Scheffersomyces stipitis: a comparative systems biology study with the Crabtree positive yeast Saccharomyces cerevisiae
© Papini et al.; licensee BioMed Central Ltd. 2012
Received: 4 May 2012
Accepted: 13 September 2012
Published: 9 October 2012
Scheffersomyces stipitis is a Crabtree negative yeast, commonly known for its capacity to ferment pentose sugars. Differently from Crabtree positive yeasts such as Saccharomyces cerevisiae, the onset of fermentation in S. stipitis is not dependent on the sugar concentration, but is regulated by a decrease in oxygen levels. Even though S. stipitis has been extensively studied due to its potential application in pentoses fermentation, a limited amount of information is available about its metabolism during aerobic growth on glucose. Here, we provide a systems biology based comparison between the two yeasts, uncovering the metabolism of S. stipitis during aerobic growth on glucose under batch and chemostat cultivations.
Starting from the analysis of physiological data, we confirmed through 13C-based flux analysis the fully respiratory metabolism of S. stipitis when growing both under glucose limited or glucose excess conditions. The patterns observed showed similarity to the fully respiratory metabolism observed for S. cerevisiae under chemostat cultivations however, intracellular metabolome analysis uncovered the presence of several differences in metabolite patterns. To describe gene expression levels under the two conditions, we performed RNA sequencing and the results were used to quantify transcript abundances of genes from the central carbon metabolism and compared with those obtained with S. cerevisiae. Interestingly, genes involved in central pathways showed different patterns of expression, suggesting different regulatory networks between the two yeasts. Efforts were focused on identifying shared and unique families of transcription factors between the two yeasts through in silico transcription factors analysis, suggesting a different regulation of glycolytic and glucoenogenic pathways.
The work presented addresses the impact of high-throughput methods in describing and comparing the physiology of Crabtree positive and Crabtree negative yeasts. Based on physiological data and flux analysis we identified the presence of one metabolic condition for S. stipitis under aerobic batch and chemostat cultivations, which shows similarities to the oxidative metabolism observed for S. cerevisiae under chemostat cultivations. Through metabolome analysis and genome-wide transcriptomic analysis several differences were identified. Interestingly, in silico analysis of transciption factors was useful to address a different regulation of mRNAs of genes involved in the central carbon metabolism. To our knowledge, this is the first time that the metabolism of S. stiptis is investigated in details and is compared to S. cerevisiae. Our study provides useful results and allows for the possibility to incorporate these data into recently developed genome-scaled metabolic, thus contributing to improve future industrial applications of S. stipitis as cell factory.
The yeast Scheffersomyces stipitis, commonly known as Pichia stipitis, is a Crabtree negative, homothallic yeast, found mainly in haploid form. S. stipis has greater respiratory capacity than S. cerevisiae due to the presence of an alternative respiration system donating electrons directly to O2 from ubiquinone, branching out before the cytochrome C complex[1, 2] and to the presence of Complex I, also lacking in S. cerevisiae. This Crabtree negative yeast is well known for its ability to ferment pentose sugars to ethanol, having one of the highest native capacity for xylose fermentation with yields on substrate between 0.35/ 0.44 g g-1 at low oxygen transfer rate. S. stipitis has therefore been studied and exploited as a source of genes for the engineering of xylose metabolisms in other microorganisms and it has also been considered as a platform cell factory for production of fuels and chemicals from lignocellulose. Differently from the Crabtree positive yeast S. cerevisiae, the regulation of fermentation in S. stipitis depends on the oxygen levels, where ethanol production sets in only when oxygen becomes limiting. There are two genes responsible for ethanol production in S. stipitis: ADH1 and ADH2, encoding the alcohol dehydrogenase complex (ADH). The activity of ADH is induced by a reduction in the oxygen tension and this regulation may be mediated by heme levels[6, 7]. Under strictly anaerobic conditions, almost no ethanol is produced and the strain cannot survive longer than 1 generation. The same pattern of induction is reported for the genes of the pyruvate decarboxylase complex (PDC) and aldehyde dehydrogenase (AlDH). This behavior is profoundly different from S. cerevisiae where ethanol production takes place under glucose excess conditions, regardless of the availability of oxygen.
In light of its attractive feature to ferment pentoses, most of the work available on S. stipitis has been performed using xylose as the sole carbon source or in mix with other sugars and no detailed physiological studies of this organisms growing on glucose are available. Such data will be important in terms of further exploiting S. stipitis as a platform cell factory for production of fuels and chemicals, particularly as glucose is a dominant sugar in biomass.
Recently a genome-scale metabolic network was reconstructed for S. stipitis[10, 11] providing an increased insight into the metabolism of this yeast. Nevertheless, still little is known about regulatory pathways in S. stipitis. Some regulatory proteins such as SNF1, GNC1 and HAP5 are known to share similarity to S. cerevisiae, but regulatory mechanisms have not been elucidated. Array-based expression studies during aerobic or oxygen limited conditions on glucose or xylose as carbon sources have been performed, showing that about half of the transcripts does not change significantly under the different conditions.
In this study, we sought to provide an insight into the metabolism of S. stipits during aerobic growth on glucose and to compare its patterns to the Crabtree positive yeast S. cerevisiae under batch and chemostat cultivations, using a systems biology approach. Besides measurement of traditional physiological parameters, we analyzed the flux distribution, intracellular metabolites levels and provide RNA-seq data to analyze gene expression levels. Additionally, to highlight the differences in regulatory network between the two yeasts, we performed in-silico analysis of known transcription factors. This work represents an attempt to integrate data from different systems biology tools to gain insight into the metabolism of S. stipitis during growth on glucose.
Results and discussion
Physiology of S. stipitis during aerobic growth on glucose
S. stipitis and S. cerevisiae were grown on glucose as the sole carbon source under both chemostat and batch conditions. For S. cerevisiae, a Crabtree positive yeast, there is a remarkable metabolic difference during these growth conditions; S. cerevisiae shows respiro-fermentative metabolism in the batch cultures, when glucose is available in excess, while a purely respiratory metabolism in the chemostat cultures when glucose is limiting, is observed. We were interested to see how S. stipitis respond to these differences in glucose concentration, and to establish eventual differences between the two different yeasts.
Physiological parameters of S. stipitis and S. cerevisiae ; aerobic, batch cultivations on minimal media with 20 gL -1 glucose as carbon source
μmax [h -1 ]
Glucose consumption rate
[C-mmol /g DW/ h]
Ysx [g /g]
YsEtOH [g /g]
YsPyr [g /g]
YsAc [g /g]
To investigate the response of S. stipitis to the presence of glucose excess and identify possible differences between growth in batch and chemostat, we performed chemostat cultivations at a dilution rate of 0.1 hr-1. Physiological analysis under this condition allowed us to establish that the yields on substrate of S. stipitis do not differ from those reported during batch cultivations. We could therefore conclude that S. stipitis shows the same behavior under the two cultivation modes and that this pattern is indeed similar to the respiratory mode observed during purely respiratory growth of S. cerevisiae.
Comparison of metabolic flux distribution during aerobic batch and chemostat cultures of S. stipitis and S. cerevisiae
To address at a metabolic level the gross physiological patterns presented above, we quantified the intracellular flux distributions of S. stipitis and compared them to that of S. cerevisiae by cultivating the two yeasts on 13C labeled glucose under aerobic batch and chemostat conditions.
Metabolic network analysis of S. stipitis has been previously reported using a different method for flux resolution, however the overall distribution of fluxes in the metabolic network was not provided.
In S. stipitis, no significant differences were present in the lower glycolytic flux or in the tricarboxylic acid cycle (TCA) during batch and chemostat, however the flux towards the pentose phosphate pathway was estimated to be slightly lower during chemostat cultures. Additionally, no external flux of metabolites (metabolites secretion) was reported, in agreement with the measured physiological parameters. The flux values of the pyruvate dehydrogenase (PDH) and of the pyruvate transport into the mitochondria are comparable between the two conditions, as well as fluxes through the TCA. Our results indicate that the metabolic fluxes of S. stipitis under the two cultivations conditions do not show remarkable differences. In contrast, in S. cerevisiae, the flux distribution in batch and chemostat conditions shows extremely different patterns, reflecting the metabolic states arising as a consequence of regulatory phenomena due to glucose repression[14, 15].
Remarkable differences are found in the tricarboxylic acid cycle (TCA): while in S. cerevisiae highly different values are found in batch and chemostat, as previously reported, in S. stipitis high values of the TCA fluxes are found regardless of the cultivation mode, however, in S. stipitis, the TCA fluxes during chemostat cultures are slightly higher than during batch culture. In S. stipitis, the flux through the oxidative part of the pentose phosphate pathway (PPP) is higher, similarly to S. cerevisiae during respiratory conditions. The flux distribution at the pyruvate branch point in S. stipitis also shows substantial differences with S. cerevisiae during batch cultures. First, the flux through the pyruvate decarboxylate reaction (PDC) in S. cerevisiae shows remarkably high values, indicating a flux towards acetaldehyde formation during glucose excess conditions; instead, during oxidative growth, this flux presents a lower value, comparable to that reported for S. stipitis. Additionally, during respiro-fermentative growth, S. cerevisiae shows secretion of intracellular metabolites (mainly ethanol but also minor amount of glycerol, pyruvate and acetate), as previously mentioned.
The flux corresponding to the anaplerotic reaction of pyruvate carboxylase (PYC) is also comparable within the two growth conditions for S. stipis, whereas in S. cerevisiae this flux is almost 10 times higher in the chemostat culture compared with the batch culture, however, when the flux between yeasts is compared, in S. stipitis is slightly lower than these reported during oxidative growth in S. cerevisiae. A similar pattern is observed for pyruvate import into the mitochondria: this flux is low during fermentative growth of S. cerevisiae and about 16 times higher in the chemostat, while in S. stipitis this flux shows constitutively high values. Other differences are found in the pyruvate dehydrogenase (PDH) flux: in S. cerevisiae during respiro-fermentative growth acetyl-CoA formation mainly occur through the PDH-bypass (PDC, acetaldehyde dehydrogenase and acetyl-CoA synthase), while, during oxidative growth, acetyl-CoA is mainly generated through the PDH reaction; in agreement with previous studies we reported a value of 4.3 for the batch and 61.7 for the chemostat culture. In contrast, S. stipitis shows similar flux values at the two growth conditions and, in S. stipitis, the PDH flux is slightly higher than that found in S. cerevisiae during oxidative growth, suggesting that in S. stipitis mitochondrial acetyl-CoA formation mainly occurs through the pyruvate dehydrogenase reaction.
A difference between the flux distribution of S. stipitis and that of S. cerevisiae during oxidative growth is found in the malic enzyme flux (MAE); the flux reported by S. stipitis, comparable in the two conditions, is higher than that presented by S. cerevisiae in batch cultivations, but lower than that found during oxidative growth of S. cerevisiae. This result, together with the lower flux value observed for the anaplerotic decarboxylation of pyruvate to oxaloacetate (PYC), suggests a different tuning of anaplerotic reactions in the two yeasts, being lower in S. stipitis, in agreement with the results reported by Fiaux et al..
Intracellular flux distribution analysis supported the presence, in S. stipitis, of one main metabolic mode, showing similar patterns to those observed for S. cerevisiae during respiratory growth; however, minor differences between the respiratory growth of S. stipitis and the respiratory growth of S. cerevisiae were identified. In S. cerevisiae instead, as previously well characterized, physiology and flux network distribution differ substantially between the two conditions as a consequence of regulatory phenomena not occurring in S. stipitis.
Intracellular metabolites analysis of S. stipitis during aerobic batch cultivations
Results from statistical analysis on intracellular metabolite analysis
Welch’s Two Sample t-Test
Total number of Biochemicals with
Total number of biochemical with
p ≤ 0.05
0.05 < p <0.1
0.05 < p < 0.1
Fold change of amino acids and nucleotides pathway with relative p-values ( Ss = S. stipitis ; Sc = S. cerevisiae )
Protein Normalized Fold Change Ss / Sc
Glycine, serine and threonine metabolism
Alanine and aspartate metabolism
glutamate, gamma-methyl ester
Phenylalanine & tyrosine metabolism
Valine, leucine and isoleucine metabolism
Urea cycle; arginine-, proline-, metabolism
Purine metabolism, (hypo)xanthine/inosine containing
Purine metabolism, adenine containing
Fold change of metabolites from carbohydrates and energy metabolism pathway with relative p-values ( Ss = S. stipitis ; Sc = S. cerevisiae )
Protein Normalized Fold Change Ss / Sc
Glycolysis, gluconeogenesis, pyruvate metabolism
fructose 1,6-diphosphate, glucose 1,6-diphosphate
Glyoxylate and dicarboxylate metabolism
Nucleotide sugars, pentose metabolism
< 0, 001
< 0, 001
< 0, 001
Fold change of metabolites involved in lipid metabolism with relative p-values ( Ss = S. stipitis ; Sc = S. cerevisiae )
Essential fatty acid
< 0, 001
Long chain fatty acid
Fatty acid, dicarboxylate
< 0, 001
Table4 shows the changes in metabolites involved carbohydrates and energy metabolism. Here, it is striking the high fold-change observed for ribulose and polyols such as arabitol and ribitol. Previous studies in Aspergillus niger demonstrated that polyols are synthesized under oxygen limiting conditions, acting as carbon storage compounds but also having a role in the maintenance of the osmotic and redox balance[17, 18]. Based on these results, we suggested the capability of S. stipitis to utilize the arabinose assimilation pathway in the opposite direction, producing arabitol and ribitol via the PPP pathway. The content of trehalose is also found to be significantly higher in S. stipitis; the increased presence of this metabolite, together with the increased adenine content compared to S. cerevisiae, might be linked to the increased biomass production. This observation is in agreement with previous results suggesting that Crabtree negative yeasts accumulates reserve carbohydrates upon glucose pulse. Metabolites of the energy metabolism do also show differences between the two yeasts. Fumarate and malate contents are higher in S. stipitis whereas succinate and malate are present in slightly lower amount. Pyruvate and acetyl-CoA are also less abundant in S. stipitis, however it is unfortunately not possible to distinguish between the cytosolic and mitochondrial fraction.
Intracellular levels of lipids are shown in Table5. The fatty acid linoleate (18:2) and the phospholipid 2-linoleoyl glycero-phosphoethanolamine have high fold-change as these lipids are naturally not abundant in S. cerevisiae. Choline content is also found to be present in higher amount in S. stipitis. 2-hydroxyglutarate, originating from the TCA intermediate 2-oxoglutarate is found to be present at higher levels in S. stipitis.
Intracellular metabolome analysis highlighted several differences in metabolites patterns of carbohydrates, energy and fatty acids metabolism. Despite some of these results might be directly connected to what observed at a phenotypic level, other differences could not be captured by physiological analysis, thus proving the validity of metabolome analysis in providing useful information for metabolic characterization.
RNA sequencing from aerobic cultivations
Not much is known about the regulatory pathways of S. stipitis and only a few works describing gene expression levels in S. stipitis have been performed. To determine gene expression levels during growth on glucose, we analyzed the transcriptome of S. stipitis in a high-throughput fashion, using RNA-seq to compare mRNAs extracted from aerobic batch and chemostat cultivations to those of S. cerevisiae. Jeffries et al. analyzed gene expressions through NimbleGen expression arrays, comparing the transcriptional response to oxygen limitation on glucose and xylose[12, 24] and showed that half of the transcript do not change expression significantly under the four different conditions. Recently Yuan et al. sequenced the transcriptome of S. stipitis growing on glucose and xylose, identifying 214 ORFs whose expression is changed during growth on the two different carbon sources.
As our goal was to capture differences in transcripts of genes of the central carbon metabolism and provide a quantitative comparison of the transcriptome of the two yeasts, we described the data obtained through RNA-seq in a quantitative fashion using FPKM (fragments per kilobase per million sequenced reads). This parameter quantifies the expression level of a certain mRNA according to its abundance and normalized it by the number of reads per samples and the gene length.
RNA-seq data provided information about the different levels of expression of genes of the central carbon metabolism between the two conditions, indicating that transcriptional regulation of central carbon metabolism differ, qualitatively and quantitatively, between the two yeasts.
In silico analysis of transcription factors
Transcription factors involved in central carbon metabolism uniquely identified for S. cerevisiae
Positive regulatory required for depression of the phospholipid biosynthetic enzymes, regulated by OPI1.
Serine-rich protein binding E-boxes of glycolytic genes and contributes to their activation. Has been found to suppress the gcr1 requirement for enolase, glyceraldehyde-3-phosphate dehydrogenase, phosphoglycerate kinase, phosphoglycerate mutase, and pyruvate kinase gene expression. It is necessary for maximal enolase expression
Transcription factor that regulates expression of several glucose transporter (HXT) genes in response to glucose.
Regulatory protein, involved in glucose repression of the SUC genes.
Transcriptional repressor controlled by Snf1 involved in controlling the transcription of SIR genes. Also involved in the response to toxic agents.
Glycosylated integral membrane protein involved in fructose-1,6-bisphosphatase (FBPase) transport and degradation.
Conserved protein involved in the degradation of the gluconeogenic enzyme fructose-1,6-bisphosphatase; also required for sporulation. Negative regulator of gluconeogenesis.
Putative zinc cluster protein of unknown function; proposed to be involved in the regulation of energy metabolism.
With the aim of describing the metabolism of S. stipitis during aerobic growth, we performed a systems-level comparison between the Crabtree negative yeast and S. cerevisiae during aerobic growth on glucose. To our knowledge, this is the first time that the metabolism of S. stipits is investigated in details during batch and chemostat cultivations on glucose. Despite the differences in genome evolution and metabolism of these two yeast species we sought to identify patterns of similarity by applying a systemic approach based on high-throughput techniques. What clearly emerges from our study is the absence of a fermentative mode for S. stipitis. This behavior is very different from S. cerevisiae where is the amount of glucose to determine its metabolic mode (respiratory or fermentative). This is in agreement with what has previously been reported for S. stipitis concerning the onset of ethanol fermentation, which is dependent on oxygen availability. The flux network of S. stipitis under both conditions show similarities to that observed for S. cerevisiae during purely respiratory growth, however, minor differences during batch and chemostat cultivations condition were identified. The estimation of intracellular fluxes based on 13C-labeling uncovered differences through the anaplerotic reactions of pyruvate carboxylase and malic enzyme, showing lower values in S. stipitis compared to those showed by S. cerevisiae during oxidative growth. Interestingly, the flux through the oxidative part of the PPP is found to be higher during batch cultivations and higher than that reported in S. cerevisiae during oxidative growth. This might be explained with a higher biomass yield on substrate reported for S. stipitis. S. stipitis showed increased pyruvate import into the mitochondria, increased flux through the PDH and TCA reactions, indicating that the Crabtree negative yeast mainly fuel the TCA through the conversion of pyruvate to acetyl-CoA via the PDH reaction.
Through metabolome analysis it was possible to assess and quantify differences in the levels of intracellular metabolites of the two yeasts during batch cultivations. Total amino-acid levels are slightly lower in S. stipitis while adenine content is found to be higher. This result might be in agreement with the observed increase in biomass yield and higher specific growth rate. Carbohydrate metabolism shows several differences, mainly in the content of polyols. This might indicate that these compounds are used as storage carbohydrate or, as observed in Aspergillus nidulans, can play a role in maintaining the redox balance. Relevant amounts of citramalate are found in S. stipitis, which is not present in S. cerevisiae. Differences in fatty acids and phospholipids are also found. The increased choline content could suggest a different regulation of the phospholipid metabolism as, through the analysis of unique transcription factors, we found the TFs INO2 and INO4 to be absent in S. stipitis. Analysis of RNA-seq data uncovered different transcriptional regulation of central carbon metabolism of the two yeasts, indicating that, in S. stipitis, transcriptional regulation is not dependent on glucose concentration. Most mRNAs of S. stipitis do not change their expression under the two conditions, in contrast to S. cerevisiae where, in the shift towards respiratory conditions, a significant number of genes change remarkably their expression. The analysis of transcription factors was particularly relevant in identifying unique transcription factors of S. cerevisiae potentially involved in controlling the onset of fermentative metabolism (Rgt1 and Sgc1). Several families of transcription factors were found to be unique for S. stipitis; unfortunately, even though the S. stipitis genome was sequenced in 2007, most of these ORF have not been assigned a function yet.
Despite the non-direct relationship between mRNA levels, metabolites and fluxes, our attempt to integrate data coming from these different techniques uncovered a robust consistency in identifying a one-mode phenotype of S. stipitis, sharing similarities to the patterns found in S. cerevisiae during purely respiratory growth. However, through integrated system-level analysis, it was possible to identify differences between the main metabolic mode of S. stipitis and the oxidative growth of S. cerevisiae, to highlights non-obvious difference during batch cultivation conditions and identify potential transcription factors involved in controlling the different response to glucose excess.
The Scheffersomyces stipitis strain used in this work was CBS 6054 obtained by T. Jeffries. The Saccharomyces cerevisiae strain CEN.PK113-7D was used as wild-type strain for comparison.
Batch and chemostat cultivations conditions for physiological characterization
The cultivations have been conducted in DasGip fermentors (DasGip, Jülich, Germany) in batch and chemostat modes, for each mode triplicates cultures were grown. A mineral salt medium was used. The medium is composed of (per liter): (NH4)2SO4, 5 g; KH2PO4, 3 g; MgSO4·7H2O, 0.5 g; Antifoam 289 (A-5551, Sigma–Aldrich, St. Louis, MO, USA), 0.05 mL; trace metals, 1 mL and vitamins, 1 mL trace metal solution. The trace metal solution consisted of (per liter): EDTA (sodium salt), 15 g; ZnSO4·7H2O, 0.45 g; MnCl2·2H2O, 1 g; CoCl2·6H2O, 0.3 g; CuSO4·5H2O, 0.3 g; Na2MoO4·2H2O, 0.4 g; CaCl2·2H2O, 0.45 g; FeSO4·7H2O, 0.3 g; H3BO3, 0.1 g and KI, 0.1 g. The pH of the trace metal solution was adjusted to 4.0 with 2 M NaOH prior to heat sterilization. The vitamin solution contained (per liter): biotin, 0.05 g; p-amino benzoic acid, 0.2 g; nicotinic acid, 1 g; Ca-pantothenate, 1 g; pyridoxine-HCl, 1 g; thiamine-HCl, 1 g and myo-inositol, 25 g. The pH of the vitamin solution was adjusted to 6.5 with 2 M NaOH. The vitamin solution was filter sterilized and stored at 4°C. The medium was supplemented 20 gL-1 of Glucose as carbon source. The precultures were used to inoculate the fermentors to an initial OD600 of 0.05.
The batch cultures were performed in 1.0 L DasGip stirrer-pro® vessels with a working volume of 0.7 L. Agitation was maintained at 600 rpm using a magnetic stirrer integrated in the BioBlock®, which maintained the temperature at 30°C. The aeration was set to 1 vvm. The pH of the medium was maintained at 5.0 by automatic addition of 2 M KOH. The temperature, agitation, gassing, pH and composition of the offgas were monitored and controlled using the DasGip monitoring and control system. Dissolved oxygen was monitored with an autoclavable polarographic oxygen electrode (Mettler Toledo, Columbus, OH, USA). The effluent gas from the fermentation was analyzed for real-time determination of oxygen and CO2 concentration by DasGip fedbatch pro® gas analysis systems with the off gas analyzer GA4 based on zirconium dioxide and two-beam infrared sensor.
For the chemostat cultivations, the medium described above (except that Glucose was in a concentration of 10 gL-1) was fed with a constant dilution rate of 0.1 hr-1 and aeration was set to 1 vvm. The working volume was kept at 0.5 L by a peristaltic effluent pump. Samples were taken after a metabolic steady state (defined as constant values of CO2 and O2 in the off-gas, as well as constant biomass concentration for at least five residence time) was achieved.
Batch and chemostat cultivations conditions for flux analysis upon 13C Glucose labeling
Metabolic flux distribution was analyzed after feeding the cultivations with 13C labeled glucose. The media used and the fermentation system was the same as described in paragraph 2.2 but glucose concentrations was 5 gL-1 for batch and 2 gL-1 for chemostat. Samples in the batch were taken in the mid-exponential phase; samples from chemostat cultivations were taken after a metabolic (see above) first and an isotopic steady state (at least 5 residence time after feeding with labeled glucose) were reached.
13C labeling experiments and flux data analysis
Flux distribution analysis was performed upon growth on 13C-labeled glucose during batch and chemostat cultivations. For batch cultures, the medium was added with 5 gL-1 of 100% D-Glucose-1-13C (13C > 99%; Isotec/ Sigma-Aldrich) and samples from triplicate cultures were withdrawn from mid-exponential phase (OD600nm = 2,5). For chemostat cultures, after reaching a metabolic steady state, the original medium containing 2gL-1 naturally labeled glucose was replaced by chemically identical medium, but where the glucose was replaced by 100% D-Glucose-1-13C (13C > 99%; Isotec/ Sigma-Aldrich).
The labeling pattern of proteinogenic amino acids was determined calculating the summed fractional labeling (SFL) as described in Gombert et al.. This calculations are based on the model previously described.
Cell dry weight was measured by filtering a known volume of the culture through a pre-dried and pre-weighed 0.45 μm pore size nitrocellulose filter (Supor®-450 Membrane Filters, PALL Life Sciences, Ann Arbor, MI, USA). The filters with the biomass were washed with water, dried for 15 min in a microwave oven at 150 W and weighed again. The optical density was determined at 600 nm using a Genesys 20 Spectrophotometer, Thermo Scientific, Wilmington, DE, USA).
The concentrations of glycerol, ethanol, acetate, succinate and pyruvate were analyzed by an isocratic HPLC (UltiMate® 3000 Nano/Capillary Autosamplers, Dionex, Sunnyvale, CA, USA) with an Aminex HPX-87 H ion exchange column (Bio-Rad, Hercules, USA) at 65°C using 5 mM H2SO4 as mobile phase at a flow rate of 0.6 mL min-1. Glucose, glycerol and ethanol were measured with a refraction index detector whereas succinate, acetate and pyruvate with an ultraviolet–visible light absorbance detector.
Treatment of sample for metabolites analysis: quenching and extraction
Sample from each condition were withdrawn from mid-exponential phase (OD600nm 2,5) and added to a 75% v/v cold methanol buffered with 12.5 mM Tricine pH 7.4, kept at −40°C in an Ethanol Bath. Pellet were harvested by centrifugation 4 min at 10,000 g at −20°C and froze on liquid nitrogen. The quenched samples were sent to Metabolon (Metabolon, Inc., Durham, NC) for extraction based on methanol/chloroform and analysis through GC-MS and LC-MS-MS. Metabolome profiling was performed as previously described[43, 44]. The untargeted metabolic profiling platform employed for this analysis was based on a combination of three independent platforms: ultrahigh performance liquid chromatography/tandem mass spectrometry (UHPLC/MS/MS) optimized for basic species, UHPLC/MS/MS optimized for acidic species, and gas chromatography/mass spectrometry (GC/MS). Metabolites were extracted from the samples using a methanol-based solvent, dried and reconstituted in chromatography solvent. The reconstituted extracts were divided into three portions and resolved using the three chromatography platform systems coupled to mass spectrometry. Samples were derivatized using trimethylsilane prior to injection into the GC/MS instrument. Metabolites were identified by matching ions chromatographic retention index and mass spectral fragmentation signatures with reference library entries created from standard metabolites. For ions that were not covered by the standards, additional library entries were added based on their unique retention time and ion signatures.
Total RNA extraction and RNA sequencing
For RNA sequencing, a 25 mL sample was withdrawn from the fermentor in the mid-exponential phase (OD600 = 2.5). The sample was cooled immediately in ice and centrifuged at 2°C and 5000 rpm for 5 min, the supernatant discarded and the biomass immediately frozen in liquid nitrogen. The total RNA was extracted from cells through mechanical disruption with glass beads, digested with DNAse and purified using the RNeasy kit (Qiagen, Hilden, Germany). The quality of the RNA was assayed using a BioAnalyzer (Agilent Technologies, Palo Alto, CA, USA). 10 ng of total RNA were sent to Lab for Life for RNA sequencing based on the Illumina Solexa platform. For construction of the library the Illumina TrueSeq kit was used.
Illumina sequencing data processing
The pair-end reads were firstly checked their quality using SolexaQA. The reads that heave Phred score less than 20 and were trimmed out using BWA trimming algorithm. The trimmed high quality reads that have length less than 25 bases were filtered out. The pre-processed reads were aligned on the reference genome of S. stipitis CBS6045 and S. cerevisiae S288c using Bowtie-Tophat software v1.3.3. With this method, more than 90% of the pre-processed reads could be aligned on their reference genome[47, 48]. The RNA-seq raw data of S. stipitis were deposited at NCBI SRA database with accession number SRS308058. For RNA-seq raw data of S. cerevisiae were retrieved from our study.
FPKM calculation, gene ontology and reporter metabolites analysis
We used the Cuffink software to estimate the gene expression level based on the parameter FPKM (fragments per kilobase of exon per million fragments mapped) and Cuffdiff to determine differential gene expression[50, 51]. The adjusted p-values from differential gene expression analysis were further overlaid on the Gene Ontology networkand genome-scale metabolic model of iIN800 and iSS884 for integrated analysis. The reporter algorithm was applied to evaluate the significant features (GOterms, metabolite) in the different conditions. The results were selected based on reporter p-value cutoff 0.01 for GOterms and 0.05 for Metabolites.
Orthologs group and TFs analysis
The protein sequences based translated from the genome sequence of S. stipitis CBS6045 and S. serevisiae S288c were used to identify orthologous group through OrthoMCL software version 1.4 using default parameters. The result from the orthologous clustering can be found inAdditional file 1. The collection of transcription factor of the both genome was retrieved from the Fungal Transcription Factor Database, and, based on the orthologs families reconstructed, a comparative of analysis of TF of the two yeasts was performed.
The authors would like to acknowledge fundings from European Research Council , Novo Nordisk Foundation, Knut and Alice Wallenberg foundation Bioinformatics Infrastructure for Life Sciences (BILS). Daniel Klevebring and Linn Fagerberg are acknowledged for technical assistance during analysis of RNA-seq data. The computational analyses were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at C3SE. Gothenburg Bioinformatics Network (GOTBIN). Funding for open access charge by Chalmers Library.
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