Prediction of Weak Acid Toxicity in Saccharomyces cerevisiaeUsing Genome-Scale Metabolic Models

Patrick B Hyland,1 Serene Lock-Sow Mun,1,2

and Radhakrishnan Mahadevan1

1Department of Chemical Engineering and Applied Chemistry,University of Toronto, Toronto, Canada

2CO2 Management Mission Oriented Research, UniversitiTeknologi PETRONAS, Tronoh, Malaysia

AbstractThe use of lignocellulosic biomass is critical for the economicproduction of transportation fuels and chemicals in renewablebioprocesses. While biomass is an abundant resource, neces-sary pretreatment to yield fermentable monosaccharides pro-duces toxic compounds that dramatically affect fermentationperformance. Weak acids such as acetic acid play an importantrole in the toxicity of lignocellulosic hydrolysate to Sacchar-omyces cerevisiae, a commonly used industrial organism. Inorder to explore the ramifications of weak acid inhibition oncellular metabolism, we adapted a genome-scale metabolicmodel of S. cerevisiae to describe toxicity of acetic acid by adecoupling mechanism. We evaluated the performance of themodel in predicting growth rates and ethanol productioncharacteristics under aerobic and anaerobic cultivations. Wefound that the model was able to capture the decreased growthduring aerobic cultivations in the presence of acetic acid, butwas unable to capture the increase in ethanol yield observed.The model was able to predict anaerobic growth rates andethanol yields; however, at conditions of higher toxicity levels,discrepancies arose. We expect that a model such as this mayfind application in the optimization of lignocellulose-basedbioprocesses in which there exists a critical economic trade-offbetween neutralization costs and product yields.

Introduction

In recent years, renewable means of producing transpor-tation fuel and chemicals have been heavily investigatedin order to reduce reliance on natural oil resources. It hasbeen previously noted that as a result of increasing global

rate of oil consumption and decreasing rate at which new fossilfuel resources are discovered, alternative sources of transpor-tation fuels and chemicals are highly sought after.1 Bioprocessesoffer an attractive alternative to petroleum-based processes bypotentially converting renewable biomass feedstock to drop-inreplacements for petroleum-derived chemicals. In particular,

bioprocesses that use lignocellulosic biomass, an abundant andrenewable resource, as feedstock are among the current areas offocus in the field of biotechnology.

Lignocellulose is composed of cellulose, hemicellulose, andlignin. Cellulose and hemicellulose are long-chain polymers ofsaccharides that must undergo enzymatic treatment to releasefermentable monomers. This process is complicated by thepresence of lignin—a complex aromatic polymer—that makes itnecessary for the biomass to undergo a pretreatment such asweak acid hydrolysis prior to enzymatic treatment.2 A signifi-cant limitation of pretreatment processes is that inhibitorycompounds are produced that significantly impact fermentationcharacteristics. Inhibitory compounds can be classified as phe-nolics, furan derivatives, or weak acids, and mechanisms ofthese inhibitor classes have been reviewed extensively.2–4

Weak acid toxicity is a particularly interesting physical phe-nomenon arising from the interaction of internal and external pHand cellular response mechanisms. The toxicity of weak acidshas largely been attributed to the acidification of the cytosol bydecoupling and anion accumulation.3,5 The decoupling mecha-nism of weak acid toxicity is a result of the ability of non-polarundissociated weak acid species to diffuse across the cellularmembrane to the near-neutral pH cytosol. Upon entering thecytosol, the acid dissociates, yielding a proton and the conjugatebase. To maintain neutral cytosolic pH, the cell uses the reverseaction of adenosine triphosphate (ATP) synthase, exportingprotons at the expense of ATP. In addition, the anion accumu-lation theory proposed by Russell states that the anionic form ofthe acid species may accumulate in the cytosol and drive thediffusion of undissociated acid across the cell membrane to-wards equilibrium.3,5

A significant weak acid inhibitor is acetic acid, which isformed largely from the degradation of hemicellulose and ispresent in hydrolysates in concentrations up to 10 g/L, depend-ing on the biomass source.6 While many studies have focused onstrain engineering for tolerance to furan derivatives and phe-nolics, there are significantly fewer that focus on improvingtolerance to weak acids.6–8 Notably, Hasunuma et al. demon-strated that a recombinant xylose-utilizing strain of Saccharo-myces cerevisiae over-expressing the TAL1 gene had improvedethanol yield in the presence of acetic acid.6 Recently, an acetic-acid-tolerant strain of S. cerevisiae was generated using agenome shuffling method.9 However, hypothesis discoveryremains a bottleneck in rational strain engineering for toleranceto weak acids.

Mathematical modeling is a powerful tool for examining themetabolism of microorganisms. Previous studies have modeled

DOI: 10.1089/ind.2013.0004 ª M A R Y A N N L I E B E R T , I N C . � VOL. 9 NO. 4 � AUGUST 2013 INDUSTRIAL BIOTECHNOLOGY 229

the impact of weak acids on microorganisms using kinetic andthermodynamic approaches; however, such studies have largelybeen for the purpose of food preservation.10–12 In the past de-cade, constraint-based metabolic models have been demon-strated to be powerful tools for describing cellular phenotypewhile not requiring enzyme kinetic parameters.13 A particularlyuseful tool in constraint-based modeling is Flux Balance Ana-lysis (FBA), wherein realistic rates of nutrient uptake based onthe growth environment are defined; a pseudo-steady state as-sumption is applied; and metabolic fluxes are determined bysolving the system as a linear programming problem. In the past,these models have successfully been used to predict experi-mental phenotypes.14,15

In this study, we attempt to describe the toxic effects ofacetic acid on the metabolism of S. cerevisiae using theiMM904 genome-scale metabolic model.16 A workflow of thestudy is presented in Fig. 1. We demonstrate that the model’spredictive power may be enhanced with the inclusion of addi-tional constraints describing the uptake of acetic acid by simplediffusion and export of protons via ATP synthase. We antici-pate that this work could be built upon to further improve thepredictive quality of metabolic models and improve their use inindustrial settings. To the best of our knowledge, this is the firstinstance of incorporating concentrations of inhibitory com-pounds present in lignocellulosic hydrolysates into an FBAframework. The model could find further use in the optimiza-tion of lignocellulosic-based bioprocesses, in which there existsa trade-off between fermentation characteristics and cost ofneutralization.

Materials and MethodsMETABOLIC MODELING

The genome-scale compartmentalized iMM904 S. cerevisiaemetabolic model was adapted to describe toxicity of acetic acidvia the uncoupling mechanism. Figure 2 shows a schematic ofreactions added to the model. The concentration of undissoci-ated acetic acid in the extracellular environment was modeled asa function of pH and concentration of acid species using theHenderson-Hasselbalch equation:

pH = pKa + log[A - ]

[HA]

� �(Equation 1)

where [A - ] and [HA] are the concentrations of dissociated andundissociated acid species, respectively. The pKa value of aceticacid used was 4.75, and the pH of cytosol was assumed to be 7.The diffusion of undissociated acid across the lipid bilayer wasmodeled as simple diffusion based on extrapolation of the linearrelationship between diffusion rate and concentration reportedby Casal et al.17 The association between proton export and ATPhydrolysis was described using the cytosolic ATP synthase re-action included in the iMM904 model. The rate of ATP hy-drolysis resulting from weak acid toxicity was fixed to be one-third of the rate of diffusion, reflective of three protons exportedper ATP hydrolysis.

Simulations of aerobic cultivations were carried out using aglucose uptake rate of 15.9 mmol/gDW/hr based on measuredexperimental rates and an oxygen uptake rate of 8.8 mmol/gDW/hr as measured by van Hoek et al.18 For anaerobic simulations,

Fig. 1. Workflow of the current study. The iMM904 metabolic model of S. cerevisiae was amended with reactions liquid chromatography todescribe weak acid toxicity by the decoupling mechanism (Left).16 S. cerevisiae CEN.PK 122 was cultivated in batch in flasks and 5-Lfermenters. Growth was monitored by measuring optical density, and metabolic concentration was determined using high-performanceliquid chromatography (HPLC) (Center). Flux balance analysis (FBA) was used to predict growth rates and product yields (Right).

Fig. 2. Reactions describing weak acid toxicity by decouplingmechanism. Non-polar undissociated weak acids may diffuseacross the plasma membrane by simple diffusion and dissociatedue to near-neutral pH of the cytosol. To maintain cytosolic pHneutrality, protons are excreted at the expense of ATP by the re-verse action of ATP synthase.

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oxygen uptake rate was set to 0 mmol/gDW/hr. FBA was con-ducted using the COBRA toolbox.19,20 Cultivations that wereconducted with no exogenous acetic acid added to the growthmedia were used to fit the first data point of the model byaltering the ATP maintenance requirement and glucose uptakerate.

STRAINS, MEDIA, AND CULTURE CONDITIONSThe prototrophic strain S. cerevisiae CEN.PK 122 was used in

all experiments. Cells were maintained on Yeast Extract Pep-tone Dextrose (YPD) medium. Preculture conditions were asfollows: cells from frozen stock were cultured overnight in YPDat 30�C and washed three times with sterile, filtered water beforebeing transferred to baffled flasks containing mineral medium(as described in a previous study) supplemented with 20 g/Lglucose and 3 g/L glacial acetic acid.21 The cells were thencultured overnight at 30�C and 250 rpm. This procedure wasused for inoculating both flask- and batch-cultivation experi-ments. In flask experiments, exogenous acetic acid concentra-tion was varied between 0–5 g/L and pH was not controlled.Aerobic batch-cultivation experiments were conducted in Infors(Basel, Switzerland) 5-L Mini Reactors (3-L working volume),and pH was controlled with the addition of 4 M KOH.

ANALYSISMetabolites were measured using high-performance liquid

chromatography (HPLC; Aminex HPX-87H column, Bio-RadLaboratories, Hercules, CA); 5 mM H2SO4, 50�C, 0.4 mL/min.Growth was monitored through optical density measurements at600nm, and cell concentration was calculated using a correlationof 1 OD600 = 0.4 gDW/L.

Results and DiscussionMODEL CHARACTERISTICS

The iMM904 metabolic model of S. cerevisiae was amendedto include reactions that describe the uncoupling mechanism ofacetic acid toxicity. Using an FBA approach, we found thatmodel predictions more closely matched experimental data ofS. cerevisiae grown in the presence of acetic acid, as comparedto the original model, which does not include reactions thataccount for weak acid toxicity. The fraction of acetic acid spe-cies existing in an undissociated form is a function of externalpH and the pKa of the compound, related by the Henderson-Hasselbalch equation (Fig. 3A). In optimal growth conditions—with an external pH of 5.5—approximately 15% of externalacetic acid exists in its undissociated form and is therefore ableto diffuse across the cellular membrane (Fig. 3A). We used theliterature values reported in Casal et al. that describe the rateof simple diffusion of undissociated acetic acid across themembrane of S. cerevisiae as a function of concentration andexternal pH.17

Upon entering the cytosol, which is nearly neutral in pH, lessthan 1% of acetic acid species exist in the undissociated form(Fig. 3A). Thus, from the Henderson-Hasselbalch equation, it isexpected that the majority of acetic acid entering the cytosol willcontribute to the acidification of the cytosol and ATP depletion.

Under the assumption of steady state, the rate of acetic aciddissociation in the cytosol must be equal to the diffusion rate ofacetic acid from the extracellular environment to the cytosol. Atsteady state, the rate of ATP hydrolysis due to weak acid toxicityis therefore equivalent to one-third the rate of cytosolic aceticacid dissociation, reflective of one molecule of ATP transport-ing three protons out of the cytosol (Fig. 3B).

As a result of diffusion of undissociated acetic acid into thecytosol, we observed changes in the flux distribution sur-rounding cytosolic acetate node (Fig. 4). Specifically, fluxthrough the cytosolic NADP + -dependent aldehyde dehydro-genase reaction (ALDD2y), converting acetaldehyde andNADP + to acetate and NADPH, was eliminated in the pres-ence of small concentrations of exogenous acetic acid. Wereasoned that this is likely due to cytosolic acetate being sup-plied by the diffusion of acetic acid into the cytosol and itssubsequent dissociation. This insight is interesting in that therefined model predicts that exogenous acetic acid is to someextent directed towards production of biomass, likely throughthe synthesis of acetyl-CoA, as was previously suggested byTaherzadeh et al.22 The balance of acetate was predicted to beexported from the cytosol through the action of an acetatetransporter (ACtr).

PREDICTION OF GROWTH UNDER AEROBIC CONDITIONSInitial aerobic cultivations were conducted in baffled shake

flasks with mineral media supplemented with 20 g/L glucoseand varying concentrations of acetic acid without buffering thechange in pH (Fig. 3C). In these cultivations, we found a linearcorrelation between growth rate decrease and increase in aceticacid concentration (Fig. 3D). Experimental growth rates werepredicted using an external pH of 5 to approximate the startingpH of the media used. Predictions were good for low concen-trations of acetic acid (up to 1 g/L), after which point the modelwas unable to capture fully the toxic effects of the compound.We reasoned that the discrepancies between the model and ex-perimental results could be partially attributed to lack of pHcontrol, particularly at higher concentrations of acetic acid. Theexternal pH of growth medium plays a critical role in the toxicityof acetic acid, as lower external pH increases the fraction of totalacetate species that remains undissociated and therefore maytransverse the cytoplasmic membrane. Under conditions thatlack pH control, thereby allowing the pH to decrease as thecultivations continue, it is expected that the toxic effects wouldbe magnified.

To account for variations in external pH, further cultivationstudies were conducted in fermenters with pH controlled to 5.5using 4M KOH. In these studies, we found a nonlinear rela-tionship between the concentration of acetic acid and decrease ingrowth rate (Fig. 5A). Low concentrations of acetic acid werefound to have less impact on growth rate as compared to thehigher concentrations, as expected based on the relative con-centrations of extracellular. undissociated acetic acid species.Although the predictions of growth rate at low concentrations ofacetic acid matched experimental data closely, we found that themodel was unable to capture toxicity at higher concentrations.We qualitatively assessed the efficacy of the model by bothincreasing and decreasing the acetic acid diffusion rate by 50%,

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corresponding to an increase and decrease in toxic effects, re-spectively. We found that despite a 1.5-fold increase in aceticacid diffusion, the model was still unable to capture the reduc-tion in growth rate experimentally determined. This resultsuggests that significant toxic effects arise from secondarymechanisms of toxicity, such as anion accumulation, osmoticstress, and increased maintenance requirement, and demon-strates that the decoupling mechanism alone is unable to accountfor the decrease in growth rate observed experimentally.5

Moreover, we found that the model was unable to captureincreases in ethanol yield due to the presence of acetic acid (Fig.5B). A previous study by Taherzadeh et al., found that thepresence of acetic acid increased the yield of ethanol in fer-mentations.22 Model predictions indicated a minor increase inethanol yield with increasing concentration of external acetic

acid, but did not match experimental results. A small portion ofthis discrepancy may be attributed to the assimilation of acetateby conversion to acetyl-CoA by acetyl coenzyme-A synthetase,although it has previously been suggested that this consumptionis small compared to the rate of acetic acid uptake.22 Moreover,varying the oxygen uptake did not improve the prediction ofethanol yield in the anaerobic case (data not shown). It is likelythat the addition of constraints that accurately reflect the phys-iology of S. cerevisiae may be included to improve predictionsand will be the subject of future studies.

PREDICTION OF GROWTH IN ANAEROBIC CONDITIONSTo evaluate the accuracy of the model, we used anaerobic

experimental data reported by Taherzadeh et al.22 In that study,the authors evaluated the impact of acetic acid on the batch

Fig. 3. Model characteristics and prediction of aerobic cultivation in baffled flasks. Henderson-Hasselbalch equation relating dissociatedacetic acid (dashed black line) and undissociated acetic acid (solid black line). Calculated using pKa of acetic acid = 4.75, pH = 5.5 (A).Diffusion rate of undissociated acetic acid (solid black line) and flux of ATP hydrolysis due to weak acid toxicity (dashed black line) toexcrete protons as a function of acetic acid concentration, simulated at pH 5.5 (B). Growth characterization of S. cerevisiae CEN.PK 122 inthe presence of 0 g/L (closed diamonds), 1 g/L (open diamonds), 2 g/L (closed triangles), 3 g/L (open triangles), 4 g/L (closed circles),4.5 g/L (open circles), and 5 g/L (closed squares) acetic acid. Error bars indicate the standard deviation between two experiments (C).Experimental growth rates of S. cerevisiae CEN.PK 122 in mineral media flasks (open diamonds) in the presence of acetic acid and predictedgrowth rates at pH 5.0 (solid black line). Error bars indicate the standard deviation of two or more experiments (D).

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fermentation of glucose to ethanol by S. cerevisiae CBS 8066and reported the fermentation characteristics at pH values of 3.5and 5.0.22 Using the same model as the aerobic case, and aglucose uptake rate reflective of the fermentations conducted byTaherzadeh et al., we predicted the growth rates and ethanolyields at pH values 3.5 and 5.0, as reported in the study (Fig. 6).Model predictions were in good agreement with experimentalvalues reported for the growth rates and ethanol yields in thepresence of acetic acid in cultivations at pH 5.0 and 3.5 (Fig. 6).However, the refined model was unable to predict the growthrate at high concentration of acetic acid at an external pH of 3.5.Moreover, in the pH 3.5 cultivations, a given increase in aceticacid concentration resulted in a larger increase in ethanol yieldthan was predicted by the refined model. In addition to sec-ondary mechanisms of weak acid toxicity that may contribute to

discrepancies observed between the experimental and modelpredicted growth rates and ethanol yields, it is possible thatadditional impacts of extracellular pH on S. cerevisiae alsocontributed to prediction error. The impacts of the pH of culti-vation media are unable to be captured by metabolic models andcould have contributed to the reduction in growth rate as well asthe increase in ethanol yields reported by Taherzadeh et al.22

Nonetheless, we found that the refined model was able to predictgrowth rates and ethanol yields for anaerobic conditions.

ConclusionsWe used constraint-based metabolic models to predict the

toxicity of acetic acid in S. cerevisiae. We amended the iMM904model of S. cerevisiae to include reactions that describe theuncoupling mechanism of weak acid toxicity and showed thatthe model could describe the trends of growth rate and ethanolyield observed in aerobic and anaerobic cultivation experimentsusing FBA.

The refined model was able to capture, to a certain extent, thetoxicity of acetic acid under aerobic cultivation; however, wefound that at higher concentrations of the inhibitor, the modelcould not capture the full toxic effect. We believe that the dis-crepancy between experimental and model results is due tothe secondary inhibitory mechanisms of weak acids that the modeldid not account for, such as anion accumulation or an increase inmaintenance requirement. Additionally, inconsistencies observedbetween experimental and predicted results could be a conse-quence of differences between the strains cultivated in experi-ments and the S. cerevisiae S288C strain upon which the metabolicmodel is based. We found that the refined model was unable topredict the trends in ethanol production in the presence of aceticacid under aerobic conditions, suggesting that the addition offurther constraints representing the physiology of S. cerevisiaemay be required.

The refined metabolic model was capable of predicting an-aerobic growth rates and ethanol yields for a pH of 5.0; however,at higher levels of toxic effect—ie, lower external pH and higherconcentration of acetic acid—discrepancies arose between the

Fig. 4. Predicted flux distribution around the cytosolic acetate nodein the presence of exogenous acetic acid. Predicted flux distribu-tion around the cytosolic acetate node in the presence of aceticacid. Values reported in mmol/gDW/hr.

Fig. 5. Prediction of aerobic batch cultivation. Experimental (open diamonds) and predicted aerobic growth rates (solid black line) ofS. cerevisiae at pH 5.5 in the presence of acetic acid. Error bars indicate standard deviation between two or more experiments. Thediffusion rate of acetic acid was increased and decreased by 50% (dashed and solid gray line, respectively) (A). Experimental (opentriangles) and predicted ethanol yields (solid black line) in aerobic batch cultivation as a function of acetic acid concentration. Experimentaland model predicted ethanol yields were normalized to their respective yields in the case of cultivation in the absence of acetic acid (B).

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model and experimental data. This result shows conclusivelythat dissociation cannot fully account for the growth defect. Webelieve this model could find application in the optimization oflignocellulosic bioprocesses, in which there exists a delicatetrade-off between cost of neutralization and process productiv-ity. It is possible that constraint-based modeling alone cannotcapture other mechanisms of toxicity such as phenolics andfuran derivatives, as they have inhibitory impacts on enzymaticactivity rather than affecting steady state yields. In these situa-tions, it is likely that coupling a constraint-based model with akinetic model could improve model predictions.

AcknowledgmentsThe prototrophic strain S. cerevisiae CEN.PK 122 used in all

experiments was a gift from Dr. Vince Martin at ConcordiaUniversity (Montreal, Canada).

Author Disclosure StatementThe authors declare no competing financial interests exist.

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Fig. 6. Prediction of anaerobic cultivation. Prediction of batch fermentation of glucose to ethanol by S. cerevisiae CBS 8066. Experimentaldata (open triangles) from Taherzadeh et al.22 Growth rate (solid black line) was predicted for pH 5.0 (A) and 3.5 (B) as well as ethanol yield(black lines) for pH 5.0 (C) and 3.5 (D).

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Address correspondence to:Radhakrishnan Mahadevan, PhD

Associate ProfessorDepartment of Chemical Engineering and Applied Chemistry

University of Toronto200 College Street, Toronto, Ontario, Canada, M5S 3E5

Phone: (416) 946-0996Fax: (416) 978-8605

E-mail: krishna.mahadevan@utoronto.ca

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