Team:XMU-China/Poster

Poster: XMU-China

AnTea-Glyphosate

Presented by Team XMU-China 2020

Jinzhu Mao¹, Shi Zhang¹, Siru Zhou¹, Meihao Ou¹, Qingliu Wang¹, Ruijie Mo¹, Ruomeng Bi¹, Shichen Geng¹, Shuling Xiao¹, Xiaoyu Wang¹, Yangqi Deng¹, YiXian Zheng¹, Zhichun Yang¹, Shengyang Zhang¹, Jisheng Xie¹, Yuan Li¹, Gezhi Xiao², Xiao-yan Zhuang², Ai-hui Zhang², Wang Yali², Fu yousi², Junhong Chen², Zinuo Huang², Yang Liang², Xingyu Chen², YanSong Chen², Fude Chen², Shiyi Zhai², Langxing Liao², Limin Qiu², Tianyu Feng², Wenyao Shao², Yajuan Peng², Haitao Wang², Yang Shi², Jiacheng Huang², Baishan Fang³

¹iGEM Student Team Member, ²iGEM Team Mentor, ³iGEM Team Primary PI, Xiamen University, Xiamen, Fujian, China

Attributions

Brainstorms: All team members.

Molecular cloning and circuit construction: All team members.

Characterization: All team members.

Proof of Concept: All team members.

Model: Yixian Zheng, Siru Zhou, Ruomeng Bi, Ruijie Mo, Jinzhu Mao, Jiacheng Huang.

Human practices: All team members.

Wiki: Shengyang Zhang, Jiacheng Huang.

Art design: Yangqi Deng, Xiaoyu Wang, Shuling Xiao, Jinzhu Mao, Ruomeng Bi, Ruijie Mo, Siru Zhou, Meihao Ou, Yixian Zheng, Shichen Geng, Zhichun Yang, Fude Chen, Jisheng Xie, Junhong Chen.

Antea-Glyphosate

Presented by Team XMU-China 2020

Jinzhu Mao¹, Shi Zhang¹, Siru Zhou¹, Meihao Ou¹, Qingliu Wang¹, Ruijie Mo¹, Ruomeng Bi¹, Shichen Geng¹, Shuling Xiao¹, Xiaoyu Wang¹, Yangqi Deng¹, YiXian Zheng¹, Zhichun Yang¹, Shengyang Zhang¹, Jisheng Xie¹, Yuan Li¹, Gezhi Xiao², Xiao-yan Zhuang², Ai-hui Zhang², Wang Yali², Fu yousi², Junhong Chen², Zinuo Huang², Yang Liang², Xingyu Chen², YanSong Chen², Fude Chen², Shiyi Zhai², Langxing Liao², Limin Qiu², Tianyu Feng², Wenyao Shao², Yajuan Peng², Haitao Wang², Yang Shi², Jiacheng Huang², Baishan Fang³, Xiamen University, Xiamen, Fujian, China

Attributions

Brainstorms: All team members.

Molecular cloning and circuit construction: All team members.

Characterization: All team members.

Proof of Concept: All team members.

Model: Yixian Zheng, Siru Zhou, Ruomeng Bi, Ruijie Mo, Jinzhu Mao.

Human practices: All team members.

Wiki: Shengyang Zhang.

Art design: Yangqi Deng, Fude Chen, Jisheng Xie, Junhong Chen.

Abstract

Tea is deeply rooted in Chinese culture. For a long period, a large amount of glyphosate has been used as a herbicide, which raises a severe problem of pesticide residues in tea and tea products.

This year XMU-China aims at developing an efficient glyphosate detection and degradation system.

There are two pathways found to degrade glyphosate:

►Glyphosate generates AMPA and glyoxylic acid through the C-N cleavage.
The product, glyoxylic acid, could be further converted into measurable signal, thus for the detection system, glyphosate is degraded by several enzymes through C-N cleavage and then transformed into a measurable fluorescence signal caused by the NADPH.

►Glyphosate generates sarcosine and phosphate acid through the C-P cleavage.
The product by C-P cleavage pathway, sarcosine, could be rapidly oxidized to become formaldehyde, a non-toxic substance. Therefore, glyphosate degradation method through C-P cleavage is preferred in our design which plans to disintegrate glyphosate to minimize the toxicity.

►Two kill switches controlled by different inducers are also proposed.

It is hoped that this project could provide new ideas for the detection and degradation of pesticide residues. Taking care of the earth by tiny bacteria, we here promise a better future of tea.

Detection

Design

Glyphosate is first converted to glyoxylate by GOX and then to glycolic acid while NADPH is consumed by GRHPR. Then, the amount of NADPH is determined by iNap.

Fig 1. Mechanism of GOX, GRHPR and iNap.

For the sake of avoiding the interference of intracellular NADPH, the surface display system was employed (Fig 2) to anchor the enzymes mentioned above on the surface of engineered bacteria. Notably, this strategy subtly solves the difficulty that there are few natural regulators which could sense glyphosate or its derivatives in the glyphosate metabolic pathways, as scarcely reported.

Fig 2. Gene circuits of detection system.

Result
Every enzyme (GOX, GRHPR and iNap) and its fusion protein forms were characterized separately. The value of optical density at 340 nm (OD₃₄₀) was used to represented the amount of NADPH, except for the experiments related to iNap.

Fig 3. Results of GOX and GRHPR. OD₃₄₀ was monitored along time in order to a, test the catalytic capability of GOX with GRHPR; b, test the enzymatic activity of anchored GOX; c, test the enzymatic activity of GRHPR; d, test the enzymatic activity of anchored GRHPR.

The single GOX could slightly catalyze the conversion of glyphosate (data not shown), while the second reaction catalyzed by GRHPR has a faster kinetic behavior (Fig 3c). When tested with GRHPR, the GOX showed moderate enzymatic activity to catalyze the conversion of glyphosate to the substrate of GRHPR, which indicates the conjugation of the second reaction could actually accelerate the conversion of glyphosate (Fig 3a). INPNC-GOX and INPNC-GRHPR retain the enzymatic activity in a large part (Fig 3b and 3d), and the latter (BBa_K3332057) is our FAVORITE.

Fig 4. Results of iNap. a, iNap fluorescence intensity with excitation at 420 nm in the presence of different concentrations of NADPH. The fluorescence intensity of b, INPNC-iNap and c, iNap-AIDA were monitored along time in the presence of different concentrations of NADPH.

iNap manifests excellent SENSITIVITY and a large dynamic range to NADPH (Fig 4a). Compared to iNap-AIDA, INPNC-iNap has a larger dynamic range since the fluorescence intensities distinguished more significantly among different groups (concentrations of NADPH) (Fig 4b and 4c). Regretfully, BrkA-iNap has not been obtained due to the limit of time.

Proof of Concept

Fig 5. Fluorescence intensity was monitored along time to test the function of reaction mixture.

Three types of E. coli BL21 (DE3) respectively carrying INPNC-GOX (BBa_K3332052), INPNC-GRHPR (BBa_K3332057), and INPNC-iNap (BBa_K3332047) were mixed to react with glyphosate and NADPH. The decrease of fluorescence intensity suggested that the detection system could work well (Fig 5).

Degradation

Design

Fig 1. Gene circuit (BBa_K3332099) of degradation system.

►Efficient phosphonate acid transmembrane transport
PhnE1E2 (encoded by phnE1 and phnE2, from Sinorhizobium meliloti 1021) could obviously enhance the transport capability of engineered bacteria to glyphosate.
►Cleavage of glyphosate
PhnJ (encoded by phnJ, from Enterobacter cloacae K7) is an essential subunit from C-P lyase which could break the C-P bond.
►RNAi
Downregulate ENDOGENOUS phnJ and phnF gene in engineered bacteria.
►AMPA derivation and degradation
PhnO (encoded by phnO, from Salmonella enterica LT2) could enhance the ability of engineered bacteria to degrade aminomethylphosphoric acid (AMPA), which would further decrease the toxicity of the derivative of glyphosate.

Result

Fig 2. Results of degradation system. a, HPLC determination of glyphosate concentration (represented by peak area). b, The transcription rate of endogenous phnJ and phnF declined, verified by RT-qPCR. c, Enzyme kinetic constants for PhnO determined with two substrates.

Every section of our design was characterized separately in E. coli BL21 (DE3). The concentration of glyphosate determined by HPLC indicates the engineered bacteria could absorb more glyphosate than the control group did (Fig 2a). And the siRNA we designed worked well to hinder the endogenous phnJ and phnF from expression, proved by RT-qPCR (Fig 2b). The kinetic constants of PhnO was obtained through the in vitro test to the double-substrate reaction (Fig 2c). Regretfully, the PhnJ hasn’t been characterized due to both the difficulty of purification and limit of time.

Proof of Concept

Fig 3. Glyphosate residue in the medium of different experimental groups after cultured for 3 hours.

Based on the results above, the degradation capability of engineered bacteria to glyphosate was determined. While the RNAi system existed, the repression to the whole phn cluster was RELIEVED so that the degradation capability was greatly enhanced (Fig 3).

Kill Switch

Kill Switch in Detection System

Fig 1. Kill switch in detection system. a, Gene circuit (BBa_K3332081) of kill switch in detection system. b, CFU assay for characterizing the killing effect of kill switch in detection system.

The toxin MazF, which is encoded by mazF, is designed to kill the engineered bacteria once they escape from the hardware to the environment lacking the inducer (L-arabinose). The number of E. coli colonies carrying BBa_K3332081 without induction decreased significantly after 8 hours, compared to the induction group.

Kill Switch in Degradation System

Fig 2. Kill switch in degradation system. a, Gene circuit (BBa_K3332078) of kill switch in degradation system. b, All the pBAD-related parameters' sensitivity of OD₆₀₀.

Formaldehyde (HCHO) will be generated during the process of degrading glyphosate. MazF would be expressed to kill the engineered E. coli when the amount of glyphosate is little in the application situations. Since all the pBAD-related parameters' sensitivity of OD₆₀₀ is very small, the replacement of promoter from pBAD (BBa_I0500) to pHCHO (BBa_K1334002) would not affect the results largely (Fig 2).

Model of Detection

Glyphosate detection system is composed of GOX, GRHPR, and iNap. GOX converts glyphosate into AMPA and glyoxylic acid; GRHPR converts glyoxylic acid into glycolic acid and consumes NADPH at the same time; iNap detects the amount of NADPH and emit fluorescence. Thus, all reactions can be concluded as following:



And those reactions can be described by the following ODE equations:

Due to the fast reaction of iNap and NADPH, the rapid equilibrium assumption was used to fit in with the experimental data, which is compared with the acquired simulation data in Fig 1.

Fig 1. The comparison between experimental data and simulation data.

In order to confirm the glyphosate threshold in detection system, the calibration curve was calculated by running different initial concentration of glyphosate and was obtained in the Fig 2, where the detection range of glyphosate is from 0.0 mM to 0.5 mM.

Fig 2. The calibration curve in the detection system (the fluorescence in 3960 seconds as a mark).

Model of Degradation

A Top-down strategy: begin with the holoenzyme’s view

For the PhnJ (exogenous), our work mainly focused on getting the structure by homologous modeling.

Fig 1. The top-down strategy. a, The structure of PhnJ (exogenous) predicted by MOE. b, The mutation site of PhnJ. c, The binding energy between PhnJ and the rest part of PhnGHIJ.

PhnJ model was predicted by SWISS-MODEL web-sever and MOE (Fig 1a). In order to improve the binding affinity between exogenous PhnJ and the rest part of PhnGHIJ, several sites in PhnJ were chosen to mutate (Fig 1b). Subsequently the protein-protein docking was implemented by the ClusPro server and the binding energy was calculated as well (Fig 1c). The decrease of binding energy indicated, at least in virtual, the mutations might lead to tighter binding between the mutated PhnJ and the rest part of PhnGHIJ.

A Bottom-up strategy: begin with the PhnJ’s view

Fig 2. The bottom-up strategy. a, The distance between Gly32 and Cys272 in PhnJ. b, The F2ARTP algorithm. c, The paths calculated by the F2ARTP algorithm. d, Total energy barrier and the total steps for PhnJ and its mutants.

The reaction mechanism of degrading glyphosate which refers to the super-distance hydrogen atom transfers are worth to study. The distance between G32 and C272 in PhnJ was showed in Fig 2a. There are three parts to evaluate the state of hydrogen atom transfers in the mechanism of a mutant, including HAT and abstraction of a hydrogen atom from the glycine residue. The “F2ARTP” (Finding the Adenosyl Radical Transfer Path) is proposed to find the possible hydrogen atom transfer path, which contains two steps: Monte Carlo sampling and Reinforcement learning (Fig 2b). The F2ARTP algorithm finally found three paths, where the path of P45Q is in blue, R21M & endogenous PhnJ is marked in red and T16S & R40Y is marked in yellow (Fig 2c). Besides, the total energy barrier of each step was calculated (Fig 2d).

Result

Fig 3. Glyphosate residue in the medium of different experimental groups after cultured for 3 hours.

R21M and T16S & R40Y mutants were tested along with other groups for the proof of concept. To some extent, the mutations of PhnJ led to improvement of degrading efficiency (Fig 3).

Model of Kill Switch

As for biosafety, the two kill switch systems were constructed for detection and degradation system, respectively (Fig 1). Both of the two kill switch design contained inverter followed by different inducible promoters: pBAD (L-arabinose) for detection and pHCHO (formaldehyde) for degradation.

Fig 1. The kill switch in detection system (a) and degradation system (b).

Both the formaldehyde and MazF protein could lead to the death of bacteria, and the model illustrates the relationship between the concentration of formaldehyde and the expression of cI lambda protein:

The relationship between expression of cI lambda and MazF protein:

The relationship between the expression of toxicity and OD₆₀₀:

And we concluded that

Hence, CFU can be altered to OD₆₀₀ values and be fitted in ODE model and experimental data in kill switch systems (Fig 2):

Fig 2. The simulation data and experimental data in kill switch system with pBAD promoter.

Due to similarity of the two kill switch systems and low sensitivity of pBAD-related parameter in OD₆₀₀ (Fig 3), it was likely to change the promoter while remained stable. Therefore, the feasibility of detection and degradation kill switch systems was proved and reinforced.

Fig 3. All the pBAD-related parameters’ sensitivity of OD₆₀₀.

Hardware & Software

To connect lab and tea garden, we also designed our own hardware and software.

Hardware

Fig 1. The overview of hardware.

The whole hardware consists of three parts:
►Cultivating Device: For resuspending the lyophilized engineered bacterial powder.
►Detection Chamber: For mixing bacterial liquid with sample liquid and testing.
►Data Transmission Device: For synchronizing data to the mobile terminal.

Fig 2. Three parts of the hardware.

Software
It is our own software that matches the detection hardware. Users can download it on Android phones. Before using our detection hardware, the phone need to be connected with hardware via bluetooth. In another word, our hardware supports bluetooth data transfer.

Fig 3. The overview of software.

After opening the detection page, users can follow the steps (Fig 4) provided on the screen preparing for detection, which means that the operation progress is easily-using to every group. While the hardware is working, users can learn the time left on the screen.

Fig 4. Schematics of the 6 steps.

A report will be given when hardware finishes working, which users can save or share as they like. All the information is stored in the database which is on the cloud server in case of data loss.

Human Practices

Background
We interviewed with the Quality Inspection Institute, retailers and tea farmers and got useful information for our project. We have learned a lot about glyphosate, tea planting and herbicide residues. And we use the information to enrich our project background.

Design
We interviewed experts in environmental ecology and biology, and got some practical suggestions from them. These suggestions helped us to improve the biosafety of the project and guide our hardware design.

Model Improvement
We interviewed experts in chemical biology and cooperated with CSU to improve our mathematical and physical modeling.

Results of Our Questionnaire
By analyzing the questionnaire, we knew the attitudes of tea farmers and consumers towards our project, and their understanding and attitude towards the problem of herbicide residues in tea.

Communicating Meetings
We attended CCiC and Southern China Regional Meeting, and gained many useful suggestions.

Safety Supporting
We accepted the guidance of professional and learned the importance of safety in Human Practices works.

Achievements

New Methodology on Glyphosate

►Parts
Contributed 103 new parts, 44 of them are basic parts. All of the parts are designed by ourselves, and most of them have been tested to insure their reliability.
Formulated a Part Collection related to SURFACE DISPLAY SYSTEM, which includes the characterization to three kinds of anchor proteins (Fig 1). Users can also combine the anchor proteins with different proteins to verify the function of the anchored proteins and treat it as a subtle way to avoid the influence of components inside the cell.

Fig 1. Schematic of gene circuits involving surface display.

►Detection System
✦Developed a convenient method based on synthetic biology to determine the concentration of glyphosate in tea food.
►Degradation System
✦Developed a highly efficient glyphosate degradation system based on synthetic biology
►Kill Switch
✦Proposed several systems to meet the needs of different scenarios in the actual application (simulate the needs of biosafety in the future).

New Hardware and Software

Developed a prototype device for the portable analysis of glyphosate.
►Hardware: Consisted of three parts: cultivating device, detection chamber, data transmission device.
►Software: Connected with hardware via bluetooth.

Fig 2. Schematic of data transmission of our device.

Business Cooperation

Reached a follow-up intention of deep cooperation with Qingshiyan Tea Co., Ltd. and Wuyishan Ruizhuang Tea Co., Ltd..

Fig 3. The companies we cooperated with.

Future Plan

►Detection System
✦Test the enzymatic activity in the real environment.
✦Further develop and test the hardware and software (Version 2.0) to achieve the deep cooperation with companies in Mount Wuyi in Fujian province.
✦Extend the scope of herbicide residue using synthetic biology to enhance the detection capability of the device.
►Degradation System
✦Test the enzymatic activity in the real environment.
✦Develop the enzymatic activity to a higher level based on further protein engineering.
►Kill Switch
✦Further characterize the proposed design and test the switch and toxin activity in the real environment.

Acknowledgements and Sponsors

ACKNOWLEDGEMENTS

PI: Prof. Baishan Fang
Prof. Yi Yang for providing the sequence of iNap
Hongyu Su for recording the voice of videos
Qinghua Yang & Tingting Chen for their kindly help in the teaching of equipment
Bocen Lin for providing the proper solution about our human practices

SPONSORS