Cell-to-cell bacterial interactions promoted by drierconditions on soil surfacesRobin 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 andecological processes in soil and other environments. Under mostconditions, natural soils are unsaturated where the fragmentedaqueous habitats and thin liquid films confine bacterial cells withinsmall volumes and close proximity for prolonged periods. We reporteffects of a range of hydration conditions on bacterial cell-levelinteractions that are marked by plasmid transfer between donorand recipient cells within populations of the soil bacterium Pseudo-monas putida. Using hydration-controlled sand microcosms, wedemonstrate that the frequency of cell-to-cell contacts under pre-scribed hydration increases with lowering water potential values(i.e., under drier conditions where the aqueous phase shrinks andfragments). These observations were supported using a mechanisticindividual-based model for linking macroscopic soil water potentialto microscopic distribution of liquid phase and explicit bacterial cellinteractions in a simplified porous medium. Model results are ingood agreement with observations and inspire confidence in theunderlying mechanisms. The study highlights important physicalfactors that control short-range bacterial cell interactions in soiland on surfaces, specifically, the central role of the aqueous phasein mediating bacterial interactions and conditions that promote ge-netic information transfer in support of soil microbial diversity.

conjugation | soil physics | vadose zone | Pseudomonas putida | HGT

Bacterial cell-to-cell interactions sustain key evolutionary andecological processes in all environments, including horizon-

tal gene transfer mediated by conjugative pili (1, 2), nutrientcross-feeding (e.g., in syntrophy; ref. 3), and chemical signalingthat shapes social behaviors (4). To interact, microorganismshave to remain in direct physical contact or within a range per-mitting effective molecular exchanges by diffusion, i.e., mostinteractions occur at the scale of individual microbes (5). Ulti-mately, cumulative cell-to-cell exchanges determine the overallbacterial activity in a given habitat, affecting large-scale fluxesand, hence, impacting global ecosystem processes (6).Bacteria that inhabit natural porous media such as soil expe-

rience life in a complex 3D pore network and on rough surfacearchitecture, with heterogeneous nutrient resources and frag-mented aqueous niches that limit their distribution, dispersion,and contact with neighbors, but can locally increase cell densityand proximity (7–11). Evidence suggests that (i) the distributionof microorganisms in soil is highly patchy, with nonrandom mi-crohabitats colonized by single cells or microcolonies (12–15),and that (ii) although soil microbial density is high in comparisonwith other habitats, cells occupy no more than a few percent ofthe pore spaces of an average soil and only a small fraction(<1%) of the available soil surfaces (9, 16, 17). This spatialcontext—together with microbial growth and motility (18)—controls the probability for a bacterial cell to encounter anothercell, and therefore to interact, in soil. Based on a detailedanalysis of bacterial distributions in hundreds of soil thin sectionsand on statistical modeling, Raynaud and Nunan (19) haveconcluded that the potential for cell-to-cell interactions in soil isrelatively limited, both in terms of the number of interacting cellsand of the number of different species to interact with. The vi-cinity of a soil bacterium typically harbors few neighbors (10 to

100 cells within a radius of 20 μm). For certain high cell density“hotspots,” e.g., in the rhizosphere (19), the number of neighborscan increase significantly. Although the distance to the nearestneighboring cell can be relatively small on average (∼10 μm indensely populated topsoils), it is highly spatially variable, and,since soil bacterial distributions show a high degree of clustering,colonized microsites are often spatially isolated (19). To sum up,the level of bacterial cell-to-cell interactions in soil may be farmore limited than would appear from the high cell density valuescommonly reported (109–1010 cells·g−1) (20) due to the highsurface area per volume of soil and the clustered spatial distri-bution of soil bacteria. In this background of limited opportu-nities for cell-to-cell interactions, additional environmentalfactors regulate the frequency and duration of cell-to-cell en-counters in soil. Developing a better mechanistic understandingof how key factors affect encounters among bacterial cells isimportant for revealing basic principles that govern soil micro-bial community diversity, dynamics, and functioning.In this study, we hypothesize that the probability of bacterial

cell-to-cell encounters and interactions on hydrated rough sur-faces such as found in soil is modified by soil properties andhydration conditions. We thus seek to link macroscopic variablessuch as the soil water potential, soil pore spaces, and internalsurface area to biological interactions that take place in aqueousmicrohabitats. Specifically, we propose that cell-to-cell bacterial

Significance

Despite a high number of microbial cells and species present insmall volumes of soil, detailed observations suggest that mostbacteria interact with only a few other individuals. These inter-actions between cells are crucial to many soil processes includingimparting genetic traits (e.g., antibiotic resistance) and microbialevolution, yet our understanding of the controlling environmentalfactors remains sketchy. Based on evidence from experiments insoil microcosms and results of a mathematical model, we dem-onstrate that a ubiquitous physical factor such as fragmentationof the aqueous phase commonly found in unsaturated soils af-fects the ranges and frequency of cell-to-cell bacterial interactions.Our findings thus help reveal some of the basic principles thatcontrol microbial life and diversity in soil environments.

Author contributions: R.T., A.E., H.K., S.E.L., and D.O. designed research; A.E. designed themathematical model; R.T., A.E., H.K., and S.E.L. performed research; R.T., A.E., H.K., andS.E.L. analyzed data; and R.T., A.E., and D.O. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

Published under the PNAS license.

Data deposition: The Modeling data and code have been deposited in the ETH ResearchCollection, https://www.research-collection.ethz.ch/handle/20.500.11850/284650 (DOI: 10.3929/ethz-b-000284650).1To whom correspondence should be addressed. Email: robin.tecon@usys.ethz.ch.2Present address: Ralph M. Parsons Laboratory for Environmental Science and Engineer-ing, Department of Civil and Environmental Engineering, Massachusetts Institute ofTechnology, Cambridge, MA 02139.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1808274115/-/DCSupplemental.

Published online September 12, 2018.

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interactions could be promoted under hydration conditions thatlocally increase the likelihood and duration of cell encounterswhile limiting cell dispersal. To systematically investigate thisquestion, we have used the exchange of a conjugative plasmid asa proxy for close encounters between bacterial cells, with the soilbacterium Pseudomonas putida (21) selected as a donor andrecipient of a broad-host range plasmid isolated from soil envi-ronments (Fig. 1). Sand microcosms were used as simple andwell-defined porous environments for assessing conjugationevents as function of hydration conditions, as determined by aprescribed matric potential (Fig. 2 and SI Appendix, Supple-mentary Methods for details). In unsaturated soil, the matricpotential results from capillary and adsorptive interactions thatretain water in pores and within roughness elements (22, 23). Thematric potential is often expressed as a negative pressure (relativeto atmospheric pressure): where zero value marks complete watersaturation, while progressively negative values correspond to drierconditions (22, 23). We use the simple experimental system (Figs.1 and 2) to directly study the relation between a biological cell-to-cell interaction (bacterial conjugation) and a physical parameter(matric potential) relevant to soil. In addition to experiments,mathematical modeling provides a means for generalization andenables evaluation of microscopic mechanisms that are inher-ently difficult to observe. Models of plasmid transfer on surfaceshave 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) haveevolved and permit mechanistic study of cell-to-cell interactions atrelevant microscales, considering physical and geometrical com-plexity found in natural habitats. Here, we have used an IBMthat explicitly integrates salient physical properties of soil and

terrestrial habitats (29–31), such as the distribution of the aqueousphase held under capillary forces on model rough surface. Bothexperiments and simulations show that the fragmentation of theaqueous phase induced by drier conditions led locally to higherrates of cell encounters and increased the number of direct cellinteractions (plasmid transfer). Overall, these results permit us todelineate a causal link between macroscopic variables (matricpotential) and the probability of bacterial interactions that takeplace at the microscale.

ResultsEffects of Hydration Conditions on Bacterial Interactions in ControlledEnvironments. To study the frequency of cell-to-cell contacts, weused the soil bacterium P. putida as the donor and recipient of aconjugative plasmid. We specifically used a plasmid that trans-fers best on surfaces (Methods), showing high rates of transfer onagar plates but very poor rates in liquid cultures (Fig. 1). Asuspension of recipient (R) and donor cells (D), mixed in a ratioof 10:1 (R:D), served to inoculate quartz sand microcosms (Fig.2) that were kept at constant temperature and matric potentialvalues for the duration of the experiment (20 h). Hydrationconditions in the microcosm ranged from relatively wet (matricpotential value of −1.2 kPa) to relatively dry (−6.5 kPa), repre-senting a wide range of saturation levels within such coarse po-rous medium (SI Appendix, Figs. S1 and S2). Fig. 3A shows theresulting number of recipient, donor, and transconjugant cells(i.e., cells that have acquired the plasmid after cell-to-cell in-teraction with a donor) that were isolated from the microcosmsand enumerated at the end of the incubation period. Bacteriasurvived and grew in all microcosms, with an average populationincrease of ∼13-fold (corresponding to an average of three tofour cell doublings during 20 h). Data showed no significantdifference in total population sizes observed across the differentmatric potentials, although we noted a slight tendency towardless cell doublings with lower matric potential values (SI Ap-pendix, Fig. S3). Therefore, the relatively limited range of matricpotential values used (approximately −1 to −7 kPa) correspondsto relatively wet conditions in most soils (23) and conducive tobacterial growth, which confirmed that changes in transfer fre-quency were mainly due to variations in constraints imposed byaqueous distribution in the microcosms. Transconjugants weredetected in all microcosms and their absolute numbers increasedwith decreasing matric potential values (Fig. 3A), although thedifference was significant only when comparing results fromthe lowest and highest matric potential values (P = 0.03 with a

Fig. 1. Bacterial conjugation to study cell-to-cell interactions. (A) Illustration ofthe conjugation process (star) on a surface between donor cells (pink) and re-cipient cells (orange), which requires direct physical contact and results in plasmidtransfer to recipients, which become transconjugants (green) over time. In thissimplified sketch, all contacts between donor and recipient cells lead to conju-gation; however, in reality, various physical and biological factors may lowerplasmid transfer efficiency. (B) Fluorescence micrograph shows an experimentwith P. putida donor and transconjugant bacterial cells immobilized on an agarsurface, respectively, shown in pseudocolors magenta and cyan. Recipient cellsare not fluorescent and, hence, not visible. (C) Control for conjugation efficiencyon solid homogeneous surfaces (0.1× TSB agar plates) and in liquid environmentwithout shaking (5 mL of 0.1× TSB). Initial R:D ratio was 10:1. After 20 h of in-cubation at 25 °C, the cells were diluted and plated on selective agar mediafollowed by cell counts (measured as colony-forming units or cfu; see SI Appen-dix, Supplementary Methods for details). Individual results from triplicate culturesare shown. Transconjugants were only detected in one of the liquid replicates.

Fig. 2. Sand microcosms with controlled hydration conditions. (A) Photo-graph shows an assembled system, with microcosms connected to a liquidmedium reservoir (0.1× TSB). The height of the liquid column (h) prescribes afixed suction to the sand phase via the ceramic plate, which mimics the ef-fects of matric potential in porous media (e.g., h = 10-cm produces a watermatric potential of ∼−1 kPa). Inset shows stereomicroscope image of thequartz sand layer. (B) Illustration of the microcosm unit, with the quartz sandgrains (0.5 g of dry sand, ∼1 mm thick) in contact with the saturated ceramicplate. Up to six units were connected to a single reservoir bottle.

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one-tailed t test). Common metrics of plasmid transfer efficiencyconfirmed that conjugation rates increased with lower matricpotential values in the microcosms by about one order of mag-nitude (SI Appendix, Table S1). We were able to visualize someof the donors and transconjugants in the sand environment at themicroscale, and the observations confirmed the occurrence of directcontacts between cells that newly acquired the conjugative plasmid(transconjugants) and donor cells growing as microcolonies (Fig.3B). Fig. 4 illustrates a conceptual view of the cell probability ofinteraction in various environments, including the role played bymatric potential in porous medium. We developed a probabilisticmodel for cell-to-cell interactions on hydrated surfaces that relieson simple spatial statistics (32) and that integrates changes in hy-dration conditions (SI Appendix, Fig. S4); however, this simplemodel was not sufficient to faithfully capture the effects of hydra-tion on conjugation rates.

Modeling of Aqueous Fragmentation, Cell Encounters, and Conjugation.We developed a mechanistic modeling framework for systematicevaluation of bacterial conjugation in soil-like surfaces under dif-ferent hydration conditions (see Methods for details). The mainassumption in the context of the study is that the spatial physicalenvironment (pore geometry and aqueous connectivity) ultimatelycontrols the rate of cell-to-cell encounters necessary for bacterialconjugation. In the model, bacteria were represented as individualagents that disperse by flagellated motion within water films, growand divide, or die, depending on the local conditions that they ex-perience. These agents populated an idealized rough surface madeof connected bonds that retain liquid by capillary forces and ad-sorption depending on their geometry and on the matric potentialprescribed to the system (29–31). In contrast to nearly saturatedconditions, lower matric potential values (i.e., drier conditions)resulted in numerous fragmented aquatic domains forming spatiallyisolated bacterial subpopulations (Fig. 5A). As a result, conjugationevents (and the resulting transconjugant cells) tended to localize inspatial hotspots where (i) water was retained and (ii) both donorand recipient cells were present (Fig. 5A). Model estimations ofconjugation rates were consistent with the experimental observa-tions and showed that the number of transconjugants increasedunder lower hydration conditions (Fig. 5B). This pattern was con-sistent across a range of cell velocity; however, an imposed slowerspeed produced results that better matched the empirical data(Fig. 5B). The simulations were conducted with similar initial celldensities and ratios (10:1, R:D) as in the experiments. Simulationresults showed the effect of increased cell motility on enhancing

conjugation process by promoting cell-to-cell encounters and,therefore, increasing the number of transconjugant cells. How-ever, when hydration conditions approached saturation, thebenefit of enhanced motility was counterbalanced by largerspatial domains visited by cells with shorter close encounterdurations (resembling conditions in bulk liquid), resulting in adecrease in conjugation rates.Individual-based modeling permits direct links between spe-

cific physical processes and the probability of cell-to-cell inter-actions. In particular, the model revealed how, all else beingequal, matric potential controls both the size and abundance ofthe microbial aqueous habitats (Fig. 6A), i.e., the aqueous frag-mentation of the system. The level of aqueous phase fragmen-tation at a given matric potential may be used to estimate theprobability of donors and recipients cooccurrence in a givenaqueous cluster. This probability increases with cell density andfor wetter conditions (Fig. 6B). Prerequisite for interactions suchas conjugation events is a donor-to-recipient encounter. We thusused the model to quantify in detail the effects of aqueous phasefragmentation (driven by hydration conditions and surface geo-metrical features) on the number of cell encounters. Fig. 6Cdetails the encounter times as a function of the largest aqueouscluster size (representing the fragmentation state of the net-work). In these simulations, individual donor and recipient cellsare tracked and their number of encounters is accumulated overtime. Results indicate a substantial increase in the number ofencounters as the aqueous phase became more fragmented (Fig.6C). The simulation results only considered donor and recipi-ent cells that experienced at least one encounter after the totalsimulation time (12 h). As shown in Fig. 6C and SI Appendix, Fig.S5, the results indicate that for connected networks (wet) wherecells swim freely, most encounters between donors and recipi-ents last only a small fraction of the total simulation time,while in a fragmented environment (drier conditions), more and

Fig. 3. Bacterial cell-to-cell interactions in sand microcosms. (A) Sand micro-cosms with controlled conditions (Fig. 2) were inoculated with ∼1 × 107 cfu of amixed suspension of P. putida recipient and donor cells (R:D ratio was 10:1). After20 h of incubation at 25 °C, the entire sand fraction was harvested to count thefinal number of recipients, donors, and transconjugants. Individual and meanresults from triplicate microcosms are shown. (B) Overlay fluorescence micro-graphs show donor cells (magenta) and transconjugants (cyan) observed after45 h of incubation at 25 °C in a sand layer. Sand particles were kept at a matricpotential of −3.6 kPa. Donors are observed as single cells or as microcolonies, andtransconjugants are only seen in direct contact to donors (Inset). Recipient cellsand sand particles are present but not visible in the fluorescence channels.

Fig. 4. Conceptual illustration of physical factors influencing cell interactions.(A) In liquid culture, local cell density fluctuates due to cell motility, and do-nors fail to encounter recipients for a sufficient amount of time. On agarsurfaces, cells are immobilized but can spread by cell division. At high celldensity, chances become high that a donor comes to contact with a recipientand conjugates. (B) On hydrated rough surfaces (e.g., in sand microcosms), theconditions are somewhat intermediary. As the rough surface dries (lowermatric potential values leading to unsaturated conditions), the aqueous do-main becomes fragmented, the cells are partially immobilized, and theprobability of cell-to-cell interaction increases.

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longer-lasting encounters are in general observed, which explainswhy more conjugation events are measured under drier condi-tions. This habitat fragmentation and spatial confinement effectprogresses until the system becomes so dry that the number andsize of aqueous clusters rapidly diminishes, leading to the declinein the total number of cell-to-cell interactions (at matric potentialvalues lower than −20 kPa, see SI Appendix, Fig. S5). Consistentwith cell-to-cell encounter time, the number of conjugation eventswas affected by aqueous phase fragmentation, cell density, anddecrease in the ratio of donor to recipient cells (SI Appendix, Fig.S6). Finally, an exponential relationship between the cell densityand the number of conjugation events was observed (SI Appendix,Fig. S6).

DiscussionWe have used bacterial conjugation in P. putida (Fig. 1) as aproxy for cell-to-cell interaction in hydration-controlled sandmicrocosms (Fig. 2), and the results supported our hypothesisthat fragmentation of the aquatic microbial habitats induced bylower water potential could locally promote encounters andinteractions of bacterial cells (Fig. 3 and SI Appendix, Fig. S7).These findings go against intuition that aqueous phase frag-mentation would limit opportunities for gene transfer in soil. Anumerical individual-based model of bacterial conjugation withrealistic representation of the physical domain and aqueousphase distribution corroborated these experimental results(Fig. 5) and offered mechanistic explanation based on thedistribution of water content in the system (Fig. 6). The modelconfirms the intuitive expectation that beyond a certain de-hydration threshold, most cell interactions cease due to van-ishing aqueous habitats (SI Appendix, Fig. S5). Experimentsand simulations suggest two main effects of matric potentialreduction on increased conjugation rate. First, motile bacteria(such as P. putida cells) are slowed down or even immobilizedwithin thin liquid films at low matric potential. This increasesthe average duration of cell-to-cell contacts, and it has beenshown experimentally that bacterial swimming and mobilityreduce the rate of plasmid transfer (33). Second, lowering

matric potential in the system increases the travel time re-quired for cell dispersal to a given distance (30) and constrainsoverall cells’ maximal dispersal range (34). (Incidentally, theseconstraints also apply to nonmotile bacteria, although theireffects are less marked than with flagellated cells.) The bio-physical processes described above are directly linked to thefragmentation of the aqueous domain, as shown by the individual-based model (Fig. 6). The connectivity of the retained aqueousphase (which underlies aqueous fragmentation) is defined hereoperationally by the minimum liquid film thickness that allowsthe passage of a bacterial cell of a given diameter. Lowering thematric 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 micrositeswith a local higher probability of cell-to-cell contacts, or “pocketsof interactions” to use the words of Raynaud and Nunan (19).Because most soils remain under unsaturated conditions mostof the time (11), we argue that the fragmentation of the soilaqueous phase and its associated constraints for microorgan-isms may be a key factor in explaining soil bacterial diversity.Moreover, the matric potential values used in our study werecloser to water saturation than values commonly measured insoil, with the so-called field capacity often ranging between −10and −33 kPa [field capacity corresponds to the water contentretained in soil once internal drainage has ceased (23), for ex-ample a few days after rainfall]. The presence of multiple con-current soil microhabitats in which species (temporarily) coexistand genetically interact has profound consequences for microbialevolution in the long term, as fragmented aqueous habitats mayrepresent a substantial barrier to gene flow between bacterialpopulations (35, 36). However, horizontally transferred genes(e.g., via a plasmid) are likely more stable in small and isolatedbacterial populations due to reduced competition. Finally, spa-tially and genetically isolated bacterial communities found in anyvolume of soil, which theoretically harbor low intracommunitybut high intercommunity diversity, could contribute to the un-equalled diversity observed at all scales, a hypothesis recentlyproposed by Rillig et al. (35) (albeit focusing on soil aggregates).Overall, our modeling and experimental findings suggest a sim-ple physical mechanism—aqueous phase fragmentation andassociated reduction of connectivity—that directly influencesthe occurrence of bacterial encounters, interactions, andgenetic exchanges in unsaturated habitats such as found inmost soils.The results permit an important distinction between the like-

lihood of cell coexistence and that of cell interaction within aconnected aqueous cluster. Simulations have shown that matricpotential (a macroscopic soil hydration state) and cell densityjointly control the probability of cooccurrence of two individualsof different types (e.g., strains, species) within an aqueous cluster(Fig. 6B), a prediction supported by previous modeling studies(37, 38). However, if cooccurrence of cell types is a prerequisitefor cell–cell interaction, it does not represent evidence for short-range contact, because two cells can coexist in an aqueous clusterwhile still being spatially separated by hundreds of microns. Forexample, in our experiments, conjugation was clearly associatedwith cell-to-cell physical contact (Fig. 3B), which was supportedby previous studies with P. putida showing that the vast majorityof plasmid transfer with rigid pili occur via direct contact (<1 μmdonor-recipient distance) (39). In that context, we should notassume direct interaction between species based solely on theircooccurrence in soil volumes typically sampled at macroscopicscales (>cubic centimeters) (7, 40). Such spatial considerationsare essential for better representation of the behavior of complexmicrobial communities (41, 42), and enabling discrimination be-tween “potential” species interactions (based on cooccurrence in asample) and “realized” interactions. We note that, although wemeasured genetic interactions (plasmid transfer) in this study,the conclusions may be applicable to metabolic or signaling

Fig. 5. Individual-based modeling of conjugation on hydrated surfaces. (A)Exemplary simulated spatial patterns of bacterial distribution in hydrated porenetworks under almost water saturated (−0.2 kPa) or relatively dry (−8.0 kPa)hydration conditions. Images are close-up views of the model representation.Water retained in the bonds of the pore network is represented with shadesof blue coloration. Each bacterium is an individual agent (colored circle) whosephysiological behavior is influenced by its local physical environment. Cells canonly move in and occupy bonds that contain water. For this reason, localspatial hotspots of conjugation are observed under drier conditions. (B) Sim-ulated number of recipients, donors, and transconjugants per unit volume andcomparison with experimental data (Fig. 3). Simulation values are averagescalculated from 15 realizations (five replicates with three independent net-works). Error bars show one SD. Simulations were conducted for two cellmotilities (0.1 and 1 μm/s). For the sake of comparison, the number of cells wasrepresented per unit volume of the experimental and modeling systems.

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interactions, as they most often rely on solute diffusion overmicroscopic distances (5, 43, 44).Admittedly, we did not explore here the wide variety of known

conjugative plasmids (45) and bacterial hosts (46), nor otherfactors that could influence the rate of bacterial conjugation,such as properties of soil surfaces, pH and oxygen levels (47),the proximity of plant roots (48), the presence of microbiota(49), or the presence of fungal hyphae (33). Any effect of thewater potential on conjugation rates in soil will thus likely re-main part of a mosaic of effects that stem from the above-mentioned factors and which lies beyond the scope of thisstudy. Instead, we highlighted the role of the physical habitat—especially the dynamic aqueous phase—as a “gatekeeper” ofbacterial interactions, enabling or preventing genetic and met-abolic exchanges among and between communities in soil. Suchsimple physical processes should not be overlooked in the dis-cussion of community interactions in the microbial world asthey can play an overarching role in the realization of biologicalfunctions.

MethodsBacterial Strains, Plasmid, and Culture Conditions. We used P. putida KT2440as recipient strain and P. putida KT2440::lacIq-pLpp-mCherry-KmR (46) asdonor strain of the cryptic broad-host range plasmid pIPO2tet::Plac::gfp (50),which encodes resistance to tetracycline. This plasmid was isolated fromthe rhizosphere (51), and it shows high rate of transfer on surfaces andrates of transfer lower by several orders of magnitude in liquid cultures(Fig. 1), which is due to the type of pili (short and rigid) encoded by pIPO2and used by P. putida to exchange the plasmid (52). The donor constitu-tively expresses the mCherry fluorescent protein as well as the LacIq re-pressor of the Plac promoter, which prevents expression of GFP from thepIPO2tet::Plac::gfp plasmid in the donor cell. Since the recipient strain lacksthe lacIq gene, GFP can be expressed in transconjugants (53) (Fig. 1B). Bothstrains were routinely grown on tryptic soy broth (TSB) (VWR International)at 30 °C with shaking at 280 rpm or on TSB agar plates at 30 °C. Tetracycline(15 μg/mL) was added in cultures of the donor strain to ensure plasmidmaintenance.

Conjugation Experiments in Sand Microcosms. A suspension of recipients anddonors with a final ratio of 10:1 (R:D) based on optical density measurements(OD600) was added (100 μL with ∼107 cfu) to the saturated microcosms. Afixed matric potential was prescribed (Fig. 2 and SI Appendix, Supplemen-tary Methods for details). After equilibration, microcosms were sealed withparafilm (to prevent evaporation) and incubated at constant temperature

(25 °C) for 20 h. Then, the entire sand phase from each microcosm was collectedby pipetting and transferred to 15-mL centrifugation tubes containing 10 mLof PBS. To detach cells from the sand particles, tubes were vortexed at max-imum speed for 10 s, followed by 2 min in an ultrasonic water bath (Branson5800; Branson Ultrasonics Corporation). Various dilutions were plated onTSB agar plates (with or without tetracycline at 15 μg/mL) to enumerateviable bacteria as cfu, and GFP and mCherry fluorescence was used to dis-criminate between transconjugants and donors (SI Appendix, SupplementaryMethods). Visualization of bacterial conjugation at the microscale was per-formed using a slightly modified microcosm setup that permitted closeraccess to the sand surface with the microscope objective (SI Appendix,Supplementary Methods).

Individual-Based Modeling of Bacterial Conjugation on Idealized HydratedSurfaces. The mathematical model abstracts the natural soil structure into2D regular pore networks consisting of v-shaped channels (30, 54). Thenetwork is composed of 40 × 40 sites that together simulate a physicaldomain of ∼3 mm × 3 mm. The structural heterogeneity of soil is imple-mented in the network model by varying the angularity of v-shaped bondsthrough a statistical probability distribution (29–31). The aqueous volumeretained in a network bond is function of matric potential and bond an-gular geometry (29). Neighboring network bonds are defined as “con-nected” (and hence grouped in a “connected aqueous cluster”) when thethickness of the liquid films retained in the bonds are above a thresholdvalue sufficient to sustain bacterial flagellar motility (30). Therefore, in ourmodel “connectivity” is defined operationally from the subjective view-point of a bacterial rod cell of a given width. (This means that thin residualliquid films existing between “disconnected” aqueous clusters may stillallow for nutrients diffusion.) The pore network environment is combinedwith an agent-based model that represents the behavior of individualbacteria in soil. Bacterial growth and substrate uptake rates at the single-cell level are based on Monod-type kinetics (see SI Appendix, Table S2 forthe growth parameters used in the model). Nutrients transport and uptakeare modeled based on Fick’s law of diffusion and mass conservations forthe whole network. A 1D reaction-diffusion model is solved for each net-work bond (31). Bacterial cell motility is often restricted in water-limitedsoil environments where viscous drag in thin water films and capillarypinning at the air-water interfaces are enhanced. To account for thisphenomenon, we have used a previously developed model that balancesself-propulsion forces of flagellated cells (FM) in bulk water with physicalrestrictions (cell-wall hydrodynamic interactions Fλ and capillary pinningforce FCa) as a function of water film thickness δ. A proportional relation-ship is considered between the cell velocity within a single bond V in thenetwork and its velocity in bulk water, V0, as follows:

Fig. 6. Modeling the effects of matric potential on aqueous fragmentation and cell encounters. (A) Model hydrated porous networks were used to determinethe 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 ofthematric potential prescribed to the network. Here, a (connected) aqueous cluster is defined by the capacity of a bacterial cell to travel across the cluster throughliquid films supporting flagellar motility. Mean values from 10 model realizations are shown. Error bars are one SD. (B) Individual-based model simulations showthe 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:Dratio 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 cellsencounter 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 simulationtime 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 timedistributions. Insets show exemplary results for the number and duration of donor-to-recipient encounters under relatively wet (−0.5 kPa, connected) and rel-atively 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.)

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VðψÞ=V0  FM − Fλðδðψ, αÞÞ− FCaðδðψ, αÞÞ

FM.

The water film thickness δ is quantified as a function of matric potential ψand bond angularity α (29, 30). Note that the simulations are performedassuming entirely random cell motility without chemotaxis. To mimic thenutrient flux in the experimental setup (porous surfaces), nutrient flux in themodel is assumed to be supplied from the bottom of the network directly toindividual bonds, and nutrients are distributed uniformly in the network. Inthe current modeling framework, plasmid conjugation from a donor to arecipient bacterium takes place if the encounter lasts more than a minimumrequired time. We assumed that the conjugation occurs if the short distancebetween a donor and recipient bacterium (less than 2 μm in our simulations)persists long enough (called “conjugation duration”). The probability ofconjugation P increases as a function of encounter duration t, and it is as-sumed to follow Poisson probability distribution

8<:

PðtÞ= 0 t < tmin

PðtÞ= λðt−tminÞ expð−λÞðt − tminÞ! t ≥ tmin

,

where λ is the sample mean of encounter duration t, and tmin is the mini-mum encounter time required to observe first conjugation. The mini-mum encounter time is considered to be 5 min based on previousobservations (55).

ACKNOWLEDGMENTS. We thank Arnaud Dechesne (Technical University ofDenmark) for the gift of the conjugative strains and Andreas Papritz (SwissFederal Institute of Technology Zürich) for his help on spatial statistics. Fi-nancial support for this work came from an Advanced Grant (to D.O.) by theEuropean Research Council (ERC-320499-“SoilLife”) and from the RTD Sys-temsX.ch project “MicroscapesX”. A.E. acknowledges funding from SwissNational Science Foundation Grant P2EZP2 175128.

1. Clewell DB (2013) Bacterial Conjugation (Springer, Berlin).2. Soucy SM, Huang J, Gogarten JP (2015) Horizontal gene transfer: Building the web of

life. Nat Rev Genet 16:472–482.3. Morris BEL, Henneberger R, Huber H, Moissl-Eichinger C (2013) Microbial syntrophy:

Interaction for the common good. FEMS Microbiol Rev 37:384–406.4. Asfahl KL, Schuster M (2017) Social interactions in bacterial cell-cell signaling. FEMS

Microbiol Rev 41:92–107.5. Franklin RB, Mills AL (2007) The Spatial Distribution of Microbes in the Environment

(Springer, Berlin).6. Jansson JK, Hofmockel KS (2018) The soil microbiome-from metagenomics to meta-

phenomics. Curr Opin Microbiol 43:162–168.7. Dechesne A, Pallud C, Grundmann GL (2007) Spatial distribution of bacteria at the

microscale in soil. The Spatial Distribution of Microbes in the Environment, edsFranklin RB, Mills AL (Springer, Berlin), pp 87–107.

8. Or D, Smets BF, Wraith JM, Dechesne A, Friedman SP (2007) Physical constraints af-fecting bacterial habitats and activity in unsaturated porous media–A review. AdvWater Resour 30:1505–1527.

9. Young IM, Crawford JW, Nunan N, Otten W, Spiers A (2008) Microbial distribution insoils: Physics and scaling. Advances in Agronomy, ed Donald LS (Academic, Cam-bridge, MA), pp 81–121.

10. Vos M, Wolf AB, Jennings SJ, Kowalchuk GA (2013) Micro-scale determinants ofbacterial diversity in soil. FEMS Microbiol Rev 37:936–954.

11. Tecon R, Or D (2017) Biophysical processes supporting the diversity of microbial life insoil. FEMS Microbiol Rev 41:599–623.

12. Foster RC (1988) Microenvironments of soil microorganisms. Biol Fertil Soils 6:189–203.

13. Grundmann GL, Debouzie D (2000) Geostatistical analysis of the distribution ofNH(4)(+) and NO(2)(−)-oxidizing bacteria and serotypes at the millimeter scalealong a soil transect. FEMS Microbiol Ecol 34:57–62.

14. Nunan N, et al. (2001) Quantification of the in situ distribution of soil bacteria bylarge-scale imaging of thin sections of undisturbed soil. FEMSMicrobiol Ecol 37:67–77.

15. Nunan N, Wu K, Young IM, Crawford JW, Ritz K (2003) Spatial distribution of bacterialcommunities and their relationships with the micro-architecture of soil. FEMSMicrobiol Ecol 44:203–215.

16. Hissett R, Gray T (1976) Microsites and time changes in soil microbe ecology. The Roleof Terrestrial and Aquatic Organisms in Decomposition Processes, eds Anderson JM,MacFayden A (Oxford Univ Press, London), pp 23–39.

17. Young I, Bengough A (2018) The search for the meaning of life in soil: An opinion. EurJ Soil Sci 69:31–38.

18. Dusenbery DB (2009) Living at Micro Scale: The Unexpected Physics of Being Small(Harvard Univ Press, Cambridge, MA).

19. Raynaud X, Nunan N (2014) Spatial ecology of bacteria at the microscale in soil. PLoSOne 9:e87217.

20. Bardgett RD, van der Putten WH (2014) Belowground biodiversity and ecosystemfunctioning. Nature 515:505–511.

21. Nelson KE, et al. (2002) Complete genome sequence and comparative analysis of themetabolically versatile Pseudomonas putida KT2440. Environ Microbiol 4:799–808.

22. Papendick R, Campbell G (1981) Theory and measurement of water potential. WaterPotential Relations Soil Microbiol 3:1–22.

23. Hillel D (2003) Introduction to Environmental Soil Physics (Academic, Cambridge, MA).24. Lagido C, Wilson IJ, Glover LA, Prosser JI (2003) A model for bacterial conjugal gene

transfer on solid surfaces. FEMS Microbiol Ecol 44:67–78.25. Prosser J, Lagido C, Glover L (2000) Gene Transfer in Microbial Biofilms. Microbial

Biosystems: New Frontiers (Atlantic Canada Society for Microbial Ecology, Halifax,Canada), pp 925–930.

26. Fox RE, Zhong X, Krone SM, Top EM (2008) Spatial structure and nutrients promoteinvasion of IncP-1 plasmids in bacterial populations. ISME J 2:1024–1039.

27. Krone SM, Lu R, Fox R, Suzuki H, Top EM (2007) Modelling the spatial dynamics ofplasmid transfer and persistence. Microbiology 153:2803–2816.

28. Merkey BV, Lardon LA, Seoane JM, Kreft JU, Smets BF (2011) Growth dependence ofconjugation explains limited plasmid invasion in biofilms: An individual-based mod-elling study. Environ Microbiol 13:2435–2452.

29. Wang G, Or D (2010) Aqueous films limit bacterial cell motility and colony expansionon partially saturated rough surfaces. Environ Microbiol 12:1363–1373.

30. Ebrahimi AN, Or D (2014) Microbial dispersal in unsaturated porous media: Charac-teristics of motile bacterial cell motions in unsaturated angular pore networks.WaterResour Res 50:7406–7429.

31. Ebrahimi AN, Or D (2015) Hydration and diffusion processes shape microbial com-munity organization and function in model soil aggregates. Water Resour Res 51:9804–9827.

32. Diggle PJ (2013) Statistical Analysis of Spatial and Spatio-Temporal Point Patterns(CRC, Boca Raton, FL).

33. Berthold T, et al. (2016) Mycelia as a focal point for horizontal gene transfer amongsoil bacteria. Sci Rep 6:36390.

34. Tecon R, Or D (2016) Bacterial flagellar motility on hydrated rough surfaces controlledby aqueous film thickness and connectedness. Sci Rep 6:19409.

35. Rillig MC, Muller LAH, Lehmann A (2017) Soil aggregates as massively concurrentevolutionary incubators. ISME J 11:1943–1948.

36. Cordero OX, Polz MF (2014) Explaining microbial genomic diversity in light of evo-lutionary ecology. Nat Rev Microbiol 12:263–273.

37. Wang G, Or D (2012) A hydration-based biophysical index for the onset of soil mi-crobial coexistence. Sci Rep 2:881.

38. Wang G, Or D (2013) Hydration dynamics promote bacterial coexistence on roughsurfaces. ISME J 7:395–404.

39. Seoane J, et al. (2011) An individual-based approach to explain plasmid invasion inbacterial populations. FEMS Microbiol Ecol 75:17–27.

40. Cordero OX, Datta MS (2016) Microbial interactions and community assembly atmicroscales. Curr Opin Microbiol 31:227–234.

41. Klitgord N, Segrè D (2011) Ecosystems biology of microbial metabolism. Curr OpinBiotechnol 22:541–546.

42. Zengler K, Palsson BO (2012) A road map for the development of community systems(CoSy) biology. Nat Rev Microbiol 10:366–372.

43. Gantner S, et al. (2006) In situ quantitation of the spatial scale of calling distances andpopulation density-independent N-acylhomoserine lactone-mediated communicationby rhizobacteria colonized on plant roots. FEMS Microbiol Ecol 56:188–194.

44. Tecon R, Or D (2017) Cooperation in carbon source degradation shapes spatial self-organization of microbial consortia on hydrated surfaces. Sci Rep 7:43726.

45. Heuer H, Smalla K (2012) Plasmids foster diversification and adaptation of bacterialpopulations in soil. FEMS Microbiol Rev 36:1083–1104.

46. Klümper U, et al. (2015) Broad host range plasmids can invade an unexpectedly di-verse fraction of a soil bacterial community. ISME J 9:934–945.

47. Stotzky G, Devanas MA, Zeph LR (1990) Methods for studying bacterial gene transferin soil by conjugation and transduction. Adv Appl Microbiol 35:57–169.

48. Van Elsas J, Trevors J, Starodub M (1988) Bacterial conjugation between pseudomo-nads in the rhizosphere of wheat. FEMS Microbiol Ecol 4:299–306.

49. Van Elsas JD, Govaert JM, Van Veen JA (1987) Transfer of plasmid pFT30 betweenbacilli in soil as influenced by bacterial population dynamics and soil conditions. SoilBiol Biochem 19:639–647.

50. Musovic S, Klümper U, Dechesne A, Magid J, Smets BF (2014) Long-term manureexposure increases soil bacterial community potential for plasmid uptake. EnvironMicrobiol Rep 6:125–130.

51. Tauch A, et al. (2002) The complete nucleotide sequence and environmental distri-bution of the cryptic, conjugative, broad-host-range plasmid pIPO2 isolated frombacteria of the wheat rhizosphere. Microbiology 148:1637–1653.

52. Bradley DE, Taylor DE, Cohen DR (1980) Specification of surface mating systemsamong conjugative drug resistance plasmids in Escherichia coli K-12. J Bacteriol 143:1466–1470.

53. Normander B, Christensen BB, Molin S, Kroer N (1998) Effect of bacterial distributionand activity on conjugal gene transfer on the phylloplane of the bush bean (Phaseolusvulgaris). Appl Environ Microbiol 64:1902–1909.

54. Long T, Or D (2007) Microbial growth on partially saturated rough surfaces: Simula-tions in idealized roughness networks. Water Resour Res 43:W02409.

55. Massoudieh A, Mathew A, Lambertini E, Nelson K, Ginn T (2007) Horizontal genetransfer on surfaces in natural porous media: Conjugation and kinetics. Vadose Zone J6:306–315.

9796 | www.pnas.org/cgi/doi/10.1073/pnas.1808274115 Tecon et al.

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