Burn wounds

“Burns are one of the most common

and devastating forms of trauma” Deirdre Church et al. 2006

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%

iGEM Groningen 2014

the smart bandage

Conventional treatments Crèmes and bandages

Bathing Preventive antibiotics

Skin transplantation

Requirements

the smart bandage

• Must be used for several days

• Must detect infections

• Must secrete infection preventing molecules (IPMs)

Pseudomonas aeruginosa

Staphylococcus aureus

Opportunistic pathogens

Gram-positive Gram-negative

“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

Our goal

To detect and prevent further

infections caused by S. aureus

and P. aeruginosa in burn

wounds.

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.

Commission of

Genetic Modification

Thomas and Lianne at COGEM.

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)

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)

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)

How does it work? NISIN PRODUCTION

Nisin kills Gram-positive bacteria

Wiedemann, I. et al. (2001)

Biobricks

Toolbox

• Nisin biobricks

• sfGFP

LactoAid

• AiiA

• DspB

• Gene constructs

From bacterium to bandage

Bandage materials

Top layer - polymethyl pentane

• Permeable to gases

• iGEM 2012, Groningen

Bandage materials

Middle layer – polyacrylamide gel

• Pore size is adjustable

• Nutrients can be added

• Cheap

• Rehydratable

Bandage materials

Bottom layer – cellulose nitrate

• Permeable to small molecules

and proteins

• iGEM 2013, TU Delft

Description of the model

Single unit Total model

Parameters – Rate Equations Nisin production assay

Optimal

concentration

2% Glucose

Models

Model-based design

Least optimal design (0 - 24 h)

Max IPMs

No IPMs

Model-based design

Best design (0 - 24 h)

Max IPMs

No IPMs

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

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

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

Results:

Cell growth in the active layer

Results: Nisin Secretion

• Nisin-sensitive strain plated

• LactoAid active layer,

with nisin-producing strain

In conclusion

“… this is an application that appeals, it would be a waste if this idea would remain at -80°C…”

We would like to thank:

Thank YOU for your attention!

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

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 =

Rate equation

F (variable,parameters) Diffusion term + Rate =

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.

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.

Model-based design

Expected most optimal design

Max IPMs

No IPMs

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

Rate equation

F (variable,parameters) Diffusion term + Rate =

Results: Protein production in

the gel 30 min 90 min 60 min

In the

Gel

On the

Gel

Toolbox

Nisin gene cluster

Nisin production