Team:BostonU/Description
Motivation
Synthetic biology tools are optimal for bioproduction
Synthetic biology has a wide array of applications in bioproduction, with the design of engineered organisms to efficiently and inexpensive produce molecules of value1.
Bioproduction at the industrial level is performed in bioreactors, apparatuses that maintain environments suitable for optimal microbial growth. A hallmark example of bioproduction is the engineering of
Saccharromyces cerevisiae to produce the anti-malarial drug artemisinin2.
The use of budding yeast to synthesize artemisinin was a major breakthrough in synthetic biology, and established an inexpensive and sustainable source of medicine to treat a major disease.
However, many compounds produced in bioreactors require heterologous expression, in which a host organism is engineered to express a relevant exogenous gene borrowed from nature. Unfortunately, heterologous expression can burden cells with high fitness costs resulting from production schemes or non-native compound outputs3. In response, production will plummet as cells either die off or break synthetic circuits to rescue fitness. A major problem facing bioproduction is inefficiency due to failing or depleted engineered organisms.
Bioproduction is a powerful approach to ameliorating reliance on fossil fuels by offering inroads to large-scale biofuel production. Specifically, exogenous expression of the well-studied enzyme xylose isomerase permits organisms that are otherwise unsuited to digest xylose, a sugar abundant in plant cell walls, towards the production of ethanol for biofuels. Saccharromyces cerevisiae is uniquely primed for producing ethanol, because its native high ethanol tolerance protects it from the potentially toxic effects of ethanol observed in other microbes4. However, the fitness costs associated with adequate heterologous expression of xylose isomerase in S. cerevisiae are so high that cells die before producing enough ethanol to be a viable source of biofuel. Thus, precise control of xylose isomerase expression is required for both the production and cell growth necessary for budding yeast to be a significant source of ethanol for biofuel.
However, many compounds produced in bioreactors require heterologous expression, in which a host organism is engineered to express a relevant exogenous gene borrowed from nature. Unfortunately, heterologous expression can burden cells with high fitness costs resulting from production schemes or non-native compound outputs3. In response, production will plummet as cells either die off or break synthetic circuits to rescue fitness. A major problem facing bioproduction is inefficiency due to failing or depleted engineered organisms.
Bioproduction is a powerful approach to ameliorating reliance on fossil fuels by offering inroads to large-scale biofuel production. Specifically, exogenous expression of the well-studied enzyme xylose isomerase permits organisms that are otherwise unsuited to digest xylose, a sugar abundant in plant cell walls, towards the production of ethanol for biofuels. Saccharromyces cerevisiae is uniquely primed for producing ethanol, because its native high ethanol tolerance protects it from the potentially toxic effects of ethanol observed in other microbes4. However, the fitness costs associated with adequate heterologous expression of xylose isomerase in S. cerevisiae are so high that cells die before producing enough ethanol to be a viable source of biofuel. Thus, precise control of xylose isomerase expression is required for both the production and cell growth necessary for budding yeast to be a significant source of ethanol for biofuel.
Inducible systems offer added control and can alleviate fitness costs
Inducible regulatory systems offer improved transcriptional control over constitutive expression. Located in promoter regions upstream of relevant genes, tunable inducible systems will initiate transcription exclusively in response to the proper stimulus.
Inducible systems offer temporal control of gene expression, alleviating fitness costs by tuning production to maximal levels without imposing growth defects.
Light Inducible Systems and Project Leo
Figure 1: A diagram of a typical small molecule inducible system. The inducer is spiked into culture, diffuses into the cell, and binds to a transcription factor. This binding causes a conformational change in the transcription factor that allows it to bind in the promoter region and activate transcription.
The current gold standard for transcriptional control relies on small molecule-inducible systems, in which exposure to a compound initiates transcription (an exceptional example is the estradiol-inducible system introduced by McIsaac et al in 20125). However, small molecule-inducible systems have limitations to their use in synthetic biology applications. Because these systems rely on small molecules, inducibility in a culture relies on diffusion, which limits spatial control. Molecule removal also requires time consuming, and often production-costly, wash steps to deplete the small molecule and deactivate transcription, limiting temporal control and output. Importantly, quantities of small molecules necessary at the industrial level come with costs too prohibitive for use in large-scale bioreactors.
The limited spatiotemporal control inherent to small-molecule induction offers a layer of control over constitutive expression, but relies on molecules that may infer a fitness cost or prove expensive at industrial scales.
Figure 2: Small molecule inducible vs. light inducible systems. The flask on the left shows how inducers diffuse into culture while the flask on the right shows how light can easily be localized and rapidly switched on and off.
Offsetting costs and improving control is accomplished with light-inducible systems, which are activated by specific pulses of light. Pulses can be easily localized and almost instantly activated or deactivated, providing superior spatiotemporal control6,7. While small molecule-inducible inputs are modeled by the slow curves of logarithmic activation and exponential deactivation, light-inducible inputs conform to the tight, crisp edges of square waves and step functions. Moreover, light apparatuses are cheaper than purchasing large amounts of inducer, lending light-inducible systems lower startup and maintenance costs which make them well suited for both small lab experiments and industrial-scale bioproduction.
Figure 3: Light inducible vs. Small molecule inducible input signals. The left side shows how light inputs can be easily switched on and off to form square waves and step functions, while the right illustrates how diffusion restricts input signals to logarithmic growth and exponential decay curves.
Despite offering more precise control for comparatively low cost, light inducible systems are insufficiently characterized, and the infrastructure necessary for light inducible systems is absent from most labs8
With Project Leo, BostonU 2018 will characterize and optimize two light-inducible systems - LOV2, a blue-light activated system9,and PhiReX, a red-light activated system10 - to improve transcriptional control.
Xylose isomerase expression can then be tuned with light inducible systems to balance production and fitness in engineered yeast optimized for biofuel production.
References
1. Cameron DE, Bashor CJ, Collins JJ. A brief history of synthetic biology. Nature Reviews Microbiology. 2014;12:381. http://dx.doi.org/10.1038/nrmicro3239.
2. Paddon CJ, Westfall PJ, Pitera DJ, et al. High-level semi-synthetic production of the potent antimalarial artemisinin. Nature. 2013;496:528. http://dx.doi.org/10.1038/nature12051.
3. Frumkin I, Schirman D, Rotman A, et al. Gene architectures that minimize cost of gene expression. Mol Cell. 2017;65(1):142-153. doi: S1097-2765(16)30715-8 [pii].
4. Nielsen J, Larsson C, van Maris A, Pronk J. Metabolic engineering of yeast for production of fuels and chemicals. Curr Opin Biotechnol. 2013;24(3):398-404. http://www.sciencedirect.com/science/article/pii/S0958166913000803. doi: //doi.org/10.1016/j.copbio.2013.03.023.
5. McIsaac RS, Oakes BL, Wang X, Dummit KA, Botstein D, Noyes MB. Synthetic gene expression perturbation systems with rapid, tunable, single-gene specificity in yeast. Nucleic Acids Res. 2013;41(4):e57. doi: 10.1093/nar/gks1313 [doi].
6. Motta-Mena L, Reade A, Mallory MJ, et al. An optogenetic gene expression system with rapid activation and deactivation kinetics. Nature Chemical Biology. 2014;10:196. http://dx.doi.org/10.1038/nchembio.1430.
7. Baaske J, Gonschorek P, Engesser R, et al. Dual-controlled optogenetic system for the rapid down-regulation of protein levels in mammalian cells. Scientific Reports. 2018;8(1):15024. https://doi.org/10.1038/s41598-018-32929-7. doi: 10.1038/s41598-018-32929-7.
8. Gerhardt KP, Olson EJ, Castillo-Hair S, et al. An open-hardware platform for optogenetics and photobiology. Scientific Reports. 2016;6:35363. http://dx.doi.org/10.1038/srep35363.
9. Zhao EM, Zhang Y, Mehl J, et al. Optogenetic regulation of engineered cellular metabolism for microbial chemical production. Nature. 2018;555:683. http://dx.doi.org/10.1038/nature26141.
10. Hochrein L, Machens F, Messerschmidt K, Mueller-Roeber B. PhiReX: A programmable and red light-regulated protein expression switch for yeast. Nucleic Acids Res. 2017;45(15):9193-9205. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5587811/. doi: 10.1093/nar/gkx610.
2. Paddon CJ, Westfall PJ, Pitera DJ, et al. High-level semi-synthetic production of the potent antimalarial artemisinin. Nature. 2013;496:528. http://dx.doi.org/10.1038/nature12051.
3. Frumkin I, Schirman D, Rotman A, et al. Gene architectures that minimize cost of gene expression. Mol Cell. 2017;65(1):142-153. doi: S1097-2765(16)30715-8 [pii].
4. Nielsen J, Larsson C, van Maris A, Pronk J. Metabolic engineering of yeast for production of fuels and chemicals. Curr Opin Biotechnol. 2013;24(3):398-404. http://www.sciencedirect.com/science/article/pii/S0958166913000803. doi: //doi.org/10.1016/j.copbio.2013.03.023.
5. McIsaac RS, Oakes BL, Wang X, Dummit KA, Botstein D, Noyes MB. Synthetic gene expression perturbation systems with rapid, tunable, single-gene specificity in yeast. Nucleic Acids Res. 2013;41(4):e57. doi: 10.1093/nar/gks1313 [doi].
6. Motta-Mena L, Reade A, Mallory MJ, et al. An optogenetic gene expression system with rapid activation and deactivation kinetics. Nature Chemical Biology. 2014;10:196. http://dx.doi.org/10.1038/nchembio.1430.
7. Baaske J, Gonschorek P, Engesser R, et al. Dual-controlled optogenetic system for the rapid down-regulation of protein levels in mammalian cells. Scientific Reports. 2018;8(1):15024. https://doi.org/10.1038/s41598-018-32929-7. doi: 10.1038/s41598-018-32929-7.
8. Gerhardt KP, Olson EJ, Castillo-Hair S, et al. An open-hardware platform for optogenetics and photobiology. Scientific Reports. 2016;6:35363. http://dx.doi.org/10.1038/srep35363.
9. Zhao EM, Zhang Y, Mehl J, et al. Optogenetic regulation of engineered cellular metabolism for microbial chemical production. Nature. 2018;555:683. http://dx.doi.org/10.1038/nature26141.
10. Hochrein L, Machens F, Messerschmidt K, Mueller-Roeber B. PhiReX: A programmable and red light-regulated protein expression switch for yeast. Nucleic Acids Res. 2017;45(15):9193-9205. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5587811/. doi: 10.1093/nar/gkx610.