Comparison of genome-wide selection strategies to identify furfural tolerance genes in ...

Comparison of Genome-Wide Selection Strategiesto Identify Furfural Tolerance Genes inEscherichia Coli

Tirzah Y. Glebes, Nicholas R. Sandoval, Jacob H. Gillis, Ryan T. Gill

Department of Chemical and Biological Engineering, University of Colorado Boulder,

Colorado; telephone: (303)492-2627; e-mail: [email protected]

ABSTRACT: Engineering both feedstock and product toler-ance is important for transitioning towards next-generationbiofuels derived from renewable sources. Tolerance tochemical inhibitors typically results in complex phenotypes,for which multiple genetic changes must often be made toconfer tolerance. Here, we performed a genome-wide searchfor furfural-tolerant alleles using the TRackable MultiplexRecombineering (TRMR) method (Warner et al. (2010),Nature Biotechnology), which uses chromosomally integratedmutations directed towards increased or decreased expres-sion of virtually every gene in Escherichia coli. We employedvarious growth selection strategies to assess the role ofselection design towards growth enrichments. We alsocompared genes with increased fitness from our TRMRselection to those from a previously reported genome-wideidentification study of furfural tolerance genes using aplasmid-based genomic library approach (Glebes et al. (2014)PLOS ONE). In several cases, growth improvements wereobserved for the chromosomally integrated promoter/RBSmutations but not for the plasmid-based overexpressionconstructs. Through this assessment, four novel tolerancegenes, ahpC, yhjH, rna, and dicA, were identified and confirmedfor their effect on improving growth in the presence offurfural.

Biotechnol. Bioeng. 2015;112: 129–140.

� 2014 Wiley Periodicals, Inc.

KEYWORDS: biofuels; metabolic engineering; directedevolution; furfural

Introduction

The ability to construct and map large-scale and high-resolution genomic libraries has advanced dramatically as theefficiency of DNA synthesis and sequencing technologiescontinues to increase. In Escherichia coli, construction andmapping tools include a knockout collection (Babaet al., 2006; Datsenko and Wanner, 2000), a collection ofall E. coli open reading frames cloned into overexpressionplasmids (Kitagawa et al., 2005), methods for parallelmapping of plasmid-based overexpression libraries (Choet al., 1998; Gill et al., 2002; Lynch et al., 2007), phage-basedrecombineering technologies like multiplex automatedgenome engineering (Wang et al., 2009), a genome-widebarcoded promoter mutation library (trackable multiplexrecombineering, or TRMR, (Warner et al., 2010)), amongothers (Alper and Stephanopoulos, 2007; Jiang et al., 2013).These tools have been broadly applied to improve under-standing of metabolic pathway optimization, chemicaltolerance, antimicrobial resistance, and the mechanisms oflaboratory evolution, to name a few (Alper et al., 2005;Bonomo et al., 2008; Dunlop et al., 2011; Gall et al., 2008;Glebes et al., 2014; Goodarzi et al. 2010; Sandoval et al., 2011;Sandoval et al., 2012; Singh et al., 2009; Spindler et al., 2013;Wang et al., 2012a; Warnecke et al., 2008; Warneckeet al., 2010; Woodruff et al., 2013a; Woodruff et al., 2013b).Here, we applied the TRMR method (Warner et al., 2010)

to perform a genome-wide search for promoter and RBS “on”or “off”mutations that confer furfural tolerance. The TRMRlibrary was constructed by integrating a dsDNA cassettecontaining a blasticidin resistance gene, a unique barcode,and either i) a promoter and strong RBS (i.e., ‘Up’) or ii) nopromoter or RBS (i.e., ‘Down’) in front of virtually every genein E. coli. The barcodes allow for specific quantification ofeach mutant within the population of �8,000 mutants byeither microarray or next-generation sequencing technology.As such, mutant allele fitness scores can be calculated bycomparing the relative allele concentration before and afterselection. Alleles with high fitness suggest that underlyingpromoter/RBS mutation confers furfural tolerance, and thus,is representative of worthwhile targets for further engineer-ing. In the seminal TRMR effort, the method was used to

Correspondence to: Ryan T. Gill

Contract grant sponsor: Seed Scholar Program through the Colorado Center for

Biorefining and Biofuels

Contract grant sponsor: NIH Pharmaceutical Biotechnology Training Grant

Contract grant sponsor: DOE BER

Contract grant number: #DE_SOOO8812

Received 6 March 2014; Revision received 20 June 2014; Accepted 25 June 2014

Accepted manuscript online 1 July 2014;

Article first published online 2 September 2014 in Wiley Online Library

(http://onlinelibrary.wiley.com/doi/10.1002/bit.25325/abstract).

DOI 10.1002/bit.25325

ARTICLE

� 2014 Wiley Periodicals, Inc. Biotechnology and Bioengineering, Vol. 112, No. 1, January, 2015 129

identify novel targets for conferring tolerance to corn stoverhydrolysate, with identification of the ahpC_Upmutant, whichwas confirmed to increase growth over 200% and speculatedto detoxify reactive oxygen species (ROS) (Warner et al.,2010).

Furfural is an inhibitor of microbial growth and metabo-lism that is present at inhibitory concentrations in ligno-cellulosic hydrolysate (Mills et al., 2009; Zaldivar et al., 1999).The toxicity of furfural has been linked to NADPH starvationresulting from the activity of NADPH-dependent furfuralreductases (Gutierrez et al., 2002; Gutierrez et al., 2006;Milleret al., 2009b), for which furfural reduction has been observedin E. coli as well as other microorganisms (Diaz et al., 1992;Delgenes et al., 1996; Franden et al., 2013). This NADPHlimitation is thought to stall biosynthetic operations likesulfur assimilation and pyrimidine biosynthesis (Milleret al., 2009a; Zheng et al., 2012). Mechanisms for conferringtolerance remain elusive, although strides have been made bytargeting various mutations to limit NADPH starvation(Miller et al., 2009a; Wang et al., 2011; Wang et al., 2012c;Wang et al., 2013; Zheng et al., 2013). Other genes have beenidentified to promote furfural reduction and tolerancevia NADH-dependent mechanisms, like fucO and ucpA(Wang et al., 2011; Wang et al., 2012c; Zheng et al., 2013).Additionally, three groups have recently shown targetingmutations to the relief of ROS accumulation, a phenotyperesulting from furfural treatment (Allen et al., 2010), canimprove tolerance to furfural or hydrolysate (Wanget al., 2012b; Warner et al., 2010; Skerker et al., 2013).Furfural treatment has been shown to reduce intracellularlevels of glutathione during the onset of ROS accumulation inyeast (Kim and Hahn, 2013), while engineering glutathionebiosynthesis results in improved fermentation performanceusing spruce hydrolysate containing over 1 g/l furfural (Asket al., 2013). Using plasmid-based genomic libraries in ourmultiSCale Analysis of Library Eenrichments (SCALEs)method (Lynch et al., 2007), we recently determined thatoverexpression of the protein chaperone GroES-EL orthe LpcA isomerase involved in lipopolysaccharide (LPS)production confer increased tolerance (Glebes et al., 2014),reinforcing the hypothesis that there are multiple genetichandles to confer tolerance.

Here, we performed growth selections with the TRMRlibrary using three different selection strategies. Similar toothers (for a recent review (Barrick and Lenski, 2013)), ourlab has shown that selection strategy can have a dramaticeffect on fitness and the identity of selected tolerance genes(Gall et al., 2008; Warnecke et al., 2008). We compare theresults of a selection performed on solid medium plates withthose performed planktonically with either a constant ordecreasing furfural gradient over several serial transfercultures. In addition to comparison among selectionstrategies, we also compare the results of the TRMR studyperformed here to a prior effort that mapped furfuraltolerance genes resulting from increased gene copy number(i.e., plasmid-based libraries using the SCALEs method(Lynch et al., 2007))(Glebes et al., 2014). This analysis

allowed us to compare the effect of promoter-based anddosage-based expression changes on identified furfuraltolerance genes. Through this comparison, we identified asubset of 35 genes that had increased fitness in both SCALEsand TRMR selections, albeit themajority of high fitness geneswere specific to the method employed (87% unique genesfrom the SCALEs selection and 95% from TRMR). Toattempt to explain such a difference, we analyzed individualclones representative either of the TRMR or SCALEs genesand measured furfural tolerance as well as mRNA expressionfor the targeted genes.

Materials and Methods

Bacterial Strains, Plasmids, and Media

E. coliMG1655 and derivatives thereof were used as hosts forstudies. Genes were amplified from MG1655 genomic DNAobtained with a Genomic DNA Extraction Kit (Qiagen;Valencia, CA). Primers were designed to amplify thepromoter and open reading frame of the gene becausepSMART-LCKan is a promoterless vector. In the cases of ninE,rimM, ybgF, and yfiB, the genes fall within an operon, so theentire operon and native promoter sequence was cloned.Cleaned amplified PCR product was ligated into pSMART-LCKan using the CloneSmart LCKan Blunt Cloning Kit(Lucigen; Middleton, WI). Ligation products were trans-formed into NEB Turbo Competent E. coli (New EnglandBioLabs). Plasmids were then harvested and transformed intoMG1655 cells made electrocompetent by serial washing withice-cold water. Confirmation of plasmids from individualcolonies were performed by agarose gel electrophoresis andsequencing. TRMR ‘Up’ mutants are derivatives of E. coliMG1655 with a synthetic cassette containing a blasticidinresistance gene, the PLtetO-1 promoter and RBS sequence(Warner et al., 2010). The ahpC_Up strain was created andconfirmed previously by (Warner et al., 2010). The yhjH_Upstrain was created by members of the Gill laboratoryaccording to the methods prescribed by Warner et al. Asummary of all strains used and corresponding genotypesis provided in Table I.

Luria-Bertani (LB) broth was used for routine applica-tions. All growth assessments were performed in MOPSminimal medium (Neidhardt et al., 1974) with 0.2 w/v%glucose. Kanamycin was used where appropriate (30mg/ml).Cultures were grown at 37�C. Furfural was added to media atthe concentrations indicated within the text.

Furfural Selections

TRMR overexpression (‘Up’) and decreased expression(‘Down’) libraries were prepared by Warner et al. (Warneret al., 2010). Libraries were recovered from thawed freezer-stocks, combined, and spiked with JWKan, a neutral-mutation barcoded mutant at 1:400 ratio of a single barcode(Warner et al., 2010). An initial sample (109 cells) washarvested by centrifugation and stored at �80�C. The mixed

130 Biotechnology and Bioengineering, Vol. 112, No. 1, January, 2015

culture was applied at 104 cells per plate to 20MOPSminimalmedium plates with 0.75 g/l furfural. Cells were harvestedafter growth appeared, pelleted, and frozen until furtherprocessing. Liquid serial batch selections were inoculatedfrom the same initial mixed library population into either aflask containing 50 ml MOPS minimal medium with 1.5 g/lfurfural (decreasing concentration loading) or 50ml 1.0 g/lfurfural (constant concentration loading). Optical densitywas monitored during growth, and cells were transferred intosubsequent rounds (to an initial OD600 of 0.02) when cultureshad reached mid-exponential phase (OD600 of 0.2–0.6). Thedecreasing concentration loading selection took place overthree rounds (1.5 g/l, 1.0 g/l, ad 0.5 g/l), while the constantconcentration loading selection contained six rounds (all at1.0 g/l). Both liquid-based selections occurred over approxi-mately 70 h, with the decreasing concentration loadinghaving three batches and the constant concentration loadinghaving six batches total. Final samples were harvested andstored for further processing. TRMR barcodes were amplifiedfrom sample genomic DNA, cleaned, prepared for micro-array analysis with control barcode loading, and analyzedwith microarrays as previously described (Warner et al.,2010). Analysis of the resulting data files to calculate allelefitness was performed as previously described (Sandovalet al., 2012; Warner et al., 2010). A standard curve wascreated, relating microarray signal to DNA concentration,as determined through the use of the control barcodes.From this curve, allele fitness (W) was calculated as theconcentration of a given allele, i, after selection divided bythe concentration of a given allele before selection, Wi¼concentrationi,selection/concentrationi,initial. Analysis of significantlyenriched gene ontologies (GO) was performed by querying

the increased fitness gene lists with the Batch Genes programon the GOEAST website, using default user settings (Zhengand Wang, 2008).Details concerning the SCALEs furfural selection have

been previously reported (Glebes et al., 2014). Briefly, 1, 2, 4,and 8 kb genomic libraries were prepared and transformedfreshly into E. coli BW25113 DrecA::FRT. Dilution plateswere prepared from the transformed culture to ensure> 105

unique transformants (10� 99% library coverage). Thetransformed libraries were then pooled, and plated on solidminimal medium with 0.75 g/l or no furfural (for controlchip). Microarray and multiscale analysis was performedaccording to the SCALEs method (Lynch et al., 2007) in orderto obtain gene fitness scores.

Growth Analysis

Overnight cultures were inoculated from freezerstocks andgrown overnight in LB. Seed cultures in MOPS minimalmedium (Neidhardt et al., 1974) were inoculated with 2 v/v%overnight culture and grown approximately two hours. Seedcultures were normalized to OD600¼ 0.195–0.200, and usedto inoculate test cultures at 10 v/v%. Test cultures were grownin MOPS minimal medium and 1.5 g/l furfural. Opticaldensity readings were monitored at 600 nm at the timepointsindicated.

Gene Expression Analysis

Cultures were prepared similarly to growth analyses, with theexception of being inoculated into MOPS minimal mediumwithout furfural. Mid-exponential cells were harvested bycentrifugation, flash frozen, and used for RNA extraction

Table I. List of strains used.

Strain name Gene(s) targeted Gene product Remarks or genotype

MG1655 N/A N/A Parent strainMG1655 pSMART N/A N/A Control strain for studiesTG01p rna RNase I MG1655/pSMART LCK-rnaTG02p ybdR Predicted oxidoreductase MG1655/pSMART LCK-ybdRTG03p dicA DNA-binding transcriptional

dual regulatorMG1655/pSMART LCK-dicA

TG04p uppP Undecaprenyl pyrophosphatephosphatase

MG1655/pSMART LCK-uppP

TG05p yhjH c-di-GMP phosphodiesterase MG1655/pSMART LCK-yhjHTG06p yhdE Nucleoside triphosphate

pyrophosphataseMG1655/pSMART LCK-yhdE

TG07p ninE Conserved protein MG1655/pSMART LCK-ninETG08p ahpC Alkyl hydroperoxide reductase,

AhpC componentMG1655/pSMART LCK-ahpC

TG09p sdaA L-serine deaminase I MG1655/pSMART LCK-sdaATG10p rimM Ribosome maturation protein MG1655/pSMART LCK-rimMTG11p ybgF Predicted periplasmic protein MG1655/pSMART LCK-ybgFTG12p yfiB Predicted outer membrane lipoprotein MG1655/pSMART LCK-yfiBTG13p ybeQ Conserved protein MG1655/pSMART LCK-ybeQTG14p ahpCF Alkyl hydroperoxide reductase MG1655/pSMART LCK-ahpCFTG05c yhjH c-di-GMP phosphodiesterase MG1655 PyhjH::BSD

RPLtetO1/pSMART LCK; TRMR ‘Up’cassette inserted in front of yhjH

TG08c ahpC Alkyl hydroperoxide reductase,AhpC component

MG1655 PahpC::BSDRPLtetO1/pSMART LCK; TRMR ‘Up’

cassette inserted in front of ahpC

Glebes et al.: Genome-Wide Mapping of Furfural Tolerance Alleles 131

Biotechnology and Bioengineering

with an RNAeasy Plus Mini Kit (Qiagen) after being treatedwith RNAprotect Cell Reagent (Qiagen). Expression analysiswas performed using the iTaq Universal SYBR GreenOne-Step Kit (Bio-rad; Hercules, CA). The transcription ofcysG was used as a reference gene (Zhou et al., 2011) toaccount for differences in sample loading.

To calculate translation initiation rate of the differenttranscript sequences, we used V 1.1 of the RBS Calculator(Salis et al., 2009; Salis, 2011) in reverse engineering mode.Translation initiation rates are reported for the gene’sannotated start codon.

Results and Discussion

Engineering tolerance to furfural is important for moreefficient production of biofuels and bioproducts (Wanget al., 2013). Here, we used the TRMR method to perform agenome-wide search for alleles that with increased ordecreased expression confer improved growth in furfural.The TRMR approach has previously been used to identifyalleles conferring tolerance to corn stover hydrolysate(Warner et al., 2010), acetate and low pH (Sandovalet al., 2012). Here, we compared three different selectionstrategies: i) a plate-based selection to minimize clonalinteractions (Glebes et al., 2014; Zheng et al., 2012), andplanktonic serial transfer cultures employing either, ii) aconstant initial furfural concentration in each batch, or iii)

decreasing furfural concentrations in each batch, which wehave previously shown increases sensitivity and selectivity ofgrowth selections (Warnecke et al., 2008) (Fig. 1A).

Population Dynamics Throughout Each Selection

An important parameter for understanding laboratoryselections is the strength of the selection, which is oftenrelated to the relative reduction in genetic diversity after anevolutionary event. To measure selective pressure here, weisolated samples through each selection, purified genomicDNA, and performed barcode analysis to calculate individualclone frequencies. The relative change in such frequenciesthrough a selection is calculated as a fitness score assigned toeach barcoded allele (Fig. 1B–D). This analysis indicated thatthe constant concentration selection had the lowest pressure,followed by the plate-based, and decreasing gradient(Table II). Specifically, the constant concentration selectionhad the smallest maximum fitness values, the greatest totalnumber of genes with increased fitness, and the least dilutionof the JWKAN control strain, a TRMR clone with themutation cassette inserted in a neutral site that does not affectE. coli growth (Warner et al., 2010).

Population dynamics among selections can also beanalyzed by pairwise comparisons of individual allele fitnessscores across different selection (Fig. 2). In this comparison, itwas observed that the plate-based selection and decreasing

Figure 1. Furfural selections performed with trackable multiplex recombineering libraries. A) Three selection schemes were performed. The first was a plate-based selection

on solid minimal mediumwith furfural. The second was a serial batch transfer selection with a decreasing concentration gradient at each batch (i.e., 1.5 g/l to 1.0 g/l to 0.5 g/l furfural

in minimal medium). The third selection was performed with serial batch transfer into constant initial concentration loading (1.0 g/l furfural in minimal medium). BD) Genome-wide

fitness plots with allele fitness values from the B) plate-based, C) decreasing concentration gradient, and D) constant concentration selections. Fitness values correspond to

locationwithin the chromosome as visualized in a clock-wise fashion. Spike amplitude is proportional to the fitness values of increased fitness alleles. Spike color corresponds to the

mutational direction of the allele: red for Up and blue for Down. Fitness values were calculated based upon the concentration of the allele after selection compared to that from the

control (unselected) population.


concentration selection were more similar than the plate-based and constant concentration selections for both ‘Up’mutations (R2 value of 0.60 vs. 0.51, respectively) and the‘Down’mutations (R2 value 0.64 vs. 0.60, respectively). Thesefindings together suggest that the decreasing concentrationselection and plate-based selection are alternative butcomplementary methods to creating strong selective pres-sures, while the traditionally used constant concentrationserial batch approach yields a weaker selective pressure.

Identification of Furfural Tolerance Genes

The highest fitness genes from each of the three TRMRselections are listed in Table III. The csiD_Up clone was the

only allele identified in the top 10 for all three furfural TRMRselections. CsiD is a predicted protein that is induced undercarbon starvation (Marschall et al., 1998), but is otherwise ofunknown function. Genes appearing within the two strongestselections include ‘Up’mutations for ydaL, yeeN, talB,and smg.TalB is a transaldolase, partially responsible for reversibleconnection between the pentose phosphate pathway (PPP)and glycolysis. TalB overexpression could potentially play arole in maintaining flux through the PPP in order to restoreNADPH lost for furfural reductions, similar to a proposedtolerance mechanism for lpcA overexpression (LpcA convertsone of the same metabolites as TalB and was found to confertolerance to furfural recently (Glebes et al., 2014)). Mutuallyshared ‘Down’ mutations are nagA and ydiA. NagA is an N-acetylglucosamine-6-phosphate deactylase, which producesglucosamine-6-phosphate (White and Pasternak, 1967), akey metabolite in the production of LPS, which was recentlysuggested as important for furfural tolerance from theSCALEs selection (Glebes et al., 2014). YdiA, also known asPpsR, is a phosphoenolpyruvate synthetase regulatoryprotein (Burnell, 2010), which regulates genes involved ingrowth on three-carbon substrates.

Table II. Summary statistics of furfural TRMR selections.

Plate-based Decreasing conc. Constant conc.

Up Down Up Down Up Down

# Increased Fitness Genes 745 684 666 671 1059 873Maximum ln(Fitness) 6.7 8.4 11.9 10.9 5.9 5.2JWKan ln(Fitness) �3.3 �5.7 �1.1

Figure 2. Comparison of gene fitness scores from furfural TRMR selections. Both liquid serial batch selections are compared to fitnesses from the plate-based selection. A) Up

fitnesses from the decreasing concentration gradient selection. B) Up fitnesses from the constant concentration selection. C) Down fitnesses from the decreasing concentration

gradient selection. D) Down fitnesses from the constant concentration selection. R2 values for each plot are A) 0.60, B) 0.51, C) 0.64, and D) 0.60.



Two additional mutations identified from the ‘Down’plate-based selection, rseB and yahF, point to mutations thatmight benefit relief of cellular stress. RseB is an anti-sigmafactor of sigma E, which specializes in cellular stress responses(e.g., heat shock). Enrichment of the rseB_Down clone herecould play a role in allowing sigma E to control responsesimportant for growth during furfural treatment. YahF is apredicted acyl-CoA synthetase which was previously identi-fied with TRMR during a growth selection under acetatetreatment (Sandoval et al., 2012). Our identification of theyahF_Down mutation in the furfural selection suggests thatthis gene might play a role in a tolerance mechanism sharedbetween furfural and acetate.

To the best of our knowledge, none of these genes inTable III (with the exception of yqhD) have previously beenimplicated in furfural tolerance. YqhD is an NADPH-dependent oxidoreductase that is reported to be one of thekey enzymes responsible for creating redox imbalance underfurfural treatment (Miller et al., 2009b). In fact, Miller et al.showed that silencing this gene confers furfural tolerance.Here, we find yqhD_Up with high fitness. Although it mightappear contradictory, the native regulation of yqhD involvesfurfural driven activation of yqhD expression by the aldehyde-sensing YqhC (Turner et al., 2011). Replacing the promoter,as done in the TRMR library, would presumably remove suchactivation, and thus affect expression in more complex andnon-intuitive manner. Genes involved in NADH-dependenttolerance mechanisms were unable to be quantified in ourefforts here due to barcode concentrations in the librarypopulation that occurred below the microarray threshold.

Comparison of TRMR and SCALEs Furfural Selections

To improve understanding of how different methods formapping tolerance genes might affect target selection forfuture engineering effort, we compared the TRMR ‘Up’ genefitness scores to previously reported SCALEs fitness scores(Fig. 3). Surprisingly, only 35 genes had increased fitness(ln(fitness> 0)) in both the TRMR and SCALEs selections,representing less than 1% of all E. coli genes assessed.Although the plate-based selections were performed similarlybetween the SCALEs and TRMR trials, a few variations

existed. For the SCALEs trials (Glebes et al., 2014), thecontrol plasmid sample used for the microarray chip washarvested from SCALEs libraries plated on minimal mediumplates without furfural, whereas in the TRMR selection,the control chip was from the combined TRMR librarypopulation at t¼ 0 of the selection (so that the three TRMRselections would be normalized against the same initialpopulation). This normalization of SCALEs data to culturesgrown on plates resulted in a lower number of increasedfitness genes (268 genes, �40% the number of increasedfitness ‘Up’ genes form the plated TRMR selection),potentially due to factoring out clones with mutationsbenefiting growth on minimal medium and not necessarilyfurfural-tolerance specific. Strain-specific differences couldalso have affected differences; the SCALEs selection wasperformed in a BW25113DrecA::FRT host, whereas the TRMRselection was performed in a MG1655 background.

With these differences in mind, our analysis still resulted inthe identification of 35 genes that were mutually enriched inboth SCALEs and TRMR selection.While the functions of theenriched genes were variable, three specific cases did matchwith previously reported data. First, thymidylate synthase,encoded by thyA, has been previously confirmed to conferfurfural tolerance through plasmid-based overexpression(Zheng et al., 2012), purportedly due to its role in de novopyrimidine biosynthesis, which could aid in furfural-relatedDNA damage repair (Khan et al., 1995). We also identifiedthis gene and confirmed its tolerance effect in our previousreport using SCALEs (Glebes et al., 2014). In our plate-basedTRMR selection, the thyA_Up clone had a slight increasedfitness (ln(fitness)¼ 0.6; 462nd highest ‘Up’ enrichment).

An alternative mechanism known for conferring toleranceis mediation of ROS induced by furfural treatment (Allenet al., 2010). Genes related to ROS mediation have been

Table III. Top 10 genes enriched in TRMR furfural selections.

Plate-based Decreasing conc. Constant conc.

Rank Up Down Up Down Up Down

1 csiD ddpF yqcC fruK ydaL aspS2 talB nagA smg yaaX dhaK ydiA3 ydaL ydiA yihY ddpF ycjU sdaA4 yeeN ycjN yqhD nagA tonB ycjN5 smg rseB yhdE nagC ddpA ygcP6 yneG yohN zipA ybjL yneG yecT7 yjaH sdaA alaS pldB rplI frmR8 acpT yecT talB ydiA csiD sra9 rplI yahF yeeN ilvM ydiJ paaI10 yccF potE csiD pdhR yohK fliO

Figure 3. Comparison of TRMR Up and SCALEs gene fitness scores. Log-

transformed fitness scores were compared for TRMR and SCALEs selections

performed on solid medium minimal media plates with 0.75 g/l furfural. SCALEs gene

fitness values have previously been reported (Glebes et al. 2014).


implicated as providing resistance to hydrolysate in Saccharo-myces cerevisiae and Zymomonas mobilis (Skerker et al., 2013) and,specifically, ahpC_Up was a high fitness hydrolysate-tolerantclone identified in a previous TRMR study (ln(fitness)¼ 5.5;(Warner et al., 2010)). In our furfural TRMR selection, theahpC_Up clone had increased fitness (ln(fitness)¼ 2.7, 50thhighest ‘Up’ enrichment in TRMR), which was higher than itsfitness in the SCALEs selection (ln(fitness)¼ 0.4), suggestingan important effect of library design on genotype-to-phenotype mapping.Lastly, genes coding for flagellar proteins undergo

repression during furfural treatment in Clostridium beijerinckiithat has been suggested as contributing towards prematuregrowth termination (Zhang and Ezeji, 2013). In ourselections, yhjH, which is involved in flagellar motilityregulation (Pesavento et al., 2008), was found with increasedfitness from both SCALEs and TRMR approaches, (yhjH_Upln(fitness)¼ 1.6, 123th highest ‘Up’ enrichment in TRMR;yhjH SCALEs ln(fitness¼ 0.7)), suggesting the importance offlagellar regulation in conferring furfural tolerance, albeit

from a yet to be elucidated mechanism. To this end, when weanalyzed our TRMR results for enrichments in specific geneontology categories, the only significantly enriched termswere related to flagella and/or chemotaxis (Table IV). It isinteresting to note that the high fitness genes from thefurfural SCALEs study (Glebes et al., 2014) included flhC,which is a regulator for flagellar genes (Bartlett et al., 1988),and flhE, which is involved in flagellar transport (Liu andOchman, 2007). Our data suggest that altered expression offlagellar regulatory and/or biosynthesis genes is a potentialmethod for conferring furfural tolerance, even though suchgenes may be removed from industrial strains for otherreasons (Marisch et al., 2013).We expected that genes with increased fitness in both

selections would be good targets for engineering furfuraltolerance (i.e., the genes labeled in Fig. 3). To this end, weconstructed clones of the top 13 genes in PSMART-LCKan(primers listed in Table V, and it is noted that we hadpreviously constructed and confirmed an overexpressionplasmid for thyA (Glebes et al., 2014)) and then tested these

Table IV. Enriched gene ontology terms from TRMR plate-based selection.

Ontology GO ID GO term P-value Genes

Biologicalprocesses

0006928 Cellular componentmovement

0.048897 ybiA, flgC, fliC, fliN, yhjH, flgI, fliH, flgK, flgB,motA, fliF, fliO, flgE, fliM, motB, ydgA, cheZ, frdC,flgG

0001539 Ciliary of flagellar motility 0.024822 flgC, fliC, fliN, flgI, fliH, flgK, flgB, motA, fliF, fliO,flgE, fliM, motB, cheZ, frdC, flgG

0048870 Cell motility 0.024822 flgC, fliC, fliN, yhjH, flgI, fliH, flgK, flgB, motA, fliF,fliO, flgE, fliM, motB, cheZ, frdC, flgG

0051674 Localization of cell 0.024822 flgC, fliC, fliN, yhjH, flgI, fliH, flgK, flgB, motA, fliF,fliO, flgE, fliM, motB, cheZ, frdC, flgG

Molecularfunction

0003774 Motor activity 0.024822 flgC, fliN, fliH, flgK, flgB, fliK, motA, fliF, flgE, fliM,flgG

Cellularcomponent

0009288 Bacterial-type flagellum 0.024822 flgC, fliC, fliN, flgI, flgN, fliH, flgK, flgB, fliK, fliF,fliO, flgE, fliM, flgM, fliR, cheZ, flgG

0044461 Bacterial-type flagellumpart

0.048897 flgC, fliC, fliN, flgI, flgK, flgB, fliK, fliF, fliO, flgE,fliM, fliR, flgG

0044463 Cell projection part 0.048897 flgC, fliC, fliN, flgI, flgK, flgB, fliK, fliF, fliO, flgE,þfliM, fliR, flgG

Table V. Primers used to amplify genomic regions for plasmid clones.

Gene target Forward primer (50–30) Reverse primer (50–30)

rna GGGAATTCCTCAATGCAGCG ATAAATCATCACGCCCGCCAybdR ACATCAACTGGCATAATGATTGTCT CGGCGATTATCGTCATGGCTdicA CCTGCTGCTTGTGCAAGTTT TCGGTCAAGTGATTTTGTATGCTuppP GTCGCATCAGGCGTTGATTG GGCGTAATCATTGAGCGTGGyhjH ATTCTTCCTGTGCCAGTCCT TCCGTTGTGGAGTGAGGAAAyhdE CGCGCCTCACGTTCAATATG GTGATTAACGTCTCTTTCAGACCGninE AGCTTCCAGAGAGTAAAAGTGTT AAAATCAGACCAGAACGCCAahpC TGCAAAAGTCGAGTAAAAGGCA GAATCCCCGGGAGCTTACACsdaA GCGCTGCAAATTGGTGTGA CCTGACGCAACAGTGGAAGTrimM CGCAGTGGTGATTACTACCC CCGACGGCCTTTTACAGCAybgF CTGGCACCCGATGGTATGTT TCGATGTAGATCACTCAACTGCTyfiB TGGCAAAAAGGGGCTGATGA GAGCGTCTTAACTAAGATTTCGCTyebQ TCCAGATCTGCGTTAGCCAT GCTCTGCGTACGGGTGAATAahpCF TGCAAAAGTCGAGTAAAAGGCA CCGGCGGGGCTTTTTAATG



clones for improved growth in 1.5 g/l furfural (Fig. 4).Surprisingly, only two of these clones, TG01p and TG03p,showed improved growth. The gene rna (TG01p) codes forribonuclease I, which has not previously been associated withfurfural tolerance, but is important for degradation of RNA,especially during recovery from starvation (Kaplan andApirion, 1975). Carbon-induced starvation was implicatedfrom the high fitness score from the csiD_Up clone that wasa top-ten enrichment in all three furfural TRMR selections(Table III). Conversely, DicA (TG03p) inhibits DicBexpression (Bejar and Bouche, 1985), which is part of theDicB-MinC complex that inhibits FtsZ at the septal ring(Johnson et al., 2002). With increased DicA expression fromthe plasmid construct, FtsZ inhibition might be reduced,resulting in differences in cell division. Identification andconfirmation of both rna and dicA as furfural tolerance geneshighlights the ability of genome-wide searches like TRMR toidentify nonobvious tolerance alleles.

Even more surprising was the observation that four clones(TG06p, TG08p, TG09p, TG11p) showed markedly reducedgrowth under furfural treatment. Importantly, each ofthese target genes (yhdE, ahpC, sdaA, ybgF, respectively) wereidentified with much higher fitness in the TRMR datacompared to the SCALEs data (Fig. 3), suggesting that thelevel of expression might dictate the ability of the gene toconfer tolerance under the conditions tested. That is, the data

Figure 4. Growth of clones constructed to test effect of gene overexpression for

increased fitness genes identified from both TRMR and SCALEs furfural selections.

Clones were constructed by amplifying the native sequence from chromosomal DNA

and ligating into pSMART-LCK vector and transformed into MG1655. Cultures were

inoculated at normalized cell loading into minimal medium with 1.5 g/l furfural and

grown for 26 h (n¼ 3; bars represent one standard error). Clones are listed in

decreasing order of SCALEs gene fitness values. Strain genotypes are provided in

Table I.

Figure 5. Comparison of furfural tolerance and expression from chromsomally-integrated construct and plasmid-based clone for yhjH overexpression. A TRMR Up cassette

was integrated upstream of yhjH to yield TG05c. The SCALEs-inspired plasmid clone (TG05p) was constructed by ligating the yhjH sequence into pSMART-LCK and transforming into

MG1655. A) Growth of strains grown in minimal medium with 1.5 g/l for 26 h. B) Transcript expression levels from TRMR and SCALEs-inspired constructs, as compared to MG1655

pSMART, with normalization to an internal reference gene. C) Transcriptional initiation rate as calculated by the RBS Calculator (Salis et al. 2009; Salis 2011) for TRMR clone

(synthetic RBS and 5 UTR sequence) compared to that from the native yhjH transcript.


in Fig. 4 was obtained using overexpression plasmids, whichmimicked the SCALEs overexpression library strategy andapparently not the TRMR promoter/RBS insertion librarystrategy. To further explore this observation, we nextobtained TRMR ‘Up’ clones for ahpC and yhjH (TG08c andTG05c, respectively), and compared tolerance and mRNAexpression levels with the plasmid based overexpressionclones.

Assessing Tolerance Differences

The ahpC gene is the leading gene in the ahpCF operon,meaning that these genes can be transcribed onto a singleRNA molecule (there are three proposed promoterstranscribing this region: two for ahpCF and one for ahpF).These two peptides work together as the AhpCF complex, analkylhydroperoxide reductase, responsible for reducingperoxides to alcohols as a means of ROS detoxification

(Seaver and Imlay, 2001). Because ahpC expression is inducedunder sulfate-starvation, which maps to known inhibitioncaused by furfural treatment (Miller et al., 2009a), wehypothesized that overexpression of ahpCwithout concurrentoverexpression of ahpF (which would be the case in ourTG08pclone, Fig. 4) would not provide tolerance. Incomparison, we reasoned that the TG08cconstruct mightconfer tolerance through potential concurrent increased ahpFexpression since the transcriptional terminator lies down-stream from ahpF. This hypothesis was supported by theobservation that ahpF was also increased during the SCALEsselection (Glebes et al., 2014). Otherwise, the difference ingrowth phenotype in furfural was hypothesized to be a resultof differing expression levels conferred by the SCALEs orTRMR-designed constructs. We constructed a plasmid strainwith ahpCF (TG14p) to compare with the TG08p and TG08cclones in order to test these hypotheses. We tested oneadditional gene from the overlapping SCALEs and TRMR

Figure 6. Comparison of furfural tolerance and expression from chromsomally-integrated construct and plasmid-based clone for ahpC overexpression. A TRMR Up cassette

was integrated upstream of ahpC to yield TG08c. The SCALEs-inspired plasmid clone (TG08p) was constructed by ligating the ahpC sequence into pSMART-LCK and transforming

intoMG1655. An additional clone, TG14p, was constructed by ligating the ahpCF operon sequence into pSMART-LCK. A) Growth of strains grown in minimal mediumwith 1.5 g/l for 30

hours. B) Transcript expression levels from TRMR and SCALEs-inspired constructs, as compared to MG1655 psMART, with normalization to an internal reference gene. C)

Transcriptional initiation rate as calculated by the RBS Calculator (Salis et al. 2009; Salis 2011) for TRMR clone (synthetic RBS and 5 UTR sequence) compared to that from the native

ahpC transcript.



data, yhjH. We chose to look at yhjH due to its role inregulation of flagellar motility, which was related to the onlyenriched GO terms from the TRMR selection (Table IV).

TG05p did not confer increased growth (Fig. 4 and 5A),but TG05c did confer increased growth in 1.5 g/l furfural(290� 24% increase over control; Fig. 5A). Similarly, we didnot find growth improvements from TG08p or TG14p(Fig. 6A), but instead observed decreased cell growth relativeto control of �54� 3% and �65� 1%, respectively.Additionally, TG14p was more inhibited infurfural thanthe ahpC-pSM construct, indicating that the reduced growth ofTG08p was not due to the lack of increased dosage of ahpF.Meanwhile, the TRMR mutant, TG08c, conferred 82� 14%increased growth relative to control. This growth improve-ment corroborates with the original TRMR study thatidentified ahpC_Upas a hydrolysate tolerant mutant (Warneret al., 2010), and we observe here the improvement directly infurfural, a component of hydrolysate.

The marked differences in growth between the TRMR andplasmid constructs motivated us to assess the significance ofexpression levels arising from these different constructs, whichfollowed the reasoning of our second hypothesis. In order tomake this assessment, we tested mRNA levels of ahpC and yhjHfrom the TRMR and plasmid constructs (Fig. 5B and 6B). Theplasmid-based constructs resulted in approximately 1–1.5 log10increase compared to control. Conversely, the chromosomallyintegrated mutations in TG08c and TG05c did not alterexpression to the same extent. This finding was unexpected, dueto the intention of the ‘Up’ mutation cassette in the TRMRdesign, where the PLtetO-1 promoter is considered to be a strongpromoter (Lutz and Bujard, 1997). This finding suggests that forthe two clones tested here, the observed phenotypemight be duemore to difference in translation resulting from optimized RBSsequences, or to alterations in native regulation, rather thandifferences in transcription. To explore this speculation further,we used the RBS Calculator (Salis et al., 2009; Salis, 2011) tocalculate translation initiation rate from themRNA sequences ofboth the native transcript and that occurring from the TRMR‘Up’ mutation. According to these calculations, the translationinitiation rate is increased in both the TRMR clones. Thetranslation initiation rate from TG05c mRNA sequence was2.0� 105 (a.u.) compared to the native sequence, which wascalculated at 2.2� 103 (a.u.), showing approximately two ordersof magnitude increase for the TRMR clone (Fig. 5C). Thetranslation initiation rates for native ahpC sequence were5.1� 104 (a.u.) and 3.1� 104 (a.u.) (there are two annotatedpromoters for ahpC; the larger value is plotted in Fig. 6C).Meanwhile, the translation initiation rate was 9.5� 104 (a.u.)for TG08c. These calculations suggest a possible role forincreased translation in the TRMR constructs for the genes ahpCand yhjH towards conferring tolerance phenotypes.

Conclusions

The use of lignocellulosic biomass as a feedstock for biofuelproduction is considered a viable route for the production ofrenewable liquid fuels. But feedstock toxicity, from inhibitory

compounds like furfural, poses a hurdle that must overcometo enable efficient cellulosic biofuel production. We used theTRMR method to track enrichments of “on” and “off”mutations in front of virtually every gene in E. coli duringfurfural growth selections. By using various selection schemes,we were able to gauge the effect of selection design towardsenrichment patterns, and through comparison to furfuralselection data using another genome-wide approach; we wereable to rapidly identify multiple furfural-tolerance genes.

Our data here relate to three key findings. The first is themarked differences in fitness scores (total number of genesenriched, maximum enrichment values, and loss of thecontrol tag in the population) based upon the type ofselection used. By these metrics, we determined that theconstant concentration selection, which is a method oftenemployed when performing directed evolution studies, wasthe “weakest.” The “strongest” selection, and perhaps mosteffective at identifying furfural tolerance genes, was a serialbatch transfer with decreasing inhibitor loadings at eachtransfer. This method was previously described as a favoredselection scheme that increased both selection specificity andsensitivity (Warnecke et al., 2008), traits that are desirable forgenotype-to-phenotype mapping studies. Our data hereparallel those findings and support this nontraditionalapproach for future selection designs.

Secondly, by using the TRMR method, we were able torapidly identify nonobvious furfural tolerance genes. Thesegenes and their functions can be added to the ever-growingcollection of genetic manipulations for conferring furfuraltolerance, with potential applications to hydrolysate and itsinhibitors, other hydrophobic compounds, or ROS generators.

Lastly, we have shown that the capability of a gene to confertolerance is highly dependent on its expression, with specificdifferences in measured transcription or predicted transla-tion initiation rates. Plasmid-based overexpression of ahpCproved to decrease growth under the conditions tested,whereas use of a synthetic promoter and optimized RBSsequence integrated on the chromosome conferred improvedgrowth. These findings strongly support the development ofgenome searching tools that are designed to be flexible andprovide controlled and predictable expression.

The authors thank S.A. Lynch and R.I. Zeitoun for constructing theyhjH_Up TRMR clone, P. Handke for help in performing the TRMRselection, and N.R. Boyle for help in creating figures. The CUMicroarray Facility is acknowledged for running the arrays. TYG wassupported by the Seed Scholar Program through the Colorado Centerfor Biorefining and Biofuels (project #11-2) and an NIH Pharmaceu-tical Biotechnology Training Grant. This project was supported by theDOE BER #DE_SOOO8812. TYG, NRS, and RTG have filed a patentapplication on some of the genes within this manuscript. The patentapplication, “Methods, compositions and use for enhancing chemicaltolerance by microorganisms,” is filed under number 13/585,828.

References

Allen SA, Clark W, McCaffery JM, Cai Z, Lanctot A, Slininger PJ, Liu ZL,Gorsich SW. 2010. Furfural induces reactive oxygen species accumula-tion and cellular damage in Saccharomyces cerevisiae. BiotechnolBiofuels 3:2.


Alper H, Miyaoku K, Stephanopoulos G. 2005. Construction of lycopene-overproducing E. coli strains by combining systematic and combinato-rial gene knockout targets. Nat biotechnol 23(5):612–616.

Alper H, Stephanopoulos G. 2007. Global transcription machineryengineering: A new approach for improving cellular phenotype. MetabEng 9(3):258–267.

Ask M, Mapelli V, Hock H, Olsson L, Bettiga M. 2013. Engineeringglutathione biosynthesis of Saccharomyces cerevisiae increases robust-ness to inhibitors in pretreated lignocellulosic materials. Microbial cellfactories 12:87.

Baba T, Ara T, Hasegawa M, Takai Y, Okumura Y, Baba M, Datsenko KA,Tomita M, Wanner BL, Mori H. 2006. Construction of Escherichia coliK-12 in-frame, single-gene knockout mutants: The Keio collection. MolSyst Biol 2:20060008.

Barrick JE, Lenski RE. 2013. Genome dynamics during experimentalevolution. Nat Rev Genet 14(12):827–839.

Bartlett DH, Frantz BB, Matsumura P. 1988. Flagellar transcriptionalactivators FlbB and FlaI: Gene sequences and 5’ consensus sequences ofoperons under FlbB and FlaI control. J Bacteriol 170(4):1575–1581.

Bejar S, Bouche JP. 1985. A new dispensable genetic locus of the terminusregion involved in control of cell division in Escherichia coli. Mol GenGenet: MGG 201(2):146–150.

Bonomo J, Lynch MD, Warnecke T, Price JV, Gill RT. 2008. Genome-scaleanalysis of anti-metabolite directed strain engineering. Metabol Eng10(2):109–120.

Burnell JN. 2010. Cloning and characterization of Escherichia coli DUF299:A bifunctional ADP-dependent kinase–Pi-dependent pyrophosphory-lase from bacteria. BMC Biochem 11:1.

Cho RJ, Fromont-Racine M, Wodicka L, Feierbach B, Stearns T, Legrain P,Lockhart DJ, Davis RW. 1998. Parallel analysis of genetic selections usingwhole genome oligonucleotide arrays. Proc Natl Acad Sci U S A95(7):3752–3757.

Datsenko KA, Wanner BL. 2000. One-step inactivation of chromosomalgenes in Escherichia coli K-12 using PCR products. Proc Natl Acad Sci US A 97(12):6640–6645.

Delgenes JH, Moletta R, JM N. 1996. Effects of lignocellulose degradationproducts on ethanol fermentations of glucose and xylose bySaccharomyces cerevisiae, Zymomonas mobilis, Pichia stipitis, andCandida shehatae. Enzyme Microbial Tech 19(3):220–225.

Diaz D, Villa P, Guerra M, Rodriguez E, Redondo D, Martinez A. 1992.Conversion of furfural into furfuryl alcohol by Saccharomyces cerevisiae354. Acta Biotechnol 12:351–354.

DunlopMJ, Dossani ZY, Szmidt HL, ChuHC, Lee TS, Keasling JD, Hadi MZ,Mukhopadhyay A. 2011. Engineering microbial biofuel tolerance andexport using efflux pumps. Mol Syst Biol 7:487.

Franden MA, Pilath HM, Mohagheghi A, Pienkos PT, Zhang M. 2013.Inhibition of growth of Zymomonas mobilis by model compoundsfound in lignocellulosic hydrolysates. Biotech Biofuels 6(1):99.

Gall S, LynchMD, Sandoval NR, Gill RT. 2008. Parallel mapping of genotypesto phenotypes contributing to overall biological fitness. Metabol Eng10(6):382–393.

Gill RT, Wildt S, Yang YT, Ziesman S, Stephanopoulos G. 2002. Genome-wide screening for trait conferring genes using DNA microarrays. ProcNatl Acad Sci U S A 99(10):7033–7038.

Glebes TY, Sandoval NR, Reeder PJ, Schilling KD, Zhang M, Gill RT. 2014.Genome-wide mapping of furfural tolerance genes in Escherichia coli.PLoS One 9(1):e87540.

Goodarzi H, Bennett BD, Amini S, Reaves ML, Hottes AK, Rabinowitz JD,Tavazoie S. 2010. Regulatory and metabolic rewiring during laboratoryevolution of ethanol tolerance in E. coli. Mol Syst Biol 6:378.

Gutierrez T, Buszko ML, Ingram LO, Preston JF. 2002. Reduction of furfuralto furfuryl alcohol by ethanologenic strains of bacteria and its effecton ethanol production from xylose. Appl Biochem Biotech 98–100:327–340.

Gutierrez T, Ingram LO, Preston JF. 2006. Purification and characterizationof a furfural reductase (FFR) from Escherichia coli strain LYO1–anenzyme important in the detoxification of furfural during ethanolproduction. J Biotechnol 121(2):154–164.

JiangW, Bikard D, Cox D, Zhang F, Marraffini LA. 2013. RNA-guided editingof bacterial genomes using CRISPR-Cas systems. Nat Biotech 31(3):233–239.

Johnson JE, Lackner LL, de Boer PA. 2002. Targeting of (D)MinC/MinD and(D)MinC/DicB complexes to septal rings in Escherichia coli suggests amultistep mechanism for MinC-mediated destruction of nascent FtsZrings. J Bacteriol 184(11):2951–2962.

Kaplan R, Apirion D. 1975. The fate of ribosomes in Escherichia coli cellsstarved for a carbon source. J Biol Chem 250(5):1854–1863.

Khan QA, Shamsi FA, Hadi SM. 1995. Mutagenicity of furfural in plasmidDNA. Cancer letters 89(1):95–99.

Kim D, Hahn JS. 2013. Roles of the Yap1 transcription factor andantioxidants in Saccharomyces cerevisiae’s tolerance to furfural and 5-hydroxymethylfurfural, which function as thiol-reactive electrophilesgenerating oxidative stress. Appl Environ Microbiol 79(16):5069–5077.

KitagawaM, Ara T, ArifuzzamanM, Ioka-Nakamichi T, Inamoto E, ToyonagaH, Mori H. 2005. Complete set of ORF clones of Escherichia coli ASKAlibrary (a complete set of E. coli K-12ORF archive): Unique resources forbiological research. DNA Res 12(5):291–299.

Liu R, Ochman H. 2007. Origins of flagellar gene operons and secondaryflagellar systems. J Bacteriol 189(19):7098–7104.

Lutz R, Bujard H. 1997. Independent and tight regulation of transcriptionalunits in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2regulatory elements. Nucleic Acids Res 25(6):1203–1210.

LynchMD,Warnecke T, Gill RT. 2007. SCALEs: Multiscale analysis of libraryenrichment. Nat Methods 4(1):87–93.

Marisch K, Bayer K, Scharl T, Mairhofer J, Krempl PM, Hummel K, Razzazi-Fazeli E, Striedner G. 2013. A comparative analysis of industrialEscherichia coli K-12 and B strains in high-glucose batch cultivations onprocess-, transcriptome- and proteome level. PLoS One 8(8):e70516.

Marschall C, Labrousse V, KreimerM,Weichart D, Kolb A, Hengge-Aronis R.1998. Molecular analysis of the regulation of csiD, a carbon starvation-inducible gene in Escherichia coli that is exclusively dependent onsigma s and requires activation by cAMP-CRP. J Mol Biol 276(2):339–353.

Miller EN, Jarboe LR, Turner PC, Pharkya P, Yomano LP, York SW, Nunn D,ShanmugamKT, Ingram LO. 2009a. Furfural inhibits growth by limitingsulfur assimilation in ethanologenic Escherichia coli strain LY180. ApplEnviron Microbiol 75(19):6132–6141.

Miller EN, Jarboe LR, Yomano LP, York SW, Shanmugam KT, Ingram LO.2009b. Silencing of NADPH-dependent oxidoreductase genes (yqhDand dkgA) in furfural-resistant ethanologenic Escherichia coli. ApplEnviron Microbiol 75(13):4315–4323.

Mills TY, Sandoval NR, Gill RT. 2009. Cellulosic hydrolysate toxicity andtolerance mechanisms in Escherichia coli. Biotech biofuels 2:26.

Neidhardt FC, Bloch PL, Smith DF. 1974. Culture medium for enter-obacteria. J Bacteriol 119(3):736–747.

Pesavento C, Becker G, Sommerfeldt N, Possling A, Tschowri N, Mehlis A,Hengge R. 2008. Inverse regulatory coordination of motility and curli-mediated adhesion in Escherichia coli. Genes Dev 22(17):2434–2446.

Salis HM. 2011. The ribosome binding site calculator. Methods Enzymol498:19–42.

Salis HM, Mirsky EA, Voigt CA. 2009. Automated design of syntheticribosome binding sites to control protein expression. Nat Biotech27(10):946–950.

Sandoval NR, Kim JY, Glebes TY, Reeder PJ, Aucoin HR, Warner JR, Gill RT.2012. Strategy for directing combinatorial genome engineering inEscherichia coli. Proc Natl Acad Sci U S A 109(26):10540–10545.

Sandoval NR, Mills TY, Zhang M, Gill RT. 2011. Elucidating acetatetolerance in E. coli using a genome-wide approach. Metabol Eng13(2):214–224.

Seaver LC, Imlay JA. 2001. Alkyl hydroperoxide reductase is the primaryscavenger of endogenous hydrogen peroxide in Escherichia coli. JBacteriol 183(24):7173–7181.

Singh A, Lynch MD, Gill RT. 2009. Genes restoring redox balance infermentation-deficient E. coli NZN111. Metabol Eng 11(6):347–354.

Skerker JM, Leon D, Price MN, Mar JS, Tarjan DR, Wetmore KM,Deutschbauer AM, Baumohl JK, Bauer S, Ibanez AB, et al. 2013.



Dissecting a complex chemical stress: Chemogenomic profiling of planthydrolysates. Mol Syst Biol 9:674.

Spindler EC, Boyle NR, Hancock RE, Gill RT. 2013. Genome-wideidentification of genes conferring energy related resistance to a syntheticantimicrobial Peptide (bac8c). PLoS One 8(1):e55052.

Turner PC, Miller EN, Jarboe LR, Baggett CL, Shanmugam KT, Ingram LO.2011. YqhC regulates transcription of the adjacent Escherichia coli genesyqhD and dkgA that are involved in furfural tolerance. J Ind MicrobiolBiotech 38(3):431–439.

Wang HH, Isaacs FJ, Carr PA, Sun ZZ, Xu G, Forest CR, Church GM. 2009.Programming cells by multiplex genome engineering and acceleratedevolution. Nature 460(7257):894–898.

Wang HH, Kim H, Cong L, Jeong J, Bang D, Church GM. 2012a. Genome-scale promoter engineering by coselection MAGE. Nat Methods9(6):591–593.

Wang J, Zhang Y, Chen Y, Lin M, Lin Z. 2012b. Global regulator engineeringsignificantly improved Escherichia coli tolerances toward inhibitors oflignocellulosic hydrolysates. Biotech Bioeng 109(12):3133–3142.

Wang X, Miller EN, Yomano LP, Shanmugam KT, Ingram LO. 2012c.Increased furan tolerance in Escherichia coli due to a cryptic ucpA gene.Appl Environ Microbiol 78(7):2452–2455.

Wang X,Miller EN, Yomano LP, Zhang X, ShanmugamKT, Ingram LO. 2011.Increased furfural tolerance due to overexpression of NADH-dependentoxidoreductase FucO in Escherichia coli strains engineered for theproduction of ethanol and lactate. Appl Environ Microbiol 77(15):5132–5140.

Wang X, Yomano LP, Lee JY, York SW, Zheng H, Mullinnix MT, ShanmugamKT, Ingram LO. 2013. Engineering furfural tolerance in Escherichia coliimproves the fermentation of lignocellulosic sugars into renewablechemicals. Proc Natl Acad Sci U S A 110(10):4021–4026.

Warnecke TE, LynchMD, Karimpour-Fard A, LipscombML,Handke P,MillsT, Ramey CJ, Hoang T, Gill RT. 2010. Rapid dissection of a complexphenotype through genomic-scale mapping of fitness altering genes.Metabol Eng 12(3):241–250.

Warnecke TE, Lynch MD, Karimpour-Fard A, Sandoval N, Gill RT. 2008. Agenomics approach to improve the analysis and design of strainselections. Metabol Eng 10(3–4):154–165.

Warner JR, Reeder PJ, Karimpour-Fard A,Woodruff LB, Gill RT. 2010. Rapidprofiling of a microbial genome using mixtures of barcodedoligonucleotides. Nat Biotech 28(8):856–862.

White RJ, Pasternak CA. 1967. The purification and properties of N-acetylglucosamine 6-phosphate deacetylase from Escherichia coli.Biochem J 105(1):121–125.

Woodruff LB, Boyle NR, Gill RT. 2013a. Engineering improved ethanolproduction in Escherichia coli with a genome-wide approach. MetabolEng

Woodruff LB, Pandhal J, Ow SY, Karimpour-Fard A,Weiss SJ,Wright PC, GillRT. 2013b. Genome-scale identification and characterization of ethanoltolerance genes in Escherichia coli. Metabol Eng 15:124–133.

Zaldivar J, Martinez A, Ingram LO. 1999. Effect of selected aldehydes on thegrowth and fermentation of ethanologenic Escherichia coli. BiotechBioeng 65(1):24–33.

Zhang Y, Ezeji TC. 2013. Transcriptional analysis of Clostridium beijerinckiiNCIMB 8052 to elucidate role of furfural stress during acetone butanolethanol fermentation. Biotechn Biofuels 6(1):66.

Zheng H, Wang X, Yomano LP, Geddes RD, Shanmugam KT, Ingram LO.2013. Improving Escherichia coli fuco for furfural tolerance bysaturation mutagenesis of individual amino acid positions. ApplEnviron Microbiol 79(10):3202–3208.

Zheng H, Wang X, Yomano LP, Shanmugam KT, Ingram LO. 2012. Increasein furfural tolerance in ethanologenic Escherichia coli LY180 by plasmid-based expression of thyA. Appl Environ Microbiol 78(12):4346–4352.

Zheng Q, Wang XJ. 2008. GOEAST: A web-based software toolkit for GeneOntology enrichment analysis. Nucleic Acids Res 36: (Web Server issue):W358–363.

Zhou K, Zhou L, Lim Q, Zou R, Stephanopoulos G, Too HP. 2011. Novelreference genes for quantifying transcriptional responses of Escherichiacoli to protein overexpression by quantitative PCR. BMCMol Biol 12:18.


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