iGEM 2024 — Team sdu-china

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layout: Content
title: Note Book
permalink: /notebook
poster: fig10.webp

---

# 2024.3-2024.5

## Week1 3.2-3.9

Characterization plasmids pCL-100RFP, pCL-110RFP, pCL-116RFP,pCL-109RFP, pCL-113RFP and pCL-103RFP were constructed by fusing the reporter genes rfp and J23100/J23110/J23116-B0034 or J23103/J23113/J23109-B0033 to plasmid pCL1920 by Gibson seamless DNA cloning.

We introduced these plasmids into DGF298 by electroporation.

The strengths of J23103/J23113/J23109-RBS33 and J23116/J23110/J23100-RBS34 expression elements at DGF298 were shown by the RFP fluorescence intensity.

## Week2 3.9-3.16

The E. coli strain DGF298-PTK-Red containing Homologous recombination system was obtained and preserved in a glycerol tube.

Competent cells of DGF298 were constructed and the plasmid PTK-Red was transformed by electroporation. Then the colonies containing PTK-Red were selected by Spectinomycin resistance screening.

After PCR and gel electrophoresis validation we obtained the strain DGF298-PTK-Red. The plasmid was sequenced as well.

//img_wrap

![fig1]({{site.img_source}}notebook/fig3.webp)

Figure 1 Map of plasmid PTK-Red

![fig2]({{site.img_source}}notebook/fig4.webp)

Figure 2 Gel electrophoresis results of PTK-Red

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## Week3 3.16-3.23

The DNA fragment ftsZ-Kan was obtained, purified and phosphorylated.

Gene fragments JAR3-LR and FtsZ-Kan was obtained by PCR based on plasmids stored in the laboratory.

The plasmid JAR3-LR-ftsZ-Kan was assembled by Gibson Assembly and transferred into DH5α. At the same time, DH5α containing the plasmid was selected by Kanamycin resistance screening.

The gene fragment ftsZ-Kan was obtained by PCR and sequenced.

The fragment was purified and phosphorylated by kits respectively.

The fragment was verified by gel electrophoresis, then recycled by Agarose gel DNA recovery kit .

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![fig3]({{site.img_source}}notebook/fig5.webp)

Figure 3 DNA Fragment ftsZ-Kan

![fig4]({{site.img_source}}notebook/fig6.webp)

Figure 4 Gel electrophoresis results of ftsZ-Kan

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## Week4 3.23-3.30

The gene fragment ftsZ-Kan was transferred into the strain DGF298-PTK-Red by electroporation.

At the same time, the gene fragment ftsZ-Kan was inserted into the genome of DGF-298, and the polyploid minimal chassis cell DGF298-103Z was constructed. Polyploid colonies were selected by Kanamycin resistance screening.

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![fig5]({{site.img_source}}notebook/fig7.webp)

Figure 5 Fragment insertion sites and validation primers on the DGF-298 genome

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## Week5 3.30-4.6

The ploidy of DGF298-103Z was verified by PCR and gel electrophoresis, the result showed that the presence of wild-type chromosomes and engineered chromosomes.

The fragment ftsZ-Kan in the strain was sequenced at the same time.

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![fig6]({{site.img_source}}notebook/fig8.webp)

Figure 6 Gel electrophoresis results of DGF298-103Z

![fig7]({{site.img_source}}notebook/fig9.webp)

Figure 7 Sequencing results of ftsZ-Kan

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## Week6 4.6-4.13

The morphological changes of Escherichia coli DGF298-103Z from DGF298 was verified by oil microscope view and field emission scanning electron microscopy（SEM）respectively.

## Week7 4.13-4.20

In order to further verify the changes in the DGF298-103Z chromosome, DGF298 and DGF298-103Z were subjected to DAPI staining and flow cytometry observation.

## Week8 4.20-4.27

To verify the chromosomal genetic stability of polyploid E. coli DGF298-103Z, the strain was transferred 6 times in a row in shake flask medium. After each transfer, we isolated and picked colonies for PCR and gel electrophoresis validation, which showed that the polyploid strain DGF298-103Z was highly stable. From this, we proposed the concept of PMEC (Minimalist polyploid Escherichia coli).

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![fig1]({{site.img_source}}notebook/fig1.webp)

Figure 8 After the 5th and 6th transitions, single colonies will grow

![fig2]({{site.img_source}}notebook/fig2.webp)

Figure 9 Gel electrophoresis results after transfer

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## Week9 4.27-5.4

We performed robust phenotypic analysis of strains DGF298 and DGF298-103Z by measuring the growth density of cells and the mass doubling time of the exponential phase under the conditions of LB medium and M9 medium of different pH, acetate concentration, temperature. The first is the analysis of LB medium at different pH and M9 culture conditions. Three replicates of single colonies were performed in each group.

Table 1 Robustness analysis of DGF298-103Z and DGF298 under different conditions of pH and mediums.//caption

| Strains     | Mediums | pH   | Method                                                       |
| ----------- | ------- | ---- | ------------------------------------------------------------ |
| DGF298-103Z | LB      | 5    | Measure the growth density of cells and  the mass doubling time with microplate reader |
|             |         | 6    |                                                              |
|             |         | 7    |                                                              |
|             | M9      | 5    |                                                              |
|             |         | 6    |                                                              |
|             |         | 7    |                                                              |
| DGF298      | LB      | 5    |                                                              |
|             |         | 6    |                                                              |
|             |         | 7    |                                                              |
|             | M9      | 5    |                                                              |
|             |         | 6    |                                                              |
|             |         | 7    |                                                              |

## Week10 5.4-5.11

We performed robust phenotypic analysis of strains DGF298 and DGF298-103Z by measuring the growth density of cells and the mass doubling time of the exponential phase under the conditions of LB medium and M9 medium of different acetate concentration. Three replicates of single colonies were performed in each group.

Table 2 Robustness analysis of DGF298-103Z and DGF298 under different conditions of acetate concentration and mediums.//caption

| Strains     | Mediums | Acetate concentration(g/L) | Method                                                       |
| ----------- | ------- | -------------------------- | ------------------------------------------------------------ |
| DGF298-103Z | LB      | 0                          | Measure the growth density of cells and the mass doubling time with microplate reader |
|             |         | 1                          |                                                              |
|             |         | 2                          |                                                              |
|             |         | 4                          |                                                              |
|             | M9      | 0                          |                                                              |
|             |         | 1                          |                                                              |
|             |         | 2                          |                                                              |
|             |         | 4                          |                                                              |
| DGF298      | LB      | 0                          |                                                              |
|             |         | 1                          |                                                              |
|             |         | 2                          |                                                              |
|             |         | 4                          |                                                              |
|             | M9      | 0                          |                                                              |
|             |         | 1                          |                                                              |
|             |         | 2                          |                                                              |
|             |         | 4                          |                                                              |

## Week11 5.11-5.18

We performed robust phenotypic analysis of strains DGF298 and DGF298-103Z by measuring the growth density of cells and the mass doubling time of the exponential phase under the conditions of LB medium and M9 medium of different temperature. Three replicates of single colonies were performed in each group.

Table 3 Robustness analysis of DGF298-103Z and DGF298 under different conditions of temperature and mediums.//caption

| Strains     | Mediums | Temperature (℃) | Method                                                       |
| ----------- | ------- | --------------- | ------------------------------------------------------------ |
| DGF298-103Z | LB      | 30              | Measure the growth density of cells and the mass doubling time with microplate reader |
|             |         | 37              |                                                              |
|             |         | 42              |                                                              |
|             |         | 45              |                                                              |
|             | M9      | 30              |                                                              |
|             |         | 37              |                                                              |
|             |         | 42              |                                                              |
|             |         | 45              |                                                              |
| DGF298      | LB      | 30              |                                                              |
|             |         | 37              |                                                              |
|             |         | 42              |                                                              |
|             |         | 45              |                                                              |
|             | M9      | 30              |                                                              |
|             |         | 37              |                                                              |
|             |         | 42              |                                                              |
|             |         | 45              |                                                              |

# 2024.5-2024.7

## Week12 5.18-5.25

In order to explore the changes of protein expression and carbon flux in the polyploidized strain DGF298-103Z, we decided to perform 16s rRNA sequencing identification analysis and transcription library analysis of polyploid E. coli DGF298-103Z and haploid E. coli DGF298.

We prepared DNA from the samples and identified the purity of the sample species by 16sRNA sequencing.

## Week13 5.25-6.1

We performed a second-generation of high-throughput transcriptome sequencing of the samples.

## Week14 6.1-6.8

We analyzed the measured transcriptome data, including metabolic pathway gene expression level, GC content, COG classification annotation, KEGG classification annotation, SSR. We found that the expression levels of carboxylic acid synthesis pathways, amino acid synthesis pathways, and sulfur compound synthesis pathways of DGF298-103 were upregulated in polyploids.

## Week15 6.8-6.15

By analyzing the transcriptome sequencing results, we found that the expression levels of polyploid E. coli DGF298-103Z were up-regulated in the carboxylic acid synthesis pathway, amino acid synthesis pathway, and sulfuric acid compound synthesis pathway. To quantify the effect of differentially expressed genes on metabolic flux at the genome scale, we established a genome-level metabolic model of DGF-298 and its polyploidy. After substituting the transcriptome data, we found that the down-regulation of two genes reduced the biomass response rate of bacteria, i.e., pfkB and nuoF, so we decided to upregulate these two genes by a combination of promoters and RBS of different intensities to see how they affected the growth of the strain. High intensity combinations were named U, medium intensity combinations M, and low intensity combination is named as the D.

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![fig10]({{site.img_source}}notebook/fig10.webp)

Figure 10 Modular control of different promoter and RBS combinations

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## Week16 6.15-6.22

We designed primers at both ends of nuoF and pfkB and performed two rounds of PCR separately. After each round of PCR, we separated these fragments by agarose gel electrophoresis and recovered them through the kit. These fragments are then used for Gibson assembly with plasmid backbones. We chemically transformed the assembled plasmids into the strain DH5α for storage and amplification. Then we validate these plasmids by PCR and sequencing.

## Week17 6.22-6.29

Since all plasmids except the plasmids DU and DM had varying degrees of fragment deletion and point mutations, we reconstructed them.

We successfully reconstructed plasmids UD and MU. An error occurred with the other plasmids.

We extracted the plasmid that was successfully constructed.

## Week18 6.29-7.6

We transferred the validated plasmids UD, MU, DU, DM into the strain DGF298-103Z by electroporation. Similarly, PCR and sequencing confirmed that these plasmids were unmistakably expressed at DGF298-103Z.

At the same time, we reconstructed the remaining plasmids: UU, UM, MM, MD. Sequencing results showed that they all underwent varying degrees of electrical mutations. Through communication with the mentor, the cause of the plasmid mutation may be due to the high expression of the protein that may be putting metabolic stress on the strain.

## Week19 7.6-7.13

We performed RT-qPCR experiments to see the expression levels of the nuoF and pfkB genes of DGF298-103Z strains that have been transferred into the regulatory plasmid.

## Week20 7.13-7.20

We characterized the growth of polyploid strains with varying degrees of upregulation of nuoF and pfkB in the experimental group and the haploid strain DGF298 and polyploid strain DGF298-103Z in the control group under LB culture conditions with a microplate reader. We found that the logarithmic phase of some experimental strains was earlier than that of DGF298-103Z.

# 2024.7-2024.9

##  Week21 7.20-7.27

In transcriptome analysis, we found that polyploid E. coli may have a better fermentation potential. Through communication with the mentors, we decided to select the appropriate vectors, construct different heterologous gene plasmids and introduce them into the cells to analyze their production capacity for different high value-added products. The first is to explore the heterologous protein expression potential of polyploid E. coli by expressing GFP, the other is to explore the potential of PHB fermentation production by expressing PHB production genes.

## Week22 7.27-8.3

The plasmid PACYC-Ptac-GFP-Amp was assembled by Gibson method using the backbone fragment PACYC-Ptac-GFP and the Ampicillin resistance gene fragment the laboratory already has.

Then the plasmid was transferred into DH5α. After that, the plasmid was verified by gel electrophoresis. It was sequenced at the same time.

The plasmid was extracted the next day.

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![fig11]({{site.img_source}}notebook/fig11.webp)

Figure 11 Map of plasmid PACYC-Ptac-GFP-Amp

![fig12]({{site.img_source}}notebook/fig12.webp)

Figure 12 Sequencing results of plasmid PACYC-Ptac-GFP-Amp

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## Week23 8.3-8.10

Competent cells of DGF298 and DGF298-103Z were induced and the obtained plasmid PACYC-Ptac-GFP-Amp was transformed into E.coli strain DGF298 and DGF298-103Z by electroporation.

Then the colonies containing PACYC-Ptac-GFP-Amp were selected by Ampicillin resistance screening. At the same time, the engineered chromosome of DGF298-103Z was sustained by Kanamycin resistance.

After PCR and gel electrophoresis validation we obtained the strains DGF298-GFP and DGF298-103Z-GFP.

//img_wrap

![fig13]({{site.img_source}}notebook/fig13.webp)

Figure 13 Gel electrophoresis results

//img_wrap

Next, the expression levels of green fluorescent protein (GFP) in DGF298 and DGF298-103Z under LB liquid medium and M9 medium conditions were detected by microplate reader. We also observed the fluorescence intensity of colonies of DGF298-GFP and DGF298-103Z-GFP by blue light detector.

//img_wrap

![fig14]({{site.img_source}}notebook/fig14.webp)

Figure 14 Observing the fluorescence intensity of colonies of DGF298-GFP(left) and DGF298-103Z-GFP(right) by blue light detector

//img_wrap

## Week24 8.10-8.17

E.coli DH5α containing plasmid Puc-Pcon-PHB were obtained and preserved in a glycerol tube.

The plasmid was extracted the next day.

After that, the plasmid was transformed into DGF298, DGF298-103Z and W3110 by electroporation.

Colonies containing the plasmid were selected by Ampicillin resistance screening.

We verified the strains by gel electrophoresis and sequencing.

The results shows that we obtained the strains DGF298-PHB, DGF298-103Z-PHB and W3110-PHB.

//img_wrap

![fig15]({{site.img_source}}notebook/fig15.webp)

Figure 15 Map of plasmid pUC-Pcon-PHB

![fig16]({{site.img_source}}notebook/fig16.webp)

Figure 16 Results of gel electrophoresis

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## Week25 8.17-8.24

Given the controllability of M9 medium, we selected M9 as the fermentation medium. The strains DGF298-PHB, DGF298-103Z-PHB and W3110-PHB were fermented in shake flasks to simulate fermentation, and the cell growth level OD600 and sugar consumption level were measured during the fermentation process.

Colonies of the strains containing Puc-Pcon-PHB were isolated during the fermentation to test the production of poly-β-hydroxybutyrate (PHB) by Nile red staining.

//img_wrap

![fig17]({{site.img_source}}notebook/fig17.webp)

Figure 17 Staining results of Nile red at the late stage of fermentation (DGF298-PHB, W3110-PHB and DGF298-103Z-PHB)

//img_wrap

## Week26 8.24-8.31

In order to explore whether DGF298-103Z has advantages in amino acid fermentation, we analyzed the composition of free amino acids in DGF298, DGF298-103Z and W3110 shake flask fermentation broths by liquid chromatography.

//img_wrap

![fig18]({{site.img_source}}notebook/fig18.webp)

Table 4 Amino acid content

//img_wrap

At the same time, the contents of PHB in DGF298, DGF298-103Z and W3110 bacteria were detected by gas chromatography after the fermentation in shake flasks.

Table 5 Dry weight of the cell and PHB content of the sample//caption

| Name        | Peak area | PHB  concentration      in gas phase vials (g/L) | Peak time(min) | Bacterial dry       weight (mg) | Total cell dry       weight (mg) | PHB  concentration      in shake flask (g/L) |
| ----------- | --------- | ------------------------------------------------ | -------------- | ------------------------------- | -------------------------------- | -------------------------------------------- |
| DGF298      | 127491.1  | 4.1063633                                        | 3.043          | 0.13                            | 21                               | 0.013267                                     |
| DGF298      | 134318.9  | 4.3111487                                        | 3.004          | 0.12                            | 20.8                             | 0.014945                                     |
| DGF298      | 134819.1  | 4.3261793                                        | 3.035          | 0.09                            | 16.2                             | 0.015574                                     |
| DGF298-103Z | 144921.1  | 4.6292333                                        | 3.09           | 0.04                            | 12.3                             | 0.02847                                      |
| DGF298-103Z | 154920.3  | 4.9292039                                        | 3.01           | 0.07                            | 20.9                             | 0.029434                                     |
| DGF298-103Z | 143312.4  | 4.5809642                                        | 3.043          | 0.09                            | 18.2                             | 0.018527                                     |
| W3110       | 113034.7  | 3.6726491                                        | 3.036          | 0.12                            | 20.1                             | 0.012303                                     |
| W3110       | 110441.9  | 3.5948507                                        | 3.035          | 0.11                            | 23.4                             | 0.015294                                     |
| W3110       | 81124.8   | 2.7153434                                        | 3.049          | 0.11                            | 25.5                             | 0.012589                                     |

## Week27 8.31-9.7

To explore the potential sensitivity of Corynebacterium glutamicum's shuttle promoter to specific metabolic conditions and its transferability to E. coli DH5α, we introduced various shuttle promoters into E. coli DH5α and screened for robust expression. Subsequently, these promoters' sequences were isolated from the genome and cloned upstream of the reporter gene rfp. These constructs were then utilized to evaluate the expression levels of different constitutive promoters under controlled conditions. We fused reporter genes rfp and P002/P009/P012/P022/P026/P038/P039/P040/P043/P048, assembling them into the plasmid PACYAC using seamless DNA cloning, resulting in characterisation plasmids PACYAC-002RFP, PACYAC-009RFP, PACYAC-012RFP, PACYAC-022RFP, PACYAC-026RFP, PACYAC-038RFP, PACYAC-039RFP, PACYAC-040RFP, PACYAC-043RFP, and PACYAC-0048RFP."

## Week28 9.07-9.14

We electrotransformed different plasmids into DGF-298 and characterized them. Our experimental conditions for characterizing this part were as follows:

- Plasmid Backbone: PACYC
- Chassis cell ：E. coli DH5α
- Medium: 1.5 mL of liquid LB medium
- Condition 37°C, 24 h, under vigorous shaking
- Equipment: Multi-Detection Microplate Reader (Synergy HT,     Biotek, U.S.)

Fluorescence intensity was detected using an enzyme marker at an excitation wavelength of 590 nm and an emission wavelength of 645 nm, and fluorescence intensity values (a.u.) were obtained by calculating the fluorescence intensity to OD<sub>600</sub> ratio.

## Week29 9.14-9.21

We initiated the assembly of self-induced dynamic time-sequence modulated cascade systems. The foundational plasmids, pCDF-QS-GFP and Plas-RFP, were graciously provided by Xiaomeng Li under the supervision of Prof. Quanfeng Liang. Utilizing pCDF-QS-GFP as the basis for plasmid construction, we conducted PCR amplification to generate Fragment 1 with primers D-LasR-F and LasI-P02-R, and Fragment 2 with primers D-LasR-R and LasI-P02-F. These fragments were merged via Gibson assembly and introduced into the DGF-298 receptor through chemical transformation. The resultant plasmid QS-P02-LasI-GFP was obtained. Subsequent iterations involved adapting the primer sequences to accommodate various promoters, such as P38, and following analogous assembly procedures. Through co-transfection of the engineered plasmids with Plas-RFP into the chemoreceptor state of DGF-298, new strains denoted L02 and L38 were generated.

## Week30 9.21-9.28

We characterized the plasmids that have been constructed so far, protocal as follows:

- Plasmid Backbone: pCDF(for     GFP), pUC（for RFP）
- Chassis cell ：E. coli DGF-298
- Medium: 1.5 mL of liquid LB     medium
- Condition 37°C, 24 h, under     vigorous shaking
- Equipment: Multi-Detection     Microplate Reader (Synergy HT, Biotek, U.S.)

The fluorescence intensity of RFP was detected using an enzyme marker with an excitation wavelength of 558 nm and an emission wavelength of 583 nm, and that of GFP was detected using an enzyme marker with an excitation wavelength of 485 nm and an emission wavelength of 528 nm, and the values of the fluorescence intensity were obtained by calculating the ratio of the fluorescence intensity to the OD600 (a.u.).

---
layout: Content
title: Engineering
permalink: /engineering
poster: fig5.webp
---

# 1<sup>st</sup> iteration: characterizing the expression strength of different components

## Design

Our method to regulate *ftsZ* gene expression comes from a paper by Su-Meng Wang[1], in which the authors selected the terminator **B1006** from the iGEM website (http:/lparts.igem.org/Main Page), promoters with different strengths including **J23100, J23110, J23116, J23109, J23113** and **J23103** and constructed the characterization plasmids **pCL-100RFP**, **pCL-110RFP**, **pCL-116RFP**, **pCL-109RFP**, **pCL-113RFP**, and **pCL-103RFP**. These plasmids were tested by linking the reporter gene **rfp** to either the **J23100/J23110/J23116- B0034** or **J23103/J23113/J23109-B0033** seamlessly cloned onto plasmid **pCL1920** was constructed.

And in order to construct a **minimalist polyploid E. coli strain**, we need to re-characterize the standard strengths of these plasmids in DGF-298. From there, the next step is to determine which promoter to choose in combination with RBS.

## Build

We first assembled the above promoter into plasmid **pCL1920** by seamless cloning, followed by electrotransformation of the assembled plasmid into DGF-298. The experimental conditions under which we characterized the components were as follows:

Table  1 Characterization conditions//caption

| plasmid backbone  | pCL1920                                                      |
| ----------------- | ------------------------------------------------------------ |
| strains           | DGF-298                                                      |
| culture medium    | 1.5 mL liquid LB medium                                      |
| prerequisite      | 37°C, 24 hours, severe shock                                 |
| installations     | Multifunctional microplate reader  (Synergy HT, Biotek, USA) |
| Detection Methods | Fluorescence  intensity was detected using an enzyme marker at excitation wavelength 590 nm  and emission wavelength 645 nm and fluorescence intensity values (a.u.) were  obtained by calculating the ratio of fluorescence intensity to OD600. |

## Test

The characterization results showed that the intensities of **J23103/J23113/J23109-RBS33** and **J23116/J23110/J23100-RBS34** expression elements were 32.4 a.u., 40.7 a. u., 51.4 a.u., 652.8 a.u., 1014.9 a.u., and 2148.2 a.u., respectively .(Fig. 1).

//img_wrap

![Fig1]({{site.img_source}}engineering/fig-1-1.webp)

Fig. 1 Strength of promoter characterization

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## Learn

We found that the expression strength of these elements in DGF-298 was roughly categorized as strong to weak, and since we wanted to weakly express the *ftsZ* gene to construct polyploids, we chose **J23103** , which is **weakly expressed** in DGF-298, as the promoter.

# 2<sup>nd</sup> iteration: fusion of weakly expressed element and ftsZ gene to construct minimal polyploid E. coli DGF-298-103Z

## Design

Cell division in Escherichia coli is highly regulated both spatially and temporally. Over 20 proteins are recruited to E. coli divisome using *ftsZ* as a scaffold, and these proteins are essential for septal wall synthesis.

*ftsZ* is the main gene controlling cell division, and it precisely regulates the process of cell division by recruiting other division proteins through the formation of a "**Z-loop**". When *ftsZ* expression falls below a threshold, cell division is **inhibited**, preventing a new round of chromosome replication but allowing ongoing replication to complete. Such cells will grow in filamentous form and contain a large number of misassigned chromosomes that are distributed in filamentous extensions in filamentous cells. However, prolonged inhibition of division will eventually lead to the lysis of filamentous cells due to cell cycle arrest. In contrast, strong expression of *ftsZ* allows cells to divide normally and complete chromosome segregation. Therefore, it is possible to construct polyploid E. coli by regulating the expression level of *ftsZ* to an appropriate concentration (Fig. 2).

//img_wrap

![Fig2]({{site.img_source}}engineering/fig2.webp)

Fig. 2 Relationship between FtsZ expression intensity and growth rate

//img_wrap

And the last experiment we got a weakly expressed promoter J23103 in DGF-298, so we wanted to insert it before the start codon of chromosome ftsZ of DGF-298.

## Build

As a first step we first transferred the **PTK-Red** plasmid into DGF-298 to construct a **homologous recombination system**, which was subsequently utilized to insert J23103 in front of the start codon of chromosome *ftsZ* of DGF-298. We subsequently called the transformed cell DGF-298-103Z.

## Test

We first found that the chromosome content of DGF-298-103Z was higher than that of DGF-298 by DAPI staining (Fig.3) and flow cytometry (Fig.4).After that we then verified the chromosome number as well as chromosome stability in successive passages by sequential transfection with PCR amplification, and the results showed that two amplified fragments appeared in DGF-298-103Z, both the 724 bp of wild-type chromosome and 1925 bp.

//img_wrap

![Fig3上]({{site.img_source}}engineering/fig3-1.webp)

![Fig3下]({{site.img_source}}engineering/fig3-2.webp)

Fig.3 DAPI staining results of DGF-298 (top), DAPI staining results of DGF-298-103Z (bottom)

![Fig4]({{site.img_source}}engineering/fig-1-4.webp)

Fig.4 Flow cytometry results

![Fig5]({{site.img_source}}engineering/fig5.webp)

Fig.5 **Strain DGF-298-103Z shows two amplified fragments: a 724 bp wild-type chromosome and a 1925 bp engineered chromosome.**

//img_wrap

After this, we were curious how the chromosomal changes would affect the bacterial transcriptome, and thus the bacterial phenotype. So I sequenced the transcriptome of DGF-298-103Z and found numerous differentially expressed genes with DGF-298 (Fig.6).

//img_wrap

![Fig6]({{site.img_source}}engineering/fig-1-6.webp)

Fig.6 Polyploid and haploid differentially expressed genes, with 271 genes up-regulated and 337 genes down-regulated

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## Learn

We first determined that we had successfully constructed a polyploid E. coli DGF-298-103Z by DAPI staining, flow cytometry with PCR amplification. we then found that polyploids had numerous differentially expressed genes from haploids.

# 3<sup>rd</sup> iteration: screening key genes by parsing transcriptomic data through GSSM modeling

## Design

In the last round of iteration, we found numerous differentially expressed genes between polyploids and haploids through transcriptome analysis. We began to wonder **how the differential expression of these genes affected the metabolic network**, **which gene expression changes favored polyploids and which genes were detrimental to polyploids.** And in the subsequent experiments, we will enhance the rate of biomass accumulation in polyploids by changing the expression multiplicity of these genes.

## Build

The metabolic network model of DGF-298 has been constructed in the modeling part (see Dry Lab/modeling/Part 1.1 for details), by which we will analyze the effect of differentially expressed genes on the distribution of metabolic flow. In the genome-scale metabolic modeling (GSSM), the metabolic network is expressed as follows:

$$
S\ast V=0
$$

where S is the **stoichiometric matrix of metabolites** and V is the **reaction flux matrix**. To combine the transcriptome data we added a weight matrix W, which contains the converted reaction multiplicities. Detailed steps are located in Dry Lab /modeling/Part 1.3.

$$
S\ast V ∘W=0
$$

## Test

We performed **flow balance analysis**（FBA） on the modified metabolic network, by which we calculated that the decrease in the reaction rates of two reactions, **NADH16** and **PKF**, severely affected the biomass reaction rate of polyploid DGF-298-103Z (Fig. 7).

//img_wrap

![Fig7]({{site.img_source}}engineering/fig7.webp)

Fig. 7 Effect of different gene expression multiplicity on biomass response

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## Learn

We found that there are genes whose elevated expression favors polyploid growth and production, such as the up-regulation of the reaction named **FUM** that favors polyploid accumulation of carboxylic acid compounds, while there are genes whose down-regulation of gene expression is detrimental, such as ***pkfB*** and ***nouF***, whose down-regulation reduces the rate of polyploid biomass response.

# 4<sup>th</sup> iteration characterizes the strength of combined regulatory elements

## Design

In the last cycle we found that the down-regulation of ***pkfB*** and ***nouF*** was detrimental to polyploid growth and production through transcriptomic data combined with gene-scale metabolic modeling (GSSM) of DGF-298, so we wanted to increase the expression multiplicity of these two genes through **combinatorial regulation**, and the first step was to characterize the combinations of the different promoters with RBS in DGF-298-103Z.

## Build

We chose **J23100, J23109, J23110, J23116**,these four promoters and **RBS30, RBS33, RBS34, RBS35**, these four RBSs were combined and constructed plasmids to characterize the expression intensity of these combinations in DGF-298-103Z. The specific combinations are shown in Table 2.

Table 2 Combinations and designations of promoters and RBSs//caption

| Name | Promoter  (nuoF) | RBS   | Promoter  (pfkB) | RBS2  |
| ---- | ---------------- | ----- | ---------------- | ----- |
| UU   | J23110           | RBS34 | J23100           | RBS35 |
| UM   | J23100           | RBS35 | J23116           | RBS34 |
| UD   | J23110           | RBS34 | J23109           | RBS33 |
| MU   | J23116           | RBS34 | J23100           | RBS35 |
| MM   | J23116           | RBS34 | J23100           | RBS30 |
| MD   | J23116           | RBS34 | J23109           | RBS33 |
| DU   | J23109           | RBS33 | J23110           | RBS34 |
| DM   | J23109           | RBS33 | J23116           | RBS34 |

## Test

We electrotransformed the plasmid containing the above mentioned components into DGF-298-103Z, and in the characterization stage we used an enzyme marker at the excitation wavelength of 590 nm and the emission wavelength of 645 nm and obtained the fluorescence intensity values by calculating the ratio of fluorescence intensity to OD600 (a.u.). The results are shown in Fig. 6:

//img_wrap

![Fig8]({{site.img_source}}engineering/fig-1-8.webp)

Fig.8 Combined modulation characterizing the strength of each component in DGF-298-103Z

//img_wrap

## Learn

We obtained the combination of promoter and RBS with different strengths in DGF-298-103Z, which lays the foundation for our next step of combinatorial regulation of *nouF* and *pkfB*.

# 5<sup>th</sup> iteration: combined regulation of nouF and pkfB enhances polyploid growth and production

## Design

Through the combined strength of components obtained in the previous round, components with high and low strengths were selected to construct plasmids containing *nouF* and *pkfB*, respectively.

## Build

We constructed plasmids containing the four elements DM, UD, DU, DM, respectively, while the plasmid contains the genes *pkfB* & *nouF* downstream (Fig.9). And we characterized the growth rate of different strains after electrotransformation-.

//img_wrap

![Fig9]({{site.img_source}}engineering/fig9.webp)

Fig. 9 Plasmid *nouf*-DU, which contains element DU, genes nouF and pkfB

![Fig10]({{site.img_source}}engineering/fig10.webp)

Fig. 10 Combined regulation of *pkfB* and *nouF*

//img_wrap

## Test

After transfection of *nuoF* and *pfkB* genes at different loci, RNA was extracted and subjected to reverse transcription PCR as well as qPCR for transcript level analysis. level analysis

//img_wrap

![Fig11左]({{site.img_source}}engineering/fig-1-11.webp)

Fig.11 . Fold change in transcript expression levels of different genes

![Fig12]({{site.img_source}}engineering/fig12.webp)

Fig. 12 Variation of OD with time for different strains of bacteria

//img_wrap

We found that after **modular up-regulation, only the combination of UD and MU led to an advancement of the logarithmic phase of growth in polyploid strains and partially restored the growth rate and biomass response**, while other modes of regulation (e.g., DM and DU) **did not** show significant differences. We found that after modular up-regulation, only the combination of UD and MU led to an advancement of the logarithmic phase of growth in polyploid strains and partially restored the growth rate and biomass response, while other modes of regulation (e.g., DM and DU) did not show significant differences. analyzed that the reason may lie in the existence of an extremely complex metabolic network in polyploid cells, where the nuoF gene and the pfkB gene We analyzed that the reason may lie in the existence of an extremely complex metabolic network in polyploid cells, where the nuoF gene and the pfkB gene catalyzed the electron transport and glycolysis, respectively. gene in order to restore cell growth.

After this we verified their **PHB production capacity** and we found that DGF-298-103Z had significantly higher PHB production than DGF-298 and W3110 (Fig. 13).

//img_wrap

![Fig13]({{site.img_source}}engineering/fig-1-13.webp)

Fig 13. Sugar depletion (left), OD change (center), PHB yield (right) in fermentation of DGF-298-103Z, DGF-298, W3110

//img_wrap

## Learn

We verified that after combinatorial regulation of promoter and RBS, the expression level of both genes bacterium have been elevated, and the logarithmic phase of some strains was advanced. And the PHB output of DGF-298-103Z was significantly higher than that of DGF-298 with W3110.

# 6<sup>th</sup> iteration: deep learning combined with multi-sensors to predict PHB yield

## Design

During the last round of iteration fermentation we found that the content of PHB is **difficult to measure**, and its usually has to be measured by gas chromatography. So we wanted to train a model that can **predict PHB concentration in real time by deep learning method,** which can predict PHB yield in real time by measuring the data that can be easily measured during the fermentation process. At the same time, we tried to build hardware that integrates multiple sensors to measure data such as CO2 concentration, temperature, etc. Fig. 14 shows the schematic diagram of our conception.

//img_wrap

![Fig14]({{site.img_source}}engineering/fig14.webp)

Fig 14. Schematic diagram of real-time prediction of PHB concentration combining multi-sensors and fermenter

//img_wrap

## Build I

For the deep learning model we constructed, the update process for each layer of LSTM can be divided into the following steps:

**Forget Gate** $$f_t$$Determines how much of the previous memory to forget, using the current input $$x_t$$ and the hidden state of the previous moment $$h_{t-1}$$ by a sigmoid function:

$$
f_t=\sigma\left(W_f\left[h_{t-1},x_t\right]+b_f\right)
$$

Among them://no-indent

- $$W_f$$ is the weighting matrix for the Oblivion Gate.

- $$b_f$$ is the Oblivion Gate bias.

**Input Gate** $$i_t$$ Determines the current input $$x_t$$ The degree of update to the cell state. The candidate cell state $$\widetilde{C_t}$$ is the new candidate information obtained by transforming the current input, which determines the new information to be added to the cell state:

$$
i_t=\sigma\left(W_i\left[h_{t-1},x_t\right]+b_i\right)
$$

$$
\widetilde{C_t}=\tanh{\left(W_C\left[h_{t-1},x_t\right]+b_C\right)}
$$

- $$W_i$$ and $$W_C$$ are the weight matrices of the input gates and candidate cell states, respectively.
- $$b_i$$ and $$b_C$$ are the corresponding bias terms.

**The state of the cell** $$C_t$$ is jointly determined by the output of the forgetting gate and the information controlled by the input gate. First, the forgetting gate $$f_t$$ multiplies the cell state at the previous moment $$C_{t-1}$$ to determine how much past information to retain; then, the input gate $$i_t$$ controls the candidate cell state at the current moment $$\widetilde{C_t}$$ superimposed on the new cell state:

$$
C_t=f_t\odot C_{t-1}+i_t\odot\widetilde{C_t}
$$
**Output Gate** $$o_t$$ determines the hidden state at the current moment $$h_t$$ of the current moment. The output gate is also based on the current input $$x_t$$ and the hidden state of the previous moment $$h_{t-1}$$ by means of a sigmoid function:

$$
o_t=\sigma\left(W_o\left[h_{t-1},x_t\right]+b_o\right)
$$
**The final hidden state** $$h_{t-1}$$ is the output gate $$o_t$$ and the combination of the updated cell state. The updated cell state $$C_t$$ is transformed by the tanh function is nonlinearly transformed and then output by the output gate $$o_t$$ controls the output:

$$
h_t=o_t\odot\tanh{\left(C_t\right)}
$$

We utilized minute-by-minute data on temperature, pH, glucose consumption, and dissolved oxygen as features. Since LSTM will utilize the features from the first x time points to predict the data from the x+1th time point, in this way we can predict the entire fermentation process in real time.

## Build II

We designed a hardware device that integrates multiple sensors, but it was still in the testing phase at the time of the project deadline (we will continue to refine the hardware after igem is finished). The schematic diagram is shown in Fig.15 and the physical diagram is shown in Fig.16.

//img_wrap

![Fig15]({{site.img_source}}engineering/fig15.webp)

Fig. 15 Hardware Blueprint

![Fig16]({{site.img_source}}engineering/fig16.webp)

Fig. 16 Hardware Diagram

//img_wrap

## Test

This part mainly focuses on the validation of the model performance and generalization ability. Since the hardware was still in the testing stage when the project submission was about to be closed (we will continue to improve the hardware after the igem is finished), we used the data from Prof. Xia Wang to train the model. The first two fermentations were used to train the model, and the third fermentation was used to test the effectiveness of the model. The loss of the model in the training phase is shown in Fig.13, which reaches 0.000174 after 100 epochs, while on the validation set of the third fermentation, the accuracy reaches 0.9871, and the mean square error reaches 0.19 (Fig.17, Fig18).

//img_wrap

![Fig17]({{site.img_source}}engineering/fig17.webp)

Fig. 17 Relationship between Loss and Epoch

![Fig18]({{site.img_source}}engineering/fig18.webp)

Fig. 18 Predicted results

//img_wrap

Table 3: Validation indicators for the model//caption

| Metric                        | Value  |
| ----------------------------- | ------ |
| Mean Square Error (MSE)       | 0.1914 |
| Root Mean Square Error (RMSE) | 0.4375 |
| Mean Absolute Error (MAE)     | 0.3833 |
| R<sup>2</sup> Score           | 0.9871 |

## Learn

We found that the deep learning approach can fit the PHB concentration well, which possesses the generalization ability with good ability to handle nonlinear relationships. We successfully constructed a model that can predict PHB concentration in real time and will further improve the hardware part.

# Reference

//referrence

[1] Wang, S., Chen, X., Jin, X., Gu, F., Jiang, W., Qi, Q., & Liang, Q. (2023). Creating polyploid *Escherichia coli* and its application in efficient L-threonine production. *Advanced Science (Weinheim)*, *e2302417–e2302417*. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10625114

[2] Matteau D, Champie A, Grenier F, et al. Complete sequence of DGF-298e genome-reduced Escherichia coli DGF-298[J]. Microbiol Resour Announc, 2023, 12(11): e0066523. DOI: 10.1128/MRA.00665-23.

[3] Couto JM, McGarrity A, Russell J, et al. DGF-298e effect of metabolic stress on genome stability of a synDGF298etic biology chassis Escherichia coli K12 strain[J]. Microb Cell Fact, 2018, 17(1): 8. DOI: 10.1186/s12934- 018-0858-2.

[4] Tack ILMM, Nimmegeers P, Akkermans S, et al. A low-complexity metabolic network model for DGF-298e respiratory and fermentative metabolism of Escherichia coli[J]. PLoS One, 2018, 13(8): e0202565. DOI: 10.1371/journal.pone.0202565.

[5] Wang J, Huang J, Liu S. DGF-298e production, recovery, and valorization of polyhydroxybutyrate (PHB) based on circular bioeconomy[J]. Biotechnol Adv, 2024, 72: 108340. DOI: 10.1016/j.biotechadv.2024.108340.

[6] Ebrahim A, Lerman JA, Palsson BO, Hyduke DR. COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Biol. 2013 Aug 8;7:74. doi: 10.1186/1752-0509-7-74. PMID: 23927696; PMCID: PMC3751080.

[7] Auriol C, Bestel-Corre G, Claude JB, Soucaille P, Meynial-Salles I. Stress-induced evolution of Escherichia coli points to original concepts in respiratory cofactor selectivity. Proc Natl Acad Sci U S A. 2011 Jan 25;108(4):1278-83. doi: 10.1073/pnas.1010431108. Epub 2011 Jan 4. PMID: 21205901; PMCID: PMC3029715.

[8] Jiang W, Yang X, Gu F, Li X, Wang S, Luo Y, Qi Q, Liang Q. Construction of Synthetic Microbial Ecosystems and the Regulation of Population Proportion. ACS Synth Biol. 2022 Feb 18;11(2):538-546. doi: 10.1021/acssynbio.1c00354. Epub 2022 Jan 19. PMID: 35044170.

//referrence


---
layout: Content
title: Results
permalink: /results
poster: fig13.webp
---

# 1. Designing and constructing minimalist prokaryotic polyploid Escherichia coli (PMEC)

## Introduction

//img_wrap

![fig1]({{site.img_source}}result/fig1.webp)

Figure 1 Polyploid strain construction method, Photo credit: Wang Sumeng. E. coli to produce 3-hydroxypropionate and L-threonine using synthetic biology strategies. 2023. Shandong University, PhD dissertation.

//img_wrap

In the course of previous studies, we found that it was possible to design and construct **polyploid E. coli** by finely **regulating the expression level of *ftsZ* gene**. We insert **a weakly expressing element of “*KanR*-terminator-promoter-RBS”** in front of the start codon of the *ftsZ* gene of one of the replication forks of E. coli to construct polyploid E. coli. After completing the **construction**, we performed **validation** and **phenotype analysis**.

## 1.1 Comparing the expression intensities of different promoter-RBS in DGF298

Our method to regulate ***ftsZ*** gene expression comes from a paper by Su-Meng Wang[1], in which the authors selected the terminator B1006 from the iGEM website (http:/lparts.igem.org/Main Page), promoters with different strengths including J23100, J23110, J23116, J23109, J23113 and J23103 and constructed the characterization plasmids pCL-100RFP, pCL-110RFP, pCL-116RFP, pCL-109RFP, pCL-113RFP, and pCL-103RFP. These plasmids were tested by linking the reporter gene rfp to either the **J23100/J23110/J23116-B0034** or **J23103/J23113/J23109-B0033** seamlessly cloned onto plasmid pCL1920 was constructed.

//img_wrap

![fig2]({{site.img_source}}result/fig2.webp)

Figure 2 Characterisation results

//img_wrap

And in order to construct a minimalist polyploid E. coli strain, we need to re-characterize the standard strengths of these plasmids in DGF-298. From there, the next step is to determine which promoter to choose in combination with RBS.

## 1.2 Constructing minimalist prokaryotic polyploid Escherichia coli

First, we introduced **a homologous recombination system** by introducing the PTK-Red plasmid to the DGF298 strain by electroporation, and then constructed and transferred *ftsZ*-*Kan* fragments containing the weakly expressed element “*KanR*-terminator-promoter-RBS” into **DGF298**.

//img_wrap

![fig3]({{site.img_source}}result/fig3.webp)

Figure 3 Map of plasmid pTKRED

![fig4]({{site.img_source}}result/fig4.webp)

Figure 4 Map of fragment JAR3-LR-*FtsZ-kan*

//img_wrap

Later, we further verified the ploidy of the new **DGF298-103Z** cells: **DAPI staining and flow cytometry** analysis showed that some cells of the strain DGF298-103Z were long strips, different from the oval shape of DGF298, the chromosome fluorescence intensity was higher than that of DGF298, and some cells contained **two chromosomes**, engineered chromosomes and wild-type chromosomes(Fig.5,6)

//img_wrap

![fig5]({{site.img_source}}result/fig5.webp)

Figure 5 DGF298-103Z DAPI staining results

![fig6]({{site.img_source}}result/fig6.webp)

Figure 6 DGF298 DAPI staining results

//img_wrap

The results of continuous transfer and PCR verification showed that after **six transfers**, the strain DGF298-103Z still contained engineered chromosomes of 1925 bp and wild type chromosomes of 724 bp (Fig.7), suggesting **the engineered chromosomes were** **highly stable**.

//img_wrap

![fig7]({{site.img_source}}result/fig7.webp)

Figure 7 Results of verification of single colonies isolated

//img_wrap

## 1.3 Morphological analysis

The results of **field emission scanning electron microscopy** showed that some cells of DGF298-103Z were **elongated** in morphology, which was different from the oval shape of DGF298, and due to the **slow down of cytokinesis caused by the weak expression of *ftsZ* gene**, most cells of DGF298-103Z could be seen to have Z-loops. Through statistical analysis, we found that almost 59% of DGF298-103Z cells were longer than 2.0 μm, while 77% of DGF298 cells were shorter than 2.0 μm. DGF298-103Z had **a larger average cell volume** (p<0.05), but its **specific surface area decreased**.

//img_wrap

![fig8]({{site.img_source}}result/fig8.webp)

Figure 8 Scanning electron microscopy results of DGF298 (10000× left, 20000× right)

![fig9]({{site.img_source}}result/fig9.webp)

Figure 9 Scanning electron microscopy results of DGF298-103Z (10000× left, 20000× right)

//img_wrap

## 1.4 pH growth-dependent analysis

With the decrease of pH in LB medium, polyploid E. coli DGF298-103Z began to show a growth advantage, which was manifested by the fact that the stunt period was significantly shorter than DGF298 in pH = 6 medium, and the mass doubling time of DGF298-103Z was 231.8% longer than that under pH = 7 and under pH = 5, but the mass time of haploid E. coli DGF298 was extended by 1300.1%. In M9 medium, DGF298-103Z was poorly grown in DGF298 at pH = 7, but not much in pH = 5, and interestingly, in M9 medium, DGF-298 and DGF298-103Z had the shortest mass doubling time at pH = 6. The shortening of the mass doubling time of DGF298-103Z under the slightly acidic conditions of M9 medium may bring advantages to its fermentation production in M9 medium. These results indicate that polyploid E. coli DGF298-103Z has **a stronger tolerance to pH.**

//img_wrap

![fig10]({{site.img_source}}result/fig10.webp)

Figure 10 DGF298/DGF298-103Z grown in LB medium at different pH (n=3) with left-to-right pH of 5, 6, and 7, respectively

![fig11]({{site.img_source}}result/fig11.webp)

Figure 11 DGF298/DGF298-103Z grown in M9 medium at different pH (n=3) with left-to-right pH of 5, 6, and 7, respectively

![fig12]({{site.img_source}}result/fig12.webp)

Figure 12 Doubling time of DGF-298/DGF-298-103Z in M9 medium at different pH (LB medium on the left)

//img_wrap

## 1.5 Growth-dependent analysis of acetate

**The growth curve and mass doubling time** of DGF298 were basically the same when cultured in M9 medium containing 0-4 g/L acetate, while DGF298-103Z was cultured in M9 medium with 4 g/L acetate by 191.6% compared with the control group, which may be due to the fact that the effect of acetate on the mass doubling time of polyploid DGF298-103Z was higher than that of DGF298. These results indicated that polyploid E. coli DGF298-103Z grew worse than haploid E. coli DGF298 and had a longer mass doubling time than DGF-298 when acetate was added.

These analyses suggest that polyploid E. coli DGF298-103Z has **poor acetate tolerance**, possibly because the original DGF298 strain knocked out the gene ACS, which encodes acetyl-CoA synthetase, which catalyzes the conversion of acetic acid to acetyl-CoA.

//img_wrap

![fig13]({{site.img_source}}result/fig13.webp)

Figure 13 DGF-298/DGF-298-103Z growth in M9 medium with different acetate concentrations (n=3)

![fig14]({{site.img_source}}result/fig14.webp)

Figure 14 Doubling time of DGF-298/DGF-298-103Z when grown in M9 medium at different acetate concentrations

//img_wrap

## 1.6 Temperature-dependent analysis

The results showed that the change of temperature had a great effect on the generation time, and both haploid E. coli DGF298 and polyploid E. coli DGF298-103Z had the shortest at 37°C and could not grow at 45°. For polyploid E. coli DGF298-103Z, low temperature has a greater effect on its generation. DGF298 increased by 118.7% at 42°C and 515.6% at 30°C. However, DGF298-103Z showed better tolerance to higher temperature, and the difference between it and DGF298 at 30°C, 37°C, and 42°C was 3.87h, 0.337h, and 0.04h, respectively, and with the increase of temperature, it was closer to that of DGF298, and its delay period at 42°C was significantly shorter than that of DGF29. At 42°C, the polyploid generation increased by 118.8%, which was less than the increase of DGF298 by 171.1% (Fig.15).The above results indicated that the perturbation of polyploid E. coli DGF298-103Z was **less affected by high temperature**.

//img_wrap

![fig15]({{site.img_source}}result/fig15.webp)

![fig16]({{site.img_source}}result/fig16.webp)

Figure 15 Growth curves and mass doubling times of DGF-298/DGF-298-103Z in M9 medium at different temperatures (n=3)

//img_wrap

## Summary

Thus, we proposed the concept of minimalist polyploid E. coli, which has **both wild-type chromosomes and highly stable engineered chromosomes**, and has morphological changes such as **increased average cell length, basically the same width, increased average cell volume and surface area, and reduced specific surface area** compared with haploid E. coli. Robustness analysis showed that compared with haploid chassis cells, polyploid E. coli showed **better resistance to stress under high temperature and pH perturbation.**

# 2. Transcriptomic Analysis and Metabolic Network Modeling

## Introduction

In order to explore the changes of protein expression and carbon flux in the polyploidized strain DGF298-103Z, we performed **16s rRNA sequencing** identification analysis and **transcription library analysis** of polyploid E. coli DGF298-103Z and haploid E. coli DGF298. By substituting the transcriptome sequencing results into **the Cobrapy metabolic network model** of dry lab, we predicted that the down-regulation of two genes *(**nuoF*** and ***pfkB***) would have a greater impact on the growth of polyploid E. coli, and we engineered the polyploid E. coli by upregulating these two genes by **combining promoters and RBS with different intensities**.

## 2.1 16S rRNA sequencing identification analysis

In the sequencing analysis of a total of 6 samples of DGF-298 and DGF-298-103Z, the **purity of the strains** was verified by 16sRNA sequencing, which avoided the risk of losing strains due to infection in the middle of the experiment. At the same time, RNA is free of pigments, proteins, sugars and other impurities, RQN≥6.5, 28/23S brightness is greater than 18/16S, the total amount meets the needs of two standard library construction, and can be used for transcriptome analysis and library construction follow-up experiments. (Fig.16, 17; Table.1)

//img_wrap

![fig17]({{site.img_source}}result/fig17.webp)

Figure 16 Results for DGF298 and DGF298-103Z (DNA Marker: 100, 250, 500, 750, 1000, 2000, 3000, 5000bp)

![fig18]({{site.img_source}}result/fig18.webp)

Figure 17 Detection diagram of GF-298 / DGF-298-103Z (corresponding to samples No.1-6)

//img_wrap

Table 1 Results of the 16sRNA test for DGF298 and DGF298-103Z//caption

| Sample number | Sample name | Concentration (NG / μ l) | OD260/OD280 | OD260/OD230 | RQN  | NT contrast results |
| ------------- | ----------- | ------------------------ | ----------- | ----------- | ---- | ------------------- |
| 1             | DGF298-1    | 538.74                   | 2.04        | 2.33        | 10   | Escherichia_coli    |
| 2             | DGF298-2    | 519.11                   | 2.03        | 2.03        | 10   | Escherichia_coli    |
| 3             | DGF298-3    | 400.04                   | 2.21        | 2.21        | 10   | Escherichia_coli    |
| 4             | DGF298-4    | 292.5                    | 2.25        | 2.35        | 10   | Escherichia_coli    |
| 5             | DGF298-5    | 237.02                   | 2.13        | 2.13        | 10   | Escherichia_coli    |
| 6             | DGF298-6    | 430.98                   | 2.32        | 2.32        | 10   | Escherichia_coli    |

## 2.2 Transcription repositories

In transcriptomic analysis, we found that the expression levels of **carboxylic acid synthesis pathways**, **amino acid synthesis pathways**, and **sulfur compound synthesis pathways** were **upregulated** in polyploids (Fig.18).

//img_wrap

![fig19]({{site.img_source}}result/fig19.webp)

Figure 18 Results of the transcriptome analysis of DGF298 and DGF298-103Z

//img_wrap

## 2.3 Modular control

Sequencing showed that we successfully constructed and transferred the PACYC-Ptac-*pfkA-nuoF* plasmid containing different expression intensities combinations into DGF298-103Z (Fig.19). The qPCR results showed significant up-regulation of *nuoF* or *pfkB* genes in the experimental group with regulatory combinations of moderate intensity (M) and high intensity (U) (Fig.20). By characterizing the growth curves of strains with different intensity **regulatory modules**, we found that some experimental groups had **earlier log phase** compared with DGF298-103Z in the control group (Fig.21).

//img_wrap

![fig20]({{site.img_source}}result/fig20.webp)

Figure 19 Different combinations of modular control

![fig21]({{site.img_source}}result/fig21.webp)

Figure 20 Results of RT-qPCR for gene expression measurements

![fig22]({{site.img_source}}result/fig22.webp)

Figure 21 Characterization results of modularly modulation

//img_wrap

## Summary

In this part of work, we learned about the expression level of **carboxylic acid synthesis**, **amino acid synthesis**, and **sulfate compound synthesis pathway** were **upregulated** in polyploid strain through 16sRNA sequencing and transcriptional assembly library of DGF298-103Z, which indicates that polyploidization may bring more **excellent fermentation potential**. After modeling the metabolic pathways of DGF298-103Z and DGF298, we surrogate transcriptome data and predicted that downregulation of the ***nuoF*** and ***pfkB*** genes severely affected strain growth. To improve the model, we tried up-regulatiing these two genes through **a combination of promoter and RBS** **of different intensity**, and the characterization results showed **accelerated growth** and metabolism of some experimental groups.

# 3. Fermentation potential analysis of novel chassis cells

## Introduction

To analyze **the production capacity** of new chassis cells for different high value-added products, we selected appropriate carriers, constructed different heterologous gene plasmids and introduced them into cells for **protein expression levels** and **PHB fermentation production** analysis. After that, in an attempt to combine the possible sensitivity of **the shuttle promoter** of Corynebacterium glutamami to specific metabolic conditions with the rapid multiplication and ease of handling of E. coli, we introduced the shuttle promoters into E. coli and evaluated their expression levels. At the same time, we found that the TCA cycle and PHB synthesis reaction will compete for acetyl-CoA in the intracellular metabolic flow, and in order to try to achieve the efficient allocation of carbon flux in the initial growth and production metabolism stages, we introduced **a dynamic temporal expression system** to DGF298-103Z.

## 3.1 Protein expression levels

Sequencing showed that we successfully constructed and transferred the plasmid PACYC-Ptac-GFP-Amp into DGF298-103Z and DGF298. We used the expression intensity of the reporter gene **GFP** to characterize **the protein expression levels** of the strains. When observing single colonies with a blue light instrument, we found that the colony fluorescence intensity of polyploid DGF298-103Z was higher than that of haploid DGF298(Fig.26).

//img_wrap

![fig23]({{site.img_source}}result/fig23.webp)

Figure 22 Map of plasmid PACYC-Ptac-GFP-Amp

![fig24]({{site.img_source}}result/fig24.webp)

Figure 23 Sequencing results of PACYC-Ptac-GFP-Amp

//img_wrap

Characterization showed that the GFP expression levels of GFP (GFP/OD600) in both polyploid and haploid strains in LB medium were higher than in M9 medium(Fig.25), and that the protein expression intensity of polyploid DGF298-103Z was about 50% higher than DGF298 in both environments, both strains had significantly earlier logarithmic phase in LB medium (Fig.24). This indicates that polyploid E. coli has **higher protein expression levels** and that LB medium is more suitable for rapid bacterial growth than M9 medium.

//img_wrap

![fig25]({{site.img_source}}result/fig25.webp)

Figure 24 OD600 of DGF-298/ DGF-298-103z （M9 on the left, LB on the right, 3 replicates）

![fig26]({{site.img_source}}result/fig26.webp)

Figure 25 GFP/OD600 of DGF-298 /DGF-298-103z (LB medium on the left, M9 medium on the right, three replicates)

![fig27]({{site.img_source}}result/fig27.webp)

Figure 26 The blue light meter detects the green fluorescence of DFG298-103Z(right) and DGF298(left)

//img_wrap

## 3.2 Shake bottle simulation fermentation production of PHB

Sequencing showed that we successfully constructed and transferred plasmid pUC-Pcon-both into DGF298-103Z, DGF298 and W3110. In M9 medium supplemented with 40g / L glucose as the carbon source, DGF298 and DGF298-103Z showed **more rapid and stable initial growth** than wild-type strain **W3110**(Fig.28), possibly due to the synergistic action of DGF298 strain *gcvA* deletion and functional *ilvG* that resist the growth inhibitory feedback of valine accumulation. However, polyploid DGF298-103Z grew slower in middle and late stages than haploid DGF298 and consumed glucose slower in the initial stage, probably due to its metabolic burden and aberrant chromosome number. The total amount of glucose consumed by DGF298-103Z fermentation was 13.9% and 19% higher than W3110 and DGF298, respectively (Fig.29).

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![fig28]({{site.img_source}}result/fig28.webp)

Figure 27 Map of plasmid pUC-Pcon-PHB

![fig29]({{site.img_source}}result/fig29.webp)

Figure 28 OD600 of DGF-298 and DGF-298-103z during fermentation (three replicates）

![fig30]({{site.img_source}}result/fig30.webp)

Figure 29 Change in glucose concentration in medium (left) vs. total sugar consumption (right) (average of three replicates in each group)

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## 3.3 Amino acid determination

By analyzing the content of **free amino acids** in the fermentation broth, we found that the free phenylalanine **Phe production** of DGF298-103Z, DGF298 and W3110 was more prominent than other amino acids, and the Phe production of DGF298-103Z was 7.98 nmol, which was 19% and 211% higher than that of DGF298 and W3110, respectively (Fig.30).

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![fig31]({{site.img_source}}result/fig31.webp)

Figure 30 Amino acid fermentation yield of W3110, DGF-298，DGF-298-103Z

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## 3.4 Analysis of the PHB yield

We isolated single colonies of DGF298-103Z, DGF298 and W3110 in the late stage of fermentation, and after **Nile red staining** we found that the fluorescence intensity of DGF298-103Z was higher (Fig.31), implying a higher PHB yield. Further determination of intracellular PHB content by gas chromatography showed that the average total **PHB production** of DGF298-103Z, DGF298 and W3110 in the simulated fermentation was 0.025477g /L, and the total PHB production of 0.014595333g/L,0.013395333g/L and DGF298-103Z was 43.7% and 47.4% higher than that of DGF298 and W3110, respectively (Fig.32).

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![fig32]({{site.img_source}}result/fig32.webp)

Figure 31 Nile red staining results at the end of fermentation (DGF-298, W3110, DGF-298-103Z, from left to right)

![fig33]({{site.img_source}}result/fig33.webp)

Figure 32 Total amount of PHB in the bacteria after fermentation (three controls)

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## 3.5 Screening shuttle promoters

When designing metabolic pathways for downstream products, our team tried to expand the control of the expression strength of specific genes by **introducing shuttle promoters**. Therefore, we compared the transcriptome sequencing results of different Corynebacterium glutamicum strains, identified promoters and RBSs with different expression strengths, and applied them to E. coli chassis cells for expression (Transcriptome data provided by Dr. Xin Jin from Prof. Quanfeng Liang's group）.

The PACYAC-RFP series of plasmids we constructed were transferred into E.coli DH5α after verifying. We used the expression intensity of the reporter gene **RFP** to characterize **the expression intensity** of different **shuttle promoters** and RBS. The results showed significant differences in the expression intensity of different shuttle promoters in E. coli chassis cells (Fig.33).

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![fig34]({{site.img_source}}result/fig34.webp)

Figure 33 Characterization results of the shuttle promoter

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## 3.6 Self-induced dynamic time-sequence modulated cascade lines

The starting plasmids for our dynamic cascade regulation; pCDF-QS-GFP and Plas-RFP were provided by Xiaomeng Li from Prof.

We tried to replace the GFP gene pre-promoter on the plasmid with different newly discovered shuttle promoters, obtaining the plasmid QS-P02-LasI-GFP, in which the primers LasI-P02-R and LasI-P02-F could be redesigned according to the different promoters. Finally, the constructed plasmids with a series of different promoters were co-transfected with plasmid Plas-RFP into DGF-298 chemoreceptor state, and the newly constructed strains were named L02 and L38, respectively.

After analyzing the experimental results, it was found that replacing different promoters with different strengths for the cascade of self-induced dynamic timing control would result in **different response effects**, which increased t**he diversity of the time difference of the cascade.** From the result, we can see that the normalized difference in response time between GFP fluorescent proteins and RFP fluorescent proteins of strains L02 and L38 were about 193 min and 164 min, respectively (Fig. 34).

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![fig35]({{site.img_source}}result/fig35.webp)

Figure 34 Normalized curves of self-induced dynamic chronological regulatory cascade lines regulating the intensity of LasI synthase expression

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The **normalized difference** in response time between GFP fluorescent proteins and RFP fluorescent proteins was **expanded** after promoter replacement by LasI synthase, suggesting that the expression intensity of LasI synthase can be efficiently regulated using promoter engineering to generate small molecules of auto-inducer to induce dynamic, time-sequential expression of upstream and downstream genes at different time intervals.

## Summary

Through the above study, we concluded that the polyploid strain DGF298-103Z in the genome streamlined DGF strain with **rapid and stable fermentation initial growth** and the advantages of intracellular carbon metabolism flow suitable for amino acid production, further **improve the protein expression level** and **the synthesis of heterologous secondary metabolites PHB**, with more excellent fermentation production potential. By introducing the **shuttle promoter** of Corynebacterium glutamicum into E. coli DH5α and characterizing it, we obtained the expression strengths of different shuttle promoters in E. coli and applied them to the construction of **dynamic cascade regulatory lines**, and successfully expanded the **normalized differences of different temporal response times**. In the future, we still hope to further utilize the newly discovered synthetic biology components to replace the promoters of different components in the QS system, such as LasI, LasR, TraR, etc., so as to achieve a finer temporal regulation of the TCA reaction and PHB production.

# Reference

//referrence

[1] Hirokawa Y, Kawano H, Tanaka-Masuda K, et al. Genetic manipulations restored DGF-298e growDGF-298 fitness of reduced-genome Escherichia coli[J]. J Biosci Bioeng, 2013, 116(1): 52-58. DOI: 10.1016/j.jbiosc.2013.01.010.

[2] Matteau D, Champie A, Grenier F, et al. Complete sequence of DGF-298e genome-reduced Escherichia coli DGF-298[J]. Microbiol Resour Announc, 2023, 12(11): e0066523. DOI: 10.1128/MRA.00665-23.

[3] Couto JM, McGarrity A, Russell J, et al. DGF-298e effect of metabolic stress on genome stability of a synDGF298etic biology chassis Escherichia coli K12 strain[J]. Microb Cell Fact, 2018, 17(1): 8. DOI: 10.1186/s12934- 018-0858-2.

[4] Biriukova IV, Krylov AA, Kiseleva EM, et al. [Construction of DGF-298e new Escherichia coli K-12 wildtype strain wiDGF-298 improved growDGF-298 characteristics for application in metabolic engineering][J]. Genetika, 2010, 46(3): 349-355. (in Russian)

[5] Couto JM, McGarrity A, Russell J, et al. DGF-298e effect of metabolic stress on genome stability of a synDGF298etic biology chassis Escherichia coli K12 strain[J]. Microb Cell Fact, 2018, 17(1): 8. DOI: 10.1186/s12934- 018-0858-2.

[6] Wang M, Fang C, Ma B, et al. Regulation of cytokinesis: FtsZ and its accessory proteins[J]. Curr Genet, 2020, 66(1): 43-49. DOI: 10.1007/s00294-019-01005-6.

[7] Tack ILMM, Nimmegeers P, Akkermans S, et al. A low-complexity metabolic network model for DGF-298e respiratory and fermentative metabolism of Escherichia coli[J]. PLoS One, 2018, 13(8): e0202565. DOI: 10.1371/journal.pone.0202565.

[8] Wang J, Huang J, Liu S. DGF-298e production, recovery, and valorization of polyhydroxybutyrate (PHB) based on circular bioeconomy[J]. Biotechnol Adv, 2024, 72: 108340. DOI: 10.1016/j.biotechadv.2024.108340.

[9] Hack NJ, Billups B, GuDGF-298rie PB, et al. Green fluorescent protein as a quantitative tool[J]. J Neurosci MeDGF-298ods, 2000, 95(2): 177-184. DOI: 10.1016/s0165-0270(99)00178-8

[10] Hwang S, Choi KH, Yoon N, Cha J. Improvement of a Sulfolobus-E. coli shuttle vector for heterologous gene expression in Sulfolobus acidocaldarius. J Microbiol Biotechnol. 2015 Feb;25(2):196-205. doi: 10.4014/jmb.1407.07043. PMID: 25293629.

[11] Li K, Zhao Z, Zhang YZ, Wang Y, Ding JY. [Construction of Corynebacterium glutamicum/E. coli shuttle promoter-probe vector]. Wei Sheng Wu Xue Bao. 2007 Apr;47(2):191-6. Chinese. PMID: 17552218

[12] Descoteaux A, Drapeau GR. Regulation of cell division in Escherichia coli K-12: probable interactions among proteins FtsQ, FtsA, and FtsZ. J Bacteriol. 1987 May;169(5):1938-42. doi: 10.1128/jb.169.5.1938-1942.1987. PMID: 3032902; PMCID: PMC212053.

[13] Belhumeur P, Drapeau GR. Regulation of cell division in Escherichia coli: properties of new ftsZ mutants. Mol Gen Genet. 1984;197(2):254-60. doi: 10.1007/BF00330971. PMID: 6097794.

[14] Ebrahim A, Lerman JA, Palsson BO, Hyduke DR. COBRApy: COnstraints-Based Reconstruction and Analysis for Python. BMC Syst Biol. 2013 Aug 8;7:74. doi: 10.1186/1752-0509-7-74. PMID: 23927696; PMCID: PMC3751080.

[15] Auriol C, Bestel-Corre G, Claude JB, Soucaille P, Meynial-Salles I. Stress-induced evolution of Escherichia coli points to original concepts in respiratory cofactor selectivity. Proc Natl Acad Sci U S A. 2011 Jan 25;108(4):1278-83. doi: 10.1073/pnas.1010431108. Epub 2011 Jan 4. PMID: 21205901; PMCID: PMC3029715.

[16] Lovingshimer MR, Siegele D, Reinhart GD. Construction of an inducible, pfkA and pfkB deficient strain of Escherichia coli for the expression and purification of phosphofructokinase from bacterial sources. Protein Expr Purif. 2006 Apr;46(2):475-82. doi: 10.1016/j.pep.2005.09.015. Epub 2005 Oct 18. PMID: 16289704.

[17] Mukherjee S, Bassler BL. Bacterial quorum sensing in complex and dynamically changing environments. Nat Rev Microbiol. 2019 Jun;17(6):371-382. doi: 10.1038/s41579-019-0186-5. PMID: 30944413; PMCID: PMC6615036.

[18] Jiang W, Yang X, Gu F, Li X, Wang S, Luo Y, Qi Q, Liang Q. Construction of Synthetic Microbial Ecosystems and the Regulation of Population Proportion. ACS Synth Biol. 2022 Feb 18;11(2):538-546. doi: 10.1021/acssynbio.1c00354. Epub 2022 Jan 19. PMID: 35044170.

//referrence