Introduction

E. coli distributes in the terminal ileum. So in order to enable EcN to survive stably in the intestine, we need to ensure that it has some competitive advantages among strains in the same genus. Therefore, in this section, we simulated the glucose competition co-culture under anaerobic conditions among the EcN model constructed in the previous step and the other five E. coli models, to verify the high competitiveness of the modified EcN in the population under anaerobic conditions with erythritol as an additional carbon source.

Data Accession and Modification

GEMs collection

Considering the construction of EcN's GEM is built based on BiGG platform, to avoid the extra workload caused by the inconsistency of different GEMs file formats, we unified the GEMs in BiGG.

The GEMs of five E. coli species were collected on the model, and the boundaries of several reactions in the model were adjusted to an anaerobic state to simulate the environment in the intestine according to the literature on model construction (Monk, J.M., et al., 2013).

Growth Medium

Since the carbon source that constitutes the major component of the human diet is glucose, the utilization of other carbon sources by the six E. coli species, including EcN, is similar. Therefore, constructing a glucose competition model under anaerobic conditions can better simulate the competition between EcN and congenic strains in the intestine. We simulated a competitive culture under equal carbon sources (10 mM for both glucose and erythritol), while the inorganic salt and other substances were considered sufficient to simulate the growth of the six strains within one hour after entering the logarithmic phase.

Construction

The basic framework of the glucose competition co-culture model for the six E. coli is roughly the same as the monoculture model. The metabolic ODEs of EcN for erythritol is shown in the previous section. In this section, the following modifications need to be made for the time derivatives of glucose to reflect the competition for glucose among the six bacteria.

$$\frac{dGlucose}{dt}=\sum_{i=1}^{6}v_{glucose}^iBiomass$$

Results

Fig2 illustrates the growth of the simulated flora within one hour of entering the logarithmic phase at an initial bacterial load of all 0.025 g/L. Within one hour, EcN (green line) was significantly higher than the remaining five bacteria that could only utilize glucose. This trend was amplified when glucose was used up, while erythritol was still adequate, for only EcN can utilize erythritol. So we can conclude that population density of EcN increases with the utilization of erythritol

In contrast, the unmodified EcN did not have a competitive advantage when co-cultured with E. coli of the same genus.

Verification

COMETS

COMETS (COmputation of Microbial Ecosystems in Time and Space) is generally used to simulate the competition for metabolites as well as secretion when multiple microorganisms are co-cultured. COMETS was proposed in 2014 (Harcombe, W.R., et al., 2014) , is also based on the GEMs and FBA.

However, COMETS also has its limitations. Firstly, as an algorithm proposed 8 years ago, its toolkit is incompatible with some SBML file formats added later, which limits its application. In addition, COMETS introduces other factors, such as the spatial structure of the cultural environment, which may make some unimportant factors have unpredictable effects on the prediction results, and the excessive complexity of the model reduces the interpretation.

Considering the above characteristics of COMETS, we built a new dFBA model for predicting co-culture outcomes instead of using the COMETS toolkit as the primary approach to predict co-cultures. We also compared the predicted results of our model with those of COMETS and found that the trends and orders of magnitude of the two matched well, giving us more confidence in our model.

MICOM

MICOM is an algorithm used to describe the co-culture of a colony of bacteria, where the GEMs of several bacteria and the culture environment can be entered.The growth rate of various bacteria in the colony and the total growth rate of the colony can be calculated, which take a wide range of substances in the environment into account throughout the process (Diener, C., et al., 2020).

However, we use our own established model because the growth rate calculated by MICOM is static and is a stable colony growth rate, which means it assumes that the material environment provided remains unchanged. It does not respond dynamically to the changes in colony growth as the material changes. However, we can still use MICOM to verify the competitive advantage of the modified EcN in a colony of six bacteria.

According to the above table, we can get three conclusions: (1) there is an increase in the growth rate of the modified EcN under the provision of erythritol (2) the competitiveness of the modified EcN colony is significantly increased under the environment of the provision of erythritol. The first two conclusions were both able to be verified with our own model project built. The third conclusion we leave to the next section.

Result Expansion

As shown in Table1, there is also a conclusion that EcN can enhance the growth rate of E. coli of the same genus. For this case, we think it is that EcN reduces the intake of glucose when it can utilize erythritol, thus providing more glucose for other bacteria. It is also possible that EcN increased the secretion of certain substances that promote the growth of other bacteria after utilizing erythritol, thus resulting in higher population growth rates.

Knockout

To investigate the above reason, we used the knockout algorithm in MICOM to obtain the changes in the growth status of other bacteria in the population when a certain bacteria in the population was removed.

It can be seen that, except for EcN, when a specific bacterium was knockout, all other bacteria were growing (the value in the graph is greater than 0), reflecting a competitive relationship. In contrast, after EcN was knockout, the growth rate of other bacteria in the population showed a decrease, reflecting a mutually beneficial relationship. Thus, it may be that EcN increased the secretion of a substance after using erythritol, ultimately increasing the population growth rate.

Flux Analysis

Based on th analysis of the fluxes in the population solution results, a significant decrease was found in the utilization of glucose by EcN compared to the other five bacteria. It indicates that EcN reduced glucose intake when it could utilize erythritol, thus providing more glucose to the other bacteria.

Conclusion

In this section, we built a glucose competitive co-culture model based on dFBA to verify that the modified EcN possesses high competitiveness when co-cultured with the same genus of E. coli when erythritol is available as an additional carbon source and validated by two more established models. The role of the modified EcN in the colony was also explored using the MICOM algorithm, and some explanations for the possible causes of the symbiotic relationship were provided.

Reference

1. Diener, C., Gibbons, S.M., Resendis-Antonio, O., 2020. miCOM: Metagenome-Scale Modeling To Infer Metabolic Interactions in the Gut Microbiota. mSystems 5, e00606-19. https://doi.org/10.1128/mSystems.00606-19

2. Harcombe, W.R., Riehl, W.J., Dukovski, I., Granger, B.R., Betts, A., Lang, A.H., Bonilla, G., Kar, A., Leiby, N., Mehta, P., Marx, C.J., Segrè, D., 2014. Metabolic Resource Allocation in Individual Microbes Determines Ecosystem Interactions and Spatial Dynamics. Cell Reports 7, 1104- 1115. https://doi.org/10.1016/j.celrep.2014.03.070

3. Monk, J.M., Charusanti, P., Aziz, R.K., Lerman, J.A., Premyodhin, N., Orth, J.D., Feist, A.M., Palsson, B.Ø., 2013. Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments. Proc. Natl. Acad. Sci. U.S A. 110, 20338-20343. https://doi.org/10.1073/pnas.1307797110 http://bigg.ucsd.edu/