assembly-quantification-co-cultures-combining-heterotrophic-yeast

Assembly and Quantification of Co-Cultures Combining Heterotrophic Yeast with Phototrophic Sugar-Secreting Cyanobacteria

With the increasing demand for sustainable biotechnologies, mixed consortia containing a phototrophic microbe and heterotrophic partner species are being ...

Michigan State University

There has been substantial recent interest in the promise of sustainable, light-driven bioproduction using cyanobacteria, including developing efforts for microbial bioproduction using mixed autotroph/heterotroph communities, which could provide useful properties, such as division of metabolic labor. However, building stable mixed-species communities of sufficient productivity remains a challenge, partly due to the lack of strategies for synchronizing and coordinating biological activities across different species. To address this obstacle, we developed an inter-species communication system using quorum sensing (QS) modules derived from well-studied pathways in heterotrophic microbes. In the model cyanobacterium,  PCC 7942 (), we designed, integrated, and characterized genetic circuits that detect acyl-homoserine lactones (AHLs), diffusible signals utilized in many QS pathways. We showed that these receiver modules sense exogenously supplied AHL molecules and activate gene expression in a dose-dependent manner. We characterized these AHL receiver circuits in parallel with  W ( W) to dissect species-specific properties, finding broad agreement, albeit with increased basal expression in . Our engineered "sender"  strains accumulated biologically synthesized AHLs within the supernatant and activated receiver strains similarly to exogenous AHL activation. Our results will bolster the design of sophisticated genetic circuits in cyanobacterial/heterotroph consortia and the engineering of QS-like behaviors across cyanobacterial populations.

Developing Cyanobacterial Quorum Sensing Toolkits: Toward Interspecies Coordination in Mixed Autotroph/Heterotroph Communities.

Microbial communities have vital roles in systems essential to human health and agriculture, such as gut and soil microbiomes, and there is growing interest in engineering designer consortia for applications in biotechnology (e.g., personalized probiotics, bioproduction of high-value products, biosensing). The capacity to monitor and model metabolite exchange in dynamic microbial consortia can provide foundational information important to understand the community level behaviors that emerge, a requirement for building novel consortia. Where experimental approaches for monitoring metabolic exchange are technologically challenging, computational tools can enable greater access to the fate of both chemicals and microbes within a consortium. In this study, we developed an in-silico model of a synthetic microbial consortia of sucrose-secreting Synechococcus elongatus PCC 7942 and Escherichia coli W. Our model was built on the NUFEB framework for Individual-based Modeling (IbM) and optimized for biological accuracy using experimental data. We showed that the relative level of sucrose secretion regulates not only the steady-state support for heterotrophic biomass, but also the temporal dynamics of consortia growth. In order to determine the importance of spatial organization within the consortium, we fit a regression model to spatial data and used it to accurately predict colony fitness. We found that some of the critical parameters for fitness prediction were inter-colony distance, initial biomass, induction level, and distance from the center of the simulation volume. We anticipate that the synergy between experimental and computational approaches will improve our ability to design consortia with novel function.

Article	Year
Developing Cyanobacterial Quorum Sensing Toolkits: Toward Interspecies Coordination in Mixed Autotroph/Heterotroph Communities. ACS synthetic biology\| PubMed ID: 36573789	2023
Predicting partner fitness based on spatial structuring in a light-driven microbial community. PLoS computational biology\| PubMed ID: 37134119	2023

Emmanuel J. Kokarakis

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