Evaluating the Assembly Dynamics in the Human Vaginal Microbiomes With Niche-Neutral Hybrid Modeling

Using 2,733 longitudinal vaginal microbiome samples (representing local microbial communities) from 79 individuals (representing meta-communities) in the states of healthy, BV (bacterial vaginosis) and pregnancy, we assess and interpret the relative importance of stochastic forces (e.g., stochastic drifts in bacteria demography, and stochastic dispersal) vs. deterministic selection (e.g., host genome, and host physiology) in shaping the dynamics of human vaginal microbiome (HVM) diversity by an integrated analysis with multi-site neutral (MSN) and niche-neutral hybrid (NNH) modeling. It was found that, when the traditional “default” P-value = 0.05 was specified, the neutral drifts were predominant (≥50% metacommunities indistinguishable from the MSN prediction), while the niche differentiations were moderate (<20% from the NNH prediction). The study also analyzed two challenging uncertainties in testing the neutral and/or niche-neutral hybrid models, i.e., lack of full model specificity – non-unique fittings of same datasets to multiple models with potentially different mechanistic assumptions – and lack of definite rules for setting the P-value thresholds (also noted as Pt-value when referring to the threshold of P-value in this article) in testing null hypothesis (model). Indeed, the two uncertainties can be interdependent, which further complicates the statistical inferences. To deal with the uncertainties, the MSN/NNH test results under a series of P-values ranged from 0.05 to 0.95 were presented. Furthermore, the influence of P-value threshold-setting on the model specificity, and the effects of woman’s health status on the neutrality level of HVM were examined. It was found that with the increase of P-value threshold from 0.05 to 0.95, the overlap (non-unique) fitting of MSN and NNH decreased from 29.1 to 1.3%, whereas the specificity (uniquely fitted to data) of MSN model was kept between 55.7 and 82.3%. Also with the rising P-value threshold, the difference between healthy and BV groups become significant. These findings suggested that traditional single P-value threshold (such as the de facto standard P-value = 0.05) might be insufficient for testing the neutral and/or niche neutral hybrid models.

Noise-Induced Versus Intrinsic Oscillation in Ecological Systems

Studies of populations oscillating through time have a long history in ecology as these dynamics can help provide insights into the causes of population regulation. A particularly difficult challenge is determining the relative role of deterministic versus stochastic forces in producing this oscillatory behavior. Another classic ecological study area is the study of spatial synchrony which also has helped unravel underlying population dynamic principles. One possible approach to understanding the causes of population cycles is based on the idea that a focus on spatiotemporal behavior, oscillations in coupled populations, can provide much further insight into the relative role of deterministic versus stochastic forces. Using ideas based on concepts from statistical physics, we develop results showing that in a system with coupling between adjacent populations, a study of spatial synchrony provides much information about the underlying causes of oscillations. Novel, to ecology, measures of spatial synchrony are a key step.

Characterization and Performance of Lactate-Feeding Consortia for Reductive Dechlorination of Trichloroethene

Understanding the underlying mechanism that drives the microbial community mediated by substrates is crucial to enhance the biostimulation in trichloroethene (TCE)-contaminated sites. Here, we investigated the performance of stable TCE-dechlorinating consortia by monitoring the variations in TCE-related metabolites and explored their underlying assembly mechanisms using 16S rDNA amplicon sequencing and bioinformatics analyses. The monitoring results indicated that three stable TCE-dechlorinating consortia were successfully enriched by lactate-containing anaerobic media. The statistical analysis results demonstrated that the microbial communities of the enrichment cultures changed along with time and were distinguished by their sample sources. The deterministic and stochastic processes were simultaneously responsible for shaping the TCE-dechlorinating community assembly. The indicator patterns shifted with the exhaustion of the carbon source and the pollutants, and the tceA-carrying Dehalococcoides, as an indicator for the final stage samples, responded positively to TCE removal during the incubation period. Pseudomonas, Desulforhabdus, Desulfovibrio and Methanofollis were identified as keystone populations in the TCE-dechlorinating process by co-occurrence network analysis. The results of this study indicate that lactate can be an effective substrate for stimulated bioremediation of TCE-contaminated sites, and the reduction of the stochastic forces or enhancement of the deterministic interventions may promote more effective biostimulation.

Microbial population dynamics are dominated by stochastic forces in a low biomass subseafloor habitat

The subseafloor is a vast global habitat that supports microorganisms that have a global scale impact on geochemical cycles. Much of the subseafloor contains endemic microbial populations that consist of small populations under growth-limited conditions. For small population sizes, the impacts of stochastic evolutionary events can have large impacts on intraspecific population dynamics and allele frequencies. These conditions are fundamentally different than those experienced by most microorganisms in surface environments, and it is unknown how small population sizes and growth-limiting conditions influence evolution and population structure. Using a two-year, high-resolution environmental time-series, we examine the dynamics of 10 microbial populations from cold, oxic crustal fluids collected from the subseafloor site North Pond, located near the mid-Atlantic ridge. The 10 microbial populations were divided into groups with distinct patterns of population dynamics based on abundance, nucleotide diversity, and changes in allele frequency. Results reveal rapid allele frequency shifts linked to different types of population interactions, including sweeps, dispersal, and clonal expansion. Dispersal plays an important role in structuring the most abundant populations in the crustal fluids. Microbial populations in the subseafloor of North Pond are highly dynamic and evolution is governed largely by the stochastic forces of dispersal and drift.ImportanceThe cold, oxic subseafloor is an understudied habitat that is difficult to access, yet important to global biogeochemical cycles and starkly different compared microbial habitats on the surface of the Earth. Our understanding of microbial evolution and population dynamics has been largely molded by studies of microbes living in surface habitats that can host 10-1,000 times more microbial biomass than has been observed in the subsurface. This study provides an opportunity to observe evolution in action within a low biomass, growth-limited environment and reveals that while microbial populations in the subseafloor can be influenced by changes in selection pressure and small-scale gene sweeps, the stochastic forces of genetic drift and dispersal have an important impact on the evolution of microbial populations. Much of the microbial life on the planet exists under growth-limited conditions and the subseafloor provides a natural laboratory to explore these fundamental biological questions.

The evolutionary scaling of cellular traits imposed by the drift barrier

Owing to internal homeostatic mechanisms, cellular traits may experience long periods of stable selective pressures, during which the stochastic forces of drift and mutation conspire to generate variation. However, even in the face of invariant selection, the drift barrier defined by the genetic effective population size, which is negatively associated with organism size, can have a substantial influence on the location and dispersion of the long-term steady-state distribution of mean phenotypes. In addition, for multilocus traits, the multiplicity of alternative, functionally equivalent states can draw mean phenotypes away from selective optima, even in the absence of mutation bias. Using a framework for traits with an additive genetic basis, it is shown that 1) optimal phenotypic states may be only rarely achieved; 2) gradients of mean phenotypes with respect to organism size (i.e., allometric relationships) are likely to be molded by differences in the power of random genetic drift across the tree of life; and 3) for any particular set of population-genetic conditions, significant variation in mean phenotypes may exist among lineages exposed to identical selection pressures. These results provide a potentially useful framework for understanding numerous aspects of cellular diversification and illustrate the risks of interpreting such variation in a purely adaptive framework.

Fungal community assembly in drought-stressed sorghum shows stochasticity, selection, and universal ecological dynamics

Community assembly of crop-associated fungi is thought to be strongly influenced by deterministic selection exerted by the plant host, rather than stochastic processes. Here we use a simple, sorghum system with abundant sampling to show that stochastic forces (drift or stochastic dispersal) act on fungal community assembly in leaves and roots early in host development and when sorghum is drought stressed, conditions when mycobiomes are small. Unexpectedly, we find no signal for stochasticity when drought stress is relieved, likely due to renewed selection by the host. In our experimental system, the host compartment exerts the strongest effects on mycobiome assembly, followed by the timing of plant development and lastly by plant genotype. Using a dissimilarity-overlap approach, we find a universality in the forces of community assembly of the mycobiomes of the different sorghum compartments and in functional guilds of fungi.

Fast Stokesian dynamics

We present a new method for large scale dynamic simulation of colloidal particles with hydrodynamic interactions and Brownian forces, which we call fast Stokesian dynamics (FSD). The approach for modelling the hydrodynamic interactions between particles is based on the Stokesian dynamics (SD) algorithm (J. Fluid Mech., vol. 448, 2001, pp. 115–146), which decomposes the interactions into near-field (short-ranged, pairwise additive and diverging) and far-field (long-ranged many-body) contributions. In FSD, the standard system of linear equations for SD is reformulated using a single saddle point matrix. We show that this reformulation is generalizable to a host of particular simulation methods enabling the self-consistent inclusion of a wide range of constraints, geometries and physics in the SD simulation scheme. Importantly for fast, large scale simulations, we show that the saddle point equation is solved very efficiently by iterative methods for which novel preconditioners are derived. In contrast to existing approaches to accelerating SD algorithms, the FSD algorithm avoids explicit inversion of ill-conditioned hydrodynamic operators without adequate preconditioning, which drastically reduces computation time. Furthermore, the FSD formulation is combined with advanced sampling techniques in order to rapidly generate the stochastic forces required for Brownian motion. Specifically, we adopt the standard approach of decomposing the stochastic forces into near-field and far-field parts. The near-field Brownian force is readily computed using an iterative Krylov subspace method, for which a novel preconditioner is developed, while the far-field Brownian force is efficiently computed by linearly transforming those forces into a fluctuating velocity field, computed easily using the positively split Ewald approach (J. Chem. Phys., vol. 146, 2017, 124116). The resultant effect of this field on the particle motion is determined through solution of a system of linear equations using the same saddle point matrix used for deterministic calculations. Thus, this calculation is also very efficient. Additionally, application of the saddle point formulation to develop high-resolution hydrodynamic models from constrained collections of particles (similar to the immersed boundary method) is demonstrated and the convergence of such models is discussed in detail. Finally, an optimized graphics processing unit implementation of FSD for mono-disperse spherical particles is used to demonstrated performance and accuracy of dynamic simulations of $O(10^{5})$ particles, and an open source plugin for the HOOMD-blue suite of molecular dynamics software is included in the supplementary material.