Bacterial processes are thus underthe constant threat of

BIOREACTORS

PRODUCTS

Bioreactors are being used more and more to source everyday products

For example, 10% of dairyfermentation collapsedue to phage infection

dxdt

Our solution involved refactoring a recently discovered bacterial

immune system

We have also explored population control of a model system using

CRISPR

This will allow for control of product synthesis in a bioreactor

Current uses in synthetic biology include genome editing, gene editing, gene expression tuning

Loci discovered in E. coli

The CRISPR craze

Variable sequences matched to phage genomes

Variable sequences predicted to function at the RNA level to protect against plasmids and phage

Demonstrated acquired resistance to phage

In vitro demonstration of sequence-directed DNA cutting

1987

2005

20072008

2013

2011ASU Georgia TechUSC

Spacer PAM

Exogenou s D NA

cas ... leader repeat repeat ...repeatcas

pre-crRNA

Cas9 crRNA

CRISPR Mechanism

...

......

...

T4

AR1HX10

RB14 ...Host genome ...

Broadly neutralizing repeat spacer repeat arrayVirus speci!c spacers

Bioinformatic pipeline: broadly neutralizing spacers

cas9 tracrRNA Repeat T4 Spacer Repeat

cas9 tracrRNA Repeat T7 Spacer Repeat

cas9 tracrRNA Repeat T4 Spacer Repeat T7 Spacer Repeat

The minimum CRISPR array: our design

Broadly immune

T4 immune

T7 immune

17

2025

35

48

63

75

100

135

180

245Cas9

Control

Ladder

163

Induction dependant survival of CRISPR array harboring clones

Growth kinetics of immune vs susceptible clones

Modeling

Basic assumptions carried through model

Bacteria are grown in a batch reactor

Bacteria with our CRISPR system are completely immune to phage infection

All phages are considered lytic

Basic Monod growth kinetics

0 2 4 6 8 10 12 14 16 18 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time [hours]

OD 60

0

Numerical Predictions€

dXdt

= µe−

XXC

⎛

⎝ ⎜

⎞

⎠ ⎟

m

+ kd⎛

⎝

⎜ ⎜

⎞

⎠

⎟ ⎟

€

X 1− e−αt( )

Monod equation dampening

0 2 4 6 8 10 12 14 16 18 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time [hours]

OD 60

0

Experimental DataNumerical Predictions

Experimental growth data fit to model

High inoculums used to mimic bioreactor seeding

0 2 4 6 8 10 12 14 16 18 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time [hours]

OD 60

0

Experimental DataNumerical Predictions

Predicative power of model for growth data

CRISPR

CRISPR

Extending the model to phage predation

Infected bacteria statistically determined using a poisson distribution

Cell bursts according to a normal distribution

0 4 8 12 160

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Time [hours]

OD

600

Predicted Total

Extending the model to phage predation

0 4 8 12 160

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Time [hours]

OD

600

Growth DataPredicted TotalPredicted InfectedPredicted Healthy

Cell Debris

Fitting experimental data to phage the predation model

CRISPR

Extending the model to with a mixed population

Susceptible to phage Resistant to phage

Infected Phage

OCH3

OH

CHO

HO

Extending the model to with a mixed population producing different compounds

CRISPR

CRISPR

Compound A

Compound B

Modeling compound production under phage predation

pH

Flav

ourin

g

Phage

Res

istan

ce

Aroma

Gas

Tol

eran

ce

Starter cultures are used and selected for several key properties

These properties are usually specific to individual strains – not all strains within a species are suitable for fermentation

pH

Flav

ourin

g

Phage

Res

istan

ce

Aroma

Gas

Tol

eran

ce

Phage ResistanceFlavouring

Combining biosynthesis of common flavors with phage immunity we can consolidate strains

Offers control over the strain balance as well as the flavor production

pH

Flav

ourin

g

Phage

Res

istan

ce

Aroma

Gas

Tol

eran

ce

Phage ResistanceFlavouring

CinnamaldehydeProducing

VanillinProducing

For this case study we choose 2 cultures, one producing cinnamaldehyde and the other producing vanillin

We envision tuning different populations in an industry that suffers from phage mediated bioreactor collapse; the yogurt industry

Resistant to Environmental Phage

Cinnamaldehyde VanillaCRISPR

CRISPR

Both strains are resistant to the environmental phage

Susceptible to Control Phage

Cinnamaldehyde VanillaCRISPR

CRISPR

Both strains are resistant to the environmental phage

Only one is immunized against the control phage

Population Control with Controlled Phage Addition

Cinnamaldehyde VanillaCRISPR

CRISPR

Only one is immunized against the control phage

VanillaCRISPR

Biosynthesis of Vanillin

OCH3

OH

CHO

Vanillin

OCH3

CoA

OH

OSO

Feruoyl-CoA

OCH3

OH

OH

Ferulic Acid

O

OHOH

OH

Caffeic Acid

O

OH

OH

p-Coumaric Acid

O

NH2

OH

OH

Tyrosine

O

TAL CMH COMT

FCS

ECH

BBa_K1129000 BBa_K1129046 BBa_K1129041

BBa_K1129024

BBa_K1129022

Biosynthesis of Vanillin

OCH3

OH

CHO

Vanillin

OCH3

CoA

OH

OSO

Feruoyl-CoA

OCH3

OH

OH

Ferulic Acid

O

OHOH

OH

Caffeic Acid

O

OH

OH

p-Coumaric Acid

O

NH2

OH

OH

Tyrosine

O

TAL CMH COMT

FCS

ECH

BBa_K1129000 BBa_K1129046 BBa_K1129041

BBa_K1129024

BBa_K1129022

Conversion of p-Coumaric acid to Caffeic Acid

0

5000000

10000000

15000000

20000000

25000000

30000000

35000000

14 14.2 14.4 14.6 14.8 15 15.2 15.4 15.6 15.8 16

Constitutive 4CMHControl

14.6

97

Abu

ndan

ce

Retention Time (min)

OHOH

OH

Caffeic Acid

O

Abundance

50 100 150 200 250 300 350 400 450 500 550

73.2

110.2147.1

267.1

307.2

351.1 454.3 513.4 554.2

396.2

219.1

m/z

4500000

4000000

3500000

3000000

2500000

2000000

1500000

1000000

5000000

14.697

Biosynthesis of Vanillin

OCH3

OH

CHO

Vanillin

OCH3

CoA

OH

OSO

Feruoyl-CoA

OCH3

OH

OH

Ferulic Acid

O

OHOH

OH

Caffeic Acid

O

OH

OH

p-Coumaric Acid

O

NH2

OH

OH

Tyrosine

O

TAL CMH COMT

FCS

ECH

BBa_K1129000 BBa_K1129046 BBa_K1129041

BBa_K1129024

BBa_K1129022

Conversion of Caffeic Acid to Ferulic Acid

0

5000000

10000000

15000000

20000000

25000000

30000000

35000000

40000000

13 13.5 14 14.5 15 15.5 16

Constitutive COMTControl

14.4

34

OCH3

OH

OH

Ferulic Acid

O

Abu

ndan

ce

Retention Time (min)

14.434

41.2

73.1

115.1146.9 191.1

249.1

293.1

338.2

386.2 442.2 485.1

120000

100000

80000

60000

40000

20000

50 100 150 200 250 300 350 400 450m/z 0

Abundance

Biosynthesis of Vanillin

OCH3

OH

CHO

Vanillin

OCH3

CoA

OH

OSO

Feruoyl-CoA

OCH3

OH

OH

Ferulic Acid

O

OHOH

OH

Caffeic Acid

O

OH

OH

p-Coumaric Acid

O

NH2

OH

OH

Tyrosine

O

TAL CMH COMT

FCS

ECH

BBa_K1129000 BBa_K1129046 BBa_K1129041

BBa_K1129024

BBa_K1129022

Conversion of Ferulic Acid to Vanilin

0

5000000

10000000

15000000

20000000

25000000

30000000

35000000

9 9.5 10 10.5 11 11.5 12

Constitutive EncH and FcS Control

10.4

86

OCH3

OH

CHO

Vanillin

10.486

Abu

ndan

ce

Retention Time (min)

45.1

73.1

104.1137.1 163.0

224.1

254.0299.1

194.1

276.1 326.2 348.2

Abundance350000

300000

250000

200000

150000

100000

50000

060 100 140 180 220 260 300 340m/z

Biosynthesis of Cinnamaldehyde

Cinnamaldehyde

CoAOSO

Cinnamoyl-CoACinnamic acid

OHO HO

NH2

OH

Phenylalanine

O

PAL (EncP) 4-CL (EncH) AtCCR1

BBa_K1129026 BBa_K1129042 BBa_K1129039

Biosynthesis of Cinnamaldehyde

Cinnamaldehyde

CoAOSO

Cinnamoyl-CoACinnamic acid

OHO HO

NH2

OH

Phenylalanine

O

PAL (EncP) 4-CL (EncH) AtCCR1

BBa_K1129026 BBa_K1129042 BBa_K1129039

Conversion of Phenylalanine to Cinnamaldehyde

0

5000000

10000000

15000000

20000000

25000000

30000000

9 9.5 10 10.5 11 11.5 12 12.5 13

10.5

80

11.8

19

Constitutive EncP, 4CL and ATCRR1Control

Abu

ndan

ce

Retention Time (min)

CinnamaldehydeCinnamic acid

HO

45.1

75.1

103.1 161.1

131.1

229.1263.1

299.1 348.2376.2

205.145000

35000

25000

15000

5000

Abundance 10.58011.819

60 140 220 300 380m/z 0

60 140 220 300 380

45.1

73.1

147.1

118.1 175.1220.1265.1

293.1

320.1359.2 409.1

Abundance1200000

1000000

800000

600000

400000

2000000

m/z

OHO

Consolida)ng  proper)es  of  different  strains  could  save  the  dairy  industry  millions

What  would  be  the  percep)on    of  this  gene)cally  modified  organism  

 by  the  end  user,    the  public.  

Industry

Academic

Professional Interviews

Developing iGEM Project

Gathering Information

Developing Public Survey

Conduct Public Survey Partners Telus World of Science

Marketing Professionals

Apply for Ethics Approval

Consult

Analyze Survey Data

Design Marketing Strategy

Human practices workflow

0  

0.5  

1  

1.5  

2  

2.5  

3  

3.5  

4  

4.5  

5  

Health  Benefit   Environmental  Impact  

Price   Health  Hazards   Taste  

Rela%ve  Level  of  C

onside

ra%on

 (5  being  M

ost  C

onside

red)    

GM  vs  Non-­‐GM  Yogurt  Considera%ons  

Non-­‐GM  Yogurt  

GM  Yogurt  

Conclusions We assembled, expressed and have started characterizing a minimal CRISPR-Cas9 system in E. coli

We developed both mathematical and numerical models to understand mono- and mixed cultures of compound-producing E. coli

We characterized compound generation for all steps in the pathway from p-coumaric acid to vanillin and confirmed cinnamaldehyde production from phenylalanine

We identified consumer concerns with genetically modified foods, proposed a feasible labeling guide and designed a marketing strategy for genetically modified foods

Acknowledgments    Dr. Steven Hallam and Dr. Joanne Fox

UBC Life Science Institute UBC Microbiology and Immunology

UBC Michael Smith Laboratories UBC Department of Chemical and Biological Engineering

UBC Faculty of Applied Sciences UBC Faculty of Science

Walter H. Gage Memorial Fund Pfizer Canada Inc.

Engineering Undergraduate Society

Submitted 40 and characterized 25 new biobrick parts

Submitted Cas9 (BBa_K1129006) as our favorite functional part and showed evidence of immunity against phage using CRISPR components.

Obtained ethics approval to survey public’s perception of GMOs with the aim to create a marketing strategy for a GM Yogurt, following interviews with dairy industry and academic professionals

Improved 3 existing caffeine biosynthesis biobricks (BBa_K1129013, BBa_K1129015, BBa_K1129017) from TU Munich 2012 by replacing a yeast consensus sequence with a bacterial ribosome-binding site for prokaryotic engineering

Characterized previous iGEM parts (from TU Munich 2012, KU Leuven 2009, Edinburgh 2007) in vanillin, cinnamaldehyde and caffeine biosynthesis pathways and were able to show functional data for substrate conversion.

We are the only team to show data for vanillin and cinnamaldehyde production in E. coli!

Currently exploring the consequences of off-target activity in the CRISPR system. We are in the process of developing a resource (SPACE-R) to assess the safety and specificity of individual spacer sequences.

Developed comprehensive models that (1) predict growth of CRISPR-expressing E. coli cultures under phage predation, visualized using ‘Gro simulation’ (2) compute the cinnamaldehyde production following population tuning with various initial starting number of viruses.

Acheivements