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Synthetic Biology & Microbial
Biofuels
George Church, MIT/Harvard DOE GtL Center
DuPont 13-Sep-2006
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, UCSF
Keasling, 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.
OFF
ON
ON
Bayer & Smolke; Isaacs & Collins 2005 Nature Biotech.
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
(= 2 E.coli genomes or 20 Mycoplasmas /chip)
Digital Micromirror Array
~1000X lower oligo costs
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
Amplify pools of 50mers using
flanking universal PCR primers and
three paths to 10X error correction
Tian et al. Nature. 432:1050; Carr & Jacobson
2004 NAR; Smith & Modrich 1997 PNAS
rE.coli: new in vivo genetic codes
Freeing 4 tRNAs, 7 codons: UAG, UUR, AGY, AGR
e.g. PEG-pAcPhe-hGH (Ambrx, Schultz) high serum stability
TTT
TTC
F
TTA
30362
TCT
11495
TAT
22516
TCC
11720
TAC
18932
TCA
9783
S
21999
TGT
Y
16601
TGC
C
8816
TAA
STOP
2703
TGA
STOP
1256
STOP
326
TGG
W
20683
17613
CGT
28382
13227
CGC
29898
20888
CGA
39188
CGG
7399
24159
AGT
11970
29385
AGC
45687
AGA
14029
AGG
43719
GGT
25918
GGC
4 18602
TCG
12166
TAG
CTT
15002
CCT
9559
CAT
CTC
15077
CCC
7485
CAC
CTA
5314
CCA
11471
CAA
71553
CCG
31515
CAG
41309
ACT
12198
AAT
34178
ACC
31796
AAC
9670
AAA
TTG
CTG
L
L
ATT
ATC
I
ATA
ATG
M
GTT
GTC
GTA
GTG
V
P
T
5967
ACA
37915
ACG
19624
AAG
24858
GCT
20762
GAT
20753
GCC
34695
GAC
14822
GCA
35918
GCG
A
27418
GAA
45741
GAG
H
Q
N
K
D
E
1
53641
GGA
24254
GGG
7048
R
4859
S
3 21862
R
2 1692
Isaacs
Church
Forster
2896
33622
Carr
Jacobson
40285
G
10893
15090
Jahnz
Schultz
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
Normalized Expression
Glycogen
metabolism metabolism
Light regulated
Prochlorococcus
10
glgA
glgB
glgC
glgX
glgP
1
0.1
0
4
8
12 16 20 24 28 32 36 40 44 48
Time (hours)
glgA
glgC
Central
Carbon
Metabol.
a-Glc-1P
ADP-Glc
glgB
a-1,4-glucosyl-glucan
glgX
glgP
Zinser et al. unpubl.
glycogen
Photosynthetic Genes in Phage
Podovirus P-SSP7 46 kb
HLIP
D1
Myovirus P-SSM2 255 kb
PC
12kb
HLIPs
Fd
D1
24kb
Myovirus P-SSM4 181 kb
HLIPs
D1
D2
~500 bp
6.4kb
Lindell, Sullivan, Chisholm et al. 2004
2.8kb
RNA Responses to Phage
14.0
ARR_0682
MED4-psbA (log2Intensity)
8
log2amount
7
6
MED4-0682 (60 aa
Conserved URF)
5
4
MED4 host
psbA
13.8
13.6
13.4
13.2
3
n=3
phage n=3 flasks
2
13.0
0
2
4
6
8
0
2
Time (h) after infection
4
6
8
time (h) after infection
phage psbA/rnpB
10-1
10-2
Phage SSP7
psbA
10-3
10-4
n=3 flasks
10-5
Lindell, Sullivan, Zinser, Chisholm
0
2
4
Time (h) after infection
6
8
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 km²
Sugarcane:344 million tons
Sugar: 23 million tons
Ethanol:14 million m³ $0.26/L (feedstock 70%)
yield increase 3.5%/yr
Dry bagasse: 50 million tons
Electricity: 1350 MW
Bagasse 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/ha
Goals: 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/L
Ammonia 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)
Escherichia
Radiation resistance (Edwards & Battista)
Tyr/Trp production & transport (Lin & Reppas)
Cutrate utilization (Rich Lenski)
Lactate production (Lonnie Ingram)
Thermotolerance (Phillipe Marliere)
Glycerol utilization (Bernahard Palsson)
Intelligent Design & Metabolic Evolution
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’).
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)
Internal view
of the aphid.
(by T. Sasaki)
Bacteriocyte
(Photo by T.
Fukatsu)
Aphids
http://buchnera.gsc.riken.go.jp
Buchnera
(Photo by
M. Morioka)
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-∆Tyr
Questions:
• 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
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:
density of ∆Trp (gBM/ml)
density of ∆Tyr (gBM/ml)
conc. of Trp (mmol/ml)
conc. of Tyr (mmol/ml)
Parameters:
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)
Invasion of advantageous mutants
‘Next Generation’
Technology Development
Multi-molecule
Affymetrix
454 LifeSci
Solexa/Lynx
AB/APG
Our role
Software
Paired ends, emulsion
Multiplexing & polony
Seq by Ligation (SbL)
Complete Genomics SbL
Gorfinkel
Polony to Capillary
Single molecules
Helicos Biosci
Pacific Biosci
Agilent
Visigen Biotech
SAB, cleavable fluors
Advisor KPCB
Nanopores
AB
Polony Sequencing Equipment
HMS/AB/APG
microscope
with xyz
controls
HPLC autosampler
(96 wells)
flow-cell
temperature
control
syringe
pump
Synthetic combinatorics & evolution of
7*7* 4.7 Mbp genomes
First
Passage
Second
Passage
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
Why low error rates?
Goal of genotyping & resequencing  Discovery of variants
E.g. cancer somatic mutations ~1E-6 (or lab evolved cells)
Consensus error rate
1E-4
4E-5
Total errors (E.coli)
Bermuda/Hapmap
454 @40X
(Human)
500
600,000
200
240,000
3E-7
Polony-SbL @6X
0
1800
1E-8
Goal for 2006
0
60
Also, effectively reduce (sub)genome target size by enrichment for
exons or common SNPs to reduce cost & # false positives.
Mutation Discovery in Engineered/Evolved E.coli
Position
Type
Gene
Location
ABI
Confirm
Comments
986,334
T>G
ompF
Promoter-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
l-3274
T>C
cI
Glu > Glu

l-red heterogeneity
l-9846
T>C
ORF61
Lys > Gly

l-red heterogeneity
Shendure, Porreca, et al. (2005) Science 309:1728
ompF - non-specific transport channel
Can increase import & export capability simultaneously
AAAGAT
CAAGAT
-12 -11 -10 -9
-8 -7
• Promoter mutation at
position (-12)
• Makes -10 box more
consensus-like
-6
• Glu-117 → Ala (in the pore)
• Charged residue known to affect
pore size and selectivity
Sequence monitoring of evolution
(optimize small molecule synthesis/transport)
8
Doubling time (hr)
7
6
5
Q1
Q3
4
Q2-1
Q2-2
3
Sequence trp-
2
EcNR1
1
0
0
10
20
30
40
50
60
70
80
90 100 110 120 130 140 150
# of passages
Reppas, Lin & Church
Co-evolution of mutual biosensors
sequenced across time & within each time-point
3 independent lines of Trp/Tyr co-culture frozen.
OmpF: 42R-> G, L, C, 113 D->V, 117 E->A
Promoter: -12A->C, -35 C->A
Lrp: 1bp deletion, 9bp deletion, 8bp deletion, IS2
insertion, R->L in DBD.
Heterogeneity within each time-point reflecting
colony heterogeneity.
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 & Microbial
Biofuels
George Church, MIT/Harvard DOE GtL Center
DuPont 13-Sep-2006
.
.
MI, OK, IL, IN, MN, KY, PA, MA, CA, NH. Because our GTL-Systems Biology Center renewal is a bit before the GTLBioenergy 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
Metabolic constraints
Regulated Capsule
TonB, DapD
& new genetic codes
for safety
Optical imaging: bacteria, viruses, and mammalian cells encoding lightemitting 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
LPS- Capsule+ Dap- for safety
DapD
7