Adresse e-mail pour l'envoi des notifications. Coauteurs Tout afficher M. Teresa M. Fernando Baquero Dpt. Tout afficher. Professor of Genetics, University of Cantabria Spain. The horizontal gene pool: bacterial plasmids and gene spread 23, , Antimicrobial agents and chemotherapy 55 8 , , Proceedings of the National Academy of Sciences 18 , , Horizontal gene transfer and the origin of species: lessons from bacteria F De la Cruz, J Davies Trends in microbiology 8 3 , , Nature , , HGT parameters fit using naked DNA predict transfer of genes released via neighbor killing surprisingly well, suggesting DNA released from cell lysis in situ is readily available for uptake.
Using this model, we characterize the impact of neighbor killing on HGT in a wide range of environmental conditions and evaluate potential inhibition strategies, and we experimentally confirm the key predictions. In this context, our dynamic model should be useful for predicting and combating HGT of antibiotic resistance. We seeded the device with a simplified community consisting of predator A. As expected, we observed spontaneous lysis of individual E.
In our initial experiments, the GFP-expressing plasmid in E. Expression of the GFP gene from this plasmid is repressed in E. We transformed E. Importantly, horizontally acquired GFP was stably maintained during cell division, giving rise to clumps of cells that were both red and green.
In the movies, note that although GFP expression in E. Kanamycin was added after seeding the device. Still images were captured at indicated times after kanamycin addition, after several independent HGT events had already occurred arrows in e-g. This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Most E. Expression of mCherry in Acinetobacter faded toward the end of the movies see also Supplemental Note 1.
Individual movies are physically separated traps within the same microfluidic chip. See caption to Video 1 for details. HGT of antibiotic resistance allows pathogenic bacteria to survive treatment with antibiotics that would kill the parental strain. Therefore, expression of GFP should indicate a newly kanamycin-resistant strain of Acinetobacter , whose de novo appearance would demonstrate the potential clinical relevance of HGT within a microbial community.
To test whether this directly observed HGT would be enough to provide a population-level selective advantage, we used our microfluidic chip to visualize functional HGT of antibiotic resistance in real time. We seeded the microfluidic device with mCherry-expressing Acinetobacter alongside E. Within about 7 hr of kanamycin addition, multiple HGT events could be visibly identified by the emergence of GFP-expressing Acinetobacter arrows in Figure 1e—g.
Only those Acinetobacter that were expressing GFP, indicating horizontal transfer of kanamycin resistance, continued to grow Figure 1h , Videos 7 , 8 , while the parental GFP-negative Acinetobacter cells became smaller and stopped dividing. The red and green, dual-labeled Acinetobacter quickly dominated the device, lysing neighboring E.
To our knowledge, these experiments represent the first real-time observation of adaptive, cross-species HGT via natural competence that rapidly enables invading cells to thrive in a new niche. Acinetobacter and E. The movies begin 7. Only Acinetobacter expressing GFP continue to grow. To test this, we quantified HGT from E. These experiments involve surface-attached cells that are developing spatially-structured communities. This is a qualitatively different condition from growth in shaking culture tubes and more similar to biofilm dynamics, although we did not study true biofilms, which take longer to mature.
In these bulk experiments, we co-cultured Acinetobacter carrying genomic spectinomycin spect resistance and E. We placed the cm resistance gene in a region of the E. We co-cultured Acinetobacter spect and E. We also cultured each species alone as controls. We quantified the efficiency of E. As expected, Acinetobacter dramatically reduced E. Transfer of the genomic cm E. Communities were seeded with 2 ul droplets containing indicated strains of Acinetobacter with genomic spect resistance, mixed with genomically cm-resistant E.
Lower limits on y-axes are the limits of detection. Spots were seeded with Acinetobacter at optical density OD five and E. Two-strain cultures dark bars correspond to the center two bars in b, while the three-strain cultures light bars corresponds to the rightmost bar in b. In contrast, survival of E. Together, these results and those reported for Vibrio cholera Borgeaud et al.
Survival of E.
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Consistent with the model, killing of E. This likely indicates a spatial effect - actively killing predator cells will be physically closest to the DNA released from their lysed prey. Together with experiments below showing HGT inhibition by extracellular DNase Figure 7e , these results support a model in which the T6SS releases DNA into the extracellular environment via lysis of neighboring cells, thereby making it available for uptake by any nearby cell, but the T6SS is not directly involved in DNA uptake Figure 2j.
To further characterize killing-enhanced HGT in Acinetobacter , we analyzed the effect of genetic context. Previous experiments have shown that A. However, those experiments were done using chemically purified, exogenously added DNA. Therefore, we experimentally confirmed them for the specific case of killing-enhanced HGT. We co-cultured Acinetobacter on agar plates with E. We also tested whether contact-dependent killing enables Acinetobacter to efficiently acquire genes from the E.
To do so, we created a bait plasmid containing kan resistance adjacent to 22 kb of Acinetobacter genomic homology. We transformed this plasmid into E. Acinetobacter acquired the kan resistance gene regardless of its location Figure 2—figure supplement 1b , but HGT was approximately fold lower when the kan gene was in the E.
Horizontal gene transfer
Considering that the bait plasmid had a pBR origin of replication, with 10—20 plasmids per cell, the per-copy difference was only about 5- to fold. Finally, we picked three double-resistant Acinetobacter clones each that had acquired either the replicating or homology plasmid for further analysis, isolated both plasmid and genomic DNA, and confirmed that the replicating plasmid pBAV1k had transferred as an episomal unit, whereas the homology plasmid pRC03H had integrated into the genome Figure 2—figure supplement 2.
While killing-enhanced HGT has now been observed in multiple genera, little is known about the population dynamics of this process, its efficiency, or how that efficiency is influenced by environmental conditions. This lack of understanding is largely due to the absence of a quantitative method to measure mechanistic HGT parameters. This is problematic, because results depend on multiple parameters extrinsic to inherent competence, including cell concentration, DNA concentration, and incubation time.
This makes results difficult to compare across experiments, conditions, strains, or even labs. A more useful approach would be to quantify transformation using variables intrinsic to the cells and the DNA, such as the DNA uptake rate and transformation efficiency per molecule of DNA. The model parameters pertaining to growth, transformation, and killing can each be measured sequentially in simplified conditions Figure 3 , Figure 3—figure supplements 1 — 3 , Table 1.
For all parameter fitting, we incubated cells in spots on agar plates, the same condition used to measure HGT in dual-species communities see Materials and methods. First, we measured growth parameters for each species separately Figure 3—figure supplements 1 — 2 , and see Materials and methods. We incubated the cell-DNA mixtures on agar plates, and then we counted the number of cells that had acquired kan resistance. To obtain values for the HGT parameters, we simultaneously fit data from time courses with either limiting Figure 3a or saturating Figure 3b DNA, and from DNA dilution series with cells harvested at different time points Figure 3c.
Third, we fit parameters for T6SS-mediated killing by mixing different concentrations of E. As with DNA uptake, we fit killing parameters simultaneously for several time courses Figure 3d—i and Figure 3—figure supplement 3 and dilution series Figure 3j. The leak rate may be due to spontaneous E.
Error bars indicate measurement standard deviations of experimental data for a single spot harvested from an agar plate, and lines represent simulations using the shared best fit parameters Table 1. Note all y-axes are log scale except for l. Orange lines indicate Acinetobacter spect-, or in k, tet-resistant , blue lines indicate transformed Acinetobacter additionally kan-resistant , green lines indicate E. The model closely matched experimental results, suggesting that DNA released from cells in situ is equivalently available for uptake as DNA purified in vitro Figure 4.
Note that counting CFUs in a spot on an agar plate is a destructive measurement, as it requires harvesting and resuspending the entire spot, so successive data points in a time course are actually from different individual spots that were seeded at the same time from the same cell mixture. Plotted is the fraction of Acinetobacter that have become double antibiotic-resistant due to HGT of kan resistance from E.
Data are from the same experiments as Figure 3d—i and Figure 3—figure supplement 3 , which shows the total CFUs. Solid lines are model predictions, and error bars are standard deviations of experimental results. Each row of plots is from a different day, and plots within a row are for varying seeding densities of the two species shown in the Figure Supplement at time 0. Experimental measurement of HGT is time-consuming and labor-intensive, limiting the study of how it is affected by environmental conditions.
However, our experimentally parameterized, mechanistic model allows us to simulate a much wider variety of conditions. This allows us to predict the most conducive conditions for HGT, the conditions in which contact-dependent killing plays an important role, and what strategies are most likely to inhibit HGT, and thus the spread of MDR.
First, to determine the effect of bacterial seeding density on the fraction of transformed Acinetobacter , we simulated surfaces seeded with varying densities of the two species and incubated for two hours. Wild-type Acinetobacter had the highest transformation frequency when both species were seeded at higher densities, allowing maximal contact Figure 5a.
This can be understood by considering that the killing rate, and thus DNA release, depends on the product of the cell counts of both species Box 1 , so the most DNA is released when both species are at high density. In contrast, the transformation frequency of killing-deficient Acinetobacter was mainly dependent on only the E. Contour levels indicate fold changes in a,b, two-fold changes in c,g, and 30 min changes in f. From these results, we calculated how the relative importance of killing for HGT depends on seeding density.
We defined the degree to which killing of E. This killing enhancement was greatest when Acinetobacter was seeded at high density and E. Next, we used our model to explore how killing-enhanced HGT interacts with incubation time. We simulated a surface seeded with both Acinetobacter and E. HGT increased with time for both the WT and the killing mutant Acinetobacter Figure 5d , but the enhancement of HGT provided by killing was greatest within the first few hours Figure 5e. Killing still enhanced HGT by more than fold for a wide range of seeding densities even after 10 hr Figure 5g , which is long enough to reach the carrying capacity.
Overall, these results show that the degree to which contact-dependent killing increases HGT to predatory bacteria is influenced by the total initial cell density, the ratio of the two species, and the length of time that the community has to grow. In the case of transferring antibiotic resistance genes, it would be desirable to inhibit HGT.
Therefore, we used our model to explore how well killing-enhanced HGT could be blocked by two potential environmental perturbations: either degradation by DNase or competitive inhibition with added DNA. All contour levels indicate two-fold differences, and all axis and colorbar scales are log10, except DNA half life in a.
The axes indicate the initial cell count A 0 and E 0 for Acinetobacter and E. We then explored how well each condition would inhibit HGT in microbial communities seeded at different cell densities and species ratios. We simulated surfaces seeded with varying numbers of E. Importantly, our model predicted DNAse to remain effective even at high initial cell density Figure 6b , whereas competing DNA was predicted to dramatically lose efficacy in that condition Figure 6f. Regardless of the inhibition strategy, killing consistently increased HGT to the wild type relative to the killing mutant, even in the presence of the inhibitors Figure 6d,h.
Finally, we experimentally tested the predictions from our model, focusing on cases with relatively high cell numbers where HGT events are detectable. With respect to seeding density, the model predicted experimental results quite well see Figure 5a—c. For WT Acinetobacter mixed with E. The HGT enhancement factor was greatest when the predator was seeded at high density, while the prey was at low density Figure 7c. Also consistent with the model, the HGT enhancement decreased for all seeding densities after overnight growth compare Figure 7c to Figure 7d.
The missing bar at the bottom indicates data below detection. Reduction is relative to the same experiment with no DNase added. Error bars indicate the propagated standard error. Units of DNase are not easily converted to DNA half life on an agar substrate, so we tested a four-fold DNase dilution series Figure 7e with the two species seeded at approximately equal numbers. When cells were seeded at fold lower density, or Acinetobacter was killing-deficient, DNase reduced HGT to an even greater extent. At fold lower seeding density, or when Acinetobacter was unable to kill E.
Nevertheless, the results were repeatable over 3 separate days, and the figure shows their average. While our model predicted the key qualitative features of cell seeding density, extracellular DNase, and competing DNA, there were some discrepancies. This may be a result of the fact that our model does not capture spatial heterogeneity in DNA concentrations. Lysed E. The discrepancies may also reflect the technical difficulty of delivering molecules evenly to real-world communities, which would be exacerbated for mature biofilms.
This threat stems from pathogens such as Acinetobacter that are able to rapidly accumulate multiple resistance genes, which can make them nearly impossible to treat. High-throughput sequencing has revealed evidence for widespread horizontal gene transfer, but unlike conjugation and phage transduction, we still know relatively little about the microbial dynamics underlying HGT via natural competence Mao and Lu, ; Johnsborg et al.
It has been observed qualitatively that killing of nearby cells - via fratricide Kreth et al. In this paper, we showed that contact-dependent killing by Acinetobacter can increase HGT rates from E. By subsequently adding kanamycin to our chips, we observed functionally adaptive emergence of newly drug-resistant bacteria in situ. Significantly, we also showed that killing by one strain in a spatially-structured community makes DNA available for uptake by nearby, non-killing cells. This highlights the role that polymicrobial interactions can play in facilitating HGT. We then developed population dynamic models for both natural transformation and contact-dependent killing, and we fit them to experimental data to obtain biologically relevant parameters.
This was not obvious a priori , particularly given that previous experiments have shown extracellular DNA to rapidly lose its transforming ability in real-world conditions Mercer et al. For contact-dependent killing, predation can occur only at the perimeter of micro-colonies, where there is contact between predator and prey cells Schwarz et al.
This allowed us to approximately account for the difference between interior and exterior prey cells, while maintaining the simplicity of a system of ordinary differential equations. See also Figure 3—figure supplement 4 for a comparison of simulations using the same parameters, fit using the restriction of killing to perimeter cells and shown in Table 1 , simulated with and without that restriction. The difference between the total number of cells and the number of perimeter cells only becomes significant when the population has grown much greater than the initial cell number compare solid blue to dashed orange lines in Figure 3—figure supplement 5 , so we would expect it to be more important in longer time courses.
Four representative micro-colonies were selected from Videos 2 and 3 to highlight the enrichment of E. Left panels: difference images were calculated for the green E. This shows where the the GFP signal dramatically decreases, indicating cell lysis between the two time points. These regions of putative cell lysis are shown in yellow. Right panels: the same movies, but without the yellow difference signal and including the transmitted light channel, for comparison.
After the mCherry signal from Acinetobacter fades see Supplemental Note 1 , the location of Acinetobacter surrounding the E. Note that some color intensity scales were chosen differently for the two panels to optimize clarity for the viewer. Using our experimentally parameterized model, we characterized contact-dependent killing-enhanced HGT in a wide range of simulated conditions. Our model revealed that killing is most important for HGT when the prey is at low density, the predator is at high density, and the interaction time is short Figure 5c,f , which was confirmed by experimental data Figure 7a—d.
Killing is less important for HGT when the prey outnumbers the predator, because enough prey DNA is released by spontaneous lysis that the additional DNA released by killing no longer provides much advantage. Similarly, killing provides less HGT enhancement when the interaction time is longer, because killing Acinetobacter deplete their donor DNA source by killing neighboring prey E.
Interestingly, this seeding ratio at which contact-dependent killing most enhances HGT - when the predator outnumbers the prey - is the same condition in which it provides the strongest competitive advantage in terms of cell growth Borenstein et al. High predator cell density is also the condition that induces fratricide-mediated HGT in Streptococci Steinmoen et al. However, T6SS regulation does not always depend on high cell density, and it can be quite complex and varied, even within strains of the same species, likely reflecting the wide range of ecological contexts and functions performed by the T6SS Miyata et al.
Given that the only demonstrated advantages are at high cell density, it remains an open question what, if any, selective advantages the T6SS may provide at lower cell density. While T6SS regulation in A. Although our model predicted neither condition would eliminated the HGT enhancement provided by killing in our model, both strategies were predicted to reduce HGT when at realistic levels. Competing DNA began to reduce HGT at about 10 9 kb or greater Figure 6e , and given that our agar spots could support 10 7 —10 8 cells, that would be 10— kb of extracellular DNA per cell at carrying capacity.
Importantly, DNase was predicted to be more effective than competing DNA at high cell seeding densities, which is the condition where contact-dependent killing-enhanced HGT is most efficient. The reduced efficacy of competing DNA in this condition is likely because the high initial killing rate releases a large amount of prey DNA at once, overcoming competitive inhibition.
Interestingly, competing DNA was nearly fold less effective than predicted, which may reflect its physical exclusion from dense microbial communities, or the fact that our model does not reflect the spatial heterogeneity of DNA released from lysing E. Spatial dependence of DNA concentration would be a valuable limitation to address in future work; nevertheless, the qualitative trends predicted by our model show its usefulness in guiding experiments and intuition.
Additionally, the observation that contact-dependent killing appears to provide a sub-population of cells with privileged access to DNA that is protected from DNase may help explain how HGT can occur so frequently in real-world environments where DNA is quickly degraded. While we used A. Indeed, careful re-examination of clinical isolates recently revealed that A. It was a clinical A.
Perhaps more importantly, A. For example, another carbapenemase gene, bla OXA23, has been found in multiple Acinetobacter species isolated from both humans and animals, including both A. In addition to helping to explain, predict, and combat the rapid spread of multi drug-resistance, particularly among Acinetobacter , the population dynamics revealed here are likely important in the microbiome and microbial evolution more broadly.
Both the T6SS Schwarz et al. Therefore, our results are likely broadly generalizable and may contribute to understanding the known increase of HGT within biofilms Madsen et al. The method presented here for quantifying natural competence with standardized parameters may also help researchers address other outstanding questions about microbial evolution Vos et al. In the light of a growing threat from antibiotic resistance, the quantitative methods presented here should greatly aid study of the mechanisms by which bacteria swap genes, shortcut evolution, and outsmart our drugs.
We obtained Acinetobacter baylyi sp. We inserted spectinomycin resistance and mCherry into a putative prophage region of the Acinetobacter genome, as described previously Murin et al. To give E. We then replaced this landing pad with the chloramphenicol marker from donor plasmid pTKIP-cat, as described previously Kuhlman and Cox, We derived our genomically integrating plasmids from and the ColE1 origin plasmid pRC03, which replicates in E. To compare acquisition of plasmid vs.
For microfluidic experiments, we seeded a custom microfluidic device Figure 1—figure supplement 1 with both Acinetobacter and E. We imaged the experiment shown in Figure 1a—d with a 40x objective and that shown in Figure 1e—k with a 60x objective. Note that microfluidics with A.
We seeded the communities with 2 ul spots of cell culture, being careful not to introduce bubbles, which can spray aerosolized cells across the plates when they burst. Before seeding, we grew the cells overnight, resuspended them at into fresh media, grew them again for 2—3 hr, washed them, and then resuspended again in fresh media. For time-course experiments, the initial cell density can be seen from the CFUs at time 0. We harvested spots by cutting out spots with a razor blade and resuspending them in ul of PBS buffer.
To count CFUs, we made serial ten-fold dilutions of the resuspended cells, spotted 2 ul of each dilution onto selective plates, and counted colonies after incubation overnight. To lower our limit of detection Figure 2b bars 2 and 4, Figure 2c bar 3 , we also spread 50 ul of resuspended cells across selective plates, achieving a theoretical limit of detection of 10 CFUs.
We isolated any plasmid DNA from these clones and from the bait E. Next, we performed PCR using primers pointing outward from the kan gene to amplify the circularized, surrounding genomic region. All three clones had the same PCR pattern Figure 2—figure supplement 2b , and sequencing confirmed that the surrounding region was Acinetobacter genome. When DNA or DNase was added to the cells, we added it to the cell mixture on ice just before spotting. We used PureLink DNase, resuspended in water at the recommended concentration 2.
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We grew this in E. Error bars in Figure 2b—d indicate the standard deviations of measurements pooled across two culture replicates, each with three measurement replicates. Culture replicates refer to distinct spots seeded with the same cell mixture at the same time. Each resuspended culture was then serially diluted, and these fold dilutions were themselves spotted onto selective plates to count CFUs measurement spots , as described above.
However, the CFUs in each of these measurement spots are expected to be Poisson-distributed, so we spotted each dilution three times, to obtain measurement replicates.
Antibiotic resistance and horizontal gene transfer
Error bars in Figure 2a are for three measurement replicates of one culture each. Where data was less than CFUs, only one larger volume measurement replicate was counted for each community, as described above. To calculate the pooled variance of HGT frequency across both types of replicates, we followed the following procedure. For each selective antibiotic condition a and each culture replicate c , calculate the sample variance of the CFU counts x across measurement replicates, s a , c 2. In particular, we calculated p-values with the MATLAB function multcompare , using the condition means and variances calculated above.
Spotting serial dilutions to count CFUs measures data on a log base 10 scale, so to compare data separated by more than one order of magnitude, we first calculated the log base 10 value of each data point Figure 2b , survival of E. To test for significance where data was below the limit of detection Figure 2b , survival of E. In our model Box 1 , Figure 2j , Table 1 , both E. All variables are absolute numbers, not concentrations. We modeled the DNA uptake and E. For contact-dependent killing, predation can occur only at the perimeter of micro-colonies where there is contact between predator and prey cells Schwarz et al.
Since there are N 0 micro-colonies, the total number of perimeter cells is:. Over a longer time scale, communities on agar plates will continue to grow slowly as fresh nutrients diffuse in and to replace dying cells, but this model focuses on the early dynamics of a growing community in the very early stages of biofilm development. Transformation of Acinetobacter by homologous recombination HR is more efficient than via replicating plasmids Palmen et al. We fit each component of the model sequentially using simplified experiments. Using these growth parameters, we then fit the killing rate by measuring CFUs in co-cultured communities over time and using that data to fit the model as integrated using ode23 in MATLAB Figure 3d—j.
For simultaneous fitting of data from multiple experiments Figure 3 , Figure 3—figure supplement 3 , we defined custom objective functions to calculate the prediction error for all data points, and then minimized the total sum of squares with lsqnonlin. We simultaneously fit the HGT curves for time course and serial dilution data to the model as integrated using ode23 Figure 3a—c. We measured plasmids per E.
While plasmid copy number may vary as conditions change e. We believe both of these phenomena are likely due to nutrient restriction, caused by Acinetobacter adhesion, growth, and eventual clogging in the channels meant to supply nutrients see the top and bottom strips in the Supplemental Movies. Indeed, in our experience, the stickiness of Acinetobacter makes microfluidic experiments much more challenging than with only lab E.
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LacI repression in E. The E. In Videos 7 — 8 , on the other hand, the media had been switched to include kanamycin, which inhibited all Acinetobacter that had not acquired resistance via HGT from E. This is likely why the GFP repression in E. Conversely, Acinetobacter appear to reduce expression of mCherry as they become more nutrient-limited. We speculate that mCherry fluorescence fades while GFP remains visible in Acinetobacter because i the mCherry gene is single-copy on the genome while the GFP plasmid is multi-copy, ii mCherry bleaches more readily and has lower intrinsic brightness than GFP Shaner et al.
Regardless, in the Supplemental Videos 1 — 6 , it is clear when and where all E. For GFP to re-emerge in an area where all E.