Biology subjects

Jones, E. W.

Publications and source records attributed to Jones, E. W..

4 recordsLinked to original sources

bioRxiv2025

How to quantify immigration from community abundance data using the Neutral Community Model

Biological communities are connected through dispersal, which regulates diversity across local and regional scales. However, dispersal is difficult to measure directly, limiting what is known about dispersals impact on species composition in complex communities. One method to measure dispersal employs the Neutral Community Model (NCM) to quantify how a local community is influenced by the immigration of individuals from a larger source community. Conveniently, the immigration rate NTm of the NCM can be fit from biological sequence abundance datasets, which are plentiful. Yet it is neither known if these estimated values reflect the ground truth, nor what sampling effort is required to yield accurate estimates. In this study we introduce two inference methods, a variance-based and a Dirichlet-Multinomial Log-Likelihood (DM-LL) method, to complement the established occupancy-based inference method. In simulations of communities that resemble activated sludge microbiomes, all inference methods were capable of estimating NTm within 10% of ground-truth, with the variance-based and DM-LL methods requiring less sampling effort. Accurate inferences require read depths greater than NTm in each sample. The three methods agree in their inferred NTm in simulations of communities experiencing weak non-neutral effects (e.g., selection), and in applications to an empirical dataset from wastewater activated sludge. Based on these findings, we propose practical sampling and methodological guidelines for quantifying immigration between highly diverse, complex communities using the NCM.

ecology↗

bioRxiv2021

A gut commensal niche regulates stable association of a multispecies microbiota

The intestines of animals are typically colonized by a complex, relatively stable microbiota that influences health and fitness, but the underlying mechanisms of colonization remain poorly understood. As a typical animal, the fruit fly, Drosophila melanogaster, is associated with a consistent set of commensal bacterial species, yet the reason for this consistency is unknown. Here, we use gnotobiotic flies, microscopy, and microbial pulse-chase protocols to show that a commensal niche exists within the proventriculus region of the Drosophila foregut that selectively binds bacteria with exquisite strain-level specificity. Primary colonizers saturate the niche and exclude secondary colonizers of the same strain, but initial colonization by Lactobacillus physically remodels the niche to favor secondary colonization by Acetobacter. Our results provide a mechanistic framework for understanding the establishment and stability of an intestinal microbiome. One-Sentence SummaryA strain-specific set of bacteria inhabits a defined spatial region of the Drosophila gut that forms a commensal niche.

microbiology↗

bioRxiv2021

Stochastic microbiome assembly depends on context

Observational studies reveal substantial variability in microbiome composition across individuals. While some of this variability can be explained by external factors like environmental, dietary, and genetic differences between individuals, in this paper we show that for the model organism Drosophila melanogaster the process of microbiome assembly is inherently stochastic and contributes a baseline level of microbiome variability even among organisms that are identically reared, housed, and fed. In germ-free flies fed known combinations of bacterial species, we find that some species colonize more frequently than others even when fed at the same high concentration. We develop a new ecological technique that infers the presence of interactions between bacterial species based on their colonization odds in different contexts, requiring only presence/absence data from two-species experiments. We use a progressive sequence of probabilistic models, in which the colonization of each bacterial species is treated as an independent stochastic process, to reproduce the empirical distributions of colonization outcomes across experiments. We find that incorporating context-dependent interactions substantially improves the performance of the models. Stochastic, context-dependent microbiome assembly underlies clinical therapies like fecal microbiota transplantation and probiotic administration, and should inform the design of synthetic fecal transplants and dosing regimes. Significance StatementIndividuals are constantly exposed to microbial organisms that may or may not colonize their gut microbiome, and newborn individuals assemble their microbiomes through a number of these acquisition events. Since microbiome composition has been shown to influence host physiology, a mechanistic understanding of community assembly has potentially therapeutic applications. In this paper we study microbiome acquisition in a highly-controlled setting using germ-free fruit flies inoculated with specific bacterial species at known abundances. Our approach revealed that acquisition events are stochastic, and the colonization odds of different species in different contexts encode ecological information about interactions. These findings have consequences for microbiome-based therapies like fecal microbiota transplantation that attempt to modify a persons gut microbiome by deliberately introducing foreign microbes.

microbiology↗

bioRxiv2017

High-dimensional microbiome interactions shape host fitness

Gut bacteria can affect key aspects of host fitness, such as development, fecundity, and lifespan, while the host in turn shapes the gut microbiome. Microbiomes co-evolve with their hosts and have been implicated in host speciation. However, it is unclear to what extent individual species versus community interactions within the microbiome are linked to host fitness. Here we combinatorially dissect the natural microbiome of Drosophila melanogaster and reveal that interactions between bacteria shape host fitness through life history tradeoffs. We find that the same microbial interactions that shape host fitness also shape microbiome abundances, suggesting a potential evolutionary mechanism by which microbiome communities (rather than just individual species) may be intertwined in co-selection with their hosts. Empirically, we made germ-free flies colonized with each possible combination of the five core species of fly gut bacteria. We measured the resulting bacterial community abundances and fly fitness traits including development, reproduction, and lifespan. The fly gut promoted bacterial diversity, which in turn accelerated development, reproduction, and aging: flies that reproduced more died sooner. From these measurements we calculated the impact of bacterial interactions on fly fitness by adapting the mathematics of genetic epistasis to the microbiome. Host physiology phenotypes were highly dependent on interactions between bacterial species. Higher-order interactions (involving 3, 4, and 5 species) were widely prevalent and impacted both host physiology and the maintenance of gut diversity. The parallel impacts of bacterial interactions on the microbiome and on host fitness suggest that microbiome interactions may be key drivers of evolution.\n\nSignificanceAll animals have associated microbial communities called microbiomes that can influence the physiology and fitness of their host. It is unclear to what extent individual microbial species versus ecology of the microbiome influences fitness of the host. Here we mapped all the possible interactions between individual species of bacteria with each other and with the hosts physiology. Our approach revealed that the same bacterial interactions that shape microbiome abundances also shape host fitness traits. This relationship provides a feedback that may favor the emergence of co-evolving microbiome-host units.

microbiology↗