded and transferred by phages, which are further regulated by S. enterica regulatory genes. We obtained S. enterica genomes from Warwick's EnteroBase and phage genomes encoding S. enterica virulence genes such as fliC, iacP, mgtB, and misL, among others, from the Integrated Microbial Genomes and Virome (IMG/VR) database (version 4). We investigated the geographical distribution of virulence genes in S. enterica and phages, analyzed the functional mechanisms and phylogenetic relationships of virulence factors and regulators, and experimentally assessed the effect of the S. enterica regulator csrA on transduction. The results may provide new scientific insights into the global scope of phage‐mediated horizontal transfer of virulence genes and offer strong support for strategies aimed at controlling the horizontal transfer of S. enterica virulence genes.

RESULTS

Geographic distribution of S. enterica virulence genes carrying bacteria and phages

To obtain a comprehensive picture of S. enterica virulence genes, we investigated the distribution of both S. enterica strains and phages that carry these virulence genes. A total of 466,136 S. enterica genomes were obtained from EnteroBase, of which 122,680 genomes (26.3%) had habitat information and belonged to 548 serotypes across 66 countries (Table S1). The S. enterica strains were primarily distributed across the Americas, spanning from 60° W to 150° W, followed by Europe (0°–30° E) and Asia (90° E–120° E) (Figure 1A). North America had the highest abundance of S. enterica (99,492 strains), followed by Asia (7344 strains) and Europe (7244 strains). Specifically, the countries with the highest abundance of S. enterica were the US (86,861 strains) and Canada (10,570 strains), followed by the UK (4451 strains), China (3925 strains), and Chile (2341 strains). The main habitats of S. enterica included food (39%), human and animal feces (32%), animals (15.2%), water (7.7%), sediments (0.8%), and soil (0.4%) (Figure 1B,C).

Global geographical distribution of *S. enterica*. (A) Patterns of global variation in *S. enterica* abundance with longitude. The x‐axis represents longitude and corresponds exactly to the longitude of the map. The y‐axis represents the abundance of *Salmonella* carrying virulence genes. The colors represent habitats. (B) Global distribution of *S. enterica* in different habitats. The size of the pie chart represents the abundance of *S. enterica*. And the colors represent habitats. (C) The abundance of four virulence genes (*fli*C, *iac*P, *mgt*B, and *mis*L) of *S. enterica* in North America, Africa, Oceania, Asia, Europe, and South America of each habitat. The colors represent habitats. Each bar represents the abundance of a specific virulence gene, with segments representing habitats.

Similarly, we investigated the geographic distribution of phages carrying S. enterica virulence genes. A total of 5178 S. enterica virulence genes (fliC, iacP, mgtB/C, misL, sitC, etc.) encoded‐phages were obtained from the IMG/VR database, which were primarily identified as Caudoviricetes (Tables S2 and S3). Of these phages, 45.73% of the phages were temperate, 33.49% were virulent, and the remaining 20.77% had uncertain lifestyles (Table S4). North America (60° W−150° W), Europe (0°−30° E), and Asia (90° E−120° E) were the top three regions reporting the highest abundance of phages carrying S. enterica virulence genes (Figure 2A). Specifically, the countries with the highest abundance of phages carrying S. enterica virulence genes were the US (1401), followed by Sweden (126), Canada (92), Denmark (84), and China (80). These phages were primarily found in six habitats, including mammalian digestive system (457), marine environments (362), nest (236), freshwater (199), soil (175), and roots (81) (Figure 2B,C). Additionally, we explored the kilopercentage ratios of uncultivated virus genomes (UViGs) carrying S. enterica virulence genes in the IMG/VR database across all habitats. Notably, the human digestive system had a relatively high prevalence of phages carrying S. enterica virulence genes, at 0.56‰ (Figure S1 and Table S5). Overall, the results demonstrate the widespread distribution of virulence genes in both S. enterica and its phages across diverse habitats worldwide, highlighting the significant threats they pose to global human health.

Global distribution of phages encoding *S. enterica* virulence genes. (A) The pattern of global variation in phage abundance with longitude. The x‐axis represents longitude and corresponds exactly to the longitude of the map. The y‐axis represents the abundance of phages carrying virulence genes. The colors represent habitats. (B) Global distribution of phages encoding *S. enterica* virulence genes in different habitats. The size of the pie chart represents the abundance of phages. And the colors represent habitats. (C) Abundance of the four genes of interest (*fli*C, *iac*P, *mgt*B, and *mis*L) in the phages in North America, Africa, Oceania, Asia, Europe, and South America of each habitat. The colors represent habitats. Each bar represents the abundance of a specific virulence gene, with segments representing habitats.

Phage‐mediated horizontal transfer of virulence genes

To explore the evolutionary history of phages carrying virulence genes and gene exchange between phages and bacteria, we analyzed phage‐bacteria interactions, the phylogenies of virulence genes encoded by both phages and S. enterica, and the predicted protein quaternary structures of phage‐encoded virulence genes. Determining the phage‐host linkage is essential for confirming the occurrence of phage‐mediated HGT. A total of 52 phages were linked to 89,871 host S. enterica strains, covering a wide range of S. enterica serotypes, for example, Typhimurium, Infantis, Enteritidis, Newport, Heidelberg, Anatum, Derby, Schwarzengrund, Montevideo, and Typhi (Figure 3A, Tables S6 and S7). This suggests a high likelihood of HGT occurring between phages carrying virulence genes and several S. enterica serotypes. Notably, S. Typhimurium and S. Infantis were the most frequently targeted serotypes by phages, with 9204 S. Typhimurium and 7746 S. Infantis strains linked to 42 and 39 phages, respectively. Together, phages carrying virulence genes can infect a wide range of S. enterica serotypes, highlighting their crucial role in facilitating the transfer of virulence genes across different S. enterica serotypes.

Phage host prediction and phylogenetic analysis of virulence genes. (A) Linkage between phages carrying virulence genes with *S. enterica,* as well as other bacteria. Left: the *S. enterica* serotypes that can match to phages carrying virulence genes, and phages match to host bacteria. Middle: the phages that can match to host. Right: bacterial hosts at the genus level, and phages only match to *S. enterica*. (B) Phylogenetic analysis of virulence gene *fli*C encoded by phages and *S. enterica*. The branch length represents the tree scale. The green branch represented a phage‐carried *fli*C, and the blue branch represented a *Salmonella*‐carried *fli*C. The color of the leaves name represents the habitat where the *fli*C is located. The bar next to the phylogenetic tree annotates the geographic location where *fli*C is located. (C) Genomic structures of phage‐encoded virulence genes. Pink represents virulence genes, yellow represents viral genes assigned V flags by DRAMV, green represents other types of genes identified, and blue represents genes with unidentified functions. (D) Predicted quaternary structures of phage‐encoded virulence genes (*fli*C, *iac*P, *mgt*B, and *mis*L).

To obtain more direct evidence of HGT between phages and S. enterica, we compared the affinities of the virulence genes encoded by S. enterica and phages. Phylogenetic tree showed that the virulence genes carried by phages were genetically close to those carried by S. enterica (Figures 3B, S2 and S3). For example, the branches of phage‐encoded fliC (IMGVR_UViG_3300011660_000005 and IMGVR_UViG_3300011573_000024) and the neighboring branches of fliC encoded by S. enterica strains (GCA_006027275.1 and GCA_006490175.1) shared the same ancestor. Moreover, the identity and coverage of fliC between S. enterica strains (GCA_006027275.1 and GCA_006490175.1) and phage (IMGVR_UViG_3300011660_000005) were equal to 100%. The identity of fliC between S. enterica strains (GCA_006027275.1 and GCA_006490175.1) and phage (IMGVR_UViG_3300011573_000024) was equal to 97.76% with 100% coverage. Phylogenetic analysis of both mgtB and misL also revealed highly similar virulence gene segments (Table S8, Figures S2 and S3). The above S. enterica and phages carried homologous virulence genes with high identity values, indicating that HGT of virulence genes had occurred between phage and S. enterica.

To investigate the expression capacity of the virulence genes, we analyzed the locations of promoters and terminators and predicted the quaternary structure of fliC, iacP, mgtB, and misL, all of which play important roles in S. enterica survival and pathogenicity. These genes were found to be positioned downstream of the promoter and upstream of the terminator (Figure 3C,D and Table S9). Based on SWISS‐MODEL analysis, the Global Model Quality Estimation (GMQE) indices for fliC, iacP, mgtB, and misL were 0.80, 0.81, 0.84, and 0.88, respectively, indicating a high quality of prediction for these virulence genes carried by phages.

The effect of S. enterica regulator csrA on phage‐mediated HGT

Although virulence genes carried by viral vectors can be transmitted to different S. enterica serotypes, S. enterica has evolved different regulatory factors to mediate HGT and gene expression processes. In this study, we selected 10 regulatory factors in S. enterica based on their widespread distribution and universal regulatory functions. These factors, which were summarized from previous research, include csrA, hns, hfq, crp, barA, sirA, cas1, cas2, cas3, and cas5. The regulatory mechanisms of these factors are illustrated in Figure S4. Regulatory genes in the S. enterica genomes were identified through BLAST analysis. The most frequently detected regulator was csrA, with a total of 122,681 occurrences, followed by hns, hfq, crp, and others, across the 122,680 S. enterica genomes. Similar to the distribution of virulence gene‐carrying S. enterica, the 10 regulator factors were also most extensively found in North America, followed by Asia and Europe. Their primary habitats included food, feces, and water (Figure 4A).

Global distribution of regulators in *S. enterica*. (A) Global distribution of virulence regulators encoded by *S. enterica*. Different colors represent different regulatory genes. And the size of the pie chart represents the abundance of genes. (B) The average copy number of regulatory genes changes with 10 serotypes from left to right: S. Enteritidis, S. Anatum, S. Infantis, S. Montevideo, S. Typhimurium, S. Derby, S. Schwarzengrund, S. Kentucky, S. Heidelberg, and S. Newport. (C) Phylogenetic tree of *csr*A. The branch length represents the tree scale. The bar and number next to it annotates the count of *csr*A in each branch.

The average copy number of each regulator was also calculated for the top 10 S. enterica serotypes, including Enteritidis, Anatum, Infantis, Montevideo, Typhimurium, Derby, Schwarzengrund, Kentucky, Heidelberg, and Newport. The average copy number of the 10 regulators was generally high across the top 10 S. enterica serotypes, ranging from 0.7440 to 1.0000. The highest average copy number of the regulators was observed in S. Enteritidis, such as csrA (1.0000), barA (0.8720), sirA (0.8721), cas1 (0.8696), cas2 (0.8696), crp (0.8722), hfq (0.8722), and hns (0.8722). Notably, the average copy number of csrA was the highest, which was almost 100% in all these serotypes (Figure 4B). In addition, 99.94% of the csrA had identical amino acid sequences (Figure 4C), suggesting the gene being highly conservative in S. enterica. Together, we found that csrA was the most widespread regulatory gene in S. enterica and was highly conserved across different serotypes. Therefore, csrA was selected for further investigation to validate its role in phage‐mediated HGT processes in S. enterica.

To validate the role of csrA in phage‐mediated HGT, we performed experiments to explore the effect of csrA on prophage cyclization and the cI repressor in S. Typhimurium strain S12. The prophage PSAP2‐2‐carrying S12 was selected as the representative S. enterica strain, and the cI repressor in the PSAP2‐2 genome was shown to mediate the phage life cycle (Figure 5A). Plasmids carrying csrA were introduced into S. Typhimurium strains via electroporation (Figure 5B and Table S10). The ability of the S12::csrA‐1 and S12::csrA‐2 with WT S12 to induce the cyclization and release of the prophage PASAP2‐2 was compared. We found that the average titer of phages released by S12::csrA‐1, S12::csrA‐2, and WT were 1.18 × 10⁶, 1.39 × 10⁶, and 1.75 × 10⁶ pfu mL⁻¹, respectively. Notably, the phages released by S12::csrA‐1 and S12::csrA‐2 were significantly fewer than those released by the WT strain (p < 0.05; Figure 5C), indicating that csrA inhibited the cyclization and release of the prophage PSAP2‐2.

Experiments to validate the effect of *csr*A on phage cyclization and cI repressor. (A) The complete genomic structure of the prophage PSAP2‐2. Specific genes are indicated with arrows, with cyan for tail protein genes and gray for other genes. (B) Strains with *E. coli* plasmid PHB20TG carrying *csr*A and strain with only plasmid PHB20TG as a control were successfully constructed. (C) Phage titers of strains overexpressing *csr*A (S12::*csr*A‐1 and S12::*csr*A‐2) and wild‐type strain (WT) after induction of prophage expression. p < 0.05 indicated significant difference between strains overexpressing *csr*A and WT strain. The relationship between the expression of the cI repressor and *csr*A at a dilution gradient of 1:16 (D) and 1:32 (E), respectively. p < 0.001 indicated a significant correlation between the expression of *csr*A and that of the cI repressor. (F) Schematic representation of the main steps and results of the experiment. The green oval boxes on the left and right represent S. Typhimurium S12 carrying an empty plasmid and a plasmid containing the *csr*A gene via electroporation method, respectively. The prophage PSAP2‐2 inserted in the host DNA is determined by the expression of its own CI repressor to enter the lysis cycle. Upon induction, the CI repressor is cleaved, the *cro* gene‐expressed protein binds the RM promoter (P _RM), represses the expression of cI, opens the left and right promoters (P _L and P _R), and the prophage enters the lysis cycle. However, the overexpression of *csr*A promotes the expression of cI repressor, which activates the RM promoter (P _RM), so that the phage tends to maintain the lysogenic cycle and reduces the release of phages. Released phages were infiltrated with S. Typhimurium S3, and the influence of *csr*A on prophage cyclization and release was quantified based on the count of phage spots on the petri dishes.

To investigate the regulatory mechanism of host gene csrA on prophage cyclization and release, we further determined the correlation between the expression of csrA and cI, which inhibits prophage cyclization by quantitative PCR (qPCR). We measured the corresponding bacterial optical density at 600 nm (OD₆₀₀) at 2, 3, 4, 5, 7, and 9 h (Figure S5) and calculated the relative expression of csrA and cI based on their Ct values. A strong positive correlation was observed between the expression of csrA and cI at dilution of 1:16 (R ² = 0.776, p < 0.001; Figures 5D) and 1:32 (R ² = 0.761, p < 0.001; Figure 5E), which indicated that csrA inhibited prophage cyclization by promoting cI expression. In conclusion, the S. enterica regulatory gene csrA was found to regulate the phage life cycle, inhibit phage infection of S. enterica, and reduce the likelihood of horizontal transfer of S. enterica virulence genes (Figure 5F).

DISCUSSION

Virulence genes of S. enterica are widely found in phages

S. enterica infection represents a major threat to human health and has resulted in considerable economic losses worldwide. However, most existing studies have primarily focused on regional distribution and antibiotic resistance, especially β‐lactamase genes of S. enterica [25, 26]. These studies do not provide a comprehensive overview of the global distribution patterns, habitats, and virulence genes carried by S. enterica. This study is the first to investigate the global geographic distribution of S. enterica. In this study, 122,680 S. enterica were mainly observed in food, feces, and animals in North America, Europe, and Asia (Figure 1). Consistent with previous regional studies, we found that the average abundance of S. enterica virulence genes and Salmonellosis outbreaks are associated with gross domestic product and population density [27, 28]. The global distribution patterns of S. enterica can help raise awareness of food safety and public health concerns in countries with a higher risk of Salmonella outbreaks.

As phages can encode and facilitate the transmission of S. enterica virulence genes through HGT, we also investigated the global distribution of phages carrying S. enterica virulence genes. We identified 1844 phages that carry S. enterica virulence genes, which were predominantly found in the digestive system, marine environments, and nests across North America, Europe, and Asia (Figure 2), demonstrating that phages are important carriers of virulence genes in addition to S. enterica. Moreover, the digestive system was the habitat with the highest abundance and proportion of phages carrying S. enterica virulence genes. S. enterica was also commonly detected in food, feces, and animal bodies, which aligns well with the habitats of the phages. Notably, fliC, mgtB, misL, and iacP were detected for the first time in phage genomes and identified as phage‐encoded auxiliary metabolic genes (AMGs) (Tables S11 and S12). These virulence genes, as phage‐encoded AMGs, not only alter the metabolism of host bacteria to enhance their virulence but also influence the spread of these genes within the bacterial community through phage‐mediated HGT. Together, this study offers a more comprehensive understanding of S. enterica virulence gene‐encoded phages and addresses several gaps in previous research.

Counteracting effects of S. enterica and phages on horizontal transfer of virulence genes

The study of phage‐mediated horizontal transfer of S. enterica virulence genes is crucial for understanding how phages influence the pathogenicity of this bacterium. Based on phylogenetic analysis, we found that virulence genes encoded by both S. enterica and phages shared common ancestors (Figure 3B) with high identity and coverage, suggesting that HGT between phages and S. enterica has occurred historically. Based on the methods for identifying HGT among bacteria described in previous studies, horizontally transferred fragments should be aligned using BLAST, with an identity of 100% and a length greater than 500 bp [29]. Specifically, the amino acid residues of the virulence gene fliC encoded by phage IMGVR_UViG_3300011660_000005 matched exactly with those carried by S. enterica strains (GCA_006490175.1 and GCA_006027275.1), which aligns with the method described above for identifying HGT. We excluded the possibility that the bacterial sequences carrying fliC might be prophages, further confirming that phage IMGVR_UViG_3300011660_000005 is responsible for transferring fliC from S. enterica. Moreover, it was predicted that the phage‐encoded fliC has a structurally intact protein and performs its function, as determined by SWISS‐MODEL (GMQE = 0.81). However, for most S. enterica and phages, the virulence genes they carried were not 100% identical to each other, suggesting that base mutations may have occurred. It is likely that phages initially served as vectors to package bacterial virulence genes and subsequently underwent rapid sequence drift during their lifecycle [30]. This indicates that phages play a major role in the transfer of virulence genes. Together, our work provides new evidence of the flow and evolution of virulence genes within microbial communities by investigating the phylogenetic relationships and sequence identity of virulence genes encoded by both phages and S. enterica.

Although the virulence genes encoded by phages are promising in assisting host bacterial virulence‐related metabolism and pathogenicity, bacteria also possess their own regulatory genes to control the transfer of exogenous genes, including phage‐encoded genes [31, 32]. The 10 S. enterica regulator genes examined in this study were widely distributed worldwide, which were detected in 70.6% of S. enterica (Table S1). Among these, the csrA gene was the most extensively detected, highly conserved, and present across various serotypes (Figure 4), suggesting its significant role in S. enterica. This was supported by previous studies, which reported that csrA regulated the expression of 1025 bacterial genes associated with carbon metabolism and transport, iron metabolism, and cell envelope [33, 34, 35]. Notably, to the best of our knowledge, this is the first work that reports the linkage between phage‐mediated transduction and the bacterial regulator gene csrA. As shown in Figure 5, the titer of phages released by the strain overexpressing csrA was significantly lower than that of phages released by wild‐type strains (p < 0.05), and csrA expression significantly promoted cI expression of prophages (p < 0.001). The inhibitory effect of csrA on phage entry into the lysis cycle is consistent with previous studies that reported the overall negative effect of csrA on gene expression [33, 36]. Together, we identified the importance of the global regulator gene csrA in S. enterica and demonstrated for the first time that csrA inhibits prophage release. The inhibition suggests that csrA may reduce phage infection in bacteria, which could indirectly mitigate the impact of phage‐mediated horizontal transfer of virulence genes, thereby influencing the evolution of bacterial communities.

Phage‐mediated horizontal transfer of S. enterica virulence genes requires further attention

Considering both phage‐encoded virulence genes and regulators, it is crucial to remain vigilant about phage‐mediated transmission of S. enterica virulence factors. While this large‐scale, data‐driven investigation provides valuable insights, it is important to acknowledge that the stringent quality filtering employed, combined with inherent limitations stemming from incomplete metadata, may introduce unavoidable biases in the analytical outcomes [37, 38, 39]. These biases may lead to an overrepresentation of virulence genes in developed regions and an underestimation of the burden of enteric fever in low‐ and middle‐income countries [40, 41]. This disparity highlights substantial gaps in the monitoring and sequencing of S. enterica and phages, likely due to economic constraints [3], which hinder the ability to track and understand the global spread and evolution of virulence factors. Based on the current available data, 36 phages were identified as polyvalent phages, as they were linked to more than one bacterial species in this study (Figure 3A), indicating the significant potential of these phages for HGT. In addition, phage infection may enhance host bacterial virulence [42]. As validated in this study, the overall transmission of S. enterica virulence genes increased due to the phage‐mediated horizontal transfer and expression of virulence genes. Although the global regulator gene csrA reduced the frequency of HGT by inhibiting prophage cyclization and release, it did not completely block phage infection. Future research should focus more on the roles of virulence factors and regulators in phage applications. Phages act as both sources and sinks for bacterial genes, offering potential opportunities for disease treatment in the context of antibiotic abuse, while also complicating the challenge of treating bacterial diseases. To minimize the risk of HGT of virulence genes, therapeutic phages should either be free of these genes or engineered to remove any virulence genes they may carry. A more comprehensive investigation into the roles and mechanisms of regulators in HGT is critical for accurately assessing the risks associated with phage‐mediated transfer and the dissemination of host virulence genes. This study is therefore essential for improving the safety and effectiveness of phage therapy applications.

CONCLUSION

In this study, we have uncovered a complex interplay between phages and S. enterica, a bacterial genus associated with a variety of diseases in humans and animals. Our research demonstrates that phages play a pivotal role in the horizontal transfer of virulence genes within S. enterica, thereby potentially enhancing its pathogenicity. However, this process is intricately balanced by regulatory genes within the host bacterium that suppress these transfers. Our findings show that virulence genes carried by both phages and S. enterica are not only widespread but also exhibit close genetic affinity, indicating a history of HGT events. This genetic proximity and shared ancestry underscore the evolutionary relationship between phages and their bacterial hosts, highlighting the dynamic nature of their coexistence. Despite the apparent propensity for HGT, our work also demonstrates that certain regulatory genes within S. enterica, such as csrA, act as natural defense mechanisms. The gene csrA, which is abundant in S. enterica, was found to impede the life cycles of prophage PSAP2‐2, by preventing its cyclization and subsequent release. This inhibition, in turn, reduces the likelihood of phage‐mediated HGT, thereby limiting the spread of virulence factors. In conclusion, these findings emphasize the crucial role of phages in driving gene exchange and evolution in S. enterica. They also suggest that the regulatory machinery of S. enterica is not only essential for modulating the bacterium's own physiology but also plays a significant role in controlling phage life cycles and shaping the dynamics of HGT processes. These insights into the interaction between phages and their hosts advance our understanding of the mechanisms underlying bacterial pathogenicity and may have significant implications for the development of innovative therapeutic strategies to treat bacterial infections.

METHODS

Global distribution of S. enterica and phages

To determine whether phages carry S. enterica virulence genes and to analyze the global distribution pattern of these genes in both phages and S. enterica, we collected as many reliable genomic datasets as possible and used them as a basis for mapping. The amino acid sequences of S. enterica target virulence genes (fimA, fliC, iacP, mgtB, mgtC, misL, pagC, spvA/B/C/R, sseI, araB, bcfC, and sitC) and regulatory genes (csrA, hns, hfq, crp, barA, sirA, cas1, cas2, cas3, and cas5) were downloaded from the KEGG database, while 466,136 S. enterica genomes were downloaded from EnteroBase (https://enterobase.warwick.ac.uk/, accessed on 29 May 2023) [43]. The region encoding these target genes in S. enterica was identified through BLAST (v2.13.0+) and filtered using the following parameters: E‐value < 0.0001, identity >70%, and coverage >70%. After filtering the fields “Country” and “Isolation Source,” 122,680 S. enterica genomes remained. Similarly, the target amino acid sequences were blasted against the IMG/VR v4 database, which contains over 15,000,000 viral genomes and genome fragments (https://img.jgi.doe.gov/cgi-bin/vr/main.cgi) [44]. After removing redundant matches, 1839 phages encoding S. enterica virulence genes were identified (E‐value < 0.0001, bit score > 40, and identity > 20%). Finally, S. enterica and phages were spatially visualized at the national level using ArcGIS 10.7 (ESRI) on a global scale, based on geographic location and habitat. The information on S. enterica and phages carrying S. enterica virulence genes is provided in Tables S1 and S2.

To investigate the proportion of uncultivated viral genomes UViGs encoding S. enterica virulence genes in each habitat, we calculated the ratios of these UViGs in this study to the total number of UViGs in the IMG/VR database. Habitats with three or fewer sampling sites or projects were excluded from this study to minimize the bias of single sample on the results. The abundance of UViGs in each habitat, both in this study and the IMG/VR database, is shown in Table S3.

Taxonomic assignment and functional annotation of phages

To classify the phages, the obtained phage genomes were scanned using HMMER (v3.1b2), aligning protein sequences with HMM models corresponding to specific phage taxon from the ViPhOG database [45] (Table S3). The hit with the highest score for each sequence was selected as the best assignment. The data set was further filtered and annotated using DRAMV [46]. AMGs were identified by combining DRAMV (v1.2.0) with VIBRANT (v1.2.0) using default parameters. The potential functions of the identified genes, including AMGs, were further validated through protein structure prediction. The SWISS‐MODEL server (https://swissmodel.expasy.org/, accessed on 11 December 2021) was used to predict the quaternary structure of each protein with GMQE score > 0.5. The BPROM server (http://www.softberry.com/, accessed on January 2, 2022) and the FindTerm server (http://www.softberry.com/, accessed on January 2, 2022) were then used to predict the promoter (threshold: 0.20) and terminator of phage‐encoded virulence genes, respectively.

Phylogenetic analysis, phage lifestyle, and putative host prediction

Amino acid sequences corresponding to virulence genes or regulatory genes encoded by phages and S. enterica were aligned using MUSCLE (v5.3) [47] and then trimmed with parameter 0.5. Trees were constructed using either IQ‐TREE (v1.5.5) [48] or MEGA11 [49]. Bootstrapping with 1000 replications was performed to assess the statistical robustness of the clustering. Phage lifestyles, including virulent and temperate, were predicted using a new model based on BERT, PhaTYP (http://phage.ee.cityu.edu.hk/phatyp, accessed on June 20, 2022 [50]; Table S4). The nucleotide sequences were inputted into the model and assigned probabilities for each lifestyle class. Two prognostic computational approaches genome (homology match and CRISPR match) were used to predict putative phage‐host linkages. The genomes of S. enterica were used to search for CRISPR spacers using CRISPRFinder (v4.3.2) [51]. Phage genomic signatures in S. enterica were identified via BLAST with the following parameters: E‐value < 0.0001, bit score ≥ 50, identity ≥ 70%, and matching length ≥ 2500 bp [52] (Tables S6 and S7). Moreover, phages linked to multiple bacterial genera were classified as polyvalent phages, meaning they can infect a wide range of hosts.

Bench trials to validate the role of regulators on HGT of S. enterica virulence genes

We conducted an experiment to investigate the role of csrA in mediating phage‐mediated horizontal transfer processes. The cyclization and release of the prophage PSAP2‐2 were compared between wild‐type S. enterica subsp. enterica serotype Typhimurium (S. Typhimurium) strain S12 (WT) and S. Typhimurium strain S12 overexpressing csrA (i.e., S12::csrA‐1 and S12::csrA‐2). If the release of prophages decreased in strains overexpressing csrA, the probability of HGT would be lower in these strains compared to the control. The phage titer (pfu mL⁻¹) of S12::csrA‐1, S12::csrA‐2, and WT strains was determined after induction of prophage expression. The expression of csrA and the cI repressor was measured by qPCR.

Construction of strains overexpressing csrA. The primers used in this study are listed in Table S10. The E. coli plasmid PHB20TG contained recombinant enzymes that can be expressed by l‐arabinose, along with a gentamicin resistance gene used as a screening marker. The desired gene sequence was amplified by PCR using primers S77 and S78. The PCR product was then cloned into the digested plasmid vector. DNA ligase was used to ligate csrA into the excised vector. Strain S12 was cultured to the logarithmic phase to prepare competent cells. A volume of 10 µL of the extracted plasmid was electroporated into the S12 cells, which were then cultured at 37°C. Two monoclonal colonies were randomly selected for purification on plates containing 15 µg mL⁻¹ gentamicin and incubated at 37°C. Strains S12::csrA‐1 and S12::csrA‐2 were constructed from the two clones, respectively. The control strain was WT carrying the plasmid PHB20TG without csrA. The mutant was further confirmed by DNA sequencing of the PCR products.

Determination of phage titer. Strains overexpressing csrA and the WT strain were incubated overnight at 37°C, and then transferred to 3 mL of LB containing 3 µL of 15 µg mL⁻¹ gentamicin and 60 µL of 20% arabinose. The strains were incubated at 37°C with shaking for 6 h, and the OD₆₀₀ was determined. Bacterial suspensions were centrifuged at 12,000 g and 4°C for 5 min. The supernatant was collected and diluted to 10⁻², 10⁻³, and 10⁻⁴ of the original concentration. A volume of 10 µL from each diluted suspension was taken, along with 200 µL of S3, incubated at 37°C, and the plaque‐forming units (pfu) were counted. The experiment was set up with three parallel control groups. Phage titer was calculated following the equation listed below

P {hage titer (pfu mL}^{- 1}) = \frac{Plaque forming unit (pfu) \times Dilution}{Volume (mL)},

(1)

qPCR and analysis of csrA and cI repressor. The S12 strain was cultured in 15 mL of LB medium and incubated at 37°C with shaking for 2, 3, 4, 5, 7, and 9 h. At each time point, the OD₆₀₀ was measured. Subsequently, 1 mL of bacterial culture was centrifuged at 12,000 g for 3 min at 4°C. RNA was extracted from the bacterial pellet using Trizol reagent and treated with DNase I to remove any residual DNA. The RNA concentration was then determined and standardized. The RNA was reverse‐transcribed into cDNA, which was diluted to 1:16 and 1:32. The experiment utilized two sets of primers, S85–86 and S87–88, for qPCR. The cycle threshold (Ct) values of csrA and the cI repressor were obtained. The correlation between the relative expression of csrA and cI repressor was analyzed using scatter plots and linear regression. The relative expression was calculated following the equation listed below,

R elative expression of gene = 2^{- C t_{gene}} .

(2)

Statistical analysis

Differences in parameters were assessed for significance using t‐test, and variable correlations were analyzed using bivariate correlation in SPSS 26.0 (SPSS Inc). Data visualization was conducted using the website: https://www.chiplot.online/, and geographic distribution was visualized using ArcGIS 10.7 (Esri).

AUTHOR CONTRIBUTIONS

Tianjing She: Writing—original draft; visualization; software; formal analysis; data curation; conceptualization; methodology. Demeng Tan: Conceptualization; methodology; software; supervision; resources; validation. Jose Luis Balcazar: Writing—review and editing; formal analysis; supervision. Ville‐Petri Friman: Writing—review and editing; formal analysis; supervision. Danrui Wang: Software; project administration; supervision; data curation. Dong Zhu: Methodology; formal analysis; supervision. Mao Ye: Conceptualization; writing—review and editing; methodology; formal analysis; project administration; supervision; resources. Mingming Sun: Supervision; resources; project administration; formal analysis; writing—review and editing; methodology; conceptualization; funding acquisition. Shujian Yuan: Investigation; conceptualization; methodology; data curation. Feng Hu: Funding acquisition; project administration; supervision; resources.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

ETHICS STATEMENT

No animals or humans were involved in this study.

Supporting information

Figure S1. Percentage of uncultivated virus genomes (UViGs) carrying Salmonella enterica (S. enterica) virulence genes in each habitat.

Figure S2. Phylogenetic tree of mgtB encoded by S. enterica and phages.

Figure S3. Phylogenetic tree of misL encoded by S. enterica and phages.

Figure S4. Regulatory mechanisms of the key regulators in S. enterica.

Figure S5. Bacterial optical density at 600 nm (OD₆₀₀) when performing quantitative PCR (qPCR) experiments.

IMT2-4-e70042-s001.docx^{(654.2KB, docx)}

Table S1. Metadata of 122,680 filtered Salmonella enterica genomes.

Table S2. Metadata of 1839 filtered phage genomes.

Table S3. Phage taxonomy.

Table S4. Phage lifestyles.

Table S5. Proportion of UViGs in each habitat.

Table S6. Host predicted by CRISPR match.

Table S7. Host predicted by homology match.

Table S8. Alignment of virulence genes carried by S. enterica and phages via BLAST.

Table S9. Prediction of promoters and terminators.

Table S10. Sequences of genes and primers for qPCR experiments.

Table S11. Phage AMGs identified by DRAMV.

Table S12. Phage AMGs identified by VIBRANT.

IMT2-4-e70042-s002.xlsx^{(6.6MB, xlsx)}

ACKNOWLEDGMENTS

This study was supported by the National Natural Science Foundation of China (42177113 and 42277115), the Key R&D Project of Jiangsu Province (Modern Agriculture; BE2022322), the Fundamental Research Funds for the Central Universities (KJJQ2025028), the Jiangsu Provincial Agricultural Independent Innovation Project (CX(24)3102), and the China Postdoctoral Science Foundation (Grant No. 2024M761435). Jose Luis Balcazar acknowledges the support from the Generalitat de Catalunya through the Consolidated Research Group grant (ICRA‐ENV 2021 SGR 01282), and from the CERCA program of the Catalan Government. This study was also supported by the Bioinformatics Center of Nanjing Agricultural University.

She, Tianjing , Tan Demeng, Balcazar Jose Luis, Friman Ville‐Petri, Wang Danrui, Zhu Dong, Ye Mao, Sun Mingming, Yuan Shujian, and Hu Feng. 2025. “Phage‐Mediated Horizontal Transfer of Salmonella enterica Virulence Genes with Regulatory Feedback From the Host.” iMeta 4, e70042. 10.1002/imt2.70042

Contributor Information

Jose Luis Balcazar, Email: jlbalcazar@icra.cat.

Mingming Sun, Email: sunmingming@njau.edu.cn.

DATA AVAILABILITY STATEMENT

Data collected for the study are publicly available from the Figshare platform, and can be downloaded from https://doi.org/10.6084/m9.figshare.26493310, https://doi.org/10.6084/m9.figshare.26527759, and https://doi.org/10.6084/m9.figshare.26531836. The data and scripts used are saved in GitHub https://github.com/TianjingShe/Salmonella-enterica. Supplementary materials (figures, tables, graphical abstract, slides, videos, Chinese translated version, and update materials) may be found in the online DOI or iMeta Science http://www.imeta.science/.

REFERENCES

1. Li, Shaoting , He Yingshu, Mann David Ames, and Deng Xiangyu. 2021. “Global Spread of Salmonella Enteritidis via Centralized Sourcing and International Trade of Poultry Breeding Stocks.” Nature Communications 12: 5109. 10.1038/s41467-021-25319-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
2. Antillón, Marina , Warren Joshua L., Crawford Forrest W., Weinberger Daniel M., Kürüm Esra, Pak Gi Deok, Marks Florian, and Pitzer Virginia E.. 2017. “The Burden of Typhoid Fever in Low‐ and Middle‐Income Countries: A Meta‐Regression Approach.” PLoS Neglected Tropical Diseases 11: e0005376. 10.1371/journal.pntd.0005376 [DOI] [PMC free article] [PubMed] [Google Scholar]
3. Meiring, James E. , Shakya Mila, Khanam Farhana, Voysey Merryn, Phillips Maile T., Tonks Susan, Thindwa Deus, et al. 2021. “Burden of Enteric Fever at Three Urban Sites in Africa and Asia: A Multicentre Population‐Based Study.” The Lancet Global Health 9: e1688–e1696. 10.1016/S2214-109X(21)00370-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
4. Choi, Eunna , Han Yoontak, Park Shinae, Koo Hyojeong, Lee Jung‐Shin, and Lee Eun‐Jin. 2021. “A Translation‐Aborting Small Open Reading Frame in the Intergenic Region Promotes Translation of a Mg²⁺ Transporter in Salmonella Typhimurium .” mBio 12: e03376‐20. 10.1128/mbio.03376-20 [DOI] [PMC free article] [PubMed] [Google Scholar]
5. Park, Myungseo , Kim Hyunkeun, Nam Daesil, Kweon Dae‐Hyuk, and Shin Dongwoo. 2019. “The mgtCBR mRNA Leader Secures Growth of Salmonella in Both Host and Non‐Host Environments.” Frontiers in Microbiology 10: 2831. 10.3389/fmicb.2019.02831 [DOI] [PMC free article] [PubMed] [Google Scholar]
6. van Asten, Alphons J. A. M. , and Dijk Jaap E.. 2005. “Distribution of ‘Classic’ Virulence Factors Among Salmonella Spp.” FEMS Immunology and Medical Microbiology 44: 251–259. 10.1016/j.femsim.2005.02.002 [DOI] [PubMed] [Google Scholar]
7. Horstmann, Julia A. , Zschieschang Erik, Truschel Theresa, de Diego Juana, Lunelli Michele, Rohde Manfred, May Tobias, et al. 2017. “Flagellin Phase‐Dependent Swimming on Epithelial Cell Surfaces Contributes to Productive Salmonella Gut Colonisation.” Cellular Microbiology 19: e12739. 10.1111/cmi.12739 [DOI] [PubMed] [Google Scholar]
8. Dorsey, Caleb W. , Laarakker Marijke C., Humphries Andrea D., Weening Eric H., and Bäumler Andreas J.. 2005. “ Salmonella enterica Serotype Typhimurium MisL is an Intestinal Colonization Factor That Binds Fibronectin.” Molecular Microbiology 57: 196–211. 10.1111/j.1365-2958.2005.04666.x [DOI] [PubMed] [Google Scholar]
9. Kim, Jin Seok , Eom Jeong Seon, Jang Jung Im, Kim Hyeon Guk, Seo Doo Won, Bang Iel‐Soo, Bang Seong Ho, Lee In Soo, and Park Yong Keun. 2011. “Role of Salmonella Pathogenicity Island 1 Protein IacP in Salmonella enterica Serovar Typhimurium Pathogenesis.” Infection and Immunity 79: 1440–1450. 10.1128/iai.01231-10 [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Penadés, José R. , Chen John, Quiles‐Puchalt Nuria, Carpena Nuria, and Novick Richard P.. 2015. “Bacteriophage‐Mediated Spread of Bacterial Virulence Genes.” Current Opinion in Microbiology 23: 171–178. 10.1016/j.mib.2014.11.019 [DOI] [PubMed] [Google Scholar]
11. Chen, Juan , Wang Peifang, Wang Chao, Wang Xun, Miao Lingzhan, Liu Sheng, Yuan Qiusheng, and Sun Shenghao. 2020. “Fungal Community Demonstrates Stronger Dispersal Limitation and Less Network Connectivity Than Bacterial Community in Sediments Along a Large River.” Environmental Microbiology 22: 832–849. 10.1111/1462-2920.14795 [DOI] [PubMed] [Google Scholar]
12. Chen, Juan , Wang Chao, Pan Ying, Farzana Shazia Shyla, and Tam Nora Fung‐Yee. 2018. “Biochar Accelerates Microbial Reductive Debromination of 2,2’,4,4’‐Tetrabromodiphenyl Ether (BDE‐47) in Anaerobic Mangrove Sediments.” Journal of Hazardous Materials 341: 177–186. 10.1016/j.jhazmat.2017.07.063 [DOI] [PubMed] [Google Scholar]
13. Sekizuka, Tsuyoshi , Yamamoto Akihiko, Komiya Takako, Kenri Tsuyoshi, Takeuchi Fumihiko, Shibayama Keigo, Takahashi Motohide, Kuroda Makoto, and Iwaki Masaaki. 2012. “ Corynebacterium ulcerans 0102 Carries the Gene Encoding Diphtheria Toxin on a Prophage Different From the C‐diphtheriae NCTC 13129 Prophage.” BMC Microbiology 12: 72. 10.1186/1471-2180-12-72 [DOI] [PMC free article] [PubMed] [Google Scholar]
14. Fillol‐Salom, Alfred , Bacigalupe Rodrigo, Humphrey Suzanne, Chiang Yin Ning, Chen John, and Penadés José R.. 2021. “Lateral Transduction Is Inherent to the Life Cycle of the Archetypical Salmonella Phage P22.” Nature Communications 12: 6510. 10.1038/s41467-021-26520-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
15. Humphrey, Suzanne , Fillol‐Salom Alfred, Quiles‐Puchalt Nuria, Ibarra‐Chávez Rodrigo, Haag Andreas F., Chen John, and Penadés José R.. 2021. “Bacterial Chromosomal Mobility via Lateral Transduction Exceeds That of Classical Mobile Genetic Elements.” Nature Communications 12: 6509. 10.1038/s41467-021-26004-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
16. Altier, Craig , Suyemoto Mitsu, and Lawhon Sara D.. 2000. “Regulation of Salmonella enterica Serovar Typhimurium Invasion Genes by Csra .” Infection and Immunity 68: 6790–6797. 10.1128/IAI.68.12.6790-6797.2000 [DOI] [PMC free article] [PubMed] [Google Scholar]
17. Lawhon, Sara D. , Frye Jonathan G., Suyemoto Mitsu, Porwollik Steffen, McClelland Michael, and Altier Craig. 2003. “Global Regulation by CsrA in Salmonella Typhimurium.” Molecular Microbiology 48: 1633–1645. 10.1046/j.1365-2958.2003.03535.x [DOI] [PubMed] [Google Scholar]
18. Fortune, Doreen R. , Suyemoto Mitsu, and Altier Craig. 2006. “Identification of CsrC and Characterization of Its Role in Epithelial Cell Invasion in Salmonella enterica Serovar Typhimurium.” Infection and Immunity 74: 331–339. 10.1128/IAI.74.1.331-339.2006 [DOI] [PMC free article] [PubMed] [Google Scholar]
19. El Mouali, Youssef , Esteva‐Martínez Guillem, García‐Pedemonte David, and Balsalobre Carlos. 2020. “Differential Regulation of CsrC and CsrB by CRP‐cAMP in Salmonella enterica .” Frontiers in Microbiology 11: 570536. 10.3389/fmicb.2020.570536 [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Teplitski, Max , Goodier Robert I., and Ahmer Brian M. M.. 2003. “Pathways Leading from BarA/SirA to Motility and Virulence Gene Expression in Salmonella .” Journal of Bacteriology 185: 7257–7265. 10.1128/JB.185.24.7257-7265.2003 [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Ko, Minsu , and Park Chankyu. 2000. “Two Novel Flagellar Components and H‐NS Are Involved in the Motor Function of Escherichia coli .” Journal of Molecular Biology 303: 371–382. 10.1006/jmbi.2000.4147 [DOI] [PubMed] [Google Scholar]
22. Sun, Dongchang , Mao Xudan, Fei Mingyue, Chen Ziyan, Zhu Tingheng, and Qiu Juanping. 2020. “Histone‐Like Nucleoid‐Structuring Protein H‐NS Paralogue StpA Activates the Type I‐E CRISPR‐Cas System Against Natural Transformation in Escherichia coli .” Applied and Environmental Microbiology 86: e00731‐20. 10.1128/AEM.00731-20 [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Nava‐Galeana, Jessica , Yakhnin Helen, Babitzke Paul, and Bustamante Víctor H.. 2023. “CsrA Positively and Directly Regulates the Expression of the pdu, pocR, and eut Genes Required for the Luminal Replication of Salmonella Typhimurium.” Microbiology Spectrum 11: e01516‐23. 10.1128/spectrum.01516-23 [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Meng, Xia , He Mengping, Chen Binjie, Xia Pengpeng, Wang Jinqiu, Zhu Chunhong, Wang Heng, and Zhu Guoqiang. 2023. “RyhB Paralogs Downregulate the Expressions of Multiple Survival‐Associated Genes and Attenuate the Survival of Salmonella Enteritidis in the Chicken Macrophage HD11.” Microorganisms 11: 214. 10.3390/microorganisms11010214 [DOI] [PMC free article] [PubMed] [Google Scholar]
25. Zhang, Zengfeng , Kuang Dai, Xu Xuebin, Zhan Zeqiang, Ren Hao, and Shi Chunlei. 2024. “Dissemination of IncC Plasmids in Salmonella enterica Serovar Thompson Recovered From Seafood and Human Diarrheic Patients in China.” International Journal of Food Microbiology 417: 110708. 10.1016/j.ijfoodmicro.2024.110708 [DOI] [PubMed] [Google Scholar]
26. Lin, Jiafu , Lv Hua, Wang Tiantian, Tao Hongkun, Zhong Yi, Zhou Yang, Tang Yibo, et al. 2024. “The Global Distribution of the Macrolide Esterase EstX From the Alpha/Beta Hydrolase Superfamily.” Communications Biology 7: 781. 10.1038/s42003-024-06473-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Wang, Yanan , Xu Xuebin, Zhu Baoli, Lyu Na, Liu Yue, Ma Sufang, Jia Shulei, et al. 2023. “Genomic Analysis of Almost 8,000 Salmonella Genomes Reveals Drivers and Landscape of Antimicrobial Resistance in China.” Microbiology Spectrum 11: e02080‐23. 10.1128/spectrum.02080-23 [DOI] [PMC free article] [PubMed] [Google Scholar]
28. Wang, Zining , Zhou Haiyang, Liu Yuhao, Huang Chenghu, Chen Jiaqi, Siddique Abubakar, Yin Rui, et al 2024. “Nationwide Trends and Features of Human Salmonellosis Outbreaks in China.” Emerging Microbes & Infections 13: 2372364. 10.1080/22221751.2024.2372364 [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Smillie, Chris S. , Smith Mark B., Friedman Jonathan, Cordero Otto X., David Lawrence A., and Alm Eric J.. 2011. “Ecology Drives a Global Network of Gene Exchange Connecting the Human Microbiome.” Nature 480: 241–244. 10.1038/nature10571 [DOI] [PubMed] [Google Scholar]
30. Glazko, Galina , Makarenkov Vladimir, Liu Jing, and Mushegian Arcady. 2007. “Evolutionary History of Bacteriophages With Double‐Stranded DNA Genomes.” Biology Direct 2: 36. 10.1186/1745-6150-2-36 [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Marraffini, Luciano A. , and Sontheimer Erik J.. 2008. “CRISPR Interference Limits Horizontal Gene Transfer in Staphylococci by Targeting DNA.” Science 322: 1843–1845. 10.1126/science.1165771 [DOI] [PMC free article] [PubMed] [Google Scholar]
32. Hullinger, Allison C. , Green Virginia E., Klancher Catherine A., Dalia Triana N., and Dalia Ankur B.. 2025. “Two Transmembrane Transcriptional Regulators Coordinate to Activate Chitin‐Induced Natural Transformation in Vibrio cholerae .” PLoS Genetics 21: e1011606. 10.1371/journal.pgen.1011606 [DOI] [PMC free article] [PubMed] [Google Scholar]
33. Potts, Anastasia H. , Vakulskas Christopher A., Pannuri Archana, Yakhnin Helen, Babitzke Paul, and Romeo Tony. 2017. “Global Role of the Bacterial Post‐Transcriptional Regulator CsrA Revealed by Integrated Transcriptomics.” Nature Communications 8: 1596. 10.1038/s41467-017-01613-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
34. Renda, Andrew , Poly Stephanie, Lai Ying‐Jung, Pannuri Archana, Yakhnin Helen, Potts Anastasia H., Bevilacqua Philip C., Romeo Tony, and Babitzke Paul. 2020. “CsrA‐Mediated Translational Activation of ymdA Expression in Escherichia coli .” mBio 11: e00849‐20. 10.1128/mBio.00849-20 [DOI] [PMC free article] [PubMed] [Google Scholar]
35. Butz, Heidi A. , Mey Alexandra R., Ciosek Ashley L., Crofts Alexander A., Davies Bryan W., and Payne Shelley M.. 2021. “Regulatory Effects of CsrA in Vibrio cholerae .” mBio 12: e03380‐20. 10.1128/mBio.03380-20 [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Gorelik, Mark G. , Yakhnin Helen, Pannuri Archana, Walker Alyssa C., Pourciau Christine, Czyz Daniel, Romeo Tony, and Babitzke Paul. 2024. “Multitier Regulation of the E. coli Extreme Acid Stress Response by CsrA.” Journal of Bacteriology 206: e00354‐23. 10.1128/jb.00354-23. [DOI] [PMC free article] [PubMed] [Google Scholar]
37. Han, Jing , Tang Hailin, Zhao Shaohua, and Foley Steven L.. 2024. “ Salmonella enterica Virulence Databases and Bioinformatic Analysis Tools Development.” Scientific Reports 14: 25228. 10.1038/s41598-024-74124-x [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Xi, Xi , Li Jiaqi, Jia Jinmeng, Meng Qiuchen, Li Chen, Wang Xiaowo, Wei Lei, and Zhang Xuegong. 2025. “A Mechanism‐Informed Deep Neural Network Enables Prioritization of Regulators That Drive Cell State Transitions.” Nature Communications 16: 1284. 10.1038/s41467-025-56475-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Li, Bi , Han Li‐Li, Du Shuai, Yu Dan‐Ting, He Ji‐Zheng, Zhang Li‐Mei, and Hu Hang‐Wei. 2023. “Cross‐Biome Soil Viruses as an Important Reservoir of Virulence Genes.” Journal of Hazardous Materials 442: 130111. 10.1016/j.jhazmat.2022.130111 [DOI] [PubMed] [Google Scholar]
40. Stanaway, Jeffrey D. , Parisi Andrea, Sarkar Kaushik, Blacker Brigette F., Reiner Robert C., Hay Simon I., Nixon Molly R., et al. 2019. “The Global Burden of Non‐Typhoidal Salmonella Invasive Disease: A Systematic Analysis for the Global Burden of Disease Study 2017.” The Lancet Infectious Diseases 19: 1312–1324. 10.1016/S1473-3099(19)30418-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
41. Weller, Daniel L. , Tierney Reese, Verlander Sarah, Bruce Beau B., and Rose Erica Billig. 2024. “Disparities in Salmonellosis Incidence for US Counties With Different Social Determinants of Health Profiles Are Also Mediated by Extreme Weather: A Counterfactual Analysis of Laboratory Enteric Disease Surveillance (LEDS) Data From 1997 Through 2019.” Journal of Food Protection 87: 100379. 10.1016/j.jfp.2024.100379 [DOI] [PMC free article] [PubMed] [Google Scholar]
42. Li, Dezhi , Liang Wei, Huang Zhiqiang, Ma Wenwen, and Liu Qing. 2024. “The Spontaneously Produced Lysogenic Prophage phi456 Promotes Bacterial Resistance to Adverse Environments and Enhances the Colonization Ability of Avian Pathogenic Escherichia coli Strain DE456.” Veterinary Research 55: 37. 10.1186/s13567-024-01292-z [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Zhou, Zhemin , Alikhan Nabil‐Fareed, Mohamed Khaled, Fan Yulei, and Agama Study Group, and Mark Achtman . 2020. “The EnteroBase User's Guide, With Case Studies on Salmonella Transmissions, Yersinia pestis Phylogeny, and Escherichia Core Genomic Diversity.” Genome Research 30: 138–152. 10.1101/gr.251678.119 [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Camargo, Antonio Pedro , Nayfach Stephen, Chen I‐Min A, Palaniappan Krishnaveni, Ratner Anna, Chu Ken, Ritter Stephan J., et al. 2023. “IMG/VR v4: An Expanded Database of Uncultivated Virus Genomes Within a Framework of Extensive Functional, Taxonomic, and Ecological Metadata.” Nucleic Acids Research 51: D733–D743. 10.1093/nar/gkac1037 [DOI] [PMC free article] [PubMed] [Google Scholar]
45. Moreno‐Gallego, Jaime Leonardo , and Reyes Alejandro. 2021. “Informative Regions in Viral Genomes.” Viruses 13: 1164. 10.3390/v13061164 [DOI] [PMC free article] [PubMed] [Google Scholar]
46. Shaffer, Michael , Borton Mikayla A., McGivern Bridget B., Zayed Ahmed A., La Rosa Sabina Leanti, Solden Lindsey M., Liu Pengfei, et al. 2020. “DRAM for Distilling Microbial Metabolism to Automate the Curation of Microbiome Function.” Nucleic Acids Research 48: 8883–8900. 10.1093/nar/gkaa621 [DOI] [PMC free article] [PubMed] [Google Scholar]
47. Edgar, Robert C . 2004. “MUSCLE: A Multiple Sequence Alignment Method With Reduced Time and Space Complexity.” BMC Bioinformatics 5: 113. 10.1186/1471-2105-5-113 [DOI] [PMC free article] [PubMed] [Google Scholar]
48. Nguyen, Lam‐Tung , Schmidt Heiko A., von Haeseler Arndt, and Minh Bui Quang. 2015. “IQ‐TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum‐Likelihood Phylogenies.” Molecular Biology and Evolution 32: 268–274. 10.1093/molbev/msu300 [DOI] [PMC free article] [PubMed] [Google Scholar]
49. Hall, B. G . 2013. “Building Phylogenetic Trees From Molecular Data With MEGA.” Molecular Biology and Evolution 30: 1229–1235. 10.1093/molbev/mst012 [DOI] [PubMed] [Google Scholar]
50. Shang, Jiayu , Tang Xubo, and Sun Yanni. 2023. “PhaTYP: Predicting the Lifestyle for Bacteriophages Using BERT.” Briefings in Bioinformatics 24: bbac487. 10.1093/bib/bbac487 [DOI] [PMC free article] [PubMed] [Google Scholar]
51. Grissa, I. , Vergnaud G., and Pourcel C.. 2007. “CRISPRFinder: A Web Tool to Identify Clustered Regularly Interspaced Short Palindromic Repeats.” Nucleic Acids Research 35: W52–W57. 10.1093/nar/gkm360 [DOI] [PMC free article] [PubMed] [Google Scholar]
52. Edwards, Robert A. , McNair Katelyn, Faust Karoline, Raes Jeroen, and Dutilh Bas E.. 2016. “Computational Approaches to Predict Bacteriophage‐Host Relationships.” FEMS Microbiology Reviews 40: 258–272. 10.1093/femsre/fuv048 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1. Percentage of uncultivated virus genomes (UViGs) carrying Salmonella enterica (S. enterica) virulence genes in each habitat.

Figure S2. Phylogenetic tree of mgtB encoded by S. enterica and phages.

Figure S3. Phylogenetic tree of misL encoded by S. enterica and phages.

Figure S4. Regulatory mechanisms of the key regulators in S. enterica.

Figure S5. Bacterial optical density at 600 nm (OD₆₀₀) when performing quantitative PCR (qPCR) experiments.

IMT2-4-e70042-s001.docx^{(654.2KB, docx)}

Table S1. Metadata of 122,680 filtered Salmonella enterica genomes.

Table S2. Metadata of 1839 filtered phage genomes.

Table S3. Phage taxonomy.

Table S4. Phage lifestyles.

Table S5. Proportion of UViGs in each habitat.

Table S6. Host predicted by CRISPR match.

Table S7. Host predicted by homology match.

Table S8. Alignment of virulence genes carried by S. enterica and phages via BLAST.

Table S9. Prediction of promoters and terminators.

Table S10. Sequences of genes and primers for qPCR experiments.

Table S11. Phage AMGs identified by DRAMV.

Table S12. Phage AMGs identified by VIBRANT.

IMT2-4-e70042-s002.xlsx^{(6.6MB, xlsx)}

Data Availability Statement

[imt270042-bib-0001] 1. Li, Shaoting , He Yingshu, Mann David Ames, and Deng Xiangyu. 2021. “Global Spread of Salmonella Enteritidis via Centralized Sourcing and International Trade of Poultry Breeding Stocks.” Nature Communications 12: 5109. 10.1038/s41467-021-25319-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0002] 2. Antillón, Marina , Warren Joshua L., Crawford Forrest W., Weinberger Daniel M., Kürüm Esra, Pak Gi Deok, Marks Florian, and Pitzer Virginia E.. 2017. “The Burden of Typhoid Fever in Low‐ and Middle‐Income Countries: A Meta‐Regression Approach.” PLoS Neglected Tropical Diseases 11: e0005376. 10.1371/journal.pntd.0005376 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0003] 3. Meiring, James E. , Shakya Mila, Khanam Farhana, Voysey Merryn, Phillips Maile T., Tonks Susan, Thindwa Deus, et al. 2021. “Burden of Enteric Fever at Three Urban Sites in Africa and Asia: A Multicentre Population‐Based Study.” The Lancet Global Health 9: e1688–e1696. 10.1016/S2214-109X(21)00370-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0004] 4. Choi, Eunna , Han Yoontak, Park Shinae, Koo Hyojeong, Lee Jung‐Shin, and Lee Eun‐Jin. 2021. “A Translation‐Aborting Small Open Reading Frame in the Intergenic Region Promotes Translation of a Mg²⁺ Transporter in Salmonella Typhimurium .” mBio 12: e03376‐20. 10.1128/mbio.03376-20 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0005] 5. Park, Myungseo , Kim Hyunkeun, Nam Daesil, Kweon Dae‐Hyuk, and Shin Dongwoo. 2019. “The mgtCBR mRNA Leader Secures Growth of Salmonella in Both Host and Non‐Host Environments.” Frontiers in Microbiology 10: 2831. 10.3389/fmicb.2019.02831 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0006] 6. van Asten, Alphons J. A. M. , and Dijk Jaap E.. 2005. “Distribution of ‘Classic’ Virulence Factors Among Salmonella Spp.” FEMS Immunology and Medical Microbiology 44: 251–259. 10.1016/j.femsim.2005.02.002 [DOI] [PubMed] [Google Scholar]

[imt270042-bib-0007] 7. Horstmann, Julia A. , Zschieschang Erik, Truschel Theresa, de Diego Juana, Lunelli Michele, Rohde Manfred, May Tobias, et al. 2017. “Flagellin Phase‐Dependent Swimming on Epithelial Cell Surfaces Contributes to Productive Salmonella Gut Colonisation.” Cellular Microbiology 19: e12739. 10.1111/cmi.12739 [DOI] [PubMed] [Google Scholar]

[imt270042-bib-0008] 8. Dorsey, Caleb W. , Laarakker Marijke C., Humphries Andrea D., Weening Eric H., and Bäumler Andreas J.. 2005. “ Salmonella enterica Serotype Typhimurium MisL is an Intestinal Colonization Factor That Binds Fibronectin.” Molecular Microbiology 57: 196–211. 10.1111/j.1365-2958.2005.04666.x [DOI] [PubMed] [Google Scholar]

[imt270042-bib-0009] 9. Kim, Jin Seok , Eom Jeong Seon, Jang Jung Im, Kim Hyeon Guk, Seo Doo Won, Bang Iel‐Soo, Bang Seong Ho, Lee In Soo, and Park Yong Keun. 2011. “Role of Salmonella Pathogenicity Island 1 Protein IacP in Salmonella enterica Serovar Typhimurium Pathogenesis.” Infection and Immunity 79: 1440–1450. 10.1128/iai.01231-10 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0010] 10. Penadés, José R. , Chen John, Quiles‐Puchalt Nuria, Carpena Nuria, and Novick Richard P.. 2015. “Bacteriophage‐Mediated Spread of Bacterial Virulence Genes.” Current Opinion in Microbiology 23: 171–178. 10.1016/j.mib.2014.11.019 [DOI] [PubMed] [Google Scholar]

[imt270042-bib-0011] 11. Chen, Juan , Wang Peifang, Wang Chao, Wang Xun, Miao Lingzhan, Liu Sheng, Yuan Qiusheng, and Sun Shenghao. 2020. “Fungal Community Demonstrates Stronger Dispersal Limitation and Less Network Connectivity Than Bacterial Community in Sediments Along a Large River.” Environmental Microbiology 22: 832–849. 10.1111/1462-2920.14795 [DOI] [PubMed] [Google Scholar]

[imt270042-bib-0012] 12. Chen, Juan , Wang Chao, Pan Ying, Farzana Shazia Shyla, and Tam Nora Fung‐Yee. 2018. “Biochar Accelerates Microbial Reductive Debromination of 2,2’,4,4’‐Tetrabromodiphenyl Ether (BDE‐47) in Anaerobic Mangrove Sediments.” Journal of Hazardous Materials 341: 177–186. 10.1016/j.jhazmat.2017.07.063 [DOI] [PubMed] [Google Scholar]

[imt270042-bib-0013] 13. Sekizuka, Tsuyoshi , Yamamoto Akihiko, Komiya Takako, Kenri Tsuyoshi, Takeuchi Fumihiko, Shibayama Keigo, Takahashi Motohide, Kuroda Makoto, and Iwaki Masaaki. 2012. “ Corynebacterium ulcerans 0102 Carries the Gene Encoding Diphtheria Toxin on a Prophage Different From the C‐diphtheriae NCTC 13129 Prophage.” BMC Microbiology 12: 72. 10.1186/1471-2180-12-72 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0014] 14. Fillol‐Salom, Alfred , Bacigalupe Rodrigo, Humphrey Suzanne, Chiang Yin Ning, Chen John, and Penadés José R.. 2021. “Lateral Transduction Is Inherent to the Life Cycle of the Archetypical Salmonella Phage P22.” Nature Communications 12: 6510. 10.1038/s41467-021-26520-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0015] 15. Humphrey, Suzanne , Fillol‐Salom Alfred, Quiles‐Puchalt Nuria, Ibarra‐Chávez Rodrigo, Haag Andreas F., Chen John, and Penadés José R.. 2021. “Bacterial Chromosomal Mobility via Lateral Transduction Exceeds That of Classical Mobile Genetic Elements.” Nature Communications 12: 6509. 10.1038/s41467-021-26004-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0016] 16. Altier, Craig , Suyemoto Mitsu, and Lawhon Sara D.. 2000. “Regulation of Salmonella enterica Serovar Typhimurium Invasion Genes by Csra .” Infection and Immunity 68: 6790–6797. 10.1128/IAI.68.12.6790-6797.2000 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0017] 17. Lawhon, Sara D. , Frye Jonathan G., Suyemoto Mitsu, Porwollik Steffen, McClelland Michael, and Altier Craig. 2003. “Global Regulation by CsrA in Salmonella Typhimurium.” Molecular Microbiology 48: 1633–1645. 10.1046/j.1365-2958.2003.03535.x [DOI] [PubMed] [Google Scholar]

[imt270042-bib-0018] 18. Fortune, Doreen R. , Suyemoto Mitsu, and Altier Craig. 2006. “Identification of CsrC and Characterization of Its Role in Epithelial Cell Invasion in Salmonella enterica Serovar Typhimurium.” Infection and Immunity 74: 331–339. 10.1128/IAI.74.1.331-339.2006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0019] 19. El Mouali, Youssef , Esteva‐Martínez Guillem, García‐Pedemonte David, and Balsalobre Carlos. 2020. “Differential Regulation of CsrC and CsrB by CRP‐cAMP in Salmonella enterica .” Frontiers in Microbiology 11: 570536. 10.3389/fmicb.2020.570536 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0020] 20. Teplitski, Max , Goodier Robert I., and Ahmer Brian M. M.. 2003. “Pathways Leading from BarA/SirA to Motility and Virulence Gene Expression in Salmonella .” Journal of Bacteriology 185: 7257–7265. 10.1128/JB.185.24.7257-7265.2003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0021] 21. Ko, Minsu , and Park Chankyu. 2000. “Two Novel Flagellar Components and H‐NS Are Involved in the Motor Function of Escherichia coli .” Journal of Molecular Biology 303: 371–382. 10.1006/jmbi.2000.4147 [DOI] [PubMed] [Google Scholar]

[imt270042-bib-0022] 22. Sun, Dongchang , Mao Xudan, Fei Mingyue, Chen Ziyan, Zhu Tingheng, and Qiu Juanping. 2020. “Histone‐Like Nucleoid‐Structuring Protein H‐NS Paralogue StpA Activates the Type I‐E CRISPR‐Cas System Against Natural Transformation in Escherichia coli .” Applied and Environmental Microbiology 86: e00731‐20. 10.1128/AEM.00731-20 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0023] 23. Nava‐Galeana, Jessica , Yakhnin Helen, Babitzke Paul, and Bustamante Víctor H.. 2023. “CsrA Positively and Directly Regulates the Expression of the pdu, pocR, and eut Genes Required for the Luminal Replication of Salmonella Typhimurium.” Microbiology Spectrum 11: e01516‐23. 10.1128/spectrum.01516-23 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0024] 24. Meng, Xia , He Mengping, Chen Binjie, Xia Pengpeng, Wang Jinqiu, Zhu Chunhong, Wang Heng, and Zhu Guoqiang. 2023. “RyhB Paralogs Downregulate the Expressions of Multiple Survival‐Associated Genes and Attenuate the Survival of Salmonella Enteritidis in the Chicken Macrophage HD11.” Microorganisms 11: 214. 10.3390/microorganisms11010214 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0025] 25. Zhang, Zengfeng , Kuang Dai, Xu Xuebin, Zhan Zeqiang, Ren Hao, and Shi Chunlei. 2024. “Dissemination of IncC Plasmids in Salmonella enterica Serovar Thompson Recovered From Seafood and Human Diarrheic Patients in China.” International Journal of Food Microbiology 417: 110708. 10.1016/j.ijfoodmicro.2024.110708 [DOI] [PubMed] [Google Scholar]

[imt270042-bib-0026] 26. Lin, Jiafu , Lv Hua, Wang Tiantian, Tao Hongkun, Zhong Yi, Zhou Yang, Tang Yibo, et al. 2024. “The Global Distribution of the Macrolide Esterase EstX From the Alpha/Beta Hydrolase Superfamily.” Communications Biology 7: 781. 10.1038/s42003-024-06473-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0027] 27. Wang, Yanan , Xu Xuebin, Zhu Baoli, Lyu Na, Liu Yue, Ma Sufang, Jia Shulei, et al. 2023. “Genomic Analysis of Almost 8,000 Salmonella Genomes Reveals Drivers and Landscape of Antimicrobial Resistance in China.” Microbiology Spectrum 11: e02080‐23. 10.1128/spectrum.02080-23 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0028] 28. Wang, Zining , Zhou Haiyang, Liu Yuhao, Huang Chenghu, Chen Jiaqi, Siddique Abubakar, Yin Rui, et al 2024. “Nationwide Trends and Features of Human Salmonellosis Outbreaks in China.” Emerging Microbes & Infections 13: 2372364. 10.1080/22221751.2024.2372364 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0029] 29. Smillie, Chris S. , Smith Mark B., Friedman Jonathan, Cordero Otto X., David Lawrence A., and Alm Eric J.. 2011. “Ecology Drives a Global Network of Gene Exchange Connecting the Human Microbiome.” Nature 480: 241–244. 10.1038/nature10571 [DOI] [PubMed] [Google Scholar]

[imt270042-bib-0030] 30. Glazko, Galina , Makarenkov Vladimir, Liu Jing, and Mushegian Arcady. 2007. “Evolutionary History of Bacteriophages With Double‐Stranded DNA Genomes.” Biology Direct 2: 36. 10.1186/1745-6150-2-36 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0031] 31. Marraffini, Luciano A. , and Sontheimer Erik J.. 2008. “CRISPR Interference Limits Horizontal Gene Transfer in Staphylococci by Targeting DNA.” Science 322: 1843–1845. 10.1126/science.1165771 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0032] 32. Hullinger, Allison C. , Green Virginia E., Klancher Catherine A., Dalia Triana N., and Dalia Ankur B.. 2025. “Two Transmembrane Transcriptional Regulators Coordinate to Activate Chitin‐Induced Natural Transformation in Vibrio cholerae .” PLoS Genetics 21: e1011606. 10.1371/journal.pgen.1011606 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0033] 33. Potts, Anastasia H. , Vakulskas Christopher A., Pannuri Archana, Yakhnin Helen, Babitzke Paul, and Romeo Tony. 2017. “Global Role of the Bacterial Post‐Transcriptional Regulator CsrA Revealed by Integrated Transcriptomics.” Nature Communications 8: 1596. 10.1038/s41467-017-01613-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0034] 34. Renda, Andrew , Poly Stephanie, Lai Ying‐Jung, Pannuri Archana, Yakhnin Helen, Potts Anastasia H., Bevilacqua Philip C., Romeo Tony, and Babitzke Paul. 2020. “CsrA‐Mediated Translational Activation of ymdA Expression in Escherichia coli .” mBio 11: e00849‐20. 10.1128/mBio.00849-20 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0035] 35. Butz, Heidi A. , Mey Alexandra R., Ciosek Ashley L., Crofts Alexander A., Davies Bryan W., and Payne Shelley M.. 2021. “Regulatory Effects of CsrA in Vibrio cholerae .” mBio 12: e03380‐20. 10.1128/mBio.03380-20 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0036] 36. Gorelik, Mark G. , Yakhnin Helen, Pannuri Archana, Walker Alyssa C., Pourciau Christine, Czyz Daniel, Romeo Tony, and Babitzke Paul. 2024. “Multitier Regulation of the E. coli Extreme Acid Stress Response by CsrA.” Journal of Bacteriology 206: e00354‐23. 10.1128/jb.00354-23. [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0037] 37. Han, Jing , Tang Hailin, Zhao Shaohua, and Foley Steven L.. 2024. “ Salmonella enterica Virulence Databases and Bioinformatic Analysis Tools Development.” Scientific Reports 14: 25228. 10.1038/s41598-024-74124-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0038] 38. Xi, Xi , Li Jiaqi, Jia Jinmeng, Meng Qiuchen, Li Chen, Wang Xiaowo, Wei Lei, and Zhang Xuegong. 2025. “A Mechanism‐Informed Deep Neural Network Enables Prioritization of Regulators That Drive Cell State Transitions.” Nature Communications 16: 1284. 10.1038/s41467-025-56475-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0039] 39. Li, Bi , Han Li‐Li, Du Shuai, Yu Dan‐Ting, He Ji‐Zheng, Zhang Li‐Mei, and Hu Hang‐Wei. 2023. “Cross‐Biome Soil Viruses as an Important Reservoir of Virulence Genes.” Journal of Hazardous Materials 442: 130111. 10.1016/j.jhazmat.2022.130111 [DOI] [PubMed] [Google Scholar]

[imt270042-bib-0040] 40. Stanaway, Jeffrey D. , Parisi Andrea, Sarkar Kaushik, Blacker Brigette F., Reiner Robert C., Hay Simon I., Nixon Molly R., et al. 2019. “The Global Burden of Non‐Typhoidal Salmonella Invasive Disease: A Systematic Analysis for the Global Burden of Disease Study 2017.” The Lancet Infectious Diseases 19: 1312–1324. 10.1016/S1473-3099(19)30418-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0041] 41. Weller, Daniel L. , Tierney Reese, Verlander Sarah, Bruce Beau B., and Rose Erica Billig. 2024. “Disparities in Salmonellosis Incidence for US Counties With Different Social Determinants of Health Profiles Are Also Mediated by Extreme Weather: A Counterfactual Analysis of Laboratory Enteric Disease Surveillance (LEDS) Data From 1997 Through 2019.” Journal of Food Protection 87: 100379. 10.1016/j.jfp.2024.100379 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0042] 42. Li, Dezhi , Liang Wei, Huang Zhiqiang, Ma Wenwen, and Liu Qing. 2024. “The Spontaneously Produced Lysogenic Prophage phi456 Promotes Bacterial Resistance to Adverse Environments and Enhances the Colonization Ability of Avian Pathogenic Escherichia coli Strain DE456.” Veterinary Research 55: 37. 10.1186/s13567-024-01292-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0043] 43. Zhou, Zhemin , Alikhan Nabil‐Fareed, Mohamed Khaled, Fan Yulei, and Agama Study Group, and Mark Achtman . 2020. “The EnteroBase User's Guide, With Case Studies on Salmonella Transmissions, Yersinia pestis Phylogeny, and Escherichia Core Genomic Diversity.” Genome Research 30: 138–152. 10.1101/gr.251678.119 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0044] 44. Camargo, Antonio Pedro , Nayfach Stephen, Chen I‐Min A, Palaniappan Krishnaveni, Ratner Anna, Chu Ken, Ritter Stephan J., et al. 2023. “IMG/VR v4: An Expanded Database of Uncultivated Virus Genomes Within a Framework of Extensive Functional, Taxonomic, and Ecological Metadata.” Nucleic Acids Research 51: D733–D743. 10.1093/nar/gkac1037 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0045] 45. Moreno‐Gallego, Jaime Leonardo , and Reyes Alejandro. 2021. “Informative Regions in Viral Genomes.” Viruses 13: 1164. 10.3390/v13061164 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0046] 46. Shaffer, Michael , Borton Mikayla A., McGivern Bridget B., Zayed Ahmed A., La Rosa Sabina Leanti, Solden Lindsey M., Liu Pengfei, et al. 2020. “DRAM for Distilling Microbial Metabolism to Automate the Curation of Microbiome Function.” Nucleic Acids Research 48: 8883–8900. 10.1093/nar/gkaa621 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0047] 47. Edgar, Robert C . 2004. “MUSCLE: A Multiple Sequence Alignment Method With Reduced Time and Space Complexity.” BMC Bioinformatics 5: 113. 10.1186/1471-2105-5-113 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0048] 48. Nguyen, Lam‐Tung , Schmidt Heiko A., von Haeseler Arndt, and Minh Bui Quang. 2015. “IQ‐TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum‐Likelihood Phylogenies.” Molecular Biology and Evolution 32: 268–274. 10.1093/molbev/msu300 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0049] 49. Hall, B. G . 2013. “Building Phylogenetic Trees From Molecular Data With MEGA.” Molecular Biology and Evolution 30: 1229–1235. 10.1093/molbev/mst012 [DOI] [PubMed] [Google Scholar]

[imt270042-bib-0050] 50. Shang, Jiayu , Tang Xubo, and Sun Yanni. 2023. “PhaTYP: Predicting the Lifestyle for Bacteriophages Using BERT.” Briefings in Bioinformatics 24: bbac487. 10.1093/bib/bbac487 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0051] 51. Grissa, I. , Vergnaud G., and Pourcel C.. 2007. “CRISPRFinder: A Web Tool to Identify Clustered Regularly Interspaced Short Palindromic Repeats.” Nucleic Acids Research 35: W52–W57. 10.1093/nar/gkm360 [DOI] [PMC free article] [PubMed] [Google Scholar]

[imt270042-bib-0052] 52. Edwards, Robert A. , McNair Katelyn, Faust Karoline, Raes Jeroen, and Dutilh Bas E.. 2016. “Computational Approaches to Predict Bacteriophage‐Host Relationships.” FEMS Microbiology Reviews 40: 258–272. 10.1093/femsre/fuv048 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Phage‐mediated horizontal transfer of Salmonella enterica virulence genes with regulatory feedback from the host

Tianjing She

Demeng Tan

Jose Luis Balcazar

Ville‐Petri Friman

Danrui Wang

Dong Zhu

Mao Ye

Mingming Sun

Shujian Yuan

Feng Hu

Abstract

Highlights

INTRODUCTION

RESULTS

Geographic distribution of S. enterica virulence genes carrying bacteria and phages

FIGURE 1.

FIGURE 2.

Phage‐mediated horizontal transfer of virulence genes

FIGURE 3.

The effect of S. enterica regulator csrA on phage‐mediated HGT

FIGURE 4.

FIGURE 5.

DISCUSSION

Virulence genes of S. enterica are widely found in phages

Counteracting effects of S. enterica and phages on horizontal transfer of virulence genes

Phage‐mediated horizontal transfer of S. enterica virulence genes requires further attention

CONCLUSION

METHODS

Global distribution of S. enterica and phages

Taxonomic assignment and functional annotation of phages

Phylogenetic analysis, phage lifestyle, and putative host prediction

Bench trials to validate the role of regulators on HGT of S. enterica virulence genes

Statistical analysis

AUTHOR CONTRIBUTIONS

CONFLICT OF INTEREST STATEMENT

ETHICS STATEMENT

Supporting information

ACKNOWLEDGMENTS

Contributor Information

DATA AVAILABILITY STATEMENT

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases