iGEM 2009 Team:Newcastle

Bac-Man: The Quest to Sequester

We have a problem…

…Cadmium• A white, silvery heavy metal• A group 12 transitional metal• Trace element

• Danger to the environment! • Neurotoxic agent – Itai-Itai disease• Causes renal damage• Induces bone demineralisation• Cancer?

Overview

So what’s the problem?...

…ACCUMULATION

What can our Project do about it?

AIMS

• Detect cadmium in the environment and remove it from the environment rendering it bio-unavailable

• Target cadmium at the source to avoid the damaging effects of accumulation.

• Remodel the life cycle of Bacillus subtilis to accommodate it’s new role and to interact with the

environment.

Our System

Choice of organism = Bacillus subtilis:• Can produce resilient, long-lasting spores• Possibility of epi-chromosomal gene transfer• Naturally lives in soil

Cadmium Intake

Cadmium sequestering

and sporulation

Stochastic Switch

Cadmium Sensing

Cadmium efflux

SUB-PROJECTS INVOLVED IN OUR DESIGN:

-Metal Infflux/Efflux-Metal Sensor-Stochastic Switch-Sporulation Tuner-Metal Sequester-Population Modelling-Chassis

Sub-projects

Overview

Cadmium Sensing

Cadmium Sensing: What is this sub-project about?

AIM – we need to produce a tightly regulated cadmium sensor in our system which produces a signal in response

Need to design a promoter regulated by cadmium and responsive to the stochastic switch’s decision

Determines: The fate of a cell according to the environement Feeds into the stochastic switch Germination ability of spores

How do we build our cadmium sensor BioBrick: Create an AND gate Use metal sensors CzrA and ArsR

Metal Sensor

Metal Sensor

Metals Sensed

ArsR As(III) Ag(I) Cu Cd

Cadmium Sensing: ArsR and CzrA

Both are metal sensitive repressors: ArsR features in Arsenic Resistance operon CzrA features in Cobalt zinc Resistance operon

Why use these two metal sensors?

For the same reason we are using the AND gate – metal sensitive promoters can sense MORE THAN ONE METAL!

Metal Sensor

Metals Sensed

czrA Zn Co Ni Cd

We know that the CadA promoter contains CzrA binding site

Solution: engineer ArsR binding site next to CadA promoter to create cadmium sensing AND gate

Cadmium Sensing: BioBrick Construct

AND gate BioBrick

ArsR binding sites

cadA promoter with CadA binding

site

RBS

Metal Sensor

Cadmium Sensing: AND gate

Cadmium Sensing: Modelling

Metal Sensor

Cadmium Sensing: Novelty and Importance

Metal Sensor

Need tightly-controlled cadmium metal sensor – single metal sensors not enough.

Usual metal-sensitive promoters are sensed by a single repressor protein only

Our metal-sensitive promoter is regulated by 2 different repressor proteins: CzrA and ArsR

Can be used in front of any gene, not only in our project but in different settings.

The concept of using two metal sensors in this way can be adapted for sensing other metals too.

Stochastic switch

Stochastic Switch: What is this sub-project?

Tuneable invertible promoter region which

Determines: Rate of Bacillus sporulation Metal sponge expression Germination ability of spores.

Degradation controller

This subproject allows us to control key aspects of the Bacillus life cycle to suit our projects needs all in a tuneable manner.

Stochastic Switch

Stochastic Switch: Novelty in design

The stochastic switch is the most novel part of our design

The switch regulates the decision to become a non-germinating metal container spore, or a spore that can go on to germinate as part of the normal life cycle

Hin invertase is used to switch on and off promoters.

We have added new bricks to our device to make the switch tuneable in three ways; 2 variable strength promoters and a degradation controller.

The switch controls 3 main aspects of the cell life cycle: Upregulating sporulation Downregulating germination Metallothionein-cotC fusion expression

Stochastic Switch

Stochastic Switch: BioBricks and Devices…

EcoRIXbalBiobrick Prefix

Rfp AccIII BalI hixC hixChin invertase

sigA(pveg)pspac NaeI

xlyA promoter

NruI

SpeIPstIBiobrick Suffix

RBS + GFP Prefix(ATG)

NheI

Key

Restriction site

Invertase site

Double terminator

RBS

‘Right facing’ promoter

‘Left facing’ promoter

• The device we had synthesised was for testing purposes an so contained IPTG and Xylose inducible promoters

• The hin invertase has a degradation tag recognised by degradation controller ssbp from E.coli.

• Sspb is placed under the control of an arabinose inducible promoter. Stochastic

Switch

Stochastic Switch: Modelling

The device had to be modelled due to the many variables that contribute to the stochastic decision; these variables can be changed to give different pulse lengths of hin in order to give a net number of flips :

Stochastic Switch

pSpac XylA

Germination activator

(FimE)

Sporulation activator

(CinR)

pSpac XylA

Sporulation activator

(CinR)

Germination activator

(FimE)

hixC hixC

hin

Pveg

hixChixC

hin

Pveg

Hin

Generally: Pveg faces Left = germination

Pveg faces Right = metal container decision. Stochastic

Switch

Stochastic Switch: Modelling

Cadmium Sequestration

AIM – to render cadmium bio-unavailable by mopping it up using a metallothionein and moving it into spores

By wrapping a spore coat protein around cadmium ions, the ions become isolated from the environment (and humans) and no longer have harmful effects.

Metallothionein = SmtA; Spore coat protein = CotC

NOVELTY – moving cadmium into resilient spores has not been accomplished before.

Future teams could apply same principle for other toxic metals.

Cadmium Sequestration: What is this sub-project

about?

Cadmium Sequestration: BioBrick Construct

Sporulation Tuning

Sporulation Tuning: Aims and Novelty

Aim:

• To adjust the natural sporulation system, instead of including something totally new.

• Find out how the percentage of the population that sporulates, and does not sporulate, when the system is affected, by example, change in protein level.

• Find the optimal percentage for the system in mind

Modelling:

• Sin (sporulation inhibition) Operon Model (CellML)

• Kin A Expression Model (COPASI)

• Sporulation Induction using Kin A Model (COPASI)

Sporulation Tuning – the sporulation trigger

The transcription factor Spo0A is a master regulator for entry into sporulation in the bacterium Bacillus subtilis [1]

Sporulation can be triggered with high efficiency in cells in the exponential phase of growth in rich medium by artificial induction of the synthesis of any one of three histidine kinases that feed phosphoryl groups into the relay [1]

For our project, we are using kinA as:

- In Bacillus subtilis, KinA is a major histidine kinase responsible for activation the sporulation pathway [2]

The KinA Expression Model illustrates how KinA is expressed while the third model shows how KinA induces sporulation

It is important to note, that a gradual increase in the Spo0A protein and activity plays a critical role in triggering sporulation and requires the action of the phosphorelay [1]

Sporulation Tuning: Modelling

Sporulation Tuning: Lab Work and Characterisation

References[1] Fujita, M. and Losick, R. 2005. Evidence that entry into

sporulation in Bacillus subtilis is governed by a gradual increase in the level and activity of the master regulator Spo0A. Genes & Development 19: 2236-2244

[2] Eswaramoorthy, P., Guo, T. and Fujita, M. 2009. In Vivo Domain-Based Functional Analysis of the Major Sporulation Sensor Kinase, Kin A, in Bacillus subtilis. Journal of Bacteriology p. 5358-5368

Sin (sporulation inhibition) Operon Model

Controls the production and activity of the repressor SinR

SinR in its active tetrametric form inhibits sporulation by repressing stage II and Spo0A promoters

Phosphorylation induces Spo0A to form active dimers which activate trascription from the P1 promoter

Population Modelling

Population Modelling: Aims and Novelty

What is the affect of modifying the bacteria's life cycle? Does the population die, if we reduce germination? How much of our bacteria will be needed to clean up an

area?

A shared environment.

Independent cells of bacteria making decisions for their life.

Each cell runs cellular models, using it's own parameters.

Population Modelling: How does it work?

Programmed in Java, uses, Jsim, and other models written in CellML and SBMLwhile integrating agent based models and biological models.

Due to each bacterial cell running independently as a thread, it used a lot of CPU power and RAM.

Powerful machines (16 CPUs) were used.

Population Modelling: Distributed Computing

The solution – Distributed Computing – By using multiple computers to spread the load.

Using Microbase – It now runs on university computers and the Amazon Elastic Compute Cloud.

Database

Amazon EC2

Cell

Server(Microbase)

Any Questions?