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20 Φεβ 2013 (πριν από 4 χρόνια και 6 μήνες)

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Genome
-
scale constraint
-
based metabolic model of

Clostridium thermocellum

Chris M. Gowen
1,3
, Seth B. Roberts
1
, Stephen S. Fong
1,2


1
Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA, USA

2
Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA, USA

3
Presenting author, contact: gowencm@vcu.edu

School of Engineering

Motivation

Cellulose

makes

up

roughly

60
%

of

the

dry

weight

of

all

plant

biomass

on

earth

and

therefore

represents

an

extremely

abundant

and

sustainable

feedstock

for

the

production

of

liquid

fuels
.

All

current

methods

for

the

biochemical

conversion

of

cellulosic

biomass

to

ethanol

for

fuel

fall

into

three

main

categories
:

Typical process





Simultaneous
Saccharification and
Fermentation




Consolidated
Bioprocessing[1]

Pretreatment

Enzymatic

Saccharification

Fermentation

Distillation

Pretreatment

Enzymatic Saccharification /

Fermentation

Distillation

Pretreatment

Biological Saccharification /

Fermentation

Distillation

Cellulase

Enzymes

Cost prohibitive due to
supplemental enzymes
and additional process
steps




Current practice in
most pilot plants,
enzymes are costly




Obviates need for
additional enzymes
and maximizes
process efficiencies

Cellulase

Enzymes

Metabolic

model

of

Clostridium

thermocellum

Clostridium

thermocellum

is

one

of

a

number

of

organisms

capable

of

direct

fermentation

of

cellulose

to

ethanol
,

and

its

cellulolytic

system

is

one

of

the

most

efficient

known

to

researchers
.

This

efficiency

is

achieved

partly

because

C
.

thermocellum

assembles

most

of

its

cellulase

enyzmes

onto

an

extracellular,

cell
-
associated

scaffold
.

The

entire

assembly

is

termed

the

“cellulosome”

and

maximizes

synergies

between

the

different

catalytic

mechanisms

of

its

cellulase

arsenal

and

subsequent

sugar

uptake
.

References and Acknowledgements

The authors would like to thank J. Paul Brooks for his computational and operations expertise, David Hogsett
and Chris Herring for discussions regarding
C. thermocellum

physiology, Lee Lynd for generously providing
C.
thermocellum

cultures and Stephen Rogers and Evert Holwerda for providing valuable assistance.




Lynd, L. R., Weimer, P. J., Van Zyl, W. H., & Pretorius, I. S. (2002). Microbial cellulose utilization: fundamentals and biot
ech
nology.
Microbiology and Molecular Biology Reviews
,
66
(3),
506
-
577.


Edwards, J. S., Covert, M., & Palsson, B. Ø. (2002). Metabolic modelling of microbes: the Flux
-
balance approach.
Environmental Microbiology
, 4(3), 133
-
140.


Lamed, R. J., Lobos, J. H., & Su, T. M. (1988). Effects of Stirring and Hydrogen on Fermentation Products of Clostridium ther
moc
ellum.
Applied and Environmental Microbiology
,
54
(5),
1216
-
1221.


Shlomi, T., Cabili, M. N., Herrgard, M. J., Palsson, B. Ø., & Ruppin, E. (2008). Network
-
Based prediction of human tissue
-
Specif
ic metabolism.
Nature Biotechnology
, 26(9), 1003
-
1010.


Burgard, A. P., Pharkya, P., & Maranas, C. D. (2003). Optknock: a bilevel programming framework for identifying gene knockout

st
rategies for microbial strain optimization.
Biotechnology and Bioengineering
,
84
(6), 647
-
657.


Fong, S. S., Burgard, A. P., Herring, C. D., Knight, E. M., Blattner, F. R., Maranas, C. D., et al. (2005). In silico design
and

adaptive evolution ofescherichia coli for production of lactic acid.
Biotechnology and Bioengineering
,
91
(5), 643
-
648.


Desai, S., Guerinot, M., & Lynd, L. (2004). Cloning of l
-
Lactate dehydrogenase and elimination of lactic acid production via gen
e knockout in thermoanaerobacterium saccharolyticum
jw/sl
-
Ys485.
Applied Microbiology and Biotechnology
,
65
(5), 600
-
605.

CREDIT: DOE Joint Genome Institute


http://genome.jgi
-
psf.org/finished_microbes/cloth/cloth.home.html

Cellulose

Cellobiose

Fructose

Ethanol

Acetate

Formate

Lactate

H
2

CO
2

The

broad

mixture

of

fermentation

byproducts

produced

by

C
.

thermocellum

is

reflective

of

the

fact

that

it

has

evolved

towards

its

own

ends,

rather

than

for

the

production

of

ethanol
.

We

demonstrate

here

the

creation

of

a

genome
-
scale

metabolic

model

of

the

metabolism

of

C
.

thermocellum

and

discuss

its

use

for

model
-
guided

metabolic

engineering

to

optimize

production

of

ethanol

from

cellulosic

substrates

in

C
.

thermocellum
.

Flux
A

Flux
B

2 A + B


C + D

A

B

C

D



Metabolites (X)

Reactions

S

v

= d[X] / dt


v1


.


.


.


.


.

0

0

0

0

0

0



Reaction A

Flux vector,
v

Mass balance
statement



s
constraint

amic
thermodyn
try
stoichiome
network

s
constraint
boundary
:
max
)
(





to
Subject
objective
cellular
fluxes
Flux balance analysis [2]

Genome

size

3.8 Mb

ORFs

3307

Included

genes

432

Enzyme

complexes

72

Isozyme

cases

70

Reactions

(excluding

exchanges)

563


Transport

56


Gene

associated

463


Non
-
gene

associated

intracellular

61


Non
-
gene

associated

transports

37

Distinct

metabolites

529

Metabolic solution space for growth of
C. thermocellum
on cellobiose
shows tradeoff between H
2

and ethanol production

Microbial Genomics 2009

Results

and

Discussion

A

constraint
-
based

genome
-
scale

metabolic

model

has

been

constructed

based

on

the

published

annotation

of

Clostridium

thermocellum’s

genome

as

well

as

the

incorporation

of

biochemical

observation
.

The

statistics

of

the

resulting

model

are

shown

in

the

table

to

the

right
.

The

model

is

unique

in

its

incorporation

of

proteomics

data

to

account

for

substrate
-
dependent

production

of

the

cellulosome
.


Combined

with

flux

balance

analysis

and

suitable

boundary

constraints,

the

model

is

able

to

closely

match

experimentally

observed

fermentation

characteristics
.

For

example,

researchers

have

long

noted

that

C
.

thermocellum

can

be

forced

to

increase

ethanol

production

by

thermodynamically

preventing

H
2

gas

production

[
3
]
.

The

three
-
dimensional

depiction

of

the

solution

space

(above,

right)

demonstrates

that

maximum

growth

rate

is

achieved

with

no

ethanol

production,

but

as

H
2

secretion

flux

is

restricted,

the

maximum

growth

rate

peak

shifts

towards

higher

ethanol

production
.

Microorganisms

generally

pursue

(either

through

evolution

or

regulation)

the

maximum

growth

rate
.

This

heuristic

also

permits

the

use

of

this

model

as

a

tool

for

computational

strain

design
.

The

graph

(below)

shows

single
-
gene

deletions

predicted

to

improve

ethanol

secretion

at

the

maximum

growth

rate
.

This

technique

can

be

used

to

inform

genetic

engineering

decision

making
.

All

reactions

available

to

an

organism

according

to

genome

annotation

and

biochemical

evidence

are

compiled

in

a

stoichiometric

matrix,

S,

which

is

part

of

a

genome
-
scale

mass
-
balance

problem
.

1

Boundary conditions are set based on observed substrate

uptake and byproduct secretion rates. Flux balance analysis

is then used to probe the resulting solution space by

maximizing a cellular objective such as growth rate within

the given constraints. The resulting vector describes the
predicted reaction fluxes throughout the model.

3

Any given flux state

can be defined as a

vector, and the

reaction matrix

combined with
boundary constraints
define the borders of a
solution space within which
the flux state must always
fall.

2

Comparison of model predictions to experimental observations


C. thermocellum iSR432

was used to simulate growth in multiple conditions. Actual (

)
and predicted (

) reaction flux rates are shown, and predicted fermentation product production rates are shown as ranges as determined by flu
x v
ariability
analysis. For each simulation, the boundary fluxes for cellobiose, acetate, and formate were constrained to match the measur
ed
fluxes during (A) chemostat
growth on cellobiose and (B) fructose, and (C) batch growth on cellobiose.


Single gene deletions for which increased ethanol production is predicted.