Chasses For All

Farren IsaacsHarris Wang

George Church

September 21, 2008SynBERC Retreat

Church LabDepartment of GeneticsHarvard Medical School

Genetic Engineering

1

2

3

n

Serial, inefficient introduction or mutation of DNA Single-few genetic changes

Cell Genome

Genomic Engineering

Parallel, site-specific, efficient introduction or mutation of DNA Explore combinatorial genomic sequence space

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Goals of Whole Genome Engineering

• Biosynthesis of new proteins– Nonnatural Amino Acids– Tagged proteins, drugs

• Optimal codons• Combinatorial genetic diversity across

whole genomes• Genome stability Safer Bio-isolation

Virus-resistant strains?

Engineered Cells with New Properties & Functionality

Technological GoalDevelop enabling genome engineeringtechnologies for small- (bp) & large-scale(KB-MB) changes to the genome

Biological Goals• Change the genetic code of E. coli• Strain-Pathway Engineering• Immutable & Stable Genomes• Therapeutic-Optimized Safe Strains• Cloning-Optimized Strains• Tagged Protein Systems

Genome Engineering Technologies: Small to Large Scale

High Efficiency -Red Homologous Recombination

High Efficiency Conjugation and Transfer of Large DNA Fragments

Versatile Engineering of Gene Elements

NUCLEOTIDES (1-10s bps)

GENOMES (kbs-Mbs)

GENES(10s-1000s bps)

Important Features• Very Efficient: >25% vs. 10-4-10-7 of standard methods)• Fast: 3 hr turnaround time (vs. 1-2 days traditionally)• Versatile: prokaryotic and eukaryotic

Applications• Synthetic Biology• Metabolic/pathway Engineering• Metagenomic Engineering• Rapid Directed Evolution• Synthetic Ecosystems• Protein/enzyme evolution• Safe Organisms

Recoding E.coli: rE.coli

TTT

F

30362 TCT

S

11495 TAT

Y

21999 TGT

C

7048

TTC 22516 TCC 11720 TAC 16601 TGC 8816

TTA

L

18932 TCA 9783 TAASTOP

STOP

2703 TGA STOP 1256

TTG 18602 TCG 12166 TAG 314 TGG W 20683

CTT

L

15002 CCT

P

9559 CAT

H

17613 CGT

R

28382

CTC 15077 CCC 7485 CAC 13227 CGC 29898

CTA 5314 CCA 11471 CAA

Q

20888 CGA 4859

CTG 71553 CCG 31515 CAG 39188 CGG 7399

ATT

I

41309 ACT

T

12198 AAT

N

24159 AGT

S

11970

ATC 34178 ACC 31796 AAC 29385 AGC 21862

ATA 5967 ACA 9670 AAA

K

45687 AGA

R

2896

ATG M 37915 ACG 19624 AAG 14029 AGG 1692

GTT

V

24858 GCT

A

20762 GAT

D

43719 GGT

G

33622

GTC 20753 GCC 34695 GAC 25918 GGC 40285

GTA 14822 GCA 27418 GAA

E

53641 GGA 10893

GTG 35918 GCG 45741 GAG 24254 GGG 15090

E. coliMG16554.7 Mb

Well understood

Fully sequenced

Genetic, Biochemical & Metabolic Research

Host for commercial utility

Robust

Remove RF1- one codon available for unnatural amino acids- new genetic code: 63 codons

1. TAG stop > TAA stop

- three codons “free”- 61 codons

2. AGR (R) > CGR (R)

tRNAs: AGY (S) > AGY (L)

3. AGY (S) > TCX (S)

tRNAs: UUR (L) > UUR (S)

3. TTR/CTX (L) > AGY (S)

In collaboration withPeter Carr & Joe Jacobson (MIT)

Combining Small- & Large-Scale Genome Engineering (GE)to Convert All UAGs UAAs

wt E. coli Small-scale GE Large-scale GE rE. coli

Small-Scale Genome Engineering:Oligonucelotide (ssDNA)-mediated Red Recombination

Obtain 25% recombination efficiency in E. coli strains lacking mismatch repair genes (mutH, mutL, mutS, uvrD, dam)

Costantino & Court. PNAS (2003)

DNA Replication Fork

Improved Recombination Efficiency (RE):10-6-10-4 0.25 (> 3 log increase!)

– Oligo length: 90mers– Increase oligo half-life: 2 phosphorothioate

bonds at 5’ & 3’ oligo ends– Conc. of oligo: > 25uM– Conc. of cells: 0.5 to 1 billion cells– DNA target: lagging strand– Minimize secondary structure (G)– Oligo pool complexity– Genetic Diversity:

• mismatches, insertions, deletions– CAD-oligo Design

Oligo Optimization RE vs. Oligo Length RE vs. [Oligo]

rE.coli Electrocycling Experimental Pipeline

Small-scale TAG TAA

codon changes

Distribution of TAA Mutations/Clone

Observed Mutations/Clone

pools

05

10152025

0 1 2 3 4 5 6 7 8 9 10

N-mutant

% o

f P

op

ula

tio

n

M ~ 3, Avg muts/clone

n = 10, # loci

c = 18, # cycles

M = n(1-(1-m)c)

Predicted Mutations/Clone

Avg Top Clone = 6.5 mutations65%

Strain Muts Strain Muts Strain Muts Strain Muts

1 8 9 6 17 8 25 8

2 10 10 8 18 9 26 9

3 8 11 7 19 8 27 9

4 7 12 9 20 8 28 9

5 8 13 5 21 7 29 6

6 7 14 7 22 7 30 8

7 9 15 6 23 8 31 9

8 7 16 4/4 24 8 32 9

Avg Top Clone = 7.8 mutations78%

~35% Total RE/cycle

20% - Total RE/cycle (m*n) 2% - Loci RE/cycle (m)

Individual

246/314 Mutations

m

Strain Characterization & Completion of TAGTAA Codon Swaps

wt strain Cycled Strains

Growth Rate (30oC) 42’ 43’ +/- 1.2’

Auxotrophy Rate - 2.6%

Recombination

Efficiency

23% 21.6% +/-2.5%

246/314: 78 % TAG TAA Conversion 314/314: 100 % TAG TAA Conversion

• Confirm Codon changes by direct Sanger Sequencing of loci regions ~1% of genome

00.5

11.5

22.5

33.5

44.5

5

Total GenomeRegion

Oligo Regions

Mu

tati

on

Fre

qu

en

cy

(1

0-4)

Mutation Frequency

0-15Cycles

Large-Scale Genome Engineering:Genome Merging via Conjugation

Large-Scale Genome Engineering:Genome Assembly via Conjugation

Step # strains # transfers Avg Size

1 32 16 143 KB

2 16 8 287 KB

3 8 4 575 KB

4 4 2 1.15 MB

5 2 1 2.3 MBF+/Hfr F-

ssDNA

10-3 – 10-2

10-6

Eff.

Genome Engineering Multiplex Automation (GEMA):Integration, automation, & standardization of tools

GEMA Prototypes

• Recoding Genomes• Strain-Pathway Engineering and Optimization• Immutable & Stable Genomes• Therapeutic-Optimized Safe Strains• Cloning-Optimized Strains• Tagged Protein Systems• … & more

Harnessing Genetic Diversity for Evolution & Engineering

Applications

Acknowledgments

NSF – SynBERC, DOE

George Church (Harvard)

Harris Wang (Harvard) Peter Carr (MIT)

Andy Tolonen (Harvard) Bram Sterling (MIT)

Nick Reppas (Harvard) Joe Jacobson (MIT)

Resmi Charalel (Harvard)

Zachary Sun (Harvard)

Laurens Kraal (Harvard)

George Xu (Harvard)

Duhee Bang (Harvard)

Craig Forest (GA. Tech)

________________________________________Farren Isaacs: farren@genetics.med.harvard.edu

Conjugation: Large-Scale Gene Transfer

• Mechanism for horizontal gene transfer– Lederberg & Tatum, CSHSQB (1946)– e.g., antibiotic resistance, metabolic functions

• DNA transfer is driven by F plasmid from an F+ Donor (D) Cell to an F- Recipient (R) Cell

• Transfer of ssDNA from D R is converted to duplex DNA by synthesis of complementary strand in the recipient cell

• ds donor DNA:– F’ transfer: circularized– Hfr transfer: incorporated into recipient chromosome via RecA-

dependant HR or degraded by RecBCD

• Probability of transferring a specific marker decreases exponentially with its distance from the origin of transfer (oriT)

– Smith, Cell (1991)

• “Direct Visualizatin of Horizontal Gene Transfer” shows much higher recombination frequencies (96.7%) than those measured with genetic markers (10-30%).

• Conjugational recombination is extremely efficient when donors and recipients are essentially gentically identical strains.

– Babic et al., Nature (2008)

F+/Hfr F-

F pilus

ssDNA

F+, Genomic oriT in Donor

APPLICATIONS

APPLICATIONS

Combining Small & Large-Scale Genome Engineering

Microscale (bp) Engineering: Oligo RecombDivide genome into 2n regions-strains:

Macroscale (KB-MB) Engineering: ConjugationPairwise assembly of 2n mutated strains

Genome

n

Small to Large-Scale Genome Engineering

Oligo Pool containing UAG codon mutations

Pool of assembly oligos

I. De novo genomeassembly

II. Oligo-mediatedRecombination:

Small-scale

III. Engineered Conjugation: Large-scale

DNA microchip

Small-Scale Genome Engineering:Oligonucelotide (ssDNA)-mediated Red Recombination

Obtain 25% recombination efficiency in E. coli strains lacking mismatch repair genes (mutH, mutL, mutS, uvrD, dam)

Costantino & Court. PNAS (2003)

DNA Replication Fork

Improved Recombination Efficiency:10-6-10-4 0.25 (> 3 log increase!)

Exo: 5’ 3’ dsDNA exonuclease

Beta: ssDNA binding protein binds to ssDNA > 35bps

Gam: inhibits RecBCD

attL int xis hin exo bet gam kil T N pL cI857

Exo Beta Gam

Oligo-mediated Recombination Experiments

90mer oligos are optimal Two oligos exhibit synergistic effect

High recombination frequencies are maintained from 0.25 to > 25 M of oligo

Recombination Efficiency vs. Oligo Length Recombination Efficiency vs. [Oligo]

Scaling: Multiplex Oligo-mediated Recombination

– Oligo length: 90mers– Increase oligo half-life: 2 phosphorothioate

bonds at 5’ & 3’ oligo ends– Conc. of oligo: up to 25uM

– Conc. of cells: 0.5 to 1 billion cells– DNA target: lagging strand– Minimize secondary structure (G)– Oligo pool complexity

Optimized variables

Oligo Pool containing TAG codon mutations

Cyclical Recombination of Oligonucleotide Pool

Oligo Pool

# cycles

Best Clone (98 %tile)

Fraction of mutated sites

Time*

11 15 7 7/11 ~2 days

54 45 23 23/54 ~5 days

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***

*

E. ColiGenome

Fraction of Cells Containing Oligo-Mediated Mutation Pilot Electrocycling Recombination Experiments

* Continuous cycling, ~3 hrs/cycle

0

5

10

15

20

25

0 1 2 3 4 5 6 7

# mutations/cloneF

req

ue

nc

y

*

*

rE. coliMG16554.7 Mb

DNA Microchip“Oligo Source”

Mutated-Recoded Strain

Large-Scale Genome Engineering:Genome Assembly via Conjugation

Step # strains # transfers Avg Size

1 32 16 143 KB

2 16 8 287 KB

3 8 4 575 KB

4 4 2 1.15 MB

5 2 1 2.3 MBF+/Hfr F-

ssDNA