GENOMICS, TRANSCRIPTOMICS, PROTEOMICS

RNA-Seq of the xylose-fermenting yeast Scheffersomycesstipitis cultivated in glucose or xylose

Tiezheng Yuan & Yan Ren & Kun Meng & Yun Feng &

Peilong Yang & Shaojing Wang & Pengjun Shi &Lei Wang & Daoxin Xie & Bin Yao

Received: 3 May 2011 /Revised: 25 August 2011 /Accepted: 23 September 2011 /Published online: 16 November 2011# Springer-Verlag 2011

Abstract Xylose is the second most abundant lignocellu-losic component besides glucose, but it cannot befermented by the widely used ethanol-producing yeastSaccharomyces cerevisiae. The yeast Scheffersomycesstipitis, however, is well known for its high native capacityto ferment xylose. Here, we applied next-generationsequencing technology for RNA (RNA-Seq) to generatetwo high-resolution transcriptional maps of the S. stipitisgenome when this yeast was grown using glucose or xyloseas the sole carbon source. RNA-Seq revealed that 5,176 of5,816 annotated open reading frames had a uniformtranscription and that 214 open reading frames weredifferentially transcribed. Differential expression analysisshowed that, compared with other biological processes,

carbohydrate metabolism and oxidation-reduction reactionswere highly enhanced in yeast grown on xylose. Measure-ment of metabolic indicators of fermentation showed that,in yeast grown on xylose, the concentrations of cysteineand ornithine were twofold higher and the concentrations ofunsaturated fatty acids were also increased. Analysis ofmetabolic profiles coincided with analysis of certaindifferentially expressed genes involved in metabolisms ofamino acid and fatty acid. In addition, we predictedprotein–protein interactions of S. stipitis through integrationof gene orthology and gene expression. Further analysis ofmetabolic and protein–protein interactions networksthrough integration of transcriptional and metabolic profilespredicted correlations of genes involved in glycolysis, thetricarboxylic acid cycle, gluconeogenesis, sugar uptake,amino acid metabolism, and fatty acid β-oxidation. Ourstudy reveals potential target genes for xylose fermentationimprovement and provides insights into the mechanismsunderlying xylose fermentation in S. stipitis.

Keywords Scheffersomyces stipitis . Xylose fermentation .

Transcriptome . RNA-Seq .Metabolic networks .

Protein–protein interactions

Introduction

Bioconversion of plant biomass to ethanol has attractedmuch attention because of the potential to utilize ethanol,rather than gasoline, as a fuel. The main structuralcomponents of plant biomass are cellulose, hemicellulose,and lignin. Glucose (hexose) and xylose (pentose) are themost abundant cellulosic and hemicellulosic components,respectively. Efficient bioconversion of lignocellulose toethanol requires both xylose and glucose utilization, which

Electronic supplementary material The online version of this article(doi:10.1007/s00253-011-3607-6) contains supplementary material,which is available to authorized users.

T. Yuan :K. Meng : P. Yang : P. Shi : B. Yao (*)Key Laboratory for Feed Biotechnology of the Ministry ofAgriculture, Feed Research Institute, Chinese Academy ofAgricultural Sciences,Beijing 100081, Chinae-mail: yaobin@caas-bio.net.cn

Y. Ren :Y. Feng : S. Wang : L. WangTEDA School of Biological Sciences and Biotechnology,Nankai University,Tianjin 300457, China

Y. Ren :Y. Feng : S. Wang : L. Wang (*)Tianjin Research Center for Functional Genomics and Biochip,Tianjin 300457, Chinae-mail: wanglei@nankai.edu.cn

D. Xie (*)School of Biological Sciences, Tsinghua University,Beijing 100084, Chinae-mail: daoxinlab@tsinghua.edu.cn

Appl Microbiol Biotechnol (2011) 92:1237–1249DOI 10.1007/s00253-011-3607-6

is essential for energy-efficient ethanol production (Margeotet al. 2009; Stephanopoulos 2007). However, bakers’ yeastSaccharomyces cerevisiae, which is commonly used inethanol production, cannot ferment xylose. The rate andyield of ethanol production from xylose in engineeredxylose-utilizing S. cerevisiae strains are substantially lowercompared with production from glucose (Matsushika et al.2009), and presence of glucose represses xylose fermenta-tion in engineered yeast strains, which is called “glucoserepression” (Weber et al. 2010).

To convert xylose into ethanol, xylose is converted intoxylulose through xylose reductase and xylitol dehydrogenasein xylose-fermenting yeasts, or through xylose isomerase inbacteria (Jeffries and Jin 2004), and xylulose is furtherconverted into ethanol through the pentose phosphatepathway (PPP) (Mussatto et al. 2010). The yeast Schefferso-myces stipitis (synonym: Pichia stipitis) (Kurtzman andSuzuki 2010) has a high native ability to ferment xylose.The S. stipitis genes encoding xylose reductase, xylitoldehydrogenase, and xylulokinase have been introduced intoS. cerevisiae strains to enhance xylose fermentation (Alperand Stephanopoulos 2009; Jin et al. 2005; Van Vleet andJeffries 2009). Compared with engineered S. cerevisiae, S.stipitis is better in the regulation of xylose metabolism,especially PPP (Fiaux et al. 2003; Jin et al. 2004).Overexpression of genes encoding non-oxidative PPPproteins, namely transketolase (TKL1), transaldolase(TAL1), ribulose-5-phosphate 3-epimerase (RPE1), orribose-5-phosphate ketol-isomerase (RKI1), can enhancePPP activity in xylose-utilizing S. cerevisiae strains (Jin etal. 2005; Matsushika et al. 2009). Besides PPP, redoxbalance (Van Vleet et al. 2008) and sugar transport (Jojimaet al. 2010) are also essential for S. cerevisiae pentosefermentation. The cited studies suggest that transcriptionalcontrol of certain genes is a strategy for improving xylosefermentation, and S. stipitis can be used for a depository ofpotential target genes as limit points involved in xylosefermentation. However, there is no guarantee that all rate-limiting steps or crucial genes for xylose fermentation in S.stipitis will be identified based only on its genome sequence(Jeffries et al. 2007). Thus, transcriptome analysis in S.stipitis should be performed.

The next-generation sequencing technology for RNA(RNA-Seq) has been applied in transcriptomics (Margueratand Bähler 2010; Mortazavi et al. 2008; Nagalakshmi et al.2008). Compared with the microarray platform (Jeffries andVan Vleet 2009), RNA-Seq can offer better dynamic range,detect very subtle changes in gene expression, characterizealternative splicing of mRNAs, detect novel transcripts, etc.(Ozsolak and Milos 2011; Wilhelm and Landry 2009).Furthermore, we have developed a strategy of applyingprotein–protein interactions (PPIs) and metabolic networkanalysis to study correlations of differentially expressed

genes (DEG) detected by RNA-Seq. Genes or proteins arepositioned in a specific network with heterogeneousfunctions in one cellular context based on genome analysis(Han et al. 2004; Hartwell et al. 1999). The “nodes” in PPIsnetworks are proteins or specific enzymes in metabolicnetworks, whereas the “edges” are their interactions. Theconcept of PPIs or metabolic networks can be used todescribe cellular processes or metabolic reactions, in whichthe importance of individual proteins can be illustrated bythe centrality of nodes (hubs) in networks. Predictingcorrelations between DEG and evaluating the centrality ofsuch genes in networks can provide potential target genesfor overcoming rate-limiting steps of xylose fermentation.

Here, we used RNA-Seq to generate two high-resolutiontranscriptional profiles of the S. stipitis genome when theyeast was grown using glucose or xylose as the sole carbonsource. We sought to predict correlations of DEG based ona subsequent analysis of PPIs and metabolic networkscombined with measurements of free amino acids and fattyacids in yeast cells as indicators of xylose fermentation.

Materials and methods

Yeast strain and growth conditions

S. stipitis CBS6054 was obtained as lyophilized powderfrom the Centraalbureau voor Schimmelcultures (CBS)Fungal Biodiversity Centre (Utrecht, The Netherlands). Itwas revived and streaked onto YPD agar medium (10 gyeast extract, 20 g tryptone, 10 g glucose, and 20 g agar/l)and grown at 30°C. Shaker flask cultivation was carried outat 30°C and 220 rpm. A single colony was transferred to a250-ml Erlenmeyer flask containing 50 ml YP medium(10 g yeast extract and 20 g tryptone/l) and grownovernight. The culture (10 ml) was then inoculated into1,000-ml flasks containing 400 ml YPG (50 g glucose perliter of YP medium) or 400 ml YPX medium (50 g xyloseper liter of YPmedium) per flask. The yeast cell mass on a dryweight basis was adjusted to be equal in the glucose andxylose samples (refer to the methods in the supplementaryfile). Yeast was cultivated in the presence of glucose orxylose until the A600 was ∼1.5 (6–8 h) and then harvested bycentrifugation at 10,000×g, 4°C for 5 min. The pellets weremixed and ground to a powder in liquid nitrogen using anoscillating mill MM-400 (Retsch, Haan, Germany) andstored at −70°C until use.

RNA sequencing

The RNA-Seq protocol involved total RNA extraction,cDNA preparation, cDNA library construction, and se-quencing. Total RNA from yeast powder was extracted

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following the standard protocol of TRIZOL Reagent(Invitrogen, Carlsbad, CA, USA). mRNAwas isolated fromtotal RNA using oligo-dT beads, fragmented by heating at94°C, and then used to synthesize cDNA using randomhexamer primers and DNA polymerase. Double-strandedcDNAwas end-repaired with Klenow polymerase, T4 DNApolymerase, and T4 polynucleotide kinase. A singleadenosine was added to the cDNA using Klenow exo-/dATP. The Illumina adapters containing primer sites forflow-cell surface annealing and sequencing were ligatedonto the repaired cDNA ends. Gel electrophoresis was usedto separate the DNA library fragments of 200–250 bp fromunligated adapters. Libraries were amplified by PCR withPhusion polymerase (NEB, Ipswich, MA, USA). Thelibraries for sequencing were denatured with sodiumhydroxide and diluted in hybridization buffer for loadingonto a single lane of an Illumina GA flow-cell (San Diego,CA, USA). Cluster formation, primer hybridization, andpaired-end 76×2 cycle sequencing were performed on anIllumina Genome Analyzer IIx using proprietary reagentsaccording to the manufacturer’s instructions.

Read mapping and expression level normalization

The reference genome of S. stipitis and annotation data weredownloaded from the web site (ftp://ftp.ncbi.nih.gov/genomes/Fungi/Scheffersomyces_stipitis_CBS_6054/). TheRNA-Seq reads were aligned to the reference genome usingthe Burrows–Wheeler Alignment Tool with default parame-ters to estimate insert sizes of the two libraries (Li andDurbin 2009). Reads were aligned to the reference genomeusing TopHat with parameters set as: -i 20 -I 10,000 -m 2 –coverage-search –closure-search –microexon-search –splice-mismatches 2, -r 20 for the glucose sample, -r 18 for thexylose sample, and -r 20 for the combined data to identifynovel transcripts (Mortazavi et al. 2008; Nagalakshmi et al.2008; Trapnell et al. 2009; Wilhelm et al. 2010). The outputsin Sequence Alignment/Map format containing aligned readsand mapping information were used for further analysis.

Identification of novel transcripts

Data for the aligned reads generated by TopHat were trans-formed into a graphic plot to serve as the input for the softwareArtemis (Rutherford et al. 2000). We manually screenedtranscription-active regions to identify novel transcripts inintergenic regions defined by S. stipitis genome annotation.

Differential expression analysis

Gene expression levels were measured as reads per kilobaseper million reads (RPKM) using the formula described byMortazavi et al. (2008). Because the 3′ end of each

transcript is less affected by the bias of reverse transcrip-tion, we defined the mid-point to the 3′ end of each gene asthe 3′ region and counted the number of reads mapped tothe 3′ region for a more confident representation of theexpression level of each gene (Nagalakshmi et al. 2008).The cutoff value for determining the background expressionlevel was the limit of RPKM for 99.9% of the total readsdistributed over 99.5% of all genes in the glucose andxylose samples. The median, the mean, and the 117thRPKM (the highest 2% of RPKMs) in the RNA-Seq datawere used to categorize the expression level as low,medium, high, or very high, respectively.

The R/Bioconductor (http://bioconductor.org/help/bioc-views/release/bioc/) package DEseq was used to model readcount data (RPKM) with negative binomial distributions andto assess DEG (p<0.05) (Anders and Huber 2010). TheBenjamini and Hochberg (B–H) multiple testing was per-formed to adjust p value and to yield significantly differentialexpression profiles from the dataset. Open reading frames(ORFs) having a ratio of RPKM in the xylose sample to theglucose sample higher than 2.0 or lower than 0.5 or a p valueof less than 0.05 were considered to indicate differentialexpression with respect to the carbon source. In addition, anR script was created using the functions in geneplotter (R/Bioconductor package) to determine the chromosomal loca-tions of ORFs.

Gene ontology and metabolic pathway analysis

The ORF-encoded proteins were categorized according tothe S. stipitis genome gene ontology (GO) annotations.GOseq (R/Bioconductor package) was used to reduce thecomplexity of RNA-Seq data and to highlight the importantbiological processes and metabolic pathways (Young et al.2010). Perl scripts were created to extract the metabolicannotation data from KEGG (Kyoto Encyclopedia of Genesand Genomes) metabolic annotation files (ftp://ftp.genome.jp/pub/kegg/genes/organisms/pic/) and XML files (ftp://ftp.genome.jp/pub/kegg/xml/kgml/metabolic/organisms/pic/).KEGGgraph (R/Bioconductor package) was used to ana-lyze the pathways using graph objects, assign genes toKEGG metabolic pathways, merge the metabolic networks,and calculate betweenness centrality of the nodes in themetabolic networks.

Real-time quantitative reverse transcriptase PCR

Real-time quantitative reverse transcriptase PCR (qPCR) wasperformed to validate the transcription levels determined byRNA-Seq. Total RNA (1 μg) was reverse transcribed intocDNA using the ImProm-II Reverse Transcription System(Promega, Fitchburg, WI, USA). Each 20-μl reactioncontained primers (250 nM final concentration), SYBR

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Premix Ex Taq (TaKaRa, Dalian, China), and cDNAtemplates (1/100 dilution), and was performed on a 7500Real-Time PCR system (ABI, Foster City, CA, USA). Thecycle threshold values (CT) were determined and the relative

fold differences were calculated by the 2�ΔΔCT method(Livak and Schmittgen 2001) using ACT1 and RPL13 as theendogenous reference genes (Vandesompele et al. 2002).Samples were run in triplicate in a 96-well plate, and eachexperiment was repeated three times. Two experiments ofindependently cultivated yeast were performed to confirmthe reproducibility of the results.

Measurement of metabolites

Yeast powder for measurements of free amino acids wassuspended in 5 ml 0.1% (v/v) Triton X-100 for 30 min at arotational speed of 100 rpm at 4°C. Protein concentration wasmeasured using a 2D Quant kit (GE Healthcare, LittleChalfont, Buckinghamshire, UK). The abundance of freeamino acids in the yeast cytoplasm was measured using anamino acid analyzer S433D (SYKAM GmbH, Gewerbering,Eresing, Germany) with a LCAK07/LI 4.6×150 mm column.Sample preparation and amino acid analysis were performedaccording to the manufacturer’s standard protocols.

Fatty acids in S. stipitis cells were measured by a gaschromatography (GC) system 7890A (Agilent, Santa Clara,CA, USA) with a flame ionization detector and an HPINNOWAX 100 m×0.32 mm×0.5 μm column. Thesamples were prepared using yeast powder after lyophili-zation and derivation of 5 ml of acetyl-chloride andmethanol (1:10, v/v) per 0.25 g powder at 80°C for120 min and stirred with 5 ml of 6% K2CO3 for 1 min.The mixture was centrifuged at 4,000×g for 5 min, and thesupernatant was recovered. Ultra-high-purity nitrogen wasused as the carrier gas at a constant flow rate of 15 ml min–1.The samples (1 μl) were injected in splitless mode (10:1).The inlet temperature was 280 °C. The temperature programbegan at 80°C with a hold time of 1 min, then increased at10°C min–1 to 160°C with a hold time of 1 min, and thenincreased at 5°C min–1 to 230°C with a hold time of 2 min.The internal standard was n-undecanoic acid (C11:0). Theexternal standards for retention index correction consistedof 20 methyl esters of C6:0, C8:0, C10:0, C11:0, C12:0,C14:0, C14:1n5, C16:0, C16:1n7, C18:0, C18:1n9c,C18:2n6c, C18:3n3, C20:0, C20:1n9, C20:4n6, C22:0,C22:1n9, C24:0, and C24:1. Ethanol concentration in themedium was measured by GC.

Metabolites in samples were measured in three parallelexperiments, and each sample was analyzed twice. Thesignificance of a difference in the content of eachmetabolite in the samples of yeast grown on glucose orxylose was determined by the Student’s t test. A p value ofless than 0.05 was considered statistically significant.

Genome comparison and generating PPIs networks

We created a Perl script to predict PPIs in S. stipitis throughgene orthology based on the assumption that highlyconserved proteins between S. stipitis and S. cerevisiaeshare similar biological functions (Rhodes et al. 2005).Amino acid sequences of S. stipitis and S. cerevisiae (ftp://ftp.ncbi.nih.gov/genomes/Fungi/Saccharomyces_cerevisiae/)were aligned using BLASTP. The orthologous alignmentsbetween the two genomes were checked manually, and amaximum of three top-scoring sequences for each sequencealignment with an E value threshold of ≤1×10–5 werechosen. The experimental PPIs information in S. cerevisiaewas obtained from the Database of Interacting Proteins(http://dip.doe-mbi.ucla.edu) (Salwinski et al. 2004). Ahomologous protein with an E value threshold of 10–10 wasrepresented as a node of PPI in S. stipitis, and an interactionbetween proteins was represented by an edge. We thencreated a Perl script to predict PPI through the method ofgene co-expression based on the assumption that co-expressedgenes are more likely to interact than genes that are not co-expressed (Rhodes et al. 2005). The expression level (RPKM)of genes involved in prediction of PPIs was 22. The PPIsinformation for S. stipitis was extracted based on theintersection of PPIs results obtained from analysis of geneorthology and gene co-expression. To simplify the retrievedPPIs networks, nodes were only defined for those transcribedgenes with known GO and certain nodes. Perl scripts werethen developed to calculate all nodes and edges interactingwith certain DEG that were used as hub nodes in a sub-PPIsnetwork. The sub-PPIs networks were presented as graphsusing Rgraphviz (R/Bioconductor package). The graphs weresimplified further by manually removing some nodes forwhich the encoded genes were uniformly transcribed.

Data integration and analysis

The genomic, transcriptional, metabolic, and PPIs data wereintegrated into an MySQL database using the databasemanagement system MySQL v5.0 (http://www.mysql.com/).Statistical analysis was performed in the R statisticalprogramming environment (http://www.r-project.org/) com-bined with SQL scripts in the database.

Results

Summary of RNA-Seq

A total of 10,523,738 pairs of reads (1.5 Gb sequenced intotal) were obtained from the RNA of yeast grown onglucose, and 12,489,114 pairs (1.8 Gb sequenced in total)were obtained for yeast grown on xylose. Over 80% of the

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reads from both samples were uniquely mapped to thegenome (Fig. 1). The average sequence depth was 102 perbase (the glucose sample) and 121 per base (the xylosesample), for the total annotated exon length of 892 Mb ofthe S. stipitis genome. The ORFs in the RNA-Seq datasetfor differential expression analysis involved 5,816 of themapped or unmapped ORFs in the reference genome.RPKM less than 2 was defined as the signal background. Inthe glucose sample, the median, the mean, and the thresholdRPKM of the highest 2% of RNA were 22, 118, and 983,respectively, and their corresponding values in the xylosesample were 22, 115, and 1,071. Thus, the transcriptionlevel of ORFs in both samples was arbitrarily classified intofive categories: very high (RPKM≥1,027), high (1,027>RPKM≥116), medium (116>RPKM≥22), low (22>RPKM≥2), and no expression (RPKM<2).

To detect novel transcripts, we merged a total of23,012,852 pairs of reads from both samples and alignedthem with the reference genome for reannotation. Weidentified 47 novel transcirpts (Table S1 in the Electronicsupplementary materials (ESM)), of which 36 codingsequences were absent in the National Center for Biotech-nology Information and RefSeq annotations. The changesof RPKM in both samples versus sequence length areshown in supplementary Fig. S1 in the ESM. In addition,we did not identify any alternative splicing sites in theglucose and xylose samples from RNA-Seq data.

Transcription profiles reveal DEG between the glucoseand xylose samples

The genome-scale analysis of transcription levels in the glucoseand xylose samples is shown in Fig. 2. The RPKM distributionfor both samples was similar (Fig. 2a). Both samples had

similar quartiles of the RPKM box plots (the glucose sample,Fig. 2b (A-G); the xylose sample, Fig. 2b (A-X)). Of 5,816annotated ORFs, 5,390 were transcribed (RPKM≥2) in eitherthe glucose or xylose sample and 5,176 transcribed ORFs(89% of the annotated ORFs) showed similar transcription inthe presence of glucose or xylose (p>0.05). Thus, based onRPKM results, the level of transcription in S. stipitis wasgenerally uniform in regardless of the carbon source.Furthermore, we classified proteins encoded by transcribedgenes into three GO categories: biological process, cellularcomponent, and molecular function. The relative number ofproteins in each of these categories was essentially unaffectedby the carbon source (Fig. S2 in the ESM). The RNA-Seqdatasets for both samples shared 1,286 GO terms and differedin eight GO terms, which were related to five overexpressedproteins in the xylose sample (p<0.05), including putativehexose transporter (locus tag: PICST_80517) and glycosylhydrolase (locus tag: PICST_34123, PICST_39160,PICST_51227, and PICST_29300).

We analyzed 214 transcribed ORFs (p<0.05) that weredifferentially expressed between the glucose and xylosesamples; in which 147 ORFs were over-transcribed and 67were under-transcribed in the xylose sample compared withthe glucose sample. The transcriptional level of DEG in thexylose sample (Fig. 2b (D-X)) was generally higher than thatin the glucose sample (Fig. 2b (D-G)). To decrease the errorrate, we then identified 43 significantly differentiallytranscribed ORFs (B–H adjusted p<0.05) (Fig. 2c). Inaddition, we found that the transcribed ORFs (RPKM≥116) (Fig. 3a) and DEG (Fig. 3b) in both samples were notclustered but located throughout eight chromosomes of S.stipitis. We then performed GO analysis based on the abovestatistical analysis. Fig. 3c presents the GO categories of theproteins encoded by the transcribed genes; most DEGencoded enzymes, proteins with binding activity, or proteinsinvolved in metabolic processes or oxidation-reduction. Asassessed by GOseq, the GO analysis revealed that the xylosesample was enriched for proteins involved in carbohydratemetabolism (GO: 0005975), oxidoreductase activity (GO:0016491), oxidation-reduction (GO: 0055114), and metabolicprocesses (GO: 0008152) (B–H adjusted p<0.05 for all).Table S2 in the ESM lists proteins belonging to suchenriched GO terms in the xylose sample. High-throughputdata from RNA-Seq indicated that carbohydrate metabolismand oxidation-reduction reactions were the core regulatorybiological processes that were upregulated in yeast inresponse to xylose, and the other processes were secondary.

Sugar metabolism

More than 860 S. stipitis genes were categorized into 73KEGG metabolic pathways; of those genes, 553 wereexpressed in both the glucose and xylose samples, and 23

Fig. 1 Summary of the RNA-Seq reads. RNA-Seq reads arecategorized as unmapped or mapped to the S. stipitis genome, andthe mapped reads are categorized into exons, introns, intergenicregions, and novel transcripts. Each category is labeled with thepercentage of total RNA-Seq reads

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were differentially expressed between the samples (TableS3 in the ESM). The most markedly modified metabolicpathways were galactose metabolism (pathwayid: 00052),pentose and glucuronate interconversions (pathwayid:00040), and PPP (pathwayid: 00030) (B–H adjusted p<0.05). We then consulted the KEGG metabolic networks toassess the roles of enzymes involved in xylose fermentation.Figure 4a presents our proposed global sugar fermentativepathway showing the flux of glucose (glucose-6-phosphate)and xylose (xylulose-5-phosphate) through glycolysis(glyceraldehyde-3-phosphate and pyruvate), the tricarboxylicacid (TCA) cycle, the PPP, and ethanol formation. Thetranscription of most enzyme-coding genes involved in PPPwas increased in the xylose sample, and certain genes (XYL1,XYL2, and XKS1) were functionally identified (Agbogbo andCoward-Kelly 2008; Jeffries and Jin 2004). RNA-Seq

demonstrated xylose-specific upregulation of other genesinvolved in sugar metabolism, namely glycoside hydrolase-coding genes (EGC1, EGC2, BGL5, BGL6, and BGL7) andFBP1 (fructose-1,6-bisphosphatase) involved in gluconeo-genesis. RNA-Seq also revealed that the expression ofmRNAs encoding key enzymes of the TCA cycle was notaffected by the carbon source; these included the pyruvatedehydrogenase complex E1–E3 (PDB1/PDA1, LAT1, andPDX1), citrate synthase (CIT1), and isocitrate dehydrogenase(IDH1 and IDH2).

Enhancement of PPP and stability of the TCA cycle atthe transcriptional level determined by RNA-Seq werevalidated by qPCR (Table S4 in the ESM). Based on RNA-Seq data, we selected six potential reference genes (ACT1,DFR1, PLB1, RPL13, RPO21, and TUB1; Vandesompele etal. 2002) whose transcription remained stable in both

Fig. 2 Statistical analysis of thetranscriptional profiles in S.stipitis cultured in the presenceof glucose or xylose. aComparison of RPKM of theORFs. RPKM in the scatter plotis presented as the logarithm forbetter visualization. Thehistograms on the upper andright portions of the scatter plotpresent the numeric distributionof RPKM in the glucose andxylose samples, respectively. bBoxplot of log2 RPKM for theglucose sample (−G) and thexylose sample (−X). A-G andA-X genome-scale RNA-Seqdata. D-G and D-X differentiallytranscribed ORFs (p<0.05). Thethree horizontal lines in theboxes show the first quartile,median, and the third quartile. cScatter plot of log2 fold changesversus the mean RPKM in bothsamples. The points above thebroken lines and those under theline represent the over- andunder-transcribed ORFs,respectively. The trianglesdenote differentially transcribedORFs (p<0.05)

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samples and 38 enzyme-coding genes involved in sugarmetabolism for quantitative expression measurements.Based on sensitivity and reproducibility of qPCR for thedifferent reference genes as well as the large range of theirrelative transcription levels, we chose ACT1 (actin) as thereference for genes expressed at the medium or high leveland RPL13 (60S ribosomal protein L13) for genesexpressed at the very high level. Except for ENO1 andARO10, transcription of 36 genes was consistent with theRNA-Seq results (Fig. 4b).

Genome comparison and prediction of PPIs

A total of 4,510 proteins in S. stipitis, a reciprocal best-hitortholog (E value of <10–5) in S. cerevisiae, and the genes

encoding 4,073 homologous proteins in S. stipitis were co-expressed in the presence of glucose or xylose (Fig. 5).Among the homologous genes, 159 were differentiallyexpressed between the xylose and glucose samples. Theknown PPIs in S. cerevisiae were used to predict PPIs in S.stipitis. However, multiple homologs in S. stipitis of asingle protein in S. cerevisiae might be detected based onamino acid sequence alignment. Thus, we considered thetop three orthologs in each alignment (E value of <10–10) toconstruct PPIs. Therefore, the PPIs that were predicted bythe method of gene orthology accounted for 60,187 PPIsinvolving 3,766 homologous proteins (E value of <10–10).Finally, the PPIs predicted through integration of geneorthology and gene co-expression (RPKM>22) accountedfor 25,166 PPIs involving 2,185 proteins. The approach of

Fig. 3 Distribution oftranscribed ORFs in S. stipitischromosomes and their GOcategories. a Eight chromo-somes are represented byhorizontal lines, of which locustags are indicated on the left.The distribution of ORFs(RPKM≥116) is indicated byticks below (yeast grown onglucose) or above (yeast grownon xylose) each horizontal line.b Distribution of overexpressed(ticks above the horizontal bluelines) and underexpressed (ticksbelow the horizontal lines)ORFs in the xylose samplecompared with the glucosesample (B–H adjusted p<0.05).c Summary of GO annotationsfor the transcribed ORFs and thenumber of ORFs for whichexpression differed between theglucose and xylose samples(indicated above each bar)

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Fig. 4 Transcriptional profiles involved in glycolysis, PPP, and theTCA cycle of S. stipitis grown in the presence of glucose or xylose (a)and comparison of the expression level of certain genes involved inthese pathways validated by RNA-Seq and qPCR (b). Metabolitesinvolved in PPP, glycolysis and the TCA cycle are in red, yellow andwhite ellipses. Enzymes and enzyme-encoding genes are in boxes.

The direction of each catalytic reaction is indicated by an arrow.Genes and the relative change in RPKM (growth on xylose vs.glucose) involved in reactions are labeled. Enzyme-coding genesoverexpressed in the xylose sample are colored red, and those uniformexpressed in both samples are colored in green

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predicting PPIs in S. stipitis by comparing orthology(Gancedo 1998; Rolland et al. 2002) and transcriptomeswith S. cerevisiae was used for analysis of correlations ofDEG.

Correlations of DEG involved in transcriptional controlof sugar utilization

Table S5 in the ESM shows possible sugar transporters in S.stipitis and their transcription in the glucose and xylosesamples. In the xylose sample, transcription of HGT2,RGT2, HXT2.4, and XUT1 was increased, of which HXT2.4and XUT1 were uniquely expressed, whereas transcriptionof SUT2–4 was repressed. Notably, HGT2 was transcribedat a very high level (RPKM=3,501) in the xylose sample,33-fold higher than its mean expression (RPKM=105) inthe glucose sample, and the transcription of RGT2 (encod-ing glucose transporter/sensor) was increased 65-fold in thexylose sample (RPKM=1,099). Furthermore, integration ofthe predicted PPIs networks and comparison of the tran-scriptomes revealed correlations of transporters encoded byDEG involved in sugar uptake in S. stipitis (Fig. 6a). Thereceptor GSF2 (involved in glucose repression) was the hub

Fig. 5 Comparison of S. stipitis and S. cerevisiae with respect totranscription from homologous genes in S. stipitis

Fig. 6 Predicted interactionsbetween proteins involved insugar uptake (a), glycolysis andthe TCA cycle (b), amino acidmetabolism and sugar signaltransduction (c), and fatty acidmetabolism, ethanol formation,and cell components (d)

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in the PPIs network, and transcription of GSF2 was alsoincreased in the xylose sample. The roles of HGT2, RGT2,and GSF2 in xylose transport in S. stipitis are little knownexcept for the HXT/XUT/SUT family of sugar transporters(Jeffries and Jin 2004; Matsushika et al. 2009; Özcan andJohnston 1999). These results indicated that transcription ofHGT2/RGT2/GSF2 might constitute a rate-limiting step ofxylose transport in S. stipitis.

The PPIs network for certain enzymes involved inglycolysis and the TCA cycle (Fig. 6b) showed that theSNF1 kinase complex, including SNF1 (carbon catabolitederepressing Ser/Thr protein kinase), SNF4 (5′-AMP-activated protein kinase), and GAL83 (a glucose repressionprotein), interacted with the enzymes of glycolysis and theTCA cycle, such as, HXK1 (hexokinase), GPM1.1/GPM1.2(phosphoglycerate mutase), and IDH1/IDH2 (isocitratedehydrogenase). This result explains why transcription ofthese genes was similar in the glucose and xylose samples.The PPIs network also showed that the regulator CAT8(controls the key enzymes of gluconeogenesis) and MIG1(a transcription factor involved in glucose repression)interacted with the SNF1 kinase complex. RNA-Seqdemonstrated that transcription of MIG1 and CAT8 wasincreased in the xylose sample. However, PPIs networkanalysis failed to detect the interacting partners of CAT8 orMIG1.

Correlations of DEG involved in amino acid metabolism

We specifically analyzed DEG among 221 genes related toKEGG amino acid metabolism. To pinpoint the effect ofcarbon source (xylose or glucose) on amino acid metabo-lism, we extracted metabolic data for 18 amino acidmetabolic pathways from the KEGG metabolic annotationof S. stipitis and merged the data into a single metabolicnetwork including 221 nodes (representing enzymes) and615 edges (representing interactions). Nodes were subcate-gorized to reveal their centralities in the merged networkand to identify the importance of enzymes involved inamino acid metabolism. The highly connected nodes (hubs)were judged by betweenness centrality based on networktopology (Jeong et al. 2001). Of 24 genes with the highestbetweenness centrality in the merged amino acid metabolicnetwork, transcription of 23 genes (except AAT2 encodingaspartate aminotransferase) was similar (p>0.10) in bothsamples. Transcription of ARG3 (encoding ornithinecarbamoyl-transferase) and GLR2 (encoding glutathionereductase) was also increased in the xylose sample. AAT2was the central node (betweenness centrality=0.0489) inthe merged network, which was located in seven KEGGpathways. The merged amino acid metabolic networkshowed that AAT2, ARG3, and GLR2 participated in themetabolism of certain amino acids (arginine, aspartate,

cysteine, methionine, proline, and tyrosine), the ornithinecycle and reduction of glutathione. Furthermore, we thenanalyzed the abundance of 23 free amino acids as well asurea in S. stipitis cultivated in glucose or xylose (Table 1).Quantitative analysis demonstrated that abundance ofcysteine and ornithine increased twice in the xylose sample,and trace urea was detected. This result indicated thatutilization of certain amino acids in S. stipitis was affectedby glucose or xylose, and that transcription of AAT2, ARG3,or GLR2 might be a regulated point of amino acidmetabolism.

To reveal the correlation between enhanced metabolismof certain amino acids and the carbon source, weconstructed a PPIs network representing correlations ofDEG involved in amino acid metabolism (Fig. 6c). ThePPIs networks showed that AAT2, GLR2, and ARG3interacted with the sugar non-fermentation transporters/

Table 1 Comparison of the contents of amino acids and urea inScheffersomyces stipitis cultivated in the presence of glucose or xylose

Compounda, b Glucose sample Xylose sample X/Gc

Alanine 86.12±1.50 76.26±1.85 0.89

Arginine 67.94±1.30 70.74±3.18 1.04

Asparagine 20.01±0.64 24.89±0.65 1.24

Aspartic acid 28.42±1.24 16.69±1.16 0.59

Cysteine 10.45±0.37 27.00±1.33 2.59

Glutamic acid 140.98±5.41 197.29±4.44 1.40

Glycine 25.94±1.17 21.29±0.91 0.82

Histidine 47.89±3.15 46.46±3.95 0.97

Isoleucine 38.31±1.78 31.82±1.43 0.83

Leucine 22.07±1.07 24.98±1.08 1.13

Lysine 62.57±1.20 66.72±16.98 1.07

Methionine 50.49±2.27 30.15±1.42 0.60

Ornithine 7.27±0.24 15.19±0.71 2.09

Phenylalanine 11.33±0.68 6.85±0.70 0.60

Phosphoserine 10.34±0.82 9.77±0.36 0.94

Proline 33.47±1.36 18.57±0.68 0.55

Serine 28.88±1.44 25.04±1.16 0.87

Threonine 31.64±1.46 23.95±0.90 0.76

Tyrosine 13.16±0.59 12.14±0.63 0.92

Urea 0 0.25±0.02 –

Valine 40.15±1.64 39.72±1.47 0.99

α-Aminobutyric acid 1.75±0.08 2.23±0.13 1.28

β-Alanine 1.65±0.09 0.91±0.11 0.55

γ-Aminobutyric acid 22.95±0.20 11.57±1.19 0.50

a The concentration unit is micrograms per milligram of the solubleprotein in the supernatant of the yeast cell extractsb Amino acids and urea with significant difference (p<0.05) inconcentration are shadedc The ratio of the number from the xylose sample to that of the glucosesample

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sensors (RGT2, SNF3, and XUT1), the protein kinases(SNF1, CBK1, KIN3, and DBF2) as sugar non-fermentation regulators, and the enzymes of sugar fermen-tation (PDB1, PFK1, and PFK2). These results indicatedthat transcription of AAT2/GLR2/ARG3, involved in aminoacid metabolism, was linked to that of genes involved inglucose/xylose fermentation.

Correlations of DEG involved in fatty acid β-oxidation, cellcomponents, and ethanol formation

RNA-Seq revealed that transcription of genes (SAD1,SAD2, SOU1, SOU2, POT11, SPS21, and SPS24) encodingthe enzymes related to fatty acid β-oxidation was increasedin the xylose sample (Table S6 in the ESM) (Platta andErdmann 2007; Visser et al. 2007) and that transcription ofSAD1, SAD2, AND SOU2 (encoding 2,4-dienoyl-CoAreductase, 9.7-fold upregulation), POT11, and SPS24 wasalso increased in the xylose sample. Furthermore, sevendifferent fatty acids were quantified, of which the levels ofsaturated fatty acids were decreased and those of unsatu-rated fatty acids were increased in the xylose sample(Table 2). Ethanol was accumulated when S. stipitisfermented glucose or xylose. The ethanol concentration inthe medium of the glucose and xylose samples at harvestwas 192.6 and 193.4 μg ml–1, respectively. Of seven genesencoding ethanol dehydrogenase, only ADH1 and ADH4were transcribed in both samples, and ADH1 was tran-scribed at a very high level (RPKM>4,000). BecauseADH1 is the key isozyme in ethanol formation (Cho andJeffries 1998; Passoth et al. 1998), we constructed a PPIsnetwork to show correlations of ADH1 involved in ethanolformation, enzymes involved in fatty acid β-oxidation, andCDC10 (septin), MYO1/MYO2/MYO5 (myosin) andECM1 (a basement membrane protein) as components ofcell structure (Fig. 6d). Thus, this result provided correla-tions of transcribed genes for enzymes involved in β-oxidation and changes in the abundance of both fatty acidsand cell components in S. stipitis, which will be potentialrate-limiting steps in ethanol formation during xylosefermentation.

Discussion

Obtaining yeast strains capable of simultaneously fer-menting glucose and xylose has been an ongoingobjective (La Grange et al. 2010; Toivari et al. 2001).RNA-Seq simplifies the screening of target genes, makingit possible to select additional genes for improving xylosefermentation and ethanol formation in S. stipitis as well asin S. cerevisiae. Due to the widespread popularity of S.cerevisiae in industrial ethanol production, comparison ofsugar fermentation between S. stipitis and S. cerevisiaeprovides new candidate mechanisms underlying xylosefermentation in yeast.

MIG1, CAT8, and SIP5 as regulators of sugar fermentation

Glucose repression in S. cerevisiae leads to the repressionof genes required for metabolism of other carbon sources,gluconeogenesis, or sugar uptake (Schüller 2003; Westholmet al. 2008). Phosphorylation of the negative regulatorMIG1 causes its deactivation with consequent derepressionof genes repressed by glucose, and the DNA-bindingproteins (CAT8 and SIP5) activate transcription of gluco-neogenic genes (Fig. S3a in the ESM). The PPIs network inS. stipitis only showed that CAT8 (a regulator of keygluconeogenesis genes) interacted with SNF1, GAL83,SSN6, and SSN7 (proteins involved in glucose repression)(Fig. S3b ESM). We noted that transcription of SIP5(encoding a protein in response to nutrient stress) wasincreased 4-fold in the xylose sample. However, no PPIsinvolving MIG1 and enzymes involved in gluconeogenesisor sugar transporters were detected. The comparisonindicates that S. stipitis may have a different mechanismfor glucose repression.

Iron uptake in the respiratory chain

Differential expression analysis demonstrated that oxidore-ductase activity (GO: 0016491) and oxidation-reduction(GO: 0055114) were remodeled when S. stipitis wascultured with xylose. Freese et al. (2011) demonstrates that

Table 2 Quantitation of fattyacids in Scheffersomyces stipitiscultivated in the presence ofglucose or xylose

aThe concentration unit of fattyacids is milligrams per gram ofdry weight of yeast pellets

Fatty acid typea Fatty acids Symbol Glucose sample Xylose sample

Saturated fatty acids Palmitic acid C16:0 3.04±0.13 2.76±0.04

Stearic acid C18:0 0.82±0.05 0.53±0.01

Lignoceric acid C24:0 0.49±0.01 0.30±0.03

Unsaturated fatty acids Palmitoleic C16:1n7 1.09±0.02 1.37±0.02

Oleic acid C18:1n9c 5.59±0.14 5.96±0.10

Linoleic acid C18:2n6c 6.09±0.14 6.75±0.11

α-Linolenic acid C18:3n3 4.99±0.12 5.06±0.09

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deletion of COX5 (encoding cytochrome c oxidase)enhances ethanol formation in S. stipitis. This is acorrelation with respect to expression of enzymes of therespiratory chain and the enhancement of oxidation-reduction reactions during glucose and xylose fermentation.In addition, iron is an important component of therespiratory chain. S. stipitis produces four kinds of irontransporters as does S. cerevisiae (Kosman 2003; Yun et al.2001). However, genes encoding ferric reductases andferroxidases are more than those in S. cerevisiae, andtranscription of these genes indicate that iron uptake in S.stipitis might be a regulated step in xylose fermentation(Table S7 in the ESM).

Signaling sensors

Our results showed that genes encoding certain membrane-spanning sensors involved in sugar utilization were differen-tially expressed between the glucose and xylose samples; forexample, RGT2 may act as a sugar sensor like its homolog inS. cerevisiae (Moriya and Johnston 2004; Rolland et al.2002). Furthermore, we investigated whether other membraneproteins may serve as sensors in response to environmentalstress. Homologous sensors (WSC1–4, MID1) in S. cerevi-siae are involved in modulating cell membrane compositionand play an important role in detecting environmental stressand other perturbations (Ma and Liu 2010). The sensorWSC3 in S. cerevisiae participates in osmotic stress responseand is a positive regulator of 1,3-β-glucan biosynthesis(Rodicio and Heinisch 2010). RNA-Seq revealed that thegenes WSC3, GRP3.4 (for a protein induced by osmoticstress), and KRE7 (encoding a glucan synthase subunit actingin cell wall assembly) were upregulated in the xylose sample.These results will help guide future studies of stress responseof S. stipitis in lignocellulosic bioconversion.

Protein phosphorylation

Salusjärvi et al. (2008) demonstrated that the patterns ofphosphorylation of hexokinase 2, glucokinase, and enolaseisoforms in glycolysis differ among xylose-grown, glucose-repressed, and glucose-derepressed recombinant S. cerevi-siae strains. Our present study suggests that the pattern ofprotein phosphorylation in S. stipitis may also differbetween glucose and xylose, although the gene expressionprofiles in S. stipitis do not necessarily reflect the patternsof post-translational modifications. Transcription of genesencoding protein kinases was detected in both the glucoseand xylose samples, and transcription of KSP1 (encodingserine/threonine protein kinase) was upregulated (p<0.05)in the xylose sample. PPIs network analysis predicted thatsome protein kinases interact with certain proteins involvedin glucose/xylose fermentation, sugar uptake, and amino

acid or fatty acid metabolism; notably, some of the proteinkinases even served as key nodes in the PPIs networks(Fig. 6b–d).

Integrated analysis of the PPIs networks expands ourunderstanding of certain crucial genes required for xylosefermentation. However, those potential target genes areinvolved in multiple metabolic pathways and thereforerequire further functional identification. Thus, investigationof the mechanisms underlying xylose fermentation requiresiterative experimental identification and bioinformaticanalysis. Our study provides the basis for such future work.

Acknowledgments This work was supported by the Ministry ofAgriculture of China—the China Modern Agriculture ResearchSystem (CARS-42) and the Key Program of Transgenic PlantBreeding (2008ZX08003-002).

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