burn wounds2014.igem.org/files/presentation/groningen_championship.pdfburn wounds 1,200,000 burn...
TRANSCRIPT
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Burn wounds
“Burns are one of the most common
and devastating forms of trauma” Deirdre Church et al. 2006
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Burn wounds
burn injuries 1,200,000
National Center for Injury Prevention and Control in the United States
hospitalized 100,000
deaths 5,000
are infection related 75%
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iGEM Groningen 2014
the smart bandage
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Conventional treatments Crèmes and bandages
Bathing Preventive antibiotics
Skin transplantation
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Requirements
the smart bandage
• Must be used for several days
• Must detect infections
• Must secrete infection preventing molecules (IPMs)
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Pseudomonas aeruginosa
Staphylococcus aureus
Opportunistic pathogens
Gram-positive Gram-negative
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“A post-antibiotic era - in which
common infections and minor injuries
can kill - far from being an apocalyptic
fantasy, is instead a very real
possibility for the 21st Century.” WHO, April 2014
World Health Organization (2014) Antimicrobial resistance: global
report on surveillance. ISBN 978 92 4 156474 8
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Our goal
To detect and prevent further
infections caused by S. aureus
and P. aeruginosa in burn
wounds.
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Why Lactococcus lactis ?
• Harmless species
• Food approved
• Produces lactate
• No spore formation
• Can be temporarily inactivated
Image adapted from a SEM scan by Joseph A. Heintz, University of Wisconsin-Madison.
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Commission of
Genetic Modification
Thomas and Lianne at COGEM.
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How does it work? DETECTION
Quorum sensing molecules
AHL (P. aeruginosa)
AIP-1 (S. aureus)
Contreras, G. et al. (2013)
LaSarre, B. and Michael J.F. (2013)
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How does it work? BIOFILM DESTRUCTION
DspB degrades the biofilms of both species
This way the other infection prevention
molecules can reach them
Mark, B.L. et al. (2001)
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How does it work? QUORUM SENSING DISRUPTION
AiiA inhibits AHL
Quorum sensing of P. aeruginosa is disrupted
P. aeruginosa expresses less virulence genes
Body can clear P. aeruginosa without problems
Kim, M.H. et al. (2005)
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How does it work? NISIN PRODUCTION
Nisin kills Gram-positive bacteria
Wiedemann, I. et al. (2001)
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Biobricks
Toolbox
• Nisin biobricks
• sfGFP
LactoAid
• AiiA
• DspB
• Gene constructs
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From bacterium to bandage
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Bandage materials
Top layer - polymethyl pentane
• Permeable to gases
• iGEM 2012, Groningen
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Bandage materials
Middle layer – polyacrylamide gel
• Pore size is adjustable
• Nutrients can be added
• Cheap
• Rehydratable
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Bandage materials
Bottom layer – cellulose nitrate
• Permeable to small molecules
and proteins
• iGEM 2013, TU Delft
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Description of the model
Single unit Total model
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Parameters – Rate Equations Nisin production assay
Optimal
concentration
2% Glucose
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Models
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Model-based design
Least optimal design (0 - 24 h)
Max IPMs
No IPMs
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Model-based design
Best design (0 - 24 h)
Max IPMs
No IPMs
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Modeling outcome
Time to reach effective concentrations
nisin aiiA dspB
12 min 18 min 18 min
24 min 30 min 30 min
114 min 138 min 144 min
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Modeling outcome
Time to reach effective concentrations
nisin aiiA dspB
12 min 18 min 18 min
24 min 30 min 30 min
114 min 138 min 144 min
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Modeling outcome
Time to reach effective concentrations
nisin aiiA dspB
12 min 18 min 18 min
24 min 30 min 30 min
114 min 138 min 144 min
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Results:
Cell growth in the active layer
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Results: Nisin Secretion
• Nisin-sensitive strain plated
• LactoAid active layer,
with nisin-producing strain
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In conclusion
“… this is an application that appeals, it would be a waste if this idea would remain at -80°C…”
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We would like to thank:
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Thank YOU for your attention!
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Wiedemann, I et al. . “pe ifi Bi di g of Nisi to the Peptidogl a Pre ursor Lipid II Co i es Pore For atio a d I hi itio of Cell Wall Bios thesis for Pote t A ti ioti A tivit . The Journal of
biological chemistry 276(3): 1772–79.
Kim, Myung Hee et al. 2005. The Mole ular “tru ture a d Catal ti Me ha is of a Quoru -
Quenching N-Acyl-L-Homoserine La to e H drolase. Proceedings of the National Academy of Sciences
of the United States of America 102(49): 17606–11.
Mark, B L et al. 2001. Cr stallographi Evide e for “u strate-Assisted Catalysis in a Bacterial Beta-
Hexosaminidase. The Journal of biological chemistry 276(13): 10330–37.
LaSarre, Breah, and Michael J Federle. . E ploiti g Quoru “e si g to Co fuse Ba terial Pathoge s. Microbiology and molecular biology reviews : MMBR 77(1): 73–111.
García-Contreras, Rodolfo, Toshinari Maeda, a d Tho as K Wood. . Resista e to Quoru -
Que hi g Co pou ds. Applied and environmental microbiology 79(22): 6840–46.
References
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Rate equations
M. Boonmee et al. Biochemical Engineering Journal 14 (2003) 127–135
Shimizu et al. Appl. Environ. Microbiol. 1999, 65(7):3134.
X1 =
X2 =
X3 =
x7 =
X8 =
X9 =
X10 =
X11 =
IPMs =
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Rate equation
F (variable,parameters) Diffusion term + Rate =
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Diffusion equations Volume fraction of the gel:
Density of IPMs:
mw - mass of the water
mb - mass of the buffer
mp - mass of the polymer
vp - partial specific volume of gel in water
vwb - partial specific volume of gel in buffer
Tong, Jane, and John L. Anderson. Biophysical journal 70.3 (1996): 1505-1513.
Fischer Hannes, et al. Protein Science 13.10 (2004): 2825-2828.
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Diffusion equations Stokes-Einstein equation:
Diffusion rates of the IPMs were found with:
Tong, Jane, and John L. Anderson. Biophysical journal 70.3 (1996): 1505-1513.
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Model-based design
Expected most optimal design
Max IPMs
No IPMs
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Growth rate (0.653 hr-1)
39 minutes
0.01
0.1
1
10
0 2 3 4 6 7 8 9 10 11
Lo
g O
D60
0nm
Time (Hours)
Parameters – Rate Equations Growth rate – L. lactis
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Rate equation
F (variable,parameters) Diffusion term + Rate =
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Results: Protein production in
the gel 30 min 90 min 60 min
In the
Gel
On the
Gel
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Toolbox
Nisin gene cluster
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Nisin production