enabling robust production of biorenewable fuels and ... · enabling robust production of...

Enabling Robust Production of Biorenewable Fuels and Chemicals from

Biomass

Laura R. Jarboe, Assistant Professor Chemical and Biological Engineering

Iowa State University

History of Biocatalysis

Biotechnology through the ages: selection for desirable traits

Biotechnology Revolution enables us to not just select for desired traits, but to understand, model and manipulate biological systems.

“Metabolic Engineering” Science 252 1991, Stephanopoulos/Vallino, Bailey

“The directed improvement of production, formation or cellular properties through the modification of specific biochemical reaction(s) or the

introduction of new one(s) with the use of recombinant DNA technology.”

Tolerance to Inhibitory Compounds

biorenewable fuel or chemical

biomass-derived sugars

Strategies for dealing with inhibition: - Selectively remove inhibitor (may increase cost) - Increase tolerance of biocatalyst to the inhibitory compound

Both ends of biocatalyst metabolism are affected by

inhibitory compounds

3/25

furfural 5-HMF acetate phenols

ethanol butanol

carboxylic acids limonene

inhibitor-sensitive biocatalyst

inhibitor-resistant biocatalyst

reverse engineering

Ideas from literature

Omics Analysis

Metabolic Evolution for Tolerance

Generalized Strategy

Rational Engineering

mechanism(s) of inhibition

Will discuss 3 examples for this strategy: furfural, carboxylic acids, pyrolytic

sugars

4/25

How to Extract Sugars from Biomass?

biorenewables

fermentable sugars

enzymes

Challenge: high enzyme cost, long

residence time

Challenge: inhibitors (i.e. acetic

acid and furfural)

acid, steam,

pressure

Work performed in Ingram Lab, University of Florida Miller, Jarboe et al AEM 2009 75:613 Miller, Jarboe et al AEM 2009 75:4315 Turner et al J Industr Microbiol Biotechnol 2010

Furfural toxicity limits biological utilization of biomass hydrolysate. How to make the

bacterial biocatalyst more robust?

D-cys

supplementation with D-cys, S2O3

2-, L-cys, cystathionine or met or use of glucose as carbon source instead of xylose improve tolerance

Transcriptome Analysis of Furfural Challenge

Hypothesis: furfural is inhibitory because its

reduction depletes NADPH pools,

limiting H2S biosynthesis

CysB

MetJ

ArgR TyrR

SF

PurR RutR

H2S

cys

homoserine

met

O-acetyl-L-serine

SAM

cys/met depletion?

stalled translation

biosynthesis intermediate accumulation

SO32-

3 NADPH ArcA altered redox ratio

furfural

furfuryl alcohol

?

Increased transcription factor activity or metabolite abundance

Decreased transcription factor activity or metabolite abundance

Rationally Improving Furfural Toxicity How can we rationally increase furfural tolerance? - Supplement with a metabolite that supplies reduced S (costly) - Grow on glucose instead of xylose (outside of our overall goal) - Engineer the biocatalyst for increased NADPH availability

+ 1 g L-1 furfural Interpretation of transcriptome data is based on known genes and pathways. Can we learn even more by reverse engineering an evolved strain?

7/25



reverse engineering (transcriptome)

Transcriptome Analysis


Furfural Strategy



8/25

Metabolic Evolution

Fresh media

Spent media

Stressful condition, cells grow poorly A random mutation confers increased stress tolerance

The mutated cell grows faster, its progeny dominate the population

Another random mutation confers even more tolerance

9/25

AM1 minimal media + 9% xylose + 1 g L-1 furfural 1mM betaine,

pH 6.5, 37C 150rpm 54 serial transfers, 0.5 – 1.3 g L-1 furfural

0 12 24 36 48 60 72

0.1

1

10

Time (h)

Cell

Mass (

g L

-1)

0 12 24 36 48 60 72

0

10

20

30

40

50

Time (h)

Eth

an

ol (g

L-1

)

evolved mutant

control

Growth

Ethanol

control

evolved mutant

Reverse Engineering a Furfural-Tolerant Mutant

What is the basis of tolerance? Would like to apply to other

biocatalysts so that they can be rationally engineered for furfural

tolerance instead of relying on evolution.

control DyqhD DdkgA DyqhD, DdkgA

parent

0.00

0.25

0.50

0.75

Cell M

ass (

g L

-1)

control DdkgA DyqhD, DdkgA

DyqhD

Parent +1 g L-1 furfural

ace

L-cys

OALS

H2S SO32-

3 NADPH

SO42-

furfural furfuryl alcohol

YqhD, DkgA

control +yqhD

evolved mutant

0.00

0.02

0.04

0.06

in v

ivo

Furf

ura

l R

ed

uctio

n

( m m

ol m

in -1

mg d

cw

-1 )

The evolved mutant found its own way to increase availability of NADPH:

silencing of the furfural reductase YqhD

Have since found that mutation of YqhC is the basis of yqhD silencing

Subsequent rational engineering described in Wang et al AEM 2011

Decreased Furfural Reduction Rate in Evolved Strain

10/25

Furfural Conclusions Strategies for increasing furfural tolerance: - Mitigate cys depletion by supplementation ($$) - Increase NADPH availability

- Use glucose as carbon source (outside our project goal) - Use transhydrogenase to convert NADH to NADPH (effective) - Silence NADPH-dependent aldehyde reductase (effective)

H2S

cys

met

cys/met depletion

SO32-

3 NADPH

furfural furfuryl alcohol

YqhD

Take-away lesson: Interpretation based on existing biocatalyst knowledge is effective, but we still have much to learn about even our most well-characterized biocatalysts

11/25

Bio-mass Derived Sugars

engineered biocatalysts

Commodity Chemicals

ethanol insulin butanol lactic acid 1,3-propanediol succinate lycopene amorphadiene

One-Use Carbon

transportation fuels

industrial chemicals

catalysis

engineered biocatalysts (E. coli, yeast)

chemical intermediates

catalysis

12/25

0.0

0.2

0.4

0.6

0.8

0 10 20 30 40

Spec

ific

gro

wth

rat

e (h

r-1)

Carboxylic acid concentration (mM)

C6

C8

C10

Problem: Carboxylic acid- producing biocatalysts must be

able to tolerate carboxylic acids at high titer. But these compounds

inhibit biocatalyst growth.

R COOH R

a-Olefins Glucose Short-Chain Carboxylic Acids

13/25



reverse engineering

Ideas from literature

Omics Analysis

Flux Analysis




14/25

gene

namefunction

Fold

Changep-value

yagU inner membrane protein that contributes to acid resistance 3.52 0.004

ffs 4.5S RNA signal recognition particle (SRP) -2.22 0.026

ybaS glutaminase 4.27 0.004

dps stationary phase nucleoid complex that sequesters iron 4.29 0.006

ompX overexpression increases sigmaE activity; outer membrane protein 2.73 0.004

ybjC predicted inner membrane protein 2.35 0.004

ompF porin; allows passage of solutes -15.86 0.001

bhsA involved in stress resistance and biofilm formation 3.34 0.002

ycgZ predicted protein 2.80 0.007

ymgA involved in biofilm formation 3.52 0.002

rpsV 30S rRNA protein subunit 4.95 0.002

gadC part of glutamate-dependent acid resistance system (AR2) 9.21 0.000

gadB part of glutamate-dependent acid resistance system (AR2) 25.07 0.000

marA regulates genes involved in resistance (antibiotics, oxidative stress, solvents, heavy metals) 4.39 0.002

flxA Qin prophage, predicted protein -6.56 0.001

cfa cyclopropane fatty acid synthase 2.09 0.007

yeeD conserved protein 3.05 0.008

yeeE putative permease 4.02 0.006

ddg palmitoleoyl acytltransferase; used to incorporate palmitoleate into lipid A instead of laurate -2.71 0.098

ygdI putative lipoprotein 2.09 0.028

mscS mechanosensitive channel; induced by osmotic stress 2.41 0.001

ygiW conserved protein 2.15 0.002

yhbW conserved protein 2.08 0.000

yhcN conserved protein 15.75 0.003

ompR regulator component of two-component system; responds to EnvZ; EnvZ senses changes in 2.58 0.011

yhiD predicted Mg-ATPase, may be involved in acid resistance 4.54 0.001

hdeB acid stress chaperone 20.83 0.000

hdeA acid stress chaperone 13.77 0.000

hdeD acid resistance membrane protein 8.80 0.001

gadE activator of glutamate-dependent acid resistance 9.73 0.004

gadW regulates glutamate dependent acid resistance system (GAD) 2.65 0.017

gadX regulates glutamate-dependent acid resistance system (GAD) 3.51 0.002

gadA glutamate decarboxylase, part of glutamate-dependent acid resistance system 4.33 0.004

osmY hyperosmitcally inducible periplasmic protein 2.76 0.001

micF anti-sense RNA, inhibits ompF translation 11.86 0.003

0

0.2

0.4

0.6

0.8

0 20 40

Gro

wth

Rat

e (h

-1)

Concentration C8 (mM)

10mM ~10% growth inhibition


15/25

Even when the external media is maintained at pH 7.0, the

presence of short-chain carboxylic acids can result in a

drop in cytoplasmic pH

HA H+ A-

HA

HA H+ A-

media: pH can be controlled

inside cell: pH cannot be controlled, pH is critical

Engineering Approach: - Utilize native acid resistance systems - Express proton-buffering peptides - Pump out the carboxylic acids

5.0

5.5

6.0

6.5

7.0

7.5

8.0

Control +20mM C8

+20mM C8 pH=7

+20mM HCl

+20mM HCl

pH=7

+2% Ethanol

Intr

ace

llula

r p

H C8

HCl

From omics analysis: C8 stress involves acid stress

Royce, Liu et al, in preparation

E. coli carboxylic acid stress/production involves membrane damage

0.0

0.1

0.2

0.3

0.4

0 10 20 30 40

Mem

bra

ne

po

lari

zati

on

Octanoic acid (mM)

0

10

20

30

40

50

60

0 10 20 30 40

% M

g2+

rel

ease

d b

y ch

loro

form

Octanoic acid (mM)

Membrane fluidity

Membrane leakage

0

50

100

150

200

0.0

0.2

0.4

0.6

0.8

10 20 30 40 Time (h)

total carboxylic acids

Mg leakage

% M

g2+

rele

ase

d r

ela

tive

to

CH

Cl 3

Car

bo

xylic

Aci

ds

Pro

du

ced

(g

/L)

The presence of carboxylic acids impacts membrane fluidity and integrity, with stronger impact

than ethanol or heat shock. Similar effects were seen during

carboxylic acid production.

Royce, Liu et al, in preparation

0.01

0.1

1

10

0 12 24

OD

55

0

Time (h)

control

+C8

parent strain

0.01

0.1

1

10

0 12 24

OD

55

0

Time (h)

control

+C8

evolved mutant

Goal: Reverse engineer fatty acid tolerance, learn new strategies for dealing with fatty acids

Genome sequence data analysis in progress

19/25

Royce, in preparation

Biorenewable Chemicals from Biomass

“Brown Gold” sugarcane bagasse

Lake Okeechobee, Florida

BIOCATALYSIS

How to Extract Sugars from Biomass?

biorenewables

fermentable sugars

Challenge: inhibitors (i.e. acetic

acid and furfural)

acid, steam,

pressure

enzymes Challenge: high

enzyme cost, long residence time

Challenge: complex, unstable mixture, low sugar content, inhibitors

Benefit: fast, cheap, applicable to any biomass type

thermochemical processing (pyrolysis)

“Hybrid” processing: Thermochemical processing of biomass, Biological utilization of thermochemical products.

Engineering Pyrolytic-Sugar Utilizing Biocatalysts Existing biocatalysts can easily be engineered for

utilization of levoglucosan as carbon/energy source with same redox, ATP demand as glucose

0.0

0.5

1.0

1.5

2.0

0 12 24 36 48

Su

gar,

wt%

time (hr)

Sugar utilization

levoglucosan

glucose

0.0

0.2

0.4

0.6

0.8

1.0

0 12 24 36 48

Eth

an

ol,

wt%

time (hr)

Ethanol production

levoglucosan

glucose

LB + pure sugars, 37C, pH 6.5 Layton et al Bioresource Tech 2011 22/25



reverse engineering



Pyrolytic Sugar Strategy



Project outcome: A list of modifications to implement in existing bacterial biocatalysts to enable pyrolytic sugar utilization.

23/25

Inhibitor Tolerance Conclusions

- Product toxicity or contaminants in “dirty sugars” limit production of biorenewable fuels and chemicals

- Reverse engineering of evolved strains can reveal (a) the mechanism of inhibition (b) useful mutations and (c) increase characterization of existing workhorse strains

- Finding the mechanism of toxicity enables rational engineering for inhibitor tolerance

24/25

Acknowledgements

Graduate Students: Liam Royce, Ping Liu Researchers: Matt Stebbins, Brittany Rover, Emily Rickenbach, Ben Hanson, Jennifer Au Collaborators: Jackie Shanks, Julie Dickerson, Ramon Gonzalez, Kai-Yu San

PI: Lonnie Ingram Researcers: Elliot Miller, Brelan Moritz, Christy Baggett Collaborators: K.T. Shanmugam, Priti Pharkya, David Nunn

Researchers: Zhanyou Chi, Tao Jin, D. Layton, M. Deaton, S. Steffen, J. Kuyper, B. Sorensen, A. Rossinger Collaborators: Zhiyou Wen, Robert C. Brown, D.W. Choi Funding: NSF Energy for Sustainability, Iowa Energy Center, ISU Bioeconomy Institute, ISU Plant Sciences Institute

EEC-0813570

CBET-1133319 Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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