Contribution

We have successfully constructed plasmids for hypoxic environment construction, which provides a feasible approach to solve the oxygen paradox. The sequences used for hypoxic environment construction and hypoxic environment monitoring have all been registered. In order to facilitate subsequent teams to better find the progeny to use for hypoxic environment construction, we list each progeny separately here.

We found from the literature that predecessors designed a sensor plasmid that expresses fluorescent protein under the control of icd promoter and is regulated by ArcA. After continuous culture of Escherichia coli containing this plasmid at different dilution rates, we found that there was a strong negative correlation between ArcA activity and specific oxygen consumption rate. This can help us detect the oxygen consumption rate in the constructed system, and then get the speed of oxygen decrease. Thus, under the constraint of initial conditions, we can get a general chart of oxygen change with time. Combined with the oxygen concentration measured by nirB at each time, we can get a more accurate data by fitting. However, ArcA has a fatal disadvantage, that is, it is regulated by two upstream promoters at the same time. It is not only affected by the icd promoter related to the oxygen consumption rate, but also regulated by the Cra promoter related to the cell growth rate. This leads to that if you directly use the natural ArcA transcription factor, the data obtained may not only be the result of the oxygen consumption rate, In addition, other factors that can regulate Cra, such as cell growth rate, increase the complexity of calculation and make the original simple model more complex; In order to avoid this situation, we checked various means such as inserting other fragments and knocking out the Cra promoter, which inactivated the Cra fragment and could not be expressed in bacteria, and thus would not affect the expression of ArcA transcription factor. Finally, under the guidance of the teacher, we decided to choose a relatively simple method to directly design its PCR primer to the part without Cra promoter, and not copy and paste the part involving Cra from the original genome, so that we can obtain ArcA only affected by the icd promoter, which can enable the modified ArcA transcription factor to completely and simply reflect the change of oxygen consumption rate, and obtain more accurate experimental data.

Figure. 1 The structure of icd after getting rid of Cra

In order to create a hypoxic environment in cells, we found the original leghemoglobin that can change the oxygen environment from the literature. leghemoglobin is a kind of plant hemoglobin, which will be inactivated after being introduced into Escherichia coli and cannot play its due role. This is mainly due to the intron of Escherichia coli, so we removed the intron (as shown in Figure 2) in the process of recombination of Escherichia coli, so that the leghemoglobin can work normally in the body of Escherichia coli. In addition, after studying the mechanism of leghemoglobin in detail, we found that heme plays a major role in leghemoglobin (as shown in Figure 3). However, the synthesis of heme is highly conservative in the organism, and is a speed limiting step in E. coli. Therefore, the heme content in the leghemoglobin may not be large, and the oxygen delivery capacity may be limited. For this problem, we want to solve it by adding heme to improve the oxygen delivery capacity of leghemoglobin. Relevant studies have also shown that this method is feasible, and the difference of expression content is shown in the figure (as shown in Figure 4). Since it is difficult to order heme on the market, we use hemin as a substitute.

Figure. 2 The plasmid of leghemoglobin after eliminating intron

Figure. 3 The structure of hemin

Figure. 4 The distinction of leghemoglobin after adding the heme

In addition to the use of leghemoglobin, we still use laccase as an original to consume oxygen in cells. Laccase is a reductive enzyme, which can catalyze the reaction between oxygen and reductive substances to achieve the effect that we consume oxygen in cells. As we plan to conduct the experiment in E. coli finally, the laccase we choose is the multi copper oxidase in E. coli - CueO. Based on the above ideas, we started the experiment, but because laccase will be located in the periplasm, laccase has no expected effect on the consumption of oxygen in cells. The main reason why laccase is located in the periplasm is that there are 28 signal peptides in the amino acid residues of laccase. To solve this problem, we reconstructed the laccase plasmid and removed the signal peptide sequence contained in the laccase. The reconstructed laccase plasmid is shown in Fig. 5. We will test the effect of laccase without signal peptide in further experiment. Since laccase in Escherichia coli is a copper containing enzyme, we also added copper ions in the process of culture to improve the expression activity of laccase (the expression difference is shown in Figure 6).

Figure. 5 The laccase plasmid without signal peptide

Figure. 6 The activity difference after adding the copper ion

[1] J F Prost;D Ngre;C Oudot;K Murakami;A Ishihama;A J Cozzone;J C Cortay(1998) Cra-dependent transcriptional activation of the icd gene of Escherichia coli.Journal of bacteriology,893-898

[2]Krainer, F. W., Capone, S., Jäger, M., Vogl, T., Gerstmann, M., Glieder, A., … Spadiut, O. (2015). Optimizing cofactor availability for the production of recombinant heme peroxidase in Pichia pastoris. Microbial Cell Factories, 14(1).

Djoko, K. Y., Chong, L. X., Wedd, A. G., & Xiao, Z. (2010). Reaction Mechanisms of the Multicopper Oxidase CueO fromEscherichia coliSupport Its Functional Role as a Cuprous Oxidase. Journal of the American Chemical Society, 132(6), 2005-2015.

Our project is able to provide an intracellular low oxygen at a mesoscopic level without the need for an artificial hypoxia environment to help cells to synthesize and store reducing products, avoid the inhibition of oxygen on reducing processes, and avoid reducing enzymes because of the action of oxygen, which caused the inability to bind to the substrate is indicated. This element can be easily produced and sustained under existing conditions. For some experiments due to the lack of low oxygen conditions, but have to be conducted under low oxygen conditions is a good help, while eliminating the complexity of anaerobic toolbox operation and possibly unnecessary losses due to equipment problems.

Other teams can further improve our elements, by the use of other more efficient promoter, oxygen transport factor or oxygen consumption factor. To make cells in the case of normal external oxygen concentration, faster to hypoxia, and can through less organic matter to maintain longer low oxygen, reduce the consumption of cells in maintaining oxygen, more energy applied to cells according to the function required according to the original design.

Machine learning provides an important technical support for many interdisciplinary disciplines. For example, "bioinformatics" tries to use information technology to study life phenomena and laws. What's more, the implementation of genome programs and the bright prospect of genetic drugs make people excited. Bioinformatics research involves the whole process from "life phenomenon" to "law discovery", which inevitably includes data acquisition, data management, data analysis, simulation experiment and other links. "Data analysis" is just the stage of machine learning technology, and various machine learning technologies have shone on this stage. The expanding scale and inherent complexity of biological data have prompted the increasing application of machine learning in biology. All machine learning techniques can match the models to the data. However, there is still much confusion for biological researchers about how to properly understand and use machine learning techniques.

Since our team initially chose to use the neural network model for parameter prediction, we encountered many problems in how to apply the BP neural network and the neural network code writing. At the same time, it consumes a lot of time in dividing the data set, making the data import and adjusting the parameters. In order to facilitate the future iGEM teams to get started more simple and convenient and use BP neural network to complete some modeling work, we specially developed a BP neural network prediction GUI (graphical user interface) with Matlab's guide function based on MATLAB compilation environment. Through this GUI, we only need to import the data needed to be trained and the data needed to be predicted, and we can export the prediction results of the BP neural network through simple parameter debugging. Since the guide function of the old version of Matlab will be replaced by the app designer function, in order to increase the universality of the software developed by our team, our team chose to develop the BP neural network prediction GUI development with the app designer function again. We also packaged the graphical user interface we developed into APP to Matlab. In addition, our team considering our development software can only run in the condition of Matlab, in order to be able to run in no Matlab environment, we also uploaded the MCR can run without Matlab environment, while installing the MCR and the software, we can develop without Matlab environment can also work normally. We dramatically increase the convenience of the software by installing it into a standalone desktop APP.

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