Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

A role for arthropods as vectors of multidrug-resistant Enterobacterales in surgical site infections from South Asia

Nature Microbiology volume 6, pages 1259–1270 (2021)Cite this article

Subjects

Abstract

Understanding how multidrug-resistant Enterobacterales (MDRE) are transmitted in low- and middle-income countries (LMICs) is critical for implementing robust policies to curb the increasing burden of antimicrobial resistance (AMR). Here, we analysed samples from surgical site infections (SSIs), hospital surfaces (HSs) and arthropods (summer and winter 2016) to investigate the incidence and transmission of MDRE in a public hospital in Pakistan. We investigated Enterobacterales containing resistance genes (blaCTX-M-15, blaNDM and blaOXA-48-like) for identification, antimicrobial susceptibility testing and whole-genome sequencing. Genotypes, phylogenetic relationships and transmission events for isolates from different sources were investigated using single-nucleotide polymorphism (SNP) analysis with a cut-off of ≤20 SNPs. Escherichia coli (14.3%), Klebsiella pneumoniae (10.9%) and Enterobacter cloacae (16.3%) were the main MDRE species isolated. The carbapenemase gene blaNDM was most commonly detected, with 15.5%, 15.1% and 13.3% of samples positive in SSIs, HSs and arthropods, respectively. SNP (≤20) and spatiotemporal analysis revealed linkages in bacteria between SSIs, HSs and arthropods supporting the One Health approach to underpin infection control policies across LMICs and control AMR.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Study flowchart.
Fig. 2: The distribution of resistance genes among HSs and species of arthropods represented as rose graphs.
Fig. 3: Antibiotic resistance profiles among different sources.
Fig. 4: E. coli phylogenetic and transmission analysis.
Fig. 5: E. cloacae phylogenetic and transmission analysis.

Similar content being viewed by others

Data availability

Sequences reads have been submitted to the European Nucleotide Archive (ENA) under the project number PRJEB40861. A list of individual accession numbers is provided in Supplementary Data 2. The following databases were used: NCBI (https://github.com/tseemann/abricate/tree/master/db/ncbi); Resfinder v.4.0 (https://cge.cbs.dtu.dk/services/ResFinder/); Plasmidfinder v.2.0 (https://cge.cbs.dtu.dk/services/PlasmidFinder/); MLST v.2.0.4 (https://cge.cbs.dtu.dk/services/MLST/), with MLST allele and profile data from https://pubmlst.org; Serotype finder (https://bitbucket.org/genomicepidemiology/serotypefinder/src/master). Source data are provided with this paper.

References

  1. Stewardson, A. J. et al. Effect of carbapenem resistance on outcomes of bloodstream infection caused by Enterobacteriaceae in low-income and middle-income countries (PANORAMA): a multinational prospective cohort study. Lancet Infect. Dis. 19, 601–610 (2019).

    Article  CAS  PubMed  Google Scholar 

  2. Logan, L. K. & Weinstein, R. A. The epidemiology of carbapenem-resistant Enterobacteriaceae: the impact and evolution of a global menace. J. Infect. Dis. 215, S28–S36 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Li, J. et al. Inter-host transmission of Carbapenemase-producing Escherichia coli among humans and backyard animals. Environ. Health Perspect. 127, 107009 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Ludden, C. et al. One Health genomic surveillance of Escherichia coli demonstrates distinct lineages and mobile genetic elements in isolates from humans versus livestock. mBio 10, e02693-18 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Davies, S. Antibiotic overuse. Aust. Nurs. Midwifery J. 21, 55 (2014).

    Google Scholar 

  6. Laxminarayan, R. et al. Antibiotic resistance—the need for global solutions. Lancet Infect. Dis. 13, 1057–1098 (2013).

    Article  PubMed  Google Scholar 

  7. Andremont, A. & Walsh, T. The role of sanitation in the development and spread of antimicrobial resistance. Glob. Heal. Dyn. 67, 68–73 (AMR Control, 2015); http://resistancecontrol.info/wp-content/uploads/2017/07/10_Andremont-Walsh.pdf

  8. O’Neill, J. Infection prevention, control and surveillance: limiting the development and spread of drug resistance. Rev. Antimicrob. Resist. (2016); https://amr-review.org/sites/default/files/Health%20infrastructure%20and%20surveillance%20final%20version_LR_NO%20CROPS.pdf

  9. Wang, Y. et al. Comprehensive resistome analysis reveals the prevalence of NDM and MCR-1 in Chinese poultry production. Nat. Microbiol. 2, 16260 (2017).

    Article  CAS  PubMed  Google Scholar 

  10. Fukuda, A. et al. Co-harboring of cephalosporin (bla)/colistin (mcr) resistance genes among Enterobacteriaceae from flies in Thailand. FEMS Microbiol. Lett. 365, fny178 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Guenther, S. et al. Environmental emission of multiresistant Escherichia coli carrying the colistin resistance gene mcr-1 from German swine farms. J. Antimicrob. Chemother. 72, 1289–1292 (2017).

    Article  CAS  PubMed  Google Scholar 

  12. Alves, T. et al. Carrier flies of multidrug-resistant Escherichia coli as potential dissemination agent in dairy farm environment. Sci. Total Environ. 633, 1345–1351 (2018).

    Article  CAS  PubMed  Google Scholar 

  13. Poudel, A. et al. Multidrug-resistant Escherichia coli, Klebsiella pneumoniae and Staphylococcus spp. in houseflies and blowflies from farms and their environmental settings. Int J. Environ. Res. Publ. Health 16, 3583 (2019).

    Article  CAS  Google Scholar 

  14. Solà-Ginés, M. et al. Houseflies (Musca domestica) as vectors for extended-spectrum β-lactamase-producing Escherichia coli on Spanish broiler farms. Appl. Environ. Microbiol. 81, 3604–3611 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Onwugamba, F. C. et al. The role of ‘filth flies’ in the spread of antimicrobial resistance. Travel Med. Infect. Dis. 22, 8–17 (2018).

    Article  PubMed  Google Scholar 

  16. Moges F. et al. Cockroaches as a source of high bacterial pathogens with multidrug resistant strains in Gondar Town, Ethiopia. BioMed Res. Int. 2016, 2825056 (2016).

  17. Obeng-Nkrumah, N. et al. Household cockroaches carry CTX-M-15-, OXA-48- and NDM-1-producing enterobacteria, and share beta-lactam resistance determinants with humans. BMC Microbiol. 19, 272 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wannigama, D. L., Dwivedi, R. & Zahraei-Ramazani, A. Prevalence and antibiotic resistance of Gram-negative pathogenic bacteria species isolated from Periplaneta americana and Blattella germanica in Varanasi, India. J. Arthropod Borne Dis. 8, 10–20 (2014).

    PubMed  Google Scholar 

  19. Tang, Q., Bourguignon, T., Willenmse, L., De Coninck, E. & Evans, T. Global spread of the German cockroach, Blattella germanica. Biol. Invasions 21, 693–707 (2019).

    Article  Google Scholar 

  20. Memona, H., Manzoor, F. & Riaz, S. Species diversity and distributional pattern of cockroaches in Lahore, Pakistan. J. Arthropod Borne Dis. 11, 239–249 (2017).

    Google Scholar 

  21. National Healthcare Safety Network Surgical Site Infection Event (SSI) (CDC, 2021).

  22. Roer, L. et al. Escherichia coli sequence type 410 is causing new international high-risk clones. mSphere 3, e00337-18 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Schaufler, K. et al. Clonal spread and interspecies transmission of clinically relevant ESBL-producing Escherichia coli of ST410—another successful pandemic clone? FEMS Microbiol. Ecol. 92, fiv155 (2016).

  24. Forde, B. M. et al. Population dynamics of an Escherichia coli ST131 lineage during recurrent urinary tract infection. Nat. Commun. 10, 3643 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Ewers, C. et al. Clonal spread of highly successful ST15-CTX-M-15 Klebsiella pneumoniae in companion animals and horses. J. Antimicrob. Chemother. 69, 2676–2680 (2014).

    Article  CAS  PubMed  Google Scholar 

  26. Lee, M. Y. et al. High prevalence of CTX-M-15-producing Klebsiella pneumoniae isolates in Asian countries: diverse clones and clonal dissemination. Int. J. Antimicrob. Agents 38, 160–163 (2011).

    Article  CAS  PubMed  Google Scholar 

  27. Reeves, P. R. et al. Rates of mutation and host transmission for an Escherichia coli clone over 3 years. PLoS ONE 6, e26907 (2011).

  28. Schürch, A. C., Arredondo-Alonso, S., Willems, R. J. L. & Goering, R. V. Whole genome sequencing options for bacterial strain typing and epidemiologic analysis based on single nucleotide polymorphism versus gene-by-gene–based approaches. Clin. Microbiol. Infect. 24, 350–354 (2018).

    Article  PubMed  CAS  Google Scholar 

  29. D’Souza, A. W. et al. Spatiotemporal dynamics of multidrug resistant bacteria on intensive care unit surfaces. Nat. Commun. 10, 4569 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Khan, B. A., Cheng, L., Khan, A. A. & Ahmed, H. Healthcare waste management in Asian developing countries: a mini review. Waste Manag. Res. 37, 863–875 (2019).

    Article  PubMed  Google Scholar 

  31. Ali, M., Chaudhry, N. & Wang, W. Assessment of hospital waste management in a major city of Pakistan. Int. J. Environ. Waste Manag. 19, 97–104 (2017).

    Article  Google Scholar 

  32. Raka, L. Prevention and control of hospital-related infections in low and middle income countries. Open Infect. Dis. J. 4, 125–131 (2010).

    Article  Google Scholar 

  33. Hawkey, P. M. Multidrug-resistant Gram-negative bacteria: a product of globalization. J. Hosp. Infect. 89, 241–247 (2015).

    Article  CAS  PubMed  Google Scholar 

  34. Goulson, D., Derwent, L. C., Hanley, M. E., Dunn, D. W. & Abolins, S. R. Predicting calyptrate fly populations from the weather, and probable consequences of climate change. J. Appl. Ecol. 42, 795–804 (2005).

    Article  Google Scholar 

  35. Kreft, S. & Eckstein, D. Global Climate Risk Index 2014: Who Suffers Most from Extreme Weather Events? Weather-Related Loss Events in 2012 and 1993 to 2012 Briefing Paper (Germanwatch, 2019); https://reliefweb.int/sites/reliefweb.int/files/resources/8551.pdf

  36. Connor, T. R. et al. CLIMB (the Cloud Infrastructure for Microbial Bioinformatics): an online resource for the medical microbiology community. Microb. Genom. 2, https://doi.org/10.1099/mgen.0.000086 (2016).

  37. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).

    Article  Google Scholar 

  38. Andrews, S. FastQC: a quality control tool for high throughput sequence data (2009); http://www.bioinformatics.babraham.ac.uk/projects/fastqc

  39. Ewels, P., Lundin, S. & Max, K. Data and text mining MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Bankevich, A. et al. Its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 9, e112963 (2014).

  45. Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Thomsen, M. C. F. et al. A bacterial analysis platform: an integrated system for analysing bacterial whole genome sequencing data for clinical diagnostics and surveillance. PLoS ONE 11, e0157718 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Chen, L., Zheng, D., Liu, B., Yang, J. & Jin, Q. VFDB 2016: hierarchical and refined dataset for big data analysis—10 years on. Nucleic Acids Res. 44, D694–D697 (2016).

    Article  CAS  PubMed  Google Scholar 

  48. Seemann, T. Aabricate (2021); https://github.com/tseemann/abricate.git

  49. Kaper, J. B., Nataro, J. P. & Mobley, H. L. T. Pathogenic Escherichia coli. Nat. Rev. Microbiol. 2, 123–140 (2004).

    Article  CAS  PubMed  Google Scholar 

  50. Lam, M. M. C. et al. Genomic surveillance framework and global population structure for Klebsiella pneumoniae. Preprint at bioRxiv (2021); https://doi.org/10.1101/2020.12.14.422303

  51. Ingle, D. J. et al. In silico serotyping of E. coli from short read data identifies limited novel O-loci but extensive diversity of O:H serotype combinations within and between pathogenic lineages. Microb. Genom. 2, e000064 (2016).

  52. Beghain, J., Bridier-Nahmias, A., Le, NagardH., Denamur, E. & Clermont, O. ClermonTyping: an easy-to-use and accurate in silico method for Escherichia genus strain phylotyping. Microb. Genom. 4, e000192 (2018).

    PubMed Central  Google Scholar 

  53. Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).

    Article  CAS  PubMed  Google Scholar 

  54. Page, A. J. et al. Sequence analysis Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 31, 3691–3693 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

  56. Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. https://doi.org/10.1186/s13059-016-0997-x (2016).

  58. Seemann, T. snpiphy (2019); https://github.com/bogemad/snpiphy

  59. Shannon, P. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We acknowledge the staff at the Specialist Antimicrobial Chemotherapy Unit (SACU) for technical assistance for MALDI–TOF identification of isolates; the staff at Liofilchem (Roseto) for providing materials and consumables for the study; and the staff at the Wales Gene Park and ARCCA for providing support and infrastructure to perform bioinformatics analysis. We thank the team of curators of the databases at EnteroBase (https://enterobase.warwick.ac.uk/) and the Institute Pasteur MLST (https://bigsdb.pasteur.fr/) for curating the MLST datasets; and all of the staff at KTH, Peshawar, for their help with this study. This study was funded by Ser Cymru Life Science Research Network Wales.

Author information

Authors and Affiliations

  1. Institute of Infection and Immunity, Cardiff University, Cardiff, UK

    Brekhna Hassan, Kirsty Sands, Georgios-Ion Serfas, Liam Clayfield, Giulia Lai, Edward Portal, William J. Watkins & Timothy R. Walsh

  2. Khyber Teaching Hospital, Peshawar, Pakistan

    Muhammad Ijaz & Asadullah Khan

  3. Ineos Institute of Antimicrobial Research, Department of Zoology, University of Oxford, Oxford, UK

    Kirsty Sands & Timothy R. Walsh

  4. Pharmaceutical Microbiology Department, Faculty of Pharmacy, Tanta University, Tanta, Egypt

    Maisra Mohammed El-Bouseary

  5. School of Biosciences, Cardiff University, Cardiff, UK

    Afifah Khan

  6. Department of Veterinary Medicine, University of Cambridge, Cambridge, USA

    Julian Parkhill

Authors
  1. Brekhna Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Ijaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Asadullah Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kirsty Sands
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Georgios-Ion Serfas
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Liam Clayfield
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Maisra Mohammed El-Bouseary
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Giulia Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Edward Portal
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Afifah Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. William J. Watkins
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Julian Parkhill
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Timothy R. Walsh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

B.H. and T.R.W. designed and guided the study and analysis. M.I. and Asadullah Khan provided the epidemiological and clinical dataset and collected the samples in this study. B.H., K.S. and T.R.W. wrote the manuscript. B.H., G.-I.S., L.C., M.M.E.-B., G.L. and Afifah Khan performed microbiology culture and sample processing in Cardiff University. B.H., K.S. and E.P. performed WGS experiments. K.S. and B.H. performed bioinformatics analysis, following guidance from J.P. W.J.W. and B.H. performed statistical analysis.

Corresponding author

Correspondence to Brekhna Hassan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Microbiology thanks Samuel Kariuki and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Prevalence of resistance genes from different sources and seasons.

Bar chart representing occurrence of blaNDM, blaOXA-48-like and blaCTX-M-15 among collected samples in percentage from arthropods (AR), SSI (surgical site infections) and hospital surfaces (HS) a. collective resistance prevalence; b. winter; c. summer.

Source data

Extended Data Fig. 2 Antibiotic resistance profiles of arthropods.

Antibiotic resistance profiles of the blaNDM and blaOXA-48-like positive isolates (n = 177) among different species of arthropods. Antibiotics are denoted as TGC = tigecycline; FOS = Fosfomycin; CIP = ciprofloxacin; CN = gentamycin; F = nitrofurantoin; AMC = amoxicillin-clavulanic acid; CTX = cefotaxime; CAZ = ceftazidime; FEP = cefepime; IPM = imipenem; MEM = meropenem; ATM = aztreonam; CS = colistin.

Source data

Extended Data Fig. 3 E. coli WGS results.

Figure showing samples source, STs, phylogroups, virulence score, antibiotic resistance genes and plasmid types among E. coli isolated from SSI, HS and arthropods (AR) samples. Sample source is identified by labelled symbols followed by STs, phylogroups and virulence score in text. Presence of antibiotic resistance genes are shown in pink and plasmid Inc groups in blue coloured squares.

Source data

Extended Data Fig. 4 E. cloacae WGS results.

Figure showing samples source, STs, virulence score, antibiotic resistance genes and plasmid types among E. cloacae isolated from SSI, HS and arthropods (AR) samples. Sample source is identified by labelled symbols followed by STs and virulence score in text. Presence of antibiotic resistance genes are shown in pink and plasmid Inc groups in blue coloured squares.

Source data

Extended Data Fig. 5 K. pneumoniae WGS results.

Figure showing samples source, STs, capsular types, virulence score, antibiotic resistance genes and plasmid types among K. pneumoniae isolated from SSI, HS and arthropods (AR) samples. Sample source is identified by labelled symbols followed by STs, capsular types and virulence score in text. Presence of antibiotic resistance genes are shown in pink and plasmid Inc groups in blue coloured squares.

Source data

Extended Data Fig. 6 K. pneumoniae phylogenetic and transmission analysis.

Phylogenetic and transmission analysis of K. pneumoniae. a, The core phylogenetic tree between samples derived from this study, including the reference genomes extracted from the NCBI database. The sources are shown as coloured circles on branches (SSIs from this study and clinical sample from databases (pink); arthropods from this study and animals originating from databases (blue); HSs from this study and environmental samples from databases (green)). ST and geographical location are displayed as text on the outside. Isolates of this study are labelled in red font and clades of ≥3 isolates from this study are shown in different coloured ranges. Missing information is denoted by NA (not available). b, The network chart between samples of different origins with ≤20 SNPs is shown. Connections between samples are represented by different styles of lines corresponding to the number of SNPs (0–5 (solid lines), 6–10 (dashed lines) and 11–20 (dotted lines) SNPs). The background colour represents the origin of the isolates (SSIs (pink); HSs (green) and arthropods (blue (cockroach) and orange (flies)) and the shape represents the ward of isolation (female (round) and male (rectangle)). The presence of target resistance genes for each sample is shown by the outline colour (blaCTX-M-15 (green), blaCTX-M-15 and blaNDM (black), blaCTX-M-15 and blaOXA-48-like (pink), blaNDM (red) and blaOXA-48-like (blue)). The exact location of the surfaces or species of insect is shown as text within the network edges.

Source data

Extended Data Fig. 7 SNP threshold sensitivity.

The histogram represents the number of transmission links at each SNP-cutoff point (0-50) among samples between SSI, HS and arthropods (AR) from isolates of a. E. coli, b. E. cloacae and c. K. pneumoniae.

Source data

Extended Data Fig. 8 Network chart of E. coli grouped by seasons.

The network chart between samples collected in summer and winter with <20 SNPs. Connections between samples are represented by different styles of lines corresponding to the number of SNPs (⎼⎼⎼ = 0 – 5, – ∙ – ∙ – = 6 – 10 and ∙ ∙∙∙∙∙∙∙∙ = 11– 20 SNPs). Colours of the edge represent the origin of the isolates (SSI: pink; HS: green and arthropods: blue and orange [cockroach: blue; ant: yellow; spider: purple and fly: orange] and shape represent ward of isolation (round: female and rectangle: male). The presence of target resistance genes for each sample are shown by outline colour of the edges (green: blaCTX-M-15, black: blaCTX-M-15 and blaNDM, pink: blaCTX-M-15 and blaOXA-48-like, red: blaNDM, blue: blaOXA-48-like). The exact location of surfaces or species of insect is shown as text within the network edges.

Source data

Extended Data Fig. 9 Network chart of K. pneumoniae grouped by seasons.

The network chart between samples collected in summer and winter with <20 SNPs. Connections between samples are represented by different styles of lines corresponding to the number of SNPs (⎼⎼⎼ = 0 – 5, – ∙ – ∙ – = 6 – 10 and ∙ ∙∙∙∙∙∙∙∙ = 11– 20 SNPs). Colours of the edge represent the origin of the isolates (SSI: pink; HS: green and arthropods: blue and orange [cockroach: blue; ant: yellow; spider: purple and fly: orange] and shape represent ward of isolation (round: female and rectangle: male). The presence of target resistance genes for each sample are shown by outline colour of the edges (green: blaCTX-M-15, black: blaCTX-M-15 and blaNDM, pink: blaCTX-M-15 and blaOXA-48-like, red: blaNDM, blue: blaOXA-48-like). The exact location of surfaces or species of insect is shown as text within the network edges.

Source data

Extended Data Fig. 10 Network chart of E. cloacae grouped by seasons.

The network chart between samples collected in summer and winter with <20 SNPs. Connections between samples are represented by different styles of lines corresponding to the number of SNPs (⎼⎼⎼ = 0 – 5, – ∙ – ∙ – = 6 – 10 and ∙ ∙∙∙∙∙∙∙∙ = 11– 20 SNPs). Colours of the edge represent the origin of the isolates (SSI: pink; HS: green and arthropods: blue and orange [cockroach: blue; ant: yellow; spider: purple and fly: orange] and shape represent ward of isolation (round: female and rectangle: male). The presence of target resistance genes for each sample are shown by outline colour of the edges (green: blaCTX-M-15, black: blaCTX-M-15 and blaNDM, pink: blaCTX-M-15 and blaOXA-48-like, red: blaNDM, blue: blaOXA-48-like). The exact location of surfaces or species of insect is shown as text within the network edges.

Source data

Supplementary information

Supplementary Information

Supplementary Figures 1-9, Supplementary Tables 1-6

Reporting Summary

Supplementary Data

Anonymised patient data

Supplementary Data

Accession Codes

Source data

Source Data Fig. 2

PCR results

Source Data Fig. 3

Antibiograms results

Source Data Fig. 4

Whole genomic sequencing and SNP analysis output (E. coli)

Source Data Fig. 5

Whole genomic sequencing and SNP analysis output (Enterobacter)

Source Data Extended Data Fig. 1

PCR results

Source Data Extended Data Fig. 2

Antibiograms results

Source Data Extended Data Fig. 3

Whole genomic sequence output (E. coli)

Source Data Extended Data Fig. 4

Whole genomic sequence output (Enterobacter)

Source Data Extended Data Fig. 5

Whole genomic sequence output (Klebsiella)

Source Data Extended Data Fig. 6

Whole genomic sequencing and SNP analysis output (Klebsiella)

Source Data Extended Data Fig. 7

SNP analysis output

Source Data Extended Data Fig. 8

SNP analysis output (E. coli)

Source Data Extended Data Fig. 9

SNP analysis output (Klebsiella)

Source Data Extended Data Fig. 10

SNP analysis output (Enterobacter)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hassan, B., Ijaz, M., Khan, A. et al. A role for arthropods as vectors of multidrug-resistant Enterobacterales in surgical site infections from South Asia. Nat Microbiol 6, 1259–1270 (2021). https://doi.org/10.1038/s41564-021-00965-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-021-00965-1

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology