Team:Virginia/Model
Recent advances in computational biology led to emergence of databases with whole-genome metabolic networks, which for simplicity we will call models. They describe the flow of metabolites within a particular organism. Applying constraint-based methods (see this figure) to a particular model the allows to make quantitative predictions about the cell phenotype while eliminating many of the complex parameters. Of particular interest to our project is flux balance analysis (FBA)[1], which allows to predict the optimal steady-state biomass yield, which is often correlated with cell growth rate and is the most likely observed phenotype[1,2]. One major advantage of FBA is that it does not require knowledge of enzymatic parameters. The mathematics of FBA is elucidated in this figure[3]
One of the primary questions for the project is the following: will the synthetic P. denitrificans strain grow better than the wild type in presence of ammonia? To answer this question, we performed comparative analysis of the two Paracoccus strains using FBA on the corresponding whole-genome metabolic model. The analysis pipeline involved a slew of open-source computational tools, which we will describe below.
First, we reconstructed a metabolic model of Paracoccus denitrificans strain DSM 413 on a complete medium using ModelSEED[4]. A complete medium is such that any nutrient, including ammonia, is available for uptake. Thus, the set of reactions included in the model is the biggest of all possible sets. Although the largest, this set is incomplete. In the next step, the model was gapfilled with all the reactions necessary for measurable cell growth.
The nature of our project dictates that we must be able to manually include several reactions, metabolites and genes (e.g. oxygenation of ammonia by the AMO enzyme complex) into the model. Such functionality is not available in ModelSEED. To implement this, we turned to COBRApy: Constraint-Based Reconstruction and Analysis[5] package written in Python. COBRApy does not natively work with ModelSEED models. To overcome this, we used Mackinac package[6] to convert the ModelSEED model into COBRApy-compatible format. Using COBRApy, we then added the new reactions into the model and performed FBA to compare the biomass yields, and hence the growth rates, of the two Paracoccus strains. The script is available here.
First, the FBA was run on the gapfilled unmodified model, which initially contained 1550 reactions and 1556 metabolites. The optimal biomass yield was found to be \( 224.3248 (\text{g dry weight}\cdot\text{h})^{-1} \). Next, we added the nitrification reactions. Below is the list of all reactions added to the model. \(\ce{Q}\) and \(\ce{QH_2}\) represent ubiquinone and ubiquinol, respectively. \( \text{UqO} \) is the ubiquinone oxidoreductase enzyme which catalyzes the last reaction, and is already present in the proteome of P. denitrificans. \[ \ce{NH_3 + QH_2 + O_2 ->[\text{AMO}] H_2O + Q + NH_2OH} \] \[ \ce{NH_3 + NAD + H_2O ->[\text{AMO}] 2H^+ + NADH + NH_2OH} \] \[ \ce{NH_2OH + O_2 ->[\text{HAO}] NO_2^- + H^+ + H_2O} \] \[ \ce{NH_2OH + 2Q + H_2O ->[\text{HAO}] NO_2^- + 2QH_2} \] \[ \ce{QH_2 ->[\text{UqO}] 2H^+ + Q} \] With the new model containing 1555 reactions and 1559 metabolites (hydroxylamine, Q and QH2 added), the optimal biomass yield of the modified model was found to be \( \boxed{228.6980~(\text{g dry weight}\cdot\text{h})^{-1}} \).
Exchange Fluxes and Flux Variability Analysis
To gain a deeper understanding of how these changes affect the metabolism of the cell, it is important to consider the difference in exchange fluxes between the wild type and synthetic strains. Exchange fluxes determine the intake and expulsion of certain extracellular metabolites, such as glucose or H+ ions or certain dipeptides. The set of exchange reactions determines the composition of the growth medium. The set of fluxes that gives rise to the optimal biomass, however, is not unique. This includes exchange fluxes. The system of stoichiometric equations for FBA is usually underdetermined, which means that there are actually infinitely many solutions to the problem, all of which are optimal up to certain degree of significance. To investigate the significance of exchange fluxes, we need to move away from analyzing specific numbers determined by FBA and instead consider ranges, or variations, of fluxes that allow for the optimal solution.
An escher map of the synthetic strain metabolism without nitrite transport block
An escher map of the synthetic strain metabolism with nitrite transport knocked out
To obtain the allowed range of fluxes, we employ a technique called Flux Variability Analysis (FVA). COBRApy package contains a module of functions which performs FVA on an existing model (see script for details). The main parameter of FVA is the so called fraction of optimum, which specifies the allowed deviation from the optimal biomass.
Results and Discussion
Analysis of the obtained data revealed that the modified biomass yield is greater than the original flux by nearly 2%, which means that the cell is expected to use the inserted nitrification pathway in order to enhance its metabolism. The above discussion of exchange fluxes elucidates how exactly it happens. Thus, our device potentially confers fitness advantage. If true, one implication is that it is possible that our synthetic strain will out-compete the native wild-type strain inside the sludge, thus eliminating the need to artificially sustain the new culture. As was stated earlier, the expected cell phenotype is the one with the largest biomass yield. This is only true under the assumption that the cell lives in an ideal or near-ideal environment with no over-population. Several studies have shown that under different growth environments, cells sometimes exhibit non-optimal yield metabolism[7,8]. We do not know whether wastewater causes a similar shift in metabolism of Paracoccus denitrificans. However, seeing as it is able to thrive in such environment gives enough reason to believe that the growth rate will not lose correlation with the biomass. Furthermore, it should be noted that this model is oblivious to protein structure and kinetics, and as such is unable to capture the full scale of effects caused by inserting the above genes into the model.
In this section, we describe a simplistic kinetic model of bioreactors which are able to perform both nitrification and denitrification. In particular, sequential batch reactors and bioreactors containing our synthetic strain of P. denitrificans are described by the model. Literature search led to discovery of a denitrification model called Activated Sludge Model with Indirect Coupling of Electrons (ASM-ICE), proposed by Pan et al. in 2013. Mathematica notebook is available here.
Conceptual depiction of ASM-ICE processes. Adapted from Pan et al.
Table of constants for ASM-ICE model. Adapted from Pan et al.
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References
[1] Oberhardt M., Chavali A., Papin J. (2009) Flux Balance Analysis: Interrogating Genome-Scale Metabolic Networks. In: Maly I. (eds) Systems Biology. Methods in Molecular Biology (Methods and Protocols), vol 500. Humana Press
[2] Feist, Adam M., and Bernhard O. Palsson. “The Biomass Objective Function.” Current opinion in microbiology 13.3 (2010): 344–349. PMC. Web. 28 July 2017.
[3] Cuevas, Daniel A. et al. “From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model.” Frontiers in Microbiology 7 (2016): 907. PMC. Web. 27 July 2017.
[4] Henry, C.S., DeJongh, M., Best, A.B., Frybarger, P.M., Linsay, B., and R.L. Stevens. High-throughput Generation and Optimization of Genome-scale Metabolic Models. Nature Biotechnology, (2010).
[5] Ebrahim, Ali et al. “COBRApy: COnstraints-Based Reconstruction and Analysis for Python.” BMC Systems Biology 7 (2013): 74. PMC. Web. 17 Aug. 2017.
[6] Mundy, Michael, Helena Mendes-Soares, and Nicholas Chia. "Mackinac: a bridge between ModelSEED and COBRApy to generate and analyze genome-scale metabolic models." Bioinformatics (2017): btx185.
[7] MLA Adadi, Roi et al. “Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network with Kinetic Parameters.” Ed. Nathan D. Price. PLoS Computational Biology 8.7 (2012): e1002575. PMC. Web. 31 July 2017.
[8] Molenaar, Douwe et al. “Shifts in Growth Strategies Reflect Tradeoffs in Cellular Economics.” Molecular Systems Biology 5 (2009): 323. PMC. Web. 31 July 2017.
