Team:UT-Tokyo/Project

INTRODUCTION

Project Overview

Mechanisms for development of living things have been investigated for a long time. In 1952, Alan Turing made a key contribution. He showed that the interaction of two substances with different diffusion rates can generate spatial concentration patterns, which are called Turing Patterns, without any preformed pattern[1]. This idea has been applied to explain hair follicle distribution of mice[2]<, stripe formation on zebrafish[3], and the determination of pigment pattern within avian feather follicles[4].

Here, We tried to reconstruct Turing Pattern by E. coli in a way of synthetic biology to understand the mechanism more. E. coli has cell-cell communication system called quorum sensing. Utilizing this system, we aimed to generate a periodical colony pattren. This project leads to further understanding of Turing Pattern and development of living things.

Turing Mechanism

Turing Pattern is generated by the interaction of two substances with different diffusion rates. These substances are called activator and inhibitor. Activator promotes its own production and the production of inhibitor, and inhibitor inhibits the production of activator(Fig.1). Inhibitor diffuses faster than activator.

Fig.1 Turing mechanism The interaction of activator and inhibitor generates patterns.

Turing focused on the difference of the diffusion rates, but later Meinhardt and Gierer explained the mechanism from the point of local self-activation and lateral inhibition[5]. This explanation is intuitively easy to understand, so we will explain Turing pattern along this explanation.

Fig.2 The generation of a periodical pattern
The initial condition is (a), and as time passes, a periodical concentration pattern is generated((b)~(f)).

At first, there is no preformed pattern in the field and the concentrations of two substances are homogeneous through the field(Fig2. (a)). However there can be a spot with a little bit high concentration of activator because of perturbation. At such a point, the concentration of activator becomes higher and higher, as well as that of inhibitor, by its function(Fig2. (b)~(c)). This corresponds to local self-activation. Since inihibitor diffuses faster than activator, the concentration of inhibitor becomes relatively high around that point and that of activator becomes low because of the inhibitory effect of inhibitor there(Fig2. (d)~(e)). This corresponds to lateral inhibition. Therefore, a spot of activator is generated. This reaction occurs at different points in the field, and the distance between each spot is regulated by the interaction of the two substances(Fig2. (f)). In this way, a periodical pattern is generated in the field.

Advantage Of Synthetic Biological Approach

Pattens of living things have been investigated for a long time, but it was not easy to prove directly if these patterns are produced by the reaction-diffusion systems or another mechanism. Living systems are so complex that most research was exclusively theoretical. Biologists still face a big problem: identification of proper molecules acting as activator and inhibitor.

We therefore reconstructed a Turing system using two advantages of synthetic biology; controllability and biological directness. We can change the diffusion rate of E. coli and the strength of inhibitory effects of inhibitor by inducing synthetic circuit, which can be a great advantage of the experimental system. Chemical system has a similar advantage, but it is far from living systems. Our system uses cells themselves for pattern formation, so it may be directly applied to developmental studies.

STRATEGY

We designed three different strategies to realize pattern formation.

Strategy 1

The first one is based on Turing’s classic model. (Figure1)

Fig.1 Model 1
Imitation of Turing's classic model performed by E. coli and AHL

Here, we consider the “reaction” between E. coli and AHL. AHL is a type of intercellular signaling molecule that can promote transcription from specific promoter when its concentration in a cell gets higher than certain threshold. This system is called ”Quorum Sensing”. And here, AHL is set to induce protein which inhibits the multiplication of E. coli.

E. coli produces(activates) AHL, and AHL inhibits the increase of E. coli indirectly. E. coli also multiples itself.

Diffusion rate of AHL is fast because its size is quite small. On the other hand, the rate of E. coli should be low considering Turing’s model, and to achieve this, we modified E. coli by knocking out certain gene concerned with its motility.

So, the control loop 1 (Figure1-1) functions as lateral inhibition, and the control loop 2 (Figure1-2) functions as local activation.

Fig.1-1 Control loop 1
This loop is long-range negative feedback, and it drives lateral inhibition when the concentration of E. coli gets higher.

Fig.1-2 Control loop 2
This loop is short-range positive feedback, and it drives local activation when the concentration of E. coli gets higher.

But this model has one potential defect: the order of the growth rate of E. coli is expected to be expressed as linear of the concentration of E. coli, and primary order multiplication may be too small as autocatalysis(local activation) in Turing’s model.

To solve this problem, we introduces another type of E. coli as co-activator, and that is the second strategy. (Figure2)

Strategy 2

Fig.2 Model 2
Improved model from Model 1

E. coli(activator) activates AHL, and AHL inhibits E. coli, as in the model in Figure 1. This loop functions as lateral inhibition. (Figure2-1)

Fig.2-1 Long-range negative feedback loop
Same loop as in Figure 1-1

Two types of E. coli (activator and co-activator) inhibits each other(Figure2-2), and this interaction is achieved by the function of the protein called Colicin. (The detail is shown in the chapter of System.)

Fig.2-2 Short-range positive feedback loop
Mutual inhibition forms positive feedback. And the slow diffusion rate of the agent substance(Colicin) makes the range of this loop short.

In this loop, the increase of $u$ causes the decrease of $v$, and the decrease of $v$ causes the increase of $u$ in turn. And the diffusion rate of Colicin is small for its big size[6]. Therefore, we can regard it as local activation.

Strategy 3

The third strategy is inspired by the pattern formation mechanism of zebrafish[3]. (Figure 3)

Fig.3 Model 3
Two types of E. coli play the role of activator and inhibitor.

In Figure 3, two types of E. coli(activator and inhibitor) reacts each other in two manners according to the distance between them.

When E. coli(activator) and E. coli(inhibitor) are close, they inhibit each other in the same way of strategy 2. (Figure 3-1) This control loop functions as local activation.

Fig.3-1 Short-range positive feedback loop
Same loop as in Figure 2-2

In addition, E. coli(activator) activates E. coli(inhibitor) through AHL in long distance(detailed scheme is explained in the chapter of System). The control loop composed of this activation and the short-range inhibition of E. coli(activator) by activated E. coli(inhibitor) functions as lateral inhibition. (Figure3-2)

Fig.3-2 Long-range positive feedback loop
"Activator" E. coli promotes the multiplication of "Inhibitor" E. coli, and "Inhibitor" inhibits the multiplication of "Activator" near of "Inhibitor"

SYSTEM

In this chapter, we give concrete constructions for the concepts explained at Strategy. First, we explain basic mechanisms which are necessary to understand our construction.

1. Motility control

1.1 CheZ

Fig.1-1 Swimming phase and tumbling phase
The motility of the cell is regulated by CheZ.
(a)Swimming phase: CheZ is expressed and inhibits CheY.
(b)Tumbling phase: CheY binds to flagellar binding proteins.

The movement of E. coli is roughly divided in two phases, swimming(a) and tumbling(b). In order to convert these phases, E. coli use flagellar binding protein called CheY which is controlled by CheZ. Under the expression of CheZ, CheY is dephosphorylated by CheZ and inactivated to bind to the flagellar mortar proteins. Consequently, the rotation of flagellar is changed and cell initiates swimming phase.[7]
In our project, we use cheZ knock out strain (JW18XX), and transfer cheZ under inducible promoter to control cell motility.

2. Cell – Cell interaction

2.1 Quorum Sensing

Fig.2-1 Quorum sensing
Blue hexagon shows AHL molecule. When population density gets higher than certain threshold, the transcription from specific promoter (such as Plux) is enhanced.

AHLs are signal molecules involved in bacterial quorum sensing (Fig 2.1). AHL can easily permeate cell membranes and can regulate the transcription of target cell. For example, in Lux system, AHL binds to LuxR dimer and that complex enhances transcription from PRlux promoter.

2.2 Colicin

Fig.2-2 Mechanism of Colicin release
This figure may a littel bit differ from actual mechanism.

Colicin E3 (ColE3) is a ribonuclease. Colicin E3 Immunity protein (ColI) binds to ColE3 and neutralize it. Colicin Lysis protein (ColL) allow ColE3 to pass thorugh cell membrane. The mechanism of colicin release has not been elucidated.[8][9]
Colicins are a cytotoxins which are released to environment and kill other related strains. Release of colicin involves one protein; Colicin Lysis Protein (ColL). Colicin lysis protein allows colicins to be released. The mechanism how the colicin lysis protein allows colicin release has not been fully elucidated, but it is sure that this protein raise membrane permiability and cause quasilysis.[8] After Colicin released, they diffuse through the medium and bind to the receptor on the target cell membrane. Then, they are imported to the cytoplasm or cytoplasmic membrane of target cell by Tol-system or Ton-system. (If you want to know the mechanism of the Colicin import, see [8])
Colicin producing cells also express Colicin Immunity Protein (ColI) in order to protect themselves from cytotoxity of colicin.
Colicins have variety cytotoxity such as DNase activity, RNase activity or Pore forming across inner membrane.
In our project, we select colicin E3 because it has low risk of safety problems. Colicin E3 specifically digests 16S rRNA which is only in bacteria.