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Leventhal, G. E.

Publications and source records attributed to Leventhal, G. E..

2 recordsLinked to original sources

Micro-scale ecology regulates particulate organic matter turnover in model marine microbial communities

The degradation of particulate organic matter in the ocean is a central process in the global carbon cycle, the mode and tempo of which is determined by the bacterial communities that assemble on particle surfaces. Although recent studies have shed light on the dynamics of community assembly on particles -which serve as hotspots of microbial activity in the ocean, the mapping from community composition to function, i.e. particle degradation, remains completely unexplored. Using a collection of marine bacteria cultured from different stages of succession on chitin micro-particles we found that the hydrolytic power of communities is highly dependent on community composition. Different particle degrading taxa -all of which were early successional species during colonization- displayed characteristic particle half-lives that differed by ~170 hours, comparable to the residence time of particles in the oceans mixed layer1. These half-lives were in general longer in multispecies communities, where the growth of obligate cross-feeders limited the ability of degraders to colonize and consume particles. Remarkably, above a certain critical initial ratio of cross-feeder to degrader cells, particle degradation was completely blocked along with the growth of all members of the community. We showed that this interaction occurred between a variety of strains of different taxonomic origins and that it only appears when bacteria interact with particles, suggesting a mechanism by which non-degrading secondary consumers occlude access to the particle resource. Overall, our results show that micro-scale community ecology on particle surfaces can have significant impact on carbon turnover in the ocean.

microbiology

Directly Estimating Epidemic Curves From Genomic Data

Modern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death or behaviour change). Birth-death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models, and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth-death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.

epidemiology