-Red recombination system Gam Exo Beta DNA replication fork - - PowerPoint PPT Presentation

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-Red recombination system Gam Exo Beta DNA replication fork - - PowerPoint PPT Presentation

Recombi nation-mediated genetic engi neering -Red recombination system Gam Exo Beta DNA replication fork Costantino & Court. PNAS (2003) Multiplex Automated Genome Engineering Expediting the design & evolution of


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  • Recombination-mediated genetic engineering
  • λ-Red recombination system

– Gam – Exo – Beta

Costantino & Court. PNAS (2003)

DNA replication fork

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Multiplex Automated Genome Engineering

Expediting the design & evolution of organisms with new & improved properties

  • Small-scale genome

engineering

  • Simultaneously

targets many genetic locations at high efficiency (>30%)

  • Combinatorial

genomic diversity across whole cell populations (109)

Nature, 7257:894-8 (2009) Science, 333:348-53 (2011)

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Wang & Isaacs et al, Nature 2009 Isaacs et al., Science. 2011; 333(6040), 348-353.

Pathway engineering New genetic codes

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Challenge 1 How can we predict the outcome?

SOLUTION: Computer modeling of multiplex recombination

Design challenges

?

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  • We assume the oligos bind

– only completely – only intended targets – at an empirically estimated efficiency in E. coli

(Wang & Isaacs et al, Nature 2009; Isaacs et al, Science 2011)

  • Our algorithm solves

– the NP-complete subset sum problem in time O(2n/2)

(Horowitz & Sahni, 1974)

– a degenerate case in polynomial time

(Wadyicki, Shah et al, 1973)

(p3 , r3 = 2r1) Pr(6r1) = Pr(r1 + r2 + r3) = p1p2p3

1 2 3

(p2 , r2 = 3r1) (p1 , r1 = r1)

1 2 3

+

1 2 1 2 3 2 3 1 2 3 1 2 3 1 2 3

+ + +

Pr(5r1) = Pr(r2 + r3) = (1 - p1)p2p3 Pr(4r1) = Pr(r1 + r2) = p1p2(1 – p3)

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How the number of mutations per cell accumulates over cycles How each mutation accumulates

  • ver cycles

How the minimum number of mutations per cell increases over cycles How many off-target binding events might happen, and how spontaneously

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3h

Challenge 2 Electroporation Challenge 3 Complex oligo pools Challenge 4 Oligo breakdown

SOLUTION: MAGE in naturally competent organisms

MAGE efficiency = rate of oligo incorporation • concentration of oligo over time

Technical challenges

Mutation Efficiency per Cycle

Science, 333:348-53 (2011)

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  • Natural competence

– Cells actively take up/incorporate DNA from their environment

  • Genetic diversity
  • Nucleotide recycling
  • HR-Mediated DNA repair
  • Advantages

– Eliminate electroporation – Increase diversity of MAGE- able organisms

Genescope, 2010

  • A. baylyi
  • B. subtilis
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  • 1. Identify suitable host organism
  • 2. Knock out mismatch repair

(MMR)

  • 3. Construct a MAGE assay

– Construct a universal cassette to quantify genetic modifications

  • 4. Construct a library of ssDNA-

binding recombinases

  • 5. Design mutagenic oligos
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– Close cousin of E. coli – High degree of natural competence – Useful natural pathways

  • Strains acquired:

  • A. baylyi ADP1 WT (Genoscope)

– ADP1ΔmutS (Genoscope)

– Prototype for all Gram-positive

  • rganisms

– Industrially relevant pathways

  • Strains acquired:

  • B. subtilis 168 (Bacillus Genetic Stock Center)

– Bs PY79 (BGSC) – Bs 1A833 (BGSC)

Acinetobacter baylyi ADP1 (Gram -) Bacillus subtilis (Gram +)

Genescope, 2010

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  • Why ΔmutS mutants?

– MMR deficient – Increased incorporation

  • f mutagenic oligos
  • E. coli strains lacking

mismatch repair genes (mutH, mutL, mutS) Improved recombination efficiency:

10-6-10-4  0.10-0.15 (> 1000 fold increase!)

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  • 1. Identify suitable host organism
  • 2. Knock out mismatch repair

(MMR)

  • 3. Construct a MAGE assay

– Construct a universal cassette to quantify genetic modifications

  • 4. Construct a library of ssDNA-

binding recombinases

  • 5. Design mutagenic oligos
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  • We needed something to test that will give a

clear change in phenotype, regardless of transformation/recombination efficiency or

  • rganism genotype
  • If designed rationally, such a cassette could

be widely applied for new organisms

  • What parts do we need?
  • How do we make it universal?
  • How do we make it?
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  • Plasmid acquired from Addgene
  • Successful transformation in

diverse bacterial species

  • High copy numbers in A. baylyi,
  • B. subtilis, and E. coli
  • BioBrick-compatible
  • We were able to show efficient

transformation in all three of

  • ur organisms

Universal Plasmid – pBAV1K-T5

(Bryksin and Matsumura, PLoS ONE, 2012) Negative control

  • B. subtilis 168

+ pBAV1K

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1. Promoter

– T5 promoter element (from pBAV1K-T5)

2. Reporter

– LacZ reporter gene (from E. coli genome)

3. Selectable marker (defective)

– Defective Chloramphenicol resistance (nonsense mutation, from E. coli EcFI5)

4. Selectable marker (active)

– Tetracycline resistance (from pBR322)

5. Terminator

– T1 terminator (from pBAV1K-T5)

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  • Relies on homology

between parts

  • 1. Generate homology

through PCR amplification

  • 2. Mix templates, amplify

with end primers Cross-over PCR

  • Second round to

complete promoters

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  • Cassette successfully amplified

(white arrow)

  • To verify function:

– Cloned into pBAV1K using BioBrick restriction sites – Transformed into E. coli and A. baylyi by electroporation, and A. baylyi using natural competence

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  • E. coli MachI

(ΔlacZ) on Kan – X-Gal – IPTG plates

  • Blue colonies on

all experimental transformations

Negative Control Positive Control (pBAV1K)

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MachI untransformed MachI transformed

Kan Kan + Cm Kan + Tet Kan Kan + Cm Kan + Tet Growth:

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  • A. baylyi ADP1 on

Kan – X-Gal – IPTG plates

  • Electroporation

and natural transformation

Negative Control Positive Control (pBAV1K)

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  • 1. Identify suitable host organism
  • 2. Knock out mismatch repair

(MMR)

  • 3. Construct a MAGE assay

– Construct a universal cassette to quantify genetic modifications

  • 4. Construct a library of ssDNA-

binding recombinases

  • 5. Design mutagenic oligos
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  • We don’t know which enzyme

will work best for each species

– Test homologs isolated from diverse species – Use bioinformatic techniques to identify putative recombinases

Integrated Recombinase Origin Gp35 Phage SPP1, B. subtilis Gp61 Phage Che9c, M. smegmantis

  • rf48

Phage A118, L. monocytogenes

  • rf245

Phage u136.2, L. lactis EF2132 Enterococcus faecalis s065 SXT element, V. cholerae Plu2935 Photorhabdus luminescens

  • rfC

Legionella pneumophila Gp20 Phage phiNM3, S. aureus recT Rac prophage, E. coli bet Phage lambda, E. coli EF2132-exo Enterococcus faecalis-exo s065-exo SXT element, V. cholerae-exo

  • rfC-exo

Legionella pneumophila-exo recT-exo Rac prophage, E. coli-exo bet-exo Phage lambda, E. coli-exo

Datta et al., PNAS (2008)

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  • To each homolog isolated, we added the T5 promoter

(pBAV1K) and XbaI and SpeI sites

  • We cloned into pBAV1K and sequenced:
  • bet
  • recT
  • plu2935
  • orfC
  • s065-exo
  • recT-exo
  • Other homologs

in progress

  • Re-cloned into pSB1C3 (BioBrick)

– bet - BBa_K810000

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  • 1. Identify suitable host organism
  • 2. Knock out mismatch repair

(MMR)

  • 3. Construct a MAGE assay

– Construct a universal cassette to quantify genetic modifications

  • 4. Construct a library of ssDNA-

binding recombinases

  • 5. Design mutagenic oligos
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Test Oligo

MAGE in naturally competent organisms

Optimal Conditions

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Spencer Katz David Lim Andriana Lebid Jason No Aaron Lewis Aaron Hakim Special thanks to Scott Strobel and Christopher Incarvito for lab space on Yale’s West Campus. Farren Isaacs, Advisor

Assistant Professor of Molecular, Cellular, and Developmental Biology, Yale University

Graduate advisors

  • Natalie Ma, Isaacs lab
  • Ryan Gallagher, Isaacs lab
  • Edward Barbieri, Isaacs lab
  • Alexis Rovner, Isaacs lab
  • Darryl Reeves, Isaacs lab
  • Jen Nguyen, Modis lab
  • Dan Spakowicz, Strobel lab
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  • Altman and Breaker labs, for bacterial strains
  • CAD/Art Services, for transparencies
  • Geneious, for software licensing
  • Jiming Wang and Wuyi Meng
  • Kappa Biosystems, for PCR reagents
  • Kaury Kucera, for primer design advice
  • Lloyd Chen, for laboratory assistance
  • New England Biolabs, for material support
  • Roeder and Grindley labs, for material support
  • Professor Clay, for material support
  • Professor Schatz & Liz Corbett for a sonicating water bath and thermocycler
  • Raymond and Beverly Sackler Institute
  • William Segraves and the Yale College Dean's Office, for student fellowships
  • Yale Science and Engineering Association
  • Yale University Department of Physics
  • Yale University Department of Molecular, Cellular, and Developmental Biology
  • Yale University Department of Molecular Biophysics & Biochemistry
  • Yale University West Campus
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  • A. baylyi

– Barbe, V., Vallenet, D., Fonknechten, N., Kreimeyer, A., Oztas, S., Labarre, L., Cruveiller, S., Robert, C., Duprat, S., Wincker, P., et al. (2004). Unique features revealed by the genome sequence of Acinetobacter sp. ADP1, a versatile and naturally transformation competent

  • bacterium. Nucleic Acids Res. 32, 5766–5779.

– de Berardinis, V., Vallenet, D., Castelli, V., Besnard, M., Pinet, A., Cruaud, C., Samair, S., Lechaplais, C., Gyapay, G., Richez, C., et al. (2008). A complete collection of single-gene deletion mutants of Acinetobacter baylyi ADP1. Mol Syst Biol 4, 174. – Metzgar, D., Bacher, J.M., Pezo, V., Reader, J., Döring, V., Schimmel, P., Marlière, P., and de Crécy-Lagard, V. (2004). Acinetobacter sp. ADP1: an ideal model organism for genetic analysis and genome engineering. Nucleic Acids Res 32, 5780–5790. – Santala, S., Efimova, E., Kivinen, V., Larjo, A., Aho, T., Karp, M., and Santala, V. (2011). Improved Triacylglycerol Production in Acinetobacter baylyi ADP1 by Metabolic Engineering. Microb Cell Fact 10, 36. – Young, D.M., Parke, D., and Ornston, L.N. (2005). Opportunities for Genetic Investigation Afforded by Acinetobacter Baylyi, a Nutritionally Versatile Bacterial Species That Is Highly Competent for Natural Transformation. Annual Review of Microbiology 59, 519–551.

  • B. subtilis

– Kunst, F., Ogasawara, N., Moszer, I., Albertini, A.M., Alloni, G., Azevedo, V., Bertero, M.G., Bessières, P., Bolotin, A., Borchert, S., et al. (1997). The complete genome sequence of the gram-positive bacterium Bacillus subtilis. Nature 390, 249–256. – Pedraza-Reyes, M., and Yasbin, R.E. (2004). Contribution of the Mismatch DNA Repair System to the Generation of Stationary-Phase- Induced Mutants of Bacillus subtilis. J. Bacteriol. 186, 6485–6491. – Wang, Y., Weng, J., Waseem, R., Yin, X., Zhang, R., and Shen, Q. (2012). Bacillus Subtilis Genome Editing Using ssDNA with Short Homology

  • Regions. Nucl. Acids Res.

– Zeigler, D.R., Prágai, Z., Rodriguez, S., Chevreux, B., Muffler, A., Albert, T., Bai, R., Wyss, M., and Perkins, J.B. (2008). The Origins of 168, W23, and Other Bacillus subtilis Legacy Strains. J. Bacteriol. 190, 6983–6995.

  • Plasmids and recombineering

– Bryksin, A.V., and Matsumura, I. (2010). Rational Design of a Plasmid Origin That Replicates Efficiently in Both Gram-Positive and Gram- Negative Bacteria. PLoS ONE 5, e13244. – Court, D.L., Sawitzke, J.A., and Thomason, L.C. (2002). GENETIC ENGINEERING USING HOMOLOGOUS RECOMBINATION. Annual Review of Genetics 36, 361. – Datta, S., Costantino, N., Zhou, X., and Court, D.L. (2008). Identification and analysis of recombineering functions from Gram-negative and Gram-positive bacteria and their phages. Proc Natl Acad Sci U S A 105, 1626–1631. – Wang, H.H., Isaacs, F.J., Carr, P.A., Sun, Z.Z., Xu, G., Forest, C.R., and Church, G.M. (2009). Programming cells by multiplex genome engineering and accelerated evolution. Nature 460, 894–898.

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  • Example Primer:
  • Key feature – temperature

difference between total Tm and Tm

  • f the annealing portion
  • Two-step PCR to generate individual

parts

  • Verified by sequencing

Cross-over PCR

Name Tm (Tm overhang/Tm anneal) Length (bp) Sequence (overhangANNEAL) p4f 71.04 (62.15/56.45) 50 (30/20) 5'-ggcgtaattaagaaagaggagaaagaatatATGAAATCTAACAATGCGCT-3' Step Temp (oC) Time 1 94 4 m 2 94 30 s 3 53 30 s 4 72 1.5 m 5 GOTO 2 7x 6 94 30 s 7 66 30 s 8 72 1.5 m 9 GOTO 6 30x 10 72 5 m 11 4 ∞

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Organism Strain Relevant Genotype

  • E. coli

EcNR2 bet, ΔmutS::CAT MachI ΔlacZ

  • A. baylyi

ADP1 WT ADP1ΔmutS ΔmutS::KanR

  • B. subtilis

168 PY79 1A833 ΔmutS::SpcR PERM739 ΔmutS::NeoR

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(p3 , r3 = 2r1) Pr(6r1) = Pr(r1 + r2 + r3) = p1p2p3

1 2 3

(p2 , r2 = 3r1) (p1 , r1 = r1)

1 2 3

+

1 2 1 2 3 2 3 1 2 3 1 2 3 1 2 3

+ + +

Pr(5r1) = Pr(r2 + r3) = (1 - p1)p2p3 Pr(4r1) = Pr(r1 + r2) = p1p2(1 – p3)