Cell-To-Cell Bacterial Interactions Promoted by Drier Conditions on Soil Surfaces

Cell-to-cell bacterial interactions promoted by drier conditions on soil surfaces

Robin Tecona,1, Ali Ebrahimia,2, Hannah Kleyera, Shai Erev Levia, and Dani Ora

aSoil & Terrestrial Environmental Physics, Department of Environmental Systems Science, Swiss Federal Institute of Technology (ETH) Zürich, 8092 Zürich, Switzerland

Edited by Steven E. Lindow, University of California, Berkeley, CA, and approved August 13, 2018 (received for review May 15, 2018)

Bacterial cell-to-cell interactions are in the core of evolutionary and 100 cells within a radius of 20 μm). For certain high cell density ecological processes in soil and other environments. Under most “hotspots,” e.g., in the rhizosphere (19), the number of neighbors conditions, natural soils are unsaturated where the fragmented can increase significantly. Although the distance to the nearest aqueous habitats and thin liquid films confine bacterial cells within neighboring cell can be relatively small on average (∼10 μmin small volumes and close proximity for prolonged periods. We report densely populated topsoils), it is highly spatially variable, and, effects of a range of hydration conditions on bacterial cell-level since soil bacterial distributions show a high degree of clustering, interactions that are marked by plasmid transfer between donor colonized microsites are often spatially isolated (19). To sum up, and recipient cells within populations of the soil bacterium Pseudo- the level of bacterial cell-to-cell interactions in soil may be far monas putida. Using hydration-controlled sand microcosms, we more limited than would appear from the high cell density values 9– 10 · −1 demonstrate that the frequency of cell-to-cell contacts under pre- commonly reported (10 10 cells g ) (20) due to the high scribed hydration increases with lowering water potential values surface area per volume of soil and the clustered spatial distri- (i.e., under drier conditions where the aqueous phase shrinks and bution of soil bacteria. In this background of limited opportunities for cell-to-cell interactions, additional environmental fragments). These observations were supported using a mechanistic factors regulate the frequency and duration of cell-to-cell en- individual-based model for linking macroscopic soil water potential counters in soil. Developing a better mechanistic understanding to microscopic distribution of liquid phase and explicit bacterial cell of how key factors affect encounters among bacterial cells is interactions in a simplified porous medium. Model results are in important for revealing basic principles that govern soil micro- good agreement with observations and inspire confidence in the bial community diversity, dynamics, and functioning. underlying mechanisms. The study highlights important physical In this study, we hypothesize that the probability of bacterial factors that control short-range bacterial cell interactions in soil cell-to-cell encounters and interactions on hydrated rough sur- and on surfaces, specifically, the central role of the aqueous phase faces such as found in soil is modified by soil properties and in mediating bacterial interactions and conditions that promote ge- hydration conditions. We thus seek to link macroscopic variables netic information transfer in support of soil microbial diversity. such as the soil water potential, soil pore spaces, and internal surface area to biological interactions that take place in aqueous conjugation | soil physics | vadose zone | Pseudomonas putida | HGT microhabitats. Specifically, we propose that cell-to-cell bacterial

acterial cell-to-cell interactions sustain key evolutionary and Significance Becological processes in all environments, including horizontal gene transfer mediated by conjugative pili (1, 2), nutrient Despite a high number of microbial cells and species present in cross-feeding (e.g., in syntrophy; ref. 3), and chemical signaling small volumes of soil, detailed observations suggest that most that shapes social behaviors (4). To interact, microorganisms bacteria interact with only a few other individuals. These inter- have to remain in direct physical contact or within a range per- actions between cells are crucial to many soil processes including

mitting effective molecular exchanges by diffusion, i.e., most imparting genetic traits (e.g., antibiotic resistance) and microbial MICROBIOLOGY interactions occur at the scale of individual microbes (5). Ulti- evolution, yet our understanding of the controlling environmental mately, cumulative cell-to-cell exchanges determine the overall factors remains sketchy. Based on evidence from experiments in bacterial activity in a given habitat, affecting large-scale fluxes soil microcosms and results of a mathematical model, we dem- and, hence, impacting global ecosystem processes (6). onstrate that a ubiquitous physical factor such as fragmentation Bacteria that inhabit natural porous media such as soil expe- of the aqueous phase commonly found in unsaturated soils af- rience life in a complex 3D pore network and on rough surface fects the ranges and frequency of cell-to-cell bacterial interactions. architecture, with heterogeneous nutrient resources and frag- Our findings thus help reveal some of the basic principles that mented aqueous niches that limit their distribution, dispersion, control microbial life and diversity in soil environments. and contact with neighbors, but can locally increase cell density and proximity (7–11). Evidence suggests that (i) the distribution Author contributions: R.T., A.E., H.K., S.E.L., and D.O. designed research; A.E. designed the of microorganisms in soil is highly patchy, with nonrandom mi- mathematical model; R.T., A.E., H.K., and S.E.L. performed research; R.T., A.E., H.K., and crohabitats colonized by single cells or microcolonies (12–15), S.E.L. analyzed data; and R.T., A.E., and D.O. wrote the paper. and that (ii) although soil microbial density is high in comparison The authors declare no conflict of interest. with other habitats, cells occupy no more than a few percent of This article is a PNAS Direct Submission. the pore spaces of an average soil and only a small fraction Published under the PNAS license. < ( 1%) of the available soil surfaces (9, 16, 17). This spatial Data deposition: The Modeling data and code have been deposited in the ETH Research context—together with microbial growth and motility (18)— Collection, https://www.research-collection.ethz.ch/handle/20.500.11850/284650 (DOI: 10. controls the probability for a bacterial cell to encounter another 3929/ethz-b-000284650). cell, and therefore to interact, in soil. Based on a detailed 1To whom correspondence should be addressed. Email: [email protected]. analysis of bacterial distributions in hundreds of soil thin sections 2Present address: Ralph M. Parsons Laboratory for Environmental Science and Engineer- and on statistical modeling, Raynaud and Nunan (19) have ing, Department of Civil and Environmental Engineering, Massachusetts Institute of concluded that the potential for cell-to-cell interactions in soil is Technology, Cambridge, MA 02139. relatively limited, both in terms of the number of interacting cells This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. and of the number of different species to interact with. The vi- 1073/pnas.1808274115/-/DCSupplemental. cinity of a soil bacterium typically harbors few neighbors (10 to Published online September 12, 2018.

www.pnas.org/cgi/doi/10.1073/pnas.1808274115 PNAS | September 25, 2018 | vol. 115 | no. 39 | 9791–9796 Downloaded by guest on September 29, 2021 interactions could be promoted under hydration conditions that locally increase the likelihood and duration of cell encounters while limiting cell dispersal. To systematically investigate this question, we have used the exchange of a conjugative plasmid as a proxy for close encounters between bacterial cells, with the soil bacterium Pseudomonas putida (21) selected as a donor and recipient of a broad-host range plasmid isolated from soil environments (Fig. 1). Sand microcosms were used as simple and well-defined porous environments for assessing conjugation events as function of hydration conditions, as determined by a prescribed matric potential (Fig. 2 and SI Appendix, Supple- mentary Methods for details). In unsaturated soil, the matric potential results from capillary and adsorptive interactions that retain water in pores and within roughness elements (22, 23). The matric potential is often expressed as a negative pressure (relative to atmospheric pressure): where zero value marks complete water Fig. 2. Sand microcosms with controlled hydration conditions. (A) Photo- saturation, while progressively negative values correspond to drier graph shows an assembled system, with microcosms connected to a liquid conditions (22, 23). We use the simple experimental system (Figs. medium reservoir (0.1× TSB). The height of the liquid column (h) prescribes a 1 and 2) to directly study the relation between a biological cell-to- fixed suction to the sand phase via the ceramic plate, which mimics the ef- cell interaction (bacterial conjugation) and a physical parameter fects of matric potential in porous media (e.g., h = 10-cm produces a water (matric potential) relevant to soil. In addition to experiments, matric potential of ∼−1 kPa). Inset shows stereomicroscope image of the mathematical modeling provides a means for generalization and quartz sand layer. (B) Illustration of the microcosm unit, with the quartz sand enables evaluation of microscopic mechanisms that are inher- grains (0.5 g of dry sand, ∼1 mm thick) in contact with the saturated ceramic ently difficult to observe. Models of plasmid transfer on surfaces plate. Up to six units were connected to a single reservoir bottle. have been reported (24, 25) but remain limited due to over- simplification of the aqueous habitats in unsaturated soil (26). More recently, individual-based models (IBMs) (26–28) have terrestrial habitats (29–31), such as the distribution of the aqueous evolved and permit mechanistic study of cell-to-cell interactions at phase held under capillary forces on model rough surface. Both relevant microscales, considering physical and geometrical com- experiments and simulations show that the fragmentation of the plexity found in natural habitats. Here, we have used an IBM aqueous phase induced by drier conditions led locally to higher that explicitly integrates salient physical properties of soil and rates of cell encounters and increased the number of direct cell interactions (plasmid transfer). Overall, these results permit us to delineate a causal link between macroscopic variables (matric potential) and the probability of bacterial interactions that take place at the microscale. Results Effects of Hydration Conditions on Bacterial Interactions in Controlled Environments. To study the frequency of cell-to-cell contacts, we used the soil bacterium P. putida as the donor and recipient of a conjugative plasmid. We specifically used a plasmid that trans- fers best on surfaces (Methods), showing high rates of transfer on agar plates but very poor rates in liquid cultures (Fig. 1). A suspension of recipient (R) and donor cells (D), mixed in a ratio of 10:1 (R:D), served to inoculate quartz sand microcosms (Fig. 2) that were kept at constant temperature and matric potential values for the duration of the experiment (20 h). Hydration conditions in the microcosm ranged from relatively wet (matric potential value of −1.2 kPa) to relatively dry (−6.5 kPa), representing a wide range of saturation levels within such coarse porous medium (SI Appendix, Figs. S1 and S2). Fig. 3A shows the resulting number of recipient, donor, and transconjugant cells (i.e., cells that have acquired the plasmid after cell-to-cell interaction with a donor) that were isolated from the microcosms and enumerated at the end of the incubation period. Bacteria survived and grew in all microcosms, with an average population Fig. 1. Bacterial conjugation to study cell-to-cell interactions. (A)Illustrationof increase of ∼13-fold (corresponding to an average of three to the conjugation process (star) on a surface between donor cells (pink) and re- four cell doublings during 20 h). Data showed no significant cipient cells (orange), which requires direct physical contact and results in plasmid difference in total population sizes observed across the different transfer to recipients, which become transconjugants (green) over time. In this matric potentials, although we noted a slight tendency toward simplified sketch, all contacts between donor and recipient cells lead to conju- less cell doublings with lower matric potential values (SI Ap- gation; however, in reality, various physical and biological factors may lower pendix, Fig. S3). Therefore, the relatively limited range of matric plasmid transfer efficiency. (B) Fluorescence micrograph shows an experiment potential values used (approximately −1to−7 kPa) corresponds with P. putida donor and transconjugant bacterial cells immobilized on an agar surface, respectively, shown in pseudocolors magenta and cyan. Recipient cells to relatively wet conditions in most soils (23) and conducive to are not fluorescent and, hence, not visible. (C) Control for conjugation efficiency bacterial growth, which confirmed that changes in transfer fre- on solid homogeneous surfaces (0.1× TSBagarplates)andinliquidenvironment quency were mainly due to variations in constraints imposed by without shaking (5 mL of 0.1× TSB). Initial R:D ratio was 10:1. After 20 h of in- aqueous distribution in the microcosms. Transconjugants were cubation at 25 °C, the cells were diluted and plated on selective agar media detected in all microcosms and their absolute numbers increased followed by cell counts (measured as colony-forming units or cfu; see SI Appen- with decreasing matric potential values (Fig. 3A), although the dix, Supplementary Methods for details). Individual results from triplicate cultures difference was significant only when comparing results from are shown. Transconjugants were only detected in one of the liquid replicates. the lowest and highest matric potential values (P = 0.03 with a

9792 | www.pnas.org/cgi/doi/10.1073/pnas.1808274115 Tecon et al. Downloaded by guest on September 29, 2021 conjugation process by promoting cell-to-cell encounters and, therefore, increasing the number of transconjugant cells. How- ever, when hydration conditions approached saturation, the benefit of enhanced motility was counterbalanced by larger spatial domains visited by cells with shorter close encounter durations (resembling conditions in bulk liquid), resulting in a decrease in conjugation rates. Individual-based modeling permits direct links between spe- cific physical processes and the probability of cell-to-cell interactions. In particular, the model revealed how, all else being equal, matric potential controls both the size and abundance of the microbial aqueous habitats (Fig. 6A), i.e., the aqueous fragmentation of the system. The level of aqueous phase fragmentation at a given matric potential may be used to estimate the Fig. 3. Bacterial cell-to-cell interactions in sand microcosms. (A)Sandmicro- cosms with controlled conditions (Fig. 2) were inoculated with ∼1 × 107 cfu of a probability of donors and recipients cooccurrence in a given aqueous cluster. This probability increases with cell density and mixed suspension of P. putida recipient and donor cells (R:D ratio was 10:1). After B 20 h of incubation at 25 °C, the entire sand fraction was harvested to count the for wetter conditions (Fig. 6 ). Prerequisite for interactions such final number of recipients, donors, and transconjugants. Individual and mean as conjugation events is a donor-to-recipient encounter. We thus results from triplicate microcosms are shown. (B) Overlay fluorescence micro- used the model to quantify in detail the effects of aqueous phase graphs show donor cells (magenta) and transconjugants (cyan) observed after fragmentation (driven by hydration conditions and surface geo- 45hofincubationat25°Cinasandlayer. Sand particles were kept at a matric metrical features) on the number of cell encounters. Fig. 6C potential of −3.6 kPa. Donors are observed as single cells or as microcolonies, and details the encounter times as a function of the largest aqueous transconjugants are only seen in direct contact to donors (Inset). Recipient cells cluster size (representing the fragmentation state of the net- and sand particles are present but not visible in the fluorescence channels. work). In these simulations, individual donor and recipient cells are tracked and their number of encounters is accumulated over t time. Results indicate a substantial increase in the number of one-tailed test). Common metrics of plasmid transfer efficiency encounters as the aqueous phase became more fragmented (Fig. confirmed that conjugation rates increased with lower matric 6C). The simulation results only considered donor and recipi- potential values in the microcosms by about one order of mag- SI Appendix ent cells that experienced at least one encounter after the total nitude ( , Table S1). We were able to visualize some simulation time (12 h). As shown in Fig. 6C and SI Appendix, Fig. of the donors and transconjugants in the sand environment at the S5, the results indicate that for connected networks (wet) where microscale, and the observations confirmed the occurrence of direct cells swim freely, most encounters between donors and recipi- contacts between cells that newly acquired the conjugative plasmid ents last only a small fraction of the total simulation time, (transconjugants) and donor cells growing as microcolonies (Fig. 3B). Fig. 4 illustrates a conceptual view of the cell probability of while in a fragmented environment (drier conditions), more and interaction in various environments, including the role played by matric potential in porous medium. We developed a probabilistic model for cell-to-cell interactionsonhydratedsurfacesthatrelies on simple spatial statistics (32) and that integrates changes in hydration conditions (SI Appendix,Fig.S4); however, this simple model was not sufficient to faithfully capture the effects of hydration on conjugation rates.

Modeling of Aqueous Fragmentation, Cell Encounters, and Conjugation. We developed a mechanistic modeling framework for systematic evaluation of bacterial conjugation in soil-like surfaces under dif- MICROBIOLOGY ferent hydration conditions (see Methods for details). The main assumption in the context of the study is that the spatial physical environment (pore geometry and aqueous connectivity) ultimately controls the rate of cell-to-cell encounters necessary for bacterial conjugation. In the model, bacteria were represented as individual agents that disperse by flagellated motion within water films, grow and divide, or die, depending on the local conditions that they ex- perience. These agents populated an idealized rough surface made of connected bonds that retain liquid by capillary forces and ad- sorption depending on their geometry and on the matric potential prescribed to the system (29–31). In contrast to nearly saturated conditions, lower matric potential values (i.e., drier conditions) resulted in numerous fragmented aquatic domains forming spatially isolated bacterial subpopulations (Fig. 5A). As a result, conjugation events (and the resulting transconjugant cells) tended to localize in spatial hotspots where (i) water was retained and (ii) both donor and recipient cells were present (Fig. 5A). Model estimations of Fig. 4. Conceptual illustration of physical factors influencing cell interactions. (A) In liquid culture, local cell density fluctuates due to cell motility, and do- conjugation rates were consistent with the experimental observa- nors fail to encounter recipients for a sufficient amount of time. On agar tions and showed that the number of transconjugants increased B surfaces, cells are immobilized but can spread by cell division. At high cell under lower hydration conditions (Fig. 5 ). This pattern was con- density, chances become high that a donor comes to contact with a recipient sistent across a range of cell velocity; however, an imposed slower and conjugates. (B) On hydrated rough surfaces (e.g., in sand microcosms), the speed produced results that better matched the empirical data conditions are somewhat intermediary. As the rough surface dries (lower (Fig. 5B). The simulations were conducted with similar initial cell matric potential values leading to unsaturated conditions), the aqueous do- densities and ratios (10:1, R:D) as in the experiments. Simulation main becomes fragmented, the cells are partially immobilized, and the results showed the effect of increased cell motility on enhancing probability of cell-to-cell interaction increases.

Tecon et al. PNAS | September 25, 2018 | vol. 115 | no. 39 | 9793 Downloaded by guest on September 29, 2021 matric potential in the system increases the travel time required for cell dispersal to a given distance (30) and constrains overall cells’ maximal dispersal range (34). (Incidentally, these constraints also apply to nonmotile bacteria, although their effects are less marked than with flagellated cells.) The biophysical processes described above are directly linked to the fragmentation of the aqueous domain, as shown by the individual- based model (Fig. 6). The connectivity of the retained aqueous phase (which underlies aqueous fragmentation) is defined here operationally by the minimum liquid film thickness that allows the passage of a bacterial cell of a given diameter. Lowering the matric potential in the porous system thus induces the forma- tion of spatially isolated (“disconnected”) aquatic microhabitats (Figs. 5 and 6) that strongly limit bacterial dispersal and inter- mixing (30, 34). Such disconnected habitats represent microsites with a local higher probability of cell-to-cell contacts, or “pockets Fig. 5. Individual-based modeling of conjugation on hydrated surfaces. (A) of interactions” to use the words of Raynaud and Nunan (19). Exemplary simulated spatial patterns of bacterial distribution in hydrated pore Because most soils remain under unsaturated conditions most networks under almost water saturated (−0.2 kPa) or relatively dry (−8.0 kPa) of the time (11), we argue that the fragmentation of the soil hydration conditions. Images are close-up views of the model representation. aqueous phase and its associated constraints for microorgan- Water retained in the bonds of the pore network is represented with shades isms may be a key factor in explaining soil bacterial diversity. of blue coloration. Each bacterium is an individual agent (colored circle) whose Moreover, the matric potential values used in our study were physiological behavior is influenced by its local physical environment. Cells can closer to water saturation than values commonly measured in only move in and occupy bonds that contain water. For this reason, local soil, with the so-called field capacity often ranging between −10 spatial hotspots of conjugation are observed under drier conditions. (B)Sim- and −33 kPa [field capacity corresponds to the water content ulated number of recipients, donors, and transconjugants per unit volume and comparison with experimental data (Fig. 3). Simulation values are averages retained in soil once internal drainage has ceased (23), for ex- calculated from 15 realizations (five replicates with three independent net- ample a few days after rainfall]. The presence of multiple con- works). Error bars show one SD. Simulations were conducted for two cell current soil microhabitats in which species (temporarily) coexist motilities (0.1 and 1 μm/s). For the sake of comparison, the number of cells was and genetically interact has profound consequences for microbial represented per unit volume of the experimental and modeling systems. evolution in the long term, as fragmented aqueous habitats may represent a substantial barrier to gene flow between bacterial populations (35, 36). However, horizontally transferred genes longer-lasting encounters are in general observed, which explains (e.g., via a plasmid) are likely more stable in small and isolated why more conjugation events are measured under drier condi- bacterial populations due to reduced competition. Finally, spa- tions. This habitat fragmentation and spatial confinement effect tially and genetically isolated bacterial communities found in any progresses until the system becomes so dry that the number and volume of soil, which theoretically harbor low intracommunity size of aqueous clusters rapidly diminishes, leading to the decline but high intercommunity diversity, could contribute to the un- in the total number of cell-to-cell interactions (at matric potential equalled diversity observed at all scales, a hypothesis recently values lower than −20 kPa, see SI Appendix,Fig.S5). Consistent proposed by Rillig et al. (35) (albeit focusing on soil aggregates). with cell-to-cell encounter time, the number of conjugation events Overall, our modeling and experimental findings suggest a sim- was affected by aqueous phase fragmentation, cell density, and ple physical mechanism—aqueous phase fragmentation and decrease in the ratio of donor to recipient cells (SI Appendix,Fig. associated reduction of connectivity—that directly influences S6). Finally, an exponential relationship between the cell density the occurrence of bacterial encounters, interactions, and and the number of conjugation events was observed (SI Appendix, genetic exchanges in unsaturated habitats such as found in Fig. S6). most soils. The results permit an important distinction between the like- Discussion lihood of cell coexistence and that of cell interaction within a We have used bacterial conjugation in P. putida (Fig.1)asa connected aqueous cluster. Simulations have shown that matric proxy for cell-to-cell interaction in hydration-controlled sand potential (a macroscopic soil hydration state) and cell density microcosms (Fig. 2), and the results supported our hypothesis jointly control the probability of cooccurrence of two individuals that fragmentation of the aquatic microbial habitats induced by of different types (e.g., strains, species) within an aqueous cluster lower water potential could locally promote encounters and (Fig. 6B), a prediction supported by previous modeling studies interactions of bacterial cells (Fig. 3 and SI Appendix,Fig.S7). (37, 38). However, if cooccurrence of cell types is a prerequisite These findings go against intuition that aqueous phase frag- for cell–cell interaction, it does not represent evidence for short- mentation would limit opportunities for gene transfer in soil. A range contact, because two cells can coexist in an aqueous cluster numerical individual-based model of bacterial conjugation with while still being spatially separated by hundreds of microns. For realistic representation of the physical domain and aqueous example, in our experiments, conjugation was clearly associated phase distribution corroborated these experimental results with cell-to-cell physical contact (Fig. 3B), which was supported (Fig. 5) and offered mechanistic explanation based on the by previous studies with P. putida showing that the vast majority distribution of water content in the system (Fig. 6). The model of plasmid transfer with rigid pili occur via direct contact (<1 μm confirms the intuitive expectation that beyond a certain de- donor-recipient distance) (39). In that context, we should not hydration threshold, most cell interactions cease due to van- assume direct interaction between species based solely on their ishing aqueous habitats (SI Appendix,Fig.S5). Experiments cooccurrence in soil volumes typically sampled at macroscopic and simulations suggest two main effects of matric potential scales (>cubic centimeters) (7, 40). Such spatial considerations reduction on increased conjugation rate. First, motile bacteria are essential for better representation of the behavior of complex (such as P. putida cells) are slowed down or even immobilized microbial communities (41, 42), and enabling discrimination be- within thin liquid films at low matric potential. This increases tween “potential” species interactions (based on cooccurrence in a the average duration of cell-to-cell contacts, and it has been sample) and “realized” interactions. We note that, although we shown experimentally that bacterial swimming and mobility measured genetic interactions (plasmid transfer) in this study, reduce the rate of plasmid transfer (33). Second, lowering the conclusions may be applicable to metabolic or signaling

9794 | www.pnas.org/cgi/doi/10.1073/pnas.1808274115 Tecon et al. Downloaded by guest on September 29, 2021 Fig. 6. Modeling the effects of matric potential on aqueous fragmentation and cell encounters. (A) Model hydrated porous networks were used to determine the largest aqueous cluster size in the domain (normalized by network size, approximately 3 × 3 mm) as well as the total number of aqueous clusters as function of the matric potential prescribed to the network. Here, a (connected) aqueous cluster is defined by the capacity of a bacterial cell to travel across the cluster through liquid films supporting flagellar motility. Mean values from 10 model realizations are shown. Error bars are one SD. (B) Individual-based model simulations show the probability that at least one donor cell and one recipient cell cooccur in a given aqueous cluster, as a function of matric potential and number of donors (R:D ratio is constant as 10:1). Mean values from 20 model realizations are shown. Error bars are one SD. (C) Simulation results show the relationship between cells encounter time and aqueous cluster size. The number of simulation time steps with encounter of a donor and a recipient cell are counted for a total simulation time of 12 h. The encounter time t is calculated for more than 1,000 donor/recipient cells and is normalized by total simulation time and averaged over the time distributions. Insets show exemplary results for the number and duration of donor-to-recipient encounters under relatively wet (−0.5 kPa, connected) and relatively dry (−8.0 kPa, fragmented) conditions, as well as the corresponding distribution of individual connected aqueous domains (marked by different colors). (Matric potential of −0.5 kPa resulted in a single blue cluster, hence we represent a slightly lower potential value of −2.0 kPa for illustrative purposes.)

interactions, as they most often rely on solute diffusion over (25 °C) for 20 h. Then, the entire sand phase from each microcosm was collected microscopic distances (5, 43, 44). by pipetting and transferred to 15-mL centrifugation tubes containing 10 mL Admittedly, we did not explore here the wide variety of known of PBS. To detach cells from the sand particles, tubes were vortexed at max- conjugative plasmids (45) and bacterial hosts (46), nor other imum speed for 10 s, followed by 2 min in an ultrasonic water bath (Branson factors that could influence the rate of bacterial conjugation, 5800; Branson Ultrasonics Corporation). Various dilutions were plated on such as properties of soil surfaces, pH and oxygen levels (47), TSB agar plates (with or without tetracycline at 15 μg/mL) to enumerate the proximity of plant roots (48), the presence of microbiota viable bacteria as cfu, and GFP and mCherry fluorescence was used to dis- (49), or the presence of fungal hyphae (33). Any effect of the criminate between transconjugants and donors (SI Appendix, Supplementary water potential on conjugation rates in soil will thus likely re- Methods). Visualization of bacterial conjugation at the microscale was per- main part of a mosaic of effects that stem from the above- formed using a slightly modified microcosm setup that permitted closer mentioned factors and which lies beyond the scope of this access to the sand surface with the microscope objective (SI Appendix, study. Instead, we highlighted the role of the physical habitat— Supplementary Methods). especially the dynamic aqueous phase—as a “gatekeeper” of bacterial interactions, enabling or preventing genetic and met- Individual-Based Modeling of Bacterial Conjugation on Idealized Hydrated abolic exchanges among and between communities in soil. Such Surfaces. The mathematical model abstracts the natural soil structure into simple physical processes should not be overlooked in the dis- 2D regular pore networks consisting of v-shaped channels (30, 54). The network is composed of 40 × 40sitesthattogethersimulateaphysical cussion of community interactions in the microbial world as ∼ × they can play an overarching role in the realization of biological domain of 3mm 3 mm. The structural heterogeneity of soil is imple- MICROBIOLOGY mented in the network model by varying the angularity of v-shaped bonds functions. through a statistical probability distribution (29–31). The aqueous volume Methods retained in a network bond is function of matric potential and bond angular geometry (29). Neighboring network bonds are defined as “con- Bacterial Strains, Plasmid, and Culture Conditions. We used P. putida KT2440 nected” (and hence grouped in a “connected aqueous cluster”)whenthe as recipient strain and P. putida KT2440::lacIq-pLpp-mCherry-KmR (46) as thickness of the liquid films retained in the bonds are above a threshold donor strain of the cryptic broad-host range plasmid pIPO2tet::Plac::gfp (50), value sufficient to sustain bacterial flagellar motility (30). Therefore, in our which encodes resistance to tetracycline. This plasmid was isolated from model “connectivity” is defined operationally from the subjective view- the rhizosphere (51), and it shows high rate of transfer on surfaces and point of a bacterial rod cell of a given width. (This means that thin residual rates of transfer lower by several orders of magnitude in liquid cultures liquid films existing between “disconnected” aqueous clusters may still (Fig. 1), which is due to the type of pili (short and rigid) encoded by pIPO2 allow for nutrients diffusion.) The pore network environment is combined and used by P. putida to exchange the plasmid (52). The donor constitu- tively expresses the mCherry fluorescent protein as well as the LacIq re- with an agent-based model that represents the behavior of individual pressor of the Plac promoter, which prevents expression of GFP from the bacteria in soil. Bacterial growth and substrate uptake rates at the single- pIPO2tet::Plac::gfp plasmid in the donor cell. Since the recipient strain lacks cell level are based on Monod-type kinetics (see SI Appendix, Table S2 for the lacIq gene, GFP can be expressed in transconjugants (53) (Fig. 1B). Both the growth parameters used in the model). Nutrients transport and uptake ’ strains were routinely grown on tryptic soy broth (TSB) (VWR International) are modeled based on Fick s law of diffusion and mass conservations for at 30 °C with shaking at 280 rpm or on TSB agar plates at 30 °C. Tetracycline the whole network. A 1D reaction-diffusion model is solved for each net- (15 μg/mL) was added in cultures of the donor strain to ensure plasmid work bond (31). Bacterial cell motility is often restricted in water-limited maintenance. soil environments where viscous drag in thin water films and capillary pinning at the air-water interfaces are enhanced. To account for this Conjugation Experiments in Sand Microcosms. A suspension of recipients and phenomenon, we have used a previously developed model that balances donors with a final ratio of 10:1 (R:D) based on optical density measurements self-propulsion forces of flagellated cells (FM) in bulk water with physical 7 (OD600) was added (100 μL with ∼10 cfu) to the saturated microcosms. A restrictions (cell-wall hydrodynamic interactions Fλ and capillary pinning fixed matric potential was prescribed (Fig. 2 and SI Appendix, Supplemen- force FCa) as a function of water film thickness δ. A proportional relation- tary Methods for details). After equilibration, microcosms were sealed with ship is considered between the cell velocity within a single bond V in the

parafilm (to prevent evaporation) and incubated at constant temperature network and its velocity in bulk water, V0,asfollows:

Tecon et al. PNAS | September 25, 2018 | vol. 115 | no. 39 | 9795 Downloaded by guest on September 29, 2021 8 − ðδðψ αÞÞ − ðδðψ αÞÞ FM Fλ , FCa , < PðtÞ = 0 t < tmin VðψÞ = V0 . F ð − Þ M λ t tmin ð−λÞ : ð Þ = exp ≥ P t ð − Þ! t tmin The water film thickness δ is quantified as a function of matric potential ψ t tmin , and bond angularity α (29, 30). Note that the simulations are performed λ assuming entirely random cell motility without chemotaxis. To mimic the where is the sample mean of encounter duration t, and tmin is the mini- nutrient flux in the experimental setup (porous surfaces), nutrient flux in the mum encounter time required to observe first conjugation. The mini- model is assumed to be supplied from the bottom of the network directly to mum encounter time is considered to be 5 min based on previous individual bonds, and nutrients are distributed uniformly in the network. In observations (55). the current modeling framework, plasmid conjugation from a donor to a recipient bacterium takes place if the encounter lasts more than a minimum ACKNOWLEDGMENTS. We thank Arnaud Dechesne (Technical University of Denmark) for the gift of the conjugative strains and Andreas Papritz (Swiss required time. We assumed that the conjugation occurs if the short distance μ Federal Institute of Technology Zürich) for his help on spatial statistics. Fi- between a donor and recipient bacterium (less than 2 m in our simulations) nancial support for this work came from an Advanced Grant (to D.O.) by the “ ” persists long enough (called conjugation duration ). The probability of European Research Council (ERC-320499-“SoilLife”) and from the RTD Sys- conjugation P increases as a function of encounter duration t, and it is as- temsX.ch project “MicroscapesX”. A.E. acknowledges funding from Swiss sumed to follow Poisson probability distribution National Science Foundation Grant P2EZP2 175128.

1. Clewell DB (2013) Bacterial Conjugation (Springer, Berlin). 29. Wang G, Or D (2010) Aqueous films limit bacterial cell motility and colony expansion 2. Soucy SM, Huang J, Gogarten JP (2015) Horizontal gene transfer: Building the web of on partially saturated rough surfaces. Environ Microbiol 12:1363–1373. life. Nat Rev Genet 16:472–482. 30. Ebrahimi AN, Or D (2014) Microbial dispersal in unsaturated porous media: Charac- 3. Morris BEL, Henneberger R, Huber H, Moissl-Eichinger C (2013) Microbial syntrophy: teristics of motile bacterial cell motions in unsaturated angular pore networks. Water Interaction for the common good. FEMS Microbiol Rev 37:384–406. Resour Res 50:7406–7429. 4. Asfahl KL, Schuster M (2017) Social interactions in bacterial cell-cell signaling. FEMS 31. Ebrahimi AN, Or D (2015) Hydration and diffusion processes shape microbial com- Microbiol Rev 41:92–107. munity organization and function in model soil aggregates. Water Resour Res 51: 5. Franklin RB, Mills AL (2007) The Spatial Distribution of Microbes in the Environment 9804–9827. (Springer, Berlin). 32. Diggle PJ (2013) Statistical Analysis of Spatial and Spatio-Temporal Point Patterns 6. Jansson JK, Hofmockel KS (2018) The soil microbiome-from metagenomics to meta- (CRC, Boca Raton, FL). phenomics. Curr Opin Microbiol 43:162–168. 33. Berthold T, et al. (2016) Mycelia as a focal point for horizontal gene transfer among 7. Dechesne A, Pallud C, Grundmann GL (2007) Spatial distribution of bacteria at the soil bacteria. Sci Rep 6:36390. microscale in soil. The Spatial Distribution of Microbes in the Environment, eds 34. Tecon R, Or D (2016) Bacterial flagellar motility on hydrated rough surfaces controlled Franklin RB, Mills AL (Springer, Berlin), pp 87–107. by aqueous film thickness and connectedness. Sci Rep 6:19409. 8. Or D, Smets BF, Wraith JM, Dechesne A, Friedman SP (2007) Physical constraints af- 35. Rillig MC, Muller LAH, Lehmann A (2017) Soil aggregates as massively concurrent – fecting bacterial habitats and activity in unsaturated porous media–A review. Adv evolutionary incubators. ISME J 11:1943 1948. Water Resour 30:1505–1527. 36. Cordero OX, Polz MF (2014) Explaining microbial genomic diversity in light of evo- – 9. Young IM, Crawford JW, Nunan N, Otten W, Spiers A (2008) Microbial distribution in lutionary ecology. Nat Rev Microbiol 12:263 273. soils: Physics and scaling. Advances in Agronomy, ed Donald LS (Academic, Cam- 37. Wang G, Or D (2012) A hydration-based biophysical index for the onset of soil mi- bridge, MA), pp 81–121. crobial coexistence. Sci Rep 2:881. 10. Vos M, Wolf AB, Jennings SJ, Kowalchuk GA (2013) Micro-scale determinants of 38. Wang G, Or D (2013) Hydration dynamics promote bacterial coexistence on rough – bacterial diversity in soil. FEMS Microbiol Rev 37:936–954. surfaces. ISME J 7:395 404. 11. Tecon R, Or D (2017) Biophysical processes supporting the diversity of microbial life in 39. Seoane J, et al. (2011) An individual-based approach to explain plasmid invasion in bacterial populations. FEMS Microbiol Ecol 75:17–27. soil. FEMS Microbiol Rev 41:599–623. 40. Cordero OX, Datta MS (2016) Microbial interactions and community assembly at 12. Foster RC (1988) Microenvironments of soil microorganisms. Biol Fertil Soils 6: microscales. Curr Opin Microbiol 31:227–234. 189–203. 41. Klitgord N, Segrè D (2011) Ecosystems biology of microbial metabolism. Curr Opin 13. Grundmann GL, Debouzie D (2000) Geostatistical analysis of the distribution of Biotechnol 22:541–546. NH(4)(+) and NO(2)(−)-oxidizing bacteria and serotypes at the millimeter scale 42. Zengler K, Palsson BO (2012) A road map for the development of community systems along a soil transect. FEMS Microbiol Ecol 34:57–62. (CoSy) biology. Nat Rev Microbiol 10:366–372. 14. Nunan N, et al. (2001) Quantification of the in situ distribution of soil bacteria by 43. Gantner S, et al. (2006) In situ quantitation of the spatial scale of calling distances and large-scale imaging of thin sections of undisturbed soil. FEMS Microbiol Ecol 37:67–77. population density-independent N-acylhomoserine lactone-mediated communication 15. Nunan N, Wu K, Young IM, Crawford JW, Ritz K (2003) Spatial distribution of bacterial by rhizobacteria colonized on plant roots. FEMS Microbiol Ecol 56:188–194. communities and their relationships with the micro-architecture of soil. FEMS 44. Tecon R, Or D (2017) Cooperation in carbon source degradation shapes spatial self- Microbiol Ecol 44:203–215. organization of microbial consortia on hydrated surfaces. Sci Rep 7:43726. 16. Hissett R, Gray T (1976) Microsites and time changes in soil microbe ecology. The Role 45. Heuer H, Smalla K (2012) Plasmids foster diversification and adaptation of bacterial of Terrestrial and Aquatic Organisms in Decomposition Processes, eds Anderson JM, populations in soil. FEMS Microbiol Rev 36:1083–1104. MacFayden A (Oxford Univ Press, London), pp 23–39. 46. Klümper U, et al. (2015) Broad host range plasmids can invade an unexpectedly di- 17. Young I, Bengough A (2018) The search for the meaning of life in soil: An opinion. Eur verse fraction of a soil bacterial community. ISME J 9:934–945. – J Soil Sci 69:31 38. 47. Stotzky G, Devanas MA, Zeph LR (1990) Methods for studying bacterial gene transfer 18. Dusenbery DB (2009) Living at Micro Scale: The Unexpected Physics of Being Small in soil by conjugation and transduction. Adv Appl Microbiol 35:57–169. (Harvard Univ Press, Cambridge, MA). 48. Van Elsas J, Trevors J, Starodub M (1988) Bacterial conjugation between pseudomo- 19. Raynaud X, Nunan N (2014) Spatial ecology of bacteria at the microscale in soil. PLoS nads in the rhizosphere of wheat. FEMS Microbiol Ecol 4:299–306. One 9:e87217. 49. Van Elsas JD, Govaert JM, Van Veen JA (1987) Transfer of plasmid pFT30 between 20. Bardgett RD, van der Putten WH (2014) Belowground biodiversity and ecosystem bacilli in soil as influenced by bacterial population dynamics and soil conditions. Soil – functioning. Nature 515:505 511. Biol Biochem 19:639–647. 21. Nelson KE, et al. (2002) Complete genome sequence and comparative analysis of the 50. Musovic S, Klümper U, Dechesne A, Magid J, Smets BF (2014) Long-term manure – metabolically versatile Pseudomonas putida KT2440. Environ Microbiol 4:799 808. exposure increases soil bacterial community potential for plasmid uptake. Environ 22. Papendick R, Campbell G (1981) Theory and measurement of water potential. Water Microbiol Rep 6:125–130. – Potential Relations Soil Microbiol 3:1 22. 51. Tauch A, et al. (2002) The complete nucleotide sequence and environmental distri- 23. Hillel D (2003) Introduction to Environmental Soil Physics (Academic, Cambridge, MA). bution of the cryptic, conjugative, broad-host-range plasmid pIPO2 isolated from 24. Lagido C, Wilson IJ, Glover LA, Prosser JI (2003) A model for bacterial conjugal gene bacteria of the wheat rhizosphere. Microbiology 148:1637–1653. transfer on solid surfaces. FEMS Microbiol Ecol 44:67–78. 52. Bradley DE, Taylor DE, Cohen DR (1980) Specification of surface mating systems 25. Prosser J, Lagido C, Glover L (2000) Gene Transfer in Microbial Biofilms. Microbial among conjugative drug resistance plasmids in Escherichia coli K-12. J Bacteriol 143: Biosystems: New Frontiers (Atlantic Canada Society for Microbial Ecology, Halifax, 1466–1470. Canada), pp 925–930. 53. Normander B, Christensen BB, Molin S, Kroer N (1998) Effect of bacterial distribution 26. Fox RE, Zhong X, Krone SM, Top EM (2008) Spatial structure and nutrients promote and activity on conjugal gene transfer on the phylloplane of the bush bean (Phaseolus invasion of IncP-1 plasmids in bacterial populations. ISME J 2:1024–1039. vulgaris). Appl Environ Microbiol 64:1902–1909. 27. Krone SM, Lu R, Fox R, Suzuki H, Top EM (2007) Modelling the spatial dynamics of 54. Long T, Or D (2007) Microbial growth on partially saturated rough surfaces: Simula- plasmid transfer and persistence. Microbiology 153:2803–2816. tions in idealized roughness networks. Water Resour Res 43:W02409. 28. Merkey BV, Lardon LA, Seoane JM, Kreft JU, Smets BF (2011) Growth dependence of 55. Massoudieh A, Mathew A, Lambertini E, Nelson K, Ginn T (2007) Horizontal gene conjugation explains limited plasmid invasion in biofilms: An individual-based mod- transfer on surfaces in natural porous media: Conjugation and kinetics. Vadose Zone J elling study. Environ Microbiol 13:2435–2452. 6:306–315.

9796 | www.pnas.org/cgi/doi/10.1073/pnas.1808274115 Tecon et al. Downloaded by guest on September 29, 2021