A Thi2p Regulatory Network Controls the Post-glucose Effect of Xylose Utilization in Saccharomyces cerevisiae

Abstract

The complete and efficient utilization of both glucose and xylose is necessary for the economically viable production of biofuels and chemicals using lignocellulosic feedstocks. Although recently obtained recombinant Saccharomyces cerevisiae strains metabolize xylose well when xylose is the sole carbon source in the medium (henceforth referred to as “X stage”), their xylose consumption rate is significantly reduced during the xylose-only consumption phase of glucose-xylose co-fermentation (“GX stage”). This post-glucose effect seriously decreases overall fermentation efficiency. We showed in previous work that THI2 deletion can alleviate this post-glucose effect, but the underlying mechanisms were ill-defined. In the present study, we profiled the transcriptome of a thi2Δ strain growing at the GX stage. Thi2p in GX stage cells regulates genes involved in the cell cycle, stress tolerance, and cell viability. Importantly, the regulation of Thi2p differs from a previous regulatory network that functions when glucose is the sole carbon source, which suggests that the function of Thi2p depends on the carbon source. Modeling research seeking to optimize metabolic engineering via TFs should account for this important carbon source difference. Building on our initial study, we confirmed that several identified factors did indeed increase fermentation efficiency. Specifically, overexpressing STT4, RGI2, and TFC3 increases specific xylose utilization rate of the strain by 36.9, 29.7, 42.8%, respectively, in the GX stage of anaerobic fermentation. Our study thus illustrates a promising strategy for the rational engineering of yeast for lignocellulosic ethanol production.

Introduction

The economic feasibility of producing biofuels and biochemicals via the industrial fermentation of lignocellulosic hydrolysates requires the full consumption of glucose and xylose, which are the most abundant sugars in this kind of material (Hou et al., 2017; Kwak et al., 2019; Li et al., 2019). Saccharomyces cerevisiae is a well-studied and robust cellular factory, but it cannot natively metabolize xylose. Engineering strategies have introduced the initial xylose metabolizing enzymes, the xylose isomerase (XI), or xylose reductase and xylitol dehydrogenase. Strategies have also focused on altered transporters and modified expression of genes encoding xylulokinase and non-oxidative pentose phosphate pathway (PPP) in S. cerevisiae. However, such recombinant strains based on the strategies mentioned above only show limited xylose utilization capacity. Additionally, directed evolution with xylose as the sole carbon source in the growth medium has led to some substantial improvements (Hou et al., 2017; Kwak et al., 2019; Li et al., 2019). However, in many cases, the mechanistic details remain unclear, and this lack of understanding has hindered progress for realizing advanced strategies to rationally engineer further improvements (Myers et al., 2019).

Factors, including metabolic genes and transcription factors (TFs), that control xylose utilization in S. cerevisiae have been the focus of specific research in recent decades. For example, it has been confirmed that XI has significant effects on the capacity of S. cerevisiae to metabolize xylose. Efficient xylose utilization by an evolved strain has been partially attributed to elevated expression levels of XI, which was accomplished via multiple-copy chromosomal integration of the heterogenous xylA gene (Zhou et al., 2012). The enhancement of another evolved strain was attributed to the improvement of XI activity in S. cerevisiae by upregulating the expression molecular chaperones (Hou et al., 2016a). Additionally, improved xylose utilization capacity in some other evolved strains was attributed to the reprogramming of their carbon metabolism regulatory networks, such as the MAP Kinase (MAPK) signaling pathway and Protein Kinase A (PKA) signaling pathways, and several TFs in these signaling pathways, such as Hog1p and Ira2p, have showed their effects on xylose metabolism (Sato et al., 2016; Osiro et al., 2018; Myers et al., 2019). These findings confirmed that globally modifying the gene expression state by regulate the key transcription factors could be a way to optimize the xylose metabolism in yeast.

The carbon source conditions at the start of fermentation also significantly affect xylose metabolism. The evolved strain metabolize xylose well when xylose is the sole carbon source (referred to as the X stage). However, their specific xylose consumption rate is generally lower in the xylose consumption phase after glucose depletion in glucose-xylose co-fermentation (referred to as the GX stage), although there still remains more than half of the xylose when cells enter the GX stage (Michael et al., 2016; Wei et al., 2018). That is the yeast cells do not recognize xylose in the GX stage as they do in the X stage. It is industrially attractive to alleviate this post-glucose effect because it significantly decreases xylose utilization and prolongs fermentation times. To date, there are insufficient data regarding the mechanisms of control of the post-glucose effect and limited strategies to overcome it.

In our previous work, we revealed that deletion of the TF gene THI2 improved the xylose consumption in the GX stage (Wei et al., 2018). Thi2p is a transcriptional activator of thiamine biosynthetic genes (Nosaka et al., 2005), but little information exists on how Thi2p affects carbon metabolism. Here, we demonstrated that deletion of THI2 does not affect the activity of xylose isomerase, which catalyzes the first step of xylose metabolism and significantly affects the metabolic efficiency. We then compared the specific transcriptome differences during the GX stage between the thi2Δ strain and parental strain and examined the effects of Thi2p target genes on xylose metabolism. We thusly discovered a Thi2p regulatory network that improved xylose utilization in the GX stage. In addition, we revealed that deleting THI2 or overexpressing its target genes MID2, STT4, and CDC42 decreased the proportion of dead cells present in cultures. Finally, we showed that overexpressing Thi2p target genes STT4 (Phosphatidylinositol-4-kinase), RGI2 (respiratory growth induced, function unknown), and TFC3 (subunit of RNA polymerase III transcription initiation factor complex) significantly enhanced xylose utilization in the GX stage of anaerobic fermentation, thereby illustrating a promising strategy for the rational engineering of yeast for lignocellulosic ethanol production. Moreover, our work illustrates the important point that yeast metabolic modeling, both in basic systems studies and in more applied efforts directed towards optimization and engineering, needs to account for the carbon-source-dependent regulatory functions of TFs like Thi2p.

Methods

Construction of Plasmids and Strains

All plasmids and strains used in this study are listed in Table 1. The ORFs of all genes were amplified from the genomic DNA of the S. cerevisiae strain CEN.PK 113-5D (Entian and Kotter, 2007) using the primers listed in Additional file 1: Supplementary Table S1. The fragments of ORFs were digested by restriction enzymes and ligated into plasmid pUC20. The genes in the resultant recombinant plasmids were under the control of the TEF1 promoter. The genes were overexpressed by transferring these recombinant plasmids into BSGX001.

Table 1.

S. cerevisiae strains and plasmids used in this study.

S. cerevisiae strains and plasmids	Description	Sources
PLASMIDS
pUG6	The plasmid with loxP-KanMX4-loxP cassette	Guldener et al., 1996
YEp-CH	Shuttle plasmid for E. coli and S. cerevisiae, GAL2p-cre-CYC1t, HygR	Li et al., 2016
pUC20	Yeast 2μ plasmid, KanMX4	Wei et al., 2018
pUC20-BDH2a	pUC20, TEF1p-BDH2-ADHt	This study
S. cerevisiae STRAINS
CEN.PK 113-5D	MATa; ura3-53	Entian and Kotter, 2007
BSGX001	CEN.PK 113-5D derivative; Ru-XI, XK, gre3::PPP, cox4Δ, AEb	Hou et al., 2016b
BSGX001(ixr1Δ)c	BSGX001 derivative, ixr1::KanMX4	This study
BSGX001(BDH2)d	BSGX001 derivative, δ1-loxp-TEF1p-BDH2-ADHt-δ2	This study

^aOther plasmids derived from pUC20 were named in the same way, and due to space limitations, they were not listed here.

^bAE, adaptive evolution in medium using xylose as the sole carbon source.

^cOther strains derived from BSGX001 with deleted genes were named in the same way, and due to space limitations, they were not listed here.

^dOther strains derived from BSGX001 with overexpressed genes were named in the same way, and due to space limitations, they were not listed here.

Gene knockout was performed by homologous recombination using a KanMX4 expression cassette, which was cloned from pUG6 (Guldener et al., 1996), to replace the target gene. The KanMX4 marker was then discarded by transferring plasmid YEp-CH into the strains and inducing the expression of Cre recombinase (Li et al., 2016).

Cultivation Conditions and Batch Fermentation

E. coli recombinant cells were cultured at 37°C in Luria–Bertani (LB) medium (5 g L⁻¹ yeast extract, 10 g L⁻¹ tryptone, 10 g L⁻¹ NaCl, pH 7.0), and 100 mg L⁻¹ ampicillin was added for the selection of transformants. Yeast cells were cultivated at 30 °C in SC-Ura medium containing 1.7 g L⁻¹ yeast nitrogen base, 5 g L⁻¹ (NH₄)₂SO₄, 0.77 g L⁻¹ CSM-Ura (Sunrise Science Products, USA) and 20 g L⁻¹ glucose as the carbon source.

Fermentation was performed in shake flasks or 1 L bioreactors according to the experimental requirements. The fermentation medium was comprised of 1.7 g L⁻¹ yeast nitrogen base, 5 g L⁻¹ (NH₄)₂SO₄, 20 g L⁻¹ glucose, and 20 g L⁻¹ xylose. Overnight cultures of a single colony were transferred into a 250 mL shake flask containing 50–60 mL fresh SC-Ura medium supplied with 20 g L⁻¹ glucose and an initial biomass of 0.23 g L⁻¹ dry cell weight (DCW) (OD₆₀₀ of 1) and cultured at 30 °C and 200 rpm for another 12–16 h. The cells were then collected and washed three times with sterile water and inoculated into the fermentation medium. Fermentation in shake flasks was performed at 30°C and 200 rpm. The initial biomass was 0.575 g L⁻¹ DCW. The anaerobic fermentation in bioreactors was performed with an initial biomass of 0.23 g L⁻¹ DCW at 30°C and pH 5.5, with 0.1 vvm nitrogen and a stirring speed of 200 rpm for ~ 30 h. The pH was maintained by automatically pumping 5 mol L⁻¹ NaOH and 5 mol L⁻¹ H₃PO₄. All fermentations were carried out in triplicate.

Quantitative PCR (qPCR) Analysis

qPCR data were analyzed according to the 2^−ΔΔCT method (Livak and Schmittgen, 2001). RNA was extracted from the cells collected from the 20 h glucose-xylose co-fermentation flasks using a UNlQ-10 Column Trizol Total RNA Isolation Kit (Sangon Biotech Co., Ltd., Shanghai, China). The cDNA was obtained using a PrimeScript^TM RT reagent Kit (TaKaRa, Japan). The gene transcription levels were determined using the equation N = 2^{Ct(reference gene)}/2^{Ct(target gene)}. ACT1 was used as the reference gene, and the t-test was applied to evaluate the differences between means.

Xylose Isomerase Activity Assay

The crude enzyme samples were prepared as previously described (Hou et al., 2016a). Yeast cells were collected at 20 h in glucose-xylose co-fermentation, then were broken by glass beads (Φ = 0.5 mm) using a FastPrep cell homogenizer (Thermo Savant, USA). The total cellular protein concentration was measured using a BCA protein assay reagent kit (Sangon Biotech Co., Ltd., Shanghai, China).

The XI activities were determined at 30°C by measuring the decrease in NADH concentration using a previously reported method (Hou et al., 2016a). Briefly, assays were performed in reaction mixtures containing 0.15 mmol L⁻¹ NADH, 10 mmol L⁻¹ MgCl₂, and 1 U of sorbitol dehydrogenase (Sigma-Aldrich, USA) in 100 mmol L⁻¹ Tris-HCl (pH 7.5) with appropriately diluted crude cell extracts. The reaction was initiated by adding 500 mmol L⁻¹ xylose. One unit of XI activity was defined as the amount of crude enzyme required to produce 1 mmol xylulose per min under the assay conditions.

Transcriptome Analysis

Samples from the batch fermentation shake flasks were taken at 20 h and subjected to transcriptome analysis. The cells in each sample were collected by centrifugation at 5,000 rpm and 4°C for 5 min and then frozen in liquid nitrogen. Total RNA was extracted using a UNIQ-10 Trizol RNA Purification Kit (Sangon Biotech, China) and then fragmented. DNA was digested with DNase I, and cDNA was synthesized by using short mRNA fragments as templates. Three independent RNA extractions were assayed for each strain. The resulting sample library was sequenced using an Illumina HiSeqTM 2000 (BGI Shenzhen, China).

Raw data from transcriptional analysis and processed data for genes exhibiting significant differences between BSGX001 (thi2Δ) and BSGX001 are available in the NCBI Gene Expression Omnibus database (GEO Accession Number: GSE119333). Significant differences were indicated by p-values of 0.001 or less, and an absolute fold-change threshold of 2.0 or greater. All annotations were derived from the Saccharomyces Genome Database (SGD) (http://www.yeastgenome.org/). Cluster analysis was performed using the Gene Ontology Slim Mapper tool supplied by the SGD (http://www.yeastgenome.org).

Analysis of Metabolites and Calculation

The concentrations of glucose, xylose, glycerol, acetate, and ethanol were measured using HPLC (Shimadzu, Japan) with an Aminex HPX-87H ion exchange column (300 ×7.8 mm, Bio-Rad, Hercules, USA). The mobile phase was 5 mmol L⁻¹ H₂SO₄ with a flow rate of 0.6 ml/min, and the temperature of the column oven was 45°C. The specific xylose utilization rate (r_xylose) was calculated using the following equation, as previously described (Wei et al., 2018):

$$\begin{array}{l}r=\frac{A_n-A_m}{\frac{1}{2}{\sum }_{i=m+1}^n(B_i+B_{i-1})\times (t_i-t_{i-1})}\end{array}$$ (1)

where r is the specific utilization during the phase from sampling point m to sampling point n; and A, B, and t are the metabolite concentration, biomass concentration, and time, respectively, at sampling points n, i, and m.

Measurement of the Proportion of Dead Cells in Culture

Yeast cells were harvested at 20, 36, and 48 h, and diluted to a suitable multiple (~ 6 ×10⁷ cells mL⁻¹). The cells were then incubated with 0.04% trypan blue for 3–10 min; dead cells were stained by trypan, while live cells were not (Bowey-Dellinger et al., 2017). The stained cells were counted manually using a hemocytometer. Samples were subjected to independent triplicate tests, each with more than 500 cells counted. For statistical analysis, the unpaired, two-tailed t-test was performed. Data with p ≤ 0.05 were considered significantly different.

Results

Increased Xylose Utilization of the THI2 Deletion Strain Is Not Related to Xylose Isomerase Activity

Xylose isomerase activity in recombinant yeast seriously affects the capacity of S. cerevisiae to utilize xylose (van Maris et al., 2007; Zhou et al., 2012; Hou et al., 2016a). The transcription level of xylA and XI activity of the thi2Δ strain and its control BSGX001 (Hou et al., 2016b) were detected to determine whether deletion of THI2 enhanced xylose utilization through increasing the xylA gene expression or directly improving the XI activity. The results showed that the transcriptional level of xylA in THI2 deletion strain is 54.2% of that in parent strain and the p-value is 0.147, which change is not significant. The xylose isomerase in THI2 deletion strain is 83.6% of parent strain (p-value is 0.048). These results suggested that neither the transcription level of xylA nor XI activity increased in the thi2Δ strain compared to BSGX001 in the GX stage (Figure 1), that is deletion of THI2 did not increase XI activity of strain.

Figure 1.

The transcription level of the Ru-xylA gene relative to reference gene ACT1 (A), and the specific activity of XI (B) of the THI2 deletion strain. The error bars indicate the standard deviation of independent experiments performed in triplicate.

The Transcriptional Profile of the THI2 Deletion Strain Suggested Engineering Strategies for Enhanced Xylose Utilization in S. cerevisiae

Transcriptional Profile of the THI2 Deletion Strain

To investigate how THI2 deletion improved the xylose utilization in the GX stage, we compared the transcriptome of THI2 deletion strain BSGX001(thi2Δ) and its parent strain BSGX001 in the GX stage. The samples of both strains were taken at 2 h after glucose depletion (20 h) in glucose-xylose co-fermentation. At this time point, ~17–18 g L⁻¹ xylose remained in the medium. The transcriptome analysis results (Table 2) revealed that 93 and 16 genes were significantly up- and downregulated, respectively, in the THI2 deletion strain during the GX stage. The Gene Ontology (GO) cluster result showed that within the Molecular Functions category, the upregulated genes were primarily clustered (cluster frequency ≥ 10%) under the GO terms hydrolase activity and DNA binding; the downregulated genes were primarily clustered under the GO terms DNA binding, transferase activity and ligase activity; within the Biological Processes category, upregulated genes did not cluster to specific GO term, while the downregulated genes clustered to GO terms RNA polymerase II promoter, cellular response to DNA damage stimulus, vitamin metabolic process, and DNA repair.

Table 2.

Gene cluster analysis of transcriptome difference of BSGX001 (thi2Δ) vs. BSGX001 at the GX stage in the aspect of Molecular Function and Biological Processes.

GOID	GO terms (Molecular Function)	Frequency	Gene(s)
UP-REGULATED
3674	Molecular function unknown	34 out of 93 genes, 36.6%	ATS1, MAK16, BOL1, BOL3, AIM2, ECM1, ERP1, PAU7, YAR023C, UIP3, MST28, YAR064W, YAR066W, YAR068W, RRT6, SPG1, RGI2, FAR10, ARV1, SYM1, YLR255C, TMA7, YLR264C-A, BOP2, CMG1, SMD2, YLR281C, YLR283W, YLR287C, COQ11, SPH1, NKP2, PEX30, YLR326W
16787	Hydrolase activity	14 out of 93 genes, 15.1%	CCR4, POP5, PHO11, YIL082W-A, CDC42, GPN3, CDD1, IRC20, MCM5, DBP9, CTS1, TAD3, SFH1, YRF1-6
3677	DNA binding	11 out of 93 genes, 11.8%	TFC3, SAW1, ECM22, EST1, RED1, NEJ1, PDR8, MCM5, YLR278C, MEC3, EST2
16740	Transferase activity	9 out of 93 genes, 9.7%	SWD1, YAT1, YIL082W-A, ERF2, IRC20, STT4, UBC12, EST2, GAS2
30234	Enzyme regulator activity	8 out of 93 genes, 8.6%	GIP4, CLN3, PEX22, GPB2, BUD14, RFU1, PIG1, GCD7
5198	Structural molecule activity	7 out of 93 genes, 7.5%	NUP60, RED1, RPS28B, MRPL15, RPL38, RPS25B, RPP0
3723	RNA binding	7 out of 93 genes, 7.5%	POP5, YIL082W-A, EST1, DBP9, GCD7, YHC1, RPP0
3735	Structural constituent of ribosome	5 out of 93 genes, 5.4%	RPS28B, MRPL15, RPL38, RPS25B, RPP0
4386	Helicase activity	4 out of 93 genes, 4.3%	IRC20, MCM5, DBP9, YRF1-6
16887	ATPase activity	3 out of 93 genes, 3.2%	MCM5, DBP9, SFH1
4518	Nuclease activity	3 out of 93 genes, 3.2%	CCR4, POP5, YIL082W-A
1071	Nucleic acid binding transcription factor activity	3 out of 93 genes, 3.2%	TFC3, ECM22, PDR8
3682	Chromatin binding	3 out of 93 genes, 3.2%	RED1, YCS4, MCM5
5085	Guanyl-nucleotide exchange factor activity	3 out of 93 genes, 3.2%	EFB1, LTE1, GCD7
19899	Enzyme binding	2 out of 93 genes, 2.2%	VPS8, GIP4
3729	mRNA binding	2 out of 93 genes, 2.2%	DBP9, YHC1
16779	nucleotidyltransferase activity	2 out of 93 genes, 2.2%	YIL082W-A, EST2
4871	Signal transducer activity	2 out of 93 genes, 2.2%	GPB2, MID2
43167	Ion binding	2 out of 93 genes, 2.2%	RBG1, NUP60
3924	GTPase activity	2 out of 93 genes, 2.2%	CDC42, GPN3
22857	Transmembrane transporter activity	2 out of 93 genes, 2.2%	THI7, CSC1
8135	Translation factor activity, RNA binding	1 out of 93 genes, 1.1%	GCD7
16491	Oxidoreductase activity	1 out of 93 genes, 1.1%	BDH2
16798	Hydrolase activity, acting on glycosyl bonds	1 out of 93 genes, 1.1%	CTS1
8233	Peptidase activity	1 out of 93 genes, 1.1%	YIL082W-A
16853	Isomerase activity	1 out of 93 genes, 1.1%	ECI1
16301	Kinase activity	1 out of 93 genes, 1.1%	STT4
988	Transcription factor activity, protein binding	1 out of 93 genes, 1.1%	TFC3
30674	Protein binding, bridging	1 out of 93 genes, 1.1%	ATG39
19843	rRNA binding	1 out of 93 genes, 1.1%	RPP0
8168	Methyltransferase activity	1 out of 93 genes, 1.1%	SWD1
8289	Lipid binding	1 out of 93 genes, 1.1%	NUP60
16829	Lyase activity	1 out of 93 genes, 1.1%	CYC3
16874	Ligase activity	1 out of 93 genes, 1.1%	LIP2
51082	Unfolded protein binding	1 out of 93 genes, 1.1%	CNE1
16791	Phosphatase activity	1 out of 93 genes, 1.1%	PHO11
DOWN-REGULATED
3674	Molecular function unknown	6 out of 16 genes, 37.5%	BSC1, PRM7, YDR246W-A, RRT5, YGL015C, YOR338W
3677	DNA binding	3 out of 16 genes, 18.8%	THI2, MGA1, IXR1
16740	Transferase activity	3 out of 16 genes, 18.8%	HOM3, TRA1, URA2
16874	Ligase activity	2 out of 16 genes, 12.5%	SNZ3, URA2
30234	Enzyme regulator activity	1 out of 16 genes, 6.3%	CIP1
1071	Nucleic acid binding transcription factor activity	1 out of 16 genes, 6.3%	MGA1
16829	Lyase activity	1 out of 16 genes, 6.3%	DAL3
16301	Kinase activity	1 out of 16 genes, 6.3%	HOM3
other	Other	1 out of 16 genes, 6.3%	BTN2
GOID	GO terms (Biological Processes)	Frequency	Gene(s)
UP-REGULATED
8150	Biological process unknown	19 out of 93 genes, 20.4%	AIM2, BDH2, PAU7, YAR023C, UIP3, YAR064W, YAR066W, YAR068W, RRT6, SPG1, YLR255C, YLR264C-A, BOP2, CMG1, YLR278C, YLR281C, YLR283W, YLR287C, YLR326W
51726	Regulation of cell cycle	9 out of 93 genes, 9.7%	CCR4, LTE1, CLN3, BUD14, CDC42, RED1, YCS4, MEC3, SFH1
33043	Regulation of organelle organization	8 out of 93 genes, 8.6%	EFB1, LTE1, BUD14, CDC42, EST1, RED1, YCS4, MCM5
6974	Cellular response to DNA damage stimulus	7 out of 93 genes, 7.5%	SAW1, NUP60, IRC20, NEJ1, MCM5, MEC3, SFH1
7059	Chromosome segregation	7 out of 93 genes, 7.5%	LTE1, GIP4, GPN3, RED1, YCS4, NKP2, SFH1
278	Mitotic cell cycle	7 out of 93 genes, 7.5%	CCR4, LTE1, CLN3, CDC42, GPN3, YCS4, SPH1
42221	Response to chemical	6 out of 93 genes, 6.5%	GPB2, CNE1, CDC42, FAR10, SPH1, MID2
6281	DNA repair	6 out of 93 genes, 6.5%	SAW1, NUP60, IRC20, NEJ1, MCM5, SFH1
6325	Chromatin organization	6 out of 93 genes, 6.5%	NUP60, SWD1, YCS4, MCM5, MEC3, SFH1
2181	Cytoplasmic translation	6 out of 93 genes, 6.5%	RBG1, TMA7, RPS28B, RPL38, RPS25B, RPP0
6605	Protein targeting	5 out of 93 genes, 5.4%	VPS8, PEX22, NUP60, GPN3, ERF2
51052	Regulation of DNA metabolic process	5 out of 93 genes, 5.4%	CCR4, SAW1, EST1, MCM5, SFH1
6366	Transcription from RNA polymerase II promoter	5 out of 93 genes, 5.4%	CCR4, GPB2, ECM22, PDR8, SFH1
48285	Organelle fission	5 out of 93 genes, 5.4%	LTE1, CDC42, GPN3, RED1, YCS4
23052	Signaling	5 out of 93 genes, 5.4%	PEX22, GPB2, CDC42, FAR10, MID2
32200	Telomere organization	5 out of 93 genes, 5.4%	SWD1, EST1, MEC3, EST2, YRF1-6
7010	Cytoskeleton organization	4 out of 93 genes, 4.3%	EFB1, ATS1, BUD14, CDC42
746	Conjugation	4 out of 93 genes, 4.3%	CDC42, FAR10, SPH1, MID2
31399	Regulation of protein modification process	4 out of 93 genes, 4.3%	GIP4, CLN3, PEX22, NUP60
6629	Lipid metabolic process	4 out of 93 genes, 4.3%	ECM22, ARV1, ECI1, STT4
902	Cell morphogenesis	4 out of 93 genes, 4.3%	BUD14, CDC42, SPH1, MID2
6310	DNA recombination	4 out of 93 genes, 4.3%	IRC20, MCM5, MEC3, YRF1-6
51169	Nuclear transport	4 out of 93 genes, 4.3%	ECM1, NUP60, GPN3, RPS28B
51321	Meiotic cell cycle	4 out of 93 genes, 4.3%	GPB2, RED1, YCS4, GAS2
6397	mRNA processing	3 out of 93 genes, 3.2%	SMD2, YHC1, MID2
8380	RNA splicing	3 out of 93 genes, 3.2%	SMD2, YHC1, MID2
6401	RNA catabolic process	3 out of 93 genes, 3.2%	CCR4, POP5, RPS28B
6260	DNA replication	3 out of 93 genes, 3.2%	CCR4, MCM5, SFH1
7114	Cell budding	3 out of 93 genes, 3.2%	ATS1, CDC42, SPH1
8033	tRNA processing	3 out of 93 genes, 3.2%	ATS1, POP5, TAD3
7124	Pseudohyphal growth	3 out of 93 genes, 3.2%	GPB2, CDC42, SPH1
51049	Regulation of transport	3 out of 93 genes, 3.2%	BUD14, ECM22, CDC42
6364	rRNA processing	3 out of 93 genes, 3.2%	MAK16, POP5, DBP9
70647	Protein modification by small protein conjugation or removal	3 out of 93 genes, 3.2%	PEX22, NUP60, UBC12
42273	Ribosomal large subunit biogenesis	3 out of 93 genes, 3.2%	MAK16, DBP9, RPP0
9451	RNA modification	2 out of 93 genes, 2.2%	ATS1, TAD3
6497	Protein lipidation	2 out of 93 genes, 2.2%	ARV1, ERF2
6869	Lipid transport	2 out of 93 genes, 2.2%	ECM22, ARV1
55085	Transmembrane transport	2 out of 93 genes, 2.2%	PEX22, THI7
70271	Protein complex biogenesis	2 out of 93 genes, 2.2%	CYC3, BUD14
18193	Peptidyl-amino acid modification	2 out of 93 genes, 2.2%	NUP60, SWD1
42594	Response to starvation	2 out of 93 genes, 2.2%	RBG1, ECM22
71554	Cell wall organization or biogenesis	2 out of 93 genes, 2.2%	MID2, GAS2
910	Cytokinesis	2 out of 93 genes, 2.2%	BUD14, SPH1
7033	Vacuole organization	2 out of 93 genes, 2.2%	CLN3, CDC42
6091	Generation of precursor metabolites and energy	2 out of 93 genes, 2.2%	RGI2, PIG1
43934	Sporulation	2 out of 93 genes, 2.2%	GPB2, GAS2
15931	Nucleobase-containing compound transport	2 out of 93 genes, 2.2%	NUP60, RPS28B
51186	Cofactor metabolic process	2 out of 93 genes, 2.2%	BOL1, COQ11
6354	DNA-templated transcription, elongation	2 out of 93 genes, 2.2%	CCR4, SFH1
51604	Protein maturation	2 out of 93 genes, 2.2%	BOL1, BOL3
1403	Invasive growth in response to glucose limitation	2 out of 93 genes, 2.2%	GPB2, CDC42
7031	Peroxisome organization	2 out of 93 genes, 2.2%	PEX22, PEX30
6417	Regulation of translation	2 out of 93 genes, 2.2%	EFB1, GCD7
48284	Organelle fusion	2 out of 93 genes, 2.2%	CLN3, CDC42
61025	Membrane fusion	2 out of 93 genes, 2.2%	CLN3, CDC42
5975	Carbohydrate metabolic process	1 out of 93 genes, 1.1%	PIG1
6413	Translational initiation	1 out of 93 genes, 1.1%	GCD7
16570	Histone modification	1 out of 93 genes, 1.1%	SWD1
6468	Protein phosphorylation	1 out of 93 genes, 1.1%	CLN3
16197	Endosomal transport	1 out of 93 genes, 1.1%	VPS8
70925	Organelle assembly	1 out of 93 genes, 1.1%	RPP0
32196	Transposition	1 out of 93 genes, 1.1%	YIL082W-A
6887	Exocytosis	1 out of 93 genes, 1.1%	CDC42
6970	Response to osmotic stress	1 out of 93 genes, 1.1%	MID2
43144	snoRNA processing	1 out of 93 genes, 1.1%	POP5
54	Ribosomal subunit export from nucleus	1 out of 93 genes, 1.1%	ECM1
8213	Protein alkylation	1 out of 93 genes, 1.1%	SWD1
6457	Protein folding	1 out of 93 genes, 1.1%	CNE1
6383	Transcription from RNA polymerase III promoter	1 out of 93 genes, 1.1%	TFC3
42255	Ribosome assembly	1 out of 93 genes, 1.1%	RPP0
6414	Translational elongation	1 out of 93 genes, 1.1%	EFB1
7005	Mitochondrion organization	1 out of 93 genes, 1.1%	STT4
32543	Mitochondrial translation	1 out of 93 genes, 1.1%	MRPL15
32787	Monocarboxylic acid metabolic process	1 out of 93 genes, 1.1%	ECI1
16050	Vesicle organization	1 out of 93 genes, 1.1%	MST28
51603	Proteolysis involved in cellular protein catabolic process	1 out of 93 genes, 1.1%	CNE1
6811	Ion transport	1 out of 93 genes, 1.1%	CSC1
6470	Protein dephosphorylation	1 out of 93 genes, 1.1%	GIP4
43543	Protein acylation	1 out of 93 genes, 1.1%	ERF2
55086	Nucleobase-containing small molecule metabolic process	1 out of 93 genes, 1.1%	CDD1
48193	Golgi vesicle transport	1 out of 93 genes, 1.1%	ERP1
6897	Endocytosis	1 out of 93 genes, 1.1%	ARV1
9408	Response to heat	1 out of 93 genes, 1.1%	NUP60
other	other	7 out of 93 genes, 7.5%	YAT1, PHO11, RFU1, LIP2, SYM1, CTS1, ATG39
DOWN-REGULATED
6366	Transcription from RNA polymerase II promoter	4 out of 16 genes, 25%	THI2, TRA1, IXR1, YOR338W
8150	Biological process unknown	4 out of 16 genes, 25%	BSC1, YDR246W-A, RRT5, YGL015C
6520	Cellular amino acid metabolic process	2 out of 16 genes, 12.5%	HOM3, URA2
6974	Cellular response to DNA damage stimulus	2 out of 16 genes, 12.5%	TRA1, IXR1
6766	Vitamin metabolic process	2 out of 16 genes, 12.5%	THI2, SNZ3
6281	DNA repair	2 out of 16 genes, 12.5%	TRA1,IXR1
6468	Protein phosphorylation	1 out of 16 genes, 6.3%	CIP1
16197	Endosomal transport	1 out of 16 genes, 6.3%	BTN2
43543	Protein acylation	1 out of 16 genes, 6.3%	TRA1
6457	Protein folding	1 out of 16 genes, 6.3%	BTN2
55086	Nucleobase-containing small molecule metabolic process	1 out of 16 genes, 6.3%	URA2
51726	Regulation of cell cycle	1 out of 16 genes, 6.3%	CIP1
42221	Response to chemical	1 out of 16 genes, 6.3%	IXR1
43934	Sporulation	1 out of 16 genes, 6.3%	YOR338W
18193	Peptidyl-amino acid modification	1 out of 16 genes, 6.3%	TRA1
51321	Meiotic cell cycle	1 out of 16 genes, 6.3%	YOR338W
6865	Amino acid transport	1 out of 16 genes, 6.3%	BTN2
746	Conjugation	1 out of 16 genes, 6.3%	PRM7
31399	Regulation of protein modification process	1 out of 16 genes, 6.3%	CIP1
278	Mitotic cell cycle	1 out of 16 genes, 6.3%	CIP1
16570	Histone modification	1 out of 16 genes, 6.3%	TRA1
6325	Chromatin organization	1 out of 16 genes, 6.3%	TRA1
6811	Ion transport	1 out of 16 genes, 6.3%	BTN2
other	Other	2 out of 16 genes, 12.5%	MGA1, DAL3

The Effect of Differentially Expressed Genes (DEGs) on Xylose Utilization

According to the significant DEGs and previously reported factors that related to the xylose metabolism of S. cerevisiae (Salusjarvi et al., 2008; Cheng et al., 2018; Wei et al., 2018), 32 upregulated genes involved in ribosomal biosynthesis, signal transducer, generation of precursor metabolites and energy, starvation response, ATPase or GTPase activity, oxidoreductase activity, cofactor metabolic process, lipid metabolic process, transmembrane transporter activity, and protein modification, and 12 downregulated genes, were chosen for follow-up investigations.

The up- and down-regulated genes were overexpressed and deleted, respectively, in strain BSGX001. The effect was then evaluated by determining the xylose-specific consumption rate of recombinant strains in shake flask fermentations at the GX stage. The results showed that all mutants had no significant effect on glucose metabolism. Overexpressing the cell wall integrity (CWI)-related genes MID2, STT4, and CDC42 increased the r_xylose of the strain by 45.9, 49.2, and 13.1%, respectively. Overexpressing stress response genes ECM22, CSC1, and BDH2 increased the r_xylose of the strain by 11.5, 13.1, and 26.2%, respectively. Overexpressing GPN3 (encoding a putative GTPase) and TFC3 (encoding a subunit of the RNA polymerase III transcription initiation factor complex) increased the r_xylose of the strain by 13.1 and 42.6%, respectively. Furthermore, overexpressing the function unknown genes BOP2 and RGI2 increased the r_xylose of the strain by 11.5 and 41.0%, respectively. Deleting CIP1, IXR1, YDR246W-A, and YGLO15C increased the r_xylose of the strain by 26.2%, 36.1, 16.4, and 14.8%, respectively (Table 3). These results suggested that deleting THI2 enhanced xylose utilization through regulating these genes (Figure 2).

Table 3.

Genes regulated by Thi2p in GX stage and their effects on xylose utilization.

Category	Strains	Gene annotation	Log2(fold changes)a	${\text{r}}_{\text{xylose}}^{\text{b}}$ (g g−1 DCW h−1)
	Control (BSGX001)			0.061 ± 0.001
OVEREXPRESSION THE UP-REGULATED GENES
RP-related genes	RPS25B	Protein component of the small (40S) ribosomal subunit	1.379	0.053 ± 0.001*
	MRPL15	Mitochondrial ribosomal protein of the large subunit	1.157	0.035 ± 0.003*
	RPL38	Ribosomal 60S subunit protein L38	1.048	0.022 ± 0.001*
	MAK16	Constituent of 66S pre-ribosomal particles	1.802	0.052 ± 0.001*
	RPPO	Conserved ribosomal protein P0 of the ribosomal stalk	1.345	0.054 ± 0.001*
	RPS28B	Protein component of the small (40S) ribosomal subunit	1.113	0.060 ± 0.000*
	DBP9	A putative ATP-dependent RNA helicase involved in 60S-ribosomal-subunit biogenesis	1.380	0.060 ± 0.000*
	POP5	Subunit of both RNase MRP and nuclear RNase P	1.616	0.050 ± 0.001*
	RBG1	Translating ribosomes	1.275	0.060 ± 0.001*
Signal transducer genes	GPB2	Multistep regulator of cAMP-PKA signaling	1.146	0.039 ± 0.003*
	MID2	Acts as a sensor for cell wall integrity signaling	1.053	0.089 ± 0.003*
	CDC42	Establishment and maintenance of cell polarity	1.241	0.069 ± 0.002*
	PEX22	Required for import of peroxisomal proteins	1.422	0.049 ± 0.001*
	FAR10	Protein involved in recovery from arrest in response to pheromone	1.051	0.060 ± 0.001*
Generation of precursor Metabolites and energy	PIG1	Glycogen synthesis	1.412	0.045 ± 0.001*
	RGI2	Involved in energy metabolism under respiratory conditions	1.136	0.086 ± 0.001*
Response to starvation	ECM22	Sterol regulatory element binding protein	1.268	0.068 ± 0.002*
ATPase activity	MCM5	An active ATP-dependent helicase	1.334	0.022 ± 0.000*
	SFH1	Component of the RSC chromatin remodeling complex	1.075	0.000 ± 0.000*
GTPase activity	GPN3	Biogenesis of RNA pol II and polIII	1.252	0.069 ± 0.001*
Oxidoreductase activity	BDH2	Putative medium-chain alcohol dehydrogenase	1.241	0.077 ± 0.001*
Transcription factor activity, protein binding	TFC3	Subunit of RNA polymerase III Transcription initiation factor complex	1.014	0.087 ± 0.000*
Cofactor metabolic process	COQ11	Putative oxidoreductase, subunit of Coenzyme Q biosynthetic complexes	1.069	0.064 ± 0.000
	BOL1	Mitochondrial matrix protein involved in Fe-S cluster biogenesis	1.014	0.060 ± 0.002
Lipid metabolic process	STT4	Phosphatidylinositol-4-kinase	1.282	0.091 ± 0.001*
	ARV1	Involved in intracellular sterol and sphingolipid transport	1.374	0.055 ± 0.003*
	ECI1	Essential for the beta-oxidation of unsaturated fatty acids	1.306	0.057 ± 0.000*
Transmembrane Transporter activity	CSC1	May be involved in detoxification	1.071	0.069 ± 0.002*
	THI7	Responsible for the uptake of thiamine	1.233	0.020 ± 0.001*
Phosphatase activity	PHO11	One of three repressible acid phosphatases	1.492	0.034 ± 0.002*
Protein modification	UBC12	Related to E2 ubiquitin-conjugating enzymes	1.696	0.046 ± 0.002*
Function unknown	BOP2	Protein of unknown function	1.882	0.068 ± 0.001*
DELETION THE DOWN-REGULATED GENES
Cellular amino acid metabolic or transport	hom3Δ	Cytoplasmic enzyme that catalyzes the first step in the common pathway for methionine and threonine biosynthesis	−1.071	0.000 ± 0.000*
Amino acid transport	btn2Δ	Modulates arginine uptake	−1.038	0.030 ± 0.001*
Cell cycle related genes	cip1Δ	Cyclin-dependent kinase inhibitor	–1.035	0.077 ± 0.002*
	yor338wΔ	Putative protein of unknown function	−1.455	0.039 ± 0.002*
Response to chemical	Ixr1Δ	Transcriptional repressor that regulates hypoxic genes during normoxia	–1.044	0.083 ± 0.003*
DNA binding	mga1Δ	Protein similar to heat shock transcription factor	−1.055	0.039 ± 0.002*
	dal3Δ	Ureidoglycolate lyase	−1.108	0.037 ± 0.001*
Molecular function Unknown	prm7Δ	Pheromone-regulated protein	−1.294	0.048 ± 0.001*
	ydr246w-AΔ	Unknown function	–1.070	0.071 ± 0.001*
	rrt5 Δ	Unknown function	−1.004	0.051 ± 0.002*
	ygl015cΔ	Null mutants accumulate cargo in the Golgi	–1.121	0.070 ± 0.005*
	Bsc1	Null mutant has increased glycogen accumulation	−1.397	0.056 ± 0.004*

Cells were cultured at 30°C in a shake flask and agitated at 200 rpm. All the data are the mean value ± standard deviation of independent triplicate tests. The bold gene names and values refers to the operations brought the positive effect in xylose utilization in the GX stage.

^*p <0.05.

^aThe THI2 deletion strain compared to the parent strain BSGX001, up-regulate represents genes with higher expression in THI2 deletion strain compared to the parent strain BSGX001, down-regulate represents the reverse operation.

^bThe specific consumption/production rates of xylose/ethanol (r_xylose/r_ethanol) were calculated from the data on the xylose consumption phase in the GX stage.

Figure 2.

Regulatory Network of Thi2p promoting xylose utilization in the GX stage. Green and red lines represent activation function and inhibition function, respectively; black lines represent unknown function. Genes with purple background frame encode transcription factors.

Overexpressing Cell Wall Integrity Related Genes MID2, STT4, and CDC42 Decreased the Proportion of Dead Cells in the Culture

Among the genes that positively enhanced xylose utilization, the genes MID2, STT4, and CDC42 belong to the CWI pathway, which may protect cells from environmental conditions that otherwise induced death (Mishra et al., 2017). We determined the proportion of dead cells in the culture of strains overexpressing these genes, as well as THI2 deletion strain, and their parent strain BSGX001. Samples were taken at 20, 36, and 48 h, respectively.

The results (Figure 3) provided information regarding three aspects of xylose utilization. First, in the phase that xylose was rapidly consumed (BSGX001, xylose fermentation, 20 h), the proportion of dead cells in the culture was low (<10%). After xylose was depleted (BSGX001, xylose fermentation, 36 and 48 h), the proportion of dead cells increased with time. The changes in nutritional condition apparently induced cell death. Second, the proportion of dead cells in the GX stage (BSGX001, glucose-xylose co-fermentation, 20, 36, 48 h) was much higher than after xylose was depleted (BSGX001, xylose fermentation, 36 and 48 h), which suggested that cell death was more significantly induced by glucose depletion than by xylose depletion. Third, overexpressing MID2, STT4, and CDC42 or deleting THI2 decreased the proportion of dead cells in the cultures. The decrease at all timepoints was significant (p-value < 0.05). Overexpression of MID2, STT4, and CDC42 or deleting THI2 enhanced xylose metabolism in the GX stage, in part, because overexpression of these genes promoted cell survival and continued metabolism.

Figure 3.

The proportion of dead cell in the culture. Cells were cultured at 30°C in a shake flask and agitated at 200 rpm. In xylose fermentation, xylose was depleted at about 30 h. In glucose-xylose co-fermentation, glucose was depleted before 20 h, while xylose was not depleted before 48 h. Samples were taken at 20, 36, and 48 h respectively. All data are the mean ± standard deviation of independent tests performed in triplicate. ^*p < 0.05. , BSGX001, xylose fermentation; , BSGX001, glucose-xylose co-fermentation; , the MID2 overexpression strain, glucose-xylose co-fermentation; , the STT4 overexpression strain, glucose-xylose co-fermentation; , the CDC42 overexpression strain, glucose-xylose co-fermentation; , the THI2 deletion strain, glucose-xylose co-fermentation.

Overexpressing STT4, RGI2, or TFC3 Enhanced Xylose Utilization in the GX Stage Under Anaerobic Conditions

The level of available oxygen has an impact on the xylose fermentation characteristics (Salusjarvi et al., 2008; Souto-Maior et al., 2009). The xylose utilization of strains overexpressing STT4, RGI2, TFC3, and MID2, or deleting IXR1, which showed the highest positive effect on xylose utilization in shake flask fermentations, were further evaluated in bioreactors under anaerobic conditions. Their glucose-xylose co-fermentation characteristics are shown in Figure 4 and Table 4. Overexpressing STT4 and RGI2 increased the specific consumption rate of xylose (r_xylose) of the strain by 36.9 and 29.7% in the GX stage, respectively. Although the specific production rate of ethanol (r_ethanol) and the ethanol yields (Y_ethanol) did not increase, the fermentation time was shortened (Figures 4A,B). Overexpressing TFC3 increased the r_xylose and r_ethanol by 42.8 and 32.5%, respectively, and this also shortened the fermentation time (Figure 4C). However, overexpression of MID2 or deletion of IXR1 did not yield positive effects on xylose utilization under anaerobic conditions, which indicated that oxygen levels played an important role in the strain utilization of xylose.

Figure 4.

Fermentation characteristics of strains overexpressing STT4, RGI2, TFC3, and MID2 or deleting IXR1 under anaerobic conditions. Cells were cultured in bioreactors at 30 °C, pH 5.5 with 0.1 vvm nitrogen and stirring at 200 rpm. (A) STT4 overexpression strain vs. BSGX001. (B) RGI2 overexpression strain vs. BSGX001. (C) TFC3 overexpression strain vs. BSGX001. (D) MID2 overexpression strain vs. BSGX001. (E) IXR1 deletion strain vs. BSGX001. Symbols: ■, glucose; ∙, xylose; ▾, glycerol; ◂, acetate; ▸, ethanol; ▴, biomass. The red solid symbols are the test strains; the black hollow symbols are the control strain BSGX001. All the data represent the mean value of independent triplicate tests.

Table 4.

The characteristics of anaerobic fermentation of strains overexpressing STT4, RGI2, TFC3, MID2, and deleting IXR1.

Strains	μa	${\text{r}}_{\text{xylose}}^{\text{b}}$ (g g−1 DCW h−1)	${\text{r}}_{\text{xylose}}^{\text{b}}$ (g g−1 DCW h−1)	${\text{Y}}_{\text{ethanolc}}^{\text{c}}$ (g g−1 sugars)
BSGX001	0.170 ± 0.001	0.407 ± 0.005	0.191 ± 0.002	0.400 ± 0.002
STT4	0.185 ± 0.002*	0.557 ± 0.003*	0.191 ± 0.002*	0.396 ± 0.003
RGI2	0.208 ± 0.001*	0.528 ± 0.000*	0.182 ± 0.004*	0.400 ± 0.005
TFC3	0.165 ± 0.000*	0.581 ± 0.002*	0.253 ± 0.003*	0.400 ± 0.006
MID2	0.191 ± 0.001*	0.486 ± 0.002*	0.185 ± 0.002*	0.360 ± 0.003
Ixr1Δ	0.150 ± 0.002*	0.381 ± 0.003*	0.188 ± 0.001	0.402 ± 0.004

All the data are the mean value ± standard deviation of independent triplicate tests. Cells were cultured in bioreactors at 30°C and pH 5.5, and a stirring speed of 200 rpm. The anaerobic condition was maintained by sparking nitrogen into the bioreactors with a speed of 0.1 vvm.

^*p < 0.05.

^aThe specific growth rates (μ) were calculated from the data on the glucose consumption phase in the glucose and xylose co-fermentation.

^bThe specific consumption rates of xylose/ethanol (r_xylose/r_ethanol) were calculated from the data on the xylose consumption phase in the GX stage.

^cThe ethanol yields (Y_ethanol) of total sugars that strain consumed.

Discussion

Despite the large amount of xylose present in many feedstocks that are commonly used in fermentation cultures in bio-industrial manufacturing, our basic understanding of xylose utilization by S. cerevisiae is limited. This lack of understanding has hindered rationally informed strategies for further improving recombinant S. cerevisiae strains to efficiently utilize this abundant carbon source. Understanding the regulatory networks controlling xylose metabolism will almost certainly inform and encourage rational engineering work focused on fully utilizing the mixed sugars in lignocellulosic hydrolysates. In the present study, we investigated the functional significance of how THI2 deletion promotes xylose utilization. We found that THI2 positively affected xylose utilization by downregulating the cell cycle-related gene CIP1 and the stress response-related gene IXR1; by upregulating the stress response-related genes MID2, STT4, CDC42, ECM22, BDH2, and CSC1; and by upregulating the cell viability-related genes GPN3 and TFC3. These results reconfirm the findings of several previous studies that have shown xylose utilization is related to stress responses, and expression of stress-resistant genes affects the xylose metabolism in engineered S. cerevisiae (Cheng et al., 2018; Cunha et al., 2018).

Furthermore, we found that deletion of THI2 increased xylose metabolism in the GX stage by regulating genes involved in the cell cycle, stress tolerance, and cell viability. Notably, these regulatory targets of Thi2p in the GX stage were apparently very different from its targets when cells are cultured in glucose (e.g., eponymous thiamine biosynthetic genes and some ribosomal protein genes) (Hu et al., 2007). A comparison of these results suggests that the function of Thi2p depends on the carbon source available, a phenomenon that has been observed for some other yeast TFs (Bergenholm et al., 2018). Further modeling investigations that are directed towards optimization and engineering through disruption of TFs, such as transcriptome engineering (Michael et al., 2016), should not ignore this important difference.

In addition, our work demonstrated that overexpression of STT4, RGI2, and TFC3 increased xylose consumption rates in both aerobic and anaerobic fermentation. Specifically, these genetically modified strains increased the r_xylose and shorted the overall fermentation time, a finding which has significance for production practices focused on improving economic efficiency. Additionally, we found that although overexpression of MID2 or deletion of IXR1 increased xylose consumption in aerobic fermentation, this effect was not seen in anaerobic fermentation. These results are not simply due to respiration, since the respiratory chain of our strains was blocked via deletion of COX4. The work of Myers et al. (2019) clearly showed large transcriptional changes to S. cerevisiae as it enters anaerobiosis in glucose or xylose. Future work resolving the apparent discrepancies between their transcriptome work and our findings for specific genetically manipulated strains will almost certainly reveal clues to deepen our understanding of how oxygen availability, beyond its role in respiration, impacts xylose metabolism in yeast and other organisms. Moreover, the stricter controlled pH condition in bioreactors than in shake flasks could also be a reason.

In summary, xylose has long been considered to be only a semi-fermentable carbon source for S. cerevisiae (Salusjarvi et al., 2008; Souto-Maior et al., 2009), and the post-glucose effect is obviously involved in shifting between carbon sources. However, this phenomenon has not been fully appreciated or understood to date. In this context, our molecular investigation of the global impacts of altering the regulatory networks controlling xylose utilization during the GX stage significantly advance our basic understanding of the mechanisms underlying such carbon source shift. Our results provide an initial proof-of-concept demonstration for new strategies to control and overcome inefficiencies for the exploitation of xylose as a carbon source in industrial biotechnology.

Data Availability

The raw data from transcriptional analysis and processed data of genes with significant differences between the thi2Δ strain and the parent strain in the GX stage are presented in the NCBI Gene Expression Omnibus database (GEO accession number: GSE119333).

Author Contributions

YS and XB conceived the original research plan. SW, PB, YL, MY, and JM designed and performed the experiments. SW, YS, XB, and JH analyzed the data. SW, YS, WL, and XB wrote and revised the manuscript. All authors read and approved the final manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Glossary

Abbreviations

CWI

cell wall integrity

DCW

dry cell weight

GEO

Gene Expression Omnibus database

GO

Gene Ontology

LB

Luria-Bertani

PPP

pentose phosphate pathway

qPCR

Quantitative Polymerase Chain Reaction

SC

synthetic complete drop-out

SC-Ura

synthetic complete drop-out uracil

TFs

transcription factors

XI

xylose isomerase.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2019.01649/full#supplementary-material