Directed evolution has been used for decades to engineer biological systems from the top-down. Generally, it has been applied at or below the organismal level, by iteratively sampling the mutational landscape in a guided search for genetic variants of higher function. Above the organismal level, a small number of studies have attempted to artificially select microbial communities and ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is still limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. To address this issue, we have developed a flexible modeling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions, in a wide range of ecological conditions. By artificially selecting hundreds of in-silico microbial metacommunities under identical conditions, we examine the fundamental limits of the two main breeding methods used so far, and prescribe modifications that significantly increase their power. We identify a range of directed evolution strategies that, particularly when applied in combination, are better suited for the top-down engineering of large, diverse, and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search for dynamically stable and ecologically and functionally resilient high-functioning communities.1 .
Directed evolution has been used for decades to engineer biological systems from the top-down. Generally, it has been applied at or below the organismal level, by iteratively sampling the mutational landscape in a guided search for genetic variants of higher function. Above the organismal level, a small number of studies have attempted to artificially select microbial communities and ecosystems, with uneven and generally modest success. Our theoretical understanding of artificial ecosystem selection is still limited, particularly for large assemblages of asexual organisms, and we know little about designing efficient methods to direct their evolution. To address this issue, we have developed a flexible modeling framework that allows us to systematically probe any arbitrary selection strategy on any arbitrary set of communities and selected functions, in a wide range of ecological conditions. By artificially selecting hundreds of in-silico microbial metacommunities under identical conditions, we examine the fundamental limits of the two main breeding methods used so far, and prescribe modifications that significantly increase their power. We identify a range of directed evolution strategies that, particularly when applied in combination, are better suited for the top-down engineering of large, diverse, and stable microbial consortia. Our results emphasize that directed evolution allows an ecological structure-function landscape to be navigated in search for dynamically stable and ecologically and functionally resilient high-functioning communities.
The social amoeba Dictyostelium discoideum is a model for a wide range of biological processes including chemotaxis, cell-cell communication, phagocytosis, and development. Interrogating these processes with modern genetic tools often requires the expression of multiple transgenes. While it is possible to transfect multiple transcriptional units, the use of separate promoters and terminators for each gene leads to large plasmid sizes and possible interference between units. In many eukaryotic systems this challenge has been addressed through polycistronic expression mediated by 2A viral peptides, permitting efficient, co-regulated gene expression. Here, we screen the most commonly used 2A peptides, porcine teschovirus-1 2A (P2A), Thosea asigna virus 2A (T2A), equine rhinitis A virus 2A (E2A), and foot-and-mouth disease virus 2A (F2A), for activity in D. discoideum and find that all the screened 2A sequences are effective. However, combining the coding sequences of two proteins into a single transcript leads to notable strain-dependent decreases in expression level, suggesting additional factors regulate gene expression in D. discoideum that merit further investigation. Our results show that P2A is the optimal sequence for polycistronic expression in D. discoideum, opening up new possibilities for genetic engineering in this model system.
The social amoeba Dictyostelium discoideum is a model for a wide range of biological processes including chemotaxis, cell-cell communication, phagocytosis, and development. Interrogating these processes with modern genetic tools often requires the expression of multiple transgenes. While it is possible to transfect multiple transcriptional units, the use of separate promoters and terminators for each gene leads to large plasmid sizes and possible interference between units. In other eukaryotic systems this challenge has been addressed through polycistronic expression mediated by 2A viral peptides, permitting efficient, co-regulated gene expression. Here, we screen the most commonly used 2A peptides, porcine teschovirus-1 2A (P2A), Thosea asigna virus 2A (T2A), equine rhinitis A virus 2A (E2A), and foot-and-mouth disease virus 2A (F2A), for activity in D. discoideum. We find that these 2A sequences are effective but that, unexpectedly, combining the coding sequences of two proteins into a single transcript leads to notable decreases in expression level with strain-dependent magnitudes, suggesting unexplored constraints of gene expression in D. discoideum that merit further investigation. Our results show that P2A is the optimal sequence for polycistronic expression, opening up new possibilities for genetic engineering in this model system.
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