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Death by a thousand cuts: the challenges and diverse landscape of lignocellulosic hydrolysate inhibitors

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Frontiers in Microbiology
March, 2014
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published: 14 March 2014
doi: 10.3389/fmicb.2014.00090

Death by a thousand cuts: the challenges and diverse
landscape of lignocellulosic hydrolysate inhibitors
Jeff S. Piotrowski*, Yaoping Zhang , Donna M. Bates , David H. Keating , Trey K. Sato , Irene M. Ong
and Robert Landick
DOE Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA

Edited by:
Shihui (Shane) Yang, National
Renewable Energy Laboratory, USA
Reviewed by:
Shiyong Peng, National Institute of
Health, USA
Qiang J. Fei, National Renewable
Energy Lab, USA
Jeff S. Piotrowski, DOE Great Lakes
Bioenergy Research Center,
University of Wisconsin-Madison,
1552 University Ave., WEI4152,
Madison, WI 53726, USA
e-mail: jpiotrowski@wisc.edu

Lignocellulosic hydrolysate (LCH) inhibitors are a large class of bioactive molecules that
arise from pretreatment, hydrolysis, and fermentation of plant biomass. These diverse
compounds reduce lignocellulosic biofuel yields by inhibiting cellular processes and
diverting energy into cellular responses. LCH inhibitors present one of the most significant
challenges to efficient biofuel production by microbes. Development of new strains that
lessen the effects of LCH inhibitors is an economically favorable strategy relative to
expensive detoxification methods that also can reduce sugar content in deconstructed
biomass. Systems biology analyses and metabolic modeling combined with directed
evolution and synthetic biology are successful strategies for biocatalyst development,
and methods that leverage state-of-the-art tools are needed to overcome inhibitors
more completely. This perspective considers the energetic costs of LCH inhibitors and
technologies that can be used to overcome their drain on conversion efficiency. We
suggest academic and commercial research groups could benefit by sharing data on LCH
inhibitors and implementing “translational biofuel research.”
Keywords: cellulosic biofuels, lignocellulosic hydrolysate inhibitors, systems biolog; y, chemical genomics,
metabolic modeling, ethanologens

Lignocellulosic biofuels offer the promise of sustainable,
domestically produced fuels with favorable carbon balances. Fastgrowing grasses like Miscanthus and agricultural residues provide fermentable sugars at lower energy and fertilizer costs than
grains (Schmer et al., 2008), making them preferable feedstocks
for advanced biofuels. Cellulosic ethanol is an obvious nextgeneration biofuel to implement given its production and delivery
infrastructures are compatible with existing fuels.
Central to the success of cellulosic ethanol is efficient conversion of biomass-derived sugars to ethanol by microbes such as
Saccharomyces cerevisiae, Escherichia coli, and Zymomonas mobilis
(Alper and Stephanopoulos, 2009; Lau et al., 2010; Yang et al.,
2010a). Under optimal conditions, these microbes are powerful
ethanologens; however, lignocellulosic hydrolysates (LCH) and
industrial scale fermentation tanks are not optimal conditions.
Thermal, osmotic, and ethanol stresses are just some of the environmental factors that inhibit fermentation and reduce yield
(Attfield, 1997; Gibson et al., 2007; Jin et al., 2013). Industrial
microbes are pushed to the limits of stress tolerance to make
biofuel production energetically favorable.
Although environmental stressors limit yields in present day
ethanol facilities, cellulosic biomass conversion comes with new
challenges. Specifically, LCH inhibitors, a group of small, bioactive molecules can significantly reduce conversion efficiency. LCH
inhibitors such as aliphatic acids, furans, and phenolics are
released or condensed from cellulose and hemicellulose during
pretreatment and hydrolysis (Larsson et al., 1999, 2000; Yang
et al., 2010a); however, chemical residues from newer hydrolysis strategies and synergies with biofuel end products (ethanol,


isobutanol) are less well studied. Removal of these inhibitors
can be expensive and may reduce titers of fermentable sugars;
some estimates suggest that detoxification can remove up to 26%
of total fermentable sugars (Larsson et al., 1999). Thus, a preferred strategy is to develop microbial strains with properties that
minimize the effects of LCH inhibitors on biofuel yields.
With commercially available industrial stains that are robust
to thermal and ethanol stress (e.g., Ethanol Red, Fermentis,
Milwaukee, WI, USA), recent attention has been directed to
overcoming the challenge of LCH inhibitors. These compounds
are ubiquitous in hydrolysates, and their abundance and composition depends on pretreatment (Chundawat et al., 2010),
feedstock (Klinke et al., 2004; Almeida et al., 2007), and seasonality (Bunnell et al., 2013; Greenhalf et al., 2013). Given their
chemical diversity, these compounds can target many cellular
processes. LCH inhibitors can also generate a substantial cellular energy drain. Cells have evolved to detoxify, excrete inhibitors,
or repair the resultant cellular damage fast enough to reproduce.
However, evolved coping mechanisms may also negatively affect
the efficiency of conversion by competing for cellular resources
(Bellissimi et al., 2009; Miller et al., 2009). Although it is in the
microbe’s best interest to use its resources to limit the effects
of LCH inhibitors and maintain cellular viability, this may be
reducing biofuel production. In this perspective, we consider the
diversity and cellular costs of LCH inhibitors from traditional
and novel pretreatment and hydrolysis strategies, describe new
technologies and their application to strain development, and
finally identify key needs of the cellulosic biofuel community that
will empower “translational biofuel research” to take discoveries
quickly to industrial scale.

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Challenges of lignocellulosic fermentation inhibitors

Prior to microbial conversion of lignocellulosic sugars into biofuel, biomass must be deconstructed into monomeric sugars by
enzymatic or chemical hydrolysis. This hydrolysis step is often
preceded by a pretreatment step that expands the plant fibers and
allows cellulolytic enzymes access to the polysaccharide matrices.
The resulting hydrolysates are complex, ill-defined mixtures that
include sugars and a diversity of bioactive molecules (Table 1).

Small acids and phenolic compounds are released from cellulose and hemicelluloses during hydrolysis and furans arise from
the dehydration of pentose and hexose monomers (Klinke et al.,
2004). Pretreatments such as acid hydrolysis, steam explosion
or NH3 expansion each impart their own “profile” of LCH
inhibitors. For example, AFEX (Ammonia Fiber EXpansion)
uses high-pressure/temperature ammonia to alter the cellulose
matrix to allow hydrolysis by cellulases (Lau and Dale, 2009), and

Table 1 | Classes of lignocellulosic hydrolysate (LCH) inhibitors and their described modes of toxicity.

Mode of action


Decreases cellular pH,
Decreases cellular ATP,
Inhibits macromolecule
Inhibits DNA synthesis/repair,
Inhibits glycolytic enzymes

Sinha, 1986; Cherrington et al.,
1990; Holyoak et al., 1996;
Stratford and Anslow, 1998;
Bellissimi et al., 2009; Ullah et al.,
2012; Ding et al., 2013

Damages membranes,
Oxidative damage,
Damages nucleic acids,
Damages proteins,
Limits sulfur assimilation,
Reduces NADH/NADPH pools,
Inhibits enzymes

Ingram, 1976; Hadi et al., 1989;
Khan et al., 1995; Zaldivar and
Ingram, 1999; Modig et al., 2002;
Miller et al., 2009; Allen et al.,
2010; Wang et al., 2013

Damages membranes,
Decreases cellular pH,
Decrease cellular ATP,
Inhibits translation,
Oxidative damage, Denatures
proteins, Damages cytoskeleton,
DNA mutagenesis, Induces

Krebs et al., 1983; Mikulášová
et al., 1990; Verduyn et al., 1992;
Chambel et al., 1999; Fitzgerald
et al., 2004; Iwaki et al., 2013;
Nguyen et al., 2014


Docherty and Kulpa, 2005

Damages membranes

King et al., 1991; Laouar et al.,

Copper, Sodium, Zirconium

Damages membranes,
nucleic acids, and enzymes

Shapiro and Ling, 1998; Schmitt
and Tampé, 2002



Luterbacher et al., 2014

Damages membranes
Damages DNA
Inhibits enzymes

Nagodawithana and Steinkraus,
1976; Dombek and Ingram, 1984;
Alexandre et al., 1994; Ibeas and
Jimenez, 1997; Huffer et al., 2011

Small acids
Acetic acid, formic acid, levulic acid

Furfural, HMF, 2-furoic acid

Ferulic acid, coumaric acid
Vanillin, Syringealdehyde
Coniferyl alcohol, Eugenol
Acetovanillin, Feruloyl amide, Coumaryl

Ionic liquids
Triton-X, Tween

Metal ions

End product inhibitors

Frontiers in Microbiology | Microbial Physiology and Metabolism

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Piotrowski et al.

this produces amide versions of inhibitors (e.g., feruloyl amide
from ferulic acid) with potentially new biological properties
(Chundawat et al., 2010).
Besides these common inhibitors, residual pretreatment chemicals may complicate fermentation. Ionic liquids are pretreatment
and hydrolysis solvents, but are toxic to many microorganisms
(Docherty and Kulpa, 2005; Ouellet et al., 2011). Alkaline hydrogen peroxide (AHP) pretreatment limits the production of furans;
however this method introduces significant amounts of Na+
from NaOH, which can cause osmotic stress (Sato et al., 2014).
Copper(II) 2,2 -bipyridine is a catalyst that enhances AHP pretreatment by reducing the H2 O2 requirement (Li et al., 2013), but
copper is toxic to most microbes. Next-generation pre-treatments
and hydrolysis methods like γ-valerolactone (Luterbacher et al.,
2014), surfactants (Sindhu et al., 2013), zirconium phosphate
catalysts (Gliozzi et al., 2014), and other incipient hydrolysis
technologies may imbue hydrolysates with novel toxicities and
synergisms with common inhibitors.
Biofuel end-products themselves are inhibitory. Ethanol can
directly damage cellular membranes, DNA, as well as inhibit
enzymes (Nagodawithana and Steinkraus, 1976; Dombek and
Ingram, 1984; Alexandre et al., 1994; Ibeas and Jimenez, 1997;
Huffer et al., 2011). The ethanologens S. cerevisiae and Z. mobilis
are not immune to ethanol toxicity at high concentrations
(Carmona-Gutierrez et al., 2012; Yang et al., 2013). Advanced
biofuels like isobutanol are toxic at significantly lower concentrations than ethanol (Brynildsen and Liao, 2009; Atsumi et al., 2010;
Huffer et al., 2011; Minty et al., 2011). Inhibition by end products
has been an area of research interest (Baez et al., 2011; McEwen
and Atsumi, 2012; Zingaro et al., 2013), and ethanol tolerance is
a pre-requisite for all industrial yeast.
Effects of inhibitors at a minimum can be additive, but an
even greater concern is the potential for synergy between LCH
inhibitors and fermentation condition, including high osmolarity and absence of O2 . Some studies have described synergies
between acetic acid, furfural, and phenolics in yeast (Oliva et al.,
2006; Ding et al., 2011), but a comprehensive evaluation of synergisms between compounds and conditions on both growth
rate and fermentation will be essential. Such assessment will be
a massive undertaking that will also require defined synthetic
hydrolysate media to permit meaningful definition of minimum
inhibitory concentrations (MICs) of the individual inhibitors,
and how this value will change in combination with other LCH
inhibitors (synergy/antagonism) and fermentation conditions.
Nevertheless, documenting interactions between inhibitors on
sugar conversion is crucial to prioritizing future research for
improved biofuel microbes.

LCH inhibitors directly affect biofuel yield as well as the production rate, which can extend fermentation time and result
in higher operating costs. In simplest terms, these inhibitors
affect conversion efficiency by depleting cellular energy resources
(e.g., ATP, NADH, NADPH; Figure 1). Some inhibitors can act
broadly and damage key enzymes of fermentation pathways


Challenges of lignocellulosic fermentation inhibitors

(Modig et al., 2002). The coping mechanisms available to the
biofuel microbes fall into 4 main categories: (i) detoxification, (ii) efflux, (iii) repair, or (iv) tolerance. The first three
are of most concern given their effects on cellular energy and
Detoxification and efflux are the most well characterized
mechanisms of inhibitor tolerance in microbes, with deep literature not only from biofuel research but also in the medical literature from a wealth of antibiotic and pharmaceutical research.
Detoxification is the major route of tolerance for aldehydes in
both bacteria and yeast. Reduction of compounds like furfural
and vanillin to less toxic alcohols by NADH/NADPH dependent reductases occurs in ethanol fermentation using S. cerevisiae
and E. coli (Jarboe, 2011). Oxidoreductase expression significantly increases in yeast and E. coli in the presence of aldehydes (Liu et al., 2008; Wang et al., 2011), depleting cellular
NADH/NADPH. This results in inhibition of NADPH-dependent
processes (e.g., assimilation of sulfur) leading ultimately to
slower conversion of sugars (Miller et al., 2009). Interestingly,
yields can be increased by disabling the detoxification pathway, suggesting that tolerance may be more energetically efficient
than detoxification (Wang et al., 2013). Alternatively, changing the source of reducing equivalent for aldehyde detox from
NADPH to NADH also can improve biofuel yield (Wang et al.,
Efflux is mediated by ATP-dependent trans-membrane pumps
that selectively or non-selectively pump out toxic compounds
usually at the cost of 1 ATP per molecule (Shapiro and Ling, 1998;
Schmitt and Tampé, 2002). The yeast S. cerevisiae has 29 different
ATP-binding cassette (ABC) efflux transporters (Decottignies and
Goffeau, 1997) and 5% of the E. coli genome is composed of genes
with ABC-transporter domains, many involved in efflux (Linton
and Higgins, 1998). In both yeast and E. coli, expression of transporters increases in response to LCH inhibitors (Schüller et al.,
2004; Lee et al., 2012; Schwalbach et al., 2012). The yeast weak acid
response is mediated by increased expression of ABC-transporters
(e.g., Pdr1p and Pdr5p) (Schüller et al., 2004; Pereira Rangel et al.,
2010). As long as cells are exposed to LCH inhibitors, a significant
fraction of cellular ATP will be diverted to efflux pumps. Of particular importance for overall efficiency of LCH conversion, ATPdepletion may have a disproportionate effect on xylose conversion
compared to glucose conversion because xylose produces less cellular energy per molecule transported (Matsushika et al., 2013).
Bellissimi et al. (2009) found that acetic acid could specifically
inhibit xylose fermentation, but that this effect could be reversed
with glucose addition. The authors posit that ATP generated from
xylose fermentation cannot match ATP depletion from the weak
acid response and efflux/proton pumps used to maintain cellular
pH. Inhibitors that require ATP-dependent coping mechanisms
can directly reduce xylose conversion. Some inhibitors can be particularly draining, affecting both NADPH and ATP pools. Ask
et al. (2013) found that furfural and HMF not only reduced cellular NAPDH in S. cerevisiae, but also elicited increased expression
of ATP-dependent efflux pumps Pdr5p and Yor1p. This suggests
that coping with furans requires both NADPH dependent detoxification and ATP-dependent efflux.

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Piotrowski et al.

FIGURE 1 | Inhibitor classes and the cellular energy consequences of
LCH inhibitors. Presented are examples from three main classes of
inhibitors and the ways cells can cope with these: efflux via pumps,
detoxification via enzymes, and repair of the damage caused by the

An unanswered question is whether the ATP-dependent action
of efflux pumps has a net positive (by inhibitor removal) or
net negative (by energy consumption) effect on biofuel production. Although the answer may vary by inhibitor, because some
inhibitors like aldehydes are more damaging to cells than others,
a general test of the positive or negative consequences of efflux
pumps for biofuel yield will help advance strategies for biofuel
microbe design.
The cellular energy costs of maintenance and repair are more
difficult to quantify but could account for significant energy
loss. If cells can repair the damage caused by fermentation
inhibitors quickly, then fermentation may proceed. Inhibitors
can acidify cells (Verduyn et al., 1992), damage cellular membranes (Russell, 1992), DNA (Allen et al., 2010), and individual
proteins (Modig et al., 2002). Repairing structures requires biogenesis; this comes at the expense of ATP, NADPH, carbon, and
nitrogen. Maintaining pH is mediated by ATP-dependent proton
pump Pma1p; and ATP cost under acidic conditions is the primary cause of reduced cellular growth (Verduyn et al., 1992; Ullah
et al., 2012). Biogenesis requires NADPH-dependent assimilation
of nutrients like sulfur, which is drained by repair enzymes.
Phenolics and furans can damage membranes, requiring more

Frontiers in Microbiology | Microbial Physiology and Metabolism

Challenges of lignocellulosic fermentation inhibitors

compounds. Each coping strategy comes at the expense of cellular energy
that is diverted from the stores used to maintain cell integrity by
chemiosmotic exchange and assimilation as well as energy required for
fermentation of sugars to fuel.

energy to maintain the proton gradient required for basic
metabolism (Ding et al., 2012; Schwalbach et al., 2012; Stratford
et al., 2013). Growth and sugar conversion will be slowed as
resources are diverted to maintenance and repair. Given that
hydrolysates contain a mixture of inhibitors with diverse modes of
action requiring all of these coping mechanisms simultaneously,
the energy drain from fermentation inhibitors is truly death by a
thousand cuts.

Strain development is an economical route to deal with
LCH inhibitors. Resistance to the suite of inhibitors requires
complex response of many cellular systems, and as such
is not easily conferred by engineering of individual genes.
Moreover, inhibitor pools can vary between hydrolysate preparations, thus even the most robust strains in ammoniapretreated hydrolysate may wither in dilute acid pretreated
hydrolysates. It is unlikely that one strain will be optimal
for all conditions. The reality is that microbial strains will
need to be tailored to specific hydrolysates through engineering and directed evolution. Accelerating this process is

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Piotrowski et al.

crucial to making new cellulosic technologies industrially
The tools of systems biology can give a detailed view of
the microbial stress response (Jozefczuk et al., 2010; Lee et al.,
2011). Transcriptomic, proteomic, and metabolomic responses
to inhibitors can be tracked in detail, and this “multiomic”
approach can give high-resolution insight to the global cellular
consequences (Miller et al., 2009; Yang et al., 2010b; Schwalbach
et al., 2012; Skerker et al., 2013; Yang et al., 2013). Advanced
techniques such as ribosome profiling can give a view into relationships between transcription and protein abundance in the
presence of LCH inhibitors (Ingolia et al., 2009; Brar et al., 2012).
Metabolic and flux-balance models are valuable in determining
energy balances within cells (Varma and Palsson, 1994; Fiaux
et al., 2003; Jin and Jeffries, 2004; Herrgård et al., 2008; TaymazNikerel et al., 2010). These models combined with a systems
biology view of protein and gene expression can be used to identify key energetic bottlenecks as targets for engineering. Recently,
Wei et al. (2013) demonstrated an elegant way to overcome the
redox cofactor imbalance in yeast designed to ferment xylose by
engineering acetate metabolism from E. coli into S. cerevisiae.
The authors combined an acetate utilization pathway that consumes NADH with a xylose utilization pathway that produces
NADH to overcome the redox imbalance of engineered xylose
fermentation. The resultant strain has both better xylose conversion and the ability to detoxify acetate (Wei et al., 2013).
Detailed accounting of ATP and NAD(P)H in the presence of
LCH inhibitors and industrial conditions will be necessary to
disentangle and understand points for rational engineering of
microbial catalysts.
Model biofuel microbes like S. cerevisiae and E. coli benefit from well-developed suites of functional genomics resources,
such as deletion mutant or overexpression collections (Giaever
et al., 2002; Baba et al., 2006; Kitagawa et al., 2006). These tools
have revealed effects of some inhibitors such as furfural (Gorsich
et al., 2006), vanillin (Endo et al., 2008; Iwaki et al., 2013),
and acetic acid (Mira et al., 2010). Genome-wide mutant collections also allow powerful studies of inhibitors via “chemical
genomics” (Giaever et al., 2004; Parsons et al., 2006; Ho et al.,
2011). This new tool in the multiomic arsenal, when combined
with the information in genetic interaction networks (Butland
et al., 2008; Costanzo et al., 2010), can allow precise predictions of the cellular targets of fermentation inhibitors. Recently,
Skerker et al. (2013) used a chemical genomics approach to
discover a previously undescribed inhibitor in acid pretreated
hydrolysate, methyl glyoxal (MG), and identified mutations that
confer MG resistance. Chemical genomics can be used to identify the chemical biological signatures within hydrolysates and
mutations conferring resistance, but more broadly can serve as
a “biological fingerprinting” technique for hydrolysate to identify variation in production, and as a method to benchmark the
biological properties of novel hydrolysates. Resources such as
the MoBY-ORF collections (Ho et al., 2009; Magtanong et al.,
2011), which are barcoded plasmids collections carrying nearly all
S. cerevisiae used to assess the effects of increased gene dose and
gene complementation, could be used with industrial, wild, and
engineered yeast to identify genetic interactions within diverse


Challenges of lignocellulosic fermentation inhibitors

yeast strains. Combined with traditional selection for resistance
and directed evolution, systems biology tools offer great potential
for strain development.
Further, new tools are now available that can accelerate strain
development. Genome editing and optimization techniques such
as CRISPR/Cas9 and MAGE allows rapid, detailed genome editing in both bacteria and eukaryotes (Wang et al., 2012; Cong
et al., 2013; DiCarlo et al., 2013; Gilbert et al., 2013). Next steps in
strain development will be tuning genomes to inhibitor tolerance
by parallel disruption or activation of inhibitor responsive genes
using CRISPR/Cas9-based systems. Additionally, large-scale gene
synthesis can be used to identify genes that not only aid xylose
utilization, but also confer inhibitor tolerance. Systems biology
tools for biocatalyst development are coalescing to a pipeline
that can keep pace with the changing landscape of fermentation
inhibitors, allowing for the rapid tailoring of ethanologens with
robust and efficient sugar to biofuel conversion from any new

Much like the interface between commercial and academic drug
discovery communities, applying next-generation biofuel technologies will require cooperative translational research. Academic
biofuel communities are developing advanced system biology
techniques whereas the commercial community excels at scale-up
commercialization. However, similar to drug discovery, these two
groups are often isolated in their research. In both cases, publicprivate partnerships such as the NIH translational medicine
initiatives (Zerhouni, 2003) and NCERC industrial partnerships
(http://www.siue.edu/ethanolresearch/), as well as shared computational resources like Kbase (http://www.kbase.us/) can help
bridge the divide.
How can researchers and funding agencies best enhance
collaboration? A key advance would be greater data sharing
about LCH inhibitors. Diverse hydrolysates and their respective
inhibitors are major variables in the field of cellulosic biofuel production, but detailed information on specific hydrolysate compositions is not broadly available. Comprehensive efforts to identify
all major LCH components and inhibitors across feedstocks and
hydrolysis treatments are needed and will require a central repository of LCH data that includes data on feedstocks, pretreatment,
hydrolysis, nutrients, and inhibitor. Chemical genomic profiling
could be used to generate “biological fingerprints” of hydrolysates
to allow comparisons among hydrolysates and identify the biological effects of LCH components by standard analytical methods.
A central resource would allow researchers to compare the composition and biological fingerprints of new hydrolysates with
existing knowledge about tolerant microbes for further strain
development. The DOE’s Systems Biology Portal, KBase, offers
the best outlet for community resources, and could serve as the
authority of the response of biocatalysts to LCH inhibitors with
open-source, community-developed analytical tools for chemical
genomics datasets.

LCH inhibitors are major barriers for cellulosic biofuels. The
cellular energy costs of coping with these compounds are a

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Piotrowski et al.

significant drain on the already thin margins of biofuel production. However, the increasingly powerful tools of systems biology
can be used to gain a detailed understanding of the cellular consequences of individual and mixtures of fermentation inhibitors,
which will serve as a basis for rational engineering of customizable
microbes. The biofuel research community would benefit from
shared computational and database resources that can improve
communication between the academic and commercial sides of

All authors are funded by the DOE Great Lakes Bioenergy
Research Center (DOE BER Office of Science DE-FC0207ER64494).

Alexandre, H., Berlot, J. P., and Charpentier, C. (1994). Effect of ethanol on
membrane fluidity of protoplasts from Saccharomyces cerevisiae and Kloeckera
apiculata grown with or without ethanol, measured by fluorescence anisotropy.
Biotechnol. Tech. 8, 295–300. doi: 10.1007/BF02428970
Allen, S. A., Clark, W., McCaffery, J. M., Cai, Z., Lanctot, A., Slininger, P. J.,
et al. (2010). Furfural induces reactive oxygen species accumulation and cellular
damage in Saccharomyces cerevisiae. Biotechnol. Biofuels 3, 2. doi: 10.1186/17546834-3-2
Almeida, J. R., Modig, T., Petersson, A., Hähn-Hägerdal, B., Lidén, G., and GorwaGrauslund, M. F. (2007). Increased tolerance and conversion of inhibitors
in lignocellulosic hydrolysates by Saccharomyces cerevisiae. J. Chem. Technol.
Biotechnol. 82, 340–349. doi: 10.1002/jctb.1676
Alper, H., and Stephanopoulos, G. (2009). Engineering for biofuels: exploiting innate microbial capacity or importing biosynthetic potential? Nat. Rev.
Microbiol. 7, 715–723. doi: 10.1038/nrmicro2186
Ask, M., Bettiga, M., Mapelli, V., and Olsson, L. (2013). The influence of HMF and
furfural on redox-balance and energy-state of xylose-utilizing Saccharomyces
cerevisiae. Biotechnol. Biofuels 6, 22. doi: 10.1186/1754-6834-6-22
Atsumi, S., Wu, T.-Y., Machado, I. M. P., Huang, W.-C., Chen, P.-Y., Pellegrini,
M., et al. (2010). Evolution, genomic analysis, and reconstruction of isobutanol tolerance in Escherichia coli. Mol. Syst. Biol. 6, 449. doi: 10.1038/msb.
Attfield, P. V. (1997). Stress tolerance: the key to effective strains of industrial baker’s
yeast. Nat. Biotechnol. 15, 1351–1357. doi: 10.1038/nbt1297-1351
Baba, T., Ara, T., Hasegawa, M., Takai, Y., Okumura, Y., Baba, M., et al. (2006).
Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants:
the Keio collection. Mol. Syst. Biol. 2. doi: 10.1038/msb4100050
Baez, A., Cho, K.-M., and Liao, J. C. (2011). High-flux isobutanol production using
engineered Escherichia coli: a bioreactor study with in situ product removal.
Appl. Microbiol. Biotechnol. 90, 1681–1690. doi: 10.1007/s00253-011-3173-y
Bellissimi, E., Van Dijken, J. P., Pronk, J. T., and Van Maris, A. J. A. (2009). Effects
of acetic acid on the kinetics of xylose fermentation by an engineered, xyloseisomerase-based Saccharomyces cerevisiae strain. FEMS Yeast Res. 9, 358–364.
doi: 10.1111/j.1567-1364.2009.00487.x
Brar, G. A., Yassour, M., Friedman, N., Regev, A., Ingolia, N. T., and Weissman, J. S.
(2012). High-resolution view of the yeast meiotic program revealed by ribosome
profiling. Science 335, 552–557. doi: 10.1126/science.1215110
Brynildsen, M. P., and Liao, J. C. (2009). An integrated network approach identifies
the isobutanol response network of Escherichia coli. Mol. Syst. Biol. 5, 277. doi:
Bunnell, K., Rich, A., Luckett, C., Wang, Y.-J., Martin, E., and Carrier, D. J. (2013).
Plant maturity effects on the physicochemical properties and dilute acid hydrolysis of switchgrass (Panicum virgatum, L.) hemicelluloses. ACS Sustain. Chem.
Eng. 1, 649–654. doi: 10.1021/sc4000175
Butland, G., Babu, M., Diaz-Mejia, J. J., Bohdana, F., Phanse, S., Gold, B., et al.
(2008). eSGA: E. coli synthetic genetic array analysis. Nat. Methods 5, 789–795.
doi: 10.1038/nmeth.1239
Carmona-Gutierrez, D., Sommer, C., Andryushkova, A., Kroemer, G., and Madeo,
F. (2012). A higher spirit: avoiding yeast suicide during alcoholic fermentation.
Cell Death Differ. 19, 913–914. doi: 10.1038/cdd.2012.31

Frontiers in Microbiology | Microbial Physiology and Metabolism

Challenges of lignocellulosic fermentation inhibitors

Chambel, A., Viegas, C. A., and Sá-Correia, I. (1999). Effect of cinnamic acid on the
growth and on plasma membrane H+–ATPase activity of Saccharomyces cerevisiae. Int. J. Food Microbiol. 50, 173–179. doi: 10.1016/S0168-1605(99)00100-2
Cherrington, C. A., Hinton, M., and Chopra, I. (1990). Effect of short-chain
organic acids on macromolecular synthesis in Escherichia coli. J. Appl. Bacteriol.
68, 69–74. doi: 10.1111/j.1365-2672.1990.tb02550.x
Chundawat, S. P. S., Vismeh, R., Sharma, L. N., Humpula, J. F., da Costa Sousa,
L., Chambliss, C. K., et al. (2010). Multifaceted characterization of cell wall
decomposition products formed during ammonia fiber expansion (AFEX)
and dilute acid based pretreatments. Bioresour. Technol. 101, 8429–8438. doi:
Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., et al. (2013). Multiplex
genome engineering using CRISPR/Cas systems. Science 339, 819–823. doi:
Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. D., Sevier, C. S.,
et al. (2010). The genetic landscape of a cell. Science 327, 425–431. doi:
Decottignies, A., and Goffeau, A. (1997). Complete inventory of the yeast ABC
proteins. Nat. Genet. 15, 137–145. doi: 10.1038/ng0297-137
DiCarlo, J. E., Norville, J. E., Mali, P., Rios, X., Aach, J., and Church, G. M. (2013).
Genome engineering in Saccharomyces cerevisiae using CRISPR-Cas systems.
Nucleic Acids Res. 41, 4336–4343. doi: 10.1093/nar/gkt135
Ding, J., Bierma, J., Smith, M. R., Poliner, E., Wolfe, C., Hadduck, A. N.,
et al. (2013). Acetic acid inhibits nutrient uptake in Saccharomyces cerevisiae:
auxotrophy confounds the use of yeast deletion libraries for strain improvement. Appl. Microbiol. Biotechnol. 97, 7405–7416. doi: 10.1007/s00253-0135071-y
Ding, M.-Z., Wang, X., Yang, Y., and Yuan, Y.-J. (2011). Metabolomic study of interactive effects of phenol, furfural, and acetic acid on Saccharomyces cerevisiae.
OMICS J. Integr. Biol. 15, 647–653. doi: 10.1089/omi.2011.0003
Ding, M.-Z., Wang, X., Yang, Y., and Yuan, Y.-J. (2012). Comparative metabolic
profiling of parental and inhibitors-tolerant yeasts during lignocellulosic
ethanol fermentation. Metabolomics 8, 232–243. doi: 10.1007/s11306-0110303-6
Docherty, K. M., and Kulpa, C. F. Jr. (2005). Toxicity and antimicrobial activity
of imidazolium and pyridinium ionic liquids. Green Chem. 7, 185–189. doi:
Dombek, K. M., and Ingram, L. O. (1984). Effects of ethanol on the Escherichia coli
plasma membrane. J. Bacteriol. 157, 233–239.
Endo, A., Nakamura, T., Ando, A., Tokuyasu, K., and Shima, J. (2008). Genomewide screening of the genes required for tolerance to vanillin, which is a
potential inhibitor of bioethanol fermentation, in Saccharomyces cerevisiae.
Biotechnol. Biofuels 1, 3. doi: 10.1186/1754-6834-1-3
Fiaux, J., Çakar, Z. P., Sonderegger, M., Wüthrich, K., Szyperski, T., and Sauer,
U. (2003). Metabolic-flux profiling of the yeasts Saccharomyces cerevisiae and
Pichia stipitis. Eukaryot. Cell 2, 170–180. doi: 10.1128/EC.2.1.170-180.2003
Fitzgerald, D. J., Stratford, M., Gasson, M. J., Ueckert, J., Bos, A., and Narbad,
A. (2004). Mode of antimicrobial action of vanillin against Escherichia coli,
Lactobacillus plantarum and Listeria innocua. J. Appl. Microbiol. 97, 104–113.
doi: 10.1111/j.1365-2672.2004.02275.x
Giaever, G., Chu, A. M., Ni, L., Connelly, C., Riles, L., Véronneau, S., et al.
(2002). Functional profiling of the Saccharomyces cerevisiae genome. Nature
418, 387–391. doi: 10.1038/nature00935
Giaever, G., Flaherty, P., Kumm, J., Proctor, M., Nislow, C., Jaramillo, D. F.,
et al. (2004). Chemogenomic profiling: identifying the functional interactions
of small molecules in yeast. Proc. Natl. Acad. Sci. U.S.A. 101, 793–798. doi:
Gibson, B. R., Lawrence, S. J., Leclaire, J. P. R., Powell, C. D., and Smart, K. A.
(2007). Yeast responses to stresses associated with industrial brewery handling.
FEMS Microbiol. Rev. 31, 535–569. doi: 10.1111/j.1574-6976.2007.00076.x
Gilbert, L. A., Larson, M. H., Morsut, L., Liu, Z., Brar, G. A., Torres, S. E., et al.
(2013). CRISPR-mediated modular RNA-guided regulation of transcription in
eukaryotes. Cell 154, 442–451. doi: 10.1016/j.cell.2013.06.044
Gliozzi, G., Innorta, A., Mancini, A., Bortolo, R., Perego, C., Ricci, M., et al. (2014).
Zr/P/O catalyst for the direct acid chemo-hydrolysis of non-pretreated microcrystalline cellulose and softwood sawdust. Appl. Catal. B Environ. 145, 24–33.
doi: 10.1016/j.apcatb.2012.12.035
Gorsich, S. W., Dien, B. S., Nichols, N. N., Slininger, P. J., Liu, Z. L., and Skory,
C. D. (2006). Tolerance to furfural-induced stress is associated with pentose

March 2014 | Volume 5 | Article 90 | 6

Piotrowski et al.

phosphate pathway genes ZWF1, GND1, RPE1, and TKL1 in Saccharomyces
cerevisiae. Appl. Microbiol. Biotechnol. 71, 339–349. doi: 10.1007/s00253-0050142-3
Greenhalf, C. E., Nowakowski, D. J., Yates, N., Shield, I., and Bridgwater, A. V.
(2013). The influence of harvest and storage on the properties of and fast pyrolysis products from Miscanthus x giganteus. Biomass Bioenergy 56, 247–259. doi:
Hadi, S. M., Shahabuddin, and Rehman, A. (1989). Specificity of the interaction
of furfural with DNA. Mutat. Res. Lett. 225, 101–106. doi: 10.1016/01657992(89)90125-5
Herrgård, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., et al.
(2008). A consensus yeast metabolic network reconstruction obtained from a
community approach to systems biology. Nat. Biotechnol. 26, 1155–1160. doi:
Ho, C. H., Magtanong, L., Barker, S. L., Gresham, D., Nishimura, S., Natarajan,
P., et al. (2009). A molecular barcoded yeast ORF library enables mode-ofaction analysis of bioactive compounds. Nat. Biotechnol. 27, 369–377. doi:
Ho, C. H., Piotrowski, J., Dixon, S. J., Baryshnikova, A., Costanzo, M., and Boone,
C. (2011). Combining functional genomics and chemical biology to identify targets of bioactive compounds. Curr. Opin. Chem. Biol. 15, 66–78. doi:
Holyoak, C. D., Stratford, M., McMullin, Z., Cole, M. B., Crimmins, K., Brown,
A. J., et al. (1996). Activity of the plasma membrane H(+)-ATPase and optimal
glycolytic flux are required for rapid adaptation and growth of Saccharomyces
cerevisiae in the presence of the weak-acid preservative sorbic acid. Appl.
Environ. Microbiol. 62, 3158–3164.
Huffer, S., Clark, M. E., Ning, J. C., Blanch, H. W., and Clark, D. S. (2011).
Role of alcohols in growth, lipid composition, and membrane fluidity of
yeasts, bacteria, and archaea. Appl. Environ. Microbiol. 77, 6400–6408. doi:
Ibeas, J. I., and Jimenez, J. (1997). Mitochondrial DNA loss caused by ethanol in
Saccharomyces flor yeasts. Appl. Environ. Microbiol. 63, 7–12.
Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. S., and Weissman, J. S. (2009).
Genome-wide analysis in vivo of translation with nucleotide resolution using
ribosome profiling. Science 324, 218–223. doi: 10.1126/science.1168978
Ingram, L. O. (1976). Adaptation of membrane lipids to alcohols. J. Bacteriol. 125,
Iwaki, A., Ohnuki, S., Suga, Y., Izawa, S., and Ohya, Y. (2013). Vanillin
inhibits translation and induces messenger ribonucleoprotein (mRNP) granule
formation in Saccharomyces cerevisiae: application and validation of highcontent, image-based profiling. PloS ONE 8:e61748. doi: 10.1371/journal.pone.
Jarboe, L. R. (2011). YqhD: a broad-substrate range aldehyde reductase with various
applications in production of biorenewable fuels and chemicals. Appl. Microbiol.
Biotechnol. 89, 249–257. doi: 10.1007/s00253-010-2912-9
Jin, M., Sarks, C., Gunawan, C., Bice, B. D., Simonett, S. P., Avanasi Narasimhan, R.,
et al. (2013). Phenotypic selection of a wild Saccharomyces cerevisiae strain for
simultaneous saccharification and co-fermentation of AFEX™ pretreated corn
stover. Biotechnol. Biofuels 6, 108. doi: 10.1186/1754-6834-6-108
Jin, Y.-S., and Jeffries, T. W. (2004). Stoichiometric network constraints on xylose
metabolism by recombinant Saccharomyces cerevisiae. Metab. Eng. 6, 229–238.
doi: 10.1016/j.ymben.2003.11.006
Jozefczuk, S., Klie, S., Catchpole, G., Szymanski, J., Cuadros-Inostroza, A.,
Steinhauser, D., et al. (2010). Metabolomic and transcriptomic stress response
of Escherichia coli. Mol. Syst. Biol. 6:364. doi: 10.1038/msb.2010.18
Khan, Q. A., Shamsi, F. A., and Hadi, S. M. (1995). Mutagenicity of furfural in
plasmid DNA. Cancer Lett. 89, 95–99. doi: 10.1016/0304-3835(95)90163-9
King, A. T., Davey, M. R., Mellor, I. R., Mulligan, B. J., and Lowe, K. C. (1991).
Surfactant effects on yeast cells. Enzyme Microb. Technol. 13, 148–153. doi:
Kitagawa, M., Ara, T., Arifuzzaman, M., Ioka-Nakamichi, T., Inamoto, E.,
Toyonaga, H., et al. (2006). Complete set of ORF clones of Escherichia coli ASKA
library (A Complete Set of E. coli K-12 ORF Archive): unique resources for
biological research. DNA Res. 12, 291–299. doi: 10.1093/dnares/dsi012
Klinke, H. B., Thomsen, A. B., and Ahring, B. K. (2004). Inhibition of
ethanol-producing yeast and bacteria by degradation products produced during pre-treatment of biomass. Appl. Microbiol. Biotechnol. 66, 10–26. doi:


Challenges of lignocellulosic fermentation inhibitors

Krebs, H. A., Wiggins, D., Stubbs, M., Sols, A., and Bedoya, F. (1983). Studies on
the mechanism of the antifungal action of benzoate. Biochem. J. 214, 657–663.
Laouar, L., Lowe, K. C., and Mulligan, B. J. (1996). Yeast responses to nonionic surfactants. Enzyme Microb. Technol. 18, 433–438. doi: 10.1016/01410229(95)00122-0
Larsson, S., Quintana-Sáinz, A., Reimann, A., Nilvebrant, N. O., and Jönsson, L.
J. (2000). Influence of lignocellulose-derived aromatic compounds on oxygenlimited growth and ethanolic fermentation by Saccharomyces cerevisiae. Appl.
Biochem. Biotechnol. 84–86, 617–632. doi: 10.1385/ABAB:84-86:1-9:617
Larsson, S., Reimann, A., Nilvebrant, N.-O., and Jönsson, L. J. (1999). Comparison
of different methods for the detoxification of lignocellulose hydrolyzates of
spruce. Appl. Biochem. Biotechnol. 77, 91–103. doi: 10.1385/ABAB:77:1-3:91
Lau, M. W., and Dale, B. E. (2009). Cellulosic ethanol production from AFEXtreated corn stover using Saccharomyces cerevisiae 424A(LNH-ST). Proc. Natl.
Acad. Sci. U.S.A. 106, 1368–1373. doi: 10.1073/pnas.0812364106
Lau, M. W., Gunawan, C., Balan, V., and Dale, B. E. (2010). Comparing the
fermentation performance of Escherichia coli KO11, Saccharomyces cerevisiae
424A(LNH-ST) and Zymomonas mobilis AX101 for cellulosic ethanol production. Biotechnol. Biofuels 3, 11. doi: 10.1186/1754-6834-3-11
Lee, M. V., Topper, S. E., Hubler, S. L., Hose, J., Wenger, C. D., Coon, J. J., et al.
(2011). A dynamic model of proteome changes reveals new roles for transcript
alteration in yeast. Mol. Syst. Biol. 7:514. doi: 10.1038/msb.2011.48
Lee, S., Nam, D., Jung, J. Y., Oh, M.-K., Sang, B.-I., and Mitchell,
R. J. (2012). Identification of Escherichia coli biomarkers responsive to
various lignin-hydrolysate compounds. Bioresour. Technol. 114, 450–456. doi:
Li, Z., Chen, C. H., Liu, T., Mathrubootham, V., Hegg, E. L., and Hodge, D.
B. (2013). Catalysis with Cu(II) (bpy) improves alkaline hydrogen peroxide
pretreatment. Biotechnol. Bioeng. 110, 1078–1086. doi: 10.1002/bit.24793
Linton, K. J., and Higgins, C. F. (1998). The Escherichia coli ATP-binding
cassette (ABC) proteins. Mol. Microbiol. 28, 5–13. doi: 10.1046/j.13652958.1998.00764.x
Liu, Z. L., Moon, J., Andersh, B. J., Slininger, P. J., and Weber, S. (2008). Multiple
gene-mediated NAD(P)H-dependent aldehyde reduction is a mechanism of in
situ detoxification of furfural and 5-hydroxymethylfurfural by Saccharomyces
cerevisiae. Appl. Microbiol. Biotechnol. 81, 743–753. doi: 10.1007/s00253-0081702-0
Luterbacher, J. S., Rand, J. M., Alonso, D. M., Han, J., Youngquist, J. T.,
Maravelias, C. T., et al. (2014). Nonenzymatic sugar production from biomass
using biomass-derived γ-valerolactone. Science 343, 277–280. doi: 10.1126/science.1246748
Magtanong, L., Ho, C. H., Barker, S. L., Jiao, W., Baryshnikova, A., Bahr, S.,
et al. (2011). Dosage suppression genetic interaction networks enhance functional wiring diagrams of the cell. Nat. Biotechnol. 29, 505–511. doi: 10.1038/
Matsushika, A., Nagashima, A., Goshima, T., and Hoshino, T. (2013). Fermentation
of xylose causes inefficient metabolic state due to carbon/energy starvation and
reduced glycolytic flux in recombinant industrial Saccharomyces cerevisiae. PloS
ONE 8:e69005. doi: 10.1371/journal.pone.0069005
McEwen, J. T., and Atsumi, S. (2012). Alternative biofuel production in nonnatural hosts. Curr. Opin. Biotechnol. 23, 744–750. doi: 10.1016/j.copbio.
Mikulášová, M., Vodnı, Š., and Pekarovièová, A. (1990). Influence of phenolics
on biomass production by Candida utilis and Candida albicans. Biomass 23,
149–154. doi: 10.1016/0144-4565(90)90032-F
Miller, E. N., Jarboe, L. R., Turner, P. C., Pharkya, P., Yomano, L. P., York, S. W., et al.
(2009). Furfural inhibits growth by limiting sulfur assimilation in ethanologenic Escherichia coli strain LY180. Appl. Environ. Microbiol. 75, 6132–6141. doi:
Minty, J. J., Lesnefsky, A. A., Lin, F., Chen, Y., Zaroff, T. A., Veloso, A. B.,
et al. (2011). Evolution combined with genomic study elucidates genetic bases
of isobutanol tolerance in Escherichia coli. Microb. Cell Fact. 10, 18. doi:
Mira, N. P., Palma, M., Guerreiro, J. F., and Sa-Correia, I. (2010). Genome-wide
identification of Saccharomyces cerevisiae genes required for tolerance to acetic
acid. Microb. Cell Fact. 9, 79. doi: 10.1186/1475-2859-9-79
Modig, T., Lidén, G., and Taherzadeh, M. J. (2002). Inhibition effects of furfural on
alcohol dehydrogenase, aldehyde dehydrogenase and pyruvate dehydrogenase.
Biochem. J. 363, 769–776. doi: 10.1042/0264-6021:3630769

March 2014 | Volume 5 | Article 90 | 7

Piotrowski et al.

Nagodawithana, T. W., and Steinkraus, K. H. (1976). Influence of the rate of ethanol
production and accumulation on the viability of Saccharomyces cerevisiae in
“rapid fermentation”. Appl. Environ. Microbiol. 31, 158–162.
Nguyen, T. T. M., Iwaki, A., Ohya, Y., and Izawa, S. (2014). Vanillin causes the activation of Yap1 and mitochondrial fragmentation in Saccharomyces cerevisiae.
J. Biosci. Bioeng. 117, 33–38. doi: 10.1016/j.jbiosc.2013.06.008
Oliva, J. M., Negro, M. J., Sáez, F., Ballesteros, I., Manzanares, P., González, A., et al.
(2006). Effects of acetic acid, furfural and catechol combinations on ethanol
fermentation of Kluyveromyces marxianus. Process Biochem. 41, 1223–1228. doi:
Ouellet, M., Datta, S., Dibble, D. C., Tamrakar, P. R., Benke, P. I., Li, C., et al.
(2011). Impact of ionic liquid pretreated plant biomass on Saccharomyces cerevisiae growth and biofuel production. Green Chem. 13, 2743. doi: 10.1039/c1gc
Parsons, A., Lopez, A., Givoni, I., Williams, D., Gray, C., Porter, J., et al. (2006).
Exploring the mode-of-action of bioactive compounds by chemical-genetic
profiling in yeast. Cell 126, 611–625. doi: 10.1016/j.cell.2006.06.040
Pereira Rangel, L., Fritzen, M., Yunes, R. A., Leal, P. C., Creczynski-Pasa, T. B., and
Ferreira-Pereira, A. (2010). Inhibitory effects of gallic acid ester derivatives on
Saccharomyces cerevisiae multidrug resistance protein Pdr5p. FEMS Yeast Res.
10, 244–251. doi: 10.1111/j.1567-1364.2010.00603.x
Russell, J. B. (1992). Another explanation for the toxicity of fermentation acids at
low pH: anion accumulation versus uncoupling. J. Appl. Bacteriol. 73, 363–370.
doi: 10.1111/j.1365-2672.1992.tb04990.x
Sato, T. K., Liu, T., Parreiras, L. S., Williams, D. L., Wohlbach, D. J., Bice, B. D., et al.
(2014). Harnessing genetic diversity in Saccharomyces cerevisiae for improved
fermentation of xylose in hydrolysates of alkaline hydrogen peroxide pretreated
biomass. Appl. Environ. Microbiol. 80, 540–554. doi: 10.1128/AEM.01885-13
Schmer, M. R., Vogel, K. P., Mitchell, R. B., and Perrin, R. K. (2008). Net energy of
cellulosic ethanol from switchgrass. Proc. Natl. Acad. Sci. U.S.A. 105, 464–469.
doi: 10.1073/pnas.0704767105
Schmitt, L., and Tampé, R. (2002). Structure and mechanism of ABC transporters.
Curr. Opin. Struct. Biol. 12, 754–760. doi: 10.1016/S0959-440X(02)00399-8
Schüller, C., Mamnun, Y. M., Mollapour, M., Krapf, G., Schuster, M., Bauer, B. E.,
et al. (2004). Global phenotypic analysis and transcriptional profiling defines
the weak acid stress response regulon in Saccharomyces cerevisiae. Mol. Biol. Cell
15, 706–720. doi: 10.1091/mbc.E03-05-0322
Schwalbach, M. S., Keating, D. H., Tremaine, M., Marner, W. D., Zhang,
Y., Bothfeld, W., et al. (2012). Complex physiology and compound stress
responses during fermentation of alkali-pretreated corn stover hydrolysate by
an Escherichia coli ethanologen. Appl. Environ. Microbiol. 78, 3442–3457. doi:
Shapiro, A. B., and Ling, V. (1998). Transport of LDS-751 from the cytoplasmic leaflet of the plasma membrane by the rhodamine-123-selective site
of P-glycoprotein. Eur. J. Biochem. FEBS 254, 181–188. doi: 10.1046/j.14321327.1998.2540181.x
Sindhu, R., Kuttiraja, M., Elizabeth Preeti, V., Vani, S., Sukumaran, R. K., and
Binod, P. (2013). A novel surfactant-assisted ultrasound pretreatment of sugarcane tops for improved enzymatic release of sugars. Bioresour. Technol. 135,
67–72. doi: 10.1016/j.biortech.2012.09.050
Sinha, R. P. (1986). Toxicity of organic acids for repair-deficient strains of
Escherichia coli. Appl. Environ. Microbiol. 51, 1364–1366.
Skerker, J. M., Leon, D., Price, M. N., Mar, J. S., Tarjan, D. R., Wetmore, K. M., et al.
(2013). Dissecting a complex chemical stress: chemogenomic profiling of plant
hydrolysates. Mol. Syst. Biol. 9, 674. doi: 10.1038/msb.2013.30
Stratford, M., and Anslow, P. A. (1998). Evidence that sorbic acid does not inhibit
yeast as a classic “weak acid preservative.” Lett. Appl. Microbiol. 27, 203–206. doi:
Stratford, M., Nebe-von-Caron, G., Steels, H., Novodvorska, M., Ueckert, J., and
Archer, D. B. (2013). Weak-acid preservatives: pH and proton movements in
the yeast Saccharomyces cerevisiae. Int. J. Food Microbiol. 161, 164–171. doi:
Taymaz-Nikerel, H., Borujeni, A. E., Verheijen, P. J. T., Heijnen, J. J., and van Gulik,
W. M. (2010). Genome-derived minimal metabolic models for Escherichia coli
MG1655 with estimated in vivo respiratory ATP stoichiometry. Biotechnol.
Bioeng. 107, 369–381. doi: 10.1002/bit.22802

Frontiers in Microbiology | Microbial Physiology and Metabolism

Challenges of lignocellulosic fermentation inhibitors

Ullah, A., Orij, R., Brul, S., and Smits, G. J. (2012). Quantitative analysis of the
modes of growth inhibition by weak organic acids in Saccharomyces cerevisiae.
Appl. Environ. Microbiol. 78, 8377–8387. doi: 10.1128/AEM.02126-12
Varma, A., and Palsson, B. O. (1994). Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type
Escherichia coli W3110. Appl. Environ. Microbiol. 60, 3724–3731.
Verduyn, C., Postma, E., Scheffers, W. A., and Van Dijken, J. P. (1992). Effect of
benzoic acid on metabolic fluxes in yeasts: a continuous-culture study on the
regulation of respiration and alcoholic fermentation. Yeast 8, 501–517. doi:
Wang, H. H., Kim, H., Cong, L., Jeong, J., Bang, D., and Church, G. M. (2012).
Genome-scale promoter engineering by coselection MAGE. Nat. Methods 9,
591–593. doi: 10.1038/nmeth.1971
Wang, X., Miller, E. N., Yomano, L. P., Zhang, X., Shanmugam, K. T., and Ingram,
L. O. (2011). Increased furfural tolerance due to overexpression of NADHDependent oxidoreductase FucO in Escherichia coli strains engineered for the
production of ethanol and lactate. Appl. Environ. Microbiol. 77, 5132–5140. doi:
Wang, X., Yomano, L. P., Lee, J. Y., York, S. W., Zheng, H., Mullinnix, M. T., et al.
(2013). Engineering furfural tolerance in Escherichia coli improves the fermentation of lignocellulosic sugars into renewable chemicals. Proc. Natl. Acad. Sci.
U.S.A. 110, 4021–4026. doi: 10.1073/pnas.1217958110
Wei, N., Quarterman, J., Kim, S. R., Cate, J. H. D., and Jin, Y.-S. (2013). Enhanced
biofuel production through coupled acetic acid and xylose consumption by
engineered yeast. Nat. Commun. 4:2580. doi: 10.1038/ncomms3580
Yang, S., Land, M. L., Klingeman, D. M., Pelletier, D. A., Lu, T.-Y. S., Martin,
S. L., et al. (2010a). Paradigm for industrial strain improvement identifies
sodium acetate tolerance loci in Zymomonas mobilis and Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. U.S.A. 107, 10395–10400. doi: 10.1073/pnas.09145
Yang, S., Pan, C., Tschaplinski, T. J., Hurst, G. B., Engle, N. L., Zhou,
W., et al. (2013). Systems biology analysis of Zymomonas mobilis ZM4
ethanol stress responses. PloS ONE 8:e68886. doi: 10.1371/journal.pone.00
Yang, S., Pelletier, D. A., Lu, T.-Y. S., and Brown, S. D. (2010b). The Zymomonas
mobilis regulator hfq contributes to tolerance against multiple lignocellulosic pretreatment inhibitors. BMC Microbiol. 10:135. doi: 10.1186/1471-218010-135
Zaldivar, J., and Ingram, L. O. (1999). Effect of organic acids on the growth
and fermentation of ethanologenic Escherichia coli LY01. Biotechnol. Bioeng.
66, 203–210. doi: 10.1002/(SICI)1097-0290(1999)66:4<203::AID-BIT1>
Zerhouni, E. (2003). The NIH Roadmap. Science 302, 63–72. doi: 10.1126/science.1091867
Zingaro, K. A., Nicolaou, S. A., and Papoutsakis, E. T. (2013). Dissecting the
assays to assess microbial tolerance to toxic chemicals in bioprocessing. Trends
Biotechnol. 31, 643–653. doi: 10.1016/j.tibtech.2013.08.005
Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Received: 16 November 2013; accepted: 18 February 2014; published online: 14 March
Citation: Piotrowski JS, Zhang Y, Bates DM, Keating DH, Sato TK, Ong IM and
Landick R (2014) Death by a thousand cuts: the challenges and diverse landscape
of lignocellulosic hydrolysate inhibitors. Front. Microbiol. 5:90. doi: 10.3389/fmicb.
This article was submitted to Microbial Physiology and Metabolism, a section of the
journal Frontiers in Microbiology.
Copyright © 2014 Piotrowski, Zhang, Bates, Keating, Sato, Ong and Landick.
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