Resumen de How genetic, social, and evolutionary interactions shape the many levels of biological complexity
Interactions between genes, between social individuals, and between the results of alternative evolutionary histories reflect the organization and context-dependent properties of each respective level of biological complexity. Genetic interactions modify the combined effect of two genes on the characteristics of an organism. Social interactions develop when some individuals of a population contribute to a common resource at a personal cost. Evolutionary interactions result when adaptation to a particular environment changes survival in unrelated conditions. We studied these three types of interactions with a combination of computational and experimental approaches using microbes. First, we evaluate the stability of interactions between metabolic genes upon changes in the genetic background. We compared the genetic interaction networks of an in silico model of Saccharomyces cerevisiae in two types of backgrounds: single deletions and accumulation of neutral mutations. Network rewiring was strongly associated to catabolic genes, revealing that they can add to an organism’s growth in different configurations thus buffering genetic perturbations.
Neutral deletion backgrounds greatly reduced both this genetic buffering and the ability to grow on alternative nutrients, connecting both environmental and genomic robustness. Second, we tracked the sustainability of a microbial community where a social cooperative interaction is essential for survival. Non-cooperative individuals tend to appear and threaten the collective effect by exploting cooperators. Using an engineered interaction between two strains of Escherichia coli we show how feedback between population and evolutionary dynamics, combined with spatial structure, can create a context where invasion by non-cooperators instead preserves the social behavior. We further analyze how the molecular implementation of a social interaction can modify such dynamics, on the synthetic E. coli system and in the natural production of an iron-scavenging molecule by Pseudomonas fluorescens. Third, we assessed the predictability of the effect of an organism’s prior history on its reaction to a novel environment. We compared the evolutionary interaction networks associated to the adaptation of a laboratory strain of E. coli to different antibiotic classes. Acquiring resistance to the same drug could nevertheless result in different responses to an alternative compound, including opposite effects on survival. We discuss how a combination of genomic architecture and historical contingency can produce these contrasting outcomes.
