Pathogen invasion-dependent tissue reservoirs and plasmid-encoded antibiotic degradation boost plasmid spread in the gut

  1. Erik Bakkeren  Is a corresponding author
  2. Joana Anuschka Herter
  3. Jana Sanne Huisman
  4. Yves Steiger
  5. Ersin Gül
  6. Joshua Patrick Mark Newson
  7. Alexander Oliver Brachmann
  8. Jörn Piel
  9. Roland Regoes
  10. Sebastian Bonhoeffer
  11. Médéric Diard
  12. Wolf-Dietrich Hardt  Is a corresponding author
  1. Institute of Microbiology, Department of Biology, ETH Zurich, Switzerland
  2. Swiss Institute of Bioinformatics, Switzerland
  3. Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zurich, Switzerland
  4. Botnar Research Centre for Child Health, Switzerland
  5. Biozentrum, University of Basel, Switzerland

Abstract

Many plasmids encode antibiotic resistance genes. Through conjugation, plasmids can be rapidly disseminated. Previous work identified gut luminal donor/recipient blooms and tissue-lodged plasmid-bearing persister cells of the enteric pathogen Salmonella enterica serovar Typhimurium (S.Tm) that survive antibiotic therapy in host tissues, as factors promoting plasmid dissemination among Enterobacteriaceae. However, the buildup of tissue reservoirs and their contribution to plasmid spread await experimental demonstration. Here, we asked if re-seeding-plasmid acquisition-invasion cycles by S.Tm could serve to diversify tissue-lodged plasmid reservoirs, and thereby promote plasmid spread. Starting with intraperitoneal mouse infections, we demonstrate that S.Tm cells re-seeding the gut lumen initiate clonal expansion. Extended spectrum beta-lactamase (ESBL) plasmid-encoded gut luminal antibiotic degradation by donors can foster recipient survival under beta-lactam antibiotic treatment, enhancing transconjugant formation upon re-seeding. S.Tm transconjugants can subsequently re-enter host tissues introducing the new plasmid into the tissue-lodged reservoir. Population dynamics analyses pinpoint recipient migration into the gut lumen as rate-limiting for plasmid transfer dynamics in our model. Priority effects may be a limiting factor for reservoir formation in host tissues. Overall, our proof-of-principle data indicates that luminal antibiotic degradation and shuttling between the gut lumen and tissue-resident reservoirs can promote the accumulation and spread of plasmids within a host over time.

Editor's evaluation

This work reveals an important feature of within-host acquisition and spread of antibiotic resistance. Focusing on a pathogen that shuttles between the gut lumen and tissue reservoirs, this study found that antibiotic degradation in the gut by resistant bacteria can promote the accumulation and spread of plasmids. This manuscript will be of interest to readers in the fields of infection biology, plasmid ecology, gut microbiomes, and antimicrobial resistance.

https://doi.org/10.7554/eLife.69744.sa0

Introduction

The accumulation of antibiotic resistance genes in pathogenic bacterial strains is an important cause of antibiotic treatment failure. Conjugative antibiotic resistance plasmids are key drivers of this accumulation, accelerating the emergence of new bacterial strains genetically resistant to antibiotics (Wright, 2007). The mechanisms that contribute to the spread of resistance plasmids within an infected host are still not fully established.

Recently, we have begun assessing the spread of plasmids within bacteria colonizing or infecting mammalian hosts. In this work, when we refer to ‘host’, we consistently refer to the mammalian host harbouring enteric bacteria. Plasmid spread within such hosts can be aided by tissue-lodged reservoirs of bacterial cells phenotypically recalcitrant to antibiotic therapy (Bakkeren et al., 2019). This process is called persistence and refers to a property of bacterial populations in which one subpopulation is killed by an antibiotic parallel to another subpopulation that is killed slowly (‘persisters’), defined by phenotypic and not genotypic properties (Balaban et al., 2019; Gollan et al., 2019). Persisters can not only lead to antibiotic treatment failure, but they also have important implications for the evolution of antibiotic resistance (Levin-Reisman et al., 2017; Liu et al., 2020; Windels et al., 2019) or virulence (Bakkeren et al., 2020; Diard et al., 2014), and serve as long-term reservoirs promoting the spread of resistance plasmids. We showed this using the model organism S. enterica serovar Typhimurium (S.Tm) (Bakkeren et al., 2019). Invasive S.Tm cells form reservoirs of recalcitrant cells inside of host tissues (i.e. tissue persister reservoirs) that are difficult to treat with antibiotics (Bakkeren et al., 2019; Claudi et al., 2014; Diard et al., 2014; Helaine et al., 2014; Kaiser et al., 2014; Stapels et al., 2018). After treatment, these S.Tm cells can re-seed the gut lumen from their reservoirs along with any plasmids they carry. In the gut lumen, this re-seeding provides the plasmids with the opportunity to conjugate into new bacteria. Thereby, plasmids can subvert persisters to ensure long-term association within a given host. Besides persisters, S.Tm tissue invasion also creates an antibiotic susceptible subpopulation within the host tissues. In the absence of antibiotic therapy, the latter cells are generally more numerous than the persisters and are thought to drive chronic infections (Claudi et al., 2014; Kaiser et al., 2014; Lawley et al., 2008; Monack et al., 2004). Moreover, cells that constitute tissue reservoirs during chronic infection are known to re-seed the gut lumen over time (Lam and Monack, 2014). However, the role of this second, non-persister, tissue-lodged pathogen population in plasmid dissemination remained to be formally established.

Although very specific characteristics define antibiotic persistence (i.e. biphasic killing curves defining the susceptible and tolerant subpopulations) (Balaban et al., 2019), there are many similarities to persistent (aka long-term, chronic) infection. Numerous bacterial pathogens can survive or evade host immune defenses and antibiotics alike in a recalcitrant state (e.g. Pseudomonas aeruginosa, Escherichia coli, Staphylococcus aureus, or Salmonella enterica) (Bakkeren et al., 2020; Gollan et al., 2019; Grant and Hung, 2013). Using mouse models, tissue reservoirs of S.Tm antibiotic persisters located in the intestinal mucosa or systemic sites can re-seed the gut lumen after the cessation of antibiotic therapy (Bakkeren et al., 2019; Diard et al., 2014; Endt et al., 2012; Kaiser et al., 2014; Onwuezobe et al., 2012). In chronically infected hosts, the pathogen population tends to diversify forming non-growing and growing subpopulations which are kept in check by immune defenses, but can rise upon neutralization of cytokines (e.g. IFNγ or TNFα) (Monack et al., 2004; Pham et al., 2020). During monoclonal infections characteristic of many experimental models, intraspecies priority effects from S.Tm colonizing a given site within the host reduce the chances of colonization by pathogen cells that are arriving later (Lam and Monack, 2014). However, gut colonization is rarely a monoclonal process. Examples of multiple Enterobacteriaceae strains co-occuring in the same host have been demonstrated in longitudinal clinical studies monitoring fecal Enterobacteriaceae populations, but also shown in swine where different strains of Salmonella colonize the lymph nodes compared to the gut lumen (León-Sampedro et al., 2021; Martinson et al., 2019; San Román et al., 2018; Tenaillon et al., 2010). Based on these considerations, we hypothesized that repeated re-seeding and re-invasion events during persistent infection may promote plasmid spread. Here, we define ‘re-seeding’ as bacterial cells exiting tissue reservoirs into the gut lumen, and ‘re-invasion’ as bacterial cells entering tissue reservoirs from the gut lumen.

Finally, we reasoned that certain resistance plasmids should themselves expedite re-seeding-transfer-invasion cycles within the infected host. In particular, this should pertain to plasmid-encoded resistance genes encoding enzymes degrading antibiotics with an extra-cytoplasmic target (e.g. beta-lactamases). Extended spectrum beta-lactamases (ESBL) capable of hydrolyzing a wide variety of beta-lactam antibiotics including ampicillin and cephalosporins are of particular concern, as beta-lactam antibiotics account for about two-thirds of the antibiotics deployed annually worldwide (World Health Organization, 2017). These periplasmic enzymes may facilitate re-seeding of sensitive recipients, as resistance plasmids encoding beta-lactamases are able to reach high density under beta-lactam treatment, and clear the local environment from antibiotics, as shown in vitro (Perlin et al., 2009). However, it had remained unclear if such donor-mediated reduction of local beta-lactam concentrations would suffice for promoting survival of susceptible recipients in vivo, fostering plasmid transfer and subsequent reservoir formation in the host’s tissue.

To address this, we quantitatively assessed the link between within-host plasmid transfer, invasion-reservoir formation-re-seeding cycles and plasmid accumulation in the host’s tissues using S.Tm mouse models for both antibiotic persistence and chronic infection. To study these processes in a proof-of-concept approach, we chose donor-recipient-plasmid combinations featuring a high conjugation efficiency and relieved potential restrictions to conjugation or reservoir formation in the mouse gut-like colonization resistance conferred by the microbiota, and priority effects of strains that already colonize tissue reservoirs. Thereby, we could demonstrate expansion of the plasmid diversity in the gut tissue reservoir, a role for the chronically infecting S.Tm subpopulation and the effect of gut luminal antibiotic degradation by beta-lactamase expressing donor strains.

Results

Plasmids that enter S.Tm in the gut lumen can be stored in intestinal tissue reservoirs

To study plasmid transfer within a host and its subsequent storage in the host tissues, we modified a mouse model from our previous work (Bakkeren et al., 2019). For most plasmid transfer experiments, we used the IncI1 plasmid P2 (a close relative of ESBL plasmids; also known as pCol1b9), which naturally occurs in S.Tm SL1344 and spreads in the gut of antibiotic-pretreated mice and during diet-elicited Enterobacteriaceae blooms (Bakkeren et al., 2021a; Bakkeren et al., 2019; Diard et al., 2017; Moor et al., 2017; Stecher et al., 2012; Wotzka et al., 2019).

For all experiments, we used streptomycin-resistant derivatives of S.Tm ATCC 14028S that naturally do not contain P2 as recipient strains. We infected 129/SvEv mice intraperitoneally (i.p.) with recipients to allow the formation of tissue reservoirs of S.Tm (Figure 1A; left side, green) (Bakkeren et al., 2019). The advantage of this i.p. infection model is threefold. First, it allows us to study plasmid transfer and subsequent tissue storage in models for antibiotic persistence (the focus of Bakkeren et al., 2019) and to extend the work to persistent infection (this work). This is attributable to the capacity of the Nramp1-positive 129/SvEv mice to limit systemic pathogen growth, as shown previously in chronic infections initiated via the intravenous or the oral route (Cunrath and Bumann, 2019; Monack et al., 2004). Second, i.p. infection bypasses the need for gut colonization, ensuring that plasmid transfer observed later in the experiment is truly a result of plasmid conjugation into recipients entering from tissue reservoirs into the gut lumen. Third, the i.p. infection model mimics tissue reservoirs that are established after systemic spread following gut colonization (and therefore allows us to study the role of persistent infection in plasmid transfer dynamics) (Bakkeren et al., 2019; Lam and Monack, 2014; Monack et al., 2004; Stecher et al., 2006). Notably, our model differs from established persistent infection models in which infections are established for >28 days from an oral inoculation (Monack et al., 2004). However, to allow us to investigate re-seeding with high sensitivity, it was important to keep the gut lumen free of the initial recipient (without using antibiotics). We would like to point out a caveat of this i.p. model, in that it eliminates the contribution of priority effects attributable to the inoculation route. As natural infections would occur via the oral colonization route, sites that are normally occupied by invading S.Tm cells (e.g. the mesenteric lymph node; mLN) are less colonized after an i.p. infection compared to an oral infection. This means that invasion events from the gut luminal side later in the experiment are likely over-estimated in this model. On the other hand, this provides us with an exquisitely sensitive system to ask if tissue re-invasion can in principle expand the plasmid reservoirs within a host.

Figure 1 with 6 supplements see all
Salmonella enterica serovar Typhimurium (S.Tm) in tissue reservoirs can re-seed the gut lumen, obtain a plasmid from Enterobacteriaceae in the gut lumen, and form new plasmid-bearing reservoirs.

(A) Proposed hypothesis. S.Tm recipients (green) establish tissue reservoirs after intraperitoneal injections and spread to organs, dependent on the Type three secretions system-2 (TTSS-2; encoded on SPI-2). The microbiota (grey) provides colonization resistance against colonization of the gut lumen. Donors (blue) colonize the gut lumen by an oral infection following a streptomycin treatment to suppress the microbiota. S.Tm recipients re-seed from their reservoirs and obtain a plasmid from donors forming a transconjugant (red). These transconjugants then form new tissue reservoirs. (B) Transconjugants are formed in the gut lumen. Mice were i.p. infected with 103 CFU of a 1:1:1 mix of 14028S SmR TAG1-3 (S.TmWT; n = 13) or 103 CFU of 14028SSPI-2 SmR (S.TmSPI-2; ssaV mutant; n = 8). On day 2 post infection, 20 mg of streptomycin was given 4 hr before 5 × 107 CFU of a 1:1:1:1 mix of S.TmAvir (invG ssaV mutant) P2TAG4-7. Selective plating was used to determine the fecal transconjugant loads. (C) Tissue reservoirs of S.Tm. Organs of mice in panel B were analysed for recipients (green circles) or transconjugants (red circles). (B–C) Dotted line indicates the detection limit. A two-tailed Mann-Whitney U-test was used to compare S.TmWT (solid circles) to S.TmSPI-2 (hollow circles) in each sample (p > 0.05 not significant (ns), p < 0.05 (*), p < 0.01 (**), p < 0.001 (***), p < 0.0001 (****)). (D) S.Tm recipients can obtain P2cat from Escherichia coli. Mice were i.p. infected with 103 CFU of 14,028S SmR TAG1 (S.TmWT; n = 12). On day 2 post infection, 20 mg of streptomycin was given 4 hr before 5 × 107 CFU of E. coli 536 P2cat. Selective plating and colony colour on MacConkey agar was used to determine the fecal transconjugant loads. (E) Tissue reservoirs of S.Tm after conjugation with E. coli. Organs in panel B were analysed for recipients (green squares) or transconjugants (red squares). (D–E) Dotted line indicates the detection limit. (B,D) Bars indicate median. (C,E) Lines indicate median. GB = gall bladder; mLN = mesenteric lymph node. (B–E) Fecal populations of donors, recipients, and transconjugants are presented in Figure 1—figure supplement 1.

Next, we treated mice with a dose of streptomycin to suppress the microbiota and allow colonization of P2cat carrying donor strains (SmR) introduced orally (Figure 1A, blue). Note that both the donor and recipient strains used in this experimental model are streptomycin-resistant. Therefore, the systemic infection by the recipients is unaffected by the antibiotic and the recipients can grow upon re-seeding of the gut lumen (=persistent systemic infection model). As the donor, we used a derivative of S.Tm SL1344 that lacks functional type three secretions systems (TTSS)-1 and -2 (invG and ssaV mutant; SL1344Avir) (Hapfelmeier et al., 2005). This prevented invasion of the S.Tm donors into tissues to exclude tissue-localized plasmid transfer. We used SL1344 derivatives to study interactions between different strains of bacteria, rather than isogenic strain interactions that would limit co-existence of donors and recipients in the gut after re-seeding (Lam and Monack, 2014; Lee et al., 2013). Feces were monitored over time to investigate plasmid transfer dynamics and mice were euthanized at day 10 post infection to assess the formation of new intracellular plasmid reservoirs by recipients that had obtained a plasmid (transconjugants; Figure 1A, red).

One day after the addition of donors (SL1344Avir P2cat), recipients started re-seeding into the gut lumen and transconjugants were detected in the feces (Figure 1B, Figure 1—figure supplement 1A). By day 10 of the experiment, transconjugants were detected in the majority of mice (8/13 mice; Figure 1B). We confirmed that plasmid transfer had occurred by performing PCRs with both plasmid- and recipient-specific primers (Figure 1—figure supplement 2). Re-seeding and plasmid acquisition depended on the TTSS-2 encoded on Salmonella pathogenicity island (SPI)-2, as recipients lacking a functional TTSS-2 (14028S ssaV mutant; S.TmSPI-2) failed to re-seed frequently, and consequently transconjugants were rarely observed (1/8 mice; Figure 1; Figure 1—figure supplement 1B). Their failure to establish substantial tissue reservoir sizes (e.g. in the spleen, liver, or gall bladder) could explain the limited re-seeding of S.TmSPI-2 (Figure 1C). However, re-seeding was not dependent on the TTSS-1 encoded on SPI-1, as mutant recipient strains that lack a functional TTSS-1 apparatus (14028S invG mutant; S.TmSPI-1) established tissue reservoirs and re-seeded the gut (Figure 1—figure supplement 3). Furthermore, we performed a control experiment to exclude the contribution of donor strains themselves to the re-seeding process of recipients. To this end, we performed the same experiment as in Figure 1A, but in parallel included a control group where donors were not added. Re-seeding was detected in both groups (2/4 mice with donors; 3/5 mice without donors by day 10 of the experiment), indicating that the presence of donors did not promote re-seeding (Figure 1—figure supplement 4).

Altogether, these results indicated that bacteria re-seeding from tissue reservoirs can gain plasmids in the gut. In contrast to previous work (Bakkeren et al., 2019), this occurred without previous selection for persister subpopulations. Performing our experiments in the absence of antibiotic treatment to select for persisters allowed us to assess the contribution of growing tissue-lodged pathogen subpopulations. Importantly, we analysed here if the gut luminal plasmid was carried back into host tissues by transconjugants. Indeed, transconjugants were detected in the mLNs of mice where gut luminal plasmid transfer was observed. Occasionally such transconjugants were also observed in other tissue reservoirs such as the spleen and liver associated with further systemic spread after tissue invasion (Figure 1C).

Next, we tested if this process could be generalized to other donors colonizing the gut lumen. For this, we used E. coli 536 carrying P2cat as a donor. This strain is naturally streptomycin-resistant and colonizes the streptomycin-pretreated mouse gut for long periods of time (Brzuszkiewicz et al., 2006; Ghalayini et al., 2019). Also in this case, re-seeding of S.Tm 14028S recipients occurred followed by the formation of transconjugants in the majority of the mice (7/12 mice) (Figure 1D, Figure 1—figure supplement 1C). Once again, re-seeding and conjugation was associated with the formation of new plasmid tissue reservoirs by invading transconjugants, primarily in the mLN (Figure 1E). To assess if our i.p. infection experimental model allowed the comparison between both persistent infection and antibiotic persistence, we performed a control experiment in which we treated mice three times with ceftriaxone 2 days after i.p. infection to select for persisters (Figure 1—figure supplement 5), as done previously (Bakkeren et al., 2019). After the addition of E. coli 536 P2cat donors, S.TmWT recipients began to re-seed in some mice. However, this occurred later and in a smaller number of the analysed mice, as antibiotic persisters are much less abundant (and need to re-enter growth) compared to the much larger population of persistently infecting S.TmWT recipient cells. Regardless, this re-seeding was also associated with plasmid transfer and the occasional formation of tissue reservoirs of transconjugants in the mLN (Figure 1—figure supplement 5).

Altogether, our data demonstrates that plasmids can subvert S.Tm derived both from tissue-lodged populations of antibiotic persisters and from persistent infections. Cells from either type of tissue-lodged population can re-enter the gut lumen and serve as recipients in interbacterial interactions. This process can be followed by re-invasion forming new tissue reservoirs, capturing a record of conjugative plasmids that had previously been present in the gut lumen.

Re-seeding of the gut lumen, plasmid transfer, and the formation of new reservoirs is limited by the gut luminal carrying capacity and the conjugation rate

Next, we examined the dynamics of pathogen-assisted plasmid transfer and storage. This should identify bottlenecks and inform about the possible routes that recipients and transconjugants take to exit and re-enter host tissues in more detail. First, we explored possible sources of persistently infecting recipients that re-seed the gut lumen by characterizing the localization of S.Tm in tissue reservoirs. We infected mice i.p. with S.Tm 14028S recipients and euthanized mice 2 days later, while keeping the gut luminal microbiota intact (at the time point when normally the donors are added). As expected based on previous work (Bakkeren et al., 2019; Grant et al., 2008; Hensel et al., 1995; Lam and Monack, 2014; Monack et al., 2004), S.Tm were found in the spleen, liver, and kidney at high density (Figure 1—figure supplement 6), which likely occurred because of blood flow to these organs immediately after i.p. infection of S.Tm (Grant et al., 2008). Although bacteria were not detected in the feces (our readout for re-seeding), S.Tm was found in the content of the small intestine (i.e. primarily the jejunum and ileum, but also the duodenum), and this was correlated with S.Tm in the tissues of these sites (Figure 1—figure supplement 6). Furthermore, we found high densities of S.Tm in the lining of the small intestinal tissues that contains the gut-associated tissues such as Peyer’s patches (here called small intestinal gut-associated lymphoid tissues [S.I. GALT]). The gall bladder contained some S.Tm, but the low densities suggest that the gall bladder is not the primary route of re-seeding in this model (as suggested in some other experimental models Lam and Monack, 2014). Future work may address if S.Tm re-seeds from the small intestinal tissues after spread from blood or lymphatic supply.

Instead, we focused on the dynamics of re-seeding followed by plasmid transfer into this luminal pathogen population and asked in which conditions this occurs. We mixed equal ratios of recipient strains harbouring three different sequence-tagged barcodes at a neutral location in the chromosome (14028S SmR TAG1-3; 1:1:1 ratio) and i.p. infected mice (same mice as in Figure 1B). As donor strains, we used SL1344Avir containing P2 labelled with four additional unique sequence tags (P2TAG4-7; 1:1:1:1 ratio). All tags can be identified using quantitative PCR (qPCR) (Grant et al., 2008). At the end of the experiment, we analysed the ratio of each of these tags in the recipient population (enriching for recipients = recipients + transconjugants) by qPCR. We plotted the relative proportion of each tag relative to all tags (both chromosomal and recipient tags) to preserve information about the proportion of plasmid tags within the recipient population. To visually correlate re-seeding of S.Tm into the gut lumen with the localization of these tags in tissue reservoirs, we sorted and color-coded the tags according to the most abundant tag in the feces. While chromosomal tags were evenly distributed in primary tissue reservoirs (e.g. spleen and liver), the feces were typically dominated by just one of the three tagged strains, implying a narrow population bottleneck (Figure 2A, Figure 2—figure supplement 1). Thus, re-seeding from these tissue reservoirs is a rare process that is followed by clonal expansion. We also analysed the gall bladder as a possible source of re-seeding, and the mLN as a site where new tissue reservoirs are formed after re-invasion of re-seeding S.Tm. Although the chromosomal tags were slightly more skewed in the gall bladder compared to the spleen and liver, there was no correlation between the rank of the tags found in the feces and in the gall bladder (Figure 2A, Figure 2—figure supplement 1A; e.g. mouse 7 contains different most abundant tags in the feces and gall bladder). Interestingly, the mLN and the feces typically shared the same most abundant chromosomal tag (~10-fold median reduction of the proportion of the most and second most abundant tag in the feces; Figure 2A), suggesting that re-seeding followed by re-invasion could be skewing this distribution, rather than a bottleneck in cells spreading to the mLN after the i.p. injection. Note that the mLN population likely reflects the population that has invaded into the gut tissue. However, due to unavoidable background contamination from separating the gut tissue from the gut lumen, we chose to examine the mLN instead. The correlation between mLN and fecal chromosomal tags was not seen in one mouse (mouse 9 in Figure 2—figure supplement 1A), in which recipients were not found at high densities in the feces, further supporting the hypothesis that the distribution of tags in the mLN is heavily influenced by re-invading cells from the gut lumen. We plotted the plasmid tags found in the transconjugant population in the same manner (Figure 2B). In mice where plasmid transfer was detected (population sizes in Figure 1—figure supplement 1A; raw tag data in Figure 2—figure supplement 1), almost all plasmid tags were found, although their distribution was skewed (Figure 2B). Importantly, the plasmid tags found in the feces were mostly also found in the mLN, implying that plasmids were carried into the mLN by transconjugants that had formed in the gut lumen (Figure 2B). In addition, the data indicated that mLN entry of the plasmid-bearing transconjugants occurred more than once in most of the mice.

Figure 2 with 4 supplements see all
Re-seeding from tissue reservoirs is a rate limiting step that suffices in conjugation and formation of new reservoirs given a sufficient carrying capacity.

(A–B) Mice in Figure 1 where re-seeding of recipients occurred (n = 10) were analysed for the distribution of sequence tags in enrichments of recipients at day 10 of the experiment (including transconjugants) by quantitative PCR (qPCR) (raw tag data shown in Figure 2—figure supplement 1). The dotted line indicates the conservative detection limit. Lines indicate the median. The proportions of tags are plotted relative to all seven tags (three recipient and four plasmid tags). (A) Recipient tags and (B) plasmid tags were sorted according the most abundant recipient or plasmid tag in the feces, in each mouse. GB = gall bladder; mLN = mesenteric lymph node. (C) Fitting of simulations to experimental data. Individual summary statistics used to fit simulations are shown in Figure 2—figure supplement 3. Parameters and method for fitting is described in Supplementary information. The migration rate of µ = 1.78 recipient CFU/g feces per day and the conjugation rate of γ = 3.16 × 10–11 events per CFU/g feces per day provide the best fit to the experimental data (red indicates rate pairs that fit the experimental data most often). (D) The migration and conjugation rates were fixed to the most likely values in panel C and the carrying capacity was varied. Recipient (green) and transconjugant (red) populations at the end of the simulation (day 10; n = 100 simulations per carrying capacity) are shown as the mean with the range of values. The lowest value was set to 1 CFU/g feces to allow visualization on a log scale. The grey dotted lines correspond to the carrying capacity at which transconjugants are present at 106 CFU/g feces, as a density threshold that we speculate should facilitate some frequency of invasion into tissue reservoirs.

As a control, we also analysed the extent to which the presence of donors in the gut lumen contributed to the rarity of re-seeding, analyzing the seven chromosomal tags contained in the recipient population of mice in Figure 1—figure supplement 4. We found that one or few tags dominated the population in the feces in both mice with or without donors, indicating that the presence of donors did not have a detectable effect on the low re-seeding frequency (Figure 2—figure supplement 2).

Next, we developed a mathematical model (Supplementary information) to explore the relative contribution of re-seeding (migration rate; µ) and plasmid transfer (conjugation rate per mating pair; donor or transconjugant to recipient; γ) to the bacterial dynamics. We fit the model to the experimental data (evenness of tags in Figure 2A–B; recipient and transconjugant population sizes in Figure 1—figure supplement 1A; Supplementary information) to reveal the most likely migration and conjugation rates (Figure 2C in red; µ = 1.78 recipient CFU/g feces per day; γ = 3.16 × 10–11 events per CFU/g feces per day; Appendix 1—table 2). This corresponds to an average per-recipient rate of migration vs. conjugation (µ, γ) of 2.1 × 10–6 per day (by dividing µ with the recipient population in host tissue reservoirs) or 3.2 × 10–2 per day respectively (by multiplying γ with the gut luminal donor population; Supplementary information). This identified recipient re-seeding as the rate limiting step in our observed plasmid transfer dynamics, although the conjugation rate influenced the evenness of the plasmid tags and the time until recipients were converted into transconjugants (Figure 2—figure supplement 3). In our experimental system, we used streptomycin to decrease colonization resistance, effectively raising the carrying capacity (parametrized at 109 CFU/g feces for Figure 2C, Figure 2—figure supplement 3). However, since conjugation is density-dependent, we reasoned that the level of colonization resistance should influence our results. To test the range of Enterobacteriaceae densities that would allow appreciable densities of transconjugants (e.g. sufficient to allow invasion into new tissue reservoirs), we fixed the migration and conjugation rates to the most likely values from Figure 2C (i.e. µ = 1.78 recipient CFU/g feces per day; γ = 3.16 × 10–11 events per CFU/g feces per day). We then varied the carrying capacity as a proxy for colonization resistance, using the population sizes of recipients and transconjugants at the end of the experiment (day 10) as a readout. While recipient densities increased proportionally to the carrying capacity, the transconjugant population size remained below 106 CFU/g feces until the carrying capacity reached 107–108 CFU/g feces (Figure 2D; grey dotted lines). To validate this with our experimental data, we re-analysed the experimental data from Figure 1B–E (pooling data from both S.TmAvir and E. coli donors), correlating the donor population size at day 10 of the experiment (as a proxy for colonization resistance) with the transconjugant populations in both the feces and the mLN at day 10 (Figure 2—figure supplement 4A-B). As suggested by our model, transconjugant formation and re-invasion was more likely in mice that harboured higher gut luminal donor densities, indicating a reduction in colonization resistance (Figure 2—figure supplement 4A-B). We confirmed this trend in simulations with both a higher and lower conjugation rate than in our experimental system (Figure 2—figure supplement 4C-D). A plasmid with a higher conjugation rate was found to be less inhibited by colonization resistance. It could rise to appreciable densities (e.g. >106 CFU/g) even with lower overall carrying capacities. Altogether, our data suggest a critical role for colonization resistance in limiting tissue-reservoir-assisted plasmid spread dynamics, but also highlights the role of the plasmid transfer rate defined by the plasmid-strain association.

Plasmids can use re-invading S.Tm to form a persistent tissue reservoir that survives antibiotics and permits spread to further strains in the gut post-antibiotic treatment

We then addressed the implications of newly formed plasmid tissue reservoirs for future plasmid transfer dynamics in the gut. For this, in the first phase of the experiment, we performed the same experiment as in Figure 1D (using S.Tm 14028S SmR as a recipient and E. coli 536 P2cat as a donor), but on day 10 we gave mice three doses of ciprofloxacin instead of stopping the experiment (second phase of the experiment; Figure 3A). As before, S.Tm re-seeded from tissue reservoirs, obtained a plasmid in the gut lumen, and transconjugants carried the plasmid into host tissues (i.e. mLN) before the ciprofloxacin treatment killed gut luminal populations and left only tissue-associated persisters (including some of the newly formed S.Tm 14028S SmR P2cat cells) to survive (Figure 3A, Figure 3—figure supplement 1A). We used ampicillin in the drinking water to suppress re-seeding of these tissue-associated reservoirs before the third phase of the experiment. In this third phase, we introduced a secondary recipient (SL1344 ΔP2) and monitored plasmid transfer (and thus the formation of secondary transconjugants) to this strain. Strikingly, secondary transconjugants were formed in most mice (3/5 mice, numbered 1–3; Figure 3A, Figure 3—figure supplement 1A; black dots), correlating with both the presence of primary transconjugants in newly formed tissue reservoirs in the mLN and re-seeding of primary transconjugants (Figure 3B; Figure 3—figure supplement 1B). Importantly, E. coli donors were not detected in this experiment after the ciprofloxacin treatment, suggesting that plasmid transfer was due to re-seeding primary transconjugants from their new reservoirs (Figure 3—figure supplement 1A-B). We performed a series of control experiments to support that plasmid transfer to secondary recipients was due to the re-seeding of primary transconjugants. In one control experiment, we performed the same experiment as in Figure 3A–B but modified the experiment in two ways. First, we euthanized a subset of mice at day 15 (when we normally added the secondary recipient) and determined that E. coli donors were not present in tissue reservoirs (Figure 3—figure supplement 2). Second, we tested to see if E. coli donors could survive antibiotic treatment generally in the absence of S.Tm. We orally introduced donors in the absence of S.Tm and performed the same antibiotic treatment regime as Figure 3A–B. After the treatment regime, we added streptomycin to the drinking water to select for any remaining E. coli. No E. coli could be detected, neither in the feces nor in tissue reservoirs (Figure 3—figure supplement 3). This confirmed that it is unlikely that E. coli donors contributed to plasmid transfer after antibiotic treatment in Figure 3A–B. Lastly, we performed a different control experiment to investigate if plasmid transfer could in principle proceed within tissues. This would address if invading secondary recipients could be receiving plasmids (within host tissues) dependent on invasion into host tissues. We infected mice i.p. with both virulent S.Tm donors (SL1344 P2cat) and 5 min later with recipients (14028S). This sequential inoculation ruled out conjugation in the inoculum. After 3 days, we examined the organs to quantify plasmid transfer. While both donors and recipients were detected in the analysed organs, transconjugants were not detected, correlating with the absence of gut luminal growth (Figure 3—figure supplement 4). Altogether, these data verified that primary transconjugants re-seeding into the gut lumen were responsible for plasmid transfer to secondary recipients in Figure 3A, and that plasmid transfer preceded re-invasion into host tissues.

Figure 3 with 4 supplements see all
Newly formed plasmid reservoirs can spread plasmids to recipients after antibiotic treatment and plasmids can accumulate in the gut dependent on tissue reservoirs.

(A) Salmonella enterica serovar Typhimurium (S.Tm) transconjugants can conjugate to secondary recipients after survival of antibiotic treatment, by survival in tissue reservoirs. Mice were i.p. infected with 103 CFU of 14028S SmR TAG1 (KanR; n = 5). On day 2 post infection, 20 mg of streptomycin was given 4 hr before 5 × 107 CFU of Escherichia coli 536 P2cat (CmR). Selective plating and colony colour on MacConkey agar was used to determine the fecal transconjugant loads (red circles). On day 10, mice were given 3 mg ciprofloxacin for 3 days in a row, along with 2 g/l ampicillin in the drinking water from day 11 to 15. On day 15, a secondary recipient (SL1344 ΔP2; AmpR) was added (5 × 107 CFU oral). Selective plating allowed the enumeration of secondary transconjugants (black circles). Bars indicate the median. Dashed black lines connect primary or secondary transconjugant populations of the same mice (mice are numbered 1–3; the same numbering is used in panel B). (B) Tissue reservoirs of S.Tm. Organs of mice in panel A were analysed for primary transconjugants and secondary transconjugants. Lines indicate the median. GB = gall bladder; mLN = mesenteric lymph node. The numbers correspond to the same mouse numbering as in panel A. (C) S.Tm recipients can allow the formation of multiple transconjugants over time dependent on multiple re-seeding events. Mice (n = 7) were i.p. infected with recipients 14028S SmR TAG1 (KanR) and donors E. coli 536 P2cat (CmR) as in panel A. On day 10, mice were given antibiotics as in panel A. On day 15, a second donor, (E. coli pESBL; AmpR) was added (5 × 107 CFU oral). Selective plating allowed the enumeration of both P2cat (red squares) and pESBL transconjugants (grey squares). Bars indicate the median. (D) Tissue reservoirs of S.Tm. Organs of mice in panel C were analysed for P2cat and pESBL transconjugants. Lines indicate the median. GB = gall bladder; mLN = mesenteric lymph node. Dashed lines connect the two transconjugant populations in the same fecal sample (two mice harbor both types of transconjugants: mouse (a) and (b); these mouse labels are also shown on the mLN population). (A–D) Dotted lines indicate the detection limits. Population sizes of all strains and subpopulations are presented in Figure 3—figure supplement 1.

Next, we tested if multiple plasmids could accumulate in the gut lumen from tissue reservoirs. We performed the first two phases of the experiment as above (Figure 3A–B), but this time in the third phase of the experiment, we added a second E. coli donor containing an ESBL resistance plasmid (pESBL) shown to conjugate efficiently both in antibiotic pre-treated mice and in mice containing a reduced complexity microbiota (Bakkeren et al., 2019; Benz et al., 2021). As in the previous experiment, P2cat transconjugants were formed in the first phase of the experiment, followed by the elimination of bacteria from the gut lumen and storage of P2cat transconjugants in tissue reservoirs in the second phase of the experiment (Figure 3C; Figure 3—figure supplement 1C-D). Following the introduction of pESBL donors, pESBL transconjugants were detected in most mice (4/7 mice) as additional recipients re-seeded from their reservoirs (Figure 3C; Figure 3—figure supplement 1C). Importantly, in two of these four mice (labelled ‘a’ and ‘b’), P2cat transconjugants were also detected in the feces, suggesting that P2cat transconjugants re-seeded from their reservoirs (Figure 3C; Figure 3—figure supplement 1C). As in the other experiments, pESBL transconjugants could also be detected in the mLN of one mouse (Figure 3D). These data indicate that invasive enteropathogens like S.Tm might allow different resistance plasmids to accumulate in tissue reservoirs in the long run. However, it is important to reiterate that invasion into the mLN is likely influenced by priority effects, making each subsequent invasion less likely. This may explain why both types of transconjugants were not found in the same mLN in the tested mice (mouse ‘a’ and ‘b’; Figure 3D). Furthermore, while in this experiment both types of plasmids did not accumulate within the same bacterial cell (likely because these are both IncI1 plasmids and are therefore incompatible), it is plausible that transconjugants containing multiple compatible plasmids could arise. Nevertheless, we show that the accumulation of two plasmids in the gut lumen can be facilitated by tissue reservoirs.

Altogether, our data indicate that tissue reservoirs of S.Tm can influence plasmid spread in the gut both by spreading plasmids to different recipients and by accumulating multiple plasmids over time.

ESBL resistance plasmids can facilitate re-seeding of susceptible S.Tm recipients from tissue reservoirs under beta-lactam treatment, and thereby promote resistance plasmid spread

Our data suggest that some plasmids are selfish genetic elements that can use persistent pathogen cells to form long-term reservoirs in host tissues. We reasoned that a subset of these plasmids (e.g. those encoding for resistances that can clear antibiotics nearby, such as beta-lactamases) may have another way to promote such transconjugant-dependent reservoir formation by protecting potential recipients in their surroundings. The survival of S.Tm in the presence of otherwise lethal concentrations of beta-lactam antibiotics mediated by ampicillin-resistant E. coli has been described in vitro, based on the extracellular acting mechanism of beta-lactamases (Perlin et al., 2009). Furthermore, antibiotic treatment is commonly used in the treatment of bacterial infections in humans and livestock and beta-lactam antibiotics account for about two-thirds of the total antibiotic usage worldwide (Cully, 2014; World Health Organization, 2014; World Health Organization, 2017). Therefore, we asked if gut luminal Enterobacteriaceae harbouring plasmids that confer resistance to the antibiotic used could facilitate survival of S.Tm recipients long enough to obtain a plasmid and survive in the gut lumen. As a class of multi-drug resistance plasmids that has drawn particular attention as a contributor to antibiotic resistance spread, and because of their extracellular mechanism of action, we chose to study ESBL plasmids under beta-lactam treatment (Bajaj et al., 2016; Carattoli, 2009; Davies and Davies, 2010; World Health Organization, 2017).

As previously, we infected S.Tm recipients (14028S SmR) i.p. into mice. One day post infection, we introduced E. coli orally either bearing pESBL (same strains used in Figure 3C–D; clinical isolate characterized and sequenced in Bakkeren et al., 2019; Benz et al., 2021), or P2cat as a control (isogenic E. coli strains; the P2cat E. coli was cured of pESBL, followed by in vitro conjugation of P2cat), and simultaneously gave the mice ampicillin in the drinking water (Figure 4). Treating mice with 0.5 g/l ampicillin in the drinking water corresponds to a daily dose of 2–6 g in a 70 kg human, close to the recommended treatment schedule of oral ampicillin of 500 mg every 6 hr (Wirfs, 2019). As expected, E. coli pESBL grew to high densities, whereas in the control animals inoculated with ampicillin-susceptible E. coli P2cat (negative control donors; chloramphenicol resistance), the E. coli could not survive (Figure 4—figure supplement 1A). Over time, S.Tm began re-seeding from their tissue reservoirs in both groups of mice (Figure 4—figure supplement 1B), but could only bloom in the gut lumen in the presence of pESBL E. coli (Figure 4A). This bloom was associated with transfer of the pESBL, followed by clonal expansion as S.Tm became resistant to ampicillin, leading to >99% of the S.Tm gut luminal population obtaining a plasmid (Figure 4B). Importantly, since S.Tm is an enteric pathogen that utilizes inflammation to bloom in the gut (Stecher et al., 2007), using an ELISA for fecal lipocalin-2, inflammation was detected in mice with pESBL E. coli, and reached particularly high levels in mice where re-seeding of S.Tm occurred early during the experiment (i.e. >103 lipocalin-2 ng/g feces; Figure 4C). Interestingly, low-grade inflammation was detected in the absence of gut luminal S.Tm (Figure 4C) and could suggest that systemic S.Tm may contribute to low-grade gut inflammation, propagated by gut luminal E. coli. To confirm that re-seeding of S.Tm was dependent on lowering the concentration of fecal ampicillin by pESBL E. coli, we measured the concentration of fecal ampicillin using mass spectrometry (Figure 4D). In the presence of E. coli pESBL, fecal ampicillin concentrations plummeted below the minimum inhibitory concentration (MIC; detected in vitro Bakkeren et al., 2019) of S.Tm (Figure 4D), likely explaining the re-seeding dynamics. Thus, donor-mediated antibiotic degradation may protect the re-seeding recipients and thereby promote pESBL transfer. Importantly, as in the previous experiments, S.Tm transconjugants formed new plasmid reservoirs in host tissues, primarily in the mLN (Figure 4E). This indicates that ESBL plasmids promote their spread in host-associated Enterobacteriaceae in two different ways: by lowering local antibiotic concentrations that ensure survival of potential recipients and by subverting tissue-lodged pathogens (persisters and growing populations alike) to form long-term reservoirs within host tissues.

Figure 4 with 1 supplement see all
Re-seeding of Salmonella enterica serovar Typhimurium (S.Tm) followed by reception of a plasmid and storage in new tissue reservoirs can occur under beta-lactam counterselection, dependent on Escherichia coli conjugative extended spectrum beta-lactamases (ESBL) plasmids.

(A–E) Mice were i.p. infected with 103 CFU of a 1:1:1:1:1:1:1 mix of 14028S SmR TAG1-7 (KanR). On day 1 post infection, mice were given 0.5 g/l ampicillin in the drinking water and either 5 × 107 CFU of E. coli P2cat (CmR; black circles; n = 10) or E. coli pESBL (AmpR; orange circles; n = 10). Dotted lines indicate detection limits. Lines connect medians on each day. A two-tailed Mann-Whitney U-test was used to compare S.TmWT+ E. coli. coli P2cat to S.TmWT+ E. coli. coli P2cat in each sample (p > 0.05 not significant (ns), p < 0.05 (*), p < 0.01 (**), p < 0.001 (***), p < 0.0001 (****)). (A) Re-seeding S.Tm was analysed by selective plating on kanamycin-containing MacConkey agar. (B) Conjugation was determined by selective and/or replica plating. The proportion transconjugants is calculated by the population size of transconjugants divided by the sum of both transconjugants and recipients. The grey dashed line indicates a proportion of 1. The dotted line indicates the conservative detection limit for transconjugants (since the proportion depends on the recipient population size), each sample has a different detection limit and therefore values can appear below the detection limit. Values in the grey box in the lower part of the y-axis are plotted to indicate mice with no re-seeding. (C) Inflammation was quantified using an ELISA for fecal lipocalin-2. (D) Fecal ampicillin was measured using mass spectrometry. The blue dashed line indicates the minimum inhibitory concentration of S.Tm in vitro (Bakkeren et al., 2019). The red dashed line indicates the concentration of ampicillin given to the mice in the drinking water. (E) Organs were analysed for transconjugant populations on day 10 of the experiment. GB = gall bladder; mLN = mesenteric lymph node.

Figure 4—source data 1

CFU, LCN2 ELISA, and ampicillin quantification data for Figure 4.

https://cdn.elifesciences.org/articles/69744/elife-69744-fig4-data1-v1.xlsx

Discussion

Our results suggest that plasmids can leverage facultative intracellular enteropathogenic bacteria to promote both the formation of plasmid reservoirs in host tissues and the spread of plasmids into and among gut luminal microbes passing through a given host. In our experiments using S.Tm infection in mice, repeated cycles of pathogen invasion and tissue reservoir formation followed by re-seeding the gut lumen allow plasmids to form long-term reservoirs in the host’s tissues (Figure 5, steps 1–5). These reservoirs act as a record of gut luminal horizontal gene transfer, here shown by conjugation (Figures 14). By assessing the genetic element’s tissue reservoir formation, our current study significantly extends the ecological implications of our previous work where we had focused on persisters, showing that these antibiotic persisters can promote the release of donors into the gut lumen (Bakkeren et al., 2019). Here, we show that tissue reservoirs can influence plasmid dynamics also without selection for persister survival in antibiotic treated tissues (Figure 5, step 1). Most likely, tissue reservoir-promoted plasmid transfer dynamics will happen during persistent infection involving phenotypically susceptible pathogen cells, provided multiple strains co-occur within a host. Since a natural ecological succession of Enterobacteriaceae has been documented in the gut (León-Sampedro et al., 2021; Martinson et al., 2019), an overarching re-seeding-conjugation-re-invasion cycle could result in the distribution of different alleles in the various strains colonizing a host. The host tissue reservoirs would dramatically expand the time scale of such transfer thereby enabling horizontal gene transfer even when the initial donor and the later recipient have never ‘co-existed’ within the host. Such co-existence-independent gene exchange could pertain to resistance genes or virulence determinants alike. In line with this hypothesis, different strains of commensal and pathogenic Enterobacteriaceae that show vastly different plasmid, prophage, and mutational profiles are often observed within a given host population or even within the same host (Desai et al., 2013; Kroger et al., 2012; Martinson et al., 2019; San Román et al., 2018; Tenaillon et al., 2010). Conversely, the same plasmid has been detected in different enterobacterial strains over time within the same patient (León-Sampedro et al., 2021). The role of the pathogen’s re-seeding-acquisition-re-invasion cycles in resistance plasmid transfer may be particularly important in cases where different S.Tm are present within the same host (e.g. the gut lumen vs. the host tissues), as observed in pig farms (San Román et al., 2018).

Working model for how the infection cycle of Salmonella enterica serovar Typhimurium (S.Tm) may promote the spread and accumulation of plasmids within a host.

Invasive S.Tm (green for recipients, red for transconjugants) can establish reservoirs within host tissues, storing their current plasmid complement in these reservoirs (n plasmids). A subpopulation of these cells can survive antibiotic treatment as persisters (smaller green or red cells), which can regrow after the withdrawal of antibiotics. In extension to previous work (Bakkeren et al., 2019), here we show that reservoirs of S.Tm that chronically infect the host can re-seed from host tissues, even in the absence of selection for persisters (step 1). Exiting the host tissues into the gut lumen is a rate-limiting process (step 2a; Bakkeren et al., 2019). This process can be facilitated by Enterobacteriaceae donors (blue; plasmid shown in black) that produce extracellularly acting plasmid-encoded antibiotic resistance genes (such as beta-lactamases; blue colour radiating from the cell indicates local degradation of beta-lactam antibiotics by beta-lactamases), which can allow the local survival of re-seeding recipients under antibiotic treatment (step 2b). Once both donors and recipients co-occur in the gut lumen at sufficient density, plasmid transfer proceeds (step 3; Bakkeren et al., 2019; Benz et al., 2021; Diard et al., 2017; Moor et al., 2017; Stecher et al., 2012; Wotzka et al., 2019). When colonization resistance is relaxed, invasive transconjugants (red) can reach sufficient densities to invade back into host tissues and form new reservoirs that now contain n + 1 plasmids (step 4). In the tissue, transconjugant subpopulations can also survive antibiotic therapy as persisters (smaller red cells). This process is repeatable in certain conditions. Either transconjugants can re-seed to donate the plasmid to further gut luminal recipients (step 5a), or recipients can re-seed to receive an additional plasmids (step 5b). Novel steps of this process, demonstrated here using the streptomycin treatment mouse model, are indicated with bold numbers, whereas steps that have been previously shown are indicated with non-bold numbers (and key references are listed in the caption).

Which factors limit plasmid storage and transfer from host-tissue reservoirs? Through population dynamic analysis, we showed that re-seeding of recipient strains from tissue reservoirs is the rate-limiting step for the formation of new transconjugant reservoirs (Figure 2; Figure 5, step 2a). This is in line with our previous work (Bakkeren et al., 2019) where we suggested that donor-re-seeding from persister reservoirs in host tissues is a rate limiting process, and reinforces the importance of understanding the mechanisms of re-seeding, host tissue invasion, and the formation of new tissue reservoirs. Previous studies have suggested the gall bladder as a route of re-seeding for Salmonella via bile secretions (Everest et al., 2001; Gonzalez-Escobedo et al., 2011; Lam and Monack, 2014). In our experimental model, we found that sequence tags in the gall bladder did not necessarily correlate with those found at highest density in the feces, suggesting, along with others (Lam and Monack, 2014), that additional routes of re-seeding may exist. Phagocytic cells such as macrophages and dendritic cells serve as reservoirs for S.Tm (Claudi et al., 2014; Helaine et al., 2014; Kaiser et al., 2014; Pham et al., 2020; Rosenberg et al., 2021; Stapels et al., 2018) and can be reactivated by IFNγ depletion (Monack et al., 2004). The high density of S.Tm in the tissues that line S.I. GALT, correlating with S.Tm found in the gut lumen of the small intestine, could suggest that trafficking of S.Tm-bearing immune cells to these sites (Habtezion et al., 2016) contributes to gut luminal re-seeding. The independence of S.Tm on its TTSS-1 for re-seeding appears to support host-mediated trafficking to the gut lumen rather than an active mechanism evolved by S.Tm, but further work is needed for confirmation.

Interventions such as vaccination have shown success in reducing the formation of tissue reservoirs and consequently plasmid transfer dynamics associated with re-seeding (Bakkeren et al., 2019; Moor et al., 2017; Moor et al., 2016). Here, we showed that once re-seeding occurred, the transfer of plasmids followed by re-invasion is likely, given a sufficiently high carrying capacity (as a proxy for decreased colonization resistance) in the gut lumen. This is because plasmid transfer is dependent on both the density of donors and recipients. Invasion may also be density-dependent (Ackermann et al., 2008); approximately 300 invasion events occur into the mLN per day initially in streptomycin-pretreated mouse models (Kaiser et al., 2013) and this likely decreases proportionally with colonization. In a healthy microbiota community, colonization resistance prohibits enterobacteriaceal blooms in >90% of the hosts (Stecher et al., 2010; Stecher and Hardt, 2011) and dramatically reduces the efficiency of plasmid transfer (Stecher et al., 2012; Wotzka et al., 2019). However, gut perturbations such as diet shifts, inflammation, antibiotic treatment, or a reduction in microbiota complexity (e.g. dysbiosis) can all lead to increases in loads of Enterobacteriaceae and consequently plasmid transfer (Figure 5, step 3) (Bakkeren et al., 2019; Barthel et al., 2003; Benz et al., 2021; Kreuzer and Hardt, 2020; Stecher et al., 2012; Stecher et al., 2007; Wotzka et al., 2019). Therefore, a healthy microbiota is integral in minimizing pathogen evolution in two complementary ways, by reducing horizontal gene transfer and by reducing the likelihood of re-invasion and subsequent formation of tissue reservoirs. Further work will be needed to validate how such perturbations accelerate the dynamics of plasmid spread and reservoir formation.

Lastly, we showed that plasmids encoding secreted antibiotic degrading enzymes can promote their own spread by an additional mechanism in vivo: protecting potential new recipients sensitive to the antibiotic (Figure 5, step 2b). Re-seeding of S.Tm, initially susceptible to the beta-lactam, can occur even during beta-lactam therapy, dependent on the presence of E. coli-producing beta-lactamase (Figure 4). The prevalence of ESBL-producing E. coli is rising in recent years, and ESBL genes are frequently plasmid-encoded (Temkin et al., 2014; World Health Organization, 2017; Yang et al., 2009). The extracellularly acting beta-lactamases are therefore public goods. In vitro this was shown to allow the survival of susceptible S.Tm in the presence of otherwise lethal concentrations of ampicillin (Perlin et al., 2009). In our experiments, we showed that the same process occurs in the gut lumen, with re-seeding S.Tm followed by conjugation, clonal expansion, and tissue re-invasion as a consequence (Figure 4). As one of the prodigal mechanisms for blooms (of resistant bacteria), antibiotic treatment could serve to amplify re-seeding followed by plasmid exchange and tissue reservoir reformation, given sufficient protection from the antibiotic in trans. This obviously depends on the presence of tissue-invasive enteropathogens in the hosts, which is quite common in today’s farming industry (San Román et al., 2018; Van Boeckel et al., 2019), as well as the mechanism of action of the antibiotic used. Many antibiotic resistance genes encode antibiotic-inactivating enzymes, even if they are not secreted (D’Costa et al., 2006), as is the case of beta-lactamases. However, it remains to be seen to what extent non-secreted inactivating enzymes can deplete local concentrations of the antibiotic in vivo. Conversely, pathogen intrinsic mechanisms transiently promoting survival may further prolong recipient survival in an antibiotic loaded gut lumen and thereby further promote plasmid spread. The transient overexpression of efflux pumps was shown to confer survival long enough to obtain a plasmid in the presence of tetracycline above the MIC in vitro (Nolivos et al., 2019). Moreover, bacterial strains that produce outer membrane vesicles have been shown to lead to an altered susceptibility to antimicrobial peptides (e.g. polymyxin B or colistin, shown in several bacteria including E. coli and Salmonella Typhi) (Manning and Kuehn, 2011; Marchant et al., 2021). One study found that polymyxin resistance could even be functionally transferred to sensitive bacteria mediated by outer membrane vesicles (Marchant et al., 2021). It is therefore plausible that mechanisms aside from secreted and diffusible enzymes (such as for beta-lactamases) could contribute to re-seeding and plasmid acquisition. However, further work will be necessary to determine different possible mechanisms for cross-protection of susceptible strains by resistant cells in the gut lumen.

Overall, here we show that plasmids can subvert tissue-associated pathogen reservoirs to promote long-term carriage within the host and subsequent spread. There seem to be two types of tissue reservoirs with partially complementary features (Figure 5). The tissue-associated reservoirs formed by enteropathogens in chronic (persistent) infections are relatively large and facilitate re-seeding of the gut lumen and subsequent plasmid transfer at relatively high rates. In contrast, persistent pathogen cells are less frequent and need to re-start growth before engaging in any luminal plasmid transfers, likely making them less efficient at luminal re-seeding. However, they ensure survival of recipients and plasmid reservoir maintenance even in cases of therapy with antibiotics to which these persisters are genetically susceptible. Although in our experimental model plasmids can benefit strongly from tissue reservoirs, their actual transfer is dependent on the strain densities in the gut lumen. Our study provides a proof-of-concept that tissue reservoirs can act as a record of evolutionary events such as plasmid transfer in the gut lumen. It is conceivable that mutational events or transfer of other mobile genetic elements, for example, bacteriophages carrying accessory genes, could also be stored in tissue reservoirs. Regardless, it remains to be seen how often such cases occur in nature, since our work identifies these processes to be dependent on the carrying capacity in the gut and conjugation rates of plasmid-strain pairs, and to what extent this contributes to the dissemination of accessory genes on mobile genetic elements, such as antibiotic resistance genes.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (Mus musculus)129S6/SvEvTacTaconic BiosciencesRRID:IMSR_TAC:129sveWild-type mouse; specified opportunistic pathogen-free mice bred under hygienic conditions in the ETH Phenomics Center
Strain, strain background (Salmonella enterica serovar Typhimurium)SL1344Hoiseth and Stocker, 1981Derivatives of this strain used in this study are listed in Table 1
Strain, strain background (Salmonella enterica serovar Typhimurium)ATCC 14028SJarvik et al., 2010Derivatives of this strain used in this study are listed in Table 1
Strain, strain background (Escherichia coli)E. coli 536Berger et al., 1982; Brzuszkiewicz et al., 2006Derivatives of this strain used in this study are listed in Table 1
Strain, strain background (Escherichia coli)E. coli ESBL15Bakkeren et al., 2019; Tschudin-Sutter et al., 2016Derivatives of this strain used in this study are listed in Table 1
Recombinant DNA reagentTAG1-7 CmR or KanRGrant et al., 2008Barcodes used for population dynamics analyses
Recombinant DNA reagentP2catStecher et al., 2012Conjugative plasmid labelled with chloramphenicol resistance
Sequence-based reagentsRT-qPCR primersGrant et al., 2008See Table 3
Sequence-based reagentsPCR primers for strain construction and validationThis study; Bakkeren et al., 2021aSee Table 3
Software, algorithmGraphPad PrismGraphPad Prism (https://graphpad.com)RRID:SCR_015807Version 8 for Windows
Software, algorithmR Project for Statistical Computinghttps://www.r-project.org/RRID:SCR_001905

Strains, plasmids, and primers used in this study

Request a detailed protocol

Bacterial strains used in this study are derivatives of S.Tm SL1344 (Hoiseth and Stocker, 1981), S.Tm ATCC 14028S (Jarvik et al., 2010), E. coli 536 (Berger et al., 1982; Brzuszkiewicz et al., 2006), or E. coli ESBL15 (Bakkeren et al., 2019; Tschudin-Sutter et al., 2016), and are listed in Table 1. For cultivation of bacterial strains, lysogeny broth (LB) medium was used containing the appropriate antibiotics (50 µg/ml streptomycin [AppliChem]; 50 µg/ml kanamycin [AppliChem]; 15 µg/ml chloramphenicol [AppliChem]; 100 µg/ml ampicillin [AppliChem]) at 37°C (or 30°C if containing pCP20 or pCP20-IncI1). Genetic constructs (e.g. gene deletions, neutral isogenic sequence tags, or the P3 plasmid) were introduced into the desired background strain using P22 HT105/1 int-201 phage transduction (Sternberg and Maurer, 1991). Bacterial plasmids used to confer resistance or for construction of strains are listed in Table 2 and were transformed into cells using electroporation.

Table 1
Strains used in this study.
Strain nameStrain numberRelevant genotypeResistance*Reference
SL1344SB300Wild-typeSmHoiseth and Stocker, 1981
ATCC 14028S14028SWild-typeNoneJarvik et al., 2010
SL1344 P2catM995cat on P2Sm, CmStecher et al., 2012
SL1344 ΔP2M1404P2 curedSmStecher et al., 2012
E. coli 536Z2366Wild-typeSmBerger et al., 1982; Brzuszkiewicz et al., 2006
E. coli 536 P2catZ2124cat on P2Sm, CmThis study
SL1344 ΔP2 pM975Z2287P2 cured; blaSm, AmpThis study
E. coli ESBL15Z2115CTX-M1 on pESBLAmpBakkeren et al., 2019; Tschudin-Sutter et al., 2016
14028S SmRT186strAB on P3SmThis study
14028S SmR TAG1Z2279WITS1-aphTSm, KanThis study
14028S SmR TAG2Z2281WITS2-aphTSm, KanThis study
14028S SmR TAG3Z2283WITS11-aphTSm, KanThis study
14028S SmR TAG4T270WITS13-aphTSm, KanThis study
14028S SmR TAG5T272WITS17-aphTSm, KanThis study
14028S SmR TAG6T274WITS19-aphTSm, KanThis study
14028S SmR TAG7T276WITS21-aphTSm, KanThis study
14028SSPI-2 SmRT284ssaV::aphTSm, KanThis study
SL1344Avir P2TAG4Z2292WITS13-cat on P2; ΔinvG ΔssaVSm, CmThis study
SL1344Avir P2TAG5Z2293WITS17-cat on P2; ΔinvG ΔssaVSm, CmThis study
SL1344Avir P2TAG6Z2294WITS19-cat on P2; ΔinvG ΔssaVSm, CmThis study
SL1344Avir P2TAG7Z2295WITS21-cat on P2; ΔinvG ΔssaVSm, CmThis study
E. coli ΔpESBLZ2156pESBL curedNoneThis study
E. coli ΔpESBL P2catT305pESBL cured; cat on P2CmThis study
14028SSPI-1 SmRT2429invG::aphTSm, KanThis study
  1. *

    Relevant resistances only: Sm = ≥50 µg/ml streptomycin; Cm = ≥15 µg/ml chloramphenicol; Kan = ≥50 µg/ml kanamycin; Amp = ≥100 µg/ml ampicillin.

Table 2
Plasmids used in this study.
Plasmid nameRelevant genotypeResistanceReference
pM975bla; used to confer ampicillin resistanceAmpHapfelmeier et al., 2005
pCP20FLP recombinaseAmp, CmDatsenko and Wanner, 2000
pCP20-IncI1 (pZ2496)FLP-bla::IncI1 repliconCmThis study
P3 (pRSF1010)strABSmKroger et al., 2012
P2 (pCol1b9)Wild-typeNoneStecher et al., 2012
P2catcatCmStecher et al., 2012
P2TAGWITS13, 17, 19,or 21-cat on P2CmBakkeren et al., 2019
pESBL (pESBL15)CTX-M-1AmpBakkeren et al., 2019; Tschudin-Sutter et al., 2016

E. coli ESBL 15 was cured of its plasmid, pESBL, using plasmid incompatibility. To create an incompatible plasmid, pCP20-IncI1 was cloned using the replicon of pESBL. A PCR amplicon of the replication initiation protein of pESBL with 831 and 420 bp upstream and downstream flanking regions, respectively (to ensure the entire replicon was cloned), was cloned into pCP20 digested with PstI and EcoRV (removing the ampicillin resistance cassette) using Gibson assembly (NEB; protocol as described by the manufacturer). The resulting plasmid, pCP20-IncI1, was electroporated into E. coli ESBL15 and grown at 30°C under chloramphenicol selection. Loss of resistance of ampicillin (and consequently loss of pESBL) was confirmed by streaking on LB with ampicillin. The resulting clones were restreaked on LB without chloramphenicol grown at 37°C to cure pCP20-IncI1.

Conjugative plasmids were transferred into the desired strain (e.g. P2cat into E. coli ∆pESBL and E. coli 536) using in vitro conjugation assays. In brief, 105 CFU from an overnight culture of the donor strain (SL1344 P2cat) was mixed with the desired recipient, allowing conjugation overnight at 37°C on a rotating wheel. Cells were plated on selective MacConkey agar to identify transconjugants by resistance phenotype and/or colour (i.e. S.Tm is lac negative and thus forms yellow colonies on MacConkey agar while E. coli is lac positive and forms red colonies).

All strains and plasmids were genotyped prior to use using the primers listed in Table 3.

Table 3
Primers used in this study.
Primer nameSequence (5' to 3')PurposeReference
ESBL15_repl_gibs_forGCC AGT TAA TAG TTT
GCG CAA CGT TGT TGC
CAT TGC TGC ACT GAG
CTA CCA TAG ATG AC
PCR amplification of IncI1 replicon from pEBSL for Gibson assemblyThis study
ESBL15_repl_gibs_revTAC AAT TAT TCC TTA CTA
TGG ACA AAA ACA TCA ATC
TGA T GTG GTT TCA
GAA CGG TGA G
PCR amplification of IncI1 replicon from pEBSL for Gibson assemblyThis study
ESBL15_ori_ver_upCCA GTT AAT AGT
TTG CGC AAC
Validation of pCP20-IncI1 cloningThis study
ESBL15_ori_intl_ revCTT TCA GCG
CTT TAT AGC G
Validation of pCP20-IncI1 cloningThis study
ESBL15_ori_intl_forCTG TTC CGA
TGA CCA TCT G
Validation of pCP20-IncI1 cloningThis study
ESBL15_ori_ver_dwCTC CAG TTT
AAA TAC AAG ACG
Validation of pCP20-IncI1 cloningThis study
ssaV-137FGCAGCGTTCCA
GGGTATTCC
Verification of ΔssaV in the chromosomeBakkeren et al., 2021a
ssaV +155 RCAGCAAGTTCT
TCTCCAGGC
Verification of ΔssaV in the chromosomeBakkeren et al., 2021a
invG-134FGAAGGCCACGA
GAACATCAC
Verification of ΔinvG in the chromosomeBakkeren et al., 2021a
invG +112 RGCGGCCTGTT
GTATTTCCGC
Verification of ΔinvG in the chromosomeBakkeren et al., 2021a
P3_SmR_forCTA GTA TGA
CGT CTG TCG C
Verification of P3This study
P3_SmR_revCAC GTT TCG
CAA CCT GTT C
Verification of P3This study
WITS1ACG ACA CCA
CTC CAC ACC TA
qPCR for TAG1Grant et al., 2008
WITS2ACC CGC AAT
ACC AAC AAC TC
qPCR for TAG2Grant et al., 2008
WITS11ATC CCA CAC
ACT CGA TCT CA
qPCR for TAG11Grant et al., 2008
WITS13GCT AAA GAC
ACC CCT CAC TCA
qPCR for TAG13Grant et al., 2008
WITS17TCA CCA GCC
CAC CCC CTC A
qPCR for TAG17Grant et al., 2008
WITS19GCA CTA TCC
AGC CCC ATA AC
qPCR for TAG19Grant et al., 2008
WITS21ACA ACC ACC
GAT CAC TCT CC
qPCR for TAG21Grant et al., 2008
ydgAGGC TGT CCG
CAA TGG GTC
qPCR for all tagsGrant et al., 2008
pagJ_fwdATC TTC GGG
GAA GGG CAC
GTC CG
14028S chromosome-specific PCRThis study
pagJ_revGCT GTA ACC
GTA AGG ATA
GTG TGC CAC AAT T
14028S chromosome-specific PCRThis study
cat_fwdGCATTTCAGTCA
GTTGCTCAATGT
ACCTATAACC
P2cat-specific PCRThis study
cat_revCGACATGGAAGC
CATCACAAACGG
P2cat-specific PCRThis study

Infection experiments

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All animal experiments were performed in 8- to 12-week-old specified opportunistic pathogen-free 129/SvEv mice. These mice contain a functional Nramp1 allele (also known as Slc11a1) and are resistant to S.Tm and therefore allow for long-term infections (Cunrath and Bumann, 2019; Stecher et al., 2006). All infection experiments were approved by the responsible authorities (Tierversuchskommission, Kantonales Veterinäramt Zürich, licenses 193/2016 and 158/2019). Sample size was not predetermined and mice were randomly assigned to treatment groups.

For all experiments, overnight cultures of recipient S.Tm (14028S SmR derivatives) containing the appropriate antibiotics were washed with sterile PBS 2× before being diluted to 104 CFU/ml in PBS. Mixtures of the appropriate strains (in the case of tagged strains) or single strains were intraperitoneally injected into mice as a 100 µl volume (~103 CFU per mouse). All mice used for plasmid transfer experiments were housed in individual cages to ensure experimental independence. For experiments that lasted longer than 10 days, mice were caged in pairs initially, but split to individual cages for the final 10 days (due to ethical reasons, mice were not individually caged for longer than 10 days).

On day 2 post infection, mice were given an oral dose of streptomycin (20 mg), as previously described for the streptomycin-pretreated mouse model (Barthel et al., 2003). Overnight cultures of donor strains (SmR; SL1344 or E. coli 536 derivatives) containing the appropriate antibiotics were subcultured for 4 hr 1:20 in 2 ml LB without antibiotics, and then washed in PBS. Four hours after streptomycin treatment, donors were given orally (~5 × 107 CFU per mouse). After 10 days, mice were either euthanized or given doses of oral ciprofloxacin (3 mg per mouse; ciprofloxacin hydrochloride monohydrate, Sigma-Aldrich, dissolved in 100 µl sterile dH2O) for 3 consecutive days, while ampicillin (2 g/l) was provided in the drinking water on days 11–15 to prevent re-seeding before the third strain was added. After each ciprofloxacin treatment, cages were changed to prevent reinfection due to coprophagy. On day 15 of the experiment, mice were either euthanized or given a third strain (either a second recipient or a second donor; AmpR). These mice were euthanized on day 22. For Figure 3—figure supplement 3, mice were not i.p. infected with 14028S, so the experiment started with an oral streptomycin treatment (20 mg) 4 hr prior to infection with E. coli 536 P2cat. The rest of the experiment was treated the same as Figure 3, but after 4 days of ampicillin in the drinking water (as above), the mice were switched to streptomycin drinking water (1 g/l) for 3 days before euthanasia.

For mice used to enrich for persisters in tissue reservoirs (Figure 1—figure supplement 5), ceftriaxone (1.5 mg ceftriaxone disodium salt hemi(heptahydrate) dissolved in 100 µl PBS; Sigma-Aldrich) was injected intraperitoneally for 3 consecutive days. Donors were added after a streptomycin treatment (as above) on day 6 and again without streptomycin on day 8 to ensure robust colonization. These mice were euthanized on day 15.

For mice used to analyse plasmid transfer within tissues (Figure 3—figure supplement 4), overnight cultures of SL1344 P2cat and 14028S SmR KanR were washed with sterile PBS 3× before being diluted to 104 CFU/ml in PBS; 50 µl of each strain was injected into the same side of the mouse by an i.p. injection, 5 min sequentially (total CFU ~ 103 CFU per mouse). Mice were kept for 3 days before euthanasia and analysis of organs.

For mice used to investigate re-seeding in the presence of ampicillin counterselection dependent on ESBL-producing E. coli (Figure 4), on day 1 post intraperitoneal infection of recipients, the mice were given 0.5 g/l ampicillin in the drinking water and given ~5 × 107 CFU pESBL or P2cat E. coli donors orally (in PBS, following a 4 hr subculture as described above). An ELISA for mouse lipocalin-2 was performed on feces (protocol according to the manufacturer; R&D Systems kit) to determine the inflammatory state of the gut.

In all mouse experiments, feces were collected daily into pre-weighed Eppendorf tubes and homogenized in PBS using a steel ball at 25 Hz for 1 min. Populations of donors, recipients, and transconjugants were diluted and enumerated using selective MacConkey agar. Replica plating was used to accurately determine the ratio of recipients or donors to transconjugants if transconjugants were on the same order of magnitude as the donors or recipients. When mice were euthanized (at day 2, 10, 15, or 22; specified in the figure legends), the spleen, liver, gall bladder, and mLN were collected and homogenized in PBS. Populations of bacteria were enumerated as for the feces. For mice euthanized at day 2 post infection (Figure 1—figure supplement 6), additional organs were collected. For the duodenum, jejunum, ileum, cecum, and colon, first the content was collected, and then 1 cm of each tissue was opened longitudinally and washed briefly in PBS before homogenization. For the purposes of this study, the small intestinal and large intestinal gut-associated lymphoid tissue (S.I. and L.I. GALT) was collected by removing the lining of the tissues (including the fat) for each of the small intestine and large intestine. For analysis of bacterial loads in the blood, 100 µl of blood was aspirated from the heart immediately after euthanasia and collected in PBS with 2% BSA and 1 mM EDTA. Blood samples were also homogenized. Dissection tools were disinfected in 70% EtOH in-between each organ to minimize the chance of cross-contamination between organs.

Analysis of population dynamics using neutral sequence tags

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Mice were infected with an equal ratio of S.Tm 14028S SmR recipient strains (total ~103 CFU) bearing sequence tags at a neutral locus in the chromosome (Grant et al., 2008). For analysis of re-seeding and plasmid transfer dynamics in Figure 2, three recipient tagged strains were used (TAG1-3 KanR; 1:1:1 ratio). On day 2 post infection, an equal ratio of donor tagged strains (S.Tm SL1344Avir P2TAG strains; TAG4-7 CmR; 1:1:1:1) were given at a total inoculum size of ~5 × 107 CFU per mouse orally. The inocula were enriched in either LB+ chloramphenicol (for donors) or LB+ kanamycin (for recipients). At the end of the experiment (i.e. on day 10 post infection), mice were euthanized and recipient+ transconjugant populations were enriched from 100 µl of the feces and organ homogenates (in parallel to selective plating) in LB supplemented with kanamycin.

For analysis of re-seeding dynamics in the presence or absence of donors (Figure 1—figure supplement 4; Figure 2—figure supplement 2), an equal ratio of S.Tm 14028S SmR recipient strains (total ~103 CFU) bearing seven sequence unique tags at a neutral locus in the chromosome were used.

Enrichments were concentrated and genomic DNA was extracted using a QIAamp DNA Mini Kit (Qiagen). qPCR analysis was performed according to temperature conditions as previously described (Grant et al., 2008) using qPCR primers specific to the TAG and a universal second primer, ydgA (Table 3). The relative proportion was calculated by dividing the DNA copy number (calculated from the CT value) of each tag detected, by the sum of all seven tags in the sample. A dilution standard of purified chromosomal DNA allowed for a correlation between DNA copy number and CT value. For each qPCR run, the detection limit was determined by the CT value of the most-diluted DNA standard. The least precise detection limit defines the conservative detection limit plotted on graphs and used for the mathematical model (8.9 × 10–5). Once the relative proportion was determined, recipient tags were separated from plasmid tags (however, the proportion remains relative to all tags). This data is presented in Figure 2—figure supplement 1. Next, the tags were sorted according to the most abundant in the feces in each given mouse (separately for recipient and plasmid tags). This ranking was conserved for analysis in other organs to allow a correlation between the most abundant tag in the organs relative to the feces. For example, if TAG2 appeared the most abundant in the feces but least abundant in the mLN, it would ranked and coloured according to the most abundant tag, and it would still appear the same colour in the mLN, despite it not being the most abundant tag in the mLN. This data is presented in Figure 2A–B. The samples in Figure 2—figure supplement 2 were analysed the same, but were not sorted according to the most abundant tag in the feces, and is instead presented as raw tag proportions relative to all seven recipient chromosomal tags.

Measurement of fecal ampicillin

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Detection and absolute quantification of ampicillin was carried out using high-performance liquid chromatography heated electrospray ionization high-resolution mass spectrometry (HPLC HESI HRMS). Fecal samples were homogenized in PBS and immediately frozen after collection. Prior to analysis, samples were thawed and centrifuged at high speed. The supernatants were analysed on a Dionex UItiMate 3000 HPLC coupled to a Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometer. Chromatographic separation was obtained on a Phenomenex Kinetex 2.6 µm XB-C18 150 × 4.6 mm column at 30°C. Water (A) and acetonitrile (B), each containing 0.1% formic acid, were employed as mobile phases. A gradient of total 12.5 min was applied at a flowrate of 800 μl/min, starting with 3% B for 2 min, 3–50% B in 3 min, 50–98% B in 5 min, 98% B for 1 min, 98–3% B in 1 min and 3% B for 0.5 min. MS settings: spray voltage (+) 3.5 kV, capillary temperature 320°C; sheath gas (57.50), auxiliary gas (16.25), sweep gas (3.25); probe heater 462.50°C; S-Lens RF (50), resolution (70.000); AGC target (3e6), microscans (1), maximum IT 200 ms, scan range 250–750 m/z. For quantification, an ampicillin standard curve was recorded using H2O diluted concentrations of 10 ng/ml, 100 ng/ml, 500 ng/ml, 1 µg/ml, 10 µg/ml, and 100 µg/ml from a 1 mg/ml ampicillin stock solution. Samples were analysed by retention time of ampicillin and the respective ion adduct [M + H]+ = 350.1169 m/z with a mass tolerance of 5 ppm; 15 µl of each sample were injected and ampicillin concentrations were calculated using the Thermo Xcalibur 2.2 Quan Browser software. The concentration given in µg/ml of supernatant was normalized to the weight of feces collected and converted to µg/ml fecal content using the average density of feces (Brown et al., 1996).

Statistical analysis

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Statistical tests on experimental data were performed using GraphPad Prism 8 for Windows. The specific test used is each figure is described in the figure caption (a test is only indicated if statistics were used). The mathematical model was fit to the experimental measurements using an approximate Bayesian computation (ABC) approach (Marjoram et al., 2003). For the fit, we considered three sets of summary statistics: the skew of the plasmid and chromosomal tag distributions, the total size of the transconjugant and recipient population on day 10, and the time at which the transconjugant and recipient populations first exceeded 106 CFU/g feces. A simulation was called ‘successful’ if all summary statistics were within three standard deviations of the experimentally observed mean of these statistics. Migration and conjugation rates were varied on a grid, all other parameters were kept fixed (Appendix 1—table 1). All R-code needed to simulate the stochastic model, estimate the most likely parameters from the experimental data, and plot the results is included in the Github repository (https://github.com/JSHuisman/Recorder; Bakkeren, 2021b copy archived at swh:1:rev:2822d696ceddeca01a2d3eb32ffcc9bd513e561a).

Appendix 1

Description of the mathematical model

We model the re-seeding of Salmonella recipients from systemic sites into the gut, and the subsequent creation of transconjugants through plasmid transfer from donors. We follow the dynamics of donor (D), recipient (R), and transconjugant (T) populations in the gut. These populations are further divided into isogenic subpopulations based on their sequence tags. The donors Dj carry a plasmid tag j ∈ 1, ..., Nj, where Nj is the number of plasmid tags (Nj = 4). Recipients Ri carry a chromosomal tag i ∈ 1, ..., Ni, where Ni is the total number of chromosomal tags (Ni = 3). Transconjugants Tij carry both a chromosomal and a plasmid tag.

To describe the dynamics of these populations, we make several assumptions. Recipients are introduced into the gut at an overall rate μ. Here, we assume this rate is constant, although it likely increases slightly during the experiment as a function of the recipient population size at systemic sites. We assume the populations of recipients and donors in the gut are well mixed, and plasmid transfer is described by mass action kinetics. Plasmids are thus transferred between donors and recipients at a constant per-contact rate γD. Once transconjugants are formed, these can also transfer their plasmid at a constant per-contact rate γT. All plasmids are isogenic; they transfer from donors or transconjugants at the same rate and can be transferred only to naïve recipients.

Resources are limited in the gut, so we model bacterial population growth as a logistic function of the population size. We assume the donor population is limited by a different resource than the recipient and transconjugant populations, since they are not isogenic (Lee et al., 2013). The populations thus reach separate carrying capacities, denoted by KD and KRT, respectively. Otherwise, the growth dynamics are assumed population-independent: the bacteria are born at a birth-rate r, are cleared at a clearance rate c (this includes both death and efflux from the gut), and similar to Bakkeren et al., 2019; we parametrize this with a residual birth-rate at carrying capacity rK=c (to ensure some continued population turnover even at high densities).

We simulate the population dynamics stochastically using the tau-leaping method from the R package adaptivetau (Johnson, 2016). The corresponding deterministic equations (in the limit of large numbers) are:

(1) Dj.=(r+rK)DjDj(c+rjDjKD)
(2) Ri.=(r+rK)Ri+1Niμj(γDDj+γTiTij)RiRi(c+ri(Ri+jTij)KRT)
(3) Tij.=(r+rK)Tij+(γDDj+γTiTij)RiTij(c+ri(Ri+jTij)KRT)

Parametrizing the mathematical model

In the main simulations, we varied the migration rate μ and the conjugation rates γD/T while the other parameters were kept fixed (summarized in Appendix 1—table 1). The population-independent parameters describing the bacterial growth dynamics (r, c, rK), as well as the carrying capacities KD/RT and inoculum density D0, were set to the values from Bakkeren et al., 2019. There they were parametrized based on experimental data from the same mouse model system (Barthel et al., 2003; Moor et al., 2017). The number of distinguishable chromosomal and plasmid tag populations matches those used in this paper. We fixed the conjugation rates from donors and transconjugants to the same value, and the carrying capacities of donors and recipients + transconjugants were set to the same value.

Appendix 1—table 1
Parameter values used in the simulations.
ParameterFunctionValueUnits
rBirth-rate44 ln(2)Per day
cClearance-rate4 ln(2)Per day
rKResidual birth-rate at carrying capacity4 ln(2)Per day
K = KD/RTCarrying capacity109CFU/g feces
D0Size of the donor inoculum107CFU/g feces
NiNumber of distinguishable chromosomal tags3Dimensionless
NjNumber of distinguishable plasmid tags4Dimensionless
μRe-seeding rate of recipients from systemic sitesUniform on the discrete grid 10–6 – 103 in 0.25 log incrementsCFU/g feces per day
γ = γD/TThe rate of conjugation from donors or transconjugants per mating pairUniform on the discrete grid 10–12 – 10–3 in 0.25 log incrementsPer CFU/g feces per day

Parameter estimation in the mathematical model

We use ABC (Marjoram et al., 2003) to infer the most likely values of migration rate μ and conjugation rate γ. We varied both parameters on a grid, running 100 simulations per parameter set. For each parameter combination, we calculated summary statistics (see below) to compare the simulations to the experimental data.

  1. The size of the recipient and transconjugant populations on day 10, respectively.

  2. The size of the recipient and transconjugant populations on day 10, respectively.

  3. The time at which the recipient and transconjugant populations first exceed 106 CFU/g feces, respectively.

For Figure 2C we calculated the likelihood of the parameter combination as the percentage of simulations that return all summary statistics within three standard deviations of the experimentally observed mean of these statistics. The experimental data used to calculate the mean of these statistics is derived from Figure 1B, Figure 1—figure supplement 1, and Figure 2A–B (n = 8). Due to the assumptions of our model, we excluded mice where re-seeding did not occur. Therefore, the migration rate calculated here may be slightly over-estimated. However, we do not expect the relative importance of re-seeding or conjugation to change due to the exclusion of these mice.

Appendix 1—table 2
Parameter estimates.
SimulationMaximum likelihood(μ,γ) pairμ Value from the marginal posterior distributionγ Value from the marginal posterior distribution
Main
text (Figure 2C)
μ = 1.78 (CFU/g
feces per day)γ = 3.16 × 10–11 (per CFU/g feces per day)
7.1 (CFU/g feces
per day)HPD: [0.03, 31.6]
9.36 × 10–10 (per
CFU/g feces per day)HPD: [1 × 10–12,
5.6 × 10–9]
  1. HPD: Highest posterior density interval.

Translation into per-recipient rates

In the mathematical model, the dynamic parameters describing migration and conjugation (μ, γD/T) are reported in different units. To facilitate their direct comparison, we can approximate the relative magnitude of these rates per recipient per day.

Recipient migration was inferred to result in 1.78 recipient CFU/g feces per day. This should be divided by the total population of recipients in systemic reservoirs (8.5 × 105; Figure 1C) to obtain a per-recipient probability of migration. These values result in an approximate probability per recipient (tissue-located) of 2.1 × 10–6 per day.

In contrast, γ is a rate constant that needs to be multiplied by two population sizes to be comparable with the migration rate, μ. To obtain a per-recipient (gut-located) conjugation rate reported as CFU/g feces per day, γ needs to be multiplied by the size of the donor population in the gut. Assuming a donor population of 109, this results in conjugation rates of 3.2 × 10–2 per day.

Dependence of transconjugant emergence on the carrying capacity in the gut

In a separate analysis, we estimated the evenness of the plasmid and chromosomal tag distributions, as well as the final recipient and transconjugant population sizes, as a function of the carrying capacity. Here, we fixed the migration rate μ and conjugation rate γ at their most likely values (μ = 1.78 CFU/g feces per day, γ = 3.16 × 10–11 per CFU/g feces per day), and varied the carrying capacity K uniformly on the grid 103–1010 in 0.25 log increments.

In addition, we repeated this set of simulations with a conjugation rate that was either 100-fold higher, or 100-fold lower than the most likely value, to illustrate a range of plausible plasmid conjugation rates (Benz et al., 2021).

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. Source data, code, and simulation results have been provided for all figures either in the article or at the following GitHub repository: https://github.com/JSHuisman/Recorder, copy archived at https://archive.softwareheritage.org/swh:1:rev:2822d696ceddeca01a2d3eb32ffcc9bd513e561a.

References

  1. Report
    1. World Health Organization
    (2014)
    Antimicrobial Resistance: Global Report on Surveillance
    World Health Organization.
  2. Report
    1. World Health Organization
    (2017)
    Global Priority List of Antibiotic-Resistant Bacteria to Guide Research, Discovery, and Development of New Antibiotics
    World Health Organization.

Decision letter

  1. Sophie Helaine
    Reviewing Editor; Harvard Medical School, United States
  2. Wendy S Garrett
    Senior Editor; Harvard T.H. Chan School of Public Health, United States

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Pathogen invasion-dependent tissue reservoirs and plasmid-encoded antibiotic degradation boost plasmid spread in the gut" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Wendy Garrett as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1. Since in Figure 1, S1 and S2 recipients only appear after donor inoculation, it is possible that oral inoculation of donors leads to a breach in the gut barrier that in turn facilitates recipient reseeding of the gut lumen. Can the authors provide insight into the mechanism of reseeding of Salmonella from the tissue to the gut? Would a TTSS-1 off mutant be unable to re-seed the gut lumen and could it serve as a negative control to exclude that re-seeding occurs as an indirect consequence of donor oral inoculation?

2. Figure 3A results need to be strengthened with controls. What happens if the secondary recipient is TTSS-1 off, prohibiting conjugation to occur in the tissues? In addition, although unlikely, it does remain formally possible that persisters of the initial E. coli donor acted as donor in this second trans-conjugation event. An experiment with tagged plasmids showing that tag distributions in the recipient for the second trans-conjugation event correlate with tag distribution in the initial transconjugant rather than the initial donor might provide a definitive answer.

3. The model used by the authors needs to be explained in a more accessible way. For example, why is recipient migration determined to be the rate limiting step for transconjugant formation? What are the experimental justifications to using 100-fold lower or higher conjugation rates in the model? Why a recipient migration rate of 1.78 events per day is a rate limiting step for transconjugant formation compared with 3.16 x 10(-11) conjugation events per CFU per g feces per day (which superficially seems like a smaller number)? Was a sensitivity analysis for the parameters that were not explored systematically (i.e. for parameters other than mu and γ) performed?

4. Figure 4: in absence of treatment with another antibiotic, please discuss that the results are not, for certain, specific to the β lactamase.

5. Have the authors detected transconjugants by other means than phenotypic antibiotic resistance to show that resistance did not emerge by spontaneous mutation (e.g. PCR or else)?

https://doi.org/10.7554/eLife.69744.sa1

Author response

Essential revisions:

1. Since in Figure 1, S1 and S2 recipients only appear after donor inoculation, it is possible that oral inoculation of donors leads to a breach in the gut barrier that in turn facilitates recipient reseeding of the gut lumen. Can the authors provide insight into the mechanism of reseeding of Salmonella from the tissue to the gut? Would a TTSS-1 off mutant be unable to re-seed the gut lumen and could it serve as a negative control to exclude that re-seeding occurs as an indirect consequence of donor oral inoculation?

We agree that the mechanism of re-seeding (and the factors that promote this process) are still not understood. This is explained by the biological features of this process which have precluded detailed mechanistic studies. First of all, re-seeding is a quite rare process that releases no more than a few S.Tm cells into the gut lumen per day. Therefore, the re-seeding even itself is extremely hard to assess by microscopy. Moreover, the routes that cells can traffic from the various tissue reservoirs (the diversity of tissue reservoirs are shown in the current Figure 1—figure supplement 6) to the gut lumen are likely dependent on the nature of the reservoirs the cells are in initially. While we believe that an in-depth investigation of the mechanism(s) of re-seeding remains out of the scope of this work, we have performed control experiments as suggested here to rule out the contribution of donor inoculation, and to confirm that TTSS-1 is not essential for re-seeding.

First, while we showed that TTSS-2 encoded on SPI-2 is important for establishing tissue reservoirs (and as a consequence, re-seeding; Figure 1B-C), we additionally tested a TTSS-1 mutant (invG mutant). We now show that active invasion because of TTSS-1 activity is not essential for either tissue reservoir formation after i.p. infection, or re-seeding from these reservoirs. This is because even with a TTSS-1 mutant, cells were found in the same tissue reservoirs as WT S.Tm recipients, and also re-seeded the gut lumen. This data is now presented in Figure 1—figure supplement 3.

Second, we also performed the suggested experiment to investigate a possible contribution of the donor inoculation process. We repeated the same experiment as in Figure 1B-C but split the mice into two groups. In one group the donors were given as usual. In the other group, donors were not given, but the streptomycin treatment was kept as in the first group. Re-seeding occurred in both treatment groups, and both the number of mice (2/4 vs 3/5 mice by day 10 of the experiment) and the population sizes of the re-seeding population were not significantly different. This data is now presented in Figure 1—figure supplement 4. We also tested the diversity of 7 recipient chromosome tags in the populations that were isolated from the feces after re-seeding in both groups: one or few tags dominated the population in both treatment groups. This data is presented in Figure 2—figure supplement 2.

Altogether, these data indicate that re-seeding is likely a rare process determined by trafficking of host cells to the gut and/or the natural circulatory/lymphatic/biliary system of the mouse, rather than an active process dependent on TTSS-1 mediated S.Tm invasion. While this may not have a direct impact on re-seeding per se, the most important phenomenon to limit robust growth after re-seeding appears to be colonization resistance conferred by the microbiota (supported by both the low chances for re-seeded luminal populations in the phases of our experiments where CR remains intact and the results of the mathematical model; Figure 2D). It remains possible that external factors like inflammation may boost immune cell recruitment leading to increased re-seeding (in our work, all tested donor strains were not able to induce inflammation, e.g. avirulent Salmonella or E. coli). Nevertheless, we show that re-seeding can in principle occur in the absence of active invasion (by both donors and recipients).

2. Figure 3A results need to be strengthened with controls. What happens if the secondary recipient is TTSS-1 off, prohibiting conjugation to occur in the tissues? In addition, although unlikely, it does remain formally possible that persisters of the initial E. coli donor acted as donor in this second trans-conjugation event. An experiment with tagged plasmids showing that tag distributions in the recipient for the second trans-conjugation event correlate with tag distribution in the initial transconjugant rather than the initial donor might provide a definitive answer.

We have done two additional experiments to address these concerns.

First, to provide more evidence that the initial donor (i.e. E. coli 536) did not contribute to the secondary trans-conjugation event, we assessed how well it is eliminated by the ciprofloxacin/ampicillin treatment regime (days 10-15). To this end, we performed the same experimental setup as in Figure 3 and the current Figure 3—figure supplement 2. To maximize our chances for detecting any E. coli 536 "survivors", we did this experiment in the absence of S.Tm, which should exclude any possible competitive interactions between the two species. We performed the antibiotic treatment protocol as in Figure 3 and the current Figure 3—figure supplement 2 but finished with streptomycin in the drinking water to select for any remaining E. coli 536 (which is streptomycin resistant) and against the microbiota. This should further increase the detection-sensitivity for any remaining E. coli that could re-seed. We did not detect any E. coli in the feces after 3 days of streptomycin in the drinking water, nor in any of the tissue reservoirs. This supported our data in Figure 3—figure supplement 2 suggesting that E. coli cannot survive the antibiotic treatment at detectable levels, but also provided further evidence that re-seeding of the initial E. coli and a contribution to the secondary plasmid transmission is unlikely. This new data is presented in Figure 3—figure supplement 3.

Second, to directly address if conjugation can in principle happen inside of tissue reservoirs, we performed an experiment in which we introduced both virulent S.Tm donors (SL1344 p2cat) and recipients (14028) directly into the tissue reservoirs by an i.p. infection (both strains introduced by i.p. injection at the same site of the mouse, 5 minutes sequentially). We then allowed them to grow and spread within and between tissue reservoirs for 3 days before euthanizing the mice and assessing transconjugant formation. Importantly, while both donors and recipients were detected at high densities in tissue reservoirs, transconjugants were not detected in any reservoirs. Therefore, this suggests that conjugation is highly unlikely in the absence of interaction of donors and recipients in the gut lumen. This supports that acquisition of the plasmid in Figure 3A is far more likely in the gut lumen after transconjugant re-seeding, rather than invasion and within-tissue plasmid acquisition by the secondary recipient strain. This new data is presented in Figure 3—figure supplement 4.

3. The model used by the authors needs to be explained in a more accessible way. For example, why is recipient migration determined to be the rate limiting step for transconjugant formation? What are the experimental justifications to using 100-fold lower or higher conjugation rates in the model? Why a recipient migration rate of 1.78 events per day is a rate limiting step for transconjugant formation compared with 3.16 x 10(-11) conjugation events per CFU per g feces per day (which superficially seems like a smaller number)? Was a sensitivity analysis for the parameters that were not explored systematically (i.e. for parameters other than mu and γ) performed?

We have added more information to the main and supplementary text to make the model more intuitive and accessible (lines 291-295; 936-947). In particular, we have added a sentence to compare both parameters (mu and γ) in similar units, which helps convey why recipient migration is the rate limiting step (lines 292-294).

In brief, we found recipient migration results in 1.78 recipient CFU/g feces per day. This should be divided by the total population of recipients in systemic reservoirs (see Figure 1C) to obtain a per-recipient probability of migration. These values result in an approximate probability per recipient (tissue-located) of 2.1 x 10-6 per day. Instead, conjugation was reported per CFU/g feces per day, and thus must be multiplied by the size of the donor population in the gut to reach a per-recipient (gut-located) probability of conjugation. Assuming the donors are at the carrying capacity of 109, this would result in values around 3 x 10-2 per day which is a much greater number.

Our decision to use a 100-fold difference in the conjugation rate in both directions (a total span of 5 orders of magnitude), was based on experience from previous work (Benz et al., 2020, ISME J, https://doi.org/10.1038/s41396-020-00819-4) where we conjugated different IncI plasmids to a range of recipients and found roughly 5 orders of magnitude difference across strains. The chosen conjugation rates are meant to illustrate general trends for more or less conjugative plasmids.

All parameters except mu and γ were parametrised using experimental data, described in greater detail in Bakkeren et al., 2019, Nature. In that study we also reported a scenario with a higher birth rate at carrying capacity (simulating inflammation) and found that it marginally affected the estimated parameters, but did not affect qualitative statements about which steps were rate limiting. We also checked the effect of more general combinations of birth and death rates in this model, and did not find noticeable differences (the dynamics were dominated by conjugation and re-seeding). In the current study we further investigated the effect of the carrying capacity K on the dynamics.

4. Figure 4: in absence of treatment with another antibiotic, please discuss that the results are not, for certain, specific to the β lactamase.

We agree that in-trans protection is best documented for β-lactam antibiotics. As β-lactams alone account for about 2/3 of the annual world-wide antibiotic consumption, the in vivo demonstration of this in trans protection should be of significant interest in itself. We have pointed this out more clearly in the revised text (lines 110-111; 398-399). While we lack direct in vivo evidence that the results relevant to Figure 4 (i.e. cross-protection of susceptible cells by resistant cells leading to HGT) are more general than for β-lactamases, there other plausible mechanisms that could contribute to cross protection that have so far been demonstrated in vitro:

1. The local concentration of antibiotics with an intracellular mechanism of action could also be depleted in the presence of resistant bacteria, depending on the mechanism of resistance. For enzymes that inactivate the antibiotic, high densities of resistant bacteria could deplete the local concentration of antibiotics after internalization. Resistance genes that can inactivate the antibiotic were identified for several classes of antibiotics (D’Costa et al., Science, 2006). It remains to be seen how efficiently these antibiotic-inactivating enzymes would function in vivo. However, this would likely be less efficient compared to secreted enzymes compared to β-lactamases.

2. Some studies have investigated another "in-trans" protection mechanism, i.e. the contribution of outer membrane vesicles in transferring resistance determinants from resistant cells to susceptible cells. In fact, the contribution of such vesicles to the survival of otherwise lethal concentrations of membrane-disrupting antibiotics such as polymyxin B and colistin have been experimentally demonstrated in several different organisms (including E. coli and Salmonella Typhi; Manning and Kuehn, BMC Microbiol, 2011; Marchant et al., 2021, Front. Microbiol.).

We have expanded the Discussion section (lines 518-533) to encompass these points.

5. Have the authors detected transconjugants by other means than phenotypic antibiotic resistance to show that resistance did not emerge by spontaneous mutation (e.g. PCR or else)?

We have now performed a control PCR on multiple clones from mouse experiments with both recipient chromosome- and plasmid-specific primers to verify that transconjugants are truly recipients that have obtained a plasmid. This data is now presented in Figure 1—figure supplement 2.

https://doi.org/10.7554/eLife.69744.sa2

Article and author information

Author details

  1. Erik Bakkeren

    Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland
    Present address
    1. Department of Biochemistry, University of Oxford, Oxford, United Kingdom
    2. Department of Zoology, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Funding acquisition
    For correspondence
    erik.bakkeren@zoo.ox.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7970-7890
  2. Joana Anuschka Herter

    Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Funding acquisition
    Competing interests
    No competing interests declared
  3. Jana Sanne Huisman

    1. Swiss Institute of Bioinformatics, Lausanne, Switzerland
    2. Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Resources, Writing – review and editing, Validation, Funding acquisition
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1782-8109
  4. Yves Steiger

    Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland
    Contribution
    Investigation, Resources, Funding acquisition
    Competing interests
    No competing interests declared
  5. Ersin Gül

    Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland
    Contribution
    Investigation, Methodology, Validation, Funding acquisition
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2873-8034
  6. Joshua Patrick Mark Newson

    Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland
    Contribution
    Investigation, Funding acquisition
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2091-7943
  7. Alexander Oliver Brachmann

    Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland
    Contribution
    Formal analysis, Investigation, Methodology, Validation, Funding acquisition
    Competing interests
    No competing interests declared
  8. Jörn Piel

    Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland
    Contribution
    Software, Resources, Supervision, Funding acquisition
    Competing interests
    No competing interests declared
  9. Roland Regoes

    Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
    Contribution
    Software, Supervision, Funding acquisition
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8319-5293
  10. Sebastian Bonhoeffer

    Institute of Integrative Biology, Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
    Contribution
    Software, Supervision, Funding acquisition
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8052-3925
  11. Médéric Diard

    1. Botnar Research Centre for Child Health, Basel, Switzerland
    2. Biozentrum, University of Basel, Basel, Switzerland
    Contribution
    Software, Supervision, Funding acquisition
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0851-4391
  12. Wolf-Dietrich Hardt

    Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland
    Contribution
    Conceptualization, Software, Project administration, Resources, Supervision, Funding acquisition
    For correspondence
    wolf-dietrich.hardt@micro.biol.ethz.ch
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9892-6420

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (407240_167121)

  • Sebastian Bonhoeffer
  • Wolf-Dietrich Hardt

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (3100030B_173338)

  • Wolf-Dietrich Hardt

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (310030_192567)

  • Wolf-Dietrich Hardt

Gebert Rüf Stiftung (GRS-060/18)

  • Wolf-Dietrich Hardt

Monique Dornonville de la Cour Foundation

  • Wolf-Dietrich Hardt

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (PP00PP_176954)

  • Mederic Diard

Botnar Research Centre for Child Health (Multi-investigator grant)

  • Mederic Diard

Boehringer Ingelheim Fonds (PhD Fellowship)

  • Erik Bakkeren

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We would like to thank the staff at RCHCI and EPIC animal facilities, and members of the Hardt, Sunagawa, and Slack labs for helpful discussions. We would also like to acknowledge the reviewers for their constructive feedback and helpful suggestions to improve this manuscript.

Ethics

All infection experiments were approved by the responsible authorities (Tierversuchskommission, Kantonales Veterinäramt Zürich, licenses 193/2016 and 158/2019).

Senior Editor

  1. Wendy S Garrett, Harvard T.H. Chan School of Public Health, United States

Reviewing Editor

  1. Sophie Helaine, Harvard Medical School, United States

Publication history

  1. Preprint posted: April 9, 2021 (view preprint)
  2. Received: April 24, 2021
  3. Accepted: November 10, 2021
  4. Version of Record published: December 7, 2021 (version 1)

Copyright

© 2021, Bakkeren et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Erik Bakkeren
  2. Joana Anuschka Herter
  3. Jana Sanne Huisman
  4. Yves Steiger
  5. Ersin Gül
  6. Joshua Patrick Mark Newson
  7. Alexander Oliver Brachmann
  8. Jörn Piel
  9. Roland Regoes
  10. Sebastian Bonhoeffer
  11. Médéric Diard
  12. Wolf-Dietrich Hardt
(2021)
Pathogen invasion-dependent tissue reservoirs and plasmid-encoded antibiotic degradation boost plasmid spread in the gut
eLife 10:e69744.
https://doi.org/10.7554/eLife.69744

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