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Goldford, J.

Publications and source records attributed to Goldford, J..

5 recordsLinked to original sources

The History of Enzyme Evolution Embedded in Metabolism

Whereas phylogenetic reconstructions are a primary record of protein evolution, it is unknown whether the deep history of enzymes are encoded at higher levels of biological organization. Here, we demonstrate that the emergence and reuse history of enzymatic folds is embedded within the web of metabolite-cofactor-enzyme interdependencies that comprise biosphere-scale metabolic reaction networks. Using a simple network analysis approach, we reconstruct the relative ordering of enzymatic fold emergence and, where possible, the first reaction(s) that each enzymatic fold catalyzed. We find that a large majority of enzymatic folds were sufficient as independent additions to open new avenues for metabolic growth. The resulting network-based histories are broadly concordant with enzyme phyletic distribution in prokaryotes, a proxy for enzyme age. Our results suggest that the earliest enzyme-mediated metabolisms were enriched for /{beta} proteins, likely due to their strong association with cofactor utilization, and that -proteins preferentially emerge at later stages. The cradle-loop barrel, a member of the small {beta}-barrel metafold, is predicted to be the founding {beta}-fold, in agreement with analyses of ribosome structure. An examination of how the protein universe responded to the biological production of molecular oxygen reveals that the adaptation of existing enzymatic folds, not novel fold emergence, was the primary driver of metabolic evolution. This work presents a self-consistent model of metabolic and enzyme evolution, key progress towards integrating diverse perspectives into a unified history of protein evolution. Significance StatementEnzyme emergence is an ongoing process that began [~]4 billion years ago. Here, we show that the modern biosphere-scale network of metabolic reactions and enzymes is an archive of enzyme history independent from, but concordant with, phylogenetics. Based on this record, we predict the order of enzyme emergence from before the last universal common ancestor up until the biological production and metabolic utilization of molecular oxygen. We find that while /{beta} proteins dominated primitive enzyme-mediated metabolism, other folds -- including the cradle-loop barrel, which is a member of the small {beta}-barrel metafold -- were likely important early contributors. This study represents key progress towards building an internally consistent, joint history of metabolic reactions and enzymes.

bioinformatics↗

Metabolic similarity and the predictability of microbial community assembly

AbstractWhen microbial communities form, their composition is shaped by selective pressures imposed by the environment. Can we predict which communities will assemble under different environmental conditions? Here, we hypothesize that quantitative similarities in metabolic traits across metabolically similar environments lead to predictable similarities in community composition. To that end, we measured the growth rate and by-product profile of a library of proteobacterial strains in a large number of single nutrient environments. We found that growth rates and secretion profiles were positively correlated across environments when the supplied substrate was metabolically similar. By analyzing hundreds of in-vitro communities experimentally assembled in an array of different synthetic environments, we then show that metabolically similar substrates select for taxonomically similar communities. These findings lead us to propose and then validate a comparative approach for quantitatively predicting the effects of novel substrates on the composition of complex microbial consortia.

ecology↗

Community ecology of phages on a clonal bacterial host

Bacteriophages are the most abundant and diverse biological entities on Earth, yet the ecological mechanisms that sustain this extraordinary diversity remain unclear. Here, we have discovered a general mechanism that allows phage diversity to outstrip the diversity of their bacterial hosts. We assembled and passaged dozens of diverse phage communities on a single, non-coevolving strain of Escherichia coli until the phage communities reached equilibrium. In all cases, we found multiple phage species coexisted stably, despite competition for a single, clonal host population. Coexistence within these communities was supported through host phenotypic heterogeneity, whereby phages specialized on cells adopting different growth phenotypes. Our experiments reveal a rich community ecology of bacteriophages where multiple phage species coexist and interact at the scale of a single bacterial host. One-Sentence SummaryDiverse communities of phages emerge stably and consistently on a clonal bacterial host, enabled by phenotypic heterogeneity.

microbiology↗

Particle-associated and free-living bacterial communities in an oligotrophic sea are affected by different environmental and anthropogenic factors

In the oceans and seas, environmental conditions change over multiple temporal and spatial scales. Here, we ask what factors affect the bacterial community structure across time, depth and size fraction during six seasonal cruises (two years) in the ultra-oligotrophic Eastern Mediterranean Sea. The bacterial community varied most between size fractions (free-living vs particle-associated), followed by depth and finally season. The free-living (FL) community was taxonomically richer and more stable than the particle-associated (PA) one, which was characterized by recurrent "blooms" of heterotrophic bacteria such as Alteromonas and Ralstonia. The heterotrophic FL and PA communities were also correlated with different environmental parameters: depth and phytoplankton correlated with the FL population, whereas PA bacteria were correlated primarily with season. A significant part of the variability in community structure could not, however, be explained by the measured environmental parameters. The metabolic potential of the PA community, predicted from 16S amplicon data, was enriched in pathways associated with the degradation and utilization of biological macromolecules, as well as plastics, other petroleum products and herbicides. The FL community was enriched in pathways for the metabolism of inositol phosphate, a potential phosphorus source, and of polycyclic aromatic hydrocarbons. Originality - Significance StatementMarine microbial populations are complex and dynamic, and the environmental drivers of the structure and function of these communities are mostly unclear. Specifically, marine microbial communities change over time, over depth and between particle-associated and free-living size fractions, yet the relative importance of each of these axes of variability is unclear. Our results highlight fundamentally different population dynamics between free-living and particle-associated marine bacteria: free living populations were more similar between seasons, whereas particle-associated populations were highly variable and exhibited "blooms" of specific clades of heterotrophic bacteria. We also suggest that the environmental conditions often measured as part of oceanographic cruises are not enough to explain most of the variability in microbial population structure. We speculate that organismal interactions and the presence of anthropogenic pollution may be also be important yet under-sampled drivers of oligotrophic marine microbial communities.

ecology↗

The Community Simulator: A Python package for microbial ecology

Natural microbial communities contain hundreds to thousands of interacting species. For this reason, computational simulations are playing an increasingly important role in microbial ecology. In this manuscript, we present a new open-source, freely available Python package called Community Simulator for simulating microbial population dynamics in a reproducible, transparent and scalable way. The Community Simulator includes five major elements: tools for preparing the initial states and environmental conditions for a set of samples, automatic generation of dynamical equations based on a dictionary of modeling assumptions, random parameter sampling with tunable levels of metabolic and taxonomic structure, parallel integration of the dynamical equations, and support for metacommunity dynamics with migration between samples. To significantly speed up simulations using Community Simulator, our Python package implements a new Expectation-Maximization (EM) algorithm for finding equilibrium states of community dynamics that exploits a recently discovered duality between ecological dynamics and convex optimization. We present data showing that this EM algorithm improves performance by between one and two orders compared to direct numerical integration of the corresponding ordinary differential equations. We conclude by listing several recent applications of the Community Simulator to problems in microbial ecology, and discussing possible extensions of the package for directly analyzing microbiome compositional data.

ecology↗