Team:XHD-Wuhan-A-China/Model

Model

Reduction of nitrate and nitrite

When the conversion rate of nitrate to nitrite is accelerated, if the conversion rate of nitrite to nitric oxide is not accelerated, it will lead to the accumulation of nitrite in organisms, which is not conducive to the survival of organisms. The purpose of this model is to screen out a suitable Nitrite Reductase (NirK) for experiment which can efficiently convert nitrite into nitric oxide. NirK enzyme with appropriate kinetic characteristics is selected from Sinorhizobium meliloti 1021, Nitrososphaera viennensis, Achromobacter xylosoxidans, Candidatus Jettenia caeni, Achromobacter cycloclastes.

Figure 1. Reaction of nitrite to nitric oxide(NO)

The reaction of nitrite to NO is shown in Figure 1, to search the optimal concentration of substrate, the Michaelis-Menten equation was used:

V is the rate of the reaction, V_max the maximum rate of the reaction, [S] is the substrate concentration, K_cat is the turnover number and K_M is the Michaelis constant, [E] is the enzyme concentration.

As for NirK, proteins were used from Sinorhizobium meliloti 1021, Nitrososphaera viennensis, Achromobacter xylosoxidans, Candidatus Jettenia caeni, Achromobacter cycloclastes, and kinetic parameters could be found in public.

Table 1. Literature kinetic parameters(Kataoka et al., 2004; Ferroni et al., 2012; Shun et al., 2018)

Organism	Abberviations	Km[mM]	Kcat[1/s]]
Sinorhizobium meliloti 1021	S.m	0.49	180
Nitrososphaera viennensis	N.v	0.29	3.1
Achromobacter xylosoxidans	A.x	0.13	172
Candidatus Jettenia caeni	C.J.c	0.25	319
Achromobacter cycloclastes	A.c	0.50	172

Figure 2. Comparing the reaction rate of nitrite reduction of different species

Firstly, the concentration of the enzyme is assumed to be 0.05Mm. The figure 2 shows that NirK from Candidatus Jettenia caeni has the highest reaction rate when the substrate concentration is low, but this kind of bacteria is not common in the laboratory. Sinorhizobium meliloti 1021 has the second highest reaction rate among these five species, and the Sinorhizobium meliloti 1021 is Similar to the research strain Sinorhizobium Fredii HH103 of this project. So it is appropriate to select nirK gene of Sinorhizobium Fredii HH103 to construct plasmid to increase nitrite consumption rate.

Figure 3. Nitrate conversion process

When napA gene is introduced into engineering bacteria, the expression level of NapA protein increases correspondingly, namely, the concentration of NapA enzyme increases, which has been verified in the experimental part(see Figure 6 of Engineering section). We qualitatively depicted the variation trend of nitrite concentration of wide type(WT) and napA-PBBR1McS2 engineering bacteria over time. The metabolic reaction pathway in Figure 3 was simulated by establishing differential equations and selecting appropriate parameters. The simulated results of nitrate concentration over time were shown in Figure 4. It can be seen from the picture that the final Nitrite concentration of napA-PBBR1MCS2 engineering bacteria after napA gene was introduced is lower than that of the WT. This qualitative result is consistent with the experimental results(See Figure 5 and 6 of Proof_Of_Concept section).

Figure 4. Nitrate concentration changes in WT and napA-pBBR1MCS2

Improvement of PyeaR promoter activity

PyeaR is the promoter of the Escherichia coli yeaR/yoaG operon, which is able to sense nitrate and nitrite. In order to better regulate the response of the promoter to nitrate, we use machine learning models to predict and design new PyeaR sequences. This model comes from this article(Synthetic promoter design in Escherichia coli based on a deep generative network)(wang et al., 2020) . The train sets contains 11884 promoter sequences and their corresponding activities.The process for predicting promoter activity is shown in Figure 5.

Figure 5. Activity prediction and selection process of mutant promoter

This model that was based on CNN(convolutional neural network) was used to predict promoter activity after random mutations. The model was used to predict PyeaR mutant promoter activity of length 50nt(-50~-1). The promoter sequences were selected form 50nt upstream of the transcription start site to the transcription start site. In order to preserve the functional integrity of the mutant PyeaR promoter, the NarL binding and Promoter -10 region remained unchanged. Nine bases in the region -29 to -21 were randomly mutated, with 4⁹(262144) total possible mutant promoters, and the activity of these promoters were predicted. The original PyeaR promoter sequence and predicted 10 highest mutant promoter sequences and their strength are shown in Figure 6 and Table 2. From table 2, it can be found that the predicted strength of the 10 highest promoters was about twice that of WT-PyeaR.

Figure 6. Predicted 10 highest mutation sequences,Capital letters represent the 9 sites of random mutation, and the red boxes represent the sites of mutation compared to the wild-type promoter

Table 2. Predicted 10 highest mutation sequences and their strength

Promoter name	Sequence of mutation sites	Predicted promoter strength(log2)
WT-PyeaR	ATGCAAATT	8.461017
PyeaR-1	CTTAGACTT	9.534228
PyeaR-2	CTTATACTT	9.531750
PyeaR-3	CTTAAACTT	9.505615
PyeaR-4	TTTAGACTT	9.478024
PyeaR-5	TTTAAACTT	9.476535
PyeaR-6	CTTACACTT	9.475526
PyeaR-7	GTTAGACTT	9.475304
PyeaR-8	CTTAGACTA	9.458674
PyeaR-9	CTTATACTA	9.447606
PyeaR-10	CTTATGCTT	9.440815

In order to verify whether the predicted mutant promoter activity were improved, we selected the top three mutant promoters with the highest predicted activity for experimental verification, and the results were shown in Figure 7. The results show that predicted mutant top-three PyeaR has a stronger promoter strength than the wild-type.

Figure 7. Verified the strength of the wild-type PyeaR and mutant PyeaR

Discussion

In this model section , firstly, in order to screen out a suitable Nitrate Reductase (NirK) for experiment which can efficiently convert nitrite into nitric oxide, the rate of reaction of nitrate reduction from 5 different species was compared. Then, we qualitatively depicted the variation trend of nitrate concentration of wide type(WT) and napA-PBBR1McS2 engineering bacteria over time by establishing differential equations and selecting appropriate parameters. Finally, we improve the PyeaR promoter activity by machine learning. Limited by computational resources, only 9 promoter sites were randomly mutated. It is believed that the more random mutation sites there are, the higher likelihood that the predicted promoter activity will improve better.

Reference

Kataoka, K., Yamaguchi, K., Kobayashi, M., Mori, T. , Bokui, N., & Suzuki, S. (2004). Structure-based engineering of Alcaligenes xylosoxidans copper-containing nitrite reductase enhances intermolecular electron transfer reaction with pseudoazurin. J. Biol. Chem. 279,53374-53378.
Shun, Kobayashi, Daisuke, Hira, Keitaro, & Yoshida, et al. (2018). Nitric oxide production from nitrite reduction and hydroxylamine oxidation by copper-containing dissimilatory nitrite reductase (nirk) from the aerobic ammonia-oxidizing archaeon, nitrososphaera viennensis. Microbes & Environments.
Ferroni, F. M. , Guerrero, S. A. , Rizzi, A. C. , & Brondino, C. D. . (2012). Overexpression, purification, and biochemical and spectroscopic characterization of copper-containing nitrite reductase from sinorhizobium meliloti 2011. study of the interaction of the catalytic copper center with nitrite and no. Journal of Inorganic Biochemistry, 114(none), 8-14.
Wang, Y. , Wang, H. , Wei, L. , Li, S. , Liu, L. , & Wang, X. . (2020). Synthetic promoter design in escherichia coli based on a deep generative network. Nucleic Acids Research. 12,6403-6412.