WHAT I LEARNED AT CSHL SYNBIO

AKA NERD CAMP

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COURSE INFORMATION16 students

• 1 tenured undergrad university• 1 Office of Naval Research• 1 Industry• 4 Postdocs• 9 Graduate students

4 instructors

• Jeff Tabor/ Rice University• Julius Lucks/ Cornell University• Karmella Haynes/ Arizona State University• David Savage/ UC-Berkeley

• Participants

• Richard Murray/ CalTech• Eric Klavins/ UW• Pam Silver/ Harvard• Adam Arkin/ UC-Berkeley• Jeff Boeke/ JHMI• Dan Gibson/ JCVI• Michelle Chang/ UC-Berkeley• Harris Wang/ Columbia• Justin Gallivan/ Emory• Michael Jewett/ Northwestern• Ron Weiss/ MIT• Andy Ellington/ UT• Jeff Hasty/ UC-San Diego

Cold Spring Harbor Laboratory

Schedule• 9-11 Lecture• 1-3 Lab work• 3-4:30 Lecture• 4:30 – 6 Lab work• 7 – 8 Lecture• 8 - 11 Lab work• 11 – 12 Bar work• 12 - ??? Lab work

LABORATORY TECHNIQUES• Golden Gate Cloning

• Gibson Cloning

• MAGE

• TXTL cell free breadboarding

SYNTHETIC BIOLOGY

Definitions:

(1) The modern synthesis of biology and engineering.

(2) The use of biological components to design circuits, devices and systems.

PARTS CIRCUITS DEVICE SYSTEMS

To be able to make circuits need to be able to assemble multiple parts at a time

GOLDEN GATE ASSEMBLY: NO MORE MULTIPLE CLONING SITES

Engler et al. PLOS ONE 3, e3647 (2008)Engler et al. PLOS ONE 4, e5553 (2009)

Standard cohesive-end cloning cuts and ligates at the recognition site.Requires the use of a MCS in the vector.

Limitation: only 1 part at a time

• Type II restriction enzymes cut N bases away from recognition site.

• BsaI recognizes GGTCCTC• Skips a base• Leaves 4 base overhang.

• Digestion and ligation occur in the same step.

• As digestion occurs the GOI is irreversibly ligated into the destination plasmid

• Multiple GOI can be ligated into a single vector because of specific overhangs.

• No need for MCS

• Very cheap

GOLDEN GATE ASSEMBLY ALLOWS MULTIPLE PARTS TO BE ASSEMBLED AT ONCE

PCR WITH GG ALLOWS THE ASSEMBLY OF ANY GOI INTO ANY PLASMID

Limitation: Designing multiple inserts can be time consuming

GIBSON ASSEMBLY ALLOWS ASSEMBLY OF MULTIPLE PARTS AT THE SAME TIME

• No restriction enzymes needed.

• DNA fragments are created with >25 bp overlap to adjacent sequence.

• All fragments are mixed into a single reaction containing exonuclease to create sticky ends

Similar ways: SLIC, CPEC, SLiCE, and GeneArt

GIBSON ASSEMBLY VERY EASY TO USE• Up to 100 mb assembly was made.• Along with Yeast TAR, this was used to create the minimal Mycoplasma

mycoides into Mycoplasma capricolum.• < $10 per reaction

MAGE: CAPABLE OF MODIFYING MORE THAN ONE GENE AT A TIME

• Multiplex genome engineering and accelerated evolution

• Existing genomic templates are used as scaffolds to produce new engineered variants.

• Uses synthetic Okazaki fragments to mutate the genome.

• Allows for in situ directed evolution

Wang et al. Nature 460 (2009)

Wang, Church. Meth Enzymol, 498 (2011)

MAGE• Deletion of mutS increases efficiency 100X

• Knock out mutS, MAGE, and then enable mutS.• No selection marker required

• Steps

• OD ~ 0.6• Heat shock/chill 4C• Electroporation of DNA• Recover cells• Repeat cycles

• Limitations:

• Only working in E. coli.• Time consuming

EXAMPLES OF MAGE USES

Expand genetic code

Replace all TGA or TAG stop codons with TAA

Will free up codon for another amino acid (xeno DNA)

Multiple gene knockouts

Hypermutations

Optimize RBS

Phenotypic plasticity / Robustness

Directed Evolution of biosynthetic pathways

CELL FREE SOLUTIONS ALLOW FOR THE PROTOTYPING OF SYNBIO CIRCUITS

PROTOTYPING BIOLOGICAL CIRCUITS USING TXTL AND RNA ATTENUATORS

Instructor: Julius Lucks, PhD: Cornell University

TA: Mellissa Takahashi: Cornell University

Chris Fall, PhD: Office of Naval Research

Shaima Al-Khabouri: Montreal, Canada

Vipul Singhal: CalTech

SYNBIO CENTRAL GOAL: ENGINEER GENE CIRCUITS

Arbitrary gene network

Decompose

Synthesize

RNA IS VERSATILE AND REGULATES GENE NETWORKS AT MANY LEVELS

RNA FunctionsTranscription Regulation

mRNA Stability

Translation Regulation

Splicing Regulation

Chromosome Regulation

Gene

5’ UTR 3’ UTR

Gene

5’ UTR 3’ UTR

TranscriptionTranslation

Stability StabilityRegulation

RNA’S VERSATILITY IS A TOOL TO ENGINEER EXPRESSION

Gene

5’ UTR 3’ UTR

RNA

Molecular Interactions

Small Molecule

Protein

TranscriptionTranslation

Stability StabilityRegulation

Control

RNA’S VERSATILITY IS A TOOL TO ENGINEER EXPRESSION

Larson et. al., Cell 132, 2008

GCCGAGA

AGGUUAA

C G A U UG

Folding

Free Bases Can Pair to Other RNAs

G C C G A G A AGGUUAA4 Bases

UUUUUUUU

Intrinsic Terminator Hairpin

DNA

RNA

RNA Polymerase

RNA Transcription

RNA FOLDS CAN REGULATE TRANSCRIPTION

RNA-SENSING TRANSCRIPTION SIMPLIFIES NETWORKS

21

(-) Antisense

(+) Antisense

Transcriptional regulator: pT181 – RNAI/RNAII

In vivo – E. coli

ON OFF

RNA STRUCTURES CAN CONTROL TRANSCRIPTION IN VIVO

TWO MAIN CHALLENGES FOR SYNTHETIC DEVICES• Living systems are

nonlinear systems

• Unpredictable behaviors

• Evolution

26

Richard Murray

• Can we use cell free systems to ‘model’ RNA genetic circuits?• Co-develop experimental and computational methods• Goal: create a paradigm shift in the way we prototype circuits

IT IS POSSIBLE TO PROTOTYPE RNA CIRCUITS USING CELL FREE TRANSCRIPTION/TRANSLATION SYSTEM

http://www.openwetware.org/wiki/breadboards

27

PHASE I – TESTING COMPONENTS

• Basic Expression of GFP or RFP module

• DNA/RNA load on the TXTL resources

• Antisense Repression Titration• Cross Talk• Plasmid and Linear DNA

28

We can express Att-1 (Attenuator) GFP in TX-TL system in Plasmid and Linear forms

ABLE TO EXPRESS RNA NETWORK IN TX-TL SYSTEM

DATA

0 20 40 60 80 100 1200

10000

20000

30000

40000

50000

60000

Plasmid

0.25 nM

0.5 nM

1 nM

2 nM

Time (min)

RF

U

0 20 40 60 80 100 1200

10000

20000

30000

40000

50000

60000

70000

Linear

0.125 nM

0.25 nM

0.5 nM

1 nM

Time (min)

RF

U

1.000

Anti = antisense

Att = attenuator: reduces the power of a signal

29

DATA

0 1000 2000 3000 4000 5000 6000 7000 80000

50

100

150

200

250

GFP expression with and without an-tisense sequence

Att1-GFP + scrambled DNA

Att1-GFP + antisense1

Att2-GFP scrambled

Att2-GFP + antisense2

Time (sec)

RF

U

Att1-GFP + scrambled

DNA

Att1-GFP + antisense1

Att2-GFP scrambled

Att2-GFP + antisense2

0

50

100

150

200

250

Mean GFP expression with and without antisense sequence -

2 hour time-point

RF

U

1.014

ANTISENSE REPRESSION WORKS IN TXTL.

30

Att1-GFP + scrambled

Att2 + anti1 Att1 + anti2 Att2-GFP + scrambled

0

2000

4000

6000

8000

10000

12000

14000 GFP Expression - Cross Talk

RF

USLIGHT CROSSTALK BETWEEN ANTISENSE MOLECULES AND OTHER ATTENUATOR

DATA

1.012

31

qPCR verification of RNA levelsDATA

2.001

0 5 10 15 20 25 30 35 40 45 50 55 60 650

2

4

6

8

10

12

14

16

18

20

0

200

400

600

800

1000

1200GFP

anti1

anti2

GFP (Fl)

time (min)

mR

NA

(n

M)

RF

U

Inhibit Rnase in experimentTease out transcription and degradation individually

modelingexperiment

32

PHASE II – TESTING A NOVEL 3 LAYER CASCADE

Anti 2

Att2 Anti1 Anti1

Att1 GFP

Ribozyme

Level 3

Level 2

Level 1

Antisense biases towards OFF

repressing the repressor (Level 2) should INCREASE GFP production

33

att1

-GFP (c

trl)

att1

-GFP (s

cram

bled)

att1

-GFP +

leve

l 2 (6

nM) +

ant

i2 (1

8nM

)

att1

-GFP +

leve

l 2 (6

nM) +

ant

i2 (1

4nM

)

att1

-GFP +

leve

l 2 (6

nM) +

ant

i2 (1

0nM

)

att1

-GFP +

leve

l 2 (4

nM) +

ant

i2 (1

4nM

)

att1

-GFP +

leve

l 2 (4

nM) +

ant

i2 (1

4nM

)

att1

-GFP +

leve

l 2 (4

nM) +

ant

i2 (1

4nM

)

att1

-GFP +

leve

l 2 (6

nM)

att1

-GFP +

leve

l 2 (4

nM)

0

20

40

60

80

100

120

140

160

180

200

RF

U

3 LEVEL CASCADE- SUCCESSDATA

1.013

2 Hour Time Point

Increasing level 3 blocks repression by level 2and INCREASES GFP Expression

3

2

1

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Phase III – Single Input ModuleConcentration Dependent Expression

Anti-1

Att-1 Anti-2

Att-2

Att-2 Att-2

RFP

GFP

Double Att-2 sequence shouldrequire less Anti-2 for repression.

As Anti-1 increases, we predict thatRFP increase should precede GFP Increase.

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Computational PredictionModeling

RFP levels higher than GFP levelsRate of RFP increase also higher

DNA DNA:RNAP:RNA att

DNA:RNAP:RNA att-att

RNA att-att-GFP + DNA + RNAP

NTP

RNA PolyNTP

NTP

DNA:RNAP:RNA att:RNA anti

DNA:RNAP:RNA att-att:RNA anti

DNA:RNAP:RNA att-att:RNA anti:RNA anti

DNA + RNAP + RNA att:RNA anti

DNA + RNAP + RNA att-att:RNA anti

DNA + RNAP + RNA att-att:RNA anti

RNA:RNase null

Translation

null

Model partial innards

37

Computational Prediction

38

The whole shebangDATA

VARY: 18,14 or 10nM

HOLD constant

both present

419-004-18 419-004-14 419-004/100

400

800

1200

1600

2000

RFP - whole cascade

RF

U

419-004-18 419-004-14 419-004/100

2000

4000

6000

8000

10000

12000

GFP - whole cascade

RF

U

OTHER THINGS I LEARNED• Project management

• Different opinions on how to be a principle investigator

• Be a good story teller.• How to choose a problem to solve.

• Aware of the things not discussed

• Very little talk about synthetic membranes/compartments.• The evolution problem.

TRELLO: ONLINE PROJECT MANAGEMENT SUITE

ALLOWS CHECKLISTS, SHARING OF FILES, ASSIGN PEOPLE TO TASKS, DEADLINES

http://www.trello.com