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Chasses For All

Chasses For All. Farren Isaacs Harris Wang George Church September 21, 2008 SynBERC Retreat Church Lab Department of Genetics Harvard Medical School. Genomic Engineering. 1. …. …. 2. …. …. 3. …. …. n. …. …. Parallel, site-specific, efficient introduction or

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Chasses For All

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  1. Chasses For All Farren Isaacs Harris Wang George Church September 21, 2008 SynBERC Retreat Church Lab Department of Genetics Harvard Medical School

  2. Genomic Engineering 1 … … 2 … … 3 … … n … … • Parallel, site-specific, efficient introduction or mutation of DNA • Explore combinatorial genomic sequence space Genetic Engineering Genome Cell • Serial, inefficient introduction or mutation of DNA • Single-few genetic changes

  3. Goals of Whole Genome Engineering • 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 Technological Goal Develop enabling genome engineering technologies for small- (bp) & large-scale (KB-MB) changes to the genome Engineered Cells with New Properties & Functionality Virus-resistant strains? • Biosynthesis of new proteins • Nonnatural Amino Acids • Tagged proteins, drugs • Optimal codons • Combinatorial genetic diversity across whole genomes • Genome stability  Safer Bio-isolation

  4. GENES (10s-1000s bps) Genome Engineering Technologies: Small to Large Scale High Efficiency -RedHomologous Recombination 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 Versatile Engineering of Gene Elements NUCLEOTIDES (1-10s bps) High Efficiency Conjugation and Transfer ofLarge DNA Fragments Applications • Synthetic Biology • Metabolic/pathway Engineering • Metagenomic Engineering • Rapid Directed Evolution • Synthetic Ecosystems • Protein/enzyme evolution • Safe Organisms GENOMES (kbs-Mbs)

  5. 1. TAG stop > TAA stop Remove RF1 - one codon available for unnatural amino acids - new genetic code: 63 codons 2. AGR (R) > CGR (R) 3. AGY (S) > TCX (S) - three codons “free” - 61 codons tRNAs: AGY (S) > AGY (L) 3. TTR/CTX (L) > AGY (S) tRNAs: UUR (L) > UUR (S) RecodingE.coli: rE.coli In collaboration with Peter Carr & Joe Jacobson (MIT) E. coli MG1655 4.7 Mb Well understood Fully sequenced Genetic, Biochemical & Metabolic Research Host for commercial utility Robust

  6. Combining Small- & Large-Scale Genome Engineering (GE)to Convert All UAGs  UAAs wt E. coli Small-scale GE Large-scale GE rE. coli

  7. 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 (DG) • Oligo pool complexity • Genetic Diversity: • mismatches, insertions, deletions • CAD-oligo Design Oligo Optimization RE vs. Oligo Length RE vs. [Oligo] Small-Scale Genome Engineering:Oligonucelotide (ssDNA)-mediated l Red Recombination Obtain 25% recombination efficiency in E. coli strains lacking mismatch repair genes (mutH, mutL, mutS, uvrD, dam) Improved Recombination Efficiency (RE): 10-6-10-4 0.25 (> 3 log increase!) DNA Replication Fork Costantino & Court. PNAS (2003)

  8. rE.coli Electrocycling Experimental Pipeline Small-scale TAG  TAA codon changes

  9. Distribution of TAA Mutations/Clone Predicted Mutations/Clone Observed Mutations/Clone Individual Spools 20% - Total RE/cycle (m*n) 2% - Loci RE/cycle (m) M = n(1-(1-m)c) M ~ 3, Avg muts/clone n = 10, # loci c = 18, # cycles 246/314 Mutations Avg Top Clone = 7.8 mutations 78% Avg Top Clone = 6.5 mutations 65% Dm ~35% Total RE/cycle

  10. 314/314: 100 % TAG  TAA Conversion • Confirm Codon changes by direct Sanger • Sequencing of loci regions ~1% of genome 0-15 Cycles Mutation Frequency Strain Characterization & Completion of TAGTAA Codon Swaps 246/314: 78 % TAG  TAA Conversion

  11. Large-Scale Genome Engineering:Genome Merging via Conjugation

  12. ssDNA F+/Hfr F- Large-Scale Genome Engineering:Genome Assembly via Conjugation Eff. 10-3 – 10-2 10-6

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

  14. GEMA Prototypes

  15. Harnessing Genetic Diversity for Evolution & Engineering Applications • Recoding Genomes • Strain-Pathway Engineering and Optimization • Immutable & Stable Genomes • Therapeutic-Optimized Safe Strains • Cloning-Optimized Strains • Tagged Protein Systems • … & more

  16. Acknowledgments 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) NSF – SynBERC, DOE ________________________________________ Farren Isaacs: farren@genetics.med.harvard.edu

  17. ssDNA F+/Hfr F- F pilus 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+, Genomic oriT in Donor

  18. APPLICATIONS

  19. APPLICATIONS

  20. Combining Small & Large-Scale Genome Engineering Genome Microscale (bp) Engineering: Oligo Recomb Divide genome into 2n regions-strains: Macroscale (KB-MB) Engineering: Conjugation Pairwise assembly of 2n mutated strains n

  21. Small to Large-Scale Genome Engineering DNA microchip Pool of assembly oligos Oligo Pool containing UAG codon mutations I. De novo genome assembly II. Oligo-mediated Recombination: Small-scale III. Engineered Conjugation: Large-scale

  22. Improved Recombination Efficiency: 10-6-10-4 0.25 (> 3 log increase!) Small-Scale Genome Engineering:Oligonucelotide (ssDNA)-mediated l Red Recombination Exo Beta Gam attL int xis hin exo bet gam kil T N pL cI857 Exo: 5’  3’ dsDNA exonuclease Beta: ssDNA binding protein binds to ssDNA > 35bps Gam: inhibits RecBCD DNA Replication Fork Costantino & Court. PNAS (2003) Obtain 25% recombination efficiency in E. coli strains lacking mismatch repair genes (mutH, mutL, mutS, uvrD, dam)

  23. Oligo-mediated Recombination Experiments Recombination Efficiency vs. Oligo Length Recombination Efficiency vs. [Oligo] • 90mer oligos are optimal • Two oligos exhibit synergistic effect • High recombination frequencies are maintained from 0.25 to > 25 mM of oligo Optimized variables • 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 (DG) • Oligo pool complexity • Scaling: Multiplex Oligo-mediated Recombination

  24. * * * * * * * Cyclical Recombination of Oligonucleotide Pool Oligo Pool containing TAG codon mutations rE. coli MG1655 4.7 Mb E. Coli Genome DNA Microchip “Oligo Source” Mutated-Recoded Strain Fraction of Cells Containing Oligo-Mediated Mutation Pilot Electrocycling Recombination Experiments * * * Continuous cycling, ~3 hrs/cycle

  25. ssDNA F+/Hfr F- Large-Scale Genome Engineering:Genome Assembly via Conjugation

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