In prokaryotic cells (which are widely used as host cells in bio- - - PowerPoint PPT Presentation

Ø In prokaryotic cells (which are widely used as host cells in bio- engineering), enzymes are often widely separated among the cytoplasm.

Ø Traditional ways to elevate the concentration of substrates and enzymes in the specific area of the prokaryotic cells include:

• DNA scaffolds
• RNA scaffolds
• Protein scaffolds

Synthetic protein scaffolds provide modular control over metabolic flux (Dueber et al. 2009). ¡

Current method Drawbacks

DNA scaffolds

• Hard to assemble
• Rely on single-strain Stem-loop Structure

RNA scaffolds

• Lack of corresponding ligadin
• The fragility of RNA

Protein scaffolds

• The protein structure is too complex
• Often hard for prokaryotic cells to correctly fold

Ø The transcription activator-like (TAL) effectors is a family-III effector in Xanthomonas that helps when they infect various plant species (Boch et al., 2010).

Fig.1 (A) The basic structure of TAL effectors. (B) 3D structure of the TAL effector bond with DNA (Deng et al., 2012).

Ø We engineered several TALE proteins by fusing them with the enzymes from the multi-enzymatic system we try to accelerate.

Ø Through this way we can “tie” those enzymes together around the plasmid backbone to elevate the local enzyme concentration, thus accelerates the reaction.

Ø Aim

• This model tries to theoretically prove the TALE-based scaffold system can

improve the multi-enzyme reaction efficiency.

Ø Method

• The core idea is to simulate multi-enzyme reaction process and compare the

behavior of TALE-based scaffold system and the none-scaffold system.

Ø The stochastic simulation starts from COLLISION while the Michaelis-Menten equation basic on the CONCENTRATION. The transform from COLLISION to CONCENTRATION requires uniform condition.

Figure 2. The compare between two methodologies. COLLISION REACTION COLLISION REACTION CONCENTRATION Condition: Uniform

MOLECULER DYNAMICS

Condition: None Condition: None stochastic simulation Michaelis-Menten

Ø Simplifying & Behavior Assumptions Ø Parameters Setting Ø Result & Analysis Ø Conclusion Ø Postscript

Ø Object

• E. coli, reactants, scaffolds and enzymes (on or off scaffolds)

Ø Geometric simplification (2D)

• Circles: E. coli, reactants and enzymes
• Lines: scaffolds

Substrate Intermediate ¡ Product Resultant/ Intermediate ¡ Product Resultant Scaffold Enzyme ¡1 Enzyme ¡2 Enzyme ¡3

Ø Geometric simplification (2D)

(a) E.coli (b) Simplified E. coli with scaffold (c) Simplified E. coli without scaffold

Ø Behavior (time driven)

• Fixed: enzymes (on and off scaffolds)
• Random thermal motion: Reactants

§ The motion direction and speed in a time step both obey uniform distribution. § All objects are restricted to move within on E. coli. § Reactants can overlap the enzymes.

• Reaction

§ Matched overlap (collision) = reaction § Once collide, reaction finished instantaneously.

Ø Behavior (time driven)

• Demo (scaffold system)

The smaller blue dots are substrates and the linked larger blue and red dots are different enzymes.

Reactant ¡mo<on ¡and ¡interac<on ¡(First ¡30 ¡<me ¡step) ¡ ​𝑤↓𝑠𝑏𝑜𝑒 Enzyme ¡1 ​𝜒↓𝑠𝑏𝑜𝑒

PARAMETERS VALUE Simula<on ¡Time ¡Step 1 Simula<on ¡Total ¡Time ¡ 2000 Reactant ¡speed ¡lower ¡limit (<mes ¡to ¡reactant ¡diameter) 0.3 Reactant ¡speed ¡upper ¡limit (<mes ¡to ¡reactant ¡diameter) 2 Scaffold ¡Quan<ty 4 Ini<al ¡Reactant ¡Quan<ty 150 Reactant ¡Diameter 3 Cell ¡Diameter 200

The ¡Constant ¡Parameters ¡SeKng ¡

GROUP ENZYME Types ¡ Distance Diameter ¡ 1 2 8 [6,8] 2 2 12 [6,8] 3 2 18 [6,8] 4 3 [12,12] [6,8,10]

The ¡Variable ¡Parameters ¡SeKng ¡ Scaffold Enzyme ¡1 Enzyme ¡2 Enzyme ¡3 Diameter

• 1. The ¡substrates ¡are ¡consumed ¡in ¡

similar ¡speed. ¡

• 2. With ¡scaffold ¡the, ¡the ¡

intermediate ¡products ¡are ¡ transformed ¡to ¡resultant ¡faster. Time Reactants ¡ Quan<ty none-­‑Scaffold Scaffold

Time Reactants ¡ Quan<ty none-­‑Scaffold Scaffold

• 1. The ¡distance ¡between ¡enzyme ¡

sites ¡is ¡larger.

• 2. Similarly, ¡the ¡substrates ¡are ¡

consumed ¡in ¡nearly ¡same ¡speed. ¡

• 3. With ¡scaffold ¡the, ¡the ¡

intermediate ¡products ¡are ¡ transformed ¡to ¡resultant ¡faster ¡ than ¡that ¡without ¡scaffold. ¡

Time Reactants ¡ Quan<ty none-­‑Scaffold Scaffold Time Reactants ¡ Quan<ty none-­‑Scaffold Scaffold

• 1. The ¡distance ¡between ¡enzyme ¡

sites ¡is ¡enlarged ¡on. ¡

• 2. Differently, ¡the ¡scaffold ¡design ¡

and ¡none-­‑scaffold ¡design ¡have ¡ similar ¡reac<on ¡efficiency.

Resultant ¡ Quan<ty Enzyme ¡Distance: ¡8 Enzyme ¡Distance: ¡12 Enzyme ¡Distance: ¡18 Time

• 1. With ¡the ¡distance ¡between ¡

different ¡enzymes ¡sites ¡on ¡one ¡ scaffold ¡enlarging, ¡the ¡the ¡effect ¡

• f ¡the ¡scaffold ¡decreasing.

Time Reactants ¡ Quan<ty none-­‑Scaffold Scaffold

• 1. The ¡mul<-­‑enzyme ¡reac<on ¡series ¡
• increases. ¡
• 2. The ¡substrates ¡are ¡also ¡

consumed ¡in ¡nearly ¡same ¡speed. ¡

• 3. But ¡the ¡effect ¡of ¡the ¡scaffold ¡is ¡

more ¡obvious ¡than ¡that ¡above.

Ø Based on our simulation, we came to the conclusion that:

• The TALE-based scaffold system can improve the multi-enzyme reaction

efficiency.

• With the multi-enzyme reaction series increasing, the effect of the scaffold is

brought out.

• With the distance between different enzymes sites on one scaffold enlarging,

the the effect of the scaffold decreasing.

Ø Equations

• Motion of reaction

​𝑤↓𝑦 =​cos⁠​(𝜒↓𝑠𝑏𝑜𝑒 ) ×​𝑤↓𝑠𝑏𝑜𝑒 ​𝑤↓𝑧 =​sin(⁠​𝜒↓𝑠𝑏𝑜𝑒 ) ×​𝑤↓𝑠𝑏𝑜𝑒

• Collision

​(𝑦↓𝑠𝑓𝑏 −​𝑦↓𝑓𝑜𝑨 )↑2 +​(𝑧↓𝑠𝑓𝑏 −​𝑧↓𝑓𝑜𝑨 )↑2 ≤​(​𝑠↓𝑠𝑓𝑏 +​𝑠↓𝑓𝑜𝑨 )↑2

Ø Simulation Environment/Platform

• MATLAB & MacBook Pro 2.8 GHz Intel Core i7 (4 core & 16G Memory)

​𝑤↓𝑠𝑏𝑜𝑒 Enzyme ¡1 ​𝜒↓𝑠𝑏𝑜𝑒

Ø Scaffold

• TALE recognition sites (BMs)

¡BM1: ¡5’-­‑GGAGGCACCGGTGG-­‑3’ ¡ ¡BM2: ¡5’-­‑GATAAACACCTTTC-­‑3’ ¡

Ø Corresponding TALEs

• TALE1/T1 (Recognizing BM1)
• TALE2/T2 ¡(Recognizing ¡BM2) ¡
• TALE3/T3 ¡(Recognizing ¡reversed ¡

BM2) ¡

¡

Ø Scaffold SCAF1 ¡(S1): ¡ SCAF2 ¡(S2): ¡ SCAF3 ¡(S3): ¡

Ø TALE/Scaffold pairs

• I. TALE1/TALE3 with SCAF1
• II. TALE1/TALE2 with SCAF2
• III. TALE1/TALE2 with SCAF3

Ø Prototypes

• Split GFP
• TALE1-GFP1/TALE2-GFP2 S2
• TALE1-GFP1/TALE2-GFP2 S3
• TALE1-GFP1/TALE3-GFP2 S1

Ø Prototypes

• IAA production

Ø Prototypes

• IAA production
• TALE1-IAAM/TALE2-IAAH S2
• TALE1-IAAM/TALE2-IAAH S3

Ø Results

• Evaluation of the binding

ability of TALE-GFP1/2 to the DNA scaffold.

Figure 1. ChIP-PCR assay of TALE-GFP binding to the scaffold in E.coli. ¡

Ø Results

v Evaluation of the binding ability of TALE-GFP1/2 to the DNA scaffold.

Figure 2. Evaluation of the TALE-DNA scaffold system by split GFP assay. ¡

Ø Results

v The function of TALE- DNA scaffold system on IAA production

Figure ¡3. ¡Increase ¡of ¡IAA ¡produc<on ¡by ¡incorpora<ng ¡the ¡IAAM ¡and ¡IAAH ¡ into ¡TALE-­‑DNA ¡scaffold ¡system. ¡ ¡

Ø TALE can be fused with other proteins WITHOUT affecting its DNA-binding ability. Ø The TALE-DNA scaffold system can effectively meet

• ur requirement of MULTI-ENZYMATIC SYSTEM

COMPARTMENTATION in prokaryotic cells. Ø The exist of this scaffold system can remarkably ACCELERATE MULTI-ENZYMATIC REACTION. Ø The length of INTERVENING SEQUENCE between BMs may affect the function of the scaffold system.

Ø To our knowledge, this is the first report using TALE system as scaffolds for the spatial organization

• f bacterial metabolism

Ø The application of this method might be extend to the eukaryotic biofactory as well. Ø We also would like to introduce this technique in manufacturing of bio- fuels, bio-materials, medicine, and even in pollutant disposal in future work.

Ø NJU_CHINA

We performed nanoparticle tracking analysis (NTA) for NJU_CHINA to help them to have a more precise determination of the quantity and size of secreted exosomes.

Figure 4. Characterization of secreted exosomes after overexpression of nSMase2 in HEK293 cells. ¡

Ø NEFU_China: Flight iGEM

• “Flight iGEM” platform aims to

build a friendly website to make the wiki development easier.

• We participated in the “Flight

iGEM” beta test to

§ improve the Human-Computer Interaction (HCI) § debug for the login bar § make sure the feasibility

• f cleariGEM template.

"Human Practices is the study of how your work affects the world, and how the world affects your work." — Peter Carr, Director of Judging

Potential Application Spread iGEM Tianhe Supercomputer Lab For Fun Song of syn-bio CiCC

Ø “Your project can not only increase the reaction rate, but also improve the quality of

• drugs. It will have a positive

influence on the development of the pharmaceutical industry. ” ——Dr. Jiang

Director of Hunan Laboratory Animal Center

Ø Help them to have a thorough understanding of iGEM and Synthetic Biology. Ø Get them prepared to start an iGEM team in their own universities. Ø Further promote the spreading of iGEM and synthetic biology.

Ø “It’s not just about running a simple model on Tianhe, it also gives another clue on how High- performance computing platform can be used in bio-technology.” ——Prof. Shaoliang Peng

NUDT Supercomputer Center

Tianhe-­‑II Computer ¡Nodes: ¡16,000 Peak ¡Performance: ¡54.9 ¡Peta ¡Flops. = ¡549,000,000,000,000,000 ¡Flops

Ø In order to activate the communication, Peking University held the Conference

• f China iGEMer Committee

(CCiC) on 14 & 15, August. NUDT_CHINA was invited to give a presentation.

Ø Adapted from Snow Globe, Matt Wertz

La... There's a window into a magical place Where everything is so good Imagine there will be no lamps But don't worry about darkness Fluorecent creatures shine on you They will know your way home every day Sometimes I want a Dararemon But where's Nobi What a pity But now there is synthetic biology It will make it So beautiful La...

FACULTY ¡ADVISORS: ¡ ¡

• Dr. ¡Lingyun ¡Zhu, ¡Dr. ¡Lvyun ¡Zhu, ¡Dr. ¡

Long ¡Liu, ¡Dr. ¡Qijun ¡Liu, ¡Dr. ¡Xiaomin ¡ Wu, ¡Dr. ¡Shaoliang ¡Peng ¡ ¡ Other ¡iGEM ¡Teams: ¡ ¡ NJU_CHINA, ¡NEFU_CHINA ¡ ¡

¡ TEAM ¡MEMBERS: ¡ Chushu ¡Zhu, ¡Dongyu ¡Fan, ¡Jiaqi ¡Sun, ¡Jie ¡ Li, ¡Juanjuan ¡Huang, ¡Nianhao ¡Xie, ¡ Qianhui ¡Zhu, ¡Xinyuan ¡Qiu, ¡Yizhou ¡ Wang, ¡Yuan ¡Zhang ¡ ¡