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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.

Variation in bacterial composition inside a host is a result of complex dynamics of microbial community assembly, but little is known about these dynamics. To deconstruct the factors that contribute to this variation, we used a combination of experimental and modeling approaches. We found that demographic stochasticity and stationary heterogeneity in the host carrying capacity or bacterial growth rate are insufficient to explain quantitatively the variation observed in our empirical data. Instead, we found that the data can be understood if the host-bacteria system can be viewed as stochastically switching between high and low growth rates phenotypes. This suggests the dynamics are significantly more complex than logistic growth used in canonical models of microbiome assembly. We develop mathematical models of this process that can explain various aspects of our data. We highlight the limitations of snapshot data in describing variation in host-associated communities and the importance of using time-series data along with mathematical models to understand microbial dynamics within a host.

The gut microbiome is essential for processing complex food compounds and synthesizing nutrients that the host cannot digest or produce, respectively. New model systems are needed to study how the metabolic capacity provided by the gut microbiome impacts the nutritional status of the host, and to explore possibilities for altering host metabolic capacity via the microbiome. Here, we colonized the nematode Caenorhabditis elegans gut with cellulolytic bacteria that enabled C. elegans to utilize cellulose, an otherwise indigestible substrate, as a carbon source. Cellulolytic bacteria as a community component in the worm gut can also support additional bacterial species with specialized roles, which we demonstrate by using Lactobacillus plantarum to protect C. elegans against Salmonella enterica infection. This work shows that engineered microbiome communities can be used to endow host organisms with novel functions, such as the ability to utilize alternate nutrient sources or to better fight pathogenic bacteria. Synopsis A microbe‐host interaction model is developed by colonizing C. elegans with functional bacteria that allow digesting long‐chain cellulose. Direct benefits include increased host larval yield and protection of other gut species against pathogens. Heterologous bacteria (e.g. Pseudomonas cellulosa ) in the gut can help C. elegans to digest cellulose, an otherwise indigestible carbon substrate. Cellulolytic bacteria can also support other bacterial species with specialized roles: Lactobacillus protected the worms against Salmonella infection, and interspecies synergy between P. cellulosa and L. plantarum conferred benefit to the host. Engineered microbiome communities may provide host organisms with novel functions, including the ability to use complex nutrient sources and to fight pathogens. C. elegans colonized with bacteria provides a model system for studying microbiome‐host interactions. Graphical Abstract A microbe‐host interaction model is developed by colonizing C. elegans with functional bacteria that allow digesting long‐chain cellulose. Direct benefits include increased host larval yield and protection of other gut species against pathogens.

Abstract Microbial evolution within polymicrobial communities is a complex process. Here, we report within-species diversification within multispecies microbial communities during experimental evolution with the nematode Caenorhabditis elegans. We describe morphological diversity in the target species Chryseobacterium gleum, which developed a novel colony morphotype in a small number of replicate communities. Alternate morphotypes coexisted with original morphotypes in communities, as well as in single-species experiments using evolved isolates. We found that the original and alternate morphotypes differed in motility and in spatial expansion in the presence of C. elegans. This study provides insight into the emergence and maintenance of intraspecies diversity in the context of microbial communities. Bacterial diversity evolves repeatedly within microbial communities, and intraspecies diversity is not obvious from community-level diversity.

Critical to our understanding of infections and their treatment is the role the innate immune system plays in controlling bacterial pathogens. Nevertheless, many in vivo systems are made or modified such that they do not have an innate immune response. Use of these systems denies the opportunity to examine the synergy between the immune system and antimicrobial agents. In this study we demonstrate that the larva of is an effective in vivo model for the study of the population and evolutionary biology of bacterial infections and their treatment. To do this we test three hypotheses concerning the role of the innate immune system during infection. We show: i) sufficiently high densities of bacteria are capable of saturating the innate immune system, ii) bacteriostatic drugs and bacteriophages are as effective as bactericidal antibiotics in preventing mortality and controlling bacterial densities, and iii) minority populations of bacteria resistant to a treating antibiotic will not ascend. Using a highly virulent strain of and a mathematical computer-simulation model, we further explore how the dynamics of the infection within the short term determine the ultimate infection outcome. We find that immune activation in response to high densities of bacteria leads to a strong but short-lived immune response which ultimately results in a high degree of mortality. Overall, our findings illustrate the utility of the model system in conjunction with established in vivo models in studying infectious disease progression and treatment.

Critical to our understanding of infections and their treatment is the role the innate immune system plays in controlling bacterial pathogens. Nevertheless, many in vivo systems are made or modified such that they do not have an innate immune response. Use of these systems denies the opportunity to examine the synergy between the immune system and antimicrobial agents. In this study we demonstrate that the larva of Galleria mellonella is an effective in vivo model for the study of the population and evolutionary biology of bacterial infections and their treatment. To do this we test three hypotheses concerning the role of the innate immune system during infection. We show: i) sufficiently high densities of bacteria are capable of saturating the innate immune system, ii) bacteriostatic drugs and bacteriophages are as effective as bactericidal antibiotics in preventing mortality and controlling bacterial densities, and iii) minority populations of bacteria resistant to a treating antibiotic will not ascend. Using a highly virulent strain of Staphylococcus aureus and a mathematical computer-simulation model, we further explore how the dynamics of the infection within the short term determine the ultimate infection outcome. We find that excess immune activation in response to high densities of bacteria leads to a strong but short-lived immune response which ultimately results in a high degree of mortality. Overall, our findings illustrate the utility of the G. mellonella model system in conjunction with established in vivo models in studying infectious disease progression and treatment.Critical to our understanding of infections and their treatment is the role the innate immune system plays in controlling bacterial pathogens. Nevertheless, many in vivo systems are made or modified such that they do not have an innate immune response. Use of these systems denies the opportunity to examine the synergy between the immune system and antimicrobial agents. In this study we demonstrate that the larva of Galleria mellonella is an effective in vivo model for the study of the population and evolutionary biology of bacterial infections and their treatment. To do this we test three hypotheses concerning the role of the innate immune system during infection. We show: i) sufficiently high densities of bacteria are capable of saturating the innate immune system, ii) bacteriostatic drugs and bacteriophages are as effective as bactericidal antibiotics in preventing mortality and controlling bacterial densities, and iii) minority populations of bacteria resistant to a treating antibiotic will not ascend. Using a highly virulent strain of Staphylococcus aureus and a mathematical computer-simulation model, we further explore how the dynamics of the infection within the short term determine the ultimate infection outcome. We find that excess immune activation in response to high densities of bacteria leads to a strong but short-lived immune response which ultimately results in a high degree of mortality. Overall, our findings illustrate the utility of the G. mellonella model system in conjunction with established in vivo models in studying infectious disease progression and treatment.

Variation in transmission plays a crucial role in shaping the dynamics of infectious diseases. Population heterogeneity is known to contribute to this variation and is often represented in epidemiological models. However, it is not always clear a priori what sources of variation should contribute meaningfully to a given scenario, and it can be challenging to infer distributions of underlying processes from data. In this study, we demonstrate the use of Caenorhabditis elegans as a tractable system in which high-quality data can be produced for experimental epidemics. We show that distributions of shedding and susceptibility in this host can be experimentally decoupled to measure heterogeneity in transmission processes. We observe and quantify super-shedding and heterogeneous susceptibility, and we show that distributions of population heterogeneity and transmission outcomes have features conserved with real-world epidemics. Our results quantify sources of heterogeneity in bacterial transmission in this small model organism and establish C. elegans as a promising quantitative model for experimental epidemics.

Heterogeneity is ubiquitous across individuals in biological data, and sample batching, a form of biological averaging, inevitably loses information about this heterogeneity. The consequences for inference from biologically averaged data are frequently opaque, particularly when the underlying populations are non-normal. Here we investigate a case where biological averaging is common - count-based measurement of bacterial load in individual Caenorhabditis elegans - to empirically determine the consequences of batching. We find that both central measures and measures of variation on individual-based data contain biologically relevant information that is useful for distinguishing between groups, and that batch-based inference readily produces both false positive and false negative results in these comparisons. These results support the use of individual rather than batched samples when possible, illustrate the importance of understanding distributions across individuals within a sample frame, and indicate the need to consider effect size when drawing conclusions from biologically averaged data.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Edits to text and figures for clarity; updated code for R 4.3.2* https://github.com/veganm/WormCFUHeterogeneity2022

Variation in bacterial composition inside a host is a result of complex dynamics of microbial community assembly, but little is known about these dynamics. To deconstruct the factors that contribute to this variation, we used a combination of experimental and modeling approaches. We found that demographic stochasticity and stationary heterogeneity in the host carrying capacity or bacterial growth rate are insufficient to explain quantitatively the variation observed in our empirical data. Instead, we found that the data can be understood if the host-bacteria system can be viewed as stochastically switching between high and low growth rates phenotypes. This suggests the dynamics significantly more complex than logistic growth used in canonical models of microbiome assembly. We develop mathematical models of this process that can explain various aspects of our data. We highlight the limitations of snapshot data in describing variation in host-associated communities and the importance of using time-series data along with mathematical models to understand microbial dynamics within a host.Competing Interest StatementThe authors have declared no competing interest.

Microbial community assembly is determined in part by interactions between taxa that colonize ecological niches available within habitat patches. The outcomes of these interactions, and by extension the trajectory of community assembly, can display priority effects - dependency on the order in which taxa first occupy these niches. The underlying mechanisms of these phenomena vary from system to system and are often not well resolved. Here, we characterize priority effects in colonization of the squash bug (Anasa tristis) by bacterial symbionts from the genus Caballeronia, using pairs of strains that are known to strongly compete during host colonization, as well as strains that are isogenic and thus functionally identical. By introducing symbiont strains into individual bugs in a sequential manner, we show that within-host populations established by the first colonist are extremely resistant to invasion, regardless of strain identity and competitive interactions. By knocking down the population of an initial colonist with antibiotics, we further show that colonization success by the second symbiont is still diminished even when space in the symbiotic organ is available and physically accessible for colonization. We propose a paradigm in which resident symbionts exclude subsequent infections by manipulating the host environment, partially but not exclusively by eliciting tissue remodeling of the symbiont organ. Host-associated microbial communities underpin critical ecosystem processes and human health, and their ability to do so is determined in turn by the various processes that shape their composition. While natural selection acts on competing genotypes and species during community assembly, the manner by which selection determines the trajectory of community assembly can differ depending on the sequence by which taxa establish within that community. We document this phenomenon, known as a priority effect, during experimental colonization of a North American insect pest, the squash bug Anasa tristis, by its betaproteobacterial symbionts in the genus Caballeronia. Our study demonstrates how stark, strain-level variation can emerge in specialized host-microbe symbioses simply through differences in the order by which strains colonize the host. Understanding the mechanistic drivers of community structure in host-associated microbiomes can highlight both pitfalls and opportunities for the engineering of these communities and their constituent taxa for societal benefit.