Biology subjects

Remien, C. H.

Publications and source records attributed to Remien, C. H..

8 recordsLinked to original sources

bioRxiv2020

Trait Evolution in Microbial Communities

Understanding the evolutionary dynamics of microbial communities is a key step towards the goal of predicting and manipulating microbiomes to promote beneficial states. While interactions within microbiomes and between microbes and their environment collectively determine the community composition and population dynamics, we are often concerned with traits or functions of a microbiome that link more directly to host health. To study how traits of a microbiome are impacted by eco-evolutionary dynamics, we recast a classic resource-mediated population dynamic model into a population genetic framework which incorporates traits. The relative fitness of each group of microbes can be explicitly written in terms of population dynamic parameters, and corresponding evolutionary dynamics emerge. Using several example systems, we demonstrate how natural selection, mutation, and shifts in the environment work together to produce changes in traits over time.

microbiology↗

bioRxiv2020

Vector dynamics influence spatially imperfect genetic interventions against disease

Background and objectivesGenetic engineering and similar technologies offer promising new approaches to controlling human diseases by blocking transmission from vectors. However, in spatially structured populations, imperfect coverage of the vector will leave pockets in which the parasite can persist. Yet movement by humans may disrupt this local persistence and facilitate eradication when these pockets are small, essentially distributing parasite reproduction out of unprotected areas and into areas that block its reproduction. MethodologyWe develop formal mathematical models of this process similar to standard Ross-Macdonald models, but (i) specifying spatial structure of two patches, with transmission blocked in one patch but not in the other, (ii) allowing temporary human movement (travel instead of migration), and (iii) considering two different modes of mosquito biting. ResultsWe find that there is no invariant effect of disrupting spatial structure with travel. For both biting models, travel out of the unprotected patch has different consequences than travel by visitors into the patch, but the effects are reversed between the two biting models. Conclusions and implicationsOverall, the effect of human travel on the maintenance of vector-borne diseases in structured habitats must be considered in light of the actual biology of mosquito abundances and biting dynamics. Lay summaryGenetic interventions against pathogens transmitted by insect vectors are promising methods of controlling infectious diseases. These interventions may be imperfect, leaving pockets where the parasite persists. How will human movement between protected and unprotected areas affect persistence? Mathematical models developed here show that the answer is ecology-dependent, depending on vector biting behavior.

ecology↗

bioRxiv2020

Cell Type Development in Chlamydia trachomatis Follows a Program Intrinsic to the Reticulate Body.

The obligate intracellular bacterial pathogen Chlamydia trachomatis (Ctr) is reliant on an unusual developmental cycle consisting of two cell forms termed the elementary body (EB) and the reticulate body (RB). The EB is infectious and utilizes a type III secretion system and preformed effector proteins during invasion, but does not replicate. The RB replicates in the host cell but is non-infectious. This developmental cycle is central to chlamydial pathogenesis. In this study we developed mathematical models of the chlamydial developmental cycle that account for potential factors influencing the timing of RB to EB cell type switching during infection. Our models predicted that two broad categories of regulatory signals for RB to EB development could be differentiated experimentally; an "intrinsic" cell autonomous program inherent to each RB or an "extrinsic" environmental signal to which RBs respond. To experimentally differentiate between these hypotheses, we tracked the expression of Ctr developmental specific promoters using fluorescent reporters and live cell imaging. These experiments indicated that EB production was not influenced by increased MOI or by superinfection, suggesting the cycle follows an intrinsic program that is not influenced by environmental factors. Additionally, live cell imaging of these promoter constructs revealed that EB development is a multistep process linked to RB growth rate and cell division. The formation of EBs followed a cell type gene expression progression with the promoters for euo and ihtA active in RBs, while the promoter for hctA was active in early EBs/intermediate cells and finally the promoters for the true late genes, hctB, scc2, and tarp active in the maturing EB. ImportanceChlamydia trachomatis is an obligate intracellular bacteria that can cause trachoma, cervicitis, urethritis, salpingitis, and pelvic inflammatory disease. To establish infection in host cells Chlamydia must complete a multi cell type developmental cycle. The developmental cycle consists of two specialized cells; the EB which mediates infection of new cells and the RB which replicates and eventually produces more EB cells to mediate the next round of infection. By developing and testing mathematical models to discriminate between two competing hypotheses for the nature of the signal controlling RB to EB cell type switching. We demonstrate that RB to EB development follows a cell autonomous program that does not respond to environmental cues. Additionally, we show that RB to EB development is a function of cell growth and cell division. This study serves to further our understanding of the chlamydial developmental cycle that is central to the bacteriums pathogenesis.

microbiology↗

bioRxiv2019

Bayesian estimation of Lassa virus epidemiological parameters: implications for spillover prevention using wildlife vaccination

Lassa virus is a significant burden on human health throughout its endemic region in West Africa, with most human infections the result of spillover from the primary rodent reservoir of the virus, the natal multimammate mouse, M. natalensis. Here we develop a Bayesian methodology for estimating epidemiological parameters of Lassa virus within its rodent reservoir and for generating probabilistic predictions for the efficacy of rodent vaccination programs. Our approach uses Approximate Bayesian Computation (ABC) to integrate mechanistic mathematical models, remotely-sensed precipitation data, and Lassa virus surveillance data from rodent populations. Using simulated data, we show that our method accurately estimates key model parameters, even when surveillance data are available from only a relatively small number of points in space and time. Applying our method to previously published data from two villages in Guinea estimates the time-averaged R0 of Lassa virus to be 1.658 and 1.453 for rodent populations in the villages of Bantou and Tanganya, respectively. Using the posterior distribution for model parameters derived from these Guinean populations, we evaluate the likely efficacy of vaccination programs relying on distribution of vaccine-laced baits. Our results demonstrate that effective and durable reductions in the risk of Lassa virus spillover into the human population will require repeated distribution of large quantities of vaccine. Author SummaryLassa virus is a chronic source of illness throughout West Africa, and is considered to be a threat for widespread emergence. Because most human infections result from contact with infected rodents, interventions that reduce the number of rodents infected with Lassa virus represent promising opportunities for reducing the public health burden of this disease. Evaluating how well alternative interventions are likely to perform is complicated by our relatively poor understanding of viral epidemiology within the reservoir population. Here we develop a novel statistical approach that couples mathematical models and viral surveillance data from rodent populations to robustly estimate key epidemiological parameters. Applying our method to existing data from Guinea yields well-resolved parameter estimates and allows us to simulate a variety of rodent vaccination programs. Together, our results demonstrate that rodent vaccination alone is unlikely to be an effective tool for reducing that public health burden of Lassa fever within West Africa.

ecology↗

bioRxiv2019

Spatial structure undermines parasite suppression by gene drive cargo

Gene drives may be used in two ways to curtail vectored diseases. Both involve engineering the drive to spread in the vector population. One approach uses the drive to directly depress vector numbers, possibly to extinction. The other approach leaves intact the vector population but suppresses the disease agent during its interaction with the vector. This second application may use a drive engineered to carry a genetic cargo that blocks the disease agent. An advantage of the second application is that it is far less likely to select vector resistance to block the drive, but the disease agent may instead evolve resistance to the inhibitory cargo. However, some gene drives are expected to spread so fast and attain such high coverage in the vector population that, if the disease agent can evolve resistance only gradually, disease eradication may be feasible. Here we use simple models to show that spatial structure in the vector population can greatly facilitate persistence and evolution of resistance by the disease agent. We suggest simple approaches to avoid some types of spatial structure, but others may be intrinsic to the populations being challenged and difficult to overcome.

evolutionary biology↗

bioRxiv2019

Gene-drive-mediated extinction is thwarted by evolution of sib mating

Genetic engineering combined with CRISPR technology has developed to the point that gene drives can, in theory, be engineered to cause extinction in countless species. Success of extinction programs now rests on the possibility of resistance evolution, which is largely unknown. For CRISPR technology, resistance may take many forms, from mutations in the nuclease target sequence to specific types of non-random population structures that limit the drive. We develop mathematical models of various deviations from random mating to consider escapes from extinction-causing gene drives. We use a version of Maynard Smiths haystack model to show that population structure can enable drive-free subpopulations to be maintained against gene drives. Our main emphasis, however, is sib mating in the face of recessive-lethal and Y-chromosome drives. Sib mating easily evolves in response to both kinds of gene drives and maintains mean fitness above 0, with equilibrium fitness depending on the level of inbreeding depression. Environmental determination of sib mating (as might stem from population density crashes) can also maintain mean fitness above 0. Translation of mean fitness into population size depends on ecological details, so understanding mean fitness evolution and dynamics is merely the first step in predicting extinction. Nonetheless, these results point to possible escapes from gene drive-mediated extinctions that lie beyond the control of genome engineering.

evolutionary biology↗

bioRxiv2019

Microbial phenotypic heterogeneity in response to a metabolic toxin: continuous, dynamically shifting distribution of formaldehyde tolerance in Methylobacterium extorquens populations

While microbiologists often make the simplifying assumption that genotype determines phenotype in a given environment, it is becoming increasingly apparent that phenotypic heterogeneity (in which one genotype generates multiple phenotypes simultaneously even in a uniform environment) is common in many microbial populations. The importance of phenotypic heterogeneity has been demonstrated in a number of model systems involving binary phenotypic states (e.g., growth/non-growth); however, less is known about systems involving phenotype distributions that are continuous across an environmental gradient, and how those distributions change when the environment changes. Here, we describe a novel instance of phenotypic diversity in tolerance to a metabolic toxin within wild-type populations of Methylobacterium extorquens, a ubiquitous phyllosphere methylotroph capable of growing on the methanol periodically released from plant leaves. The first intermediate in methanol metabolism is formaldehyde, a potent cellular toxin that is lethal in high concentrations. We have found that at moderate concentrations, formaldehyde tolerance in M. extorquens is heterogeneous, with individual cells minimum tolerance levels ranging between 0 mM and 8 mM. This form of heterogeneity is continuous in terms of threshold (in the range of maximum tolerances possible), yet binary in outcome (at a given formaldehyde concentration, cells either grow normally or die, with no intermediate phenotype), and it is not associated with any detectable genetic mutations. Moreover, tolerance distributions within the population are dynamic, changing over time in response to growth conditions. We characterized this phenomenon using bulk liquid culture experiments, colony growth tracking, flow cytometry, time-lapse microscopy, and genome resequencing. Finally, we used mathematical modeling to better understand the processes by which cells change phenotype, and found evidence for both stochastic, bidirectional phenotype diversification and responsive, directed phenotype shifts, depending on the growth substrate and the presence of toxin.

microbiology↗

bioRxiv2018

Parameter Identifiability of the Generalized Lotka-Volterra Model for Microbiome Studies

BackgroundPopulation dynamic models can be used in conjunction with time series of species abundances to infer interactions. Understanding microbial interactions is a prerequisite for numerous goals in microbiome research; predicting how populations change over time, determining how manipulations of microbiomes affect dynamics, and designing synthetic microbiomes to perform tasks are just a few examples. As such, there is great interest in adapting population dynamic theory for microbial systems. Despite the appeal, numerous hurdles exist. One hurdle is that the data commonly obtained from DNA sequencing yield estimates of relative abundances, while population dynamic models such as the generalized Lotka-Volterra model track absolute abundances or densities. It is not clear whether relative abundance data alone can be used to infer parameters of population dynamic models such as the Lotka-Volterra model.\n\nResultsWe used structural identifiability analyses to determine the extent to which time series of relative abundances can be used to parameterize the generalized Lotka-Volterra model. We found that only with absolute abundance data to accompany relative abundance estimates from sequencing can all parameters be uniquely identified. However, relative abundance data alone do contain information on relative interaction strengths, which is sufficient for many studies where the goal is to estimate key interactions and their effects on dynamics. Our results also indicate that the relative interaction rates that can be estimated using relative abundance data provide ample information to estimate relative changes of absolute abundance over time. Using synthetic data for which we know the underlying structure, we found our results to be robust to modest amounts of both process and measurement error.\n\nConclusionsFitting the generalized Lotka-Volterra model to time-series sequencing data typically requires either assuming a constant population size or performing additional measurements to obtain absolute abundances. We have found that these assumptions are not strictly necessary because relative abundance data alone contain sufficient information to estimate relative rates of interaction, and thus to infer key drivers of microbial population dynamics.

microbiology↗