towards intelligent probiotics
DESCRIPTION
Towards Intelligent Probiotics. Chris Brasseaux , EE David Golynskiy , Bio/ Crim Tyler Guinn, BioChem /EE Sameer Sant , Bio/Econ Mitu Bhattatiry , Biomed (Columbia) Nimi Bhattatiry (High School) Jose Alfredo Flores (Monterey Mexico). Towards Intelligent Probiotics. Introduction - PowerPoint PPT PresentationTRANSCRIPT
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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)
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Towards Intelligent Probiotics
1. Introduction
2. Immunobot: sensor-taxis
3. Killbot: population control
4. More
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Towards Intelligent Probiotics
1. Introduction
2. Immunobot: sensor-taxis
3. Killbot: population control
4. More
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Towards Intelligent Probiotics
• 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
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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
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Towards Intelligent Probiotics
• “Device 1”: Immunobot– Immune interface– Drug delivery
• “Device 2”: Killbot– System control
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Towards Intelligent Probiotics
1. Introduction
2. Immunobot: sensor-taxis
3. Killbot: population control
4. More
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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
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FGF Receptor (FGFR)
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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
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Connection to Chemotaxis
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Immunobot System
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Cloning…
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More Cloning
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…and more cloning
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Experiments
1. Transformed E.Coli Dh5a2. Incubated with nitrates for production of the receptor3. Introduced heparin and FGF 4. Performed fluorescent microscopy measurements
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Experiments
Negative Control
10uM heparin
100uM heparin
1mM heparin
15nM FGF220
225
230
235
240
245
250
255
260
PyeaR_FGFR-ToxR and ctx_GFP after 1 hourM
ean
Fluo
resc
ence
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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
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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
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Interface to Chemotaxis
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Chemotaxis
Image from Roland Institute at Harvard Image from Roland McGraw Hill
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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).
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Towards Intelligent Probiotics
1. Introduction
2. Immunobot: sensor-taxis
3. Killbot: population control
4. More
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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)
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A Glucose Repressible Killbot
Time
Deliv
ery/
Gluc
ose
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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
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-1.66533453693773E-16
0.2
0.4
0.6
0.8
1
1.2
Inte
grat
ed F
luor
esce
nt In
tens
ity
Killbot Experiments
The killbots eliminate the majority of the cells
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-1.66533453693773E-16
0.2
0.4
0.6
0.8
1
1.2
Inte
grat
ed F
luor
esce
nt In
tens
ity
Killbot Experiments
The addition of glucose in the medium increases the population two-fold
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Towards Intelligent Probiotics
1. Introduction
2. Immunobot: sensor-taxis
3. Killbot: population control
4. More
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Android Apps
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Android Apps
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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
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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.
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Dr. Leonidas BlerisDr. Hyun-Joo Nam
Dr. Lan MaNeha Kashyap
Lagnajeet PradhanKristina Ehrhardt