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Genomic census of invasive nontyphoidal Salmonella infections reveals global and local human-to-human transmission

Nature Medicine volume 31, pages 2325–2334 (2025) Cite this article

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Abstract

Extraintestinal infections caused by Enterobacteriaceae represent a global concern, further exacerbated by the growing prevalence of antimicrobial resistance (AMR). Among these, invasive nontyphoidal Salmonella (iNTS) infections have become increasingly challenging to manage, and their global spread remains poorly understood. Here we compiled 1,115 patient records and generated a comprehensive genomic dataset on iNTS. Age and sex emerged as significant risk factors, with Salmonella Enteritidis identified as a major cause. We observed serovar-specific AMR patterns, with notable resistance to fluoroquinolones and third-generation cephalosporins. A global phylogenomic analysis of Enteritidis revealed three distinct clades, highlighting the accumulation of AMR determinants during its international spread. Importantly, our genomic and transmission analyses suggest that iNTS infections may involve human-to-human transmission, with diarrheal patients acting as potential intermediaries, deviating from typical zoonotic pathways. Collectively, our newly generated cohort and iNTS genomic dataset provide a framework for precise local iNTS burden and underscore emerging transmission trends.

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Fig. 1: Geographic distribution and temporal trend in extraintestinal cases defined as iNTS infections in China.
The alternative text for this image may have been generated using AI.
Fig. 2: Genomic characterization of 1,115 iNTS isolates for individual extraintestinal infection cases in China over the past three decades (1993–2023).
The alternative text for this image may have been generated using AI.
Fig. 3: AMR potential and their associated mobile elements among the iNTS isolates.
The alternative text for this image may have been generated using AI.
Fig. 4: Phylogenetic analysis of 1,077 global iNTS serovar Enteritidis genomes.
The alternative text for this image may have been generated using AI.
Fig. 5: The genetic relationship among the Salmonella Enteritidis isolates from different sources in China.
The alternative text for this image may have been generated using AI.

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Data availability

All complete genomic data for Salmonella is available at BioProject number PRJNA1080262. The accession numbers of these strains are listed in Supplementary Table 7. Source data are provided with this paper.

Code availability

The code used in this paper is as follows: Abricate v.1.0.1 (https://github.com/tseemann/abricate), Snippy v.4.4.4 (https://github.com/tseemann/snippy), SISTR v.1.1.1 (https://github.com/phac-nml/sistr_cmd), Seqsero2 v.1.2.1 (https://github.com/denglab/SeqSero2) SNP-dists v.0.7.0 (https://github.com/tseemann/snp-dists) IQ-TREE v.1.6.12 (https://github.com/iqtree/iqtree2). Staramr v.0.9.1 (https://github.com/phac-nml/staramr) Invasive Index (no version number; http://www.github.com/UCanCompBio/invasive_salmonella) Rhierbaps v. 1.1.4 (https://github.com/gtonkinhill/rhierbaps) BacAnt v. 3.4.0 (https://github.com/xthua/bacant) BEAST v.2.7.6 (https://beast.community/) TreeAnnotator v2.6.7 (https://beast.community/treeannotator) FigTree v.1.4.3 (http://tree.bio.ed.ac.uk/software/Figtree/) Treetime v.0.11.3 (https://github.com/neherlab/treetime) MOB-suit v.3.1.9 (https://github.com/phac-nml/mob-suite) Prokka v.1.14.6 (https://github.com/tseemann/prokka) Roary v.3.13.0 (https://sanger-pathogens.github.io/Roary/) A pipline for locating resistance genes on integrons and transposons can be found on GitHub at https://github.com/tjiaa/INTS_Code.

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Acknowledgements

This work was supported by the National Program on Key Research Project of China (grant no. 2022YFC2604201) and the EU’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 861917 – SAFFI, Zhejiang Provincial Natural Science Foundation of China (grant nos. LZ24C180002 and LR19C180001), Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City (grant no. 2021JJLH0083) and the Research Funds of Hangzhou Institute for Advanced Study, UCAS. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Haiyang Zhou, Chenghao Jia, Ping Shen.

Authors and Affiliations

  1. Department of Veterinary Medicine, College of Animal Sciences, Zhejiang University, Hangzhou, People’s Republic of China

    Haiyang Zhou, Chenghao Jia, Chenghu Huang, Zining Wang, Yan Li & Min Yue

  2. Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, People’s Republic of China

    Haiyang Zhou, Chenghao Jia, Chenghu Huang, Lin Teng, Zining Wang & Min Yue

  3. Hainan Institute of Zhejiang University, Sanya, People’s Republic of China

    Haiyang Zhou, Chenghao Jia, Chenghu Huang, Zining Wang, Yan Li & Min Yue

  4. State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, People’s Republic of China

    Ping Shen & Yonghong Xiao

  5. Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, People’s Republic of China

    Beibei Wu

  6. Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, People’s Republic of China

    Haoqiu Wang

  7. School of Clinical Medicine, University of Cambride, Cambridge, UK

    Stephen Baker

  8. Institut Pasteur, Université Paris Cité, Unité des Bactéries pathogènes entériques, Paris, France

    François-Xavier Weill

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  1. Haiyang Zhou
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Contributions

H.Z. designed and conducted experiments, analyzed, interpreted data and wrote the paper. C.J., C.H., L.T., Z.W. and Y.L. designed experiments, analyzed and interpreted data. P.S., B.W., H.W. and Y.X. provided Salmonella strains and associated patient information. S.B. and F-X.W. contributed to the revision and polishing of the paper and provided interpretation of the data. M.Y. supervised the project, revised the paper and served as the corresponding author, managing submission and journal correspondence.

Corresponding author

Correspondence to Min Yue.

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The authors declare no competing interests.

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Nature Medicine thanks Nick Thomson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Alison Farrell and Joao Monteiro, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 The percentage of bacterial bloodstream isolates in a collaborative Chinese surveillance system.

Data was collected by the Blood Bacterial Resistant Investigation Collaborative System (BRICS) in China between 2014 and 2021. In general, two major groups, Gram-negative or Gram-positive bacteria, were summarized.

Source data

Extended Data Fig. 2 The gender distribution across various Chinese regions.

(a) Gender distribution in accumulated year groups. (b) Gender distribution in various geographical locations: the regions marked with different colours on the map represent the four geographical divisions of China, including Northern, Central, Eastern, and Western China.

Source data

Extended Data Fig. 3 The predicted resistance towards fluoroquinolones.

(a) Resistance detection percentage (dot) and the number of fluoroquinolone resistance isolates (bar) in the top eleven serovars. (b) Resistance detection percentage (dot) and the number of fluoroquinolone resistance isolates (bar) across different periods.

Source data

Extended Data Fig. 4 The distribution of blaCTX-M genes.

(a) Percentage of blaCTX-M genes detection (dot) and the number of isolates carrying blaCTX-M genes (bar) in the top eleven serovars. (b) Percentage of blaCTX-M genes detection (dot) and the number of isolates carrying blaCTX-M gene (bar) across different periods.

Source data

Extended Data Fig. 5 The antimicrobial resistant determinant and plasmid for invasive non-typhoidal Salmonella (iNTS) serovars.

(a) The heat map of plasmid detection rate across various Salmonella serovars. (b) The bubble map of frequency and the total number of antimicrobial-resistant genes detected among the top three serovars (Enteritidis, Typhimurium, 4,[5],12:i:-) through the five time periods of each column (before 2010, 2011–2013, 2014–2016, 2017–2019 and 2020–2023). The colour indicates the percentage of antimicrobial resistance determinants in an individual serovar over a particular period. The bubble size indicates the number of iNTS isolates for a specific group. (c) Among the 483 Enteritidis isolates, we determined the proportion of strains carrying pSENV. The detection of pSENV was based on plasmid typing and the presence of the spv gene; plasmids that had the same plasmid type as the pSENV of Enteritidis strain P125109 and simultaneously contained the spv gene were identified as pSENV.

Source data

Extended Data Fig. 6 Temporal phylogenetic tree of global Enteritidis.

(a) Temporal phylogenetic tree produced with BEAST for a subset of 847 representative isolates. Four color-coded bars are annotated to indicate the source of isolation, HierBAPS Clade, sub-HierBAPS Clade, and cluster. The appearance time and 95% highest posterior density (HPD) intervals for the C&E Africa Clade, Global-a Clade, Global-b Clade, and Global-c Clade are labeled with corresponding color-coded fonts. (b) Results of root-to-tip regression analyses.

Source data

Extended Data Fig. 7 Antimicrobial resistance (AMR) and virulence potential of 1,077 global serovar Enteritidis isolates.

(a) AMR pattern in isolates across different regions and phylogenetic clades. (b) The invasive index of different clades and regions. The invasive index was calculated by a program that relies on the DeltaBS metric for identifying deleterious mutations in protein-coding genes. Center line, median; box limits, upper and lower quartiles.

Source data

Extended Data Fig. 8 Comparative genomic analysis of the Chinese isolates to the African isolates.

(a) Pangenome tree of 1077 global isolates. A total of 584 Shell genes (genes present in 15% to 94% of all strains) are annotated on the tree, with blue indicating the presence of a gene and white indicating its absence. Three colored bars represent the geographical origins of the isolates and their corresponding clades. (b) The prevalence of different genes across strains from Central & Eastern Africa, West Africa, China, and the rest of the world. This heatmap illustrates genes with a prevalence difference greater than 60% between strains from Central & Eastern Africa and China, along with their corresponding functional classifications. (c)The genomes of Chinese strains are compared with the representative strain D7795 from Central & Eastern Africa and analyzed the types and proportions of single nucleotide mutations. (d) Among all the variants that introduced premature stop codons, 6 genes show frequency greater than 50%. The bar chart was used to show the proportion of these variants in the Chinese strains. ‘✱’ represents the appearance of a stop codon at this position, terminating the translation of the amino acid.

Source data

Supplementary information

Reporting Summary (download PDF )

Supplementary Tables 1–7. (download XLSX )

Supplementary Table 1. The risk index of different age groups. Supplementary Table 2. Gender distribution in different regions and periods in China. Supplementary Table 3. MDR rates of strains collected from patients across different age groups. Supplementary Table 4. Information for 1,077 global Enteritidis dataset. Supplementary Table 5. Information for 800 China Enteritidis dataset. Supplementary Table 6. The detection rate (%) of MGEs in Enteritidis from different sources. Supplementary Table 7. The general information of 1,115 China iNTS cases and genomes.

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Source Data Extended Data Fig. 7 (download XLSX )

Statistical source data.

Source Data Extended Data Fig. 8 (download XLSX )

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Zhou, H., Jia, C., Shen, P. et al. Genomic census of invasive nontyphoidal Salmonella infections reveals global and local human-to-human transmission. Nat Med 31, 2325–2334 (2025). https://doi.org/10.1038/s41591-025-03644-4

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