George Church, MIT/Harvard DOE GtL CenterDuPont 13-Sep-2006

Synthetic Biology & Microbial Biofuels

Our DOE Biofuels Center goals & strengths

1. Basic enabling technologies: omics, models,

genome synthesis, evolution, sequencing

2. Harnessing new insights from ecosystems.

3. Improving photosynthetic and conversion efficiencies.

4. Fermentative production of alcohols & biodiesel.

Synthetic Biology Engineering Research Center (SynBERC) $16M NSF, IGEM

UC-Berkeley, Harvard, MIT, UCSFKeasling, Lim, Endy, Church, Prather, Voigt, Knight

Parts, Devices, Chassis, Thrust in biochemical engineering

Stress & parasite resistance

Engineering a mevalonate pathway in Escherichia coli for production of terpenoids. Martin VJ, et al. Nat. Biotech 2003

Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Ro DK, et al. Nature. 2006 8

Programmable ligand-controlled riboregulators to monitor metabolites.

Bayer & Smolke; Isaacs & Collins 2005 Nature Biotech.

ON

ON

OFF

Genome & Metabolome Computer Aided Design (CAD)

4.7 Mbp new genetic codes new amino acids 7*7 * 4.7 Mbp mini-ecosystems biosensors, bioenergy, high secretors, DNA & metabolic isolation

•Top Design Utility, safety & scalability

CAD-PAM Synthesis (chip & error correction)

Combinatorics Evolution Sequence

How? 10 Mbp of oligos / $1000 chip

8K Atactic/Xeotron/Invitrogen

Photo-Generated Acid

Sheng , Zhou, Gulari, Gao (Houston)

12K Combimatrix Electrolytic

44K Agilent Ink-jet standard reagents

380K Nimblegen Photolabile 5'protection

Tian et al. Nature. 432:1050; Carr & Jacobson 2004 NAR; Smith & Modrich 1997

PNAS

~1000X lower oligo costs

(= 2 E.coli genomes or 20 Mycoplasmas /chip)

Amplify pools of 50mers using flanking universal PCR primers and three paths to 10X error correction

Digital Micromirror Array

rE.coli: new in vivo genetic codes

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 326 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

Freeing 4 tRNAs, 7 codons: UAG, UUR, AGY, AGRe.g. PEG-pAcPhe-hGH (Ambrx, Schultz) high serum stability

IsaacsChurch

Forster

CarrJacobson

JahnzSchultz

1

2

3

4

Our DOE Biofuels Center goals & strengths

1. Basic enabling technologies: omics, models,

genome synthesis, evolution, sequencing

2. Harnessing new insights from ecosystems.

3. Improving photosynthetic and conversion efficiencies.

4. Fermentative production of alcohols & biodiesel.

Prochlorococcus 40ºN - 40ºS Chisholm et al.

Ocean chl a (Aug 1997 –Sept 2000)Provided by the SeaWiFS Project, NASA

-Glc-1P ADP-Glc -1,4-glucosyl-glucan glycogenCentralCarbonMetabol.

glgC

glgX

glgA glgB

glgP

Glycogen metabolism

Time (hours)

0 4 8 12 16 20 24 28 32 36 40 44 48

Nor

mal

ized

Exp

ress

ion

0.1

1

10

glgAglgBglgCglgXglgP

Zinser et al. unpublZinser et al. unpubl..

Light regulated Prochlorococcus metabolism

Photosynthetic Genes in Phage

Podovirus P-SSP7 46 kb

PC HLIPs Fd D1

12kb 24kb

PC HLIPs Fd D1

12kb 24kb

~500 bp

HLIPs D1 D2

6.4kb 2.8kb

~500 bp

Myovirus P-SSM4 181 kbHLIPs D1 D2

6.4kb 2.8kb

Lindell, Sullivan, Chisholm et al. 2004Lindell, Sullivan, Chisholm et al. 2004

HLIP D1

Myovirus P-SSM2 255 kb

RNA Responses to Phage

MED4-0682 (60 aa Conserved URF)

Phage SSP7 psbA

MED4 host psbA

Lindell,Lindell, Sullivan, Zinser, ChisholmSullivan, Zinser, Chisholm

Our DOE Biofuels Center goals & strengths

1. Basic enabling technologies: omics, models,

genome synthesis, evolution, sequencing

2. Harnessing new insights from ecosystems.

3. Improving photosynthetic and conversion efficiencies.

4. Fermentative production of alcohols & biodiesel.

Brazil’s Bioethanol

Land use:45,000 km²Sugarcane:344 million tons Sugar: 23 million tonsEthanol:14 million m³ $0.26/L (feedstock 70%)

yield increase 3.5%/yrDry bagasse: 50 million tonsElectricity: 1350 MWBagasse ash 2.5% (vs 40% for coal), nearly no sulfur. Burns at low temperatures, so low nitrogen oxides.

Saccharum officinarum

Our DOE Biofuel Center Goals

Miscanthus v Panicum (switchgrass) 22 v 10 tons/haGoals: 2kg Hybrid seeds v 2 tons rhizomes

self-destruction to aid crop rotation, pretreatment$0.10/L goal (NEB >4, corn-EtOH:1.3 soy-diesel:1.93)

Pretreatment $0.03/LAmmonia fiber explosion (AFEX), dilute acid

Integrated cellulases & fermentation to ethanol, butanol, biodiesel, alkanes $0.02/L

High Ethanol (low Lactate, Acetate)

Butanol pathways

Lab Evolution collaborations

Sacharomyces Growth on cellulose (Lee Lynd)Ethanol resistance (Greg Stephanopoulos)

EscherichiaRadiation resistance (Edwards & Battista)Tyr/Trp production & transport (Lin & Reppas)Cutrate utilization (Rich Lenski)Lactate production (Lonnie Ingram)Thermotolerance (Phillipe Marliere)Glycerol utilization (Bernahard Palsson)

Fong SS, Burgard AP, Herring CD, Knight EM, Blattner FR, Maranas CD, Palsson BO. In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol Bioeng. 2005 91(5):643-8.

Rozen DE, Schneider D, Lenski RE Long-term experimental evolution in Escherichia coli. XIII. Phylogenetic history of a balanced polymorphism. J Mol Evol. 2005 61(2):171-80

Andries K, et al. (J&J) A diarylquinoline drug active on the ATP synthase of Mycobacterium tuberculosis. Science. 2005 307:223-7.

Shendure et al. Accurate Multiplex Polony Sequencing of an Evolved Bacterial Genome Science 2005 309:1728 (Select for secretion & ‘altruism’).

Intelligent Design & Metabolic Evolution

Competition & cooperation

• Cooperation between two auxotrophs– Overall fitness depends on secretion– Over-production, increase of export

• Competition among each sub-population– The fastest growing one wins– Increase of uptake

• Coupling between evolution of import and export properties?– Amplified genes– Transporter & pore genes

Cross-feeding symbiotic systems:aphids & Buchnera

• obligate mutualism• nutritional interactions: amino acids and vitamins• established 200-250 million years ago• close relative of E. coli with tiny genome (618~641kb)

Aphids

Internal view of the aphid. (by T. Sasaki)

Bacteriocyte (Photo by T. Fukatsu)

Buchnera (Photo by M. Morioka)

http://buchnera.gsc.riken.go.jphttp://buchnera.gsc.riken.go.jp

Shigenobu et al. Genome sequence of the endocellular bacterial symbiont of aphids Buchnera sp.APS. Nature 407, 81-86 (2000).

Shigenobu et al. Genome sequence of the endocellular bacterial symbiont of aphids Buchnera sp.APS. Nature 407, 81-86 (2000).

ODE based simulation of population dynamics of cross-feeding ∆Trp-∆TyrQuestions:

• When mixed in minimum medium, how do the cell population and the amino acid concentrations change over time?

• What happens when the strains evolve?– improve on amino acid

imports– improve on amino acid

synthesis and/or exports

Governing ODE system

density of ∆Trp (gBM/ml)

density of ∆Tyr (gBM/ml)

conc. of Trp (mmol/ml)

conc. of Tyr (mmol/ml)

growth rate constant of ∆Trp ([(mmol/ml Trp)-hr]-1)

growth rate constant of ∆Tyr ([(mmol/ml Tyr)-hr]-1)

Tyr excretion rate constant of ∆Trp (mmol/gBM-hr)

Trp excretion rate constant of ∆Tyr (mmol/gBM-hr)

=0.05 Trp requirement of ∆Trp (mmol/gBM)

=0.13 Tyr requirement of ∆Tyr (mmol/gBM)

Initial conditions:

density of ∆Trp (gBM/ml)density of ∆Tyr (gBM/ml)conc. of Trp (mmol/ml)conc. of Tyr (mmol/ml)

growth rate constant of ∆Trp ([(mmol/ml

Trp)-hr]-1) growth rate constant of ∆Tyr ([(mmol/ml

Tyr)-hr]-1)

Tyr excretion rate constant of ∆Trp

(mmol/gBM-hr)

Trp excretion rate constant of ∆Tyr

(mmol/gBM-hr)

=0.05 Trp requirement of ∆Trp

(mmol/gBM)

=0.13 Tyr requirement of ∆Tyr

(mmol/gBM)

“Steady-state” solution:

Variables:

Parameters:

Invasion of advantageous mutants

‘Next Generation’ Technology Development

Multi-molecule Our roleAffymetrix Software454 LifeSci Paired ends, emulsionSolexa/Lynx Multiplexing & polonyAB/APG Seq by Ligation (SbL)Complete Genomics SbLGorfinkel Polony to Capillary

Single molecules Helicos Biosci SAB, cleavable fluorsPacific Biosci Advisor KPCBAgilent Nanopores Visigen Biotech AB

HPLC autosampler

(96 wells)syringe pump

Polony Sequencing EquipmentHMS/AB/APG

microscope

with xyz

controls

flow-cell

temperature

control

trp/tyrA pair of genomes shows the best co-growth

Reppas, Lin & Church ; Shendure et al. Accurate Multiplex Polony Sequencing of an Evolved Bacterial Genome(2005) Science 309:1728

SecondPassage

First Passage

Synthetic combinatorics & evolution of 7*7* 4.7 Mbp genomes

Consensus error rate Total errors (E.coli)

(Human)

1E-4 Bermuda/Hapmap 500

600,000

4E-5 454 @40X 200 240,000

3E-7 Polony-SbL @6X 0 1800

1E-8 Goal for 2006 0 60

Goal of genotyping & resequencing Discovery of variantsE.g. cancer somatic mutations ~1E-6 (or lab evolved cells)

Why low error rates?

Also, effectively reduce (sub)genome target size by enrichment for exons or common SNPs to reduce cost & # false positives.

Position Type Gene LocationABI

ConfirmComments

986,334 T > G ompFPromoter-

10 Only in evolved strain

985,797 T > G ompF Glu > Ala Only in evolved strain

931,960 ▲8 bp lrp frameshift Only in evolved strain

3,957,960 C > T ppiC 5' UTR MG1655 heterogeneity

-3274 T > C cI Glu > Glu red heterogeneity

-9846 T > CORF6

1Lys > Gly red heterogeneity

Mutation Discovery in Engineered/Evolved E.coli

Shendure, Porreca, et al. (2005) Science 309:1728

• Glu-117 → Ala (in the pore)

• Charged residue known to affect pore size and selectivity

• Promoter mutation at position (-12)

• Makes -10 box more consensus-like

-12 -11 -10 -9 -8 -7 -6

AAAGAT

CAAGAT

Can increase import & export capability simultaneously

ompF - non-specific transport channel

0

1

2

3

4

5

6

7

8

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150

# of passages

Do

ub

lin

g t

ime

(h

r)

Q1

Q3

Q2-1

Q2-2

EcNR1

Sequence monitoring of evolution(optimize small molecule synthesis/transport)

Sequence trp-

Reppas, Lin & Church

3 independent lines of Trp/Tyr co-culture frozen.

OmpF: 42R-> G, L, C, 113 D->V, 117 E->APromoter: -12A->C, -35 C->ALrp: 1bp deletion, 9bp deletion, 8bp

deletion, IS2 insertion, R->L in DBD.

Heterogeneity within each time-point reflecting colony heterogeneity.

Co-evolution of mutual biosensorssequenced across time & within each time-point

Our DOE Biofuels Center goals & strengths

1. Basic enabling technologies: omics, models,

genome synthesis, evolution, sequencing

2. Harnessing new insights from ecosystems.

3. Improving photosynthetic and conversion efficiencies.

4. Fermentative production of alcohols & biodiesel.

George Church, MIT/Harvard DOE GtL CenterDuPont 13-Sep-2006

Synthetic Biology & Microbial Biofuels

.

.MI, OK, IL, IN, MN, KY, PA, MA, CA, NH. Because our GTL-Systems Biology Center renewal is a bit before the GTL-Bioenergy Research Centers, we're on target for an integrated SB-BRC including strengths in :A. Technology development, ecological & economical modeling: Franco Cerrina (U. Wisc EE), George Church (MIT/HMS), Ed DeLong (MIT BE), Chris Marx (Harvard OEB), Penny Chisholm (MIT Civil Eng). These basic enabling technologies feed into all of the other aims. We are improving our pipeline from 1. metagenomics (single cell sequencing) to 2. datamining to 3. combinatorial (semi)synthetic library formation, to 4. lab-evolution, then 5. sequencing.B. Innovative macromolecular production and structural studies. William Shih (DFCI),James Chou(Harvard), Phil Laible (ANL). William & James have made a breakthrough using DNA-nanotubes which greatly improves the NMR structures including membrane proteins. . We also have world leaders in high-resolution cryo-EM. Phil has developed an impressive what to produce large quantities of pure membrane proteins. My group is scaling-up DNA preps to the multi-gram levels. Membrane and ligno-cellulosic compartments are previous blind-spots for structural genomics which we are addressing.C. Synthetic & systems biology: Daniel Segre (BU BME) Nina Lin (MSU), Pam Silver (HMS SysBiol), Drew Endy (MIT), Jim Collins (BU BME), Anthony Forster (VUMC), Joseph Jacobson (MIT ML). We are proposing a BioFoundry in collaboration with Codon Devices) to bring the cost down of open-wetware and genome-engineering. This includes novel ways to improve accuracy of synthesis and in vivo homologous recombination especially organisms with previously 'challenging' genetics. Phage-, bacterial-, and in vitro- display systems for evolution of enzymes & subsystems. Ref: Building a Fab for Biology D. Phototrophs: Fred Ausubel (Harvard), Wayne Curtis (Penn State U ChE), Clint Chapple (Purdue) Arabidopsis lignins, Richard Dixon (Noble Plant Science Center, OK) Medicago lignins & digestability, Stephen Long, (U Ill Champaign) Mischanthus. It is clear that food crops can support only a tiny fraction of our energy needs, while plants growing in marginal lands (Miscanthus at 60 tons/ha), Panicum, and Populus tricocarpa offer the best starting points. We are engineering these to maximize yield, tolerate stress, and self-destruct when harvested. We also are engineering algae for higher yield/lower cost than grasses, and specialized applications including power plant gases with Greenfuel Tech Corp).E. Microbial metabolic engineering & fermentation, including ligno-cellulose to alcohols & alkanes: Greg Stephanopoulos (MIT ChE) E.coli & Saccharomyces, Lee Lynd (Dartmouth Eng) Clostridia, Lonnie Ingram (U FL) E.coli, Kristala Jones Prather (MIT ChE) E.coli, Thomas Jeffries (USDA, WI) Pichia. We are collecting/evolving enzyme systems to extend the range of input substrates and output fuels and specialty chemicals.

Smart therapeutics example: Environmentally controlled invasion of cancer cells by engineered

bacteria. Anderson et al. J Mol Biol. 2006

Optical imaging: bacteria, viruses, and mammalian cells encoding light- emitting proteins reveal the locations of primary tumors & metastases in animals. Yu, et al. Anal. Bioanal. Chem. 2003.

accumulate in tumors at ratios in excess of 1000:1 compared with normal tissues. http://www.vionpharm.com/tapet_virulence.html

Metabolic constraintsRegulated Capsule

TonB, DapD& new genetic codes

for safety

LPS- Capsule+ Dap- for safety

7

DapD