Towards Intelligent Probiotics

Towards Intelligent Probiotics

Chris Brasseaux, EEDavid Golynskiy, Bio/CrimTyler Guinn, BioChem/EESameer Sant, Bio/Econ

Mitu Bhattatiry, Biomed (Columbia)Nimi Bhattatiry (High School)

Jose Alfredo Flores (Monterey Mexico)


1. Introduction

2. Immunobot: sensor-taxis

3. Killbot: population control

4. More


1. Introduction



4. More


• Live microorganisms that give benefit to host (e.g. live cultures in yogurt)

• May be used as therapeutic tool

• Address disorders afflicting the intestine

• May result in problematic tissue damage

Towards Intelligent Probiotics• Human bowel symbionts represent

engineering platform

• Intelligent probiotics are user-controlled and confer health benefits to host

• Stanford 2009: regulated lymphocytes to control inflammation

• Goals• Interface with immune system to produce

localization at damage site• Enable population control


• “Device 1”: Immunobot– Immune interface– Drug delivery

• “Device 2”: Killbot– System control


1. Introduction



4. More

Signal Detection• Fibroblasts play important role in wound healing

• Fibroblast growth factors (FGFs) are paracrine heparin-binding proteins that trigger fibroblast differentiation

• FGFs can represent wound signals

FGF Receptor (FGFR)

FGF Receptor Interface

• Kolmer et al., 1994 achieved a similar effect with two constructs:– Chimeric receptor: maltose receptor fused to ToxR

transcription factor – CTX cloned upstream of lacZ genes

Image from Kolmer, 1994

Connection to Chemotaxis

Immunobot System

Cloning…

More Cloning

…and more cloning

Experiments

1. Transformed E.Coli Dh5a2. Incubated with nitrates for production of the receptor3. Introduced heparin and FGF 4. Performed fluorescent microscopy measurements

Experiments

Negative Control

10uM heparin

100uM heparin

1mM heparin

15nM FGF220

225

230

235

240

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250

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260

PyeaR_FGFR-ToxR and ctx_GFP after 1 hourM

ean

Fluo

resc

ence

Experiments

Negative Control

10uM heparin

100uM heparin

1mM heparin

15nM FGF210

215

220

225

230

235

240

245

250

255

PyeaR_FGFR-ToxR and ctx_GFP after 4 hoursM

ean

Fluo

resc

ence

Experiments

no in-duction

1uM heparin

10uM heparin

100uM heparin

1mM heparin

15nM FGF 30nM FGF210

215

220

225

230

235SCP-ToxR-FGFR and ctx-GFP after 1hour

Mea

n Fl

uore

scen

ce

Interface to Chemotaxis

Chemotaxis

Image from Roland Institute at Harvard Image from Roland McGraw Hill

ModelingThe signaling network from the input of external ligand signal to the output of the tumbling state of a E coli cell can be quantitatively described by a modular model.

The model is formulated based on the law of mass action and Michaelis-Menten mechanism and contains four relatively independent modules.

Module 1: Activation of ToxR receptorModule 2: Transcription/translation of CheZModule 3: CheY dephosphorylation by the CheZ proteinModule 4: The tumbling activity of E coli is characterized by the so-called “bias”, which is defined as the ratio of the time of directed movement and the total time. It is experimentally measured that the bias is a Hill function dependent on the concentration of phosphorylated CheY (Cluzel, Surette et al. 2000).


1. Introduction



4. More

Killbot: A suicide mechanism

This mechanism uses two plasmids:1. PcstA*-RBS-LuxI-double terminator (Berkeley 2006) 2. AHL Inducible Colicin E2 with GFP (Calgary 2008)

A Glucose Repressible Killbot

Time

Deliv

ery/

Gluc

ose

Killbot Experiments

Population 1 (Immunobot) Population 1 (Immunobot) + more Population 2 (Killbot)

Population 1 (Immunobot) + Population 2 (Killbot)

• BL21 colicin sensitive cells (used same OD)• Population 1: AHL inducible colicin E2 with GFP• Population 2: glucose-repressible AHL producer

-1.66533453693773E-16

0.2

0.4

0.6

0.8

1

1.2

Inte

grat

ed F

luor

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nt In

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ity

Killbot Experiments

The killbots eliminate the majority of the cells

-1.66533453693773E-16

0.2

0.4

0.6

0.8

1

1.2

Inte

grat

ed F

luor

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nt In

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Killbot Experiments

The addition of glucose in the medium increases the population two-fold


1. Introduction



4. More

Android Apps

Android Apps

Biobricks

Name Type Description Designer LengthBBa_K569001 Composite PcstA+RBS+LuxI+double terminator Mitu Bhattatiry 937

BBa_K569013 Composite PyeaR+ToxR+FGFR David Golynskiy & Tyler Guinn 1536

BBa_K569014 Composite SCP+ToxR+FGFR David Golynskiy & Tyler Guinn 1471

Name Type Description Designer LengthBBa_K569000 Intermediate RBS+LuxI+dblterm Mitu Bhattatiry 798BBa_K569003 Composite Phototaxis Receptor+eYFP Jose Alfredo Flores 2918BBa_K569004 Composite Phototaxis Receptor+GFP Jose Alfredo Flores 2916BBa_K569005 Composite Ctx-CheZ David Golynskiy & Tyler Guinn 790BBa_K569005 Composite Ctx-CheZ David Golynskiy & Tyler Guinn 790BBa_K569006 Composite Ctx-CheY David Golynskiy & Tyler Guinn 571BBa_K569007 Coding CheZ mutant David Golynskiy & Tyler Guinn 664BBa_K569010 Composite ctx+CheZ mutant David Golynskiy & Tyler Guinn 1701BBa_K569011 Coding FGFR David Golynskiy & Tyler Guinn 792BBa_K569012 Composite ToxR+FGFR David Golynskiy & Tyler Guinn 1430BBa_K569017 Coding CheY David Golynskiy & Tyler Guinn 418

http://partsregistry.org/wiki/index.php?title=Part:BBa_K569001














Accomplishments

1. Built new BioBricks relating to wound sensing and chemotactic abilities

2. Demonstrated that some of them, particularly the killbot, seem to work as expected.

3. Improved the characterization existing BioBricks, and included our experience in the appropriate Registry page.

4. Qualifying for MIT will allow us to test the different promoter-receptor constructs with longer induction times, different ligand concentrations, and the chemotaxis experiments with controlled gradient settings.

Dr. Leonidas BlerisDr. Hyun-Joo Nam

Dr. Lan MaNeha Kashyap

Lagnajeet PradhanKristina Ehrhardt

Documents

Towards Intelligent Probiotics