Thanks to: Broad Inst., DARPA-BioComp, DOE-GTL, EU-MolTools,

NGHRI-CEGS, NHLBI-PGA, NIGMS-CECBSR, PhRMA, Lipper Foundation

Agencourt, Ambergen, Atactic, BeyondGenomics, Caliper, Genomatica, Genovoxx, Helicos, MJR, NEN, Nimblegen, ThermoFinnigan, Xeotron/Invitrogen

For more info see: arep.med.harvard.edu

BU BME retreat 23-Jun-2004 9:45-10:30 Seacrest, N. Falmouth, MA

Optimal Combinatorial Biology & Genome Engineering

Exponential technologies

1E-6

1E-4

1E-2

1E+0

1E+2

1E+4

1E+6

1935 1945 1955 1965 1975 1985 1995 2005

IPS/$

bp/$

#web sites

Polony bp/min

Shendure J, Mitra R, Varma C, Church GM (May 2004) Advanced Sequencing Technologies: Methods & Goals. Nature Reviews of Genetics 5, 335 -344.

ABI

010101 0101001010001101010101001011001011001010001101010010010 111010

0101010101001010001101010101001011001011001010001101010010010111010

010101010010101101010

101001000101100100011010100100111010

01010100101101010101000010110010000101001001010

Programming cells with DNA

vs.

Digital computers simulating cellsCells simulating digital computers

Drugs & devices simulating human systems

01010100101101010101000010110010000101001001010

01010100101101010101000010110010000101001001010

01010100101101010101000010110010000101001001010

01010100101101010101000010110010000101001001010

Engineering complex systems (comparative genomics)

Stedman et al. (2004) [Masticatory] Myosin gene mutation correlates with anatomical changes in the human

lineage Nature 428, 415 - 418

DNA RNA Proteins

Metabolites

Replication rate

Environment

Biosystems Engineering Integrating Measures & Models

Microbes Cancer & stem cells Darwinian optimaIn vitro replicationSmall multicellular organisms

RNAiInsertionsSNPs

interactions

Now that we have 200 genomes, why sequence?

Once per organism• Phylogenetic footprinting, biodiversity• RNA splicing & chromatin modification patterns.• Cell-lineage during development• NA "aptamers" & Ab for any protein

Once per person• Preventative medicine & genotype–phenotype associations

Frequently• Cancer: mutation sets for individual clones, loss-of-heterozygosity• B & T-cell receptor diversity: Temporal profiling, clinical • New & old pathogen "weather map", biowarfare sensors• DNA computing & lab selections

Shendure et al. 2004 Nature Rev Gen 5, 335.

Why 'single molecule' sequencing?

(1) Single-cell analyses , e.g. Preimplantation (PGD)

(2) Co-occurrence on a molecule, complex, cell e.g. RNA splice-forms(3) Cost: $1K-100K "personal genomes"http://grants.nih.gov/grants/guide/rfa-files/RFA-HG-04-003.html

(4) Precision: Counting 109 RNA tags (to reduce variance)

(~5e5 RNAs per human cell)Fixed 5e3 5e4 5e6 5e9 (goal) Costs EST SAGE MPSS Polony-FISSeq (polymerase colony)

Polony Fluorescent In Situ Sequencing Libraries

Greg PorrecaAbraham Rosenbaum

1 to 100kb Genomic1 to 100kb Genomic

L R

M

L R

PCRbead

Sequencingprimers

Selectorbead

2x20bp after MmeI2x20bp after MmeI

Dressman et al PNAS 2003 emulsion

Cleavable dNTP-Fluorophore (& terminators)

Mitra,RD, Shendure,J, Olejnik,J, Olejnik,EK, and Church,GM (2003) Fluorescent in situ Sequencing on Polymerase Colonies. Analyt. Biochem. 320:55-65

Reduce

or

photo-cleave

Polony-FISSeq: up to 2 billion beads/slideWhite= Fe-core pixels, Cy5 primer (570nm) ; Cy3 dNTP (666nm)

Jay Shendure

• # of bases sequenced (total) 23,703,953

• # bases sequenced (unique) 73

• Avg fold coverage 324,711 X

• Pixels used per bead (analysis) ~3.6

• Read Length per primer 14-15 bp

• Insertions 0.5%

• Deletions 0.7%

• Substitutions (raw) 4e-5

• Throughput: 360,000 bp/min

Polony FISSeq Stats

Current capillary sequencing 1400 bp/min (600X speed/cost ratio, ~$5K/1X)

(This may omit: PCR , homopolymer, context errors)Shendure

CD44 Exon Combinatorics (Zhu & Shendure)

• Alternatively Spliced Cell Adhesion Molecule• Specific variable exons are up-or-down-regulated in

various cancers (>2000 papers)• v6 & v7 enable direct binding to chondroitin sulfate,

heparin…

Zhu,J, et al. Science. 301:836-8.

V1

V2

V3

V4

V5

V6

V7

V8

V9

V1

0

RNA exon examples

auto-regridded

& quan-titated

Zhu,J, Shendure,J, Mitra, RD, Church, GM (2003) Science. 301:836-8.Single Molecule Profiling of Alternative Pre-mRNA Splicing.

Zhu J, Shendure J, Mitra RD, Church GM. Science 301:836-8. Single molecule profiling of alternative pre-mRNA splicing.

EXON PATTERN Eph4 Eph4bDD TOTALEph4 FRATIO LSTP-PV------------7-8-9-10 609 764 1373 1.17 1E-4--------------8-9-10 320 390 710 1.13 3E-2----------6-7-8-9-10 431 251 682 -1.85 4E-18------4-5-6-7-8-9-10 218 216 434 -1.08 2E-1----------------9-10 68 143 211 1.96 7E-7--------5-6-7-8-9-10 86 39 125 -2.37 2E-6----3-4-5-6-7-8-9-10 40 56 96 1.30 9E-2------4-5---7-8-9-10 16 74 90 4.30 2E-9--2-3-4-5-6-7-8-9-10 44 28 72 -1.69 1E-21-2-3-4-5-6-7-8-9-10 22 5 27 -4.73 3E-4--------5---7-8-9-10 5 19 24 3.53 3E-3----3-4-5---7-8-9-10 1 15 16 13.95 4E-4--2-3-4-5---7-8-9-10 1 10 11 9.30 5E-3

Eph4 = murine mammary epithelial cell line

Eph4bDD = stable transfection of Eph4 with MEK-1 (tumorigenic)

CD44 RNA isoforms

DNA RNA Proteins

Metabolites

Replication rate

Environment

Biosystems Engineering Integrating Measures & Models

Escherichia Darwinian optima Prochlorococcus mutant suboptimality

Homo

RNAiInsertionsSNPs

interactions

Integer Stochiometric matrix

(Roche/ExPASy)

Metabolic Pathways Cellular Processes

Xi

MembraneVtransport

Vsyn Vdeg

Vgrowth

Growth: c1Xi+ c2X2+... +cmXm Biomass

Flux ratios at each branch point yields optimal polymer composition for replication

Xi=const.

vj=0

0 5 10 15 20 25 30 35 40 4510

-6

10-4

10-2

100

102

AcCoA

CoA

ATP

FADNADH

Xi = metabolites

Ci =

coe

ff. i

n gr

owth

rea

ctio

nBiomass composition

Edwards & Palsson, PNAS 2000, BMC Bioinf. 2000

Optimize flow from input C,N,P to Biomass

GTP

Trp

LeuAlaArg

Gly

Cys

Ser

Asn Asp His

CTPUTP

SucCoA

Val

Glu Gln

PhePro

Ile

Lys

Met

Tyr

Thr

dACGT

Minimization of Metabolic Adjustment (MoMA)

Linear Programming (LP) to find optima, Quadratic (QP) to find closest points

x,y are two of the 100s of flux dimensions

Wild-typeoptimum

Mutantoptimum

Mutantinitially

(closest point)

Mutant Wild type(feasible flux polyhedra)

Objective function = growth flux hyperplanes

Segre, Vitkup, & Church PNAS 99: 15112-7

12C 13C

MS/NMR FluxRatio Data

0 50 100 150 2000

20

40

60

80

100

120

140

160

180

200

1

2

3

456

78

9

10

11121314

15

16

17 18

-50 0 50 100 150 200 250-50

0

50

100

150

200

250

1

2

3456

78

910

11121314

1516

17

18

Experimental Fluxes

Pre

dic

ted

Flu

xes

-50 0 50 100 150 200 250-50

0

50

100

150

200

250

1

2

3

456

78

910

111213

14

15

16

1718

pyk (LP)

WT (LP)

Experimental Fluxes

Pre

dic

ted

Flu

xes

Experimental Fluxes

Pre

dic

ted

Flu

xes

pyk (QP)

=0.91p=8e-8

=-0.06p=6e-1

=0.56p=7e-3

Flux Data C009-limited

Reproducibility of mass competition

Correlation between two selection experiments

Badarinarayana, et al. Nature Biotech.19: 1060

Essential 142 80 62Reduced growth 46 24 22

Non essential 299 119 180 p = 4∙10-3

Essential 162 96 66Reduced growth 44 19 25

Non essential 281 108 173 p = 10-5

MOMA

FBA

Competitive growth data

2 p-values

4x10-3

1x10-5

Position effects Novel redundancies

On minimal media

negative small selection effect

Hypothesis: next optima are achieved by regulation of activities.

LP

QP

Motif Co-occurrence, comparative genomics, RNA clusters, and/or ChIP2-location data

P= 10-6 to 10-11

Genome Res. 14:201–208Bulyk, McGuire,Masuda,Church

Synthetic testing of DNA motif combinations

1.3 2.4 (1.3 in argR)

1.1 1.3

0.7 2.5

0.2 1.4

1.4 3.5

RNA Ratio (motif- to wild type) for each flanking gene

Bulyk, McGuire,Masuda,Church Genome Res. 14:201–208

Systems Biology Loop

Synthesis /Perturbation

Model

Experimental design

(Systematic)

Data

Proteasome targetingGenome Engineering

Engineering BioSystems Perturbations

Action Specificity %KO "Design"

Small molecules (drugs) Fast Varies Varies Hard

Antibodies Fast Varies Varies Hard

RNAi Slow Varies Medium OK

Insertion "traps" Slow Yes Varies Random

Proteasome targeting Fast Excellent Medium Easy

Homologous recombination Slow Perfect Complete Easy

Programming proteasome

targeting

Janse, DM, Crosas,B Finley,D & Church, GM (2004) Localization to the Proteasome is Sufficient

for Degradation.

Synthetic Genomes & Proteomes. Why?

• Test or engineer cis-DNA/RNA-elements •Access to any protein (complex) including post-transcriptional modifications• Affinity agents for the above.• Mass spectrometry standards, protein design• Utility of molecular biology DNA-RNA-Protein

in vitro "kits" (e.g. PCR, SP6, Roche)

Toward these goals design a chassis:• 115 kbp genome. 150 genes.• Nearly all 3D structures known.• Comprehensive functional data.

PURE translation utility (yet room for improvement)

Removing tRNA-synthetases, RNases & proteases makes feasible:

Optimal mRNA structure & codon usage

Lee et al. 2004 J Immunol Methods. 284:147-57. Selection of scFvs specific for HBV DNA polymerase using ribosome display.

Forster et al. 2003Programming peptidomimetic syntheses by translating genetic codes designed de novo. PNAS 100:6353-7.

Klammt et al. 2004 Eur J Biochem. 271:568-80. High level cell-free expression & specific labeling of integral membrane proteins.

Shimizu et al. 2001 Nat Biotechnol. 19:751-5. Cell-free translation reconstituted with purified components.

in vitro genetic codes

5'

mS yU eU

UGGUUG CAG

AAC... GUU A 3'GAAACCAUG

fM TN V E

| | | | | || | |

5' Second base 3'

U

A

C

C U

mSyU

eU

A C U

G

A

0

500

1000

1500

2000

2500

3000

3500

30 40 50 60 70 80

3H-E dpm

time (min.)

fM yU mS eU E |

Forster, et al. (2003) PNAS 100:6353-7

80% average yieldper unnatural coupling.

bK = biotinyllysine , mS = Omethylserine eU=2-amino-4-pentenoic acid yU = 2-amino-4-pentynoic acid

Mirror world : resistant to enzymes, parasites, predators

L-amino acids & D-ribose (rNTPs, dNTPs)

Transition: EF-Tu, peptidyl transferase, DNA-ligase

D-amino acids & L-ribose (rNTPs, dNTPs)

Dedkova, et al. (2003) Enhanced D-amino acid incorporation into protein by modified ribosomes. J Am Chem Soc 125, 6616-7

Escherichia coli Mycoplasma 3D structureColiphage 29 DNA polymerase + +Coliphage P1 Cre recombinase - + >Coliphage Lox/Cre recombinase site - +Coliphage T7 RNA polymerase + + >Coliphage T7 RNA polymerase initiation site + + >Coliphage T7 RNA polymerase termination site + +RNase P RNA + -RNase P protein + + >RNase P site/RNA primer for DNA polymerase + +Small subunit 16S ribosomal RNA + +All 21 small subunit ribosomal proteins (1-21) + except 1,21 +Large subunit 5S ribosomal RNA + +Large subunit 23S ribosomal RNA + +Large subunit 23S rRNA G2445>m2G methylase: unknown ? -Large subunit 23S rRNA U2449>dihydroU synthetase: unknown ? -Large subunit 23S rRNA U2457>pseudoU synthetase ? -Large subunit 23S rRNA C2498>Cm methylase: unknown ? -Large subunit 23S rRNA A2503>m2A methylase: unknown ? -Large subunit 23S rRNA U2504>pseudoU synthetase ? -All 33 large subunit ribosomal proteins (1-7,9-11,13-25,27-36) + except 25, 30 +Translational initiation factor 1 + +Translational initiation factor 2 + +Translational initiation factor 3 + +Translational elongation factor Tu + +Translational elongation factor Ts + +Translational elongation factor G + +Translational release factor 1 + +Translational release factor 2 - +Translational release factor Gln methylase + +Translational release factor 3 - +Ribosome recycling factor + +33/45 Transfer RNAs (see Fig. 2) 29/33 +tRNA(I) C34>lysidine synthetase ? +tRNA(R) A34>I deaminase ? +tRNA(ASV) U34>cmo5U (=V) synthetase: unknown - -tRNA(R) U34>2sU Cys desulfurase - +tRNA(R) nm5U34 methylase ? +tRNA(R) U34>cmnm5U GTPase ? +tRNA(R) U34>cmnm5U synthetase ? +tRNA(R) cmnm5U34>nm5U,mnm5U synthetase ? -tRNA(R) G37 N1-methylase + +tRNA(RNIKM) A37>t6A N6-threonylcarbamoyl-A synthetase: unknown + -tRNA(CLFSWY) A37>i6A synthetase - +tRNA(CLFSWY) i6A37>s2i6A(ms2i6A) synthetase - +All 22 aminoacyl-tRNA synthetase subunits (20 enzymes) + except G subunit, Q + except G subunitMet-tRNA formyltransferase + +Chaperonin DnaK + +Chaperonin GroEL + +Chaperonin GroES + +

Total genes = 150Forster & Church

Oligos for 150 & 776

synthetic genes(for E.coli minigenome & M.mobile whole genome

respectively)

Up to 760K Oligos/Chip18 Mbp for $700 raw (6-18K genes)

<1K Oxamer Electrolytic acid/base 8K Atactic/Xeotron/Invitrogen Photo-Generated Acid Sheng , Zhou, Gulari, Gao (U.Houston) 24K Agilent Ink-jet standard reagents 48K Febit 100K Metrigen 380K Nimblegen Photolabile 5'protection Nuwaysir, Smith, Albert

Tian, Gong, Church

Improve DNA Synthesis CostSynthesis on chips in pools is 5000X less expensive per

oligonucleotide, but amounts are low (1e6 molecules rather than usual 1e12) & bimolecular kinetics slow with square of concentration decrease!)

Solution: Amplify the oligos then release them.

10 50 10 => ss-70-mer (chip)

20-mer PCR primers with restriction sites at the 50mer junctions

Tian, Gong, Sheng , Zhou, Gulari, Gao, Church

=> ds-90-mer

=> ds-50-mer

Improve DNA Synthesis Accuracyvia mismatch selection

Tian & Church

Genome assembly

Challenges: 1. Tandem, inverted and dispersed repeats (hierarchical assembly, size-selection and/or scaffolding)2. Reduce mutations (goal <1e-6 errors) to reduce # of intermediates 3. >30 kbp homologous recombination (Nick Reppas)

Stemmer et al. 1995. Gene 164:49-53. Single-step assembly of a gene and entire plasmid from large numbers of oligodeoxyribonucleotides.

50

75

125 225 425 825 … 100*2^(n-1)

M 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

DNA Templates

RNA Transcripts

All 30S-Ribosomal-protein DNAs & mRNAs synthesized in vitro

s190.5kb0.3kb

NimblegenXeotron/Atactic

Wild-type

DNA Templates

Tian, Gong, Sheng , Zhou, Gulari, Gao, Church

Improving synthesis accuracy 9-fold

MethodTotal

bp#

ClonesTrans-ition

Trans-version Deletion Addition Bp/error

Hyb selection, PCR 23641 9 7 3 5 2 1391Gel selection, PCR 24546 35 28 12 11 3 455

No selection, ligation+PCR 6093 25 6 6 22 4 160

No selection, PCR 9243 21 25 13 19 1 159

Tian & Church

Extreme mRNA makeover for protein expression in vitro

RS-2,4,5,6,9,10,12,13,15,16,17,and 21 detectable initially.

RS-1, 3, 7, 8, 11, 14, 18, 19, 20 initially weak or undetectable.

Solution: Iteratively resynthesize all mRNAs with less mRNA structure.

Tian & Church

20w 20m 17w 17m 16w 16m

10kd

W: wild-typeM: modified

Western blot based on His-tags

Enabling technologies

• Multi-Gene Assembly• Protein, peptidomimetic synthesis• CAD/CAM & Design for manufacturing

• Automated homologous recombination for E.coli & embryonic stem cells• Fidelity enhancements• Sequencing 107 bp/$ ($1K/human)

Thanks to: DOE-GTL, DARPA-BioComp, NIGMS-CECBSR,

NGHRI-CEGS, PhRMA, EU-MolTools, NHLBI-PGA,

Broad Inst., Lipper Foundation

Agencourt, Ambergen, Atactic, BeyondGenomics, Caliper, Genomatica, Genovoxx, MJR, NEN, Nimblegen, ThermoFinnigan, Xeotron/Invitrogen

For more info see: arep.med.harvard.edu

BU BME retreat 23-Jun-2004 9:45-10:30 Seacrest, N. Falmouth, MA

Optimal Combinatorial Biology & Genome Engineering

.

Improve DNA Synthesis accuracySynthesis on a chip pools of "construction" ~50-mers and two

complementary "selection" ~26-mers (Left & Right)

10 50 10 => ss-70-mer (chip)

Tian, Gong, Sheng , Zhou, Gulari, Gao, Church

=> ds/ss-50-mer (amplif/restrict)

10 26 10 => ss-56-mer (chip)

20-mer PCR primers (one biotinylated)

Biotin=> ss-76-mer (amplif/avidin)

Improve DNA Synthesis Accuracyvia D-HPLC or MutS

Smith & Modrich (1997) PNAS 94: 6847–50. Removal of polymerase-produced mutant sequences from PCR products. MutHLS Cleaves at GATC near mismatches. Lowers error rate from 6e-6 to 6e-7.

Bellanne-Chantelot et al. (1997) Mutat Res. 382:35-43. Search for DNA sequence variations using a MutS-based technology.

Mulligan & Tabone (2002) US Patent 6,664,112. Methods for improving the sequence fidelity of synthetic doublestranded-oligonucleotides.