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, Synechococcus elongatus PCC 7942 (S. elongatus), 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 Escherichia coli W (E. coli W) to dissect species-specific properties, finding broad agreement,
albeit with increased basal expression in S. elongatus. Our engineered “sender” E. coli 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.
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.
The present study investigates the negative impact of a marine polluter on the marine environment. Plastics degraded into micro and macroplastics harm the environment in many ways. Finding their way to the oceans cause increase in temperature at the surface and cooling in deeper waters. Degrading macroplastics releases potent greenhouse gases. More importantly, they are implicated to cause climate change. Plastic in the ocean affects its ability to act as a carbon sink by decelerating the “biogeochemical cycle of carbon”. The ocean is the largest natural sink for anthropogenic greenhouse gases. Various short- and long-term measures are also proposed to curb the flow of plastic waste into the Oceans.
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