Articles
https://doi.org/10.1038/s41587-021-01195-w
1
LanzaTech Inc., Skokie, IL, USA. 2
Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL, USA. 3
Chemistry of Life
Processes Institute, Northwestern University, Evanston, IL, USA. 4
Center for Synthetic Biology, Northwestern University, Evanston, IL, USA. 5
Biosciences
Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA. 6
Graduate School of Genome Science and Technology, University of Tennessee, Knoxville,
TN, USA. 7
Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, USA. 8
Simpson Querrey Institute, Northwestern University,
Chicago, IL, USA. 9
These authors contributed equally: Fungmin Eric Liew, Robert Nogle, Tanus Abdalla. ✉e-mail: ching.leang@lanzatech.com;
m-jewett@northwestern.edu; michael.koepke@lanzatech.com
T
he accelerating climate crisis has intensified demand for
environmentally sustainable, carbon-negative manufacturing
of chemicals and materials. Most commodity chemicals
are derived exclusively from fresh fossil resources, such as oil,
natural gas and coal. Acetone and isopropanol (IPA) are two such
examples, with a combined global market of more than US$10
billion. Both molecules are industrial solvents as well as platform
chemicals for the production of materials such as acrylic
glass (polymethyl methacrylate) and polypropylene. Additionally,
acetone has potential in the fuel sector to reduce emissions and
enhance the performance of existing fuels1,2
and as a precursor
for green diesel or jet fuel substitutes3,4
. IPA is also a disinfectant;
its use in World Health Organization-recommended sanitizer
formulations against severe acute respiratory syndrome coronavirus
2 (SARS-CoV-2)5
led to supply shortages and surging prices
during the Coronavirus Disease 2019 (COVID-19) pandemic.
Acetone is mostly produced as a byproduct of phenol production
using the cumene process. Manufacture of both acetone
and IPA is dependent on propene cracking or reforming, which
are energy-intensive processes that create hazardous waste and
greenhouse gas (GHG) emissions6
. No viable green chemistry
alternatives are available.
Biomanufacturing by fermentation enables sustainable, purposeful
production of desired chemicals using ‘above-ground’ carbon
sources. The acetone-butanol-ethanol (ABE) process developed by
Chaim Weizmann in the first half of the 20th century was one of
the first industrial fermentation processes for chemicals produc-
tion7
. In this process, acetone, butanol and ethanol are produced in
a mixed fermentation (typically in a ratio of 3:6:1) by the solventogenic
Clostridium species Clostridium acetobutylicum, Clostridium
beijerinckii, Clostridium saccharobutylicum or Clostridium saccharoperbutylacetonicum
from sugar or starch feedstocks7
. A few
ABE strains that contain a primary–secondary alcohol dehydrogenase
(sAdh) also produce low levels of IPA8
. At its peak, global
ABE production reached more than 500 metric tons per annum,
but eventually the ABE process was phased out owing to limited
selectivity to one desired product and high substrate costs and
was continued in only a few countries, such as South Africa and
Taiwan, until the 1980s7
. To develop alternative fermentation production
systems, many studies have sought to decouple acetone and
IPA production from butanol synthesis and transfer the respective
pathways to heterologous hosts or to develop novel synthetic
pathways9,10
. Substantial strain11,12
and process development13
has
been carried out in Escherichia coli, and, although improvements
Carbon-negative production of acetone and
isopropanol by gas fermentation at industrial
pilot scale
Fungmin Eric Liew1,9
, Robert Nogle1,9
, Tanus Abdalla1,9
, Blake J. Rasor   2,3,4
, Christina Canter1
,
Rasmus O. Jensen1
, Lan Wang1
, Jonathan Strutz1
, Payal Chirania5,6
, Sashini De Tissera1
,
Alexander P. Mueller1
, Zhenhua Ruan1
, Allan Gao1
, Loan Tran1
, Nancy L. Engle   5
, Jason C. Bromley1
,
James Daniell   1
, Robert Conrado1
, Timothy J. Tschaplinski   5
, Richard J. Giannone5
,
Robert L. Hettich   5
, Ashty S. Karim   2,3,4
, Séan D. Simpson1
, Steven D. Brown1
, Ching Leang   1 ✉,
Michael C. Jewett   2,3,4,7,8 ✉ and Michael Köpke   1 ✉
Many industrial chemicals that are produced from fossil resources could be manufactured more sustainably through fermentation.
Here we describe the development of a carbon-negative fermentation route to producing the industrially important chemicals
acetone and isopropanol from abundant, low-cost waste gas feedstocks, such as industrial emissions and syngas. Using
a combinatorial pathway library approach, we first mined a historical industrial strain collection for superior enzymes that we
used to engineer the autotrophic acetogen Clostridium autoethanogenum. Next, we used omics analysis, kinetic modeling and
cell-free prototyping to optimize flux. Finally, we scaled-up our optimized strains for continuous production at rates of up to
~3 g/L/h and ~90% selectivity. Life cycle analysis confirmed a negative carbon footprint for the products. Unlike traditional production
processes, which result in release of greenhouse gases, our process fixes carbon. These results show that engineered
acetogens enable sustainable, high-efficiency, high-selectivity chemicals production. We expect that our approach can be readily
adapted to a wide range of commodity chemicals.
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Articles NatureBiotechnology
in selectivity have been made, low yields remain a major hurdle
for commercialization. In the case of acetone and IPA, the maximum
theoretical yield for fermentative production from sugars is
capped at 50% owing to formation of CO2 during the breakdown of
sugars and acetone synthesis (pyruvate and acetoacetate decarboxylase
reactions)14
.
Gas fermentation using autotrophic organisms is an alternative
to sugar fermentation. In this approach, instead of breaking down
C5 or C6 sugar units, microbes build up products from C1 units,
such as carbon oxides (CO or CO2), which are found, for example, in
waste gases from heavy industry (for example, steel mills, processing
plants or refineries) or in syngas generated from any biomass
resource (for example, unsorted and non-recyclable municipal solid
waste, agricultural waste or organic industrial waste)15,16
. Capturing
and recycling these waste carbon streams before they enter the
atmosphere or environment offers routes to carbon-negative manufacturing
and a circular economy. To our knowledge, there is no
autotroph that produces acetone or IPA natively, but low-level production
of acetone and IPA (up to a rate of 3.8 mg/L/h in autotrophic
mode) has been demonstrated in several autotrophic hosts
through genetic engineering14,17–24
.
Among the range of autotrophic organisms, anaerobic acetogens
are particularly attractive as they do not require light as their source
of energy (providing an easier route for scale-up) and make use of
the most efficient of the known CO2 fixation pathways (the reductive
acetyl-CoA/Wood–Ljungdahl pathway)25–27
. Since the first demonstration
of genetic engineering of an acetogen in 2010 (ref. 28
), considerable
advances in the genetic toolbox of acetogens have been
made29,30
. Genetic engineering of acetogens has enabled small-scale
production of more than 50 molecules from C1 gases, including
industrially important alcohols (butanol, hexanol, IPA, etc.), diols
(monoethylene glycol, propanediol, butanediol, etc.), acids (lactic
acid, succinic acid, etc.), ketones (acetone and methyl ethyl ketone)
and dienes (butylene, isoprene, etc.)15,17–19,28–30
. However, efforts to
develop efficient production strains have been hampered by the lack
of high-throughput strain engineering workflows and a pathway to
industrial scale-up29
. The only commodity chemical that has been
produced by gas fermentation at industrial scale, to our knowledge,
is ethanol, which was achieved by our lab using a naturally selected,
non-engineered strain of Clostridium autoethanogenum that synthesizes
ethanol as a native product16
. In previous work, we optimized
the process for high productivities at g/L/h levels and selectivity of
more than 90% and have implemented it at two industrial plants,
with an annual production of more than 90,000 metric tons15,29,31
.
Building on these advances, here we describe an efficient,
carbon-negative fermentation process for production of acetone
and IPA by rewiring C. autoethanogenum from an ethanol producer
to a producer of acetone or IPA using enzymes from a historical
industrial strain collection, and we demonstrate scale-up of the process
to industrial pilot scale.
Results
Our strategy to achieve efficient acetone and IPA production relied
on a three-pronged approach of pathway, strain and process optimization
(Fig. 1). Specifically, we integrated high-throughput strain
engineering workflows, omics analysis, cell-free systems, kinetic
modeling, fermentation scale-up and life-cycle analysis (LCA).
We first identified optimal sets of heterologous pathway enzymes
to carry out the desired molecular transformations. Then, we optimized
our strains for enhanced flux to product. Finally, we carried
out process optimization, scale-up and LCA.
Pathway optimization. Despite the long history of ABE fermen-
tation7
, the available pool of enzyme variants for heterologous
acetone biosynthesis, specifically thiolase (ThlA), CoA transferase
(CtfAB) and acetoacetate decarboxylase (Adc), is limited (Fig. 2a).
In all previous studies9–14,17–24
, only genes derived from two reference
strains—natural isolates C. acetobutylicum ATCC824 and
C. beijerinckii NCIMB8052—have been explored. To expand the
sequence space of available enzymes, we mined a recently sequenced
collection of 272 ABE strains (assembled by David T. Jones of Otago
University and herein referred to as the ‘DJ collection’), which comprises
a myriad of optimized industrial strains from four decades of
biotechnological development, including production strains from
the last industrial ABE operations in South Africa and Taiwan7,32,33
.
We identified a total of 41 unique and not previously described
acetone biosynthesis enzymes in the DJ collection (ThlA: 6, CtfA:
11; CtfB: 13 and Adc: 11; Supplementary Table 1). These enzymes
had an identity of 72.4–99.8% to the enzymes of reference strains
(Supplementary Figs. 1–4). Several alcohol dehydrogenases, but no
sAdh enzymes, were found to be present in the DJ collection.
From the identified 41 new enzymes, we selected a representative
subset of 30 enzymes (ThlA: 4, CtfA: 6; CtfB: 10 and Adc: 10;
Fig. 2b and Supplementary Table 1) to create a combinatorial plasmid
library with the aim of optimizing acetone production. This
included reference enzymes from ATCC824 and NCIMB8052 as
well as two thiolases from Clostridium kluyveri, which we had used
successfully for butanol production in the past34
. Because of this previous
thiolase prototyping effort34
, we focused mainly on CtfAB and
Adc enzyme variants. Enzyme variants included in the library were
combined with three non-repetitive promoters of varying strengths
(high, medium and low)35
to assess acetone production in terms of
enzyme abundance via increased expression. To construct the various
combinations, we developed a hierarchical approach using three
donor vectors and one expression vector based on the modular and
universal pMTL80000 vector system36
, facilitated through Golden
Gate assembly and counterselection (Fig. 2c and Supplementary
Text). Our approach ensured that each enzyme was driven by a
unique promoter forming separate transcription units. Functional
transcription units were formed only in the final expression vector
to avoid any bias or unwanted recombination in the intermediate
host Escherichia coli. After assembly in E. coli, sequencing of the
resulting combinatorial plasmids showed good diversity, confirming
that all promoters and genes were present and relatively evenly
distributed (within ±10%) (Supplementary Fig. 5). In addition to
the original sequences, there were point mutations identified for
some variants (Supplementary Fig. 5). Unless this resulted in a
frameshift, we still carried these mutant variants forward.
Because C. autoethanogenum possesses a native primary–secondary
alcohol dehydrogenase (sAdh, encoded by
CAETHG_0553) known to reduce acetone to IPA37,38
, we generated
a C. autoethanogenum sAdh knockout (KO) strain (Δ0553)
to enable screening of the library in isolation of this reducing
+ +
Pathway
optimization
Strain
optimization
Process
optimization
Fermentation
development
& scale-up
Life cycle analysis
LCA
Cell-free prototyping
Omics
m/z
Metabolic modeling
Engineered
enzymes
Genome
mining
Combinatorial library
Fig. 1 | Overview of our three-pronged approach for pathway, strain and
process optimization. Overview of applied tools and strategies to advance
acetone and IPA production from waste gases from a proof of concept to
industrial level.
Nature Biotechnology | VOL 40 | March 2022 | 335–344 | www.nature.com/naturebiotechnology336
ArticlesNatureBiotechnology
equivalent consuming reaction. After transformation into Δ0553,
a total of 247 strains harboring distinct acetone biosynthesis
pathway designs (including all possible promoter–gene combinations
for the reference enzymes) were obtained and screened
(Supplementary Table 2). We initially investigated screening in
a plate-based format, but because acetone is too volatile and we
wanted to perform the screening under representative conditions
using gases as sole energy and carbon source, we performed the
screen in a closed system using pressurized bottles. To our knowledge,
this represents the largest library built and screened in an
autotroph by at least an order of magnitude.
All of the growth-tested gene variants produced acetone
with a wide range of end-point titers (up to 100 mM) observed
(Fig. 2d and Supplementary Table 2). Varying promoters alone led
to an 11-fold improvement in acetone production, which could be
improved another two-fold when further combined with enzyme
variants from the DJ collection. The best combination was composed
of thiolase from ATCC824, CtfA Var 8 (DJ033; 72.22%
identity to reference), CtfB Var 5 (DJ033; 96.83% identity to reference)
and Adc Var 1 (found across a range of DJ strains; 75.72%
identity to reference). Notably, the second-best combination
included variants that had several point mutations to the original
sequence in CtfAB (Supplementary Table 2). The top five
designs were moved forward to continuous fermentation testing
and genome integration. IPA production was not observed in any
of the strains (demonstrating the efficacy of Δ0553 for avoiding
acetone conversion), but we were surprised to measure production
of 3-hydroxybutyrate (3-HB), which we traced back to
native enzymes interacting with intermediates of the introduced
acetone pathway.
After optimizing the acetone pathway, we next screened a library
of C. autoethanogenum sAdh enzymes37
in Δ0553 for conversion of
acetone to IPA. Wild-type sAdh and engineered variants S199A and
S199Rthatshowednear-completeconversion(>97%)of20 gL−1
offed
acetone during growth on gas (Supplementary Fig. 6) were moved forward
for testing in continuous fermentation and genome integration.
DJ049
->
<- DJ078
<- DJ074
<- DJ037
DJ012->
NCIMB8052->
<-DJ036
<-DJ225
D
J124
->
<-DJ033
<-DJ051
<- DJ071
Tree scale: 0.01
DJ077 ->
<-DJ225
<-DJ030
DJ087 ->
<-DJ012
<-DJ033
<- DJ193
<-DJ051NCIMB8052->
DJ074 ->
DJ069->
DJ049->
Tree scale: 0.1
<- DJ077<- DJ074
<- D
J030
<- DJ087
<-DJ037
DJ069 ->
NCIMB8052 ->
DJ193 ->
DJ225->
<-DJ051
<-DJ012
DJ049 ->
Tree scale: 0.01
<- Ckl thlA1
<- Ckl thlA2
DJ259thlA2->
ATCC824 thlA ->
Tree scale: 0.1
a
CoA
O
OH
OO
OH
O
CoA
CoA
OO
CoA
O
CO2 O
Acetoacetyl-CoA Acetoacetate Acetone
Acetate Acetyl-CoA
Thl CtfAB Adc
cb
thl
donor vector
C. autoethanogenum Expression vector
P1 T1 P2 T2 P3 T3
T1 P2 T2 P3 T3
Antibiotic
selection marker
Gram (Ð)
replicon
Gram (+)
replicon
ctfA/ctfB
donor vector
adc
donor vector
CO2 + H2
WoodÐLjungdahl
pathway
Thl
CtfA
CtfB
Adc
d
Reference strains
2× acetyl-CoA
0
10
20
30
40
50
60
70
80
90
100
Acetonetiter(mM)
247 combinatorial strains
Fig. 2 | Pathway optimization. a, Acetone and IPA biosynthesis pathway from gases. b, Sequence mining of the DJ collection led to identification of a
large diversity of acetone biosynthesis enzyme sequences, including 65 unique and not previously described enzymes. Scale is in substitutions per
position; expanded trees are found in Supplementary Figs. 1–4. c, Combinatorial library assembly strategy to refactor selected acetone biosynthesis genes.
d, Acetone end-point titers observed in screening 247 strains with unique acetone pathway designs; designs that use genes from wild-type reference
strains are highlighted in red.
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Articles NatureBiotechnology
Strain optimization. With pathway enzymes identified, we next
optimized our production strains for increased titers. Specifically,
we used a genome-scale model16,39,40
and evolutionary algorithm41,42
to predict gene KOs that would increase flux to acetone and eliminate
unwanted byproducts, particularly 2,3-butanediol (2,3-BDO)
and 3-HB. These byproducts have high boiling points and would
accumulate during continuous fermentation with a recycle loop,
adding separation costs. The model identified eight gene KO targets
predicted to increase acetone production flux (Supplementary
Table 3), including the acetolactate decarboxylase reaction (budA
gene) in the well-characterized 2,3-BDO pathway38
. However, the
model did not identify genes responsible for 3-HB production in
the existing network.
Therefore, we performed an in silico homology search based
on enzymes described in the literature that perform similar reactions
(Fig. 3a). For example, a 3-HB dehydrogenase (Bdh) has been
described in Cupriavidus43,44
capable of reducing acetoacetate to
3-HB. Such activity would be directly competing with Adc, and
combinatorial strain designs with low Adc expression were among
the highest 3-HB titers (Supplementary Table 2). Several putative
Bdh isozymes as well as other promiscuous alcohol dehydrogenases,
including sAdh or butanediol dehydrogenases, are present in the
C. autoethanogenum genome38,45
. Likewise, a reduction to 3-HB
might also manifest at the CoA level, whereby acetoacetyl-CoA is
converted to 3-HB-CoA as described in the butanol pathway of
C. acetobutylcium (Hbd) or PHB pathway of Cupriavidus46,47
. This
reaction is also part of the fatty acid biosynthesis pathway. Although
respective FadBJ and Fab enzymes have been shown to be primarily
active on acyl carrier protein (ACP)-bound substrates, they have been
showntoacceptCoA-boundsubstratestoo48
.TheCoAmoiety,inturn,
can be cleaved off by a thioesterase; several enzymes of this functional
class are present in the C. autoethanogenum genome. Thioesterases
might also directly act on acetyl-CoA or acetoacetyl-CoA, limiting
flux into the pathway or competing with the CoA transferase, but do
not provide energy conservation. A total of 13 candidate genes were
identified in the C. autoethanogenum genome that alone or in combination
might be responsible for 3-HB formation.
Combinatorial KO of such a large matrix of gene targets was
infeasible with current in vivo workflows in C. autoethanogenum.
Even iterative KO of a down-selected set would take several months
and risk missing the correct genes. To address this challenge, we
adapted the in vitro Prototyping and Rapid Optimization of
Biosynthetic Enzymes (iPROBE) approach that we recently developed
to prototype biosynthetic pathways34
to now prototype KO
candidate targets. The concept was to accelerate host strain engineering
using cell-free systems by assessing the effect of enriching a
single target effector protein on acetone biosynthesis (that is, does a
KO candidate decrease acetone yields).
b c
3-HB
IPA
Acetone
3-HB
IPA
Acetone
Reference DJ collection
1586
2046
0420
1741
0555
1841
1743
0654
0385
0553
0718
1524
1780
Protein
Control
1586
2046
0420
1741
0555
1841
1743
0654
0385
0553
0718
1524
1780
Effector
gene
candidates
Acetone-
producing
pathway
C. autoethanogenum
Proteomic
measurements
Thioesterase
competition
Alcohol
dehydrogenase
competition
CoA-reductase
competition
d
Base strain
∆0553
∆0553∆1524
∆0553∆1524∆1586
3-HB
IPA
Acetone
Ethanol
8 20 32 24 50 90mM mM
0 0 0 0 0 0 24
40
log2
abundance
1586
2046
0420
1741
0718
1524
1780
0555
1841
1743
0654
0385
0553
CoA
OOH
3-hydroxybutyryl-CoA
OH
OOH
3-hydroxybutyrate
OH
Isopropanol
NAD(P)H NAD(P)+
CoA
O
OH
OO
CoA
CoA
OO
CO2
O
Acetoacetyl-CoA
Acetoacetate
Acetone
Acetate
Acetyl-CoA CoA CoA
NAD(P)H NAD(P)+
NAD(P)H
NAD(P)+
2× acetyl-CoA
a
ND
ND
ND
ND
Pathway
enzymes
CFE
Effector gene
Individually add
gene product
Fig. 3 | Strain optimization. a, In silico homology search identified 13 enzymes candidates native to C. autoethanogenum that were suspected to lead to
unwanted 3-HB formation by interacting with the heterologous acetone pathway. b, Cell-free prototyping of the identified C. autoethanogenum effector
candidate enzymes on acetone production using our iPROBE system. Effector gene candidates were produced via CFE and then spiked into E. coli cell
extracts individually enriched with acetone biosynthesis enzymes. Concentrations of 3-HB, acetone and IPA were measured. Results indicate that
C. autoethanogenum enzymes 1586, 0420, 0553 and 1524 cause the greatest decrease in acetone titers. Two sets of acetone enzymes were used, one
with reference enzymes resulting in moderate acetone production and one with enzymes from the DJ collection resulting in high acetone productivity
(mean ± 1 s.d. for n = 3 for each set). c, Expression levels of identified candidate enzymes (ND, not detected) in C. autoethanogenum (n = 3). Only three
of the four identified candidate enzymes are expressed (1586, 0553 and 1524) but not 0420. d, Improvements in selectivity of acetone over byproducts
3-HB, IPA and ethanol by serial parallel KO of selected effector candidate enzymes 1586, 0553 and 1524 in C. autoethanogenum (n = 3). A complete
selectivity breakdown is presented in Supplementary Fig. 8. Selectivity is the proportion of energy in the consumed feed gas that goes toward the product
of interest on an electron basis.
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ArticlesNatureBiotechnology
First, we used cell-free gene expression (CFE) to create an array
of cell extracts individually enriched with acetone biosynthesis
enzymes and effector candidate enzymes. 14
C-leucine incorporation
confirmed sufficient soluble enzyme expression (Supplementary
Fig. 7). Next, we established in vitro acetone production from glucose
using native E. coli catabolism coupled with two different combinations
of ThlA, CtfAB and Adc at final concentrations of 0.50 µM
each. The first combination using reference genes produced around
30 mM acetone, whereas the second combination that incorporates
sequences from the DJ collection produced nearly three times more
acetone (Supplementary Table 4). The gene KO candidate enzymes
were then added to cell-free acetone biosynthesis reactions at 1 µM,
mimicking overexpression. A substantial decrease in acetone titer
with the addition of enzyme 0553 identified this genomic KO as
a priority for deletion in the production strain. Enzymes 1586, 0420
and 1524 were also classified as detrimental to acetone biosynthesis
(Fig. 3b). Similar trends were observed for both acetone biosynthesis
combinations, although enzyme 1524 did not decrease acetone
titer for the second combination, which might be due to more favorable
kinetics for acetone production with these enzymes.
3-HB effector candidates identified by iPROBE were then successively
knocked-out in cells to eliminate byproducts and increase
acetone production. Notably, our proteomics of C. autoethanogenum
combinatorial strains, as well as previous RNA sequencing
and proteomics studies16,39
, indicated that not all of these gene KO
candidates are significantly expressed (Fig. 3c and Supplementary
Tables 5 and 6). In particular, 0420 showed negligible expression;
therefore, genes 0553, 1586 and 1524 were prioritized for sequential
KO. Although Δ0553Δ1524 only showed a 5% reduction in 3-HB,
the triple KO strain Δ0553Δ1524Δ1586 no longer produced measurable
levels of 3-HB (Fig. 3d and Supplementary Fig. 8). No obvious
negative phenotypic effects or reduction in growth rate were
observed from these KOs.
Next, we targeted the 2,3-BDO pathway, which our genome-scale
model predicted would be accomplished by elimination of the budA
gene (CAETHG_2932). In parallel, we chromosomally integrated
the optimized acetone pathway operons identified in this study to
increase stability and avoid the requirement for antibiotics. In the
resulting strain Δ0553Δ1524Δ1586Δ2932::Pfer-thlA-ctfAB-adc, acetone
production increased 27-fold from less than 2 mol% to more
than 50 mol%, with acetate and ethanol as the only other products
(Supplementary Fig. 8).
Because acetate, which is a precursor for ethanol production in
acetogens49,50
, is required as a co-substrate in the acetone pathway
as part of the CoA transferase reaction (Fig. 2a), it cannot be eliminated
completely. To further optimize flux to acetone, select strains
were analyzed by omics measurements and kinetic modeling for
bottlenecks in the pathways and tuning of enzyme levels tested in
our iPROBE system.
Proteomic measurements were conducted on ten strains from
our combinatorial library. These analyses included four strains
across the production range (ranks 1, 14, 26 and 150 of 247) and six
strains that had the same set of reference genes but driven by different
promoters with varied strengths (Fig. 4a and Supplementary
Table 6). A total of 1,914 distinct proteins were quantifiable, with
acetone pathway-specific enzymes all among the top 3.2% by
median abundance. Statistical analysis showed that acetone production
correlated well with the abundance of acetone pathway
enzymes, with the lowest abundance found in the lowest-producing
strains. Consistently, the two subunits of CtfAB were found to have
relatively lower expression levels compared to ThlA and Adc, even
if driven by the strongest promoter (Fig. 4a).
We then generated an ensemble of acetone-producing C. autoethanogenum
models (Supplementary Fig. 9) with different kinetic
parameters following a recently described approach51
. The ensemble
was pruned to include only models whose predictions matched
experimental data. By simulating changes to the expression level of
each enzyme associated with central metabolism and the acetone
pathway, the model predicted that overexpressing CtfAB would
increase flux to acetone (Fig. 4b).
Using our iPROBE system, we next tested this hypothesis by varying
levels of the different acetone pathway enzymes and confirmed
that increasing CtfAB abundance improved acetone production by
the largest amount. ThlA, CtfAB and Adc were expressed by CFE
and added to in vitro biosynthesis reactions at a range of concentra-
tions34
. First, Thl and Adc were co-titrated at low, medium and high
concentrations (0.05, 0.1 and 0.5 µM, respectively) with CtfAB con-
stantat0.5 µM,increasinginvitroacetonetiterbythree-fold(Fig.4c).
Next, CtfAB was titrated at concentrations ranging from 0.05 to
1.50 µM with Thl and Adc constant at 0.5 µM, increasing acetone
titer from the limit of detection to 74.58 ± 6.69 mM (Fig. 4c).
Taken together, our omics measurements, kinetic modeling
and cell-free prototyping all suggested CtfAB as the limiting step
for acetone production. We, therefore, added a second copy of
CtfAB by expressing the CtfAB variant and promoter combination
that registered the highest proteomic abundance on a plasmid
in strains Δ0553Δ1524Δ1586Δ2932::Pfer-thlA-ctfAB-adc(-sAdh),
which led to an increase of more than 40% in acetone/IPA selectivity
(Supplementary Fig. 10).
Process optimization. Starting with the best plasmid-based strains
from the combinatorial library screen, we next established a continuous
fermentation process for acetone in a benchtop continuous
stirred-tank reactor (CSTR). The fermentation process showed
an average 12 d of stable steady state production (Supplementary
Fig. 11) before declining. Ethanol and acetone were the main fermentation
products along with acetate, 2,3-BDO and 3-HB as
minor products (Supplementary Fig. 8). Whole-genome sequencing
showed that no mutations were found related to a decline in
performance. Strain Δ0553Δ1524Δ1586 with the same plasmid
performed similarly without measurable 3-HB formation
(Supplementary Fig. 8). Using a genomically integrated strain
Δ0553Δ1524Δ1586Δ2932::Pfer-thlA-ctfAB-adc led to a substantial
improvement in acetone selectivity and culture stability, extending
steady state production to ~3 weeks before runs were stopped
(Fig. 5a and Supplementary Fig. 11). Based on concentrations in
the broth, acetone productivity was ~2.5 g/L/h with ~80% gas utilization
(Fig. 5d and Supplementary Fig. 11). Actual productivities
are likely higher considering that acetone is highly volatile, and we
observed acetone in a condenser added to the off-gas line.
Basedontheestablishedacetoneprocess,weinvestigatedwhether
the performance would translate to IPA production. sAdh wild-type
and variants that had shown best conversion rates of fed acetone
were tested in context of the developed acetone strains. Of the variants,
S199R exhibited the highest titer and stability for IPA production.
Chromosomal integrated strain Δ0553Δ1524Δ1586Δ2932::
Pfer-thlA-ctfAB-adc-sAdh performed similarly to our acetone strain
after small adjustments in fermentation regimen. As IPA is less volatile,
we observed higher productivities of up to ~3 g/L/h with ~85%
gas utilization (Fig. 5d and Supplementary Fig. 11) and also slightly
increased selectivities (Fig. 5b). For both acetone and IPA strains,
expressing an extra copy of CtfAB further increased performance in
the CSTR, achieving up to ~90% selectivity (Fig. 5a,b).
Given the successful engineering of C. autoethanogenum to heterologously
produce acetone and IPA, both processes were scaled
up by a factor of 60× and from the bench to a field pilot. This
included transitioning from a 2-L benchtop CSTR to a 120-L pilot
plant using a loop reactor, in which important parameters, such as
pressure, can be assessed (Fig. 5c)15,29
. Several campaigns were carried
out with strains Δ0553Δ1524Δ1586Δ2932::Pfer-thlA-ctfAB-adc
(-sAdh) (which did not have the extra copy of CtfAB) under similar
fermentation conditions and gas composition. Strain performance
Nature Biotechnology | VOL 40 | March 2022 | 335–344 | www.nature.com/naturebiotechnology 339
Articles NatureBiotechnology
in continuous culture, with respect to product selectivity and productivity
as well as stability in the pilot reactor, correlated well with
performance in the benchtop CSTR-based fermentations across a
range of production rates (Fig. 5d). Productivity varied owing to
several factors, most notably strain genotype and bacterial dilution
rates due to different reactor configurations. Small variations are
expected given the use of lower-grade bulk chemicals and process
water and elevated pressure (1.4 bar) at pilot scale.
We used LCA to compare the GHG emissions of acetone and IPA
produced using the scaled-up gas fermentation process with current
fresh fossil-based production via propene and the cumene process
(Fig. 6a). Chemical and energy inputs and product yield were
determined using pilot-scale data coupled with design data from
industrial-scale ethanol production15,29
, which uses steel mill off-gas
feedstock with utilities (for example, electricity and steam) (Fig. 6b
and Supplementary Tables 9 and 10). The GHG emissions of acetone
and IPA produced by the gas fermentation process (Fig. 6c) are
−1.78 kgCO2e/kg acetone and −1.17 kgCO2e/kg IPA, respectively.
The negative value is due to avoided off-gas emissions combined
with low process emissions (Fig. 6c), effectively locking carbon
into the product. In contrast, traditional petrochemical production
routes result in substantial GHG emissions of 2.55 kgCO2e/kg for
acetone and 1.85 kgCO2e/kg for IPA.
Discussion
Despite recent progress in fermentative processes, production of
commodity chemicals by sugar fermentation remains difficult to
scale52,53
. Only a few examples have reached industrial level, including
1,3-propanediol, 1,4-butanediol, isobutanol, farnesene, lactic
acid and succinic acid—all of which required substantial financial
0
20
40
60
80
100
120
1 × 109
1 × 1010
1 × 1011
1 × 1012
1 × 1013
0.50
0.10
0.05
0.05
0.10
0.50
[Adc](µM)
[Thl] (µM)
ATCC824
Var8
Var5
Var1
Proteinabundance
[Acetone](mM)
Combinatorial strains
1 14 26 150 47 68 112 125 182 203
[CtfA]
[CtfB]
= 0.5 µM
Cell-free enzymes
Acetone-
producing
pathway
+ +
b c
a
Predictedfluxtoacetone+IPA
Fold change CtfAB
0.68 0.8 1 1.25
Boundary reaction
Intracellular reaction
Biomass reaction
Intracellular metabolite
Biomass
ATP, ADP
Co-factors (NAD(P)/H, Fd, etc.)
Same metabolites
CO CO2 H2
Acetate
Ethanol
ButanediolLactate
IPA
Acetone
3-HB
90
0
2.10
2.15
2.20
2.25
2.30
2.35
[Thl]
[Adc]
= 0.5 µM
C.klu_thlA1
Var5
Var6
Var4
Var1
Var9
Var8
Var6
ATCC824
Var6
Var7
Var7
ATCC824
NCIMB8052
NCIMB8052
NCIMB8052
ATCC824
NCIMB8052
NCIMB8052
NCIMB8052
ATCC824
NCIMB8052
NCIMB8052
NCIMB8052
ATCC824
NCIMB8052
NCIMB8052
NCIMB8052
ATCC824
NCIMB8052
NCIMB8052
NCIMB8052
ATCC824
NCIMB8052
NCIMB8052
NCIMB8052
Rank
Promoter 1 3 2
Gene
3 2 1 3 1 2 2 1 3 3 1 2 1 3 2 2 1 3 2 3 1 3 2 1 1 2 3
* * * * * * * * ** * * * * * * * * ** * **
Wood–Ljungdahlpathway
Biomass
Maintenance
reactions
Co-factor
regeneration
PyruvateAcetyl-CoA
0
10
20
30
40
50
60
70
80
90
0.05 0.1 0.2 0.3 0.4 0.5 0.75 1 1.5
Acetone(mM)
CtfAB (µM)
mM
Fig. 4 | Flux optimization. a, Protein abundance (area under the curve) for acetone biosynthesis enzymes (ThlA, yellow; CtfAB, green; Adc, blue) of ten
selected combinatorial library strains: four strains across the production range (ranks 1, 14, 26 and 150 of 247) and six strains with fixed reference genes
but different promoter combinations (mean ± 1 s.d. for n = 3; all data points shown). Asterisks represent significance abundance differences (P ≤ 0.05)
compared to lowest producer strain (rank 203) as assessed by a two-sided t-test corrected for multiple comparisons by the Benjamini–Hochberg method.
Exact P values for each comparison are listed in Supplementary Table 6. Proteomics samples were taken from cells harvested during mid-log growth, and
overlayed acetone production values are end-point values (n = 3 with all data shown). b, Kinetic ensemble modeling (n = 50 trained models) predicted that
total flux to acetone and IPA would increase as CtfAB expression increases. No models were able to reach steady state for the 1.5-fold perturbation. An
expanded representation of the core metabolic network can be found in Supplementary Fig. 9. c, Cell-free prototyping enabled precise modulation of Thl
and Adc and of CtfAB to demonstrate the strong dependence of acetone titer on the concentration of CtfAB (mean ± 1 s.d. for n = 3).
Nature Biotechnology | VOL 40 | March 2022 | 335–344 | www.nature.com/naturebiotechnology340
ArticlesNatureBiotechnology
and human investment54,55
. The economics of turning one commodity
(sugar) into another commodity and operating in batch processing
mode is challenging; even for high-yielding pathways, it requires
extremely high productivities and titers53
. In the case of acetone
and IPA production from sugars, the maximum yield is 50%14
.
Despite substantial development and highly engineered systems
that showed improvements over the historical ABE process7
, only
moderate productivities of up to 0.15 g/L/h for acetone and 0.6 g/L/h
for IPA have been achieved11,13
.
Using waste resources instead of sugars as feedstocks decouples
production from commodity prices. The first industrial-scale gas
fermentation plants are now in operation and are turning waste into
ethanol in a continuous fermentation using acetogen C. autoethano-
genum15,29,31
. Ethanol is a native product of acetogens16
, but, through
genetic engineering, synthesis of a range of non-native commodity
chemicals has been demonstrated at laboratory scale. However,
achieved efficiencies (<100 mg/L/h) and selectivities (<10%)
remain low, and production is not sustainable over longer periods
3-HB
IPA
Acetone
Ethanol
2,3-BDO
Acetate
Biomass
∆0553∆1524∆1586
∆2932::Pfer-thlA
-ctfAB-adc-sAdh
sAdh WT S199A S199R S199R S199R
+ CtfAB
ba c
0
10
20
30
40
50
60
70
80
90
100
Productselectivity(%)
0
10
20
30
40
50
60
70
80
90
100
Productselectivity(%)
∆0553∆1524∆1586∆2932
::Pfer-thlA-ctfAB-adc
+ CtfAB
∆0553∆1524∆1586
∆2932::Pfer-thlA-ctfAB
-adc + sAdh
Lab
IPA
Acetone
PilotLab*
++
d
0
0.2
0.4
0.6
0.8
1
0 1 2 3
Selectivity
Productivity (g/L/h)
Fig. 5 | Process optimization and scale-up. a, Continuous acetone production in lab-scale CSTRs with an integrated strain. Expression of an extra copy
of CtfAB led to a considerable improvement in selectivity to acetone (mean ± 1 s.d. for n = 3). b, Continuous IPA production in lab-scale CSTRs. IPA
production was optimized through a combination of different sADH variants and expressing an extra copy of CtfAB and integration of the pathway (mean
± 1 s.d. for n = 3). c, 120-L scalable pilot reactor at LanzaTech’s Freedom Pines Biorefinery site. d, Comparison of key performance metrics selectivity and
productivity across a spectrum of individual runs performed in lab-scale CSTRs (small circles, small squares and crosses) and pilot scale (large circles
and large squares) for acetone (blue) and IPA (green) strains. Crosses indicate strains with an extra copy of CtfAB. Selectivity is the proportion of energy
in the consumed feed gas that goes toward the product of interest on an electron basis. Data points represent maximum steady state conditions where
metabolite and gas profiles were stable over multiple days. Respective run profiles are shown in Supplementary Fig. 11. WT, wild-type.
Propene
Reforming
Methanol
Methanol-to-
propene
Syngas
Methanol
synthesis
Gasification
IPAAcetone
Benzene
Phenol
+
Hydration
+
Current production routes
from fresh fossil
Production from waste carbon
IPAAcetone
Gasification
Gas
fermentation
+ Animal feed
+ biogas
Coal Oil Natural gas Emissions MSW Biomass
a b c
Avoided off-gasProcessTotal emissions
1.85
Ð1.17
2.55
Ð1.79
Ð3.0
Ð2.0
Ð1.0
0
1.0
2.0
3.0GHGemissionskgCO2
/kg
Fresh
fossil
Fresh
fossil
Waste
carbon
Waste
carbon
Acetone IPA
Cracking
System boundary
Feedstock:
Steel mill off-gas
(avoided emissions)
Gas conditioning/
compression
Gas fermentation
Product recovery
Co-products: Anaerobic
digestion
Other outputs: Main product:
Acetone or IPATreated water
Animal feed,
biogas
Water
recycle
Other inputs:
Chemicals,
utilities, water
Cumene process
CO2
Fig. 6 | Life cycle analysis. a, Comparison of current routes to acetone and IPA and the developed gas fermentation process (starting from industrial waste
gases, municipal solid waste (MSW) or biomass). Steps that are particularly energy intensive or result in toxic waste are indicated by symbols. b, Process
flow diagram of LanzaTech’s gas fermentation process that shows the various inputs, outputs and process steps as well as the system boundary used for
LCA. c, LCA analysis compares GHG emissions (shown in black) of traditional production routes with the gas fermentation route. Where traditional routes
result in GHG emissions, gas fermentation avoids GHG emissions, effectively fixing carbon into the product. The effects of process (blue) and avoided
off-gas (green) are indicated (see Supplementary Tables 9 and 10 for details).
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Articles NatureBiotechnology
in continuous mode. For acetone and IPA specifically, the highest
reported autotrophic production rates are 3.8 mg/L/h with a selectivity
of 1.2%17–24
, results that are insufficient for commercialization53
.
The bioprocess described here achieves industrially relevant
productivities of up to ~3 g/L/h and showed continuous production
from C1 gases for prolonged periods (~3 weeks) without a decrease
in performance. Engineered strains achieved high selectivities of up
to 90%, which were previously reported only in acetogens for the
native products acetate and ethanol. As acetogens live on the thermodynamic
edge of life, it has long been an open question whether
they can be engineered to synthesize complex molecules at high
selectivity56,57
. Our results show that this is possible.
To achieve the observed level of performance, we relied on a
three-pronged approach comprising innovations in pathway optimization,
strain optimization and process development. Our approach
has several key features. First, we explored a large sequence space of
biosynthetic genes to identify an optimal pathway design. Whereas
previous studies aiming to optimize autotrophic acetone or IPA
production explored only a handful of designs that all relied on the
same gene sequences from two natural isolates17–24
, we mined a large
genome collection of industrial strains32,33
and screened a library
of hundreds of designs, which led to a significant increase in production
level. In some instances, even a few mutations resulted in
several-fold improvement in performance. The developed combinatorial
library framework that enabled us to prototype this design
space will be applicable to generation and screening of other pathways
in acetogens and other autotrophs.
Second, to achieve high selectivity, we implemented a substantial
level of strain engineering. Previous studies relied on plasmid-based
pathway expression with a maximum of one additional genome
modification. Our final production strain comprised multiple
genome modifications, including pathway integration, four gene
KOs and overexpression of two genes. To our knowledge, this represents
the largest number of combined genome modifications in
an autotroph or Clostridium strain. However, our original pool of
candidates to KO was even larger, and it would have taken many
months to years to generate all possible KO combinations. To guide
this effort, we applied a recently described cell-free framework
(iPROBE)34
to rapidly prototype candidate genes for KO efficacy,
streamlining an often laborious and time-consuming strain generation
process17,29
. A common challenge in metabolic engineering is
that annotations of enzymes are often poor, and many enzymes are
promiscuous and present as multiple isoenzymes58–60
. In our case,
we observed unwanted byproducts when introducing our heterologous
pathway from interactions with native enzymes, lowering overall
yield and increasing downstream separation requirements. An
in silico search yielded a large number of candidate enzymes, and
considering that these may act in combination, iPROBE34
, which
was applied here in a different way, allowed us to narrow down this
list to a few key candidate enzymes in days, enabling precision gene
KOs to effectively shut down byproduct formation. Our work also
highlights the value of recent approaches to systematically expand
genome-scale models to incorporate putative reactions arising from
enzyme promiscuity61
.
Working both in vitro (using iPROBE)34
and in vivo, we interwove
omics measurements, kinetic ensemble modelling and systems
biology analyses to identify critical bottlenecks in the production
pathway that, when alleviated, led to considerably improved flux
to the target product. Although we speculated that the thiolase step
was the bottleneck in our pathway (given that the Claisen condensation
of two molecules of acetyl-CoA to form acetoacetyl-CoA is
an endergonic reaction)62
, our analysis correctly predicted that, in
fact, the downstream CoA transferase step is most critical (providing
an important pull for the pathway). Careful balancing of the
pathway turned out to be critical; for example, acetate production
could not be simply eliminated as it is required as co-factor for the
CoA transferase. To enable IPA production, we screened a library
of engineered sADH variants. Variant S199R, which has been engineered
to have a less rigid active site cavity, was thereby found to
provide the best performance, matching the in vitro data for catalytic
activity on acetone37
.
Third, we scaled-up the process in a 120-L field pilot using a
loop reactor designed to maximize energy efficiency and resembling
industrial gas fermentation operations carried out at 46,000
metric tons per annum scale15,29
. A key challenge with the scale-up
of fermentations is the increasing heterogeneity of the bioreactor
environment with scale31,55
. Achieving high rates of gas conversion
in a single pass is a desirable performance metric for a gas fermentation
process31
. However, achieving this means that there will be
zones within the bioreactor where the fermentation broth has a
very high gas substrate content (for example, where the gas substrate
is continuously delivered into the reactor) and other zones
with very low gas substrate content (for example, where any unreacted
gas substrates and inert gases exit the bioreactor). Microbes
within the reactor cycle through these different zones continuously.
This heterogenous environment can be challenging to accurately
reproduce at a bench scale. It is, therefore, important to understand
the limits of metabolic robustness, as was done in detail for ethanol
production50
. Thermodynamic and kinetic models allowed us
to explore and optimize pathway balance and metabolic robustness
for acetone and IPA production. Developing process monitoring
and control strategies that enable product selectivity and yields to
be maintained despite these fluctuations within the bioreactor environment
is a key challenge when scaling a gas fermentation technology.
Specifically, defining the mechanisms and degree to which the
gas, chemical and physical environment can influence selectivity is
critical for dynamically adjusting one parameter to compensate for
imposed fluctuations in another. We performed rigorous testing of
performance across a spectrum of conditions, with selectivity maintained
over a range of production rates. Even though the field pilot
had been designed with the ethanol process in mind, scale-up of
acetone and IPA was similar to previous scale-up of the ethanol process,
and a good correlation between bench scale and pilot scale was
observed across a range of production rates, with small variations
due to process setup and elevated pressure levels. The observed performance
provides confidence that the developed strains and fermentation
process will translate to industrial scale.
The flexibility of fermentation to use the same bioreactor infrastructure
for multiple conversions (for example, ethanol, acetone and
IPA) is a key benefit over traditional chemical manufacturing (where
plants are typically purpose-built for a single conversion process).
The other key benefit offered by gas fermentation is the negative
carbon footprint. Traditional chemical manufacturing processes are
associated with substantial release of GHG emissions into the atmosphere
(2.55 kgCO2e/kg of produced acetone and 1.85 kgCO2e/kg
of produced IPA, respectively). In contrast, gas fermentation allows
manufacturing of the same products while also avoiding release of
GHG emissions, effectively fixing carbon into the product. LCA
confirmed that acetone and IPA produced by gas fermentation have
a negative carbon footprint of −1.78 kgCO2e/kg of produced acetone
and −1.17 kgCO2e/kg of produced IPA. Our bioprocess provides a
green alternative to today’s production routes to these important
chemicals, which exclusively rely on fresh fossil feedstocks. Thus,
our work promotes implementation of a circular economic model
in which the carbon from agriculture, industrial and societal waste
streams is recycled into a chemical synthesis value chain and displaces
manufacture of products from fresh fossil resources.
Online content
Any methods, additional references, Nature Research reporting
summaries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of
Nature Biotechnology | VOL 40 | March 2022 | 335–344 | www.nature.com/naturebiotechnology342
ArticlesNatureBiotechnology
author contributions and competing interests; and statements of
data and code availability are available at https://doi.org/10.1038/
s41587-021-01195-w.
Received: 6 October 2020; Accepted: 9 December 2021;
Published online: 21 February 2022
References
	1.	 Aguado-Deblas, L. et al. Acetone prospect as an additive to allow the use of
castor and sunflower oils as drop-in biofuels in diesel/acetone/vegetable oil
triple blends for application in diesel engines. Molecules 25, 2935 (2020).
	2.	 Elfasakhany, A. Performance and emissions analysis on using acetone–
gasoline fuel blends in spark-ignition engine. Eng. Sci. Technol. Int. J. 19,
1224–1232 (2016).
	3.	 Anbarasan, P. et al. Integration of chemical catalysis with extractive
fermentation to produce fuels. Nature 491, 235–239 (2012).
	4.	 Ryan, C. F. et al. Synthesis of aviation fuel from bio-derived isophorone.
Sustain. Energy Fuels 4, 1088–1092 (2020).
	5.	 Kratzel, A. et al. Inactivation of severe acute respiratory syndrome
coronavirus 2 by WHO-recommended hand rub formulations and alcohols.
Emerg. Infect. Dis. 26, 1592–1595 (2020).
	6.	 Wernet, G. et al. The ecoinvent database version 3 (part I): overview and
methodology. Int. J. Life Cycle Assess. 21, 1218–1230 (2016).
	7.	 Jones, D. T. & Woods, D. R. Acetone–butanol fermentation revisited.
Microbiol. Rev. 50, 484–524 (1986).
	8.	 Ismaiel, A. A., Zhu, C. X., Colby, G. D. & Chen, J. S. Purification and
characterization of a primary–secondary alcohol dehydrogenase from two
strains of Clostridium beijerinckii. J. Bacteriol. 175, 5097–5105 (1993).
	9.	 Hanai, T., Atsumi, S. & Liao, J. C. Engineered synthetic pathway for
isopropanol production in Escherichia coli. Appl. Environ. Microbiol. 73,
7814–7818 (2007).
	10.	May, A. et al. A modified pathway for the production of acetone in
Escherichia coli. Metab. Eng. 15, 218–225 (2013).
	11.	Liang, L. et al. CRISPR EnAbled Trackable genome Engineering for
isopropanol production in Escherichia coli. Metab. Eng. 41, 1–10 (2017).
	12.	Soma, Y., Yamaji, T., Matsuda, F. & Hanai, T. Synthetic metabolic bypass for a
metabolic toggle switch enhances acetyl-CoA supply for isopropanol
production by Escherichia coli. J. Biosci. Bioeng. 123, 625–633 (2017).
	13.	Jojima, T., Inui, M. & Yukawa, H. Production of isopropanol by metabolically
engineered Escherichia coli. Appl. Microbiol. Biotechnol. 77, 1219–1224 (2008).
	14.	Jones, S. W. et al. CO2 fixation by anaerobic non-photosynthetic mixotrophy
for improved carbon conversion. Nat. Commun. 7, 12800 (2016).
	15.	Simpson, S. D. & Köpke, M. Pollution to products: recycling of ‘above ground’
carbon by gas fermentation. Curr. Opin. Biotechnol. 65, 180–189 (2020).
	16.	Marcellin, E. et al. Low carbon fuels and commodity chemicals from waste
gases—systematic approach to understand energy metabolism in a model
acetogen. Green Chem. 18, 3020 (2016).
	17.	Kato, J. et al. Metabolic engineering of Moorella thermoacetica for
thermophilic bioconversion of gaseous substrates to a volatile chemical. AMB
Express 11, 59 (2021).
	18.	Hoffmeister, S. et al. Acetone production with metabolically engineered
strains of Acetobacterium woodii. Metab. Eng. 36, 37–47 (2016).
	19.	Banerjee, A., Leang, C., Ueki, T., Nevin, K. P. & Lovley, D. R. A
lactose-inducible system for metabolic engineering of Clostridium ljungdahlii.
Appl. Environ. Microbiol. 80, 2410–2416 (2014).
	20.	Garrigues, L., Maignien, L., Lombard, E., Singh, J. & Guillouet, S. E.
Isopropanol production from carbon dioxide in Cupriavidus necator in a
pressurized bioreactor. N. Biotechnol. 56, 16–20 (2020).
	21.	Lee, H. J., Son, J., Sim, S. J. & Woo, H. M. Metabolic rewiring of synthetic
pyruvate dehydrogenase bypasses for acetone production in cyanobacteria.
Plant Biotechnol. J. 18, 1860–1868 (2020).
	22.	Torella, J. P. et al. Efficient solar-to-fuels production from a hybrid
microbial-water-splitting catalyst system. Proc. Natl Acad. Sci. USA 112,
2337–2342 (2015).
	23.	Hirokawa, Y., Dempo, Y., Fukusaki, E. & Hanai, T. Metabolic engineering for
isopropanol production by an engineered cyanobacterium, Synechococcus
elongatus PCC 7942, under photosynthetic conditions. J. Biosci. Bioeng. 123,
39–45 (2016).
	24.	Zhou, J., Zhang, H., Zhang, Y., Li, Y. & Ma, Y. Designing and creating a
modularized synthetic pathway in cyanobacterium Synechocystis enables
production of acetone from carbon dioxide. Metab. Eng. 14, 394–400 (2012).
	25.	Claassens, N. J., Cotton, C. A. R., Kopljar, D. & Bar-Even, A. Making
quantitative sense of electromicrobial production. Nat. Catal. 2,
437–447 (2019).
	26.	Fast, A. G. & Papoutsakis, E. T. Stoichiometric and energetic analyses of
non-photosynthetic CO2-fixation pathways to support synthetic biology
strategies for production of fuels and chemicals. Curr. Opin. Chem. Eng. 1,
380–395 (2012).
	27.	Bar-Even, A., Noor, E. & Milo, R. A survey of carbon fixation pathways
through a quantitative lens. J. Exp. Bot. 63, 2325–2342 (2012).
	28.	Köpke, M. et al. Clostridium ljungdahlii represents a microbial production
platform based on syngas. Proc. Natl Acad. Sci. USA 107, 13087–13092 (2010).
	29.	Fackler, N. et al. Stepping on the gas to a circular economy: accelerating
development of carbon-negative chemical production from gas fermentation.
Ann. Rev. Chem. Biomol. Eng. 12, 439–470 (2021).
	30.	Jin, S. et al. Synthetic biology on acetogenic bacteria for highly efficient
conversion of C1 gases to biochemicals. Int. J. Mol. Sci. 21, 7639 (2020).
	31.	Takors, R. et al. Using gas mixtures of CO, CO2 and H2 as microbial
substrates: the do’s and don’ts of successful technology transfer from
laboratory to production scale. Microb. Biotechnol. 11, 606–625 (2018).
	32.	Jones, D. T. Applied acetone–butonal fermentation. In: Clostridia:
Biotechnology and Medical Applications (eds Dürre, P. & Bahl, H.)
125–168 (Wiley, 2001).
	33.	Jones, D. T. & Keis, S. Origins and relationships of industrial
solvent-producing clostridial strains. FEMS Microbiol. Rev. 17,
223–232 (1995).
	34.	Karim, A. S. et al. In vitro prototyping and rapid optimization of biosynthetic
enzymes for cellular design. Nat. Chem. Biol. 16, 912–919 (2020).
	35.	Krüger, A. et al. Development of a clostridia-based cell-free system
for prototyping genetic parts and metabolic pathways. Metab. Eng. 62,
95–105 (2020).
	36.	Heap, J. T., Pennington, O. J., Cartman, S. T. & Minton, N. P. A modular
system for Clostridium shuttle plasmids. J. Microbiol. Methods 78,
79–85 (2009).
	37.	Maddock, D. J., Patrick, W. M. & Gerth, M. L. Substitutions at the cofactor
phosphate-binding site of a clostridial alcohol dehydrogenase lead to
unexpected changes in substrate specificity. Protein Eng. Des. Sel. 28,
251–258 (2015).
	38.	Köpke, M. et al. Reconstruction of an acetogenic 2,3-butanediol pathway
involving a novel NADPH-dependent primary–secondary alcohol
dehydrogenase. Appl. Environ. Microbiol. 80, 3394–3403 (2014).
	39.	Valgepea, K. et al. H2 drives metabolic rearrangements in gas-fermenting
Clostridium autoethanogenum. Biotechnol. Biofuels 11, 55 (2018).
	40.	de Souza Pinto Lemgruber, R. et al. Systems-level engineering and
characterisation of Clostridium autoethanogenum through heterologous
production of poly-3-hydroxybutyrate (PHB). Metab. Eng. 53, 14–23 (2019).
	41.	Maia, P., Rocha, I. & Rocha, M. Identification of robust strain designs via
tandem pFBA/LMOMA phenotype prediction. In: GECCO 2017: Proceedings
of the Genetic and Evolutionary Computation Conference Companion
1661–1668 (Association for Computing Machinery, 2017).
	42.	Zitzler, E., Laumanns, M. & Thiele, L. SPEA2: improving the Strength Pareto
Evolutionary Algorithm. In: Proceedings of the Fifth Conference on
Evolutionary Methods for Design 95–100 (Association for Computing
Machinery, 2001).
	43.	Takanashi, M. & Saito, T. Characterization of two 3-hydroxybutyrate
dehydrogenases in poly(3-hydroxybutyrate)-degradable bacterium, Ralstonia
pickettii T1. J. Biosci. Bioeng. 101, 501–507 (2006).
	44.	Segawa, M., Wen, C., Orita, I., Nakamura, S. & Fukui, T. Two
NADH-dependent (S)-3-hydroxyacyl-CoA dehydrogenases from
polyhydroxyalkanoate-producing Ralstonia eutropha. J. Biosci. Bioeng. 127,
294–300 (2019).
	45.	Tan, Y., Liu, Z.-Y., Liu, Z. & Li, F.-L. Characterization of an acetoin
reductase/2,3-butanediol dehydrogenase from Clostridium ljungdahlii DSM
13528. Enzyme Microb. Technol. 79–80, 1–7 (2015).
	46.	Kim, J., Chang, J. H., Kim, E. J. & Kim, K. J. Crystal structure of (R)-
3-hydroxybutyryl-CoA dehydrogenase PhaB from Ralstonia eutropha.
Biochem. Biophys. Res. Commun. 443, 783–788 (2014).
	47.	Boynton, Z. L., Bennet, G. N. & Rudolph, F. B. Cloning, sequencing, and
expression of clustered genes encoding beta-hydroxybutyryl-coenzyme A
(CoA) dehydrogenase, crotonase, and butyryl-CoA dehydrogenase from
Clostridium acetobutylicum ATCC 824. J. Bacteriol. 178, 3015–3024 (1996).
	48.	Vick, J. E. et al. Escherichia coli enoyl-acyl carrier protein reductase (FabI)
supports efficient operation of a functional reversal of the β-oxidation cycle.
Appl. Environ. Microbiol. 81, 1406–1416 (2015).
	49.	Liew, F. et al. Metabolic engineering of Clostridium autoethanogenum for
selective alcohol production. Metab. Eng. 40, 104–114 (2017).
	50.	Mahamkali, V. et al. Redox controls metabolic robustness in the
gas-fermenting acetogen Clostridium autoethanogenum. Proc. Natl Acad. Sci.
USA 117, 13168–13175 (2020).
	51.	Greene, J., Daniell, J., Köpke, M., Broadbelt, L. & Tyo, K. E. J. Kinetic
ensemble model of gas fermenting Clostridium autoethanogenum for
improved ethanol production. Biochem. Eng. J. 148, 46–56 (2019).
	52.	Wehrs, M. et al. Engineering robust production microbes for large-scale
cultivation. Trends Microbiol. 27, 524–537 (2019).
	53.	Hoff, B. et al. Unlocking nature’s biosynthetic power—metabolic engineering
for the fermentative production of chemicals. Angew. Chem. Int. Ed. Engl. 60,
2258–2278 (2021).
Nature Biotechnology | VOL 40 | March 2022 | 335–344 | www.nature.com/naturebiotechnology 343
Articles NatureBiotechnology
	54.	Nielsen, J. & Keasling, J. D. Engineering cellular metabolism. Cell 164,
1185–1197 (2016).
	55.	Crater, J. S. & Lievense, J. C. Scale-up of industrial microbial processes. FEMS
Microbiol. Lett. 365, 138 (2018).
	56.	Bertsch, J. & Müller, V. Bioenergetic constraints for conversion of syngas to
biofuels in acetogenic bacteria. Biotechnol. Bioeng. 8, 210 (2015).
	57.	Schuchmann, K. & Müller, V. Autotrophy at the thermodynamic limit of life:
a model for energy conservation in acetogenic bacteria. Nat. Rev. Microbiol.
12, 809–821 (2014).
	58.	Erb, T. J. Back to the future: why we need enzymology to build a synthetic
metabolism of the future. Beilstein J. Org. Chem. 15, 551–557 (2019).
	59.	Diether, M., Nikolaev, Y., Allain, F. H. & Sauer, U. Systematic mapping of
protein-metabolite interactions in central metabolism of Escherichia coli. Mol.
Syst. Biol. 15, e9008 (2019).
	60.	Kim, H. M., Chae, T. U., Choi, S. Y., Kim, W. J. & Lee, S. Y. Engineering of an
oleaginous bacterium for the production of fatty acids and fuels. Nat. Chem.
Biol. 15, 721–729 (2019).
	61.	Amin, S. A., Chavez, E., Porokhin, V., Nair, N. U. & Hassoun, S. Towards
creating an extended metabolic model (EMM) for E. coli using enzyme
promiscuity prediction and metabolomics data. Microb. Cell Fact. 18,
109 (2019).
	62.	Vögeli, B. et al. Archaeal acetoacetyl-CoA thiolase/HMG-CoA synthase
complex channels the intermediate via a fused CoA-binding site. Proc. Natl
Acad. Sci. USA 115, 3380–3385 (2018).
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
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ArticlesNatureBiotechnology
Methods
Strains and chemicals. C. autoethanogenum DSM19630, a derivate of type strain
DSM10061, obtained from the German Collection of Microorganisms and Cell
Cultures, was used50
. Strains were handled using anaerobic techniques and media
described earlier40,50
. Molecular-grade chemicals (Sigma-Aldrich) and RO water
were used for laboratory experiments; for piloting, industrial chemicals and
process water were used. For cloning and cell-free prototyping, E. coli strains
NEB Express (NEB), DH5α (Thermo Fisher Scientific) and BL21 Star (DE3)
(Life Technologies) were used.
Sequence mining. Sequences for 272 industrial ABE strains are available through
GenBank (Supplementary Table 7). Acetone biosynthesis genes (Supplementary
Table 1) were extracted from these genomes by E.C. numbers (ThlA: E.C. 2.3.1.9,
CtfAB E.C. 2.8.3.9 and Adc E.C. 4.1.1.4) annotated in the Joint Genome Institute
Integrated Microbial Genomes & Microbiomes platform63
. The nucleotide
sequences were in silico translated, and the resulting amino acid sequences were
aligned using ClustalW (version 2.1)64
as a part of the Geneious (R9.1.8) software
package (Biomatters). Phylogenetic trees were generated using IQ-TREE (version
1.6.12) software65
with the mod LG + G4 substitution model. This model was
chosen by running the first tree using the model finder option66
. The resulting trees
were visualized using the Interactive Tree Of Life (iTOL) (version 5) web service67
.
Combinatorial library. The acetone combinatorial library employed a
hierarchical Golden Gate assembly approach that first employed AarI to
construct the donor vectors, before combining with a recipient vector to
perform the final assembly using BsaI. The counter-selectable marker ccdB
(flanked with BsaI) was cloned into pMTL82251-Pfer/wl/pfor
36
(Supplementary
Table 1) to generate the recipient vectors. Each of these promoters has a built-in
ribosomal binding site (Supplementary Table 1). Combinatorial-hierarchical
designs were generated using the TeselaGen DESIGN module (version 19)
(TeselaGen Biotechnology)68
. The construction of donor vectors involved AarI
assembly into pTypeIIs vector (Invitrogen) using two DNA parts: amplicon
containing transcriptional terminator and promoter that was subcloned into
plasmid pCR-Blunt (Invitrogen) and amplicon containing thlA, ctfA-ctfB or
adc. Primer sequence (Supplementary Table 8) and a detailed step-by-step
description can be found in the Supplementary Text and Supplementary
Fig. 11. After BsaI assembly in E. coli, the resulting acetone combinatorial
plasmids were sequence verified using MiSeq (Illumina) as described below.
After transformation40
into C. autoethanogenum Δ0553 strain, the resulting
acetone combinatorial strains were subjected to Schott bottle growth on 1.5-bar
synthetic gas blend (50% CO, 10% H2, 30% CO2 and 10% N2; Airgas) for 8 d
before samples were collected for OD600nm measurement and high-performance
liquid chromatography (HPLC) analysis as described below.
Gene expression. Plasmids for expression of sAdh variants and an extra copy
of CtfAB were constructed as follows, before transformation and screening, as
described above. The CtfAB overexpression vectors were each built using seamless
cloning techniques using the same plasmid backbone as the combinatorial library.
Oligonucleotides used for amplifying the backbone and the overexpression insert,
also from the combinatorial library, are listed in Supplementary Table 8. Plasmids
for overexpressing sAdh variants were obtained from previous work37,38
. Testing
of the sAdh variants was conducted in the same growth conditions as described
above with the addition of an acetone spike, 20 g L−1
final concentration, 24 h after
inoculation. Each bottle was immediately sampled for HPLC analysis as described
below, followed by daily samples for the next 8 d.
Chassis strain construction. Chromosomal gene insertion and deletion mutants
were constructed via homologous recombination based on the methods described
in Liew et al.69
. In brief, shuttle plasmids containing homology arms were designed
using the TeselaGen DESIGN module (version 19) (TeselaGen Biotechnology)68
,
assembled and then transformed40
into C. autoethanogenum. Screening of correct
insertion or deletion mutants were carried out using PCR and confirmed by
DNA sequencing as described below. Oligonucleotides used for amplification of
homology arms and PCR screening are listed in Supplementary Table 8.
Genome-scale and kinetic modeling. Genome-scale modeling was carried
out with model iCLAU786 (ref. 70
) expanded by missing secondary alcohol
dehydrogenase and acyl-CoA thioesterase reactions as specified in Supplementary
Table 3. COBRApy (version 0.8.2) and OptFlux (version 3.2.10) were used for
simulations with Gurobi (version 7.0.2) optimizer71,72
. Strain optimization targets
were identified using Strength Pareto Evolutionary Algorithm 2 (ref. 42
), with
mutant strain phenotypes predicted using parsimonious flux balance analysis and
minimization of metabolic adjustment41
. The multi-objective strain optimization
evaluated designs with a maximum of 20 gene KOs and excluded essential and
transport reactions as targets. The optimization objective functions maximized
product flux and minimized the number of KOs, subject to maintaining a
minimum of 20% of wild-type growth rate. Kinetic ensemble modeling was carried
out as described previously51
. The core metabolic network used has been expanded
with the acetone pathway and is shown in Supplementary Fig. 9.
Cell-free prototyping. Crude E. coli cell extracts were prepared as previously
described73
, using BL21 Star (DE3) cells (Life Technologies). CFE was carried
out based on the Cytomim system74
, and the expression of each enzyme was
quantified using radioactive leucine incorporation assays as previously described
using a MicroBeta2 instrument (PerkinElmer)75
. Acetone biosynthesis genes
(Supplementary Table 1) and effector gene candidates (Supplementary Table 4)
were codon-optimized for E. coli, cloned into pJL1 plasmid (Addgene, 69496)
for CFE and sequence-confirmed as described below. Cell-free acetone
biosynthesis reactions contained 120 mM glucose, 8 mM magnesium glutamate,
10 mM ammonium glutamate, 134 mM potassium glutamate, 100 mM Bis-Tris
buffer, 0.5 mM kanamycin, 1 mM adenosine triphosphate, 1 mM nicotinamide
adenine dinucleotide, 1 mM coenzyme A and varying concentrations of acetone
pathway enzymes produced in CFE. Acetone pathway enzymes were added at a
final concentration of 0.5 µM for gene KO prototyping. Buffer pH was adjusted
with glacial acetic acid to decrease reaction pH to ~6 for increased activity of
acetone pathway enzymes76
. After 20 h, reactions were quenched with 20 µl
of 10% (w/v) trichloroacetic acid, and precipitated proteins were removed by
centrifugation for 10 min at 20,000g. Supernatant was transferred to fresh 1.5-ml
tubes and centrifuged again for 10 min at 20,000g to remove residual protein
before chromatography.
DNA sequencing. Sequence confirmation of PCR amplicons, assembled plasmid
constructs and generated strains was carried out using MiSeq (Illumina). Clonal
plasmids were isolated using the QIAprep Miniprep Kit (Qiagen) following the
protocol. Paired-end libraries for next-generation sequencing were produced
using the Nextera XT Kit (Illumina) and quantified by Quant-iT (Thermo Fisher
Scientific) before 2× 300-bp sequencing on a MiSeq Reagent Kit V3 (Illumina).
Sequences were processed, aligned and checked for mismatches using the Geneious
(R9.1.8) software package (Biomatters).
Analytics. Biomass concentrations were determined using a spectrophotometer
(OD600nm) and dry cell weight measurements as described previously34,40
.
Metabolites 2,3-BDO, 3-HB, acetate, acetone, ethanol and IPA from
fermentation broth (or cell-free assay supernatants (5-µl injection)) were measured
using an Agilent 1100 and 1200 series HPLC system as described previously34
.
Samples were separated with 5 mM sulfuric acid flowing at 0.6 ml min−1
on a
Rezex ROA-Organic Acid H+ (8%) LC Column (Aminex) at 20 °C. Metabolites
were quantified using a refractive index detector based on the retention time of
standard solutions for each compound.
Headspace gas composition data were measured by gas chromatography using
a 490 Micro GC (Agilent) with two installed channels. Channel 1 was a 10-m
Mol-Sieve column running at 70 °C, with 200-kPa argon and a backflush time of
4.2 s; channel 2 was a 10-m PPQ column running at 90 °C, with150-kPa helium
and no backflush. The injector temperature for both channels was set at 70 °C.
Run times were set to 120 s, but all peaks of interest usually eluted before 100 s.
In each case, data were collected using Agilent OpenLab CDS ChemStation
software.
Proteomics. For proteomics samples, cells were grown in bottles to mid-log
phase (OD600nm = 0.8–0.9) as described above. Cell pellets were harvested by
centrifugation, followed by supernatant removal, rapid freezing in liquid nitrogen
and storage at −80 °C until analysis. Cells were lysed by bead-beating in Tris-HCl
(100 mM at pH 8.0), adjusting sample to 4% SDS, and heat treatment (95 °C for
10 min). Crude protein was obtained by centrifugation at 21,000g for 10 min
followed by quantification with a NanoDrop OneC spectrophotometer (Thermo
Fisher Scientific). Samples were adjusted to 10 mM dithiothreitol (10 min at 95 °C)
to reduce proteins and then 30 mM iodoacetamide (20 min at room temperature
in darkness) and cleaned up via protein aggregation capture77
. Aggregated
protein (on magnetic Sera-Mag (GE Healthcare) beads) was then digested with
proteomics-grade trypsin (1:75 w/w, Promega) in 100 mM Tris-HCl pH 8.0
overnight at 37 °C and again for 3 h at 37 °C the next day. Tryptic peptides were
then collected, acidified to 0.5% formic acid, filtered through a 10-kDa MWCO
spin filter (Vivaspin 2, Sartorius) and quantified by NanoDrop OneC. Three
micrograms of peptides were then analyzed by 1D LC–MS/MS using a Vanquish
uHPLC coupled directly to an Orbitrap Q Exactive mass spectrometer running
Xcalibur (version 4.2.47) data collection software (Thermo Fisher Scientific), as
previously described78
. Peptides were separated by a 180-min organic gradient
across an in-house pulled nanospray emitter packed with 15 cm of 1.7-µm Kinetex
reversed-phase resin (Phenomenex). Peptide fragmentation spectra were analyzed/
sequenced by Proteome Discoverer (version 2.3) software (Thermo Fisher
Scientific), and peptides were quantified by chromatographic area under the curve.
C. autoethanogenum reference proteome (UniProt UP000017590) plus acetone
biosynthesis enzymes were used as reference. Peptide abundances were summed
to their respective proteins, and protein abundances were log2-transformed and
normalized with InfernoRDN (version 1.1.6687.24196)79
.
Fermentation and piloting. Continuous fermentation was carried out under
strictly anaerobic conditions at 37 °C in 2 L of BioFlo 115 CSTR reactors (New
Brunswick) with consistent media supply and gas flow as described previously34,50
.
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Articles NatureBiotechnology
CSTR runs used a synthetic gas blend of 50% CO, 10% H2, 30% CO2 and 10%
N2 (Airgas) at atmospheric pressure and a condenser to minimize loss of volatile
products. Pilot runs were performed with a blend of 50% CO, 10% H2, 20%
CO2 and 20% N2 and pressure of 1.4 bar in a custom pilot reactor (Fig. 5c) at
LanzaTech’s Freedom Pines site. Medium dilution rates used were between 1.0
and 6.5 d−1
and gas flow rates from 200 to 800 standard cubic centimeters per
minute (SCCM) for CSTRs and from 11.5 to 23 normal liters per minute (NLPM)
for pilot reactors. After an initial ramp as described earlier, cultures were run as
continuous chemostats. Pathway and culture integrity were routinely assessed
using DNA sequencing as described above. Gas and metabolite measurements
were automatically sampled every 1–3 h and analyzed as described above. Samples
for biomass were taken at least three times a day and processed as described
above. For productivity and selectivity calculations, only concentrations in the
broth were considered (not considering any stripped product). Selectivity is
a biocatalyst-specific measure that describes the proportion of energy in the
consumed feed gas that goes toward the product of interest on electron basis. Gas
utilization is calculated as follows: % of CO consumed = ((% COout × Outlet Gas
flow) − (% COin × Inlet Gas flow)) / (% COin × Inlet Gas flow).
LCA. The scope of the LCA is cradle to gate, meaning that end-of-life is excluded.
Carbon in the steel mill off-gas would end up in the atmosphere without the gas
fermentation process, so a credit is taken for carbon sequestered in the acetone
(2.27 kgCO2e/kg acetone) and IPA (2.20 kgCO2e/kg IPA). The steel mill off-gas
carries no burden of upstream steel mill emissions as the off-gas is an unintended
consequence of the steel-making process. Therefore, the GHG emissions of steel
production do not change with the addition of the gas fermentation process. The
LCA treats the biogas and C. autoethanogenum microbial protein co-products
using system expansion by taking credits for the former replacing natural gas and
the latter replacing soybean meal based on protein content. Inputs that contributed
less than 5% of the total GHG emissions were excluded from the analysis. Emission
factors (Supplementary Tables 9 and 10) were taken directly from the GREET 1
model (https://greet.es.anl.gov/) and ecoinvent database6
.
Visualization. Figure 1 was created with BioRender.
Statistical information. All statistical information provided in this manuscript
is derived from n = 3 or more independent experiments unless otherwise noted
in the text or figure legends. Error bars represent 1 s.d. of the mean derived from
these experiments. All error bars propagated errors. These data do not represent
a distribution of measured data but, rather, a calculation with propagated errors.
Statistical analyses of proteomics were performed with Perseus (version 1.6.14.0)80
.
Statistical significance (P ≤ 0.05) was assessed using two-tailed t-tests against the
strain ranked 203 (C1; lowest acetone producer), and P values were adjusted using
Benjamini–Hochberg correction (false discovery rate ≤ 0.05).
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
All data presented in this manuscript are available as supplementary data files.
Supplementary Tables 1-10 are available on Zenodo: https://doi.org/10.5281/
zenodo.5838304. All proteomics raw data are available at the ProteomeXchange
Consortium via the MassIVE repository (ftp://massive.ucsd.edu/MSV000085940/)
(MassIVE accession: MSV000085940; ProteomeXchange accession: PXD020853).
All genome sequences are available through the Joint Genome Institute Integrated
Microbial Genomes & Microbiomes system platform or the National Center
of Biotechnology Information GenBank under accession numbers provided in
Supplementary Table 7. Any additional data may be available from the authors
upon reasonable request. Materials are available upon reasonable request and
under a material transfer agreement, but strains may require a license.
References
	63.	Chen, I. M. A. et al. The IMG/M data management and analysis
system v.6.0: new tools and advanced capabilities. Nucleic Acids Res. 49,
D751–D763 (2021).
	64.	Larkin, M. A. et al. Clustal W and Clustal X version 2.0. Bioinformatics 23,
2947–2948 (2007).
	65.	Nguyen, L., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast
and effective stochastic algorithm for estimating maximum-likelihood
phylogenies. Mol. Biol. Evol. 32, 268–274 (2014).
	66.	Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin,
L. S. ModelFinder: fast model selection for accurate phylogenetic estimates.
Nat. Methods 14, 587–589 (2017).
	67.	Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and
new developments. Nucleic Acids Res. 47, 256–259 (2019).
	68.	Fero, M. J., Craft, J. K., Trang, V. & Hillson, N. J. Combinatorial-hierarchical
DNA library design using the TeselaGen DESIGN Module with j5. Methods
Mol. Biol. 2205, 19–47 (2020).
	69.	Liew, F. M. et al. Gas fermentation—a flexible platform for commercial scale
production of low-carbon-fuels and chemicals from waste and renewable
feedstocks. Front. Microbiol. 7, 694 (2016).
	70.	Valgepea, K. et al. Arginine deiminase pathway provides ATP and boosts
growth of the gas-fermenting acetogen Clostridium autoethanogenum. Metab.
Eng. 41, 202–211 (2017).
	71.	Ebrahim, A., Lerman, J. A., Palsson, B. O. & Hyduke, D. R. COBRApy:
COnstraints-Based Reconstruction and Analysis for Python. BMC Syst. Biol.
7, 74 (2013).
	72.	Gonçalves, E., Pereira, R., Rocha, I. & Rocha, M. Optimization approaches
for the in silico discovery of optimal targets for gene over/underexpression. J.
Comput. Biol. 19, 102–114 (2012).
	73.	Kwon, Y. C. & Jewett, M. C. High-throughput preparation methods of crude
extract for robust cell-free protein synthesis. Sci. Rep. 5, 8663 (2015).
	74.	Jewett, M. C. & Swartz, J. R. Mimicking the Escherichia coli cytoplasmic
environment activates long-lived and efficient cell-free protein synthesis.
Biotechnol. Bioeng. 86, 19–26 (2004).
	75.	Jewett, M. C., Calhoun, K. A., Voloshin, A., Wuu, J. J. & Swartz, J. R. An
integrated cell-free metabolic platform for protein production and synthetic
biology. Mol. Syst. Biol. 4, 220 (2008).
	76.	Karim, A. S., Rasor, B. J. & Jewett, M. C. Enhancing control of cell-free
metabolism through pH modulation. Synth. Biol. 5, ysz027 (2020).
	77.	Batth, T. S. et al. Protein aggregation capture on microparticles enables
multipurpose proteomics sample preparation. Mol. Cell. Proteomics 18,
1027–1035 (2019).
	78.	Walker, C., Ryu, S., Giannone, R. J., Garcia, S. & Trinh, C. T. Understanding
and eliminating the detrimental effect of thiamine deficiency on the
oleaginous yeast Yarrowia lipolytica. Appl. Environ. Microbiol 86,
e02299–19 (2020).
	79.	Taverner, T. et al. DanteR: an extensible R-based tool for quantitative analysis
of -omics data. Bioinformatics 28, 2404–2406 (2012).
	80.	Tyanova, S. et al. The Perseus computational platform for comprehensive
analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).
Acknowledgements
We would like to thank members of LanzaTech’s Synthetic Biology, Strain
Development, Process Integration, Analytics, Engineering Design & Development,
AI & Modeling, Computational Biology and Freedom Pines teams for their support
and conversations about this work, in particular A. Juminaga, A. Quattlebaum, A.
Shah, J. Winkler, J. Cogan, L. Fantroy, M. Maas, M. Martin, N. Gayner, N. Fackler, R.
C. Tappel, S. Nagaraju, S. Chong, V. Reynoso, W. P. Mitchell and W. Allen. We would
also like to thank the Joint Genome Institute DNA synthesis team for their support
and conversations on this work, in particular J.-F. Chen, M. Harmon-Smith, R. Evans
and Y. Yoshikuni. Funding: Acetone strain and process development, genome-scale
modeling, LCA work and initial pilot runs were supported by the U.S. Department
of Energy Bioenergy Technologies Office under contract nos. DE-EE0007566 and
CRADA/NFE-16-06364 between LanzaTech and the Oak Ridge National Laboratory
(F.L., R.N., T.A., C.C., R.O.J., L.W., J.S., P.C., S.D.T., Z.R., A.G., L.T., N.L.E., J.C.B.,
J.D., R.C., T.J.T., R.J.G., R.L.H., S.D.S., S.D.B., C.L. and M.K.). Cell-free prototyping
work was funded by the U.S. Department of Energy Office of Science, Biological
and Environmental Research Division, Genomic Science Program, under contract
nos. DE-SC0018249 and FWP ERKP903 (F.L., B.J.R., R.O.J., N.L.E., T.J.T., R.J.G.,
R.L.H., A.S.K., S.D.S., S.D.B., M.C.J. and M.K.). This manuscript was co-authored by
UT-Battelle under contract no. DE-AC05-00OR22725 with the U.S. Department of
Energy (T.J.T., N.L.E., R.J.G. and R.L.H.). DNA synthesis for the gene libraries was
supported by the Joint Genome Institute Community Science Program under award
no. CSP-503280; https://doi.org/10.46936/10.25585/60001121 (M.C.J. and M.K.).
The work conducted by the U.S. Department of Energy Joint Genome Institute
(https://ror.org/04xm1d337), a DOE Office of Science User Facility, is supported
by the Office of Science of the U.S. Department of Energy operated under contract
no. DE-AC02-05CH11231. B.J.R. is supported by a National Defense Science and
Engineering Graduate Fellowship (award ND-CEN-017-095). We also thank the
following investors in LanzaTech’s technology: BASF, CICC Growth Capital Fund
I, CITIC Capital, Indian Oil Company, K1W1, Khosla Ventures, the Malaysian Life
Sciences Capital Fund, L. P., Mitsui, the New Zealand Superannuation Fund, Novo
Holdings A/S, Petronas Technology Ventures, Primetals, Qiming Venture Partners,
Softbank China and Suncor.
Author contributions
T.J.T., A.S.K., S.D.S., S.D.B., C.L., M.C.J. and M.K. designed the study. R.O.J. and F.L.
performed the sequence mining. F.L. developed the combinatorial library framework.
F.L., R.N., R.O.J., L.W., C.L. and M.K. performed all strain engineering. A.P.M. and
M.K. performed in silico analysis. J.S. and J.D. performed modeling. B.J.R. and A.S.K.
performed all cell-free experiments. T.A., Z.R., L.T., J.C.B. and S.D.B. performed all
fermentation experiments. C.C., A.G. and R.C. performed LCA. P.C., N.L.E., R.J.G. and
R.L.H. performed all omics experiments. E.L., R.N., T.A., B.J.R., A.S.K., C.L. and M.K.
performed data analysis and visualization. E.L., R.N., T.A., C.L., B.J.R., A.S.K, M.C.J. and
M.K. wrote the manuscript. All authors edited and reviewed the manuscript.
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Competing interests
F.L., R.N., T.A., C.C., R.O.J., L.W., J.S., S.T., A.P.M., A.G., L.T., J.C.B., J.D., R.C., S.D.S.,
S.D.B., C.L. and M.K. are current or former employees of LanzaTech, a for-profit
company pursuing commercialization of the acetone and isopropanol gas fermentation
process discussed here. M.C.J. consults for and has joint funding with LanzaTech. F.L.
and M.K. are co-inventors on granted US patent 9,365,868 (assigned to LanzaTech)
related to the production of acetone and isopropanol by fermentation of a gaseous
substrate comprising CO that incorporates discoveries incorporated in this manuscript.
M.K. is co-inventor on granted US patent 9,550,979 (assigned to LanzaTech) related
to alcohol dehydrogenases with engineered metabolic activity that incorporates
discoveries incorporated in this manuscript. R.O.J., A.P.M. and M.K. are co-inventors
on US patent application 2015/0152445 (assigned to LanzaTech) related to inactivation
of secondary alcohol dehydrogenases that incorporates discoveries incorporated in
this manuscript. C.L. is an inventor on provisional US patent application 63/035,739
(assigned to LanzaTech) related to integration of acetone and isopropanol biosynthesis
gene variants that incorporates discoveries incorporated in this manuscript. R.N. is
an inventor on provisional US patent application 63/083,257 (assigned to LanzaTech)
related to expressing multiple copies of acetone and isopropanol biosynthesis gene
variants that incorporates discoveries incorporated in this manuscript. M.C.J. and A.S.K.
are co-inventors on a US provisional patent application that incorporates discoveries
described in this manuscript. Their interests are reviewed and managed by Northwestern
University in accordance with their conflict of interest policies. All other authors declare
no competing interests.
Additional information
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41587-021-01195-w.
Correspondence and requests for materials should be addressed to
Ching Leang, Michael C. Jewett or Michael Köpke.
Peer review information Nature Biotechnology thanks Corinne Scown, Shang-Tian Yang
and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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