Difference between revisions of "Team:Amsterdam/Produce"

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       <div class="summary-col-mid">
 
       <div class="summary-col-mid">
 
       <p class="summary-text">
 
       <p class="summary-text">
         A major requisite of cyano-cell factories, according to expert's opinion, is that they  must be able to produce in a stable fashion under industrial conditions. A recent quantitative analysis of the various ways to convert the energy of photons to chemical bonds has revealed that the direct utilization of sunlight is the most efficient [1]. This however means that cells will be exposed to diurnal regimes in which they will inevitably be exposed to periods of darkness. Our goal here is to achieve the first photoautotrophic cell factories that are able to stably produce fumarate around the clock.
+
         A major requisite of cyano-cell factories, according to expert"s opinion, is that they  must be able to produce in a stable fashion under industrial conditions. A recent quantitative analysis of the various ways to convert the energy of photons to chemical bonds has revealed that the direct utilization of sunlight is the most efficient [1]. This however means that cells will be exposed to diurnal regimes in which they will inevitably be exposed to periods of darkness. Our goal here is to achieve the first photoautotrophic cell factories that are able to stably produce fumarate around the clock.
 
         <br/>
 
         <br/>
 
       </p>
 
       </p>
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         Synechocystis
 
         Synechocystis
 
         </i>
 
         </i>
         leads to a stable cell factory that produces fumarate as it grows during the day. Nevertheless, at night our cells do not produce fumarate, since at night, they don't grow. To overcome this challenge, we have taken a systems biology approach which interweaves theory, modeling, and experimentation to implement stable nighttime production of fumarate. We theorized that we can redirect the nighttime flux towards fumarate production by removing a competing pathway via knockout of the
+
         leads to a stable cell factory that produces fumarate as it grows during the day. Nevertheless, at night our cells do not produce fumarate, since at night, they don"t grow. To overcome this challenge, we have taken a systems biology approach which interweaves theory, modeling, and experimentation to implement stable nighttime production of fumarate. We theorized that we can redirect the nighttime flux towards fumarate production by removing a competing pathway via knockout of the
 
         <i>
 
         <i>
 
         zwf
 
         zwf
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         </i>
 
         </i>
 
         and other cyanobacteria are often grown in large outdoor ponds or in greenhouses [2], where natural solar radiation is the primary source of light. This means that the cultures are subject to an oscillating light-dark cycle. Therefore, we aim to make a cyanobacterial cell factory that is able to produce fumarate in a stable fashion during ,not only the day, but also the night.
 
         and other cyanobacteria are often grown in large outdoor ponds or in greenhouses [2], where natural solar radiation is the primary source of light. This means that the cultures are subject to an oscillating light-dark cycle. Therefore, we aim to make a cyanobacterial cell factory that is able to produce fumarate in a stable fashion during ,not only the day, but also the night.
         <br/>
+
         <br>
        We mimicked industrial conditions in the lab by tailoring commercially available photobioreactors (MC1000-OD, PSI, Czech Republic) to be capable of simulating dynamic white light regimes. This involved developing new algorithms to incorporate the on-line measurements (e.g. OD
+
          We mimicked industrial conditions in the lab by tailoring commercially available photobioreactors (MC1000-OD, PSI, Czech Republic) to be capable of simulating dynamic white light regimes. This involved developing new algorithms to incorporate the on-line measurements (e.g. OD
        <sub>
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          <sub>
          730
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          730
        </sub>
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          </sub>
        ) with the desired oscillatory light intensity patterns, which then had to be coded into the in-house
+
          ) with the desired oscillatory light intensity patterns, which then had to be coded into the in-house
        <a class="in-text-link" href="https://gitlab.com/mmp-uva/pycultivator" target="_blank">
+
          <a class="in-text-link" href="https://gitlab.com/mmp-uva/pycultivator" target="_blank">
          software package
+
          software package
        </a>
+
          </a>
        that controls the photobioreactors. These relatively complex
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          that controls the photobioreactors. These relatively complex
        <a class="in-text-link" href="https://2017.igem.org/Team:Amsterdam/Model" target="_blank">
+
          <a class="in-text-link" href="https://2017.igem.org/Team:Amsterdam/Model" target="_blank">
          sinusoidal functions
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          sinusoidal functions
        </a>
+
          </a>
        that we deduced may then also be optionally coupled with
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          that we deduced may then also be optionally coupled with
        <a class="in-text-link" href="https://2017.igem.org/Team:Amsterdam/Model" target="_blank">
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          <a class="in-text-link" href="https://2017.igem.org/Team:Amsterdam/Model" target="_blank">
          algorithms
+
          algorithms
        </a>
+
          </a>
        that generate stochasticity resembling the one cells encounter in production scenarios. In combination, these new developments allow us to use lab-scale photobioreactors to mimic industrial settings operating at high cell densities in which cells perceive fluctuating light intensities on top of the sinusoidal light regimes inherent to day-night cycles. This effort, albeit time consuming and with little application of synthetic biology methods, was crucial to ensure the connectivity of our metabolic engineering strategies to the
+
          that generate stochasticity resembling the one cells encounter in production scenarios. In combination, these new developments allow us to use lab-scale photobioreactors to mimic industrial settings operating at high cell densities in which cells perceive fluctuating light intensities on top of the sinusoidal light regimes inherent to day-night cycles. This effort, albeit time consuming and with little application of synthetic biology methods, was crucial to ensure the connectivity of our metabolic engineering strategies to the
        <a class="in-text-link" href="https://2017.igem.org/Team:Amsterdam/HP/Gold_Integrated" target="_blank">
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          <a class="in-text-link" href="https://2017.igem.org/Team:Amsterdam/HP/Gold_Integrated" target="_blank">
          "real-world"
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          "real-world"
        </a>
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          </a>
        beyond the academic laboratorium.
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          beyond the academic laboratorium.
 +
        </br>
 
         </p>
 
         </p>
 
         <p class="collapsible-main-header" id="stable">
 
         <p class="collapsible-main-header" id="stable">
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         <p>
 
         <p>
 
         Stable production at the industrial scale is a challenge of biotechnology. Production rates are often not sustained and will diminish throughout the cultivation. Furthermore, maximal production rates cannot be reached again by the same culture, even with the addition of fresh medium. This is due to the phenomena of strain instability [3].
 
         Stable production at the industrial scale is a challenge of biotechnology. Production rates are often not sustained and will diminish throughout the cultivation. Furthermore, maximal production rates cannot be reached again by the same culture, even with the addition of fresh medium. This is due to the phenomena of strain instability [3].
         <br/>
+
         <br>
        This generalized phenomenon can be easily understood in the light of evolution theory, Darwinian selection and population dynamics. By introducing heterologous production pathways, cellular resources are forcibly diverted towards an extraneous product, and away from anabolic processes (i.e. growth). Cells which then lose the ability to produce the product are able to grow faster and eventually take over the population based on simple Darwinian selection, resulting in the irreversible loss of production [4].
+
          This generalized phenomenon can be easily understood in the light of evolution theory, Darwinian selection and population dynamics. By introducing heterologous production pathways, cellular resources are forcibly diverted towards an extraneous product, and away from anabolic processes (i.e. growth). Cells which then lose the ability to produce the product are able to grow faster and eventually take over the population based on simple Darwinian selection, resulting in the irreversible loss of production [4].
        <br/>
+
          <br/>
        Promising solutions to strain instability can involve the alignment of production of the desired compound with the fitness of the cell, i.e. the cell must produce in order to grow. One method for stable production is to knock-out genes whose proteins recycle anabolic byproducts [5].
+
          Promising solutions to strain instability can involve the alignment of production of the desired compound with the fitness of the cell, i.e. the cell must produce in order to grow. One method for stable production is to knock-out genes whose proteins recycle anabolic byproducts [5].
 +
        </br>
 
         </p>
 
         </p>
 
       </div>
 
       </div>
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           Experts in biotechnology
 
           Experts in biotechnology
 
         </a>
 
         </a>
         have indicated to us at the onset of this project that the process unpredictability that emerges from the instability of engineered strains in production settings is one of the major technical hurdles of the field [6]. This occurs because most commonly used metabolic engineering approaches make products in direct competition with biomass formation, which imposes high fitness burdens on production strains. This ultimately leads to a rapid appearance of suppressor mutations, for instance in the form of insertions or deletions, that impair the culture's ability to form product [7][8]. So called
+
         have indicated to us at the onset of this project that the process unpredictability that emerges from the instability of engineered strains in production settings is one of the major technical hurdles of the field [6]. This occurs because most commonly used metabolic engineering approaches make products in direct competition with biomass formation, which imposes high fitness burdens on production strains. This ultimately leads to a rapid appearance of suppressor mutations, for instance in the form of insertions or deletions, that impair the culture"s ability to form product [7][8]. So called
 
         <b>
 
         <b>
 
           <i>
 
           <i>
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         has pioneered the development of a method to design growth-coupled strategies based on a completely different principle. Instead of using energy or redox regeneration, this is now based on the direct stoichiometric coupling of pathways uniquely responsible for the formation of biomass precursors to the production of target compounds. This is achieved through the deletion of the native metabolic route(s) that cells have to reintroduce side-products of anabolism, leading to their accumulation, and hence, ensuring their growth-coupled production.
 
         has pioneered the development of a method to design growth-coupled strategies based on a completely different principle. Instead of using energy or redox regeneration, this is now based on the direct stoichiometric coupling of pathways uniquely responsible for the formation of biomass precursors to the production of target compounds. This is achieved through the deletion of the native metabolic route(s) that cells have to reintroduce side-products of anabolism, leading to their accumulation, and hence, ensuring their growth-coupled production.
 
         <br/>
 
         <br/>
         This concept has been developed into an algorithm to 'Find Reactions Usable In Tapping Side-products' - FRUITS. By analyzing existing genome-scale metabolic models, it identifies anabolic side-products that can be coupled to cell growth by the deletion of their re-utilization pathway(s). This pipeline is freely-available at
+
         This concept has been developed into an algorithm to "Find Reactions Usable In Tapping Side-products" - FRUITS. By analyzing existing genome-scale metabolic models, it identifies anabolic side-products that can be coupled to cell growth by the deletion of their re-utilization pathway(s). This pipeline is freely-available at
 
         <a class="in-text-link" href="https://gitlab.com/mmp-uva/fruits.git" target="_blank">
 
         <a class="in-text-link" href="https://gitlab.com/mmp-uva/fruits.git" target="_blank">
 
           https://gitlab.com/mmp-uva/fruits.git
 
           https://gitlab.com/mmp-uva/fruits.git
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         </p>
 
         </p>
 
         <p>
 
         <p>
         \[2.1 \frac{\mu E}{s^{2}}='240\sin' (2\pi\cdot (\frac{t}{24}+\frac{1}{4}) )-120\]
+
         \[2.1 \frac{\mu E}{s^{2}}="240\sin" (2\pi\cdot (\frac{t}{24}+\frac{1}{4}) )-120\]
 
         </p>
 
         </p>
 
         <p>
 
         <p>
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           -1
 
           -1
 
         </sup>
 
         </sup>
         . To transform these QPs to a more familiar unit, we multiplied all QP's  by  a conversion factor that converts OD
+
         . To transform these QPs to a more familiar unit, we multiplied all QP"s  by  a conversion factor that converts OD
 
         <sub>
 
         <sub>
 
           720
 
           720
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         </p>
 
         </p>
 
         <p>
 
         <p>
         2.2 \[yield_{day} ='\frac{\triangle' [fumarate]}{\triangle OD}\] 2.3 \[yield_{night} ='\frac{\triangle' [fumarate]}{mean OD}\] 2.4 \[Qp_{day} ='\frac{yield_{day}}{8' \ Hours}\cdot148\ mg \cdot L^{-1} \cdot OD^{-1}\] 2.5 \[Qp_{night}='\frac{yield_{night}}{16' \ Hours}\cdot148\ mg \cdot L^{-1} \cdot OD^{-1} \] 2.6 \[Qp_{daily} ='\frac{yield_{day}+yield_{night}}{24' \ Hours}\cdot148\ mg \cdot L^{-1} \cdot OD^{-1}\]
+
         2.2 \[yield_{day} ="\frac{\triangle" [fumarate]}{\triangle OD}\] 2.3 \[yield_{night} ="\frac{\triangle" [fumarate]}{mean OD}\] 2.4 \[Qp_{day} ="\frac{yield_{day}}{8" \ Hours}\cdot148\ mg \cdot L^{-1} \cdot OD^{-1}\] 2.5 \[Qp_{night}="\frac{yield_{night}}{16" \ Hours}\cdot148\ mg \cdot L^{-1} \cdot OD^{-1} \] 2.6 \[Qp_{daily} ="\frac{yield_{day}+yield_{night}}{24" \ Hours}\cdot148\ mg \cdot L^{-1} \cdot OD^{-1}\]
 
         </p>
 
         </p>
 
       </div>
 
       </div>
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           Synechocystis
 
           Synechocystis
 
         </i>
 
         </i>
         is a polyploid, each cell containing 4 to 20 copies of the genome depending on the physiological state and environmental conditions [19]. Polyploidy poses a challenge in creating a stable mutant strain, since the newly introduced genes have to be 'fully segregated' (i.e. present in all chromosome copies) to avoid that they revert to wild type in future generations. However, a fully segregated promoter library in
+
         is a polyploid, each cell containing 4 to 20 copies of the genome depending on the physiological state and environmental conditions [19]. Polyploidy poses a challenge in creating a stable mutant strain, since the newly introduced genes have to be "fully segregated" (i.e. present in all chromosome copies) to avoid that they revert to wild type in future generations. However, a fully segregated promoter library in
 
         <i>
 
         <i>
 
           Synechocystis
 
           Synechocystis
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       <ol class="references-list">
 
       <ol class="references-list">
 
         <li>
 
         <li>
         Lips, D., Schuurmans, J. M. M., dos Santos, F. B., &amp; Hellingwerf, K. J. (2017). Many ways towards 'solar fuel': Quantitative analysis of the most promising strategies and the main challenges during scale-up. Energy &amp; Environmental Science.
+
         Lips, D., Schuurmans, J. M. M., dos Santos, F. B., &amp; Hellingwerf, K. J. (2017). Many ways towards "solar fuel": Quantitative analysis of the most promising strategies and the main challenges during scale-up. Energy &amp; Environmental Science.
 
         </li>
 
         </li>
 
         <li>
 
         <li>

Revision as of 11:56, 23 November 2017

Production


A major requisite of cyano-cell factories, according to expert"s opinion, is that they must be able to produce in a stable fashion under industrial conditions. A recent quantitative analysis of the various ways to convert the energy of photons to chemical bonds has revealed that the direct utilization of sunlight is the most efficient [1]. This however means that cells will be exposed to diurnal regimes in which they will inevitably be exposed to periods of darkness. Our goal here is to achieve the first photoautotrophic cell factories that are able to stably produce fumarate around the clock.

Produce

Overview

Synechocystis does not naturally produce fumarate. However, model guided engineering found that removing a single gene within Synechocystis leads to a stable cell factory that produces fumarate as it grows during the day. Nevertheless, at night our cells do not produce fumarate, since at night, they don"t grow. To overcome this challenge, we have taken a systems biology approach which interweaves theory, modeling, and experimentation to implement stable nighttime production of fumarate. We theorized that we can redirect the nighttime flux towards fumarate production by removing a competing pathway via knockout of the zwf gene. Additionally, we also took inspiration from nature and speculated that the incorporation of the glyoxylate shunt would further increase our nighttime production of fumarate. Our models corroborate these predictions, however, they also suggest that the stability of the glyoxylate shunt is sensitive to the timing of when the shunt is turned on (i.e. expressed). We therefore took a robust approach to incorporate the glyoxylate shunt enzymes under ideal expression conditions.

Highlights

  • Engineered a Δ fumC Δ zwf Synechocystis strain, that uses different fumarate production strategies during day and night.
  • Developed a method to make fully segregated libraries in polyploid organisms
  • Created the first fully segregated library representing the entire genome (99.9% confidence) of Synechocystis upstream of the glyoxylate shunt genes. This library is now ready to be tested to further increase nighttime fumarate production.
  • Stable production of fumarate directly from CO 2 around the clock (Nighttime fumarate production rate of 2.96 mM grDW -1 hour -1 Daytime fumarate production rate of 9.24 l mM grDW -1 hour -1 Titer of 48.48 mg L -1 ) [Disclaimer: our experimental design was aimed mostly at proof-of-principle. Much higher titers (>230 mg/L) are possible if economically more favorable due to downstream costs].

References

  1. Lips, D., Schuurmans, J. M. M., dos Santos, F. B., & Hellingwerf, K. J. (2017). Many ways towards "solar fuel": Quantitative analysis of the most promising strategies and the main challenges during scale-up. Energy & Environmental Science.
  2. René H. Wijffels, Olaf Kruse, and Klaas J. Hellingwerf. "Potential of industrial biotechnology with cyanobacteria and eukaryotic microalgae". In: Current Opinion in Biotechnology 24.3 (2013), pp. 405-413.
  3. Patrik R. Jones. "Genetic Instability in Cyanobacteria - An Elephant in the Room?" In: Frontiers in Bioengineering and Biotechnology 2.May (2014), pp. 1-5.
  4. Wei Du, S. Andreas Angermayr, Joeri A. Jongbloets, Douwe Molenaar, Herwig Bachmann, Klaas J. Hellingwerf, and Filipe Branco dos Santos. "Nonhierarchical flux regulation exposes the fitness burden associated with lactate production in Synechocystis sp. PCC6803". In: ACS Synthetic Biology (2016), acssynbio.6b00235.
  5. Wei Du, Joeri A. Jongbloets, Coco van Boxtel, Hugo Pineda Hernandez, David Lips, Brett G. Oliver, Klaas J. Hellingwerf, and Filipe Branco dos Santos. "Alignment of microbial fitness with engineered product formation: Obligatory coupling between acetate production and photoautotrophic growth". 2017.
  6. Teusink B, Smid EJ. Modelling strategies for the industrial exploitation of lactic acid bacteria. Nat Rev Microbiol. 2006;4:46-56
  7. Darmon E, Leach DR. Bacterial genome instability. Microbiol Mol Biol Rev. 2014;78:1-39.
  8. Renda BA, Hammerling MJ, Barrick JE. Engineering reduced evolutionary potential for synthetic biology. Mol Biosyst. 2014;10:1668-78.
  9. Feist AM, Zielinski DC, Orth JD, Schellenberger J, Herrgard MJ, Palsson BO. Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli . Metab Eng. 2010;12:173-86
  10. Erdrich P, Knoop H, Steuer R, Klamt S. Cyanobacterial biofuels: new insights and strain design strategies revealed by computational modeling. Microb Cell Fact. 2014;13:128.
  11. Nogales, J., Gudmundsson, S., Knight, E. M., Palsson, B. O., & Thiele, I. (2012). Detailing the optimality of photosynthesis in cyanobacteria through systems biology analysis. Proceedings of the National Academy of Sciences, 109(7), 2678-2683.
  12. Bachmann H, Molenaar D, Branco dos Santos F, Teusink B. Experimental evolution and the adjustment of metabolic strategies in lactic acid bacteria. FEMS Microbiol Rev. 2017;41 Supp_1:S201-19.
  13. Bryson V, Szybalski W. Microbial Selection. Science. 1952;116:45-51.
  14. Angermayr, S. A. & Hellingwerf, K. J. On the Use of Metabolic Control Analysis in the Optimization of Cyanobacterial Biosolar Cell Factories. J. Phys. Chem. B (2013). doi:10.1021/jp4013152
  15. Ni Wan, Drew M. DeLorenzo, Lian He, Le You, Cheryl M. Immethun, George Wang, Ed- ward E.K. Baidoo, Whitney Hollinshead, Jay D. Keasling, Tae Seok Moon, and Yinjie J. Tang. "Cyanobacterial carbon metabolism: Fluxome plasticity and oxygen dependence". In: Biotechnology and Bioengineering 114.7 (2017), pp. 1593-1602.
  16. Du, W., Jongbloets, J. A., Hernandez, H. P., Bruggeman, F. J., Hellingwerf, K. J., & dos Santos, F. B. (2016). Photonfluxostat: A method for light-limited batch cultivation of cyanobacteria at different, yet constant, growth rates. Algal Research, 20, 118-125.
  17. Lee J. Sweetlove, Katherine F M Beard, Adriano Nunes-Nesi, Alisdair R. Fernie, and R. George Ratcliffe. "Not just a circle: Flux modes in the plant TCA cycle". In: Trends in Plant Science 15.8 (2010), pp. 462-470
  18. Tu, Benjamin P., and Steven L. McKnight. "Metabolic cycles as an underlying basis of biological oscillations." Nature reviews Molecular cell biology 7.9 (2006): 696-701.
  19. Biology, C. & Soppa, J. Microbiology The ploidy level of Synechocystis sp. PCC 6803 is highly variable and is influenced by growth phase and by chemical and physical external parameters. (2016)
  20. Cheah, Y.E., Albers, S.C. & Peebles, C.A.M. A novel counter-selection method for markerless genetic modification in Synechocystis sp. PCC 6803. Biotechnol. Prog. 29, 23-30 (2013).
  21. Lopez-maury, L., Garcia-dominguez, M., Florencio, F. J. & Reyes, J.C. A two-component signal transduction system involved in nickel sensing in the cyanobacterium. 43, 247-256 (2002).
  22. Griese, M. & Lange, C. Ploidy in cyanobacteria. 323, 124-131 (2011).
  23. Zhang, Shuyi, and Donald A. Bryant. "Biochemical validation of the glyoxylate cycle in the cyanobacterium Chlorogloeopsis fritschii strain PCC 9212." Journal of Biological Chemistry 290.22 (2015): 14019-14030.