# Escherichia coli killing by epidemiologically successful sublineages of Shigella sonnei is mediated by colicins

P. Malaka de Silva, Rebecca Bennett, Lauriane Kuhn, Patryk Ngondo, Lorine Debande, Elisabeth Njamkepo, Brian Ho, François-Xavier Weill, Benoît Marteyn, Claire Jenkins, et al.

► To cite this version:

P. Malaka de Silva, Rebecca Bennett, Lauriane Kuhn, Patryk Ngondo, Lorine Debande, et al. Escherichia coli killing by epidemiologically successful sublineages of Shigella sonnei is mediated by colicins. EBioMedicine, 2023, 97, pp.104822. 10.1016/j.ebiom.2023.104822. hal-04613482

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# *Escherichia coli* killing by epidemiologically successful sublineages of *Shigella sonnei* is mediated by colicins

<figure><img src="image_1.png" /></figure>

Check for
updates

P. Malaka De Silva,ª,h Rebecca J. Bennett,ª,h Lauriane Kuhn,ᵇ Patryk Ngondo,ᶜ Lorine Debande,ᶜ Elisabeth Njamkepo,ᵈ Brian Ho,ᵉ François-Xavier Weill,ᵈ Benoît S. Marteyn,ᶜ Claire Jenkins,¹ and Kate S. Bakerª,g,\*

oa

OPEN ACCESS

## Summary

**Background** *Shigella* sp. are enteric pathogens which causes >125 million cases of shigellosis annually. *S. sonnei* accounts for about a quarter of those cases and is increasingly prevalent in industrialising nations. Being an enteric pathogen, *S. sonnei* benefits from outcompeting gut commensals such as *Escherichia coli* to establish itself and cause disease. There are numerous mechanisms that bacterial pathogens use to outcompete its rivals including molecules called colicins. A Type 6 Secretion System (T6SS) was recently described as contributing to *E. coli* killing in *S. sonnei*.

**Methods** We used Bulk Phenotyping of Epidemiological Replicates (BPER) which combined bacterial Genome Wide Association Studies (bGWAS) and high throughput phenotyping on a collection of *S. sonnei* surveillance isolates to identify the genetic features associated with *E. coli* killing and explore their relationship with epidemiological behaviour. We further explored the presence of colicins and T6SS components in the isolates using genomics, laboratory experimentation, and proteomics.

**Findings** Our bGWAS analysis returned known and novel colicin and colicin related genes as significantly associated with *E. coli* killing. *In silico* analyses identified key colicin clusters responsible for the killing phenotype associated with epidemiologically successful sub-lineages. The killing phenotype was not associated with the presence of a T6SS. Laboratory analyses confirmed the presence of the key colicin clusters and that killing was contact-independent.

**Interpretation** Colicins are responsible for *E. coli* killing by *S. sonnei*, not a T6SS. This phenotype contributes to shaping the observed epidemiology of *S. sonnei* and may contribute to its increasing prevalence globally. BPER is an epidemiologically relevant approach to phenotypic testing that enables the rapid identification of genetic drivers of phenotypic changes, and assessment of their relevance to epidemiology in natural settings.

**Funding** Biotechnology and Biological Sciences Research Council, Biotechnology and Biological Sciences Research Council Doctoral Training Partnership studentship, Wellcome Trust, Medical Research Council (UK), French National Research Agency.

**Copyright** © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

**Keywords:** Shigella; Colicins; Interbacterial competition; T6SS

## Introduction

*Shigella* sp. cause >125 million cases of shigellosis that result in ~212,000 deaths annually and understanding

the biological drivers of its success as a pathogen is critical to public health.¹,² *S. sonnei* accounts for 24% of cases of shigellosis worldwide and contributes

eBioMedicine
2023;97: 104822

Published Online 6 October
2023

https://doi.org/10.

1016/j.ebiom.2023.

104822

<footnote>ªDepartment of Clinical Infection, Microbiology, and Immunology, Institute for Infection, Veterinary, and Ecological Sciences (IVES), University of Liverpool, Liverpool, United Kingdom</footnote>

<footnote>ᵇPlateforme protéomique Strasbourg Esplanade FR1589 du CNRS, Université de Strasbourg, Strasbourg, France</footnote>

<footnote>ᶜUniversité de Strasbourg, CNRS, Architecture et Réactivité de l'ARN, UPR9002, F-67000, Strasbourg, France</footnote>

<footnote>ᵈInstitut Pasteur, Université Paris Cité, Unité des Bactéries pathogènes entériques, Centre National de Référence des *Escherichia coli*, *Shigella* et *Salmonella*, Paris, F-75015, France</footnote>

<footnote>ᵉInstitute of Structural and Molecular Biology, University College London and Birkbeck, London, UK</footnote>

<footnote>fGastro and Food Safety (One Health) Division, UK Health Security Agency, Colindale, London, UK</footnote>

<footnote n="9">⁹Department of Genetics, University of Cambridge, Downing Place, Cambridge, UK</footnote>

<footnote>*Corresponding author. Department of Clinical Infection, Microbiology, and Immunology, Institute for Infection, Veterinary, and Ecological Sciences (IVES), University of Liverpool, Liverpool, UK.</footnote>

<footnote>E-mail address: kb827@cam.ac.uk (K.S. Baker).</footnote>

<footnote>ᵇAuthors contributed equally.</footnote>
# *Escherichia coli* killing by epidemiologically successful sublineages of *Shigella sonnei* is mediated by colicins

<figure><img src="image_2.png" /></figure>

Check for
updates

P. Malaka De Silva,ª,h Rebecca J. Bennett,ª,h Lauriane Kuhn,ᵇ Patryk Ngondo,ᶜ Lorine Debande,ᶜ Elisabeth Njamkepo,ᵈ Brian Ho,ᵉ François-Xavier Weill,ᵈ Benoît S. Marteyn,ᶜ Claire Jenkins,¹ and Kate S. Bakerª,g,\*

oa

OPEN ACCESS

## Summary

**Background** *Shigella* sp. are enteric pathogens which causes >125 million cases of shigellosis annually. *S. sonnei* accounts for about a quarter of those cases and is increasingly prevalent in industrialising nations. Being an enteric pathogen, *S. sonnei* benefits from outcompeting gut commensals such as *Escherichia coli* to establish itself and cause disease. There are numerous mechanisms that bacterial pathogens use to outcompete its rivals including molecules called colicins. A Type 6 Secretion System (T6SS) was recently described as contributing to *E. coli* killing in *S. sonnei*.

**Methods** We used Bulk Phenotyping of Epidemiological Replicates (BPER) which combined bacterial Genome Wide Association Studies (bGWAS) and high throughput phenotyping on a collection of *S. sonnei* surveillance isolates to identify the genetic features associated with *E. coli* killing and explore their relationship with epidemiological behaviour. We further explored the presence of colicins and T6SS components in the isolates using genomics, laboratory experimentation, and proteomics.

**Findings** Our bGWAS analysis returned known and novel colicin and colicin related genes as significantly associated with *E. coli* killing. *In silico* analyses identified key colicin clusters responsible for the killing phenotype associated with epidemiologically successful sub-lineages. The killing phenotype was not associated with the presence of a T6SS. Laboratory analyses confirmed the presence of the key colicin clusters and that killing was contact-independent.

**Interpretation** Colicins are responsible for *E. coli* killing by *S. sonnei*, not a T6SS. This phenotype contributes to shaping the observed epidemiology of *S. sonnei* and may contribute to its increasing prevalence globally. BPER is an epidemiologically relevant approach to phenotypic testing that enables the rapid identification of genetic drivers of phenotypic changes, and assessment of their relevance to epidemiology in natural settings.

**Funding** Biotechnology and Biological Sciences Research Council, Biotechnology and Biological Sciences Research Council Doctoral Training Partnership studentship, Wellcome Trust, Medical Research Council (UK), French National Research Agency.

**Copyright** © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

**Keywords:** Shigella; Colicins; Interbacterial competition; T6SS

## Introduction

*Shigella* sp. cause >125 million cases of shigellosis that result in ~212,000 deaths annually and understanding

the biological drivers of its success as a pathogen is critical to public health.¹,² *S. sonnei* accounts for 24% of cases of shigellosis worldwide and contributes

eBioMedicine
2023;97: 104822

Published Online 6 October
2023

https://doi.org/10.

1016/j.ebiom.2023.

104822

<footnote>ªDepartment of Clinical Infection, Microbiology, and Immunology, Institute for Infection, Veterinary, and Ecological Sciences (IVES), University of Liverpool, Liverpool, United Kingdom</footnote>

<footnote>ᵇPlateforme protéomique Strasbourg Esplanade FR1589 du CNRS, Université de Strasbourg, Strasbourg, France</footnote>

<footnote>ᶜUniversité de Strasbourg, CNRS, Architecture et Réactivité de l'ARN, UPR9002, F-67000, Strasbourg, France</footnote>

<footnote>ᵈInstitut Pasteur, Université Paris Cité, Unité des Bactéries pathogènes entériques, Centre National de Référence des *Escherichia coli*, *Shigella* et *Salmonella*, Paris, F-75015, France</footnote>

<footnote>ᵉInstitute of Structural and Molecular Biology, University College London and Birkbeck, London, UK</footnote>

<footnote>fGastro and Food Safety (One Health) Division, UK Health Security Agency, Colindale, London, UK</footnote>

<footnote n="9">⁹Department of Genetics, University of Cambridge, Downing Place, Cambridge, UK</footnote>

<footnote>*Corresponding author. Department of Clinical Infection, Microbiology, and Immunology, Institute for Infection, Veterinary, and Ecological Sciences (IVES), University of Liverpool, Liverpool, UK.</footnote>

<footnote>E-mail address: kb827@cam.ac.uk (K.S. Baker).</footnote>

<footnote>ᵇAuthors contributed equally.</footnote>
Research in context

Evidence before this study

*Shigella sonnei* causes the acute diarrhoeal disease shigellosis and is increasing globally in prevalence for reasons which are not fully understood. As there is competition among inhabitants of the human gut for establishment, mechanisms for interbacterial competition are crucial for survival, however their role in an epidemiological context is poorly characterised. Colicins are a known mechanism of interbacterial competition among Enterobacteriaceae and have also been described in *Shigella* sp. An alternative competition mechanism, a Type 6 Secretion System was also recently described in an *S. sonnei* organism and was suggested to contribute to its epidemiological success. Identifying key determinants of interbacterial competition in, and their relationship with the epidemiology of, globally important diarrhoeal pathogens is thus a significant knowledge gap that requires innovative approaches.

Added value of this study

Our study identified the genetic determinants of *E. coli* killing
in an unbiased way (bacterial Genome Wide Association
Studies) and revealed multiple known and novel colicin and

colicin-related genes. Contrastingly we found no association
of the phenotype with T6SS configuration. We further
identify the key colicin clusters that are responsible for killing
in S. sonnei among an epidemiologically characterised
collection of surveillance isolates. We show that
epidemiologically successful sub-lineages contained colicins
which were expressed extracellularly and mediated contact
independent killing.

Implications of all the available evidence

Our study demonstrates the importance of colicins in
mediating epidemiologically relevant interbacterial
competition in S. sonnei and refutes the importance of a T6SS
in this system. We have further identified several novel
apparently colicin-related genes for further functional
investigation. More broadly, our study framework of using
'epidemiological replicates' represents a move away from
model organism approaches where combined high
throughput phenotyping, genomic, and laboratory, analyses
can rapidly identify genetic determinants of high
epidemiological relevance in bacterial populations.

proportionately more to the disease burden in indus-
trialising and high-income countries (HICs) where it is
primarily transmitted through travel and among certain
risk groups e.g., men who have sex with men (MSM).<sup>3–5</sup>
Recent genomic epidemiology studies of S. sonnei have
revealed a concerning emergence of highly, and exten-
sively drug resistant strains<sup>6–13</sup> leading to it being
designated a WHO priority organism for AMR.<sup>14</sup> While
global subtyping systems can help us trace the shifting
population dynamics, there is a need to further leverage
these genomic epidemiology studies to better under-
stand the biology of the organism and identify what
drives certain sublineages to success.<sup>15</sup>

An example relevant to this study is the existence of
four co-circulating subclades of MSM-associated
*S. sonnei* in the UK during 2008–2014, three of which
endured in time and spread internationally, and one of
which did not [¹⁶ and see further details below]. Being an
enteric pathogen, *S. sonnei* is in constant competition
with the other members of the gut microbiota which
acts as the barrier to successful infection and invasion of
the gut epithelial cells in the colon.¹⁷ Interbacterial
competition mechanisms are thought to be important
for a multitude of bacterial pathogens such as *Bur-
kholderia cepacia*, *Vibrio cholerae*, *Klebsiella pneumoniae*,
and *Salmonella* spp.¹⁸⁻²² Therefore, mechanisms that are
advantageous for interbacterial competition are likely to
be beneficial for *S. sonnei*.

There is a plethora of interbacterial competitions
mechanisms described, including colicins and Type VI
secretion systems (T6SSs); the latter of which was

recently described in S. sonnei.²³,²⁴ T6SSs are speci-
alised secretion systems found in Gram negative bac-
teria capable of delivering a wide variety of effectors
(usually antibacterial proteins) directly into the target
cells.²⁵,²⁶ T6SSs function via a contractile sheath that
propels a needle-like structure containing the spike
complex with the effector protein into the adjacent
target cell in a contact-dependent manner.²⁷ In contrast
to T6SS, colicins are not contact-dependent as they are
secreted into the surrounding environment and trans-
located through the outer membrane of target cells by
parasitising the existing protein translocation systems
that perform other important biological functions in
the sensitive cells.²⁸ These toxic proteins are produced
by many enteric bacteria and are usually encoded on a
plasmid alongside the colicin lysis protein that is
responsible for colicin release and the immunity pro-
tein that protects the host from its own colicins.²⁹,³⁰
Colicins have been studied extensively for more than
three decades as a major component of inter-bacterial
warfare with varying modes of action.³¹⁻³³ Being a
contact-independent process, production, and secre-
tion of colicins with respect to complex bacterial spatial
structures has been a focus of study in the recent years
and how it depends on sensing and responding to the
surroundings.³⁴,³⁵ While the importance of colicins for
interbacterial competition/persistence have been
shown *in vivo* with reports of colicins promoting di-
versity of the gut microbiota,³⁶⁻³⁸ the impact of colicins
on bacterial population structures in real world settings
remain poorly explored.
Research in context

Evidence before this study

*Shigella sonnei* causes the acute diarrhoeal disease shigellosis and is increasing globally in prevalence for reasons which are not fully understood. As there is competition among inhabitants of the human gut for establishment, mechanisms for interbacterial competition are crucial for survival, however their role in an epidemiological context is poorly characterised. Colicins are a known mechanism of interbacterial competition among Enterobacteriaceae and have also been described in *Shigella* sp. An alternative competition mechanism, a Type 6 Secretion System was also recently described in an *S. sonnei* organism and was suggested to contribute to its epidemiological success. Identifying key determinants of interbacterial competition in, and their relationship with the epidemiology of, globally important diarrhoeal pathogens is thus a significant knowledge gap that requires innovative approaches.

Added value of this study

Our study identified the genetic determinants of *E. coli* killing
in an unbiased way (bacterial Genome Wide Association
Studies) and revealed multiple known and novel colicin and

colicin-related genes. Contrastingly we found no association
of the phenotype with T6SS configuration. We further
identify the key colicin clusters that are responsible for killing
in S. sonnei among an epidemiologically characterised
collection of surveillance isolates. We show that
epidemiologically successful sub-lineages contained colicins
which were expressed extracellularly and mediated contact
independent killing.

Implications of all the available evidence

Our study demonstrates the importance of colicins in
mediating epidemiologically relevant interbacterial
competition in S. sonnei and refutes the importance of a T6SS
in this system. We have further identified several novel
apparently colicin-related genes for further functional
investigation. More broadly, our study framework of using
'epidemiological replicates' represents a move away from
model organism approaches where combined high
throughput phenotyping, genomic, and laboratory, analyses
can rapidly identify genetic determinants of high
epidemiological relevance in bacterial populations.

proportionately more to the disease burden in indus-
trialising and high-income countries (HICs) where it is
primarily transmitted through travel and among certain
risk groups e.g., men who have sex with men (MSM).<sup>3–5</sup>
Recent genomic epidemiology studies of S. sonnei have
revealed a concerning emergence of highly, and exten-
sively drug resistant strains<sup>6–13</sup> leading to it being
designated a WHO priority organism for AMR.<sup>14</sup> While
global subtyping systems can help us trace the shifting
population dynamics, there is a need to further leverage
these genomic epidemiology studies to better under-
stand the biology of the organism and identify what
drives certain sublineages to success.<sup>15</sup>

An example relevant to this study is the existence of
four co-circulating subclades of MSM-associated
*S. sonnei* in the UK during 2008–2014, three of which
endured in time and spread internationally, and one of
which did not [¹⁶ and see further details below]. Being an
enteric pathogen, *S. sonnei* is in constant competition
with the other members of the gut microbiota which
acts as the barrier to successful infection and invasion of
the gut epithelial cells in the colon.¹⁷ Interbacterial
competition mechanisms are thought to be important
for a multitude of bacterial pathogens such as *Bur-
kholderia cepacia*, *Vibrio cholerae*, *Klebsiella pneumoniae*,
and *Salmonella* spp.¹⁸⁻²² Therefore, mechanisms that are
advantageous for interbacterial competition are likely to
be beneficial for *S. sonnei*.

There is a plethora of interbacterial competitions
mechanisms described, including colicins and Type VI
secretion systems (T6SSs); the latter of which was

recently described in S. sonnei.²³,²⁴ T6SSs are speci-
alised secretion systems found in Gram negative bac-
teria capable of delivering a wide variety of effectors
(usually antibacterial proteins) directly into the target
cells.²⁵,²⁶ T6SSs function via a contractile sheath that
propels a needle-like structure containing the spike
complex with the effector protein into the adjacent
target cell in a contact-dependent manner.²⁷ In contrast
to T6SS, colicins are not contact-dependent as they are
secreted into the surrounding environment and trans-
located through the outer membrane of target cells by
parasitising the existing protein translocation systems
that perform other important biological functions in
the sensitive cells.²⁸ These toxic proteins are produced
by many enteric bacteria and are usually encoded on a
plasmid alongside the colicin lysis protein that is
responsible for colicin release and the immunity pro-
tein that protects the host from its own colicins.²⁹,³⁰
Colicins have been studied extensively for more than
three decades as a major component of inter-bacterial
warfare with varying modes of action.³¹⁻³³ Being a
contact-independent process, production, and secre-
tion of colicins with respect to complex bacterial spatial
structures has been a focus of study in the recent years
and how it depends on sensing and responding to the
surroundings.³⁴,³⁵ While the importance of colicins for
interbacterial competition/persistence have been
shown *in vivo* with reports of colicins promoting di-
versity of the gut microbiota,³⁶⁻³⁸ the impact of colicins
on bacterial population structures in real world settings
remain poorly explored.
Here we sought to identify the mechanisms in S. sonnei responsible for interbacterial competition with a relevant microbiota competitor, *Escherichia coli*, and relate this to established epidemiological understandings. To do so, we leveraged a collection of sequenced isolates from a previous cross-sectional genomic epidemiology study of national surveillance in the United Kingdom from 2008 to 2014.¹⁶ As we were interested in comparing the behaviour of epidemiological sublineages, we conducted our experimental replicates at an epidemiologically relevant level (i.e., testing many clinical isolates from each sublineage rather than testing fewer representatives multiple times) in an experimental framework we call Bulk Phenotyping of Epidemiological Replicates (BPER). We advocate BPER as a concept for moving on from extrapolating from model organisms in the genomic era while also overcoming some of the complications of using poorly characterised clinical isolates for laboratory studies. By coupling BPER to bacterial Genome Wide Association Studies (bGWAS), we demonstrate that *E. coli* killing in S. sonnei is mediated by colicins found in epidemiologically successful sublineages.

## Methods

### Strains, their whole genome sequences, and phylogenetic tree construction

The main collection of S. sonnei genomes (n = 164 isolates, Table 1) used in this study originated from the archive of isolates from the national reference laboratory of UK Health Security Agency collected from 2008 to 2014 and has been described before.¹⁶ Sequencing data for all isolates is deposited in the European Nucleotide Archive and individual accession numbers are provided in Supplementary Table S1. An additional isolate, CIP 106374, which was also previously described was included in the bioinformatic analyses and sequenced as mentioned in Supplementary appendix along with the full details of phylogenetic tree construction and genotyping.

In addition to S. sonnei, several E. coli strains were used in this study including the laboratory strain MG1655, and three diverse isolates received at the National Reference Laboratory during 2023: including a commensal ST10 (O15:H16), an ST6199 (O1:H7), and a pathogenic ST131 (O25:H4) (Table 2).

### Growth conditions for bacterial strains

All bacterial strains were grown in tryptone soy broth (TSB) (Millipore Sigma, UK) at 37 °C in a shaking incubator (220 rpm) unless otherwise stated. Antibiotic selection where applicable was carried out using kanamycin or azithromycin both at 30 µg/ml.

### Initial screen of subclade representatives for killing

Overnight pre-cultures of both S. sonnei and E. coli were diluted 1:100 (v/v) in fresh TSB and grown to mid-log phase. 10 µl of competition mixture containing E. coli: S. sonnei (1:10) was spotted onto a nitrocellulose membrane on a tryptone soy agar plate and incubated overnight at 37 °C. Competition mixture was then washed off into 500 µl of sterile PBS, serially diluted and plated on selective media for CFU enumeration.

### BPER using cell sorter

Culturable S. sonnei isolates corresponding to genomes (n = 159) were grown overnight in 96 well flat bottom plates (Greiner Bio One, UK) containing 150 µl of TSB and diluted 1:100 (v/v) into fresh TSB and grown for 2 h. E. coli was grown and diluted into fresh media as was done for initial killing assays and 880 µl of mid-log phase culture was diluted in 52 ml of TSB and 130 µl of that was distributed into each well of the 96-well plate. 20 µl of the mid-log phase S. sonnei cultures were then added to each well to make up a final volume of 150 µl with 1:10 (E. coli: S. sonnei) of the competition mixture and incubated overnight. The overnight competition mixtures were then diluted and GFP expressing E. coli cells which had survived the competition with S. sonnei were counted using a Bio-Rad ZE5 Cell Analyzer in a

<table><thead><tr><th>Genotype</th><th>Isolates (N)</th><th>Name</th><th>Original name</th><th>Epidemiological summary</th><th>Reference</th></tr></thead><tbody><tr><td>3.6.1</td><td>10</td><td>CipR_parent</td><td>-</td><td>Subclade from which ciprofloxacin-resistant sublineage emerged</td><td>15</td></tr><tr><td>3.6.1.1</td><td>16</td><td>CipR</td><td>Ciprofloxacin-resistant Pop2</td><td>Triple QRDR<sup>a</sup> mutation ciprofloxacin-resistant sublineage</td><td>39,40</td></tr><tr><td>3.6.1.1.3</td><td>4</td><td>-</td><td>-</td><td>Ciprofloxacin-resistant</td><td>39,41</td></tr><tr><td>3.6.1.1.3.1</td><td>7</td><td>CipR MSM1</td><td>MSM Clade 1</td><td>MSM-linked ciprofloxacin resistant isolates</td><td>41</td></tr><tr><td>3.6.2</td><td>9</td><td>Central Asia III Subclade</td><td>-</td><td>Associated with areas in Central Asia</td><td>41,42</td></tr><tr><td>3.6.3</td><td>9</td><td>Central Asia III Subclade</td><td>-</td><td>Associated with areas in Central Asia</td><td>39,41</td></tr><tr><td>3.7.16</td><td>7</td><td>Global III Subclade</td><td>-</td><td>-</td><td>43</td></tr><tr><td>3.7.18</td><td>19</td><td>Global III Subclade</td><td>MSM Clade 3</td><td>MSM-associated</td><td>44</td></tr><tr><td>3.7.25</td><td>46</td><td>MSM4</td><td>MSM Clade 4</td><td>MSM-associated</td><td>41</td></tr><tr><td>3.7.29.1.2</td><td>7</td><td>VN2 MSM2</td><td>MSM Clade 2</td><td>MSM-associated. Emerging from sweep 2 of Vietnam clone</td><td>7,45</td></tr><tr><td>3.7.30.4.1</td><td>6</td><td>OJC</td><td>OJC-associated</td><td>Associated with the Orthodox Jewish communities in Israel, UK, USA and Europe</td><td>42</td></tr></tbody></table>

<caption>Table 1: Population structure summary of S. sonnei genotypes (n = 164) in this study.<sup>b</sup></caption>

<footnote><sup>a</sup>QRDR = quinolone resistance determining region. <sup>b</sup>For brevity, only genotypes that included >4% of isolates are included in the table. Full genotyping data is available in Supplementary Table S1.</footnote>
Here we sought to identify the mechanisms in S. sonnei responsible for interbacterial competition with a relevant microbiota competitor, *Escherichia coli*, and relate this to established epidemiological understandings. To do so, we leveraged a collection of sequenced isolates from a previous cross-sectional genomic epidemiology study of national surveillance in the United Kingdom from 2008 to 2014.¹⁶ As we were interested in comparing the behaviour of epidemiological sublineages, we conducted our experimental replicates at an epidemiologically relevant level (i.e., testing many clinical isolates from each sublineage rather than testing fewer representatives multiple times) in an experimental framework we call Bulk Phenotyping of Epidemiological Replicates (BPER). We advocate BPER as a concept for moving on from extrapolating from model organisms in the genomic era while also overcoming some of the complications of using poorly characterised clinical isolates for laboratory studies. By coupling BPER to bacterial Genome Wide Association Studies (bGWAS), we demonstrate that *E. coli* killing in S. sonnei is mediated by colicins found in epidemiologically successful sublineages.

## Methods

### Strains, their whole genome sequences, and phylogenetic tree construction

The main collection of S. sonnei genomes (n = 164 isolates, Table 1) used in this study originated from the archive of isolates from the national reference laboratory of UK Health Security Agency collected from 2008 to 2014 and has been described before.¹⁶ Sequencing data for all isolates is deposited in the European Nucleotide Archive and individual accession numbers are provided in Supplementary Table S1. An additional isolate, CIP 106374, which was also previously described was included in the bioinformatic analyses and sequenced as mentioned in Supplementary appendix along with the full details of phylogenetic tree construction and genotyping.

In addition to S. sonnei, several E. coli strains were used in this study including the laboratory strain MG1655, and three diverse isolates received at the National Reference Laboratory during 2023: including a commensal ST10 (O15:H16), an ST6199 (O1:H7), and a pathogenic ST131 (O25:H4) (Table 2).

### Growth conditions for bacterial strains

All bacterial strains were grown in tryptone soy broth (TSB) (Millipore Sigma, UK) at 37 °C in a shaking incubator (220 rpm) unless otherwise stated. Antibiotic selection where applicable was carried out using kanamycin or azithromycin both at 30 µg/ml.

### Initial screen of subclade representatives for killing

Overnight pre-cultures of both S. sonnei and E. coli were diluted 1:100 (v/v) in fresh TSB and grown to mid-log phase. 10 µl of competition mixture containing E. coli: S. sonnei (1:10) was spotted onto a nitrocellulose membrane on a tryptone soy agar plate and incubated overnight at 37 °C. Competition mixture was then washed off into 500 µl of sterile PBS, serially diluted and plated on selective media for CFU enumeration.

### BPER using cell sorter

Culturable S. sonnei isolates corresponding to genomes (n = 159) were grown overnight in 96 well flat bottom plates (Greiner Bio One, UK) containing 150 µl of TSB and diluted 1:100 (v/v) into fresh TSB and grown for 2 h. E. coli was grown and diluted into fresh media as was done for initial killing assays and 880 µl of mid-log phase culture was diluted in 52 ml of TSB and 130 µl of that was distributed into each well of the 96-well plate. 20 µl of the mid-log phase S. sonnei cultures were then added to each well to make up a final volume of 150 µl with 1:10 (E. coli: S. sonnei) of the competition mixture and incubated overnight. The overnight competition mixtures were then diluted and GFP expressing E. coli cells which had survived the competition with S. sonnei were counted using a Bio-Rad ZE5 Cell Analyzer in a

<table><thead><tr><th>Genotype</th><th>Isolates (N)</th><th>Name</th><th>Original name</th><th>Epidemiological summary</th><th>Reference</th></tr></thead><tbody><tr><td>3.6.1</td><td>10</td><td>CipR parent</td><td>-</td><td>Subclade from which ciprofloxacin-resistant sublineage emerged</td><td>15</td></tr><tr><td>3.6.1.1</td><td>16</td><td>CipR</td><td>Ciprofloxacin-resistant Pop2</td><td>Triple QRDR<sup>a</sup> mutation ciprofloxacin-resistant sublineage</td><td>39,40</td></tr><tr><td>3.6.1.1.3</td><td>4</td><td>-</td><td>-</td><td>Ciprofloxacin-resistant</td><td>39,41</td></tr><tr><td>3.6.1.1.3.1</td><td>7</td><td>CipR MSM1</td><td>MSM Clade 1</td><td>MSM-linked ciprofloxacin resistant isolates</td><td>41</td></tr><tr><td>3.6.2</td><td>9</td><td>Central Asia III Subclade</td><td>-</td><td>Associated with areas in Central Asia</td><td>41,42</td></tr><tr><td>3.6.3</td><td>9</td><td>Central Asia III Subclade</td><td>-</td><td>Associated with areas in Central Asia</td><td>39,41</td></tr><tr><td>3.7.16</td><td>7</td><td>Global III Subclade</td><td>-</td><td>-</td><td>43</td></tr><tr><td>3.7.18</td><td>19</td><td>Global III Subclade</td><td>MSM Clade 3</td><td>MSM-associated</td><td>44</td></tr><tr><td>3.7.25</td><td>46</td><td>MSM4</td><td>MSM Clade 4</td><td>MSM-associated</td><td>41</td></tr><tr><td>3.7.29.1.2</td><td>7</td><td>VN2 MSM2</td><td>MSM Clade 2</td><td>MSM-associated. Emerging from sweep 2 of Vietnam clone</td><td>7,45</td></tr><tr><td>3.7.30.4.1</td><td>6</td><td>OJC</td><td>OJC-associated</td><td>Associated with the Orthodox Jewish communities in Israel, UK, USA and Europe</td><td>42</td></tr></tbody></table>

<caption>**Table 1:** Population structure summary of S. sonnei genotypes (n = 164) in this study.<sup>b</sup></caption>

<footnote><sup>a</sup>QRDR = quinolone resistance determining region. <sup>b</sup>For brevity, only genotypes that included >4% of isolates are included in the table. Full genotyping data is available in Supplementary Table S1.</footnote>
<table><caption>Table 2: Summary of bacterial isolates (n = 163) used in this study and their origins.</caption><thead><tr><th>Strains</th><th>Isolates (N)</th><th>Description</th><th>Reference</th></tr></thead><tbody><tr><td>S. sonnei<sup>31</sup></td><td>159</td><td>Cross-sectional isolates from routine surveillance during 2008–2014 collected by Public Health England (now UKHSA).</td><td>16</td></tr><tr><td>S. sonnei CIP 106347</td><td>1</td><td>S. sonnei clinical isolate from the collection at Institut Pasteur, previously described as having a functional T6SS</td><td>24</td></tr><tr><td>E. coli MG1655</td><td>1</td><td>E. coli MG1655 strain with a chromosomally encoded, constitutively expressed GFP marker and a kanamycin resistance marker</td><td>Previously used in<sup>46</sup></td></tr><tr><td>E. coli LAES2471692004</td><td>1</td><td>Clinical isolate of E. coli belonging to ST6199 (O1:H7) received at the National Reference Laboratory</td><td>This study</td></tr><tr><td>E. coli LAE3140732004</td><td>1</td><td>Clinical isolate of E. coli belonging to ST131 (O25:H4) received at the National Reference Laboratory</td><td>This study</td></tr><tr><td>E. coli LAE31441672004</td><td>1</td><td>Clinical isolate of E. coli belonging to ST10 (O15:H16) received at the National Reference Laboratory</td><td>This study</td></tr></tbody><tfoot><tr><td colspan="4"><sup>46</sup>Various—see Supplementary Table S1 n = 159 as five isolates (compared with Table 1) failed to grow.</td></tr></tfoot></table>

total of 10,000 events per well. The percentage of GFP expressing cells were calculated using FCS Express version 7 (De Novo Software) and plotted onto the phylogenetic tree using iTOL.<sup>47</sup>

## BPER growth assays using plate reader

Competition mixtures were set up similar to the cell sorter method albeit in a black 96 well plate (Greiner Bio One, UK). Fluorescence signal from the *E. coli* in the completion was measured every 15 min using a Synergy H1 multi-mode plate reader (BioTek Instruments) and a clear GFP signal corresponding to the optical density—based growth curve for *E. coli* was considered *E. coli* survival and used as a binary phenotype for *E. coli* killing.

## Genome wide association study

Draft assemblies of all isolates were assembled using Unicycler v0.4.8<sup>48</sup> and annotated using Prokka v1.14.6.<sup>49</sup> To generate the input for the GWAS kmer analysis, kmers were counted from assemblies using fsmlite v1.0. Lastly, to generate the input file for the GWAS COG analysis, Roary v1.007002 was utilised to generate a gene presence/absence Rtab file containing the presence or absence of each gene in each isolate.<sup>50</sup>

After generation of appropriate inputs, GWAS was carried out using Pyseer v1.3.6.<sup>51</sup> Pyseer uses linear models with fixed or mixed effects to estimate the effect of genetic variation in a bacterial population on a phenotype of interest, while accounting for potential confounding by population structure. For this investigation, presence or absence of the *E. coli* killing phenotype was utilised as the categorical phenotype (Supplementary Table S1). To account for population structure, all analyses were supplemented with phylogenetic distances from the mid-point rooted core genome phylogeny (above). Pyseer analyses were run using the linear mixed model (LMM). Specifically, the COG analyses were run using the gene presence absence file generated from Roary and the phylogenetic distances from the mid-point rooted core genome phylogeny. The default filters of Pyseer were utilised to filter out very low frequency COGs. Further GWAS investigations were carried out using Scoary v1.6.16<sup>52</sup>

utilising the gene presence/absence.csv file generated via the Roary pangenome pipeline as well as a trait file consisting of the *E. coli* killing phenotype for each sample.

## Colicin database construction and detection

To investigate the presence or absence of specific colicins within the isolates, a large quantity of colicin sequences were collated. Over 10,000 colicins from over 50 species of bacteria were collated from the European Nucleotide Archive as well as including some isolates from previously published sources.<sup>53</sup> A multi-FASTA file containing the collated colicin sequences was utilised to generate a custom database via the prepareref command of ARIBA v2.14.6 where prepareref removes erroneous data and runs cd-hit to cluster the sequences based on a user-defined similarity threshold (90% in our case). ARIBA v2.14.6 was then run with the FASTQ files of all isolates, a percentage sequence ID cut-off of 95% and the colicin database to report which sequences were observed in each isolate. The *E. coli* isolates were screened for the colicins and colicin related genes using ABRicate v1.0.1 (https://github.com/tseemann/abricate) under the default settings using our custom colicin database. The database of colicin sequences are publicly available through figshare (10.6084/m9.fishshare.20768260.v1).

## Statistical testing

All statistical analyses were performed using R v3.6.1. To determine the significant difference between the T6SS profiles of the killing versus the non-killing isolates, an independent t-test was undertaken comparing the number of T6SS elements in each group (i.e., killing and non-killing). Further independent t-test were undertaken to determine the association of the killing phenotype with each detected colicin cluster to determine their order of iterative addition as 'key colicin clusters' (see Results).

## Exploring the presence of the T6SS

The 18 components of the full T6SS were extracted from the annotated Nanopore sequence of *S. sonnei* CIP 106347 and compiled into a singular multi-FASTA file.
<table><thead><tr><th>Strains</th><th>Isolates (N)</th><th>Description</th><th>Reference</th></tr></thead><tbody><tr><td>S. sonnei<sup>31</sup></td><td>159</td><td>Cross-sectional isolates from routine surveillance during 2008–2014 collected by Public Health England (now UKHSA).</td><td>16</td></tr><tr><td>S. sonnei CIP 106347</td><td>1</td><td>S. sonnei clinical isolate from the collection at Institut Pasteur, previously described as having a functional T6SS</td><td>24</td></tr><tr><td>E. coli MG1655</td><td>1</td><td>E. coli MG1655 strain with a chromosomally encoded, constitutively expressed GFP marker and a kanamycin resistance marker</td><td>Previously used in<sup>46</sup></td></tr><tr><td>E. coli LAES2471692004</td><td>1</td><td>Clinical isolate of E. coli belonging to ST6199 (O1:H7) received at the National Reference Laboratory</td><td>This study</td></tr><tr><td>E. coli LAE3140732004</td><td>1</td><td>Clinical isolate of E. coli belonging to ST131 (O25:H4) received at the National Reference Laboratory</td><td>This study</td></tr><tr><td>E. coli LAE31441672004</td><td>1</td><td>Clinical isolate of E. coli belonging to ST10 (O15:H16) received at the National Reference Laboratory</td><td>This study</td></tr></tbody></table>

<caption>**Table 2:** Summary of bacterial isolates (n = 163) used in this study and their origins.</caption>

total of 10,000 events per well. The percentage of GFP expressing cells were calculated using FCS Express version 7 (De Novo Software) and plotted onto the phylogenetic tree using iTOL.⁴⁷

## BPER growth assays using plate reader

Competition mixtures were set up similar to the cell sorter method albeit in a black 96 well plate (Greiner Bio One, UK). Fluorescence signal from the *E. coli* in the completion was measured every 15 min using a Synergy H1 multi-mode plate reader (BioTek Instruments) and a clear GFP signal corresponding to the optical density—based growth curve for *E. coli* was considered *E. coli* survival and used as a binary phenotype for *E. coli* killing.

## Genome wide association study

Draft assemblies of all isolates were assembled using Unicycler v0.4.8⁴⁸ and annotated using Prokka v1.14.6.⁴⁹ To generate the input for the GWAS kmer analysis, kmers were counted from assemblies using fsm-lite v1.0. Lastly, to generate the input file for the GWAS COG analysis, Roary v1.007002 was utilised to generate a gene presence/absence Rtab file containing the presence or absence of each gene in each isolate.⁵⁰

After generation of appropriate inputs, GWAS was carried out using Pyseer v1.3.6.⁵¹ Pyseer uses linear models with fixed or mixed effects to estimate the effect of genetic variation in a bacterial population on a phenotype of interest, while accounting for potential confounding by population structure. For this investigation, presence or absence of the *E. coli* killing phenotype was utilised as the categorical phenotype (Supplementary Table S1). To account for population structure, all analyses were supplemented with phylogenetic distances from the mid-point rooted core genome phylogeny (above). Pyseer analyses were run using the linear mixed model (LMM). Specifically, the COG analyses were run using the gene presence absence file generated from Roary and the phylogenetic distances from the mid-point rooted core genome phylogeny. The default filters of Pyseer were utilised to filter out very low frequency COGs. Further GWAS investigations were carried out using Scoary v1.6.16⁵²

utilising the gene presence/absence.csv file generated via the Roary pangenome pipeline as well as a trait file consisting of the *E. coli* killing phenotype for each sample.

## Colicin database construction and detection

To investigate the presence or absence of specific colicins within the isolates, a large quantity of colicin sequences were collated. Over 10,000 colicins from over 50 species of bacteria were collated from the European Nucleotide Archive as well as including some isolates from previously published sources.⁵³ A multi-FASTA file containing the collated colicin sequences was utilised to generate a custom database via the prepareref command of ARIBA v2.14.6 where prepareref removes erroneous data and runs cd-hit to cluster the sequences based on a user-defined similarity threshold (90% in our case). ARIBA v2.14.6 was then run with the FASTQ files of all isolates, a percentage sequence ID cut-off of 95% and the colicin database to report which sequences were observed in each isolate. The *E. coli* isolates were screened for the colicins and colicin related genes using ABRicate v1.0.1 (https://github.com/tseemann/abricate) under the default settings using our custom colicin database. The database of colicin sequences are publicly available through figshare (10.6084/m9.fishshare.20768260.v1).

## Statistical testing

All statistical analyses were performed using R v3.6.1. To determine the significant difference between the T6SS profiles of the killing versus the non-killing isolates, an independent t-test was undertaken comparing the number of T6SS elements in each group (i.e., killing and non-killing). Further independent t-test were undertaken to determine the association of the killing phenotype with each detected colicin cluster to determine their order of iterative addition as ‘key colicin clusters’ (see Results).

## Exploring the presence of the T6SS

The 18 components of the full T6SS were extracted from the annotated Nanopore sequence of *S. sonnei* CIP 106347 and compiled into a singular multi-FASTA file.

<footnote n="37">³⁷Various—see Supplementary Table S1 n = 159 as five isolates (compared with Table 1) failed to grow.</footnote>
SRST2 v0.2.0⁵⁴ was then utilised for the purpose of T6SS component gene detection as a custom database. The T6SS components within the multi-FASTA were not pre-clustered and therefore were assigned to gene clusters based on 90% nucleotide similarity via CD-HIT v4.8.1.⁵⁵ Python scripts provided as part of the SRST2 package were utilised to parse the clusters and generate a SRST2 compatible database. SRST2 gene detection was then run utilising the custom T6SS database.

## Role of funders

Funders did not have any role in study design, data collection, data analyses, interpretation, or writing of report.

## Results

### The epidemiology and global context of the isolate collection

A collection of *S. sonnei* genomes used in this study came from a cross sectional subsample of routine microbiological surveillance in the United Kingdom from between 2008 and 2014 (n = 164).¹⁶ The original epidemiological study revealed the presence of four distinct clades which were co-circulating among MSM. In this study, Clades 1, 2, and 4 showed markers of population level epidemiological success (specifically international spread and prolonged circulation) relative to Clade 3, which was not successful.¹⁶ Notably, a recently described international global genotyping framework has renamed these Clades 1–4 (above) as Subclades 3.6.1.1.3.1 (CipR.MSM1), 3.7.29.1.2 (VN2.MSM2), 3.7.18, and 3.7.25 (MSM4) respectively.¹⁵ These isolates were utilised along with CIP 106347, which was previously used in studies of a putative *S. sonnei* T6SS²⁴ to construct a detailed phylogeny complemented with genotype assignations (Fig. 1).

All *S. sonnei* belonged to the globally disseminated multidrug resistant lineage of *S. sonnei*, Lineage 3 (Table 1, Fig. 1). Further genotyping showed that most isolates (61%, n = 101) belonged to Clade 3.7, particularly Subclade 3.7.25 (MSM4, 28%, n = 46) and Subclade 3.7.18 (12%, n = 19) (Table 1). The remaining isolates (39%, n = 64) belonged to Subclade 3.4.1 (Latin American III) and various Subclades of Clade 3.6 (Central Asia III) including 3.6.1 (CipR_parent), 3.6.1.1 (CipR), 3.6.1.1.3, 3.6.1.1.3.1 (CipR MSM1), 3.6.2, and 3.6.3 (Table 1). Correlating the genotype names with epidemiological history revealed that various globally important subclades of *S. sonnei* were captured in our dataset including internationally disseminating antimicrobial resistant genotypes (Table 1 and Supplementary Table S1). Thus, our collection contained a breadth of the diversity of the globally disseminated Lineage 3.

### E. coli killing is common and associated with genotype in S. sonnei

After an initial low throughput screen revealed differences in the *E. coli* killing phenotype among Subclade

representatives (Supplementary Figure S1), we scaled our approach to include the entire collection of *S. sonnei*. We did this so our laboratory assays were being replicated at our desired level of inference i.e., we wanted to relate the phenotype to the epidemiological behaviour of different genomic subtypes, so we needed multiple clinical isolates that belonged to those subtypes, rather than biological or technical replicates (though for robustness we also included duplicate technical replicates), which we call BPER.

Owing to the number of isolates involved and our ambition to scale the BPER approach further in future studies, we assayed the killing phenotype in a high-throughput manner. Specifically, we used cell sorting to measure the proportion (%) of green-fluorescent *E. coli* remaining after overnight competition with *S. sonnei* in a 96-well format. As a cell sorter is a specialist equipment, we also correlated these results with a binary read out of green fluorescence following a simple plate-reader based growth assay which is more broadly available. Introducing a threshold of positivity (20%) among the cell sorting results of these assays led to 99% concordance between the two approaches (Fig. 1, Supplementary Table S1, see methods). We found that most 82% (131/159) of *S. sonnei* displayed *E. coli* killing and that phenotype clustered with genotype (Fig. 1). The continuous data (from cell sorting) revealed a spectrum of difference in the phenotype with the proportion of *E. coli* following competition being between 0% and 89% (mean = 14%). Associating the phenotype against the population structure of *S. sonnei* revealed that Subclades of the Central Asia III Clade (3.6.3 and 3.6.2), 3.7.18 (MSM Clade 3), and 3.7.30.4.1 (a Subclade associated with transmission among Orthodox Jewish Communities) were predominately non-killing with the remaining Subclades being predominately killing (Fig. 1). However, differences among isolates belonging to the same Subclade (i.e., epidemiological replicates) demonstrated phenotypic variation (e.g., genotype 3.7.18 where isolates ranged from 0.1% to 88.44% with a mean of 25.15%), highlighting the value of the BPER approach when assaying clinical isolates for inference at an epidemiological level.

To further explore the relevance of this phenotype for *S. sonnei* in the real world, we also determined killing phenotypes of the *S. sonnei* collection against clinical *E. coli* isolates, including both commensal and other pathogenic types. As expected, clinical isolates of *E. coli* exhibited greater levels of survival from the *S. sonnei* competitors (Supplementary Figure S3, Supplementary Table S1). Interestingly, the degree of *E. coli* killing by the *S. sonnei* strains used scaled intuitively with the nature of the *E. coli* strain (i.e., killing of lab strain > commensal strain > pathogenic strains) and the colicin and colicin-related protein content of the *E. coli* strains (see below). Specifically, and critically for the translatability of these results, the commensal Multi
SRST2 v0.2.0⁵⁴ was then utilised for the purpose of T6SS component gene detection as a custom database. The T6SS components within the multi-FASTA were not pre-clustered and therefore were assigned to gene clusters based on 90% nucleotide similarity via CD-HIT v4.8.1.⁵⁵ Python scripts provided as part of the SRST2 package were utilised to parse the clusters and generate a SRST2 compatible database. SRST2 gene detection was then run utilising the custom T6SS database.

## Role of funders

Funders did not have any role in study design, data collection, data analyses, interpretation, or writing of report.

## Results

### The epidemiology and global context of the isolate collection

A collection of *S. sonnei* genomes used in this study came from a cross sectional subsample of routine microbiological surveillance in the United Kingdom from between 2008 and 2014 (n = 164).¹⁶ The original epidemiological study revealed the presence of four distinct clades which were co-circulating among MSM. In this study, Clades 1, 2, and 4 showed markers of population level epidemiological success (specifically international spread and prolonged circulation) relative to Clade 3, which was not successful.¹⁶ Notably, a recently described international global genotyping framework has renamed these Clades 1–4 (above) as Subclades 3.6.1.1.3.1 (CipR.MSM1), 3.7.29.1.2 (VN2.MSM2), 3.7.18, and 3.7.25 (MSM4) respectively.¹⁵ These isolates were utilised along with CIP 106347, which was previously used in studies of a putative *S. sonnei* T6SS²⁴ to construct a detailed phylogeny complemented with genotype assignations (Fig. 1).

All *S. sonnei* belonged to the globally disseminated multidrug resistant lineage of *S. sonnei*, Lineage 3 (Table 1, Fig. 1). Further genotyping showed that most isolates (61%, n = 101) belonged to Clade 3.7, particularly Subclade 3.7.25 (MSM4, 28%, n = 46) and Subclade 3.7.18 (12%, n = 19) (Table 1). The remaining isolates (39%, n = 64) belonged to Subclade 3.4.1 (Latin American III) and various Subclades of Clade 3.6 (Central Asia III) including 3.6.1 (CipR_parent), 3.6.1.1 (CipR), 3.6.1.1.3, 3.6.1.1.3.1 (CipR MSM1), 3.6.2, and 3.6.3 (Table 1). Correlating the genotype names with epidemiological history revealed that various globally important subclades of *S. sonnei* were captured in our dataset including internationally disseminating antimicrobial resistant genotypes (Table 1 and Supplementary Table S1). Thus, our collection contained a breadth of the diversity of the globally disseminated Lineage 3.

### E. coli killing is common and associated with genotype in S. sonnei

After an initial low throughput screen revealed differences in the *E. coli* killing phenotype among Subclade

representatives (Supplementary Figure S1), we scaled our approach to include the entire collection of *S. sonnei*. We did this so our laboratory assays were being replicated at our desired level of inference i.e., we wanted to relate the phenotype to the epidemiological behaviour of different genomic subtypes, so we needed multiple clinical isolates that belonged to those subtypes, rather than biological or technical replicates (though for robustness we also included duplicate technical replicates), which we call BPER.

Owing to the number of isolates involved and our ambition to scale the BPER approach further in future studies, we assayed the killing phenotype in a high-throughput manner. Specifically, we used cell sorting to measure the proportion (%) of green-fluorescent *E. coli* remaining after overnight competition with *S. sonnei* in a 96-well format. As a cell sorter is a specialist equipment, we also correlated these results with a binary read out of green fluorescence following a simple plate-reader based growth assay which is more broadly available. Introducing a threshold of positivity (20%) among the cell sorting results of these assays led to 99% concordance between the two approaches (Fig. 1, Supplementary Table S1, see methods). We found that most 82% (131/159) of *S. sonnei* displayed *E. coli* killing and that phenotype clustered with genotype (Fig. 1). The continuous data (from cell sorting) revealed a spectrum of difference in the phenotype with the proportion of *E. coli* following competition being between 0% and 89% (mean = 14%). Associating the phenotype against the population structure of *S. sonnei* revealed that Subclades of the Central Asia III Clade (3.6.3 and 3.6.2), 3.7.18 (MSM Clade 3), and 3.7.30.4.1 (a Subclade associated with transmission among Orthodox Jewish Communities) were predominately non-killing with the remaining Subclades being predominately killing (Fig. 1). However, differences among isolates belonging to the same Subclade (i.e., epidemiological replicates) demonstrated phenotypic variation (e.g., genotype 3.7.18 where isolates ranged from 0.1% to 88.44% with a mean of 25.15%), highlighting the value of the BPER approach when assaying clinical isolates for inference at an epidemiological level.

To further explore the relevance of this phenotype for *S. sonnei* in the real world, we also determined killing phenotypes of the *S. sonnei* collection against clinical *E. coli* isolates, including both commensal and other pathogenic types. As expected, clinical isolates of *E. coli* exhibited greater levels of survival from the *S. sonnei* competitors (Supplementary Figure S3, Supplementary Table S1). Interestingly, the degree of *E. coli* killing by the *S. sonnei* strains used scaled intuitively with the nature of the *E. coli* strain (i.e., killing of lab strain > commensal strain > pathogenic strains) and the colicin and colicin-related protein content of the *E. coli* strains (see below). Specifically, and critically for the translatability of these results, the commensal Multi
<figure><img src="image_2.png" /><figcaption>**Fig. 1:** The distribution of *E. coli* killing activity and colicin clusters associated with the phenotype across genotypically diverse Lineage 3 *S. sonnei*. The tree is a rooted maximum likelihood phylogeny for 165 *S. sonnei* Lineage 3 isolates. Red circles overlaying terminal branches/ nodes indicate isolates which were utilised for colicin assay and mass spectrometry. The horizontal blue bars show a continuous measure of *E. coli* killing (GFP%–see methods) with a vertical red line highlighting a cut off (≤20%) for *E. coli* killing. A binary killing/non-killing phenotype</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Fig. 1:** The distribution of *E. coli* killing activity and colicin clusters associated with the phenotype across genotypically diverse Lineage 3 *S. sonnei*. The tree is a rooted maximum likelihood phylogeny for 165 *S. sonnei* Lineage 3 isolates. Red circles overlaying terminal branches/ nodes indicate isolates which were utilised for colicin assay and mass spectrometry. The horizontal blue bars show a continuous measure of *E. coli* killing (GFP%–see methods) with a vertical red line highlighting a cut off (≤20%) for *E. coli* killing. A binary killing/non-killing phenotype</figcaption></figure>
Locus Sequence Type 10 (ST10) was killed by a greater proportion of *S. sonnei* strains than the other, more pathogenic sequence types (STs) (55/159 (35% 95% Confidence Interval [CI] 23%-43%) compared with 10% (16/159, 95% CI 4-24%) for ST6199 and 8% (12/159, 95% CI 7-23%) for ST131, *Supplementary Figure S3*, *Supplementary Table S1*).

**GWAS indicates that colicins are responsible for E. coli killing in S. sonnei**

To identify genetic factors responsible for *E. coli* killing, we conducted bGWAS for association with the *E. coli* killing phenotype (using pyseer and MG1655, see methods) and focused on those results that related to predicted/annotated genes. This revealed a variety of genetic factors that were significantly positively associated with the *E. coli* killing phenotype including 3187 kmers within 22 genes (short sequence fragments of length k = 10-100, maxp>15) and 64 clustered orthologous groups (COGs) (lrt p-value <0.05) (*Supplementary Table S2*).

Investigating the 22 genes which contained kmers significantly associated with killing revealed one hypothetical protein encoding gene and 21 of known function; six of which were colicin related. These were IBDECAPI_05363, *cim*, *cnl*, *cea*, *ceaC* and *col* (Fig. 2, *Supplementary Table S2*). IBDECAPI_05363 was the strongest result (i.e., had the highest effect size and lowest p-value, see Fig. 2) and displayed 99% similarity to the *E. coli* E1 colicin immunity protein. The *cim* gene was originally described on the *E. coli* plasmid CloDF13 and is known to be bacteriocinogenic and *cnl* encodes a lysis protein for colicin N originally described as encoded on the *E. coli* plasmid pCHAP4.⁵⁶,⁵⁷ The genes *cea*, *col* and *ceaC* encode for the colicins E1, E2 and E3 respectively and were originally identified on small plasmids ColE1, ColE2 and ColE3.⁵⁸,⁵⁹ In further support of the contribution of colicins to the killing phenotype, two of these genes (*cnl* and *ceaC*) were the first and ninth best supported genes in the COG analysis, and a COG analysis using an alternative GWAS approach (scoary, see methods) also identified the colicin related genes *imm* and *cnl* as strongly associated with the *E. coli* killing phenotype (*Supplementary Table S2*). Owing to the significant burden of evidence that colicins were responsible for the *E. coli* killing phenotype, we explored the distribution of these proteins further (see below).

Genes containing kmers positively associated with the killing phenotype that were not obviously related to colicins included phage related genes (n = 4), T3SS related genes (n = 3), DNA modification/binding genes (n = 2), protein deglycase gene (n = 1) and plasmid replication/

transfer genes (n = 5) (*Supplementary Table S2*). Notably, the plasmid replication/transfer genes from the kmer analysis (KENDMLBB_03776, BNHHFJPC_03747, PFCCDMEB_03709, BNHHFJPC_03743 and IBDECAPI_04568) encoded for replication protein B (IBDECAPI_04568), repE (BNHHFJPC_03743) with the latter (RepE protein) also being present in the COG analysis. Replication protein B and RepE have been shown to initiate replication of both chromosomal segments and plasmids within *E. coli* highlighting its potential for initiation of replication of small plasmids, such as those encoding colicins.⁶⁰ It is possible that these plasmid-related genes were associated to the killing phenotype by virtue of their co-location with colicin-related genes.

**Various colicins are widely distributed in S. sonnei**

To determine the distribution of colicins in *S. sonnei* we screened the genomes against a custom database of >10,000 colicin sequences that grouped into 145 clusters (methods). Although this revealed that widespread distribution of colicins (all isolates contained ≥1 colicin), no single colicin or cluster showed perfect concordance with the presence of the killing phenotype across *S. sonnei* (*Supplementary Table S1*, Fig. 1), suggesting no single protein was responsible for the phenotype. To determine which colicin clusters were most likely responsible for the killing phenotype, we determined the association of colicin clusters with the binary *E. coli* killing phenotype and ordered cluster inclusion based on statistical support for the association with the phenotype (i.e., starting with the lowest p-value first) with each cluster being added until all *E. coli* killing could be explained without clusters overlapping with the non-killing phenotype (Fig. 1). This process resulted in six key clusters being identified that explain the distribution of the *E. coli* killing in *S. sonnei*.

To further validate the association of colicins with the *E. coli* killing phenotype, we selected 15 isolates representative of the breadth of the key colicin clusters distributed throughout the phylogeny and two isolates that did not display *E. coli* killing as controls (Fig. 1). We then extracted culture supernatants of these isolates (n = 17) to confirm the presence of the colicins in the supernatant. Laboratory assay for contact-independent killing confirmed that filtered supernatants from the 15 colicin cluster-containing *S. sonnei* killed *E. coli* MG1655 while those from the two non-killing isolates did not (*Supplementary Figure S2*). Mass spectrometry on filtered supernatants (details in *Supplementary Appendix*) from all isolates (100%, 15 of 15) that displayed *E. coli* killing contained at least one contributing colicin in the total

<footnote>determined by plate reading is depicted in the colour strip closest to the tree followed by *S. sonnei* genotype, assignment to co-circulating MSM clades, and key colicins clusters associated with the killing phenotype. Individual colicins are shown by different colours labelled, by their European Nucleotide Archive accession numbers) and are grouped (and in columns) by arbitrarily named genetic clusters generated by the ARIBA package (see methods and *Supplementary Table S2*).</footnote>
Locus Sequence Type 10 (ST10) was killed by a greater proportion of *S. sonnei* strains than the other, more pathogenic sequence types (STs) (55/159 (35% 95% Confidence Interval [CI] 23%-43%) compared with 10% (16/159, 95% CI 4-24%) for ST6199 and 8% (12/159, 95% CI 7-23%) for ST131, *Supplementary Figure S3*, *Supplementary Table S1*).

**GWAS indicates that colicins are responsible for E. coli killing in S. sonnei**

To identify genetic factors responsible for *E. coli* killing, we conducted bGWAS for association with the *E. coli* killing phenotype (using pyseer and MG1655, see methods) and focused on those results that related to predicted/annotated genes. This revealed a variety of genetic factors that were significantly positively associated with the *E. coli* killing phenotype including 3187 kmers within 22 genes (short sequence fragments of length k = 10-100, maxp>15) and 64 clustered orthologous groups (COGs) (lrt p-value <0.05) (*Supplementary Table S2*).

Investigating the 22 genes which contained kmers significantly associated with killing revealed one hypothetical protein encoding gene and 21 of known function; six of which were colicin related. These were IBDECAPI_05363, *cim*, *cnl*, *cea*, *ceaC* and *col* (Fig. 2, *Supplementary Table S2*). IBDECAPI_05363 was the strongest result (i.e., had the highest effect size and lowest p-value, see Fig. 2) and displayed 99% similarity to the *E. coli* E1 colicin immunity protein. The *cim* gene was originally described on the *E. coli* plasmid CloDF13 and is known to be bacteriocinogenic and *cnl* encodes a lysis protein for colicin N originally described as encoded on the *E. coli* plasmid pCHAP4.⁵⁶,⁵⁷ The genes *cea*, *col* and *ceaC* encode for the colicins E1, E2 and E3 respectively and were originally identified on small plasmids ColE1, ColE2 and ColE3.⁵⁸,⁵⁹ In further support of the contribution of colicins to the killing phenotype, two of these genes (*cnl* and *ceaC*) were the first and ninth best supported genes in the COG analysis, and a COG analysis using an alternative GWAS approach (scoary, see methods) also identified the colicin related genes *imm* and *cnl* as strongly associated with the *E. coli* killing phenotype (*Supplementary Table S2*). Owing to the significant burden of evidence that colicins were responsible for the *E. coli* killing phenotype, we explored the distribution of these proteins further (see below).

Genes containing kmers positively associated with the killing phenotype that were not obviously related to colicins included phage related genes (n = 4), T3SS related genes (n = 3), DNA modification/binding genes (n = 2), protein deglycase gene (n = 1) and plasmid replication/

transfer genes (n = 5) (*Supplementary Table S2*). Notably, the plasmid replication/transfer genes from the kmer analysis (KENDMLBB_03776, BNHHFJPC_03747, PFCCDMEB_03709, BNHHFJPC_03743 and IBDECAPI_04568) encoded for replication protein B (IBDECAPI_04568), repE (BNHHFJPC_03743) with the latter (RepE protein) also being present in the COG analysis. Replication protein B and RepE have been shown to initiate replication of both chromosomal segments and plasmids within *E. coli* highlighting its potential for initiation of replication of small plasmids, such as those encoding colicins.⁶⁰ It is possible that these plasmid-related genes were associated to the killing phenotype by virtue of their co-location with colicin-related genes.

**Various colicins are widely distributed in S. sonnei**

To determine the distribution of colicins in *S. sonnei* we screened the genomes against a custom database of >10,000 colicin sequences that grouped into 145 clusters (methods). Although this revealed that widespread distribution of colicins (all isolates contained ≥1 colicin), no single colicin or cluster showed perfect concordance with the presence of the killing phenotype across *S. sonnei* (*Supplementary Table S1*, Fig. 1), suggesting no single protein was responsible for the phenotype. To determine which colicin clusters were most likely responsible for the killing phenotype, we determined the association of colicin clusters with the binary *E. coli* killing phenotype and ordered cluster inclusion based on statistical support for the association with the phenotype (i.e., starting with the lowest p-value first) with each cluster being added until all *E. coli* killing could be explained without clusters overlapping with the non-killing phenotype (Fig. 1). This process resulted in six key clusters being identified that explain the distribution of the *E. coli* killing in *S. sonnei*.

To further validate the association of colicins with the *E. coli* killing phenotype, we selected 15 isolates representative of the breadth of the key colicin clusters distributed throughout the phylogeny and two isolates that did not display *E. coli* killing as controls (Fig. 1). We then extracted culture supernatants of these isolates (n = 17) to confirm the presence of the colicins in the supernatant. Laboratory assay for contact-independent killing confirmed that filtered supernatants from the 15 colicin cluster-containing *S. sonnei* killed *E. coli* MG1655 while those from the two non-killing isolates did not (*Supplementary Figure S2*). Mass spectrometry on filtered supernatants (details in *Supplementary Appendix*) from all isolates (100%, 15 of 15) that displayed *E. coli* killing contained at least one contributing colicin in the total

<footnote>determined by plate reading is depicted in the colour strip closest to the tree followed by *S. sonnei* genotype, assignment to co-circulating MSM clades, and key colicins clusters associated with the killing phenotype. Individual colicins are shown by different colours labelled, by their European Nucleotide Archive accession numbers) and are grouped (and in columns) by arbitrarily named genetic clusters generated by the ARIBA package (see methods and *Supplementary Table S2*).</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 2: Genes associated with E. coli killing across S. sonnei (including measures of association).** The bubble plot shows the genes (text labels in graph field) associated with the *E. coli* killing phenotype. Each gene is represented by a bubble scaled in size by the number of sequence elements (kmers) contained within the gene that were associated with the killing phenotype. The position on the axes captures the effect size (x-axis) and statistical support (y-axis as negative logarithm of the p-value) for the association with the killing phenotype. The bubbles are coloured according to the average Minor Allele Frequency (MAF) of the kmers relating to the gene according to the inlaid heat map. Red stars indicate those genes with colicin or colicin-related function.</figcaption></figure>

peptides identified in the samples and, for 87% (13 of 15) of isolates, the proteins matched to a unique peptide sequence of an individual colicin (Supplementary Table S3). Mass spectrometry also confirmed that, the supernatants from the two isolates that did not display *E. coli* killing did not yield any matches to these colicin sequences further confirming the role of colicins in *E. coli* killing (Supplementary Table S3).

To interpret our varied killing results across *E. coli* strains we also screened these strains against the colicin database. The screened genomes of the *E. coli* challenge strains revealed that while the MG1655 and ST10 strains both contained only two (and identical) colicin and colicin related proteins, the ST6199 and ST131 strains contained an additional five proteins each (Supplementary Table S6, see below and methods).

**E. coli killing in vitro in S. sonnei is not mediated by T6SS**

Our results highlight the critical role of colicins in mediating *in vitro* *E. coli* killing in *S. sonnei*. As a

previous study described a functional T6SS in *S. sonnei* clinical isolate CIP 106347 that was hypothesised to contribute to competition with *E. coli* and *S. flexneri*, we also explored the possible role of the putative T6SS in *E. coli* killing.²⁴ To do this we re-sequenced CIP106347 and extracted the gene sequences for predicted proteins in the region of the T6SS system. Predicted proteins were used as the reconstituted locus was inconsistent with that originally described²⁴ and contained multiple interruptions that would render the apparatus non-functional (Supplementary Figure S4). The assembly is available in GenBank under accession number CP109775.1 and the T6SS locus is at coordinates 3535861–3560362. In fact, one or more of the key components of the T6SS (e.g., N terminus of TssC) were absent in 100% of our isolates and in CIP 106347, suggesting that the T6SS in *S. sonnei* is likely non-functional (Fig. 3). Consistent with this suggestion is the lack of a correlation between the presence and absence of components of the CIP 106347 T6SS locus
<figure><img src="image_4.png" /><figcaption>**Fig. 2: Genes associated with E. coli killing across S. sonnei (including measures of association).** The bubble plot shows the genes (text labels in graph field) associated with the *E. coli* killing phenotype. Each gene is represented by a bubble scaled in size by the number of sequence elements (kmers) contained within the gene that were associated with the killing phenotype. The position on the axes captures the effect size (x-axis) and statistical support (y-axis as negative logarithm of the p-value) for the association with the killing phenotype. The bubbles are coloured according to the average Minor Allele Frequency (MAF) of the kmers relating to the gene according to the inlaid heat map. Red stars indicate those genes with colicin or colicin-related function.</figcaption></figure>

peptides identified in the samples and, for 87% (13 of 15) of isolates, the proteins matched to a unique peptide sequence of an individual colicin (Supplementary Table S3). Mass spectrometry also confirmed that, the supernatants from the two isolates that did not display *E. coli* killing did not yield any matches to these colicin sequences further confirming the role of colicins in *E. coli* killing (Supplementary Table S3).

To interpret our varied killing results across *E. coli* strains we also screened these strains against the colicin database. The screened genomes of the *E. coli* challenge strains revealed that while the MG1655 and ST10 strains both contained only two (and identical) colicin and colicin related proteins, the ST6199 and ST131 strains contained an additional five proteins each (Supplementary Table S6, see below and methods).

**E. coli killing in vitro in S. sonnei is not mediated by T6SS**

Our results highlight the critical role of colicins in mediating *in vitro* *E. coli* killing in *S. sonnei*. As a

previous study described a functional T6SS in *S. sonnei* clinical isolate CIP 106347 that was hypothesised to contribute to competition with *E. coli* and *S. flexneri*, we also explored the possible role of the putative T6SS in *E. coli* killing.²⁴ To do this we re-sequenced CIP106347 and extracted the gene sequences for predicted proteins in the region of the T6SS system. Predicted proteins were used as the reconstituted locus was inconsistent with that originally described²⁴ and contained multiple interruptions that would render the apparatus non-functional (Supplementary Figure S4). The assembly is available in GenBank under accession number CP109775.1 and the T6SS locus is at coordinates 3535861–3560362. In fact, one or more of the key components of the T6SS (e.g., N terminus of TssC) were absent in 100% of our isolates and in CIP 106347, suggesting that the T6SS in *S. sonnei* is likely non-functional (Fig. 3). Consistent with this suggestion is the lack of a correlation between the presence and absence of components of the CIP 106347 T6SS locus
Tree scale: 100

E. coli phenotype

<figure><img src="image_4.png" /></figure>

Non-killing

<figure><img src="image_5.png" /></figure>

Killing

T6SS components

<figure><img src="image_6.png" /></figure>

Present

<figure><img src="image_7.png" /></figure>

Absent

E. coli phenotype

T6SS components

<figure><img src="image_8.png" /></figure>
Tree scale: 100

E. coli phenotype

<figure><img src="image_5.png" /></figure>

Non-killing

<figure><img src="image_6.png" /></figure>

Killing

T6SS components

<figure><img src="image_7.png" /></figure>

Present

<figure><img src="image_8.png" /></figure>

Absent

E. coli phenotype

T6SS components

<figure><img src="image_9.png" /></figure>
and the killing phenotype in our studies (Fig. 3), though
notably our liquid media experimental model is insuf-
ficient to evaluate a functional T6SS. Thus, the T6SS
genotypic profiles suggest that T6SS is not responsible
for our phenotype and is likely non-functional across
Lineage 3 S. sonnei.

Discussion

Outcompeting commensals like *E. coli* in the gut is an
important aspect for *Shigella* for surviving and estab-
lishing in their niche. Since *Shigella* is known to have a
relatively lower infectious dose, we can expect that the
mechanisms by which *Shigella* establishes itself are
relatively efficient.⁶¹ Therefore, it is important to inves-
tigate these mechanisms to better understand the dy-
namics of the complex gut microbial communities that
lead to infection, and correlate this with established
epidemiological understandings. While there will be
several mechanisms at play in the complex environ-
ments and microbial communities that the pathogens
live in, we demonstrated here the crucial role colicins
play in interbacterial competition with *E. coli* and the
potential consequences of their actions at an epidemi-
ological level.⁶²,⁶³ The important role of colicins in the
genus *Shigella* could be further evidenced by recent re-
ports of colicins being discovered in *S. flexneri* 2a⁶⁴ and a
previous description of colicin plasmid acquisition in
establishing *S. sonnei* in Vietnam.⁴⁵ Our findings
strengthen this evidence base by our use of a diverse
collection of real-world isolates and assay of the
phenotype with replication at an epidemiologically
relevant level through BPER.

The high prevalence of *E. coli* killing throughout the
*S. sonnei* phylogeny suggests that this is an epidemio-
logically important phenotype. This is further supported
by the only non-killing Subclade (3.7.18, Global III
subclade (MSM-associated Clade 3)) being outcompeted
by *E. coli* killing Subclades (CipR MSM1 (3.6.1.1.3.1),
MSM4 (3.7.25), and VN2 MSM2 (3.7.29.1.2)) in a sce-
nario where the Subclades were known to be co-
circulating in a single patient population.¹⁶ Of course,
phenotypes other than *E. coli* killing will also contribute
to the competition dynamics among different geno-
types. Indeed, our previous work identified that Sub-
clade 3.7.18 was also comparatively distinct in lacking a
low fitness cost azithromycin resistance plasmid,
pKSR100.¹⁶,⁴⁶ Non-killing phenotypes were also
conserved in other Subclades of Central Asia III (3.6.2
and 3.6.3) and 3.7.40.1 (Global III OJC). For the majority

of Lineage 3 clades, the *E. coli* killing ability was
conserved through clade expansion indicating its po-
tential to contribute to the global success of Lineage
3 S. sonnei. However, it is important to consider the real-
world relevance of our findings. Our extended BPER
experiments with other *E. coli* competitors showed that
the killing phenotype is less prominent in clinical iso-
lates. This may be due to a variety of factors, including
the presence of the O-antigen in the clinical isolates
which has previously been shown to reduce colicin activ-
ity.⁶⁵ Importantly however, our killing phenotype was
more concordant with killing of ST10, a common
commensal ST which represents the type of *E. coli* most
likely to be competing with *S. sonnei*. Bacteriocins have
previously been shown to be important in the virulence
of pathogenic extraintestinal *E. coli*⁶⁶ and this is via a
number of different mechanisms of actions for those
colicins present.³⁰ Consistent with this finding, *S. sonnei*
were less able to kill the pathogenic ST6199 and ST131
*E. coli* strains, which contained more colicins and colicin
immunity proteins than MG1655 and ST10. Hence, we
have shown that colicins are the key driving factor
responsible for *E. coli* killing with relevance to
commensal strains.

We advocate the BPER approach as a way of assess-
ing the phenotypes in an epidemiologically relevant way.
Since most studies carry out laboratory experiments of
phenotypes using either type strains or a limited num-
ber of clinical isolates, the level of applicability of what
could occur in a real-world setting might not be fully
captured. Therefore, we propose that using a collection
of real-world isolates in laboratory phenotype experi-
ments and having epidemiological replicates where
possible rather than biological replicates offer greater
insight into relevant pathogen biology. We found that, in
the case of this phenotype, BPER could be implemented
in a simple, cost-effective experimental setup with no/
minimal compromise on accuracy (only two mis-
matches between the experiments using flow cytometry
and plate reader, Supplementary Table S1). Here, by
working with a real-world isolate collection with known
epidemiological outcomes we could work backwards to
identify the factors that helped shape the observed
epidemiology and deepen our understanding of the
biology in a targeted, comparatively rapid manner.

We have shown that *in vitro* *E. coli* killing by *S. sonnei*
is common (although the killing percentage reduced
with the use of clinically isolated *E. coli* strains possibly
due to bacteriocins produced by those *E. coli* strains⁶⁷)
and likely mediated by colicins not via a T6SS, as

**Fig. 3: Genotypic T6SS profiles across Lineage 3 S. sonnei.** The tree is a rooted maximum likelihood phylogeny for 165 S. sonnei Lineage 3 isolates. A red colour block is shown to the right of the CIP 106347 isolate (in which the T6SS was previously described). The killing/non-killing phenotype is depicted in the colour strip closest to the tree followed by colour strips depicting the presence/absence of individual T6SS components, both coloured according to the inlaid keys.
and the killing phenotype in our studies (Fig. 3), though
notably our liquid media experimental model is insuf-
ficient to evaluate a functional T6SS. Thus, the T6SS
genotypic profiles suggest that T6SS is not responsible
for our phenotype and is likely non-functional across
Lineage 3 S. sonnei.

Discussion

Outcompeting commensals like *E. coli* in the gut is an
important aspect for *Shigella* for surviving and estab-
lishing in their niche. Since *Shigella* is known to have a
relatively lower infectious dose, we can expect that the
mechanisms by which *Shigella* establishes itself are
relatively efficient.⁶¹ Therefore, it is important to inves-
tigate these mechanisms to better understand the dy-
namics of the complex gut microbial communities that
lead to infection, and correlate this with established
epidemiological understandings. While there will be
several mechanisms at play in the complex environ-
ments and microbial communities that the pathogens
live in, we demonstrated here the crucial role colicins
play in interbacterial competition with *E. coli* and the
potential consequences of their actions at an epidemi-
ological level.⁶²,⁶³ The important role of colicins in the
genus *Shigella* could be further evidenced by recent re-
ports of colicins being discovered in *S. flexneri* 2a⁶⁴ and a
previous description of colicin plasmid acquisition in
establishing *S. sonnei* in Vietnam.⁴⁵ Our findings
strengthen this evidence base by our use of a diverse
collection of real-world isolates and assay of the
phenotype with replication at an epidemiologically
relevant level through BPER.

The high prevalence of *E. coli* killing throughout the
*S. sonnei* phylogeny suggests that this is an epidemio-
logically important phenotype. This is further supported
by the only non-killing Subclade (3.7.18, Global III
subclade (MSM-associated Clade 3)) being outcompeted
by *E. coli* killing Subclades (CipR MSM1 (3.6.1.1.3.1),
MSM4 (3.7.25), and VN2 MSM2 (3.7.29.1.2)) in a sce-
nario where the Subclades were known to be co-
circulating in a single patient population.¹⁶ Of course,
phenotypes other than *E. coli* killing will also contribute
to the competition dynamics among different geno-
types. Indeed, our previous work identified that Sub-
clade 3.7.18 was also comparatively distinct in lacking a
low fitness cost azithromycin resistance plasmid,
pKSR100.¹⁶,⁴⁶ Non-killing phenotypes were also
conserved in other Subclades of Central Asia III (3.6.2
and 3.6.3) and 3.7.40.1 (Global III OJC). For the majority

of Lineage 3 clades, the *E. coli* killing ability was
conserved through clade expansion indicating its po-
tential to contribute to the global success of Lineage
3 S. sonnei. However, it is important to consider the real-
world relevance of our findings. Our extended BPER
experiments with other *E. coli* competitors showed that
the killing phenotype is less prominent in clinical iso-
lates. This may be due to a variety of factors, including
the presence of the O-antigen in the clinical isolates
which has previously been shown to reduce colicin activ-
ity.⁶⁵ Importantly however, our killing phenotype was
more concordant with killing of ST10, a common
commensal ST which represents the type of *E. coli* most
likely to be competing with *S. sonnei*. Bacteriocins have
previously been shown to be important in the virulence
of pathogenic extraintestinal *E. coli*⁶⁶ and this is via a
number of different mechanisms of actions for those
colicins present.³⁰ Consistent with this finding, *S. sonnei*
were less able to kill the pathogenic ST6199 and ST131
*E. coli* strains, which contained more colicins and colicin
immunity proteins than MG1655 and ST10. Hence, we
have shown that colicins are the key driving factor
responsible for *E. coli* killing with relevance to
commensal strains.

We advocate the BPER approach as a way of assess-
ing the phenotypes in an epidemiologically relevant way.
Since most studies carry out laboratory experiments of
phenotypes using either type strains or a limited num-
ber of clinical isolates, the level of applicability of what
could occur in a real-world setting might not be fully
captured. Therefore, we propose that using a collection
of real-world isolates in laboratory phenotype experi-
ments and having epidemiological replicates where
possible rather than biological replicates offer greater
insight into relevant pathogen biology. We found that, in
the case of this phenotype, BPER could be implemented
in a simple, cost-effective experimental setup with no/
minimal compromise on accuracy (only two mis-
matches between the experiments using flow cytometry
and plate reader, Supplementary Table S1). Here, by
working with a real-world isolate collection with known
epidemiological outcomes we could work backwards to
identify the factors that helped shape the observed
epidemiology and deepen our understanding of the
biology in a targeted, comparatively rapid manner.

We have shown that *in vitro* *E. coli* killing by *S. sonnei*
is common (although the killing percentage reduced
with the use of clinically isolated *E. coli* strains possibly
due to bacteriocins produced by those *E. coli* strains⁶⁷)
and likely mediated by colicins not via a T6SS, as

**Fig. 3: Genotypic T6SS profiles across Lineage 3 S. sonnei.** The tree is a rooted maximum likelihood phylogeny for 165 S. sonnei Lineage 3 isolates. A red colour block is shown to the right of the CIP 106347 isolate (in which the T6SS was previously described). The killing/non-killing phenotype is depicted in the colour strip closest to the tree followed by colour strips depicting the presence/absence of individual T6SS components, both coloured according to the inlaid keys.
previously suggested.²⁴ This was supported by the lack of a fully intact T6SS in any of the isolates under study and no association of putative T6SS components with the killing phenotype. However, it is important to note that T6SS is a contact-dependent killing mechanism that would be more appropriately evaluated by *in vivo* experiments in animal models. However, this would be an inappropriate experiment to undertake with a large bank of strains, and unnecessary given that the genomic evidence suggests there are no intact T6SS. We did however find that our liquid media competition experiments matched the outcomes of our initial solid-media experiments for a subset of strains (Supplementary Figure S1, Supplementary Table S1), consistent with the killing phenotype being driven by colicins. Furthermore, this is well supported by associations between colicin clusters and the *E. coli* killing phenotype and confirmation of the secretion and functionality of colicins *in vitro*.

Excitingly, our work also identified several novel genes that were associated with the *E. coli* killing phenotype not known to be part of colicin synthesis or activity (i.e., four phage related genes and one hypothetical gene). These may have some currently unknown relationship with the colicins (e.g., phage proteins may be involved in the mobility of the colicin plasmids) or have as yet unknown functions. In any case, these genes represent ideal candidates for future studies investigating the mechanisms of interbacterial competition in *Shigella*. Furthermore, although, not practicable at the scale used in this study, mutation experiments with individual isolates are needed to definitively confirm (or reconfirm) the colicin-mediated killing (and/or immunity). However, previous work has shown that colicins produced by *S. sonnei* facilitate *E. coli* killing further supporting our findings.⁶⁸ Ultimately testing mutants of colicins, their immunity proteins, and associated genes of unknown function in *in vivo* models represent be exciting avenues of future research to explore the mechanisms more fully. Meanwhile, at an epidemiological scale, our BPER approach has demonstrated the likely importance of interbacterial competition from a panel of diverse strains supporting the importance of this phenotype in shaping bacterial population dynamics.

### Contributors

P.M.D.S—Methodology, Formal analysis, Investigation, Writing—original draft, Writing—Review & editing, Visualisation, R.J.B—Methodology, Software, Formal analysis, Investigation, Writing—original draft, Writing—Review & editing, Visualisation.

L.K.—Methodology, Formal analysis, Investigation, Data curation, Writing—Review & editing, Visualisation.

P.N.—Methodology, Formal analysis, Investigation, Data curation, Writing—Review & editing, Visualisation.

L.D.—Methodology, Writing—Review & editing.

E.N.—Methodology, Formal analysis, Investigation, Data curation, Writing—Review & editing.

B.H.—Conceptualisation, Supervision, Writing Review & editing, Supervision.

F.-X.W.—Conceptualisation, Resources, Writing—Review & editing, Supervision.

B.S.M.—Conceptualisation, Resources, Writing—Review & editing, Supervision.

C.J.—Conceptualisation, Resources, Writing—Review & editing.

K.S.B.—Conceptualisation, Methodology, Formal analysis, Resources, Writing—original draft, Writing—Review & editing, Visualisation, Supervision, Funding acquisition.

### Data sharing statement

Sequencing data for all isolates are deposited in the European Nucleotide Archive and individual accession numbers are provided in Supplementary Table S1.

The colicin sequence database used in this study is publicly available through figshare via https://doi.org/10.6084/m9.figshare.20768260.v1.

Mass spectrometry data are deposited in the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD036656.

### Declaration of interests

Authors declare no competing interests.

### Acknowledgements

P.M.D.S and K.S.B. are supported by a Biotechnology and Biological Sciences Research Council project grant BB/V009184/1. R.J.B is funded by a Biotechnology and Biological Sciences Research Council Doctoral Training Partnership studentship BB/M011186/1. K.S.B was also supported by a Wellcome Trust Clinical Research Career Development Award 106690/A/14/Z and drew on funding from Medical Research Council MR/R020787/1. K.S.B is affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR-HPRU) in Gastrointestinal Infections at University of Liverpool in partnership with the United Kingdom Health Security Agency (UKHSA), in collaboration with University of Warwick. The views expressed are those of authors and not necessarily those of the NHS, NIHR, Department of Health and Social Care or UKHSA. Authors acknowledge the staff and facilities of GeneMill foundry, Shared Research Facilities, University of Liverpool for their help in setting up and the use of cell sorting facilities. The mass spectrometry instrumentation was funded by the LABEX: ANR-10-LABX-0036 NETRNA, a funding from the state managed by the French National Research Agency as part of the investments for the future program. Authors would like to thank Michael Bottery (University of Manchester) for his kind gift of *E. coli* MG1655 strain and Serge Mostowy (London School of Hygiene and Tropical Medicine) for kindly providing the GFP reporter plasmid used in this study.

## Appendix A. Supplementary data

Supplementary data related to this article can be found at https://doi.org/10.1016/j.ebiom.2023.104822.

### References

1. Bardhan P, Faruque AS, Naheed A, Sack DA. Decrease in shigellosis-related deaths without Shigella spp.-specific interventions, Asia. *Emerg Infect Dis.* 2010;16(11):1718-1723.

2. Khalil IA, Troeger C, Blacker BF, et al. Morbidity and mortality due to shigella and enterotoxigenic Escherichia coli diarrhoea: the global burden of disease study 1990-2016. *Lancet Infect Dis.* 2018;18(11):1229-1240.

3. Baker KS, Dallman TJ, Ashton PM, et al. Intercontinental dissemination of azithromycin-resistant shigellosis through sexual transmission: a cross-sectional study. *Lancet Infect Dis.* 2015;15(8):913-921.

4. Ingle DJ, Easton M, Valcanis M, et al. Co-Circulation of multidrug-resistant Shigella among men who have sex with men in Australia. *Clin Infect Dis.* 2019;69(9):1535-1544.

5. Thompson CN, Duy PT, Baker S. The rising dominance of *Shigella sonnei*: an intercontinental shift in the etiology of bacillary dysentery. *PLoS Neglected Trop Dis.* 2015;9(6):e0003708.

6. Simms I, Field N, Jenkins C, et al. Intensified shigellosis epidemic associated with sexual transmission in men who have sex with
previously suggested.²⁴ This was supported by the lack of a fully intact T6SS in any of the isolates under study and no association of putative T6SS components with the killing phenotype. However, it is important to note that T6SS is a contact-dependent killing mechanism that would be more appropriately evaluated by *in vivo* experiments in animal models. However, this would be an inappropriate experiment to undertake with a large bank of strains, and unnecessary given that the genomic evidence suggests there are no intact T6SS. We did however find that our liquid media competition experiments matched the outcomes of our initial solid-media experiments for a subset of strains (Supplementary Figure S1, Supplementary Table S1), consistent with the killing phenotype being driven by colicins. Furthermore, this is well supported by associations between colicin clusters and the *E. coli* killing phenotype and confirmation of the secretion and functionality of colicins *in vitro*.

Excitingly, our work also identified several novel genes that were associated with the *E. coli* killing phenotype not known to be part of colicin synthesis or activity (i.e., four phage related genes and one hypothetical gene). These may have some currently unknown relationship with the colicins (e.g., phage proteins may be involved in the mobility of the colicin plasmids) or have as yet unknown functions. In any case, these genes represent ideal candidates for future studies investigating the mechanisms of interbacterial competition in *Shigella*. Furthermore, although, not practicable at the scale used in this study, mutation experiments with individual isolates are needed to definitively confirm (or reconfirm) the colicin-mediated killing (and/or immunity). However, previous work has shown that colicins produced by *S. sonnei* facilitate *E. coli* killing further supporting our findings.⁶⁸ Ultimately testing mutants of colicins, their immunity proteins, and associated genes of unknown function in *in vivo* models represent be exciting avenues of future research to explore the mechanisms more fully. Meanwhile, at an epidemiological scale, our BPER approach has demonstrated the likely importance of interbacterial competition from a panel of diverse strains supporting the importance of this phenotype in shaping bacterial population dynamics.

### Contributors

P.M.D.S—Methodology, Formal analysis, Investigation, Writing—original draft, Writing—Review & editing, Visualisation, R.J.B—Methodology, Software, Formal analysis, Investigation, Writing—original draft, Writing—Review & editing, Visualisation.

L.K.—Methodology, Formal analysis, Investigation, Data curation, Writing—Review & editing, Visualisation.

P.N.—Methodology, Formal analysis, Investigation, Data curation, Writing—Review & editing, Visualisation.

L.D.—Methodology, Writing—Review & editing.

E.N.—Methodology, Formal analysis, Investigation, Data curation, Writing—Review & editing.

B.H.—Conceptualisation, Supervision, Writing Review & editing, Supervision.

F-X.W.—Conceptualisation, Resources, Writing—Review & editing, Supervision.

B.S.M.—Conceptualisation, Resources, Writing—Review & editing, Supervision.

C.J.—Conceptualisation, Resources, Writing—Review & editing.

K.S.B.—Conceptualisation, Methodology, Formal analysis, Resources, Writing—original draft, Writing—Review & editing, Visualisation, Supervision, Funding acquisition.

### Data sharing statement

Sequencing data for all isolates are deposited in the European Nucleotide Archive and individual accession numbers are provided in Supplementary Table S1.

The colicin sequence database used in this study is publicly available through figshare via https://doi.org/10.6084/m9.figshare.20768260.v1.

Mass spectrometry data are deposited in the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD036656.

### Declaration of interests

Authors declare no competing interests.

### Acknowledgements

P.M.D.S and K.S.B. are supported by a Biotechnology and Biological Sciences Research Council project grant BB/V009184/1. R.J.B is funded by a Biotechnology and Biological Sciences Research Council Doctoral Training Partnership studentship BB/M011186/1. K.S.B was also supported by a Wellcome Trust Clinical Research Career Development Award 106690/A/14/Z and drew on funding from Medical Research Council MR/R020787/1. K.S.B is affiliated to the National Institute for Health Research Health Protection Research Unit (NIHR-HPRU) in Gastrointestinal Infections at University of Liverpool in partnership with the United Kingdom Health Security Agency (UKHSA), in collaboration with University of Warwick. The views expressed are those of authors and not necessarily those of the NHS, NIHR, Department of Health and Social Care or UKHSA. Authors acknowledge the staff and facilities of GeneMill foundry, Shared Research Facilities, University of Liverpool for their help in setting up and the use of cell sorting facilities. The mass spectrometry instrumentation was funded by the LABEX: ANR-10-LABX-0036 NETRNA, a funding from the state managed by the French National Research Agency as part of the investments for the future program. Authors would like to thank Michael Bottery (University of Manchester) for his kind gift of *E. coli* MG1655 strain and Serge Mostowy (London School of Hygiene and Tropical Medicine) for kindly providing the GFP reporter plasmid used in this study.

## Appendix A. Supplementary data

Supplementary data related to this article can be found at https://doi.org/10.1016/j.ebiom.2023.104822.

### References

1. Bardhan P, Faruque AS, Naheed A, Sack DA. Decrease in shigellosis-related deaths without Shigella spp.-specific interventions, Asia. *Emerg Infect Dis.* 2010;16(11):1718-1723.

2. Khalil IA, Troeger C, Blacker BF, et al. Morbidity and mortality due to shigella and enterotoxigenic Escherichia coli diarrhoea: the global burden of disease study 1990-2016. *Lancet Infect Dis.* 2018;18(11):1229-1240.

3. Baker KS, Dallman TJ, Ashton PM, et al. Intercontinental dissemination of azithromycin-resistant shigellosis through sexual transmission: a cross-sectional study. *Lancet Infect Dis.* 2015;15(8):913-921.

4. Ingle DJ, Easton M, Valcanis M, et al. Co-Circulation of multidrug-resistant Shigella among men who have sex with men in Australia. *Clin Infect Dis.* 2019;69(9):1535-1544.

5. Thompson CN, Duy PT, Baker S. The rising dominance of *Shigella sonnei*: an intercontinental shift in the etiology of bacillary dysentery. *PLoS Neglected Trop Dis.* 2015;9(6):e0003708.

6. Simms I, Field N, Jenkins C, et al. Intensified shigellosis epidemic associated with sexual transmission in men who have sex with
men–Shigella flexneri and S. sonnei in England, 2004 to end of February 2015. *Euro Surveill.* 2015;20(15):21097.

7 Bardsley M, Jenkins C, Mitchell HD, et al. Persistent transmission of shigellosis in england is associated with a recently emerged multidrug-resistant strain of *Shigella sonnei*. *J Clin Microbiol.* 2020;58(4).

8 Charles H, Prochazka M, Thorley K, et al. Outbreak of sexually transmitted, extensively drug-resistant *Shigella sonnei* in the UK, 2021-22: a descriptive epidemiological study. *Lancet Infect Dis.* 2022;22:1503.

9 Chung The H, Rabaa MA, Pham Thanh D, et al. South Asia as a reservoir for the global spread of ciprofloxacin-resistant *Shigella sonnei*: a cross-sectional study. *PLoS Med.* 2016;13(8):e1002055.

10 Fischer N, Maex M, Mattheus W, et al. Genomic epidemiology of persistently circulating MDR *Shigella sonnei* strains associated with men who have sex with men (MSM) in Belgium (2013-19). *J Antimicrob Chemother.* 2021;77(1):89-97.

11 Locke RK, Greig DR, Jenkins C, Dallman TJ, Cowley LA. Acquisition and loss of CTX-M plasmids in Shigella species associated with MSM transmission in the UK. *Microb Genom.* 2021;7(8):000644.

12 Zayet S, Klopfenstein T, Pierron A, et al. *Shigella sonnei*, an emerging multidrug-resistant sexually transmitted pathogen in Franche-Comte, France. *Emerg Microbes Infect.* 2021;10(1):1702-1705.

13 Moreno-Mingorance A, Espinal P, Rodriguez V, et al. Circulation of multi-drug-resistant *Shigella sonnei* and Shigella flexneri among men who have sex with men in Barcelona, Spain, 2015-2019. *Int J Antimicrob Agents.* 2021;58(3):106378.

14 Tacconelli E, Carrara E, Savoldi A, et al. Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. *Lancet Infect Dis.* 2018;18(3):318-327.

15 Hawkey J, Paranagama K, Baker KS, et al. Global population structure and genotyping framework for genomic surveillance of the major dysentery pathogen, *Shigella sonnei*. *Nat Commun.* 2021;12(1):2684.

16 Baker KS, Dallman TJ, Field N, et al. Horizontal antimicrobial resistance transfer drives epidemics of multiple Shigella species. *Nat Commun.* 2018;9(1):1462.

17 Anderson M, Sansonetti PJ, Marteyn BS. Shigella diversity and changing landscape: insights for the twenty-first century. *Front Cell Infect Microbiol.* 2016;6:45.

18 MacIntyre DL, Miyata ST, Kitaoka M, Pukatzki S. The Vibrio cholerae type VI secretion system displays antimicrobial properties. *Proc Natl Acad Sci U S A.* 2010;107(45):19520-19524.

19 Hsieh PF, Lu YR, Lin TL, Lai LY, Wang JT. Klebsiella pneumoniae type VI secretion system contributes to bacterial competition, cell invasion, type-1 fimbriae expression, and in vivo colonization. *J Infect Dis.* 2019;219(4):637-647.

20 Sabinelli-Sousa S, de Araujo-Silva AL, Hespanhol JT, Bayer-Santos E. Revisiting the steps of Salmonella gut infection with a focus on antagonistic interbacterial interactions. *FEBS J.* 2022;289(14):4192-4211.

21 Sana TG, Flaugnatti N, Lugo KA, et al. Salmonella Typhimurium utilizes a T6SS-mediated antibacterial weapon to establish in the host gut. *Proc Natl Acad Sci U S A.* 2016;113(34):E5044-E5051.

22 Myers-Morales T, Oates AE, Byrd MS, Garcia EC. Burkholderia cepacia complex contact-dependent growth inhibition systems mediate interbacterial competition. *J Bacteriol.* 2019;201(12):e00012.

23 Sorbara MT, Pamer EG. Interbacterial mechanisms of colonization resistance and the strategies pathogens use to overcome them. *Mucosal Immunol.* 2019;12(1):1-9.

24 Anderson MC, Vonaesch P, Saffarian A, Marteyn BS, Sansonetti PJ. *Shigella sonnei* encodes a functional T6SS used for interbacterial competition and niche occupancy. *Cell Host Microbe.* 2017;21(6):769-776.e3.

25 Coulthurst S. The Type VI secretion system: a versatile bacterial weapon. *Microbiology (Read)*. 2019;165(5):503-515.

26 Zoued A, Brunet YR, Durand E, et al. Architecture and assembly of the Type VI secretion system. *Biochim Biophys Acta.* 2014;1843(8):1664-1673.

27 Cianfanelli FR, Monlezun L, Coulthurst SJ. Aim, load, fire: the type VI secretion system, a bacterial nanoweapon. *Trends Microbiol.* 2016;24(1):51-62.

28 Lazdunski CJ, Bouveret E, Rigal A, Journet L, Lloubes R, Benedetti H. Colicin import into Escherichia coli cells. *J Bacteriol.* 1998;180(19):4993-5002.

29 Riley MA. Molecular mechanisms of colicin evolution. *Mol Biol Evol.* 1993;10(6):1380-1395.

30 Cascales E, Buchanan SK, Duche D, et al. Colicin biology. *Microbiol Mol Biol Rev.* 2007;71(1):158-229.

31 Yamada M, Ebina Y, Miyata T, Nakazawa T, Nakazawa A. Nucleotide sequence of the structural gene for colicin E1 and predicted structure of the protein. *Proc Natl Acad Sci U S A.* 1982;79(9):2827-2831.

32 Pugsley AP, Goldzahl N, Barker RM. Colicin E2 production and release by Escherichia coli K12 and other Enterobacteriaceae. *J Gen Microbiol.* 1985;131(10):2673-2686.

33 Horak V. Seventy colicin types of *Shigella sonnei* and an indicator system for their determination. *Zentralbl Bakteriolog.* 1994;281(1):24-29.

34 Mavridou DAI, Gonzalez D, Kim W, West SA, Foster KR. Bacteria use collective behavior to generate diverse combat strategies. *Curr Biol.* 2018;28(3):345-355.e4.

35 Cornforth DM, Foster KR. Competition sensing: the social side of bacterial stress responses. *Nat Rev Microbiol.* 2013;11(4):285-293.

36 Gillor O, Giladi I, Riley MA. Persistence of colicinogenic Escherichia coli in the mouse gastrointestinal tract. *BMC Microbiol.* 2009;9:165.

37 Kirkup BC, Riley MA. Antibiotic-mediated antagonism leads to a bacterial game of rock-paper-scissors in vivo. *Nature.* 2004;428(6981):412-414.

38 Riley MA, Gordon DM. The ecological role of bacteriocins in bacterial competition. *Trends Microbiol.* 1999;7(3):129-133.

39 Chung The H, Boinett C, Pham Thanh D, et al. Dissecting the molecular evolution of fluoroquinolone-resistant *Shigella sonnei*. *Nat Commun.* 2019;10(1):1-13.

40 Chung The H, Rabaa MA, Thanh DP, et al. Introduction and establishment of fluoroquinolone-resistant *Shigella sonnei* into Bhutan. *Microb Genom.* 2015;1(6):e000042-e.

41 Baker K, Dallman T, Field N, et al. Genomic epidemiology of *Shigella in the United Kingdom* shows transmission of pathogen sublineages and determinants of antimicrobial resistance. 2018.

42 Baker KS, Dallman TJ, Behar A, et al. Travel- and community-based transmission of multidrug-resistant *Shigella sonnei* lineage among international orthodox Jewish communities. 2016;22(9):1545-1553.

43 Holt KE, Baker S, Weill FX, et al. *Shigella sonnei* genome sequencing and phylogenetic analysis indicate recent global dissemination from Europe. *Nat Genet.* 2012;44(9):1056-1059.

44 Baker KS, Campos J, Pichel M, et al. Whole genome sequencing of *Shigella sonnei* through PulseNet Latin America and Caribbean: advancing global surveillance of foodborne illnesses. *Clin Microbiol Infect.* 2017;23(11):845-853.

45 Holt KE, Thieu Nga TV, Thanh DP, et al. Tracking the establishment of local endemic populations of an emergent enteric pathogen. *Proc Natl Acad Sci U S A.* 2013;110(43):17522-17527.

46 Malaka De Silva P, Stenhouse GE, Blackwell GA, et al. A tale of two plasmids: contributions of plasmid associated phenotypes to epidemiological success among Shigella. *Proc Biol Sci.* 2022;289(1980):20220581.

47 Letunic I, Bork P. Interactive Tree of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. *Nucleic Acids Res.* 2021;49(W1):W293-W296.

48 Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycer: resolving bacterial genome assemblies from short and long sequencing reads. *PLoS Comput Biol.* 2017;13(6):e1005595.

49 Seemann T. Prokka: rapid prokaryotic genome annotation. *Bioinformatics.* 2014;30(14):2068-2069.

50 Page AJ, Cummins CA, Hunt M, et al. Roary: rapid large-scale prokaryote pan genome analysis. *Bioinformatics.* 2015;31(22):3691-3693.

51 Lees JA, Galardini M, Bentley SD, Weiser JN, Corander J. pyseer: a comprehensive tool for microbial pangenome-wide association studies. *Bioinformatics.* 2018;34(24):4310-4312.

52 Brynildsrud O, Bohlin J, Scheffer L, Eldholm V. Rapid scoring of genes in microbial pan-genome-wide association studies with Soary. *Genome Biol.* 2016;17(1):238.

53 Hahn-Lobmann S, Stephan A, Schulz S, et al. Colicins and salmocins—new classes of plant-made non-antibiotic food antibiotics. *Front Plant Sci.* 2019;10:437.

54 Inouye M, Dashnow H, Raven LA, et al. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. *Genome Med.* 2014;6(11):90.
men–Shigella flexneri and S. sonnei in England, 2004 to end of February 2015. *Euro Surveill.* 2015;20(15):21097.

7 Bardsley M, Jenkins C, Mitchell HD, et al. Persistent transmission of shigellosis in england is associated with a recently emerged multidrug-resistant strain of *Shigella sonnei*. *J Clin Microbiol.* 2020;58(4).

8 Charles H, Prochazka M, Thorley K, et al. Outbreak of sexually transmitted, extensively drug-resistant *Shigella sonnei* in the UK, 2021-22: a descriptive epidemiological study. *Lancet Infect Dis.* 2022;22:1503.

9 Chung The H, Rabaa MA, Pham Thanh D, et al. South Asia as a reservoir for the global spread of ciprofloxacin-resistant *Shigella sonnei*: a cross-sectional study. *PLoS Med.* 2016;13(8):e1002055.

10 Fischer N, Maex M, Mattheus W, et al. Genomic epidemiology of persistently circulating MDR *Shigella sonnei* strains associated with men who have sex with men (MSM) in Belgium (2013-19). *J Antimicrob Chemother.* 2021;77(1):89-97.

11 Locke RK, Greig DR, Jenkins C, Dallman TJ, Cowley LA. Acquisition and loss of CTX-M plasmids in Shigella species associated with MSM transmission in the UK. *Microb Genom.* 2021;7(8):000644.

12 Zayet S, Klopfenstein T, Pierron A, et al. *Shigella sonnei*, an emerging multidrug-resistant sexually transmitted pathogen in Franche-Comte, France. *Emerg Microbes Infect.* 2021;10(1):1702-1705.

13 Moreno-Mingorance A, Espinal P, Rodriguez V, et al. Circulation of multi-drug-resistant *Shigella sonnei* and Shigella flexneri among men who have sex with men in Barcelona, Spain, 2015-2019. *Int J Antimicrob Agents.* 2021;58(3):106378.

14 Tacconelli E, Carrara E, Savoldi A, et al. Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis. *Lancet Infect Dis.* 2018;18(3):318-327.

15 Hawkey J, Paranagama K, Baker KS, et al. Global population structure and genotyping framework for genomic surveillance of the major dysentery pathogen, *Shigella sonnei*. *Nat Commun.* 2021;12(1):2684.

16 Baker KS, Dallman TJ, Field N, et al. Horizontal antimicrobial resistance transfer drives epidemics of multiple Shigella species. *Nat Commun.* 2018;9(1):1462.

17 Anderson M, Sansonetti PJ, Marteyn BS. Shigella diversity and changing landscape: insights for the twenty-first century. *Front Cell Infect Microbiol.* 2016;6:45.

18 MacIntyre DL, Miyata ST, Kitaoka M, Pukatzki S. The Vibrio cholerae type VI secretion system displays antimicrobial properties. *Proc Natl Acad Sci U S A.* 2010;107(45):19520-19524.

19 Hsieh PF, Lu YR, Lin TL, Lai LY, Wang JT. Klebsiella pneumoniae type VI secretion system contributes to bacterial competition, cell invasion, type-1 fimbriae expression, and in vivo colonization. *J Infect Dis.* 2019;219(4):637-647.

20 Sabinelli-Sousa S, de Araujo-Silva AL, Hespanhol JT, Bayer-Santos E. Revisiting the steps of Salmonella gut infection with a focus on antagonistic interbacterial interactions. *FEBS J.* 2022;289(14):4192-4211.

21 Sana TG, Flaugnatti N, Lugo KA, et al. Salmonella Typhimurium utilizes a T6SS-mediated antibacterial weapon to establish in the host gut. *Proc Natl Acad Sci U S A.* 2016;113(34):E5044-E5051.

22 Myers-Morales T, Oates AE, Byrd MS, Garcia EC. Burkholderia cepacia complex contact-dependent growth inhibition systems mediate interbacterial competition. *J Bacteriol.* 2019;201(12):e00012.

23 Sorbara MT, Pamer EG. Interbacterial mechanisms of colonization resistance and the strategies pathogens use to overcome them. *Mucosal Immunol.* 2019;12(1):1-9.

24 Anderson MC, Vonaesch P, Saffarian A, Marteyn BS, Sansonetti PJ. *Shigella sonnei* encodes a functional T6SS used for interbacterial competition and niche occupancy. *Cell Host Microbe.* 2017;21(6):769-776.e3.

25 Coulthurst S. The Type VI secretion system: a versatile bacterial weapon. *Microbiology (Read)*. 2019;165(5):503-515.

26 Zoued A, Brunet YR, Durand E, et al. Architecture and assembly of the Type VI secretion system. *Biochim Biophys Acta.* 2014;1843(8):1664-1673.

27 Cianfanelli FR, Monlezun L, Coulthurst SJ. Aim, load, fire: the type VI secretion system, a bacterial nanoweapon. *Trends Microbiol.* 2016;24(1):51-62.

28 Lazdunski CJ, Bouveret E, Rigal A, Journet L, Lloubes R, Benedetti H. Colicin import into Escherichia coli cells. *J Bacteriol.* 1998;180(19):4993-5002.

29 Riley MA. Molecular mechanisms of colicin evolution. *Mol Biol Evol.* 1993;10(6):1380-1395.

30 Cascales E, Buchanan SK, Duche D, et al. Colicin biology. *Microbiol Mol Biol Rev.* 2007;71(1):158-229.

31 Yamada M, Ebina Y, Miyata T, Nakazawa T, Nakazawa A. Nucleotide sequence of the structural gene for colicin E1 and predicted structure of the protein. *Proc Natl Acad Sci U S A.* 1982;79(9):2827-2831.

32 Pugsley AP, Goldzahl N, Barker RM. Colicin E2 production and release by Escherichia coli K12 and other Enterobacteriaceae. *J Gen Microbiol.* 1985;131(10):2673-2686.

33 Horak V. Seventy colicin types of *Shigella sonnei* and an indicator system for their determination. *Zentralbl Bakteriolog.* 1994;281(1):24-29.

34 Mavridou DAI, Gonzalez D, Kim W, West SA, Foster KR. Bacteria use collective behavior to generate diverse combat strategies. *Curr Biol.* 2018;28(3):345-355.e4.

35 Cornforth DM, Foster KR. Competition sensing: the social side of bacterial stress responses. *Nat Rev Microbiol.* 2013;11(4):285-293.

36 Gillor O, Giladi I, Riley MA. Persistence of colicinogenic Escherichia coli in the mouse gastrointestinal tract. *BMC Microbiol.* 2009;9:165.

37 Kirkup BC, Riley MA. Antibiotic-mediated antagonism leads to a bacterial game of rock-paper-scissors in vivo. *Nature.* 2004;428(6981):412-414.

38 Riley MA, Gordon DM. The ecological role of bacteriocins in bacterial competition. *Trends Microbiol.* 1999;7(3):129-133.

39 Chung The H, Boinett C, Pham Thanh D, et al. Dissecting the molecular evolution of fluoroquinolone-resistant *Shigella sonnei*. *Nat Commun.* 2019;10(1):1-13.

40 Chung The H, Rabaa MA, Thanh DP, et al. Introduction and establishment of fluoroquinolone-resistant *Shigella sonnei* into Bhutan. *Microb Genom.* 2015;1(6):e000042-e.

41 Baker K, Dallman T, Field N, et al. Genomic epidemiology of *Shigella in the United Kingdom* shows transmission of pathogen sublineages and determinants of antimicrobial resistance. 2018.

42 Baker KS, Dallman TJ, Behar A, et al. Travel- and community-based transmission of multidrug-resistant *Shigella sonnei* lineage among international orthodox Jewish communities. 2016;22(9):1545-1553.

43 Holt KE, Baker S, Weill FX, et al. *Shigella sonnei* genome sequencing and phylogenetic analysis indicate recent global dissemination from Europe. *Nat Genet.* 2012;44(9):1056-1059.

44 Baker KS, Campos J, Pichel M, et al. Whole genome sequencing of *Shigella sonnei* through PulseNet Latin America and Caribbean: advancing global surveillance of foodborne illnesses. *Clin Microbiol Infect.* 2017;23(11):845-853.

45 Holt KE, Thieu Nga TV, Thanh DP, et al. Tracking the establishment of local endemic populations of an emergent enteric pathogen. *Proc Natl Acad Sci U S A.* 2013;110(43):17522-17527.

46 Malaka De Silva P, Stenhouse GE, Blackwell GA, et al. A tale of two plasmids: contributions of plasmid associated phenotypes to epidemiological success among Shigella. *Proc Biol Sci.* 2022;289(1980):20220581.

47 Letunic I, Bork P. Interactive Tree of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. *Nucleic Acids Res.* 2021;49(W1):W293-W296.

48 Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycer: resolving bacterial genome assemblies from short and long sequencing reads. *PLoS Comput Biol.* 2017;13(6):e1005595.

49 Seemann T. Prokka: rapid prokaryotic genome annotation. *Bioinformatics.* 2014;30(14):2068-2069.

50 Page AJ, Cummins CA, Hunt M, et al. Roary: rapid large-scale prokaryote pan genome analysis. *Bioinformatics.* 2015;31(22):3691-3693.

51 Lees JA, Galardini M, Bentley SD, Weiser JN, Corander J. pyseer: a comprehensive tool for microbial pangenome-wide association studies. *Bioinformatics.* 2018;34(24):4310-4312.

52 Brynildsrud O, Bohlin J, Scheffer L, Eldholm V. Rapid scoring of genes in microbial pan-genome-wide association studies with Soary. *Genome Biol.* 2016;17(1):238.

53 Hahn-Lobmann S, Stephan A, Schulz S, et al. Colicins and salmocins—new classes of plant-made non-antibiotic food antibiotics. *Front Plant Sci.* 2019;10:437.

54 Inouye M, Dashnow H, Raven LA, et al. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. *Genome Med.* 2014;6(11):90.
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