Team:NCKU Tainan/Analysis

CO2 utilization result analysis

Amount of CO2 uptake

At first, we set up a closed system to model CO2 uptake by E. coli. After a period of time, concentration of CO2 uptake reached a balance. The result can tell that more than 50% air CO2 diffuse into E. coli in a closed system.

Fig. 1 CO2 uptake under closed system

However, we cannot set a CO2 utilization system in a closed system. No matter our application is continuous culture or fed-batch culture, air CO2 concentration will be constant, which means a boundary condition. Therefore, we model again under open system. Under constant air CO2 condition, CO2 diffusion speed changed with time and we categorized them into three phases, which are steady climb phase, transition phase, and saturated phase. The time segments of these three phases depend on CA activity. We assume A, B, C as time intervals of three phases. The cover area in the result shows that different time interval refers to different amount of CO2 uptake by E. coli. Its CO2 uptake rate change with time as well. As a result, we collect the CO2 uptake rate and then calculate with the xylose consumed rate and pyruvate produced rate. What’s more, the main reaction of CO2 in engineered E. coli happened between rubp to 3PGA, which is another part we are going to discuss.

  1. Engineered E. coli with CA

    2. Time A was about 10 min (or 700 s) with [CO2 uptake] reach 1 mM, causing 25% CO2 uptake in total.

    Time B was about 1 hour (or 3500 s) with [CO2 uptake] equals to [air CO2], causing 75% CO2 uptake in an hour.

    Time C was about 1.5 hour (or 8000 s) for [CO2 uptake] reach balance with the highest CO2 uptake percentage, 90%.

    Time interval Xylose supplied Concentration CO2 uptake (mM) Xylose (mM/s) Pyruvate (mM/s) Total CO2 uptake percentage
    A 0.2% 1 0.008 0.016 25%
    B 0.2% 2.045 0.00264 0.00528 75%
    C 0.2% 2.15 0.001131 0.002263 90%

    Table 1

    xylose consumed rate and pyruvate produced rate under different CO2 uptake time interval with CA

  2. Engineered E. coli without CA

    3. Time A was about 10 min (or 700 s) with [CO2 uptake] reach 1 mM, causing 25% CO2 uptake in total.

    Time B was about 1 hour (or 3500 s) with [CO2 uptake] equals to [air CO2], causing 75% CO2 uptake in an hour.

    Time C was about 1.5 hour (or 8000 s) for [CO2 uptake] reach balance with the highest CO2 uptake percentage, 88%.

    Time interval Xylose supplied Concentration CO2 uptake (mM) Xylose (mM/s) Pyruvate (mM/s) Total CO2 uptake percentage
    A 0.2% 1 0.0036345 0.007504 28%
    B 0.2% 2.045 0.0017486 0.003888 72%
    C 0.2% 2.15 0.001631 0.00388 88%

    Table 2

    xylose consumed rate and pyruvate produced rate under different CO2 uptake time interval without CA

    From table 1 and table 2, we knew that the highest pyruvate produced rate happened under the lowest CO2 uptake percentage. Besides, pyruvate produced rate of engineered E. coli with CA is higher than that of engineered E. coli without CA. Although our working space was open system that we cannot sense precise data of the change of CO2 concentration. Through pyruvate produced rate, we can easily recognize which phase of CO2 uptake and then figure out the percentage of total CO2 uptake. It is the method we calculate how much CO2 uptake by our engineered E. coli.

    A

    B

    C

    Fig. 3 result of xylose and pyruvate under A, B, C, time interval

    Actually, xylose consumed rate is slightly related to has little relationship with the CO2 uptake rate, since that xylose metabolism wasn’t just a single pathway. What we can analysis is that the pyruvate produced rate being correlation with CO2 uptake rate, which help us to define the question that how much CO2 uptake by engineered E. coli. It can also fit with experiment data easily. Next, we discuss about the true CO2 reaction in E. coli CO2 utilization bypass pathway. Every single mole of CO2 uptake will react with one mole of rubp and then produce 2 mole of 3PGA.

    Fig. 4 result of rubp and 3PGA during CO2 uptake

    Since that rubp and 3PGA are just intermediate products in metabolism, their concentration is quite low. Besides, results of three CO2 uptake time interval showed similar. We still can see that 3PGA produced is 2 times larger then rubp produced. We then calculate their produced rate in three CO2 uptake time interval.

    Time interval Rubp produced rate (mM/s) 3PGA produced rate (mM/s)
    A 4.25E-09 6.35E-10
    B 3.89E-09 8.42E-09
    C 1.76E-09 3.71E-09

Carbon metabolism flux

The main Xylose metabolism in E. coli was PP pathway and glycolysis. As for recombinant E. coli, it has multiple xylose metabolic pathways, and we can simplify them into original pathway and CO2 Bypass pathway. Therefore, we need to define the percentage of xylose, which is consumed by engineered E. coli, entering CO2 bypass pathway and utilize CO2.

It costs a lot of time to get absolute metabolic flux of CO2 in engineered E. coli and require feed of 13CO2 during cultivation. Since the metabolic flux of the original metabolic pathway is quite stable, the relative metabolic flux of CO2-utilization over that of the original metabolic pathway could show a quantitative understanding on the CO2 utilization efficiency. This relative value was MFICO2[1], a term as metabolic flux index of CO2-utilization pathway in heterotrophic engineered E. coli. This is the percentage of xylose that we mentioned above.

Fig. 5 carbon flux in engineered E. coli

X：Actual 3PGA detected from the original pathway = 3PGA0

Y：Actual 3PAG detected from CO2 bypass pathway = 3PGA’

a：3PGA generated from the central pathway

b：CO2 fixed by the CO2 bypass pathway

c：mol of 3PGA0 into downstream

d : mol of 3PGA’ into downstream

To define the MFICO2, we use CO2 fixed by the CO2 bypass pathway, noted as b, divided by the 3PGA generated from the central pathway, noted as a. We also assume c is mol of 3PGA¬0 and d is mol of 3PGA’ that channels into downsteam metabolism. After metabolism, (a+b) mol of 3PGA0 and b mol of 3PGA’ are generated.

Besides, X and Y represent the actual 3PGA detected from the original pathway and CO2 bypass pathway, which show in 3PGA0 and 3PGA’ in the fig. 1, respectively. In the experiment, we use 13C-labeled CO2 and unlabeled sugar to get the amount of 3PGA0 and 3PGA’. However, it was reported that 3.45% of unlabeled 3PGA, which is noted as 3PGA’, will convert to its isotopic during the culturing E. coli strains in medium. Eventually, we concluded these situation into two equations.

$${y = b + 3.45\% \times (a+b) - d ......(1)}$$

$${x = (1-3.45\%) \times (a+b) - c ....(2)}$$

Since d/c = y/x under a metabolic steady-state, we derive equation (1) and (2) into a final relationship between a, b, x, and y.

$${MFI(Metabolic flux index) = {b \over a} = {{0.97y-0.03x} \over {1.03x-0.97y}}}$$

As a result, we only need the amount of 3PGA0 and 3PGA’ to calculate MFICO2. Through modelling, we supply 0.4% xylose and 5% CO2 to get the data of 3PGA0 and 3PGA’, which helps us to adjust the rate between xylose and CO2 sources.

Fig 6. The result of 3PGA produced form PP pathway (original metabolism) and from CO2 bypass pathway.

Time MFICO2
10min 0.35256
1 hr 0.35247
2.5 hr 0.3524

Table 3 MFICO2 at different time

Fitting Experiment data

The purpose of modelling is to predict the result before doing experiment data. Our model focus on the metabolism pathway in engineered E. coli, trying to understand how E. coli utilize CO2. The result can fit with our experiment result to see if our model method was correct. However, most of our experiments is about cell growth under different condition. There are too many factors affect biomass and we cannot list all of them into model. Therefore, pyruvate, was chosen to represent the trend of biomass since that the metabolism of pyruvate downstream process is quite clear with abundant research have been done.

Fig. 7 pyruvate produced under different CO2 uptake condition (model result)

Fig.8 cell growth under different CO2 condition (experiment data)

The final goal of our project is to prove that our engineered E. coli could successfully consumed CO2 into its metabolism. CO2 convert into pyruvate through E. coli and then express on its cell growth. Therefore, we can conclude that pyruvate production will have correlation with biomass, which confirm that our model is reasonable to show the result with pyruvate production.

Reference

  1. Fuyu G, Guoxia L, Xiaoyun Z, Jie Z, Zhen C and Yin L. Quantitative analysis of an engineered CO2-fixing Escherichia coli reveals great potential of heterotrophic CO2 fixation. Gong et al. Biotechnology for Biofuels, 2015, 8:86.
  2. citric acid cycle from Brenda, web : https://www.brenda-enzymes.org/pathway_index.php?ecno=&brenda_ligand_id=Alpha-ketoglutarate&organism=Escherichia+coli&pathway=citric_acid_cycle&site=pathway
  3. Uwe Sauer, Bernhard J. E. The PEP—pyruvate—oxaloacetate node as the switch point for carbon flux distribution in bacteria. FEMS Microbiology Reviews, Volume 29, Issue 4, 1 September 2005, Pages 765–794.
  4. Mugihito O, Hideaki S, Yukihiro T, Noriko M, Tatsuya S, Masahiro O, Ayaaki I, and Kenji S. Kinetic modeling and sensitivity analysis of xylose metabolism in Lactococcus lactis IO-1. Journal of Bioscience and Bioengineering VOL. 108 No. 5, 376–384, 2009.
  5. Akira W., Keisuke N., Tomohiro H., Ryohei S. & Toshio I. Reaction mechanism of phosphoribulokinase from a cyanobacterium, Synechococcus PCC7942. Photosynthesis Research 56: 27–33, 1998
  6. Guillaume G. B., Tcherkez, Graham D. Farquhar, and T. John Andrews. Despite slow catalysis and confused substrate specificity, all ribulose bisphosphate carboxylases may be nearly perfectly optimized Proc Natl Acad Sci U S A. 2006 May 9; 103(19): 7246–7251.
  7. Yun L. and Keith A. M. Determination of Apparent Km Values for Ribulose 1,5- Bisphosphate Carboxylase/Oxygenase (Rubisco) Activase Using the Spectrophotometric Assay of Rubisco Activity. Plant Physiol. (1991) 95, 604-609
  8. Rong-guang Z, C. Evalena A., Alexei S., Tatiana S., Elena E., Steven B., Cheryl H. A., Aled M. E., Andrzej J., and Sherry L. M. Structure of Escherichia coli Ribose-5-Phosphate Isomerase: A Ubiquitous Enzyme of the Pentose Phosphate Pathway and the Calvin Cycle Structure, Vol. 11, 31–42, January, 200
  9. Inês L., Joana F., Christine C., Sandra M., Nuno S., Nilanjan R., Anabela C., and Joana T. Ribose 5-Phosphate Isomerase B Knockdown Compromises Trypanosoma brucei Bloodstream Form Infectivity PLoS Negl Trop Dis. 2015 Jan; 9(1): e3430.
  10. Singh2006 TCA mtu model1. SBML2LATEX. Web : http: //www.ra.cs.uni-tuebingen.de/software/SBML2LaTeX
  11. Jun Shen, Modeling the glutamate–glutamine neurotransmitter cycle, Front. Neuroenergetics, 28 January 2013
  12. Xueyang Feng and Huimin Zhao, Investigating xylose metabolism in recombinant Saccharomyces cerevisiae via 13C metabolic flux analysis, Microb Cell Fact. 2013; 12: 114.
  13. David Runquist, Bärbel Hahn-Hägerdal and Maurizio Bettiga, Increased expression of the oxidative pentose phosphate pathway and gluconeogenesis in anaerobically growing xylose-utilizing Saccharomyces cerevisiae, Microbial Cell Factories 2009, 8:49
  14. Kalle Hult rev 2005, 2007 Linda Fransson Department of Biotechnology KTH, Stockholm, Enzyme kinetics, An investigation of the enzyme glucose-6- phosphate isomerase
  15. Model name: “Mosca2012 - Central Carbon Metabolism Regulated by AKT”, SBML2LATEX. Web : http: //www.ra.cs.uni-tuebingen.de/software/SBML2LaTeX
  16. Ettore M., Roberta A., Carlo M., Annamaria B., Gianfranco C. and Luciano M., Computational modeling of the metabolic states regulated by the kinase Akt, Front. Physiol., 21 November 2012
  17. Jacqueline E. G., Christopher P. L., Maciek R. A., Comprehensive analysis of glucose and xylose metabolism in Escherichia coli under aerobic and anaerobic conditions by 13C metabolic flux analysis, Metabolic Engineering Volume 39, January 2017, Pages 9-18
  18. N. Nuray Ulusu, Cihangir Şengezer, Kinetic mechanism and some properties of glucose-6- phosphate dehydrogenase from sheep brain cortex, Türk Biyokimya Dergisi [Turkish Journal of Biochemistry–Turk J Biochem] 2012; 37 (4) ; 340–347
  19. Stefania H., Katy M., Carlo C., Morena M., and Franco D., 6-Phosphogluconate Dehydrogenase Mechanism EVIDENCE FOR ALLOSTERIC MODULATION BY SUBSTRATE, J Biol Chem. 2010 Jul 9; 285(28): 21366–21371.
  20. K. Nielsen, P.G. Sørensen, F. Hynne, H.-G. Busse, Sustained oscillations in glycolysis: an experimental and theoretical study of chaotic and complex periodic behavior and of quenching of simple oscillations, Biophysical Chemistry 72 (1998) 49–62
  21. UniProtKB - A0RV30 from web : https://www.uniprot.org/uniprot/A0RV30
Follow us
Contact us

igem.ncku.tainan@gmail.com
No.1, Daxue Rd., East Dist., Tainan City 701, Taiwan (R.O.C.)