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Ecological drift during colonization drives within-host and between-host heterogeneity in an animal-associated symbiont

  • Jason Z. Chen ,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review & editing

    jche226@emory.edu

    Affiliation Department of Biology, Emory University, Atlanta, Georgia, United States of America

  • Zeeyong Kwong,

    Roles Data curation, Formal analysis, Investigation, Methodology

    Affiliation Laboratory of Bacteriology, National Institutes of Allergy and Infectious Diseases, Hamilton, Montana, United States of America

  • Nicole M. Gerardo,

    Roles Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Visualization, Writing – review & editing

    Affiliation Department of Biology, Emory University, Atlanta, Georgia, United States of America

  • Nic M. Vega

    Roles Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing

    Affiliations Department of Biology, Emory University, Atlanta, Georgia, United States of America, Department of Physics, Emory University, Atlanta, Georgia, United States of America

Abstract

Specialized host–microbe symbioses canonically show greater diversity than expected from simple models, both at the population level and within individual hosts. To understand how this heterogeneity arises, we utilize the squash bug, Anasa tristis, and its bacterial symbionts in the genus Caballeronia. We modulate symbiont bottleneck size and inoculum composition during colonization to demonstrate the significance of ecological drift, the noisy fluctuations in community composition due to demographic stochasticity. Consistent with predictions from the neutral theory of biodiversity, we found that ecological drift alone can account for heterogeneity in symbiont community composition between hosts, even when 2 strains are nearly genetically identical. When acting on competing strains, ecological drift can maintain symbiont genetic diversity among different hosts by stochastically determining the dominant strain within each host. Finally, ecological drift mediates heterogeneity in isogenic symbiont populations even within a single host, along a consistent gradient running the anterior-posterior axis of the symbiotic organ. Our results demonstrate that symbiont population structure across scales does not necessarily require host-mediated selection, as it can emerge as a result of ecological drift acting on both isogenic and unrelated competitors. Our findings illuminate the processes that might affect symbiont transmission, coinfection, and population structure in nature, which can drive the evolution of host–microbe symbioses and microbe–microbe interactions within host-associated microbiomes.

Introduction

A persistent paradox in the study of host–microbe symbioses is that, like microbes in natural environments, microbial symbionts exhibit enormous strain diversity [18]. This is observed even when natural selection, imposed by specialized interactions with their hosts, is expected to erode genetic variation. Different mechanisms, based on environmental selection or host variation, are typically invoked to explain the maintenance of symbiont genetic variation, often in terms of host benefit [9,10]. However, these hypotheses do not account for how host-associated consortia assemble as ecological communities, which embeds this genetic variation within patches in physical space [11,12]. This is an inherently stochastic process that generates heterogeneity [1315]. Heterogeneity in host-associated microbial communities manifests at 2 scales: as heterogeneity in colonization between hosts, and as spatial heterogeneity across tissues and organs within each host. At both scales, it is critical to understand how this heterogeneity emerges during establishment of symbiosis, which drives the evolution, ecology, and physiology of both host and microbe.

While the ecological processes that create heterogeneity during community assembly have been studied with mathematical models (e.g., [16]), validation of these models in empirical studies using natural, ecologically realistic communities, including host-associated microbial communities [1214,17], is scarce. Some of these processes are deterministic, acting on specific traits that allow or hinder establishment of a taxon in a predictable, niche-based fashion. However, community assembly is also governed by dispersal between habitats. Dispersal imparts a stochastic element on community assembly [14]: Taxa immigrate and establish in new patches in a probabilistic manner, in part because they experience transient reductions, called bottlenecks, in population size [18]. These bottlenecks intensify ecological drift (i.e., stochastic variation in community composition). Since the proposal of Hubbell’s unified neutral theory of biodiversity, the relative role of stochastic processes such as ecological drift in community assembly, compared with deterministic niche-based processes such as between-species interactions, has been a matter of continuous study [1921].

In the context of host–microbe mutualistic symbioses, hosts impose stringent ecological selection during community assembly by filtering out or sanctioning non-beneficial and pathogenic microbes [10,2225]. While this paradigm can explain the consistency with which hosts can acquire symbionts while excluding nonsymbiotic taxa (Fig 1D), it does not explain how these symbiont communities differ between individuals (Fig 1E), nor can it account for spatial structure in communities within the host. To illustrate the importance of ecological drift during the establishment of even highly specific symbioses, we employ the squash bug, Anasa tristis (Fig 1A), as a model. A. tristis is host to specific symbionts in the β-proteobacterial genus Caballeronia (previously referred to in the literature as the Burkholderia “SBE” clade [26] or the B. glathei-like clade [27]), which it requires for survival and normal development to adulthood [28]. Acquisition of Caballeronia occurs through the environment after nymphs (immature insects) molt into the second instar. Once they successfully colonize the host, symbionts are housed in hundreds of sacs called crypts, which form 2 rows running along a specific section of the midgut, called the M4 (Fig 1B). Unlike many other insect symbionts, Caballeronia can be isolated from bugs and established in pure culture in the laboratory. Because A. tristis nymphs hatch from their eggs symbiont-free, the symbiosis can be reconstituted anew every generation by feeding cultured symbionts to these nymphs in the laboratory [7,29].

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Fig 1. The squash bug, A. tristis, engages in a specialized host–microbe symbiosis with bacteria in the genus Caballeronia.

(A) A second instar squash bug (A. tristis) dissected to reveal the M4 section of the midgut, colonized with Caballeronia symbionts expressing the sfGFP fluorescent protein. (B) Inset: The fine structure of the M4, consisting of 2 rows of hundreds of sac-like crypts lining a central lumen. Crypts are colonized at high density with Caballeronia symbionts expressing the sfGFP fluorescent protein. The scale bar represents 250 μm. (C) Relative abundance of the top 20 Caballeronia 16s V3-V4 amplicon sequence variants (ASVs) within 9 bugs across 3 field localities in Georgia, Indiana, and North Carolina, with different colors representing different ASVs. The data underlying this figure can be found in S1 Data. (D) A common notion is that because hosts with obligate microbial symbionts are under uniquely strong selection to transmit and maintain particular symbiotic taxa, selection (gray) rather than drift (blue) broadly governs the outcome of community assembly in these highly specialized microbiomes. In this paradigm, communities (left and middle clusters) invariably are dominated by symbionts (green) that have higher within-host fitness than non-symbionts (brown). As a result, rare, highly divergent communities (top right) containing non-symbionts might emerge only by chance, when symbionts undergo extremely tight transmission bottlenecks (right cluster). However, this paradigm does not explain compositional heterogeneity among symbiont communities, in which competing symbiont strains may be functionally identical in host benefit and ability to colonize. (E) We posit that ecological drift plays an important role in structuring symbiont strain diversity by counteracting within-host selection in symbiont populations during colonization. As in (D), selection (gray) acts on differences in relative fitness between competing strains (magenta and green) to drive community convergence towards a mean composition (left cluster). However, the effect of selection is only apparent when symbiont populations undergo weak population bottlenecks during host colonization. Drift (blue) minimizes the impact of selection, such that even if competing symbiont strains differ substantially in within-host fitness, heterogeneity in community composition emerges as if this fitness difference is absent, i.e., neutrally (middle and right clusters).

https://doi.org/10.1371/journal.pbio.3002304.g001

Because the host relies on Caballeronia strains as its symbiotic partners, its digestive tract imposes strong selection to favor Caballeronia colonization [25,30], as in other specialized systems [22]. As a result, Caballeronia constitutes the vast majority of the microbial community within the M4 symbiotic organ, even though squash bug nymphs are exposed to diverse environmental microbes on squash fruit and plants [28]. However, this and similar bug-Caballeronia symbioses are extremely nonspecific below the genus level [31], with distantly related symbiont isolates conferring nearly the same degree of host benefit [7,32]. In accordance with this apparent lack of specificity, we observe that within-host Caballeronia communities from wild squash bug populations vary widely in their composition [7] (Fig 1C). So, beyond the coarse ecological filter that the host insect applies against nonsymbiotic taxa [25,30], little is known about the ecological processes that maintain within- and between-host diversity of this beneficial symbiont.

Here, we explore the hypothesis that both within- and between-host diversity in symbiont populations arise stochastically as a result of ecological drift during infection [14]. First, we set out to explore a range of conditions under which this pattern might emerge, incorporating neutral competition (where all cells are isogenic, and thus functionally equivalent, individuals) [20] and interspecies competition (where cells are genetically distinct, but still equally host beneficial) between symbiont strains. By experimentally manipulating transmission bottleneck size, we show that ecological drift alone can account for heterogeneity between hosts, segregating strains between hosts and decreasing the probability of coinfection. Using isogenic coinfections, we additionally demonstrate that the symbiotic organ imposes spatial heterogeneity on within-host populations, whereby separate crypts are colonized by different strains. Our results demonstrate the role of ecological drift in the assembly of a highly specialized host–microbe system and in structuring symbiont population diversity across scales.

Results

Ecological drift is sufficient to generate variation in colonization outcome

We reasoned that if ecological drift plays a role in generating heterogeneity in symbiont populations between hosts, it should generate greater and greater heterogeneity under smaller and smaller inoculum densities, which represent tightening transmission bottlenecks in our experiments. Specifically, the neutral model [33] implies that under tight bottlenecks, which shrink the effective size of a local community, colonization outcomes should be bimodal, with hosts dominated by clonal lineages, regardless of strain identity [16,19]. By contrast, when host control determines colonization, altering the inoculum size should have minimal impact on community composition across hosts. Additionally, if strong competition between symbionts determines the outcomes of colonization, individual hosts should be mono-colonized across a broad range of inoculum densities. To test this, we implemented a simple experimental design (Fig 2A), previously applied to human pathogens and legume nodule symbionts, that modulates transmission bottleneck size while maintaining the relative abundance of each strain during transmission [34,35]. To minimize the involvement of selection, we used isogenic, green- and red-fluorescently labeled isolates of C. zhejiangensis GA-OX1, a highly beneficial strain isolated previously from A. tristis [7]. Because our experiments involved only 2 competitors, we used the bimodality coefficient [14,36] to quantify heterogeneity in community composition. The bimodality coefficient is a composite measure of skewness and kurtosis, which is maximized for a distribution with equal weight at the extrema of the data. As this measure can be sensitive to small sample sizes, we also calculated the Hartigan’s dip statistic, which represents a more robust general measure of multimodality. We inoculated second instar squash bug nymphs with approximately 1:1 mixtures of GA-OX1 sfGFP with GA-OX1 RFP, diluted to produce inocula ranging from approximately 106 to 101 CFU/μL (S1A Fig). These inoculum densities are within the natural range of variation in symbiont density that hosts might encounter, whether in freshly deposited adult feces (105 to 5 × 106 CFUs/μL), on which nymphs can feed to acquire symbionts [37], or in soil, in which Caballeronia is present at lower densities alongside many other microbes [38,39].

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Fig 2. The strength of ecological drift mediates variability in the outcome of symbiont colonization.

(A) Experimental design. Symbionts previously isolated from squash bugs, made to stably express green or red fluorescent proteins (GFP or RFP), are grown individually in liquid culture. Liquid cultures are combined at a predetermined ratio, then the mixture is diluted at various concentrations in the inoculation medium such that the inoculum density (a proxy for transmission bottleneck size) varies over several orders of magnitude while retaining the relative abundance of each strain across inocula. (B) Variable colonization outcomes associated with different transmission bottleneck sizes in isogenic co-inoculation, using Caballeronia zhejiangensis GA-OX1 sfGFP and RFP. Blue X marks indicate the mean % GA-OX1 RFP associated with each inoculum treatment, ranging from 101 to 106 CFU/μL. Points represent individual nymphs, and the color of each point and its position along the y-axis represent the percent relative abundance of GA-OX1 RFP colonies among all fluorescent colonies recovered from each nymph. Magenta points represent nymphs from which only RFP colonies were recovered, green points represent nymphs from which only sfGFP colonies were recovered, and faded magenta/green colonies represent coinfected nymphs. Violin plots associated with each treatment depict the shape of the distribution in relative RFP abundance. Asterisks indicate significantly multimodal infection outcomes as determined by Hartigan’s dip test, at a significance value of p < 0.05. Below each violin plot, the success rate of colonization is indicated, as the number of nymphs that were successfully colonized with Caballeronia out of all nymphs sampled. Trials were aggregated across multiple runs. The data underlying this figure can be found in S2 Data. (C) Bimodality coefficients calculated from results in panel B. Large blue dots indicate bimodality coefficients calculated from all bugs in each treatment; boxplots indicate bimodality coefficients calculated by jackknife resampling in each treatment. The 0.555 threshold (marked with a dotted line) indicates the bimodality coefficient expected from a uniform distribution. Asterisks indicate significantly multimodal infection outcomes as indicated by Hartigan’s dip test, at a significance level of p < 0.05. The data underlying this figure can be found in S2 Data.

https://doi.org/10.1371/journal.pbio.3002304.g002

Consistent with the neutral model, under the highest inoculum densities, corresponding to the loosest bottlenecks, differences between the M4 communities of individuals are minimized, with a slight bias in favor of the sfGFP strain (Figs 2B and S1B). The slight bias towards sfGFP colonization could be due to toxic aggregation of the dTomato fluorescent protein, which has been observed in eukaryotic cells [40]. As inoculum density decreases, and thus as transmission bottlenecks tighten, individual infections become increasingly dominated by one or the other strain, causing the bimodality coefficient to increase (Figs 2C and S1C and S1 Table). Below 100 CFU/μL, individual infections are comprised of mostly either sfGFP or RFP, manifesting as a weakly but significantly bimodal outcome (bimodality coefficient = 0.677, Hartigan’s dip statistic = 0.152, p < 2.2 × 10−16) (S1 Table). Fluorescence images of whole nymphs provided qualitative confirmation of our results, with more heterogeneity observed between nymphs at lower inoculum densities (S2 Fig). Through this set of experiments, we show that ecological drift is sufficient to drive heterogeneous colonization outcomes.

Ecological drift maintains coexistence between competing strains across separate hosts

Having illustrated the action of transmission bottlenecks on a single symbiont genetic background, we next sought to understand how they would act on genetically distinct host-beneficial strains. If ecological drift has an effect even when selection can act on competitive differences between strains, we should see a similar result to our previous experiment, with bimodality increasing with tightening transmission bottlenecks. We tested C. zhejiangensis GA-OX1 alongside C. sp. nr. concitans SQ4a [28], which represent 2 lineages within the Caballeronia genus (S3 Fig) [27,41] but are nonetheless equally beneficial for host developmental time and survivorship in the laboratory [7,28]. SQ4a was previously labeled with GFPmut3 [28,42] and was additionally labeled with sfGFP and dTomato for this study using the same constructs [43] that were applied to GA-OX1 above.

First, we demonstrated that GA-OX1 and SQ4a compete under an in vitro approximation of natural conditions within the host midgut (Fig 3A). In trials where SQ4a sfGFP and RFP were grown together as liquid cultures in filter-sterilized zucchini squash extract, both strains were recovered at high densities after 24 h. On the other hand, when either SQ4a strain was grown with a counter-labeled GA-OX1, SQ4a almost always went extinct (T test, p < 0.001, n = 10). Labeled GA-OX1 strains grew to high densities regardless of whether they were growing alongside SQ4a or the counter-labeled GA-OX1. These data suggest that GA-OX1 is the superior competitor to SQ4a under these culture conditions.

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Fig 3. Ecological drift mediates heterogeneity in host infection by competing strains.

(A) Competitive interactions between 2 symbiont strains in filter-sterilized zucchini squash extract. GA-OX1 and SQ4a differentially labeled with sfGFP or RFP were cocultured with a strain with the opposite fluorescent marker, either of an isogenic background or of the other species, and then assayed after 24 h for abundance. Data are pooled across fluorophore reciprocal swaps. White boxplots represent growth with a heterospecific competitor, while gray boxplots represent growth with an isogenic competitor. The data underlying this figure can be found in S3 Data. (B) Fluorescence image of one cohort of squash bug nymphs fed a combination of SQ4a sfGFP and GA-OX1 RFP, at a combined density of approximately 5,000 CFU/μL. Green indicates the presence of SQ4a sfGFP in nymphs, and magenta indicates the presence of GA-OX1 RFP. Nymphs without fluorescence were not successfully colonized with either symbiont strain. Scale bar indicates 5 mm. (C) Variable colonization outcomes associated with different transmission bottleneck sizes in two-species co-inoculation, using C. sp. nr. concitans SQ4a GFPmut3 and C. zhejiangensis GA-OX1 RFP. Blue X marks indicate the percent GA-OX1 RFP associated with each inoculum treatment, ranging from 101 to 104 CFU/μL. Points represent individual nymphs, and the color of each point and its position along the y-axis represent the percent relative abundance of GA-OX1 RFP colonies among all fluorescent colonies recovered from each nymph. Magenta points represent nymphs from which only GA-OX1 RFP colonies were recovered, green points represent nymphs from which only SQ4a GFPmut3 colonies were recovered, and faded magenta/green points represent coinfected nymphs. Violin plots associated with each treatment depict the shape of the distribution in relative GA-OX1 RFP abundance. Below each violin plot, the success rate of colonization is indicated, as the number of nymphs that were successfully colonized with Caballeronia out of all nymphs sampled. Asterisks indicate significantly multimodal infection outcomes as determined by Hartigan’s dip test, at a significance level of p < 0.05. The data underlying this figure can be found in S4 Data. (D) Bimodality coefficients calculated from results in panel C. Large blue dots indicate bimodality coefficients calculated from all bugs in each treatment; boxplots indicate bimodality coefficients calculated by jackknife resampling in each treatment. The 0.555 threshold (marked with a dotted line) indicates the bimodality coefficient associated with a uniform distribution. Asterisks indicate significantly multimodal infection outcomes as indicated by Hartigan’s dip test, at a significance value of p < 0.05. The data underlying this figure can be found in S4 Data.

https://doi.org/10.1371/journal.pbio.3002304.g003

We next co-colonized hosts with mixtures of GA-OX1 RFP and SQ4a GFPmut3, using the same experimental design as in isogenic-strain colonization. Moderately high to low inoculum densities all resulted in strong bimodality in infection outcomes, where individual hosts were dominated either by GA-OX1 or by the competitor SQ4a (Fig 3B and 3C and S2 Table). Only at high inoculum density (≥104 CFUs) were infections biased in favor of GA-OX1 (Fig 3C). This bimodality was qualitatively reproducible across different combinations of SQ4a and GA-OX1 expressing different fluorophores from different synthetic constructs (Figs 3B and S4 and S3 and S4 Tables). Bimodality coefficients were consistently higher at high colonization densities in interspecific competition experiments than neutral competition experiments (Figs 3D, S4B, and S4D and S2S4 Tables); this is expected, as neutral competition should produce unimodal populations when colonization rates are high.

Ecological drift during colonization generates within-host spatial heterogeneity

The squash bug symbiotic organ, called the M4, contains hundreds of crypts (Fig 1A and 1B). Because each crypt is filled with its own population of symbionts, we asked whether symbiont composition might exhibit between-crypt heterogeneity within the host, consistent with previous unquantified observations from related insect-Caballeronia models [25,44]. If crypts indeed contain heterogeneous populations, we would expect crypts to contain mostly RFP- and mostly GFP-expressing symbionts, as opposed to highly similar populations composed of one or both types. We also asked if within-host heterogeneity might be sensitive to inoculum density in the same manner as between-host heterogeneity. If so, we would expect greater heterogeneity among crypts within a host when symbionts are subjected to tighter transmission bottlenecks during host colonization.

We systematically characterized within-host spatial heterogeneity by co-inoculating nymphs with 1:1 mixtures of counter-labeled GA-OX1 at approximately 106 and 102 CFU/μL, as above. Co-infected nymphs were selected by screening whole insects under fluorescence prior to dissection. By imaging freshly dissected whole guts from coinfected nymphs, we observed that the M4 does impose spatial heterogeneity on symbiont populations, with individual crypts varying in GFP and RFP intensity even at colonization with 106 symbiont CFU/μL (Fig 4A). However, there is a clear gradient in the degree of heterogeneity among crypts along the length of the M4, with anterior crypts being co-colonized and posterior crypts being singly infected (Fig 4B). We quantified this gradient by measuring the variance in RFP intensity relative to GFP along the length of the M4 (Fig 4C). Contrary to our expectations, we saw that nymphs colonized with just 102 symbiont CFU/μL also exhibited this gradient, with anterior crypts being co-colonized despite a 10,000-fold reduction in inoculum density (Fig 4B and 4C). Thus, patterns of heterogeneity within the host are consistent over 4 orders of magnitude in inoculum density. Even when microbe–microbe competition is nearly neutral, host anatomy appears to impose spatial structure on symbiont populations.

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Fig 4. The squash bug symbiotic organ (the M4) imposes spatial heterogeneity on Caballeronia populations within the host.

(A) Tilescan of the entire M4 of a representative second-instar nymph fed combined 106 CFU/μL GA-OX1 sfGFP and RFP, dissected and linearized to illustrate symbiont colonization along its length. Individual panels represent the merged GFP and RFP channels (top), only the GFP channel (middle), and only the RFP channel (bottom). In each panel, the anterior end of the M4 is oriented to the left and the posterior end is oriented to the right. The intensely magenta, spindle-shaped organ is the M4b, which is functionally distinct from the crypts that house the symbiont population. The scale bar represents 1 mm. (B) Anterior and posterior crypts from 3 nymphs fed 102 CFU/μL (left) and 3 other nymphs fed 106 CFU/μL (right) GA-OX1 sfGFP and RFP, dissected and prepared as in (A). Panels represent the merging of GFP, RFP, and DIC (differential interference contrast) channels. For each specimen, the anterior crypts are on the left and the posterior crypts are on the right. The scale bar represents 500 μm. Raw images for all specimens are available at https://doi.org/10.15139/S3/YZPBGY. (C) Ratio of normalized RFP intensity relative to normalized GFP intensity (left) and variance in this ratio within a sliding window (right) along a transect from the anterior to the posterior of the M4. Nymphs were either inoculated with 102 CFU/μL (n = 3, top) or 106 CFU/μL (n = 4, bottom), and different colored lines represent the trajectories of these values associated with each nymph. The data underlying this figure can be found in S7 Data.

https://doi.org/10.1371/journal.pbio.3002304.g004

Discussion

Previous research on a suite of closely related insect-Caballeronia symbioses has demonstrated both heterogeneity in symbiont composition and low diversity of symbiont populations within hosts [7,31,45,46]. In the present study, we reveal the processes that underlie these patterns are consistent with stochastic colonization, which results in strong ecological drift as symbionts establish in their host insects. By modulating transmission bottleneck sizes of inocula containing isogenic, nearly neutrally competing strains, we show that ecological drift alone can generate heterogeneity in colonization outcome between different hosts, consistent with the neutral theory of biodiversity [20,21]. The transmission bottlenecks in our experiments are likely to be within the range of natural variation in transmission bottleneck size through natural routes [37,38] of symbiont transmission in A. tristis and related insects [47], suggesting that our results pertain to how drift affects symbiont population structure in wild bug-Caballeronia assemblages.

Next, by manipulating bottleneck sizes of inocula containing different symbiont species, we not only experimentally demonstrate the role of ecological drift in maintaining genetic diversity but also highlight the role of between-symbiont competition. Ecological drift generates variation in founding populations between hosts, while competition drives homogeneity in symbiont populations during subsequent proliferation inside a single host. The effect is bimodality in symbiont colonization, even when transmission bottlenecks are loose. Our results mirror findings from similar studies using plant communities, where competitive asymmetries between species also exaggerate the effect of ecological drift [19], and call attention to the role that inter-symbiont competition might play during the early stages of colonization in other host–microbe systems [13,4850].

Generality of drift in generating heterogeneity in symbiotic systems

Although we implemented our experiments using horizontal transmission, many symbioses exhibit elaborate, host-controlled mechanisms that ensure vertical transmission [5155]. Despite host control, vertically transmitted symbionts, including obligate insect mutualists [56], are not immune to the effects of ecological drift, which acts on communities regardless of how they disperse. Indeed, some vertically transmitted symbionts undergo extreme transmission bottlenecks [5759], exaggerating the intensity of drift, and vertically transmitted symbionts also compete for host colonization [5962]. Thus, we should expect drift to generate heterogeneity in vertically transmitted infections [63,64] in a similar manner as we observed in our horizontal transmission experiments.

Based on our findings, we argue that the role of ecological drift is inadequately considered in host–microbe associations [13,14,34,35,65]. Notably, as long as ecologically overlapping microbes are capable of colonizing a within-host niche, host benefit, partner choice, and coevolutionary history may be unnecessary to explain between-host variation in microbiomes [2,5,15,49,64,6669]. This is of course not to say that niche-based ecology does not affect these communities or their evolution (e.g., [70]). In our system, there is limited diversity in the microbial symbiont community, and interactions between strains are likely dominated by competition [5,44,59,71,72]. By contrast, multispecies communities may contain facilitative interactions, such as signaling crosstalk, cross-feeding, metabolic division of labor, and complementary host-provisioning [8,7380], all of which could fundamentally alter ecological dynamics. Nonetheless, the neutral model often performs surprisingly well in explaining patterns in multispecies communities [21], suggesting that drift and other stochastic processes should at least be considered when attempting to explain patterns of diversity within more specialized symbioses as well.

The pervasive effect of ecological drift suggests it may also play a key but undervalued role in the evolution of specialized host–microbe symbioses. First, ecological drift can override selection and maintain strain variation within a host population, by providing refugia for suboptimal or less competitive symbionts. In addition, by driving compositional variation between host-associated microbiomes, ecological drift can expose taxonomically or functionally distinctive strains and communities to selection [61,8185]. If a distinctive microbiome can maintain its association with a particular host lineage, coevolution with the host may eventually occur. By simultaneously maintaining genetic variation among symbionts and generating heterogeneity in symbiont community composition, we argue that ecological drift could provide another explanation for the paradox of variation in host–microbe mutualisms [9].

Beyond its role in generating between-host heterogeneity, ecological drift also generates heterogeneity in symbiont populations within a host. In the squash bug, we found that gut crypts, a unique anatomical feature of the M4 symbiotic organ, generate heterogeneity by segregating strains into discrete compartments within the same host. This has parallels in other symbiotic organs, including the crypts in the light organs and accessory nidamental glands of sepiolid squid [8688], coralloid roots and root nodules in cycads and legumes [35,89,90], and pores in human skin [91]. Because we show that such compartmentalization acts even on isogenic cells, we propose that within-host population spatial structure, as with between-host population structure, is not adequately explained by either host selection or microbial competition, and is instead characterized by stochastic colonization of different crypts [35]. While spatial heterogeneity frequently emerges as a result of between-strain interactions within in vitro communities [9296], here, the anatomy of a host forcefully imposes it even in the apparent absence of such interactions. How squash bugs and other multicellular hosts benefit from subjecting their symbiont populations to such elaborate compartmentalization remains an open question [97,98].

Although we have discussed how ecological drift results in segregation of genetic variation within the host, we were surprised to find that population diversity within individual crypts is apparently independent of inoculum density. We expected that the degree of population admixture within the crypts would depend on inoculum density, with crypts being predominantly coinfected at high inoculum density and predominantly singly infected at low inoculum density, as has been demonstrated in vitro systems on plates and in microfluidics experiments [99,100]. However, we instead observed an anterior-posterior gradient of admixture for all co-colonized bugs, consistent across 4 orders of magnitude in initial inoculum density. This suggests that in vivo colonization processes impose distinct conditions that generate structure in Caballeronia populations. We know almost nothing about symbiont colonization at the single-cell level in the squash bug. However, we speculate that the host permits colonization of individual crypts by only a limited number of symbiont cells and that inoculation of individual crypts continues to some extent after the initial colonization event by movement of propagules within the symbiont organ. Further study is necessary to ascertain whether coinfection within single crypts affects within-host symbiont evolution and host fitness, as predicted by others [101], or creates opportunities for horizontal gene transfer [102].

In this work, we illustrate the role of ecological drift in shaping symbiont host populations at multiple scales. Our findings highlight the effect of ecological drift during colonization by maintaining heterogeneity in symbiont populations both within and between hosts. We posit that ecological drift can weaken selection from microbe–microbe interactions within the host, while also setting the stage for the evolution of these same processes. These results contribute to our understanding of the role that stochastic dynamics play in the assembly of ecological communities, even in ancient, highly specific host–microbe associations subjected to extensive host control [21].

Methods

Study system

Squash bugs (Anasa tristis) were maintained on yellow crookneck squash plants (Cucurbita pepo “Goldstar”) in 1 ft3 mesh cages. Hatchlings were maintained on pieces of surface-sterilized organic zucchini in plastic rearing boxes, where they remain aposymbiotic (i.e., Caballeronia-free, though not necessarily free of other microbes). Hatchlings molt to the second instar, the life stage competent for symbiont colonization, after 2 days of feeding. Nymphs utilized in this experiment were typically 1 week old or less.

Caballeronia symbionts C. sp. SQ4a and C. zhejiangensis GA-OX1 were originally isolated from wild squash bugs at different localities in northeastern Georgia, United States of America. SQ4a and GA-OX1 form phenotypically very distinct colonies on nutrient agar (NA; 3 g/L yeast extract, 5 g/L peptone, 15 g/L agar) and are not closely related within the genus Caballeronia [41] (S3 Fig). Cultures were typically grown on NA plates or in Luria Bertani (LB) Lennox broth with low salt (Sigma-Aldrich L3022) at 25°C. Unless otherwise stated, 2 ml broth cultures were initiated from colony picks of 3- to 4-day-old colonies grown on NA at 25°C, and grown overnight with shaking at 200 rpm at 25°C.

Strain construction

The mini-Tn7 system [103] facilitates the stable, orientation-specific introduction of foreign DNA into bacterial genomes at a neutral intergenic site, attTn7, with minimal effects on phenotype and fitness in vitro [104106]. To make readily distinguishable but otherwise isogenic symbiont strains, we genomically integrated a green fluorescent protein (sfGFP; henceforth GFP) and a red fluorescent protein (dTomato; henceforth RFP) into SQ4a and GA-OX1 using improved versions of previously developed mini-Tn7 vectors (Table 1) [103]. The conjugative Escherichia coli K12 strain SM10(λpir) harboring pTn7xKS-sfGFP or pTn7xKS-dTomato (Table 1), which were a generous gift from Travis Wiles [43], as well as an E. coli parent of the same strain harboring helper plasmid pTNS2 [103], were plated with SQ4a and GA-OX1 at high density on LB plates with salt (10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl, 15 g/L agar). After 24 to 48 h of incubation at 30°C, matings were harvested into LB Lennox low-salt broth with a lytic coliphage, T7, to eradicate E. coli. After further incubation for 4 h at 30°C shaking at 200 to 225 rpm, cultures were plated on NA amended with 1 mM isopropyl-β-D-1-thiogalactoside (IPTG) and 10 μg/ml gentamicin to select for successful integrants. Colonies on selective plates were screened for fluorescence and frozen at −80°C as 20% v/v glycerol stocks.

To confirm stability of fluorophore expression, newly constructed strains were streaked on NA plates and visually assessed for fluorescence after 2 days. To confirm site- and orientation-specificity of mini-Tn7-GmR integration, we ran PCR to amplify the fragment between the endogenous glmS gene (GA-OX1: 5′ AGGCGCGTTGAAGCTCAAGG 3′; SQ4a: 5′ CGCTGGAGCCGCAAATCATC 3′) and the inserted aacC gentamicin resistance marker (aacC-83F: 5′ GTATGCGCTCACGCAACTGG 3′). We did not screen for insertion at additional sites, as to our knowledge the strains of Caballeronia we used have only 1 attTn7 site.

Competition assays in vitro

During the summer growing season, squash bugs feed on macerated cell contents, xylem, and phloem in tissues from squash plants and fruits [110,111]. To replicate microbial competition in this environment, competition assays in liquid culture were conducted in filter-sterilized zucchini squash extract. In short, juice from organic zucchini fruits was extracted in a juicer, combined, and filtered to remove large suspended particles. This filtrate was then centrifuged at 10,000 ×g for 3 h to pellet suspended particles, then filter-sterilized through a 0.2 μm filter and stored at −20°C.

To initiate competition assays, GA-OX1 and SQ4a, labeled with sfGFP or dTomato as described above, were initially streaked from frozen 20% glycerol stocks onto NA plates and incubated at 30°C for 48 h. Individual colonies from each plate were inoculated into 2 ml of LB media and incubated in a shaking incubator (New Brunswick Scientific Excella E25) at 25°C for 12 h with shaking at 225 rpm. All cultures were equalized to an optical density (OD) of 1.0 by adding 100 μl of each culture to a 96-well plate and taking readings with a Synergy HTX multimode plate reader. The equalized cultures were spun down with an Eppendorf centrifuge 5424 R and washed with 1 ml of 1× phosphate-buffered saline (PBS) 3 times.

Monocultures of GA-OX1 and SQ4a were combined to form counterlabeled self versus self and self versus competitor cocultures, for a total of 4 combinations. Self versus self cocultures contained the same Caballeronia strain, differing only in the fluorescent protein, while self versus competitor cocultures contained different Caballeronia strains, also differing in the fluorescent protein. All cocultures were set up in 500 μl of a 1:1 mixture of filter-sterilized zucchini squash extract and PBS, and then incubated for 24 h at 30°C with shaking at 225 rpm. As described above, cocultures were dilution plated on NA and incubated a further 20 to 24 h, and single colonies containing each fluorophore were distinguished and counted under a dissecting scope. We confirmed that SQ4a and GA-OX1 do not appear to inhibit each other intensely on NA plates, suggesting that plating cocultures on NA provides an unbiased count for both competitors (S5 Fig).

Competition assays in vivo

The generalized protocol for competition assays with varying transmission bottlenecks in vivo is presented in Fig 2A. GFP- and RFP-labeled Caballeronia strains were streaked out from glycerol stocks onto NA and incubated at 25°C for at least 3 days. To initiate liquid cultures, single colonies were picked into 2 ml LB in glass tubes and incubated at 25°C with shaking at 200 rpm; to account for different growth rates between SQ4a and GA-OX1, a glass inoculating loop was used to pick up an entire colony of SQ4a, while a p10 micropipettor tip was used to extract a small plug from part of a single GA-OX1 colony.

To prepare inocula for feeding, cultured bacterial cells were washed to remove LB. Two hundred μl of culture was spun down at 10,000 ×g at 4°C for 2 min. The supernatant was removed, and the pellet was resuspended in 1,000 μl 1× PBS. After a second centrifugation, the pellet was resuspended in 200 μl 1× PBS to bring the cells to their original culture density. For quality assurance, 10-fold serial dilutions were carried out using 30 μl washed cells in 270 μl PBS in a 96-well plate, and 50 μl each of the washed GFP- and RFP-labeled strains were then diluted into 400 μl of a complex feeding solution (a 1:1 mixture of filter-sterilized zucchini squash extract and PBS; for neutral competition trials) or a defined feeding solution (2% m/v glucose 10% v/v PBS; for interspecies competition trials), in each case containing 20 μl of a nontoxic blue dye (1 mM erioglaucine disodium). We found that the defined feeding solution improved nymphal feeding response and better prevented bacterial population growth during the inoculation time window, with minimal impact on our experimental results (S6 Fig). Nymphs previously starved overnight for 15 to 25 h in clean plastic rearing boxes (7 cm × 7 cm × 3 cm) were supplied with 120 μl of a single inoculum treatment blotted on quartered sectors of 55 mm diameter qualitative filter paper (Advantec MFS N015.5CM). Nymphs were then allowed to wander and feed ad libitum for 2 to 3 h. After this brief inoculation period, nymphs were housed singly in 24-well plates with small pieces of organic zucchini to develop for 3 days. Just before and after the inoculation period, inocula were serially diluted as above to quantify the concentration of each strain and ensure that no substantial growth or death of either strain occurred during the inoculation period (S6 Fig).

On the fourth day after inoculation, nymphs were killed in 70% denatured ethanol, surface sterilized in 10% bleach for 5 to 10 min, washed off again in 70% ethanol, and immersed in approximately 20 μl droplets of 1× PBS. Whole nymphs, when applicable, were imaged on an Olympus SZX16 stereomicroscope with an Olympus XM10 monochrome camera and Olympus cellSens Standard software ver. 1.13. Nymphs were immersed in a shallow volume of PBS in 6 cm plastic petri dishes, and images were taken in darkfield (30 ms exposure 11.4 dB gain), brightfield (autoexposure, 11.4 dB gain), a GFP channel (autoexposure, 18 dB gain), and an RFP channel (autoexposure, 11.4 dB gain). Darkfield and brightfield images were merged in FIJI version 1.54f using the Image Calculator plugin, and the result was then merged with the GFP channel, RFP channel, or both. M4s were individually dissected from nymphs, and the degree of green and red colonization was qualitatively estimated under a fluorescent microscope. Each M4 was then held in 300 μl 1× PBS in Eppendorf tube and crushed with a sterile micropestle; 30 μl of homogenate was serially diluted in 270 μl PBS and immediately dilution plated onto NA. Plates were then incubated at 30°C for about 24 h. Counts of GFP and RFP fluorescent colonies were recorded after refrigeration at 4°C for at least 24 h to enhance fluorescent protein expression. The count data of GFP and RFP colonies yielded by our sampling procedure almost always reflected our qualitative observations of GFP and RFP colonization inside the M4, suggesting that our data accurately represent the colonization state within live insects.

Microscopy of within-host symbiont populations

Bugs were inoculated and allowed to develop for 4 days as described above with approximately 60 and 931,000 CFU/μl inocula containing GA-OX1 GFP and RFP. We then intentionally screened individual insects for co-colonization, and only these insects were selected for dissection and microscopy. From each bug, the whole gut was dissected in a 20 to 30 μl droplet of PBS in a 30 mm diameter plastic dish. The M4 was stretched out to its full length, and straightened out as much as possible by severing tracheoles associated with the crypts and flipping the M4 over to minimize the number of twists in the M4. This was critical to minimize aberrations in fluorescence intensity and colocalization due to overlap between multiple crypts. The M4 was anchored at the posterior end by the tip of the bug abdomen and at the anterior end by the M1-M3 sections of the midgut, and cleaned several times by pipetting off debris, fat body, and hemocytes with clean PBS. Finally, the whole preparation was re-immersed in 2,550 μl of PBS, to which 1 μl of M9 buffer containing 1% Triton-X100 was added to aid the spreading of the droplet.

Gut preparations, which degrade or dry rapidly, were imaged as soon as possible. Tilescan images were taken using a Leica DMi8 inverted widefield light microscope with a Leica DFC9000 GT fluorescence camera and Leica Application Suite X ver. 3.4.2.18368 software. Automated tilescans were taken with a 10× objective lens with brightfield, DIC, GFP, and RFP channels. Fluorescent channels were established by filter sets. The GFP channel was set to: bandpass filter 470/40 nm emission, dichroic mirror 495 nm, emission 525/50 nm. The dsRed channel was set to: 546/11 nm excitation, dichroic mirror 560 nm, 630/75 nm emission. As each sample is unique, care was taken to set GFP and RFP channel exposure times manually according to the most intense pixels in the entire M4 (usually in the posterior crypts) to minimize signal saturation in any part of the preparation. Due to the convoluted shape of the M4, images were taken with and without autofocus, and stitched images were visually assessed to determine which images were more useful. The repetitive structure of the M4, composed of nearly identically sized, regularly spaced crypts, also necessitated a lower overlap value between tiles for tilescans, as low as 2%. LAS X software was used to merge tiles from tilescans without smoothing for quantitative analysis.

Statistical analysis

All statistical analyses were conducted in R version 4.1.1, and the R package ggplot2 (version 3.4.2) was used for all data visualization. Because multiple trials were run for inoculation experiments, and some trials recovered very low numbers of infected nymphs, we binned nymphs from multiple trials into discrete treatment groups, based on inoculum size, for analysis. For neutral competition experiments, which utilized isogenic GA-OX1 GFP and RFP, we measured the proportion of GA-OX1 RFP extracted from each host. For interspecies competition experiments, utilizing different combinations of SQ4a and GA-OX1, we measured the proportion of GA-OX1 compared to the sum of all green and red fluorescent colonies extracted from each host.

Raw colony counts of each fluorescent strain recovered from each individual insect were converted into proportions for analysis and visualization. To quantify between-host heterogeneity in symbiont colonization for each inoculation treatment, we calculated a bimodality coefficient [36] using the R package mousetrap (version 3.2.0), as well as the population variance, for each inoculation treatment. Using the R package dip test (version 0.76–0), we also implemented Hartigan’s dip test [112], which calculates a dip statistic (S1S4 Tables) based on the shape of the cumulative distribution function of a dataset. We considered a distribution to deviate from unimodality if p-values from the dip test repeatedly fell below the threshold of 0.05.

To quantify within-host spatial structure, a linear region of interest (ROI) was sampled from one complete row of crypts from each sample to obtain GFP and RFP intensities. Crossover of the ROI from one side of the M4 to the next was occasionally necessary to follow that row through each twist of the M4. The identical ROI was translated to obtain GFP and RFP intensities from the empty background immediately adjacent to the crypts. GFP and RFP intensities at each point along the M4 were normalized by subtracting the background signal from the same point outside the M4. For the RFP channel, the difference between crypt and background signal was occasionally less than 0; in these rare cases, the normalized RFP intensity at that point was assigned a value of 1. The log-transformed ratio of RFP to GFP intensity was measured from each pixel along the ROI. In addition, the variance in this value was calculated by iteratively sampling pixels from within a sliding interval 10% of the length of the ROI.

Supporting information

S1 Fig. Unaggregated infection outcomes resulting from neutral competition during isogenic co-colonization.

The data underlying this figure can be found in S2 Data. (A) Relative GA-OX1 RFP abundance within inocula used in isogenic colonization trials. Vertical lines demarcate which trials were aggregated for analysis in Fig 1. Blue X marks indicate the inoculum density, and the percent relative abundance of GA-OX1 RFP, in each trial. The dotted horizontal line represents the average relative abundance of GA-OX1 RFP (46.5%) across all trials. (B) Variable colonization outcomes associated with different transmission bottleneck sizes in isogenic co-inoculation, disaggregated from Fig 2B. Blue X marks indicate the inoculum density, and the percent relative abundance of GA-OX1 RFP, in each trial. Points indicate successfully colonized nymphs associated with each inoculation trial, and the color of each point and its position along the y-axis represent the percent relative abundance of GA-OX1 RFP colonies among all fluorescent colonies recovered from each nymph. Magenta points represent insects containing only RFP colonies, green points represent insects containing only GFP colonies, and faded magenta/green colonies are co-colonized. Note that multiple points overlap, particularly at the extremes of 0% and 100% RFP composition, due to the absence of jittering. (C) Bimodality coefficients calculated from unaggregated trials. Bimodality coefficients (black points) calculated from data in panel B. The 0.555 threshold (marked with a dotted line) indicates the bimodality coefficient expected from a uniform distribution.

https://doi.org/10.1371/journal.pbio.3002304.s001

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S2 Fig. Isogenic coinfections of A. tristis nymphs over 5 orders of magnitude in inoculum density.

Fluorescence images of nymphs from a single cohort colonized with different densities of GA-OX1 sfGFP and GA-OX1 RFP, ranging from 101 to 106 CFU/μl. Nymphs inoculated with only GA-OX1 sfGFP or only GA-OX1 RFP serve as controls (top 2 rows); the bottom 3 rows show nymphs from mixed inoculation trials (GA-OX1 GFP + RFP). Note that the red fluorescent protein dTomato is brighter in whole-body preparations of nymphs than the green fluorescent protein sfGFP, due to increased absorbance of green light by living tissue [113] and the high stability of free dTomato under physiological conditions.

https://doi.org/10.1371/journal.pbio.3002304.s002

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S3 Fig. Symbiont strains SQ4a and GA-OX1 represent distinct clades within the genus Caballeronia.

A whole genome-based phylogeny of selected species and previously isolated Anasa tristis symbionts, representing major clades within the genus Caballeronia as defined by Peeters and colleagues [27], including the experimental strains C. sp. nr. concitans SQ4a and C. zhejiangensis GA-OX1. The phylogeny was constructed using RealPhy [114], with Burkholderia cepacia as the reference genome, using default settings except for a gap threshold of 0.1 and setting the model of evolution to GTR. Support values are bootstrap values based on 100 replicates. In addition to SQ4a and GA-OX1, Caballeronia strains A33M4c and IN-ML1 were also previously isolated from A. tristis [7,28]. SMT4a is a Paraburkholderia terricola soil isolate that can colonize A. tristis [28,41]. GenBank assemblies are as follows: GCF_023631065.1 (GA-OX1), GCF_022879815.1 (A33M4c), GCF_022627895.1 (C. zhejiangensis), GCF_023631085.1 (INML1), GCF_001544875.2 (C. hypogeia), GCF_000402035.1 (C. insecticola), GCF_023170545.1 (SQ4a), GCF_001544615.1 (C. concitans), GCF_902833485.1 (C. glathei), GCF_902859805.1 (P. sediminicola), GCF_022879555.1 (SMT4a), and GCA_009586235.1 (B. cepacia). Bootstrapping and tree files are available at https://doi.org/10.15139/S3/YZPBGY.

https://doi.org/10.1371/journal.pbio.3002304.s003

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S4 Fig. Different combinations of competing GA-OX1 and SQ4a fluorescent strains yield qualitatively similar responses to increasing stochasticity in transmission.

(A) Variable colonization outcomes associated with different transmission bottleneck sizes in two-species co-inoculation, using C. sp. nr. concitans SQ4a sfGFP and C. zhejiangensis GA-OX1 RFP. Blue X marks indicate the mean percent GA-OX1 RFP associated with each inoculum treatment, ranging from 100 to 105 CFU/μl. Points represent individual nymphs, and the color of each point and its position along the y-axis represent the percent relative abundance of GA-OX1 RFP colonies among all fluorescent colonies recovered from each nymph. Magenta points represent nymphs from which only GA-OX1 RFP colonies were recovered, green points represent nymphs from which only SQ4a sfGFP colonies were recovered, and faded magenta/green points represent coinfected nymphs. Violin plots associated with each treatment depict the shape of the distribution in relative GA-OX1 RFP abundance. Below each violin plot, the success rate of colonization is indicated, as the number of nymphs that were successfully colonized with Caballeronia out of all nymphs sampled. These values were not recorded for the 100−101 treatment and thus omitted. Asterisks indicate significantly multimodal infection outcomes as determined by Hartigan’s dip test, at a significance level of p < 0.05. The data underlying this figure can be found in S5 Data. (B) Bimodality coefficients calculated from results in panel A. Large blue dots indicate bimodality coefficients calculated from all bugs in each treatment; boxplots indicate coefficients calculated by jackknife resampling in each treatment. Colonization is bimodal (bimodality coefficient > 0.555) across several orders of magnitude of inoculum density. The 0.555 threshold (marked with a dotted line) indicates the bimodality coefficient associated with a uniform distribution. The data underlying this figure can be found in S5 Data. Note that for the 100−101 treatment, the sample size was insufficient for jackknife resampling. (C) Variable colonization outcomes associated with different transmission bottleneck sizes in two-species co-inoculation, using C. sp. nr. concitans SQ4a RFP and C. zhejiangensis GA-OX1 sfGFP. Blue X marks indicate the mean percent GA-OX1 GFP associated with each inoculum treatment, ranging from 101 to 105 CFU/μl. Points represent individual nymphs, and the color of each point and its position along the y-axis represent the percent relative abundance of GA-OX1 GFP colonies among all fluorescent colonies recovered from each nymph. Magenta points represent nymphs from which only SQ4a RFP colonies were recovered, green points represent nymphs from which only GA-OX1 sfGFP colonies were recovered, and faded magenta/green points represent coinfected nymphs. Violin plots associated with each treatment depict the shape of the distribution in relative GA-OX1 GFP abundance. Below each violin plot, the success rate of colonization is indicated, as the number of nymphs that were successfully colonized with Caballeronia out of all nymphs sampled. Asterisks indicate significantly multimodal infection outcomes as determined by Hartigan’s dip test, at a significance level of p < 0.05. The data underlying this figure can be found in S6 Data. (D) Bimodality coefficients calculated from results in panel C. Large blue dots indicate bimodality coefficients calculated from all bugs in each treatment; boxplots indicate coefficients calculated by jackknife resampling in each treatment. Colonization is bimodal (bimodality coefficient >0.555) across several orders of magnitude of inoculum density. The 0.555 threshold (marked with a dotted line) indicates the bimodality coefficient associated with a uniform distribution. The data underlying this figure can be found in S6 Data.

https://doi.org/10.1371/journal.pbio.3002304.s004

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S5 Fig. GA-OX1 and SQ4a do not exhibit strong inhibition on nutrient agar.

Spots of GA-OX1 RFP and SQ4a sfGFP plated side-by-side at high and low densities on nutrient agar. (A) A dense culture of GA-OX1 RFP spotted adjacent to single SQ4a sfGFP colonies. (B) A dense culture of SQ4a sfGFP spotted adjacent to single SQ4a sfGFP colonies. (C) A dense culture of SQ4a sfGFP spotted adjacent to single GA-OX1 RFP colonies.

https://doi.org/10.1371/journal.pbio.3002304.s005

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S6 Fig. Changes in titer of each strain during all inoculation trials.

Bacterial strain titers before and after inoculation trials. The data underlying this figure can be found in S2, S4, S5, and S6 Data files. (A) GA-OX1 GFP and RFP (cf Figs 2B, 2C, and S1). (B) SQ4a GFPmut3 and GA-OX1 RFP (cf Fig 3C and 3D). (C) SQ4a sfGFP and GA-OX1 RFP (cf S4A and S4B Fig). (D) GA-OX1 sfGFP and SQ4a RFP(cf S4C and S4D Fig).

https://doi.org/10.1371/journal.pbio.3002304.s006

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S1 Table. Community heterogeneity in competition trials between GA-OX1 sfGFP and GA-OX1 RFP.

Bimodality coefficients, population variances, species-level Fst, and dip statistics calculated from competition trials between GA-OX1 sfGFP and GA-OX1 RFP.

https://doi.org/10.1371/journal.pbio.3002304.s007

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S2 Table. Community heterogeneity in competition trials between SQ4a GFPmut3 and GA-OX1 RFP.

Bimodality coefficients, population variances, species-level Fst, and dip statistics calculated from competition trials between SQ4a GFPmut3 and GA-OX1 RFP.

https://doi.org/10.1371/journal.pbio.3002304.s008

(XLSX)

S3 Table. Community heterogeneity in competition trials between SQ4a sfGFP and GA-OX1 RFP.

Bimodality coefficients, population variances, species-level Fst, and dip statistics calculated from competition trials between GA-OX1 RFP and SQ4a sfGFP.

https://doi.org/10.1371/journal.pbio.3002304.s009

(XLSX)

S4 Table. Community heterogeneity in competition trials between SQ4a RFP and GA-OX1 sfGFP.

Bimodality coefficients, population variances, species-level Fst, and dip statistics calculated from competition trials between GA-OX1 GFP and SQ4a RFP.

https://doi.org/10.1371/journal.pbio.3002304.s010

(XLSX)

S1 Data. Bacterial symbiont community composition in wild squash bug populations.

Sequence counts of the top 20 Caballeronia 16s V3-V4 amplicon sequence variants (ASVs) from 9 bugs across 3 field localities in Georgia, Indiana, and North Carolina. Publicly available data from [7].

https://doi.org/10.1371/journal.pbio.3002304.s011

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S2 Data. Insect inoculation trials using Caballeronia zhejiangensis GA-OX1 sfGFP and GA-OX1 RFP.

Colony count data of GA-OX1 sfGFP and GA-OX1 RFP colonization trials from each insect, including insects from which no fluorescent colonies were recovered.

https://doi.org/10.1371/journal.pbio.3002304.s012

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S3 Data. Competition between Caballeronia zhejiangensis GA-OX1 and Caballeronia sp.

SQ4a. Colony count data of counterlabeled GA-OX1 and SQ4a strains grown in liquid coculture over 24 h, including both same-strain and interspecific combinations.

https://doi.org/10.1371/journal.pbio.3002304.s013

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S4 Data. All bugs dissected in colonization trials using SQ4a GFPmut3 and GA-OX1 RFP.

Colony count data of SQ4a GFPmut3 and GA-OX1 RFP colonization trials from each insect, including insects from which no fluorescent colonies were recovered.

https://doi.org/10.1371/journal.pbio.3002304.s014

(XLSX)

S5 Data. All bugs dissected in colonization trials using SQ4a sfGFP and GA-OX1 RFP.

Colony count data of SQ4a sfGFP and GA-OX1 RFP colonization trials from each insect, including insects from which no fluorescent colonies were recovered.

https://doi.org/10.1371/journal.pbio.3002304.s015

(XLSX)

S6 Data. All bugs dissected in colonization trials using SQ4a RFP and GA-OX1 sfGFP.

Colony count data of SQ4a RFP and GA-OX1 GFPmut3 colonization trials from each insect, including insects from which no fluorescent colonies were recovered.

https://doi.org/10.1371/journal.pbio.3002304.s016

(XLSX)

S7 Data. RFP and GFP fluorescence in bugs co-colonized with GA-OX1 sfGFP and SQ4a RFP.

Red and green fluorescence intensities measured along digital transects of dissected symbiotic organs (M4s).

https://doi.org/10.1371/journal.pbio.3002304.s017

(ZIP)

Acknowledgments

We thank Gerardo, de Roode, Vega, and Levin lab members for helpful comments on this manuscript. In particular, we wish to thank Joselyne Chavez for supplying ASV diversity data from squash bugs; Anthony Junker for important advice on microscopy and image analysis; Sandra Mendiola and Erik Edwards for maintaining squash bug lab colony stocks and squash plants; and Kayla Stoy, Justine Garcia, and Patrick Stillson for the isolation and genomic characterization of Caballeronia strains used in this study. We also thank Travis Wiles and Elena Wall for contributing the plasmids that made this project possible.

References

  1. 1. Bose JL, Wollenberg MS, Colton DM, Mandel MJ, Septer AN, Dunn AK, et al. Contribution of Rapid Evolution of the luxR-luxI Intergenic Region to the Diverse Bioluminescence Outputs of Vibrio fischeri Strains Isolated from Different Environments. Appl Environ Microbiol. 2011;77:2445–2457. pmid:21317265
  2. 2. Breusing C, Xiao Y, Russell SL, Corbett-Detig RB, Li S, Sun J, et al. Ecological differences among hydrothermal vent symbioses may drive contrasting patterns of symbiont population differentiation. mSystems. 2023;0:e00284–e00223. pmid:37493648
  3. 3. LaJeunesse TC, Parkinson JE, Gabrielson PW, Jeong HJ, Reimer JD, Voolstra CR, et al. Systematic Revision of Symbiodiniaceae Highlights the Antiquity and Diversity of Coral Endosymbionts. Curr Biol. 2018;28:2570–2580.e6. pmid:30100341
  4. 4. Lan Y, Sun J, Chen C, Wang H, Xiao Y, Perez M, et al. Endosymbiont population genomics sheds light on transmission mode, partner specificity, and stability of the scaly-foot snail holobiont. ISME J. 2022;16:2132–2143. pmid:35715703
  5. 5. Rahman A, Manci M, Nadon C, Perez IA, Farsamin WF, Lampe MT, et al. Competitive interference among rhizobia reduces benefits to hosts. Curr Biol. 2023;33:2988–3001.e4. pmid:37490853
  6. 6. Rotman ER, Bultman KM, Brooks JF, Gyllborg MC, Burgos HL, Wollenberg MS, et al. Natural strain variation reveals diverse biofilm regulation in squid-colonizing Vibrio fischeri. J Bacteriol. 2019:JB.00033-19. pmid:30782630
  7. 7. Stoy KS, Chavez J, De Las CV, Talla V, Berasategui A, Morran LT, et al. Evaluating coevolution in a horizontally transmitted mutualism. Evolution. 2023;77:166–185. pmid:36622711
  8. 8. Zheng H, Perreau J, Powell JE, Han B, Zhang Z, Kwong WK, et al. Division of labor in honey bee gut microbiota for plant polysaccharide digestion. Prox Natl Acad Sci U S A. 2019;116:25909–25916. pmid:31776248
  9. 9. Heath KD, Stinchcombe JR. Explaining mutualism variation: a new evolutionary paradox? Evolution. 2014;68:309–317. pmid:24303853
  10. 10. Yoder JB, Tiffin P. Sanctions, Partner Recognition, and Variation in Mutualism. Am Nat. 2017;190:491–505. pmid:28937817
  11. 11. Adair KL, Douglas AE. Making a microbiome: the many determinants of host-associated microbial community composition. Curr Opin Microbiol. 2017;35:23–29. pmid:27907842
  12. 12. Miller ET, Svanbäck R, Bohannan BJM. Microbiomes as Metacommunities: Understanding Host-Associated Microbes through Metacommunity Ecology. Trends Ecol Evol. 2018;33:926–935. pmid:30266244
  13. 13. Jones EW, Carlson JM, Sivak DA, Ludington WB. Stochastic microbiome assembly depends on context. Proc Natl Acad Sci U S A. 2022;119:e2115877119. pmid:35135881
  14. 14. Vega NM, Gore J. Stochastic assembly produces heterogeneous communities in the Caenorhabditis elegans intestine. PLoS Biol. 2017;15:e2000633. pmid:28257456
  15. 15. Burns AR, Miller E, Agarwal M, Rolig AS, Milligan-Myhre K, Seredick S, et al. Interhost dispersal alters microbiome assembly and can overwhelm host innate immunity in an experimental zebrafish model. Proc Natl Acad Sci U S A. 2017;114:11181–11186. pmid:28973938
  16. 16. Orrock JL, Watling JI. Local community size mediates ecological drift and competition in metacommunities. Proc Biol Sci. 2010. pmid:20236983
  17. 17. Robinson CD, Bohannan BJ, Britton RA. Scales of persistence: transmission and the microbiome. Curr Opin Microbiol. 2019;50:42–49. pmid:31629296
  18. 18. Obadia B, Güvener ZT, Zhang V, Ceja-Navarro JA, Brodie EL, Ja WW, et al. Probabilistic Invasion Underlies Natural Gut Microbiome Stability. Curr Biol. 2017;27:1999–2006.e8. pmid:28625783
  19. 19. Gilbert B, Levine JM. Ecological drift and the distribution of species diversity. Proc Biol Sci. 2017;284:20170507. pmid:28566486
  20. 20. Hubbell SP. Neutral theory in community ecology and the hypothesis of functional equivalence. Funct Ecol. 2005;19:166–172.
  21. 21. Sieber M, Pita L, Weiland-Bräuer N, Dirksen P, Wang J, Mortzfeld B, et al. Neutrality in the Metaorganism. PLoS Biol. 2019;17:e3000298. pmid:31216282
  22. 22. Nyholm SV, McFall-Ngai M. The winnowing: establishing the squid–vibrio symbiosis. Nat Rev Microbiol. 2004;2:632–642. pmid:15263898
  23. 23. Oono R, Anderson CG, Denison RF. Failure to fix nitrogen by non-reproductive symbiotic rhizobia triggers host sanctions that reduce fitness of their reproductive clonemates. Proc Biol Sci. 2011;278:2698–2703. pmid:21270038
  24. 24. Kiers ET, Rousseau RA, West SA, Denison RF. Host sanctions and the legume–rhizobium mutualism. Nature. 2003;425:78–81. pmid:12955144
  25. 25. Itoh H, Jang S, Takeshita K, Ohbayashi T, Ohnishi N, Meng X-Y, et al. Host–symbiont specificity determined by microbe–microbe competition in an insect gut. Proc Natl Acad Sci U S A. 2019:201912397. pmid:31636183
  26. 26. Itoh H, Aita M, Nagayama A, Meng X-Y, Kamagata Y, Navarro R, et al. Evidence of Environmental and Vertical Transmission of Burkholderia Symbionts in the Oriental Chinch Bug, Cavelerius saccharivorus (Heteroptera: Blissidae). Appl Environ Microbiol. 2014;80:5974–5983. pmid:25038101
  27. 27. Peeters C, Meier-Kolthoff JP, Verheyde B, De Brandt E, Cooper VS, Vandamme P. Phylogenomic Study of Burkholderia glathei-like Organisms, Proposal of 13 Novel Burkholderia Species and Emended Descriptions of Burkholderia sordidicola, Burkholderia zhejiangensis, and Burkholderia grimmiae. Front Microbiol. 2016:7. pmid:27375597
  28. 28. Acevedo TS, Fricker GP, Garcia JR, Alcaide T, Berasategui A, Stoy KS, et al. The Importance of Environmentally Acquired Bacterial Symbionts for the Squash Bug (Anasa tristis), a Significant Agricultural Pest. Front Microbiol. 2021;12:2655. pmid:34671328
  29. 29. Mendiola SY, Stoy KS, DiSalvo S, Wynn CL, Civitello DJ, Gerardo NM. Competitive Exclusion of Phytopathogenic Serratia marcescens from Squash Bug Vectors by the Gut Endosymbiont Caballeronia. Appl Environ Microbiol. 2022;88:e01550–e01521. pmid:34669447
  30. 30. Ohbayashi T, Takeshita K, Kitagawa W, Nikoh N, Koga R, Meng X-Y, et al. Insect’s intestinal organ for symbiont sorting. Proc Natl Acad Sci U S A. 2015;112:E5179–E5188. pmid:26324935
  31. 31. Kikuchi Y, Hosokawa T, Fukatsu T. An ancient but promiscuous host–symbiont association between Burkholderia gut symbionts and their heteropteran hosts. ISME J. 2011;5:446–460. pmid:20882057
  32. 32. Hunter MS, Umanzor EF, Kelly SE, Whitaker SM, Ravenscraft A. Development of Common Leaf-Footed Bug Pests Depends on the Presence and Identity of Their Environmentally Acquired Symbionts. Appl Environ Microbiol. 2022;88:e01778–e01721. pmid:34986009
  33. 33. Hubbell SP. Local Community Dynamics under Ecological Drift. The Unified Neutral Theory of Biodiversity and Biogeography. Princeton, New Jersey: Princeton University Press; 2001. p. 76–112. Available from: https://press.princeton.edu/books/paperback/9780691021287/the-unified-neutral-theory-of-biodiversity-and-biogeography-mpb-32.
  34. 34. Moxon ER, Murphy PA. Haemophilus influenzae bacteremia and meningitis resulting from survival of a single organism. Proc Natl Acad Sci U S A. 1978;75:1534–1536. pmid:306628
  35. 35. Gage DJ. Analysis of Infection Thread Development Using Gfp- and DsRed-Expressing Sinorhizobium meliloti. J Bacteriol. 2002;184:7042–7046.
  36. 36. Pfister R, Schwarz K, Janczyk M, Dale R, Freeman J. Good things peak in pairs: a note on the bimodality coefficient. Front Psychol. 2013;4. Available from: https://www.frontiersin.org/articles/10.3389/fpsyg.2013.00700. pmid:24109465
  37. 37. Villa SM, Chen JZ, Kwong Z, Acosta A, Vega NM, Gerardo NM. Specialized acquisition behaviors maintain reliable environmental transmission in an insect-microbial mutualism. Curr Biol. 2023;33:2830–2838.e4. pmid:37385254
  38. 38. Kikuchi Y, Hosokawa T, Fukatsu T. Insect-Microbe Mutualism without Vertical Transmission: a Stinkbug Acquires a Beneficial Gut Symbiont from the Environment Every Generation. Appl Environ Microbiol. 2007;73:4308–4316. pmid:17483286
  39. 39. Itoh H, Hori T, Sato Y, Nagayama A, Tago K, Hayatsu M, et al. Infection dynamics of insecticide-degrading symbionts from soil to insects in response to insecticide spraying. ISME J. 2018;12:909–920. pmid:29343832
  40. 40. Bindels DS, Haarbosch L, van Weeren L, Postma M, Wiese KE, Mastop M, et al. mScarlet: a bright monomeric red fluorescent protein for cellular imaging. Nat Methods. 2017;14:53–56. pmid:27869816
  41. 41. Stillson PT, Baltrus DA, Ravenscraft A. Prevalence of an Insect-Associated Genomic Region in Environmentally Acquired Burkholderiaceae Symbionts. Appl Environ Microbiol. 2022;88:e02502–e02521. pmid:35435710
  42. 42. Kikuchi Y, Fukatsu T. Live imaging of symbiosis: spatiotemporal infection dynamics of a GFP-labelled Burkholderia symbiont in the bean bug Riptortus pedestris. Mol Ecol. 2014;23:1445–1456. pmid:24103110
  43. 43. Wiles TJ, Wall ES, Schlomann BH, Hay EA, Parthasarathy R, Guillemin K. Modernized Tools for Streamlined Genetic Manipulation and Comparative Study of Wild and Diverse Proteobacterial Lineages. MBio. 2018:9. pmid:30301859
  44. 44. Ohbayashi T, Cossard R, Lextrait G, Hosokawa T, Lesieur V, Takeshita K, et al. Intercontinental Diversity of Caballeronia Gut Symbionts in the Conifer Pest Bug Leptoglossus occidentalis. Microbes Environ. 2022;37:ME22042. pmid:35965097
  45. 45. Gook D-H, Jung M, Kim S, Lee D-H. Species diversity of environmentally-transmitted bacteria colonizing Riptortus pedestris (Hemiptera: Alydidae) and symbiotic effects of the most dominant bacteria. In Review. 2023.
  46. 46. Ravenscraft A, Thairu MW, Hansen AK, Hunter MS. Continent-Scale Sampling Reveals Fine-Scale Turnover in a Beneficial Bug Symbiont. Front Microbiol. 2020:11. pmid:32636818
  47. 47. Kikuchi Y, Yumoto I. Efficient Colonization of the Bean Bug Riptortus pedestris by an Environmentally Transmitted Burkholderia Symbiont. Appl Environ Microbiol. 2013;79:2088–2091. pmid:23291548
  48. 48. García-Bayona L, Comstock LE. Bacterial antagonism in host-associated microbial communities. Science. 2018;361:eaat2456. pmid:30237322
  49. 49. Romero Picazo D, Dagan T, Ansorge R, Petersen JM, Dubilier N, Kupczok A. Horizontally transmitted symbiont populations in deep-sea mussels are genetically isolated. ISME J. 2019;13:2954–2968. pmid:31395952
  50. 50. Shen P, Lees JA, Bee GCW, Brown SP, Weiser JN. Pneumococcal quorum sensing drives an asymmetric owner–intruder competitive strategy during carriage via the competence regulon. Nat Microbiol. 2019;4:198. pmid:30546100
  51. 51. Bright M, Bulgheresi S. A complex journey: transmission of microbial symbionts. Nat Rev Microbiol. 2010;8:218–230. pmid:20157340
  52. 52. Buchner P. Endosymbiosis of Animals with Plant Microorganisms. New York, NY: Interscience Publishers; 1965.
  53. 53. Dan H, Ikeda N, Fujikami M, Nakabachi A. Behavior of bacteriome symbionts during transovarial transmission and development of the Asian citrus psyllid. PLoS ONE. 2017;12:e0189779. pmid:29240843
  54. 54. Luan J-B, Shan H-W, Isermann P, Huang J-H, Lammerding J, Liu S-S, et al. Cellular and molecular remodelling of a host cell for vertical transmission of bacterial symbionts. Proc R Soc B. 2016;283:20160580. pmid:27358364
  55. 55. Maire J, Parisot N, Galvao Ferrarini M, Vallier A, Gillet B, Hughes S, et al. Spatial and morphological reorganization of endosymbiosis during metamorphosis accommodates adult metabolic requirements in a weevil. Proc Natl Acad Sci U S A. 2020;117:19347–19358. pmid:32723830
  56. 56. Thia JA, Zhan D, Robinson K, Umina PA, Hoffmann AA, Yang Q. “Drifting” Buchnera genomes track the microevolutionary trajectories of their aphid hosts. bioRxiv. 2023:p. 2023.11.17.567149.
  57. 57. Ciche TA, Kim K, Kaufmann-Daszczuk B, Nguyen KCQ, Hall DH. Cell Invasion and Matricide during Photorhabdus luminescens Transmission by Heterorhabditis bacteriophora Nematodes. Appl Environ Microbiol. 2008;74:2275–2287. pmid:18281425
  58. 58. Kaltenpoth M, Goettler W, Koehler S, Strohm E. Life cycle and population dynamics of a protective insect symbiont reveal severe bottlenecks during vertical transmission. Evol Ecol. 2010;24:463–477.
  59. 59. Perreau J, Zhang B, Maeda GP, Kirkpatrick M, Moran NA. Strong within-host selection in a maternally inherited obligate symbiont: Buchnera and aphids. Proc Natl Acad Sci U S A. 2021;118:e2102467118. pmid:34429360
  60. 60. Kondo N, Shimada M, Fukatsu T. Infection density of Wolbachia endosymbiont affected by co-infection and host genotype. Biol Lett. 2005;1:488–491. pmid:17148240
  61. 61. Lima A, Lubatti G, Burgstaller J, Hu D, Green AP, Di Gregorio A, et al. Cell competition acts as a purifying selection to eliminate cells with mitochondrial defects during early mouse development. Nat Metab. 2021;3:1091–1108. pmid:34253906
  62. 62. Sobanski J, Giavalisco P, Fischer A, Kreiner JM, Walther D, Schöttler MA, et al. Chloroplast competition is controlled by lipid biosynthesis in evening primroses. Proc Natl Acad Sci U S A. 2019;116:5665–5674. pmid:30833407
  63. 63. Ant TH, Sinkins SP. A Wolbachia triple-strain infection generates self-incompatibility in Aedes albopictus and transmission instability in Aedes aegypti. Parasit Vectors. 2018;11:295. pmid:29751814
  64. 64. Ellegaard KM, Engel P. Genomic diversity landscape of the honey bee gut microbiota. Nat Commun. 2019;10:1–13. pmid:30683856
  65. 65. Kono M, Zafar MA, Zuniga M, Roche AM, Hamaguchi S, Weiser JN. Single Cell Bottlenecks in the Pathogenesis of Streptococcus pneumoniae. PLoS Pathog. 2016;12:e1005887. pmid:27732665
  66. 66. Burns AR, Stephens WZ, Stagaman K, Wong S, Rawls JF, Guillemin K, et al. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 2016;10:655–664. pmid:26296066
  67. 67. Ortiz A, Vega NM, Ratzke C, Gore J. Interspecies bacterial competition regulates community assembly in the C. elegans intestine. ISME J. 2021;15:2131–2145. pmid:33589765
  68. 68. Rothschild D, Weissbrod O, Barkan E, Kurilshikov A, Korem T, Zeevi D, et al. Environment dominates over host genetics in shaping human gut microbiota. Nature. 2018;555:210–215. pmid:29489753
  69. 69. Łukasik P, Newton JA, Sanders JG, Hu Y, Moreau CS, Kronauer DJC, et al. The structured diversity of specialized gut symbionts of the New World army ants. Mol Ecol. 2017;26:3808–3825. pmid:28393425
  70. 70. Gude S, Pinçe E, Taute KM, Seinen A-B, Shimizu TS, Tans SJ. Bacterial coexistence driven by motility and spatial competition. Nature. 2020;578:588–592. pmid:32076271
  71. 71. McIlroy SE, Cunning R, Baker AC, Coffroth MA. Competition and succession among coral endosymbionts. Ecol Evol. 2019;9:12767–12778. pmid:31788212
  72. 72. Speare L, Cecere AG, Guckes KR, Smith S, Wollenberg MS, Mandel MJ, et al. Bacterial symbionts use a type VI secretion system to eliminate competitors in their natural host. Proc Natl Acad Sci U S A. 2018:201808302. pmid:30127013
  73. 73. Dial DT, Weglarz KM, Aremu AO, Havill NP, Pearson TA, Burke GR, et al. Transitional genomes and nutritional role reversals identified for dual symbionts of adelgids (Aphidoidea: Adelgidae). ISME J. 2022;16:642–654. pmid:34508228
  74. 74. Dodge R, Jones EW, Zhu H, Obadia B, Martinez DJ, Wang C, et al. A symbiotic physical niche in Drosophila melanogaster regulates stable association of a multi-species gut microbiota. Nat Commun. 2023;14:1557. pmid:36944617
  75. 75. Giri S, Oña L, Waschina S, Shitut S, Yousif G, Kaleta C, et al. Metabolic dissimilarity determines the establishment of cross-feeding interactions in bacteria. Curr Biol. 2021;31:5547–5557.e6. pmid:34731676
  76. 76. Łukasik P, Nazario K, Leuven JTV, Campbell MA, Meyer M, Michalik A, et al. Multiple origins of interdependent endosymbiotic complexes in a genus of cicadas. Proc Natl Acad Sci U S A. 2018;115:E226–E235. pmid:29279407
  77. 77. Ponnudurai R, Kleiner M, Sayavedra L, Petersen JM, Moche M, Otto A, et al. Metabolic and physiological interdependencies in the Bathymodiolus azoricus symbiosis. ISME J. 2017;11:463–477. pmid:27801908
  78. 78. Zélé F, Magalhães S, Kéfi S, Duncan AB. Ecology and evolution of facilitation among symbionts. Nat Commun. 2018;9:4869. pmid:30451829
  79. 79. Hosni T, Moretti C, Devescovi G, Suarez-Moreno ZR, Fatmi MB, Guarnaccia C, et al. Sharing of quorum-sensing signals and role of interspecies communities in a bacterial plant disease. ISME J. 2011;5:1857–1870. pmid:21677694
  80. 80. Vega NM, Allison KR, Samuels AN, Klempner MS, Collins JJ. Salmonella typhimurium intercepts Escherichia coli signaling to enhance antibiotic tolerance. Proc Natl Acad Sci U S A. 2013;110:14420–14425. pmid:23946425
  81. 81. Harumoto T, Lemaitre B. Male-killing toxin in a bacterial symbiont of Drosophila. Nature. 2018;557:252–255. pmid:29720654
  82. 82. Jones MW, Fricke LC, Thorpe CJ, Vander Esch LO, Lindsey ARI. Infection Dynamics of Cotransmitted Reproductive Symbionts Are Mediated by Sex, Tissue, and Development. Appl Environ Microbiol. 2022;88:e00529–e00522. pmid:35730939
  83. 83. Richardson KM, Ross PA, Cooper BS, Conner WR, Schmidt TL, Hoffmann AA. A male-killing Wolbachia endosymbiont is concealed by another endosymbiont and a nuclear suppressor. PLoS Biol. 2023;21:e3001879. pmid:36947547
  84. 84. Serra P, Navarro B, Forment J, Gisel A, Gago-Zachert S, Di Serio F, et al. Expression of symptoms elicited by a hammerhead viroid through RNA silencing is related to population bottlenecks in the infected host. New Phytol. 2023:n/a. pmid:37148189
  85. 85. Zhou J, Liu W, Deng Y, Jiang Y-H, Xue K, He Z, et al. Stochastic Assembly Leads to Alternative Communities with Distinct Functions in a Bioreactor Microbial Community. MBio. 2013;4:10.1128/mbio.00584-12. pmid:23462114
  86. 86. Collins AJ, LaBarre BA, Won BSW, Shah MV, Heng S, Choudhury MH, et al. Diversity and Partitioning of Bacterial Populations within the Accessory Nidamental Gland of the Squid Euprymna scolopes. Appl Environ Microbiol. 2012;78:4200–4208. pmid:22504817
  87. 87. Sun Y, LaSota ED, Cecere AG, LaPenna KB, Larios-Valencia J, Wollenberg MS, et al. Intraspecific Competition Impacts Vibrio fischeri Strain Diversity during Initial Colonization of the Squid Light Organ. Appl Environ Microbiol. 2016;82:3082–3091. pmid:27016564
  88. 88. Wollenberg MS, Ruby EG. Population Structure of Vibrio fischeri within the Light Organs of Euprymna scolopes Squid from Two Oahu (Hawaii) Populations. Appl Environ Microbiol. 2009;75:193–202. pmid:18997024
  89. 89. Costa J-L, Paulsrud P, Lindblad P. Cyanobiont diversity within coralloid roots of selected cycad species. FEMS Microbiol Ecol. 1999;28:85–91.
  90. 90. Costa J-L, Romero EM, Lindblad P. Sequence based data supports a single Nostoc strain in individual coralloid roots of cycads. FEMS Microbiol Ecol. 2004;49:481–487. pmid:19712296
  91. 91. Conwill A, Kuan AC, Damerla R, Poret AJ, Baker JS, Tripp AD, et al. Anatomy promotes neutral coexistence of strains in the human skin microbiome. Cell Host Microbe. 2022. pmid:34995483
  92. 92. McNally L, Bernardy E, Thomas J, Kalziqi A, Pentz J, Brown SP, et al. Killing by Type VI secretion drives genetic phase separation and correlates with increased cooperation. Nat Commun. 2017;8:14371. pmid:28165005
  93. 93. Wong JPH, Fischer-Stettler M, Zeeman SC, Battin TJ, Persat A. Fluid flow structures gut microbiota biofilm communities by distributing public goods. Proc Natl Acad Sci U S A. 2023;120:e2217577120. pmid:37307459
  94. 94. Xiong L, Cao Y, Cooper R, Rappel W-J, Hasty J, Tsimring L. Flower-like patterns in multi-species bacterial colonies. Weigel D, Walczak AM, Seminara A, editors. Elife. 2020;9: e48885. pmid:31933477
  95. 95. Yanni D, Kalziqi A, Thomas J, Ng SL, Vivek S, Ratcliff WC, et al. Life in the coffee-ring: how evaporation-driven density gradients dictate the outcome of inter-bacterial competition. arXiv:170703472 [cond-mat, physics:physics, q-bio]. 2017. Available from: http://arxiv.org/abs/1707.03472.
  96. 96. Yanni D, Márquez-Zacarías P, Yunker PJ, Ratcliff WC. Drivers of Spatial Structure in Social Microbial Communities. Curr Biol. 2019;29:R545–R550. pmid:31163168
  97. 97. Chomicki G, Werner GDA, West SA, Kiers ET. Compartmentalization drives the evolution of symbiotic cooperation. Philos Trans R Soc Lond B Biol Sci. 2020;375:20190602. pmid:32772665
  98. 98. Fronk DC, Sachs JL. Symbiotic organs: the nexus of host–microbe evolution. Trends Ecol Evol. 2022;37:599–610. pmid:35393155
  99. 99. Dal Co A, van Vliet S, Kiviet DJ, Schlegel S, Ackermann M. Short-range interactions govern the dynamics and functions of microbial communities. Nat Ecol Evol. 2020;4:366–375. pmid:32042125
  100. 100. van Gestel J, Weissing FJ, Kuipers OP, Kovács ÁT. Density of founder cells affects spatial pattern formation and cooperation in Bacillus subtilis biofilms. ISME J. 2014;8:2069. pmid:24694715
  101. 101. Frank SA. Host–symbiont conflict over the mixing of symbiotic lineages. Proc Biol Sci. 1996;263:339–344. pmid:8920255
  102. 102. Russell SL, Pepper-Tunick E, Svedberg J, Byrne A, Castillo JR, Vollmers C, et al. Horizontal transmission and recombination maintain forever young bacterial symbiont genomes. PLoS Genet. 2020;16:e1008935. pmid:32841233
  103. 103. Choi K-H, Gaynor JB, White KG, Lopez C, Bosio CM, Karkhoff-Schweizer RR, et al. A Tn7-based broad-range bacterial cloning and expression system. Nat Methods. 2005;2:443–448. pmid:15908923
  104. 104. Enne VI, Delsol AA, Davis GR, Hayward SL, Roe JM, Bennett PM. Assessment of the fitness impacts on Escherichia coli of acquisition of antibiotic resistance genes encoded by different types of genetic element. J Antimicrob Chemother. 2005;56:544–551. pmid:16040624
  105. 105. Lambertsen L, Sternberg C, Molin S. Mini-Tn7 transposons for site-specific tagging of bacteria with fluorescent proteins. Environ Microbiol. 2004;6:726–732. pmid:15186351
  106. 106. Mamat U, Hein M, Grella D, Taylor CS, Scholzen T, Alio I, et al. Improved mini-Tn7 Delivery Plasmids for Fluorescent Labeling of Stenotrophomonas maltophilia. Appl Environ Microbiol. 2023;0:e00317–e00323. pmid:37195181
  107. 107. Miller VL, Mekalanos JJ. A novel suicide vector and its use in construction of insertion mutations: osmoregulation of outer membrane proteins and virulence determinants in Vibrio cholerae requires toxR. J Bacteriol. 1988;170:2575–2583.
  108. 108. Simon R, Priefer U, Pühler A. A Broad Host Range Mobilization System for In Vivo Genetic Engineering: Transposon Mutagenesis in Gram Negative Bacteria. Bio/Technology. 1983;1:784–791.
  109. 109. Teal TK, Lies DP, Wold BJ, Newman DK. Spatiometabolic Stratification of Shewanella oneidensis Biofilms. Appl Environ Microbiol. 2006;72:7324–7330. pmid:16936048
  110. 110. Bonjour EL, Fargo WS, Webster JA, Richardson PE, Brusewitz GH. Probing Behavior Comparisons of Squash Bugs (Heteroptera: Coreidae) on Cucurbit Hosts. Environ Entomol. 1991;20:143–149.
  111. 111. Neal JJ. Xylem transport interruption by Anasa tristis feeding causes Cucurbita pepo to wilt. Entomol Exp Appl. 1993;69:195–200.
  112. 112. Hartigan JA, Hartigan PM. The Dip Test of Unimodality. Ann Stat. 1985;13:70–84.
  113. 113. Deliolanis NC, Kasmieh R, Würdinger T, Tannous BA, Shah K, Ntziachristos V. Performance of the Red-shifted Fluorescent Proteins in deep-tissue molecular imaging applications. J Biomed Opt. 2008;13:044008. pmid:19021336
  114. 114. Bertels F, Silander OK, Pachkov M, Rainey PB, van Nimwegen E. Automated Reconstruction of Whole-Genome Phylogenies from Short-Sequence Reads. Mol Biol Evol. 2014;31:1077–1088. pmid:24600054