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 .

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Full development of IL-17 producing CD4+ T helper cells (TH17 cells) requires the transcriptional activity of both orphan nuclear receptors RORα and RORγt. However, RORα is considered functionally redundant to RORγt; therefore, the function and therapeutic value of RORα in TH17 cells is unclear. Here, using mouse models of autoimmune and chronic inflammation, we show that expression of RORα is required for TH17 cell pathogenicity. T-cell-specific deletion of RORα reduces the development of experimental autoimmune encephalomyelitis (EAE) and colitis. Reduced inflammation is associated with decreased TH17 cell development, lower expression of tissue-homing chemokine receptors and integrins, and increased frequencies of Foxp3+ T regulatory cells. Importantly, inhibition of RORα with a selective small molecule antagonist mostly phenocopies our genetic data, showing potent suppression of the in vivo development of both chronic/progressive and relapsing/remitting EAE, but with no effect on overall thymic cellularity. Furthermore, use of the RORα antagonist effectively inhibits human TH17 cell differentiation and memory cytokine secretion. Together, these data suggest that RORα functions independent of RORγt in programming TH17 pathogenicity and identifies RORα as a safer and more selective therapeutic target for the treatment of TH17-mediated autoimmunity.