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Rosean, S.

Publications and source records attributed to Rosean, S..

2 recordsLinked to original sources

No Trade-Offs Required: Cross-Feeding From Survival Alone

Cross-feeding relationships shape the composition of many microbial communities, yet the evolutionary processes that give rise to them remain poorly understood. Most theoretical and experimental work has therefore focused on minimal scenarios, particularly the stable cross-feeding polymorphisms that evolve in asexual populations growing on a single energy source (Helling et al., 1987). Yet replicate experiments do not always produce cross-feeding populations, raising the question of why genetically identical populations evolving under identical conditions can follow different evolutionary trajectories (Treves et al., 1998). Here we present a bare-bones agent-based model of evolution in a chemostat. We show that selection for energy acquisition alone is sufficient to promote the evolution of cross-feeding, without invoking mechanisms specific to metabolic exchange. The resulting communities nevertheless differ across replicate simulations, reproducing the qualitative variability observed experimentally. Significance StatementMicrobial communities often depend on cross-feeding, in which one cells metabolic product becomes anothers energy source. Existing explanations typically invoke trade-offs between metabolic tasks or other mechanisms specific to cross-feeding itself. Using large-scale in silico simulations of evolution in a chemostat, we show that no such explanation is required. A population that competes for metabolic energy by utilizing a primary resource and then releasing a product that may itself serve as an energy source can evolve into a mixed population of organisms that specialize in the primary resource alongside others that specialize in the secondary one. Energy-based probabilistic death and reproduction are sufficient to produce this coexistence and to reproduce the mixed outcomes seen in laboratory evolution experiments.

evolutionary biology↗

Regulatory Landscape Enrichment Analysis (RLEA) using gaiaAssociation

MotivationTo understand whether sets of genomic loci are enriched at the regulatory loci of one or more cell types, we developed the gaiaAssociation package to perform Regulatory Landscape Enrichment Analysis (RLEA). RLEA is a novel analytical process that tests for enrichment of sets of loci in cell type-specific open chromatin regions (OCRs) in the genome. ResultsWe demonstrate that the application of RLEA to genome-wide association study (GWAS) data reveals cell types likely to be mediating the phenotype studied, and clusters OCRs based on their shared regulatory profiles. GaiaAssociation is Python code that is freely available for use in functional genomics studies. Availability and ImplementationGaia Association is available on PyPi (https://pypi.org/project/gaiaAssociation/0.6.0/#description) for pip download and use on the command line or as an inline Python package. Gaia Association can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation. Contactjohn.greally@einsteinmed.edu

bioinformatics↗