Synthetic biology microbial biofuels
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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.

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Synthetic biology microbial biofuels

Synthetic Biology & Microbial Biofuels

George Church, MIT/Harvard DOE GtL Center

DuPont 13-Sep-2006


Our doe biofuels center goals strengths

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

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


Synthetic biology microbial biofuels

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

Programmable ligand-controlled riboregulators to monitor metabolites.

OFF

ON

ON

Bayer & Smolke; Isaacs & Collins 2005 Nature Biotech.


Genome metabolome computer aided design cad

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)

How? 10 Mbp of oligos / $1000 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

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

4

1

Isaacs

Church

Forster

Carr

Jacobson

Jahnz

Schultz

3

2


Our doe biofuels center goals strengths1

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 40 n 40 s chisholm et al

Prochlorococcus40ºN - 40ºS Chisholm et al.

Ocean chl a (Aug 1997 –Sept 2000)

Provided by the SeaWiFS Project, NASA


Synthetic biology microbial biofuels

Light regulated Prochlorococcus metabolism

glgA

glgB

glgC

Central

Carbon

Metabol.

a-Glc-1P

ADP-Glc

glycogen

a-1,4-glucosyl-glucan

glgX

glgP

Zinser et al. unpubl.


Photosynthetic genes in phage

HLIP

D1

Photosynthetic Genes in Phage

Podovirus P-SSP746 kb

Myovirus P-SSM2255 kb

PC

PC

HLIPs

HLIPs

Fd

Fd

D1

D1

12kb 24kb

12kb 24kb

Myovirus P-SSM4 181 kb

HLIPs

HLIPs

D1

D1

D2

D2

~500

~500

bp

bp

6.4kb

6.4kb

2.8kb

2.8kb

Lindell, Sullivan, Chisholm et al. 2004


Rna responses to phage

RNA Responses to Phage

MED4 host

psbA

MED4-0682 (60 aa Conserved URF)

Phage SSP7

psbA

Lindell,Sullivan, Zinser, Chisholm


Our doe biofuels center goals strengths2

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

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

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

High Ethanol (low Lactate, Acetate)


Butanol pathways

Butanol pathways


Lab evolution collaborations

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

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

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

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)

Buchnera (Photo by M. Morioka)

Aphids

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


Synthetic biology microbial biofuels

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


Synthetic biology microbial biofuels

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

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:

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)

density of ∆Trp (gBM/ml)

density of ∆Tyr (gBM/ml)

conc. of Trp (mmol/ml)

conc. of Tyr (mmol/ml)

Governing ODE system


Synthetic biology microbial biofuels

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:


Synthetic biology microbial biofuels

Invasion of advantageous mutants


Next generation technology development

‘Next Generation’ Technology Development

Multi-molecule Our role

Affymetrix Software

454 LifeSci Paired ends, emulsion

Solexa/Lynx Multiplexing & polony

AB/APG Seq by Ligation (SbL)

Complete Genomics SbL

Gorfinkel Polony to Capillary

Single molecules

Helicos Biosci SAB, cleavable fluors

Pacific Biosci Advisor KPCB

Agilent Nanopores

Visigen Biotech AB


Synthetic biology microbial biofuels

Polony Sequencing EquipmentHMS/AB/APG

microscope

with xyz

controls

HPLC autosampler

(96 wells)

flow-cell

syringe pump

temperature

control


Synthetic combinatorics evolution of 7 7 4 7 mbp genomes

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

Second

Passage

First

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

Why low error rates?

Goal of genotyping & resequencing  Discovery of variants

E.g. cancer somatic mutations ~1E-6 (or lab evolved cells)

Consensus error rateTotal 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

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

Mutation Discovery in Engineered/Evolved E.coli

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


Synthetic biology microbial biofuels

ompF - non-specific transport channel

AAAGAT

CAAGAT

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

Can increase import & export capability simultaneously

  • 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


Sequence monitoring of evolution optimize small molecule synthesis transport

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

Sequence trp-

Reppas, Lin & Church


Synthetic biology microbial biofuels

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 strengths3

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 biofuels1

Synthetic Biology & Microbial Biofuels

George Church, MIT/Harvard DOE GtL Center

DuPont 13-Sep-2006


Synthetic biology microbial biofuels

.


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.

.


Synthetic biology microbial biofuels

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 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


Lps capsule dap for safety

LPS- Capsule+ Dap- for safety

DapD

7


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