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Transcriptome and metabolome analyses reveal global behaviour of a genetically engineered methanol-independent Pichia pastoris strain
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Process Biochemistry journal homepage: www.elsevier.com/locate/procbio
Transcriptome and metabolome analyses reveal global behaviour of a genetically engineered methanol-independent Pichia pastoris strain Lei Shia, Jinjia Wanga, Xiaolong Wanga, Yuanxing Zhanga,b, Zhiwei Songc,d, Menghao Caia, ⁎ Xiangshan Zhoua, a
State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China Shanghai Collaborative Innovation Center for Biomanufacturing, Shanghai 200237, China c Bioprocessing Technology Institute, Singapore d Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore b
A R T I C LE I N FO
A B S T R A C T
Keywords: Pichia pastoris Δmig1Δmig2Δnrg1-Mit1 AOX1 promoter Glycerol Methanol Glutamic acid decarboxylase
Pichia pastoris is greatly used as a protein expression system based on tightly regulated aldehyde oxidase 1 (AOX1) promoter (PAOX1) that is induced by methanol; however, the use of methanol of highly expensive and dangerous. By deleting transcription repressors and overexpressing transcription activator of the PAOX1, a methanol-independent strain MF1 (Δmig1Δmig2Δnrg1-Mit1) has been previously constructed. This study investigated the transcriptomic and metabolomic profiles of the MF1 strain in response to different carbon sources, including methanol, glucose, and glycerol, in comparison with the wild-type strain. AOX expression was observed in all groups except the wild-type strains cultured in glucose and glycerol. Genes involved in methanol utilization and peroxisome biosynthesis were mainly upregulated in the mutant strain grown on glucose or glycerol. Metabolomics data showed significant increase of the products related to metabolic pathways in amino acid and energy metabolism in the mutant strain grown in glycerol. Genes such as glutamic acid decarboxylase (GAD) and some metabolites were both involved in the pathways such as GABAergic synapse. GAD1, GAD2, and succinate may be associated with the recombinant protein expression system of the mutant MF1 strain of P. pastoris grown on glucose or glycerol.
1. Introduction The methylotrophic yeast Pichia pastoris is most extensively used beyond Escherichia coli in the production of a wide variety of recombinant proteins [1]. The most important factor contributing to the popularity of this expression system is the ability to produce heterologous proteins at high levels using alcohol oxidase 1 (AOX1) promoter PAOX1 [2]. The PAOX1 promoter is strongly induced only in response to methanol but repressed in the presence of glucose, glycerol or ethanol and other alternative carbon sources [3]. However, the use of methanol in large-scale industrial fermentation processes can be highly expensive and dangerous for its toxicity and flammability [4]. Additionally, its special and high requirements on transportation, storage, and operator's occupational skills, the need for high oxygen supply and heat removal during fermentation, and the byproduct of methanol metabolism hydrogen peroxide (H2O2) leading to the degradation of specific recombinant proteins [5] also pose challenges to the industrial
application of the P. pastoris methanol-induced expression system. Several alternatives to methanol for the aforementioned processing have been investigated. Different promoters such as PGAP [6], PICL1 [7], PTEF1 [8], PPGK1 [9], PYPT1 [10], PPHO89 [11] and PGCW14 [12] have been investigated; however, none of these have been widely adopted because of their weak or non-controllable features. Although the mixed carbon substrate feeding strategy by combining methanol with other carbon source such as glucose [13], glycerol [14], or sorbitol [15] reduces oxygen consumption and H2O2 production without compromising protein yield, it does not completely eliminate the need of methanol. Furthermore, cis-acting regulatory element rewiring as a frequently used genetic engineering approach for potentially controlling PAOX1 has previously been investigated. By deletion or duplication of the putative transcription factor-binding sites within the promoter, some of the variants of PAOX1 were able to produce moderate levels of recombinant proteins without methanol [16,17]. Recently, trans-acting regulatory elements of PAOX1 have also been identified and comprehensively
⁎ Corresponding author at: State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China. E-mail address: [email protected] (X. Zhou).
https://doi.org/10.1016/j.procbio.2018.10.014 Received 9 August 2018; Received in revised form 23 October 2018; Accepted 25 October 2018 1359-5113/ © 2018 Elsevier Ltd. All rights reserved.
Please cite this article as: Shi, L., Process Biochemistry, https://doi.org/10.1016/j.procbio.2018.10.014
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Life Technologies) according to the manufacturer’s protocol, RNA concentration was measured using Qubit® RNA Assay Kit with Qubit® 2.0 Flurometer (Life Technologies, CA, USA), and RNA integrity was assessed with the RNA Nano 6000 Assay Kit by the Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Sequencing libraries were generated using NEBNext® Ultra™ Directional RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s instructions, and library quality was assessed with the Agilent Bioanalyzer 2100 system and sequenced on an Illumina Hiseq 2000 platform. Quality control of raw data (raw reads) in fastq format was processed by removing reads containing adapter, ploy-N, and low-quality data. All the downstream analyses were based on clean, high-quality data. Paired-end clean reads were aligned to the reference genome using TopHat v2.0.9 [26]. HTSeq v0.5.4p3 software [27] was used to count the number of reads mapped to each gene. The number of reads per kilobase of exon region per million mapped reads (RPKM) was calculated to quantify gene expression levels. Differential expression analysis of two conditions/ groups was performed using the R package DESeq (1.10.1) [28]. The resulting P-values were adjusted for multiple testing using Benjamini and Hochberg’s method [29]. Genes with an adjusted P-value < 0.05 and |log2(fold change, FC)| ≥ 1 were considered significantly differentially expressed genes (DEGs). The clustering algorithms of hierarchical clustering and k-means clustering of genes were used. KOBAS software was used to test the statistical enrichment of DEGs in KEGG pathways [30], with the threshold of corrected P-value < 0.05. The transcriptome sequence of methanol-independent Pichia pastoris strain has been deposited at National Center of Biotechnology Information (NCBI) Sequence Read Archive (SRA) [31] under the accession numbers SRP132617.
investigated. Particularly, Prm1 and Mit1, belonging to Zn(II)2Cys6 family transcription factors, which positively activate PAOX1, in response to methanol were shown to bind to PAOX1 at a different site [18,19], while Nrg1 was observed to act as a transcriptional repressor of PAOX1 [19]. Besides, many genes, including the hexose transporter Hxt1 [20] or the glucose sensor Gss1 [21], are known to regulate derepression of glucose-regulated genes. Thus, genetic engineering of the trans-acting elements of PAOX1 for methanol-free protein expression is possible. Mig1 and Mig2 are zinc finger proteins and both are involved in yeast glucose repression [22]. By overexpression of Mit1 under a constitutive GAP promoter in a strain with triple-deletion of Nrg1, Mig1, and Mig2, we successfully constructed a robust methanol-free PAOX1 strain MF1 (Δmig1Δmig2Δnrg1-Mit1), which expresses 77% GFP in glycerol compared to the wild-type in methanol [23]. However, how these genetic variations affect global cell metabolism and protein synthesis remain unexplored. Moreover, gene-to-metabolite correlation and distribution in different pathways can be well interpreted through the comprehensive analysis of gene expression (transcriptome) and metabolite accumulation (metabolome) [24,25]. Thus, transcriptome and metabolome analyses were performed in this study to plot variations in the metabolic and gene expression profile of the MF1 strain using different carbon sources in comparison with the wild-type strain; this will facilitate further assessment of the novel genetically engineered strain for extensive pharmaceutical application. 2. Materials and methods 2.1. Strain cultivation The strain MF1 (Δmig1Δmig2Δnrg1-Mit1) stored in −80 °C was inoculated directly into 250 mL flasks containing 50 mL yeast nitrogenbase/dextrose (YND) medium (0.67% yeast nitrogen base medium (YNB) and 1% dextrose) as the first seed culture. After cultivation at 30 °C for 24 h with agitation (200 rpm), the seed cultures (12.5 mL) were used to inoculate into 500 mL flasks containing 100 mL YND medium as the second seed culture. The wild-type strain GS115 (WT) was cultured in MGY medium (0.67% YNB and 1% glycerol) under the same conditions. After 16 h of cultivation at 30 °C, batch cultivation was started according to the inoculum concentration of 1:10. Continuous cultivations were performed at a working volume of 3.5 L in a 5 L stirred tank bioreactor at 30 °C, pH 5.0 (controlled by 25% ammonium hydroxide), and a minimum of 30% dissolved oxygen tension. When carbon source was depleted, and the concentration of dissolved oxygen increased sharply, the chemostat medium using glucose (20.0 g/ L), glycerol (20.0 g/L), or methanol (20.0 g/L) as the sole carbon source was supplemented. While feeding in the chemostatic medium, a peristaltic pump was used to drain the fermentation broth to maintain a constant volume of the fermentation broth. Samples were taken when the specific growth rate reached μ = 0.03 h−1. AOX enzyme activity was measured by a colorimetric assay, as described previously [20]. Six groups, including wild-type strain in dextrose/glucose (W_D), wild-type strain in glycerol (W_G), wild-type strain in methanol (W_M), mutant strain in dextrose/ glucose (M_D), mutant strain in glycerol (M_G), and mutant strain in methanol (M_M) were analyzed in the present study. The detailed information of medium was described elsewhere [23]. For RNA-seq analysis, two biological replicates were available for each strain. For LC–MS\GC–MS analysis (metabolome), the chemostat sample was divided into five 2 mL aliquots (five identical samples) for each strain. After centrifugation at 4 °C and 5000 r/min, the supernatant was removed and the cells were rapidly cooled with liquid nitrogen and placed at −80 °C.
2.3. Metabolome analysis Metabolomic profile analysis was performed by Metabolon Inc. (Durham, NC), as previously described [32]. In brief, each sample was accessioned into the Metabolon laboratory information management system (LIMS) system and was assigned by LIMS as a unique identifier. Samples were prepared using the automated MicroLab STAR® system from Hamilton Company. The resulting extract was divided into two fractions: one for analysis by LC and another for analysis by GC. The representative (QA/QC) samples were used to evaluate the process control for the study. The liquid chromatography/mass spectrometry (LC/MS, LC/MS2) portion of the platform was based on a Waters ACQUITY UPLC and a Thermo-Finnigan LTQ mass spectrometer. The MS analysis alternated between MS and data-dependent MS2 scans using dynamic exclusion. For gas chromatography/mass spectrometry (GC/MS), samples were analyzed using a Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole mass spectrometer. The samples for GC/MS analysis were vacuum dried for 24 h prior to being derivatized under dried nitrogen using bistrimethyl-silyl-triflouroacetamide (BSTFA). The GC column comprised 5% benzene and the temperature of the ramp was set from 40 °C to 300 °C for a 16-min period. Also, accurate LC/MS determination and MS/MS fragmentation (LC/MS/MS) was conducted for ions with counts higher than 2 million and the parent ion as well as fragments. The bioinformatics system comprised four components, the LIMS, the data extraction and peak-identification software, data processing tools for QC and compound identification, and a collection of information interpretation and visualization tools for data analysis. It was based on a database server running Oracle 10.2.0.1 Enterprise Edition (Redwood Stores, CA). Metabolites were identified by comparison to library entries of purified standards or recurrent unknown entities using the Metabolon LIMS system. For pairwise comparisons, Welch’s t-tests and/or Wilcoxon’s rank-sum tests were used to identify differential metabolites. For other statistical analyses, ANOVA procedures (e.g., repeated measures ANOVA) were used. A random forest approach was applied for clustering [33]. Statistical analyses were performed using
2.2. Transcriptome analysis Total RNA was extracted using the RiboPure™-Yeast Kit (Ambion, 2
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the program “R” [34].
involved in Subcluster_1; 90 genes, including MIG1 and MIG2, in Subcluster_3; and 168 genes, including MIT1, in Subcluster_7 (Fig. 2). Moreover, a strong negative correlation of gene expression trend between those in Subcluster_1 and Subcluster_3 was found. AOX gene expression was detected in four groups of samples except in W_D and W_G. There were 73 up-regulated DEGs overlapped in comparisons of W_M vs. W_G, W_M vs. W_D, M_G vs. W_G, and M_D vs. W_D. Pathway enrichment analysis of those 73 up-regulated genes showed that these genes were mainly associated with methanol utilization and peroxisome biosynthesis (Supplementary Table 1). To investigate the difference in the transcriptome of the two expression systems (methanol induced and glycerol induced) in primary metabolism pathways (Supplementary Fig. 2), comparative analysis between M_G and W_G was performed. Fig. 3 depicted DEGs in the metabolic pathway of methanol utilization and the peroxisome biosynthetic pathway. The genes involved in two pathways were significantly upregulated for M_G compared with W_G, among which, AOX1 and AOX2 were significantly upregulated by 15.8-fold and 8.3fold, respectively. Some other genes, such as S-formylglutathione hydrolase (FGH), formate dehydrogenase (FDH), and dihydroxyacetone synthase (DAS1 and DAS2), were also highly expressed (Fig. 3A) in M_G compared with W_G. Fig. 3C showed that the expression levels of Peroxisomal Biogenesis Factor 1 (PEX1), PEX5, PEX6, PEX13 and PEX14 in M_G were much higher than those in W_G. For glycerol metabolism (Fig. 3E), the mitochondrial glycerol-3-phosphate dehydrogenase gene (GUT2), the active glycerol importer gene (STL1), and the homolog of S. cerevisiae putative passive glycerol channel gene (YFL054C) exhibited no noticeable changes at the transcriptional levels in M_G and W_G, while were higher than those in W_M. However, the cytosolic glycerol kinase gene (GUT1) was downregulated in M_G compared with the expression level of that in W_G, and close to the level of W_M. For primary metabolism, no significant changes were observed in the glycolytic pathway (Fig. 3D), TCA cycle (Fig. 3F), and pentose phosphate pathway (PPP, Fig. 3B) between mutant and wild type grown on glycerol at the transcriptional level, except that ribose-5-phosphate ketolisomerase (RPI) involved in PPP was upregulated in M_G as compared with W_G. The newly developed P. pastoris expression system produces exogenous proteins in glycerol. Expression of the genes related to protein secretion pathway was investigated through the comparative analysis of M_G and W_M. Based on the threshold of adjusted P-value < 0.05 and |log2(fold change, FC)| ≥ 1), we found upregulation of UGP1 and GFA1 involved in the process of nucleotide sugar synthesis, UGGT involved in the process of quality, and YPS2 and PAS_chr3_1157 involved in the process of proteolysis, along with downregulation of PAS_chr2-1_0706 and PNO1 involved in Golgi N-glycan processing (Supplementary Table 2).
2.4. Integrated analysis of transcriptome and metabolome To integrate transcriptome and metabolomics data, the IMPaLA tool [35] was used for over-representation analysis (ORA) and joint analysis of both sets of data based on the KEGG database [36]. The pathways significantly enriched by differential metabolites and DEGs were identified. When the number of significant genes or metabolites is relatively small, the joint analysis is preferable. The thresholds were num_overlapping_genes ≥ 1, num_overlapping_metabolites ≥ 1, and P_joint < 0.05. 3. Results 3.1. Alternation of gene expression levels of P. pastoris under different carbon sources The concentration (> 50 ng/μl), purity (OD260/280 > 1.8) and integrity [value of RNA Integrity Number (RIN) > 7.0] of extracted total RNA were sufficient for sequencing. In total, approximately 402,803,524 reads were obtained from the samples of six groups grown under six different conditions. As a result, 2,263, 2,349, and 2,403 DEGs were obtained from M_D, M_G, and M_M, respectively, in comparison with the respective wild-type genes. In total, 975 overlapping genes were identified from three comparisons. The hierarchical clustering, as illustrated in Fig. 1, demonstrated that the wild-type and mutant strains grown on methanol clustered together, except that the wild-type strain was devoid of AOX expression in glucose and glycerol conditions (supplementary Fig. 1). The k-means clustering of all DEGs in four comparisons indicated that 52 genes, including AOX1, were
3.2. Alteration of metabolites revealed by metabolomic profile A total of 233 known compounds were identified from the metabolic profiles by LC–MS/MS and GC–MS analyses. The total metabolomics data were summarized in supplementary table 3. The principal component analysis (PCA) revealed that the wild-type strain was significantly separated from the mutant strains in glucose and glycerol unlike the partial overlap in methanol (Fig. 4). Similarly, hierarchical clustering demonstrated that the wild-type and mutant strains grown on methanol clustered together; however, they were separated based on the genotype when grown on glucose or glycerol (Fig. 5). The LC/MS-GC/MS platform used methanol as an intermediate reagent so that the methanol content was not detected during the methanol metabolic pathway analysis. Only the levels of fructose-6-phosphate (F6P) and fructose-1, 6-bisphosphate (FBP) were determined and they were found to be elevated in M_G and M_D compared with W_D and W_G (Supplementary Fig. 3). Enhanced levels of glycolysis-, PPP-, and TCA cycle-related metabolites and amino acids were also observed
Fig. 1. Hierarchical clustering analysis of differentially expressed genes. D, G, M represent glucose, glycerol, and methanol chemostat medium, respectively; M represents mutant strain, and W represents wild-type strain. 3
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Fig. 2. k-means clustering of differentially expressed genes. The gray lines in each subgraph represent the relative expression of a gene in a cluster under different experimental conditions, and the blue line represents the average of the relative expression of all genes in the cluster under different experimental conditions. The x-axis represents experimental conditions, the y-axis represents relative expression, and the number at the top of the subgraph represents the number of genes contained in each cluster. Each of these groups was compared with the expression of the gene in M_D (mutant strain cultured in glucose medium) as a reference value (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
in strains grown on glycerol compared with those in the strains grown on methanol and glucose (Figs. 6A and B). Moreover, the levels of branched-chain amino acids, including valine and isoleucine, were
increased in the mutant strain grown in glucose and particularly in glycerol (Supplementary Fig. 4). However, the NAD level was enhanced in three mutant strains regardless of the nutrient source 4
Process Biochemistry xxx (xxxx) xxx–xxx 4.14E-05 6.43E-06 0.00276 0.000281 0.0227 0.0421 0.000514 0.00329 0.0396 0.0887 0.0573 0.251 0.346 citrate; succinate; cis-aconitate; thiamin diphosphate methionine; arginine; aspartate; tyrosine; lysine; putrescine citrate; succinate; aspartate pantothenate; aspartate; 3-hydroxypropanoate; spermidine aspartate; arginine putrescine; arginine; spermidine succinate; flavin mononucleotide (FMN); phosphate citrate; succinate; cis-aconitate; glycerate succinate; thiamin diphosphate succinate putrescine; spermidine lysine succinate C00158;C00042;C00417;C00068 C00073;C00062;C00049;C00082;C00047;C00134 C00158;C00042;C00049 C00864;C00049;C01013;C00315 C00049;C00062 C00134;C00062;C00315 C00042;C00061;C00009 C00158;C00042;C00417;C00258 C00042;C00068 C00042 C00134;C00315 C00047 C00042
(Supplementary Fig. 5). A significant accumulation of the phospholipid catabolites ethanolamine, glycerophosphorylcholine (GPC), glycerophosphoethanolamine, and glycerol-3-phosphate (G3P) was also observed in the mutant strain (Supplementary Fig. 6). The metabolomic alteration of the two expression systems (methanol induced and glycerol induced) was analyzed by comparing M_G and W_M with W_G as a reference. As shown in Fig. 7, most of the metabolites levels in PPP, glycolytic pathway, and TCA of M_G were higher than those of W_M and W_G, except that the 2-oxoglutarate (AKG) and isocitrate (ICIT) of W_G were higher than M_G, and were lowest in W_M. In addition, the metabolite accumulation levels of GN6P and F6P were comparable between W_M and W_G. 3.3. Pathways enriched by differential metabolites and DEGs There were 2263 DEGs and 199 differential metabolites, 2349 DEGs and 160 differential metabolites, and 2403 DEGs and 75 differential metabolites from M_D vs. W_D, M_G vs. W_G, and M_M vs. W_M, respectively. There were 975 genes and 60 metabolites were overlapped among these 3 comparison groups. Pathway enrichment analysis on the overlapped genes and metabolites revealed that thirteen pathways, such as TCA cycle, were significantly enriched by overlapping DEGs and differential metabolites (Table 1). Especially, glutamic acid decarboxylase/glutamate decarboxylase (GAD1) and GAD2, as well as metabolites of succinate (C00042) and aspartate (C00049) were enriched in four pathways, including alanine, aspartate, and glutamate metabolism, beta-alanine metabolism, GABAergic synapse and butanoate metabolism. Moreover, succinate (C00042) also related to some other pathways, including oxidative phosphorylation and TCA cycle, which indicated the crucial role of succinate. 4. Discussion Changes in the transcriptional and metabolite profiles of P. pastoris grown in different carbon sources were compared using the mass profile data of transcriptome and metabolome. Specific genes and processes which were potentially associated with gene expression pattern of AOX1 as well as metabolic signature alterations demonstrated the potential of Δmig1Δmig2Δnrg1-Mit1 to serve as a host line for recombinant protein production. In addition, the integrated analysis potentially revealed the underlying mechanism of the application potential of the mutant strain. AOX was expressed in four groups of samples except in W_D and W_G, which demonstrated the significance of Δmig1Δmig2Δnrg1-Mit1 as an effective host line for recombinant protein production. Similarly, clustering patterns between transcriptome and metabolome indicated that the global transcript and metabolite profiles were strongly complementing each other. PCA of metabolome revealed a clear separation between wild-type and mutant strains in the presence of glucose and glycerol. This indicated that both genotype and nutrient-dependent alterations influenced metabolic signature. In contrast, only a partial overlap was observed between wild-type and mutant strains grown on methanol. Similarly, hierarchical clustering demonstrated that wildtype and mutant strains grown on methanol clustered together; however, except that wild-type strain has no AOX1 gene expression in glucose and glycerol conditions, other samples had AOX1 gene expression. These findings confirm methanol-induced AOX1 expression in the wild-type strain, as previously reported [37]. Moreover, the study also demonstrated that up-regulated genes in strain with a positive AOX expression were mainly enriched in methanol metabolic pathway and peroxisome biosynthesis. Genes, including FGH, FDH, DAS1, and DAS2, involved in the methanol metabolism pathway were significantly up-regulated in M_G compared with W_G; however, their expression was lower than that observed in W_M. A similar trend in the expression of genes involved in peroxisome biosynthesis was observed. This suggested that genes involved in the
ACO1;PCK1;MDH1 CPA1;CPA2 NIT2;GAD1;GAD2 GAD1;GAD2 ARG2;OTC;ARG1 ARG2;ODC1;ARG1;LAP3 CYC1;COX15;COX17 ACO1;MDH1 PCK1;GLO1;MDH1 HAP1;GAD1;GAD2 LAP3;ODC1 OXSM GAD1;GAD2 Citrate cycle (TCA cycle) Protein digestion and absorption Alanine_ aspartate and glutamate metabolism beta-Alanine metabolism Arginine biosynthesis Arginine and proline metabolism Oxidative phosphorylation Glyoxylate and dicarboxylate metabolism Pyruvate metabolism GABAergic synapse Glutathione metabolism Biotin metabolism Butanoate metabolism
0.00119 0.132 0.00187 0.0199 0.000408 0.00038 0.0672 0.0164 0.00256 0.024 0.0553 0.021 0.0164
P values Description of metabolites overlapping_genes Pathways
Table 1 Pathways enriched by the overlapping DEGs and differential metabolites.
P values
overlapping_metabolites
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Fig. 3. Expression levels of the genes involved in the main metabolism pathway. Asterisk indicates the significant difference between mutant/glycerol condition and wild-type/glycerol condition.
Fig. 4. Principal component analysis (PCA) of the metabolome data of different samples. PCA revealed a distinct separation between WT and mutants in the presence of glucose and glycerol that may reflect both genotype and nutrient dependent alterations in the metabolic signature. In contrast, partial overlap was observed between WT and mutant samples grown on methanol.WT, wildtype. Mut, mutant.
pathways of methanol metabolism and peroxisome biosynthesis in M_G exhibited an expression level similar to those in W_M. Importantly, in P. pastoris, number of peroxisomes was strongly reduced in cells in which methanol utilization is repressed while are abundantly present in methanol-grown cells [38]. For primary metabolism, no significant changes in gene expression profile of the glycolytic pathway, TCA cycle, and PPP were observed between mutant and wild-type strains grown on glycerol at the transcriptional level; however, RPI involved in PPP was upregulated in M_G compared with W_G. From the k-means clustering results, when the relative expression level of genes similar to the AOX1 gene expression pattern increased, the relative expression levels of genes similar to the MIG1 and MIG2 gene expression patterns decreased, suggesting that there are potential repressors in genes similar to the MIG1 and MIG2 gene expression patterns.
Fig. 5. Hierarchical clustering of metabolites of WT and mutant strains in different medium.Wild-type and mutant strains grown on methanol clustered together; however, they were separated by the genotype when grown on glucose or glycerol.
Evidence demonstrates that the methanol-free PAOX1 start-up mutant strain induced by glycerol could produce recombinant proteins nearly as efficiently as the wide-type in methanol [23]. In addition, metabolomics revealed enhanced levels of amino acids and glycolysis-, PPP-, and TCA cycle-related metabolites in wild-type strains grown on glycerol compared with those in the strains grown on methanol, and indicated that glycerol could serve as a favorable carbon source in this study [39]. Notably, mutant strains grown on glycerol exhibited the 6
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Fig. 6. Enhanced levels of amino acids (A) and glycolysis-, pentose phosphate pathway (PPP)-, and TCA cycle-related metabolites (B) were observed in wild-type yeast grown on glycerol as compared with those in the strains grown on glucose or methanol, this suggests that the former is the best carbon source observed in this study.
dual role as a signaling molecule. This dual role of it could be similar to some other metabolites, such as glutamate [45]. GABA is degraded to succinate (C00042) via the GABA shunt. The concentration of GABA in Fusarium oxysporum was increased in anaerobiosis [46]. Kaliňák et al. speculated that the GABA shunt is active during glucose catabolism compared to that of gluconeogenesis in Saccharomyces cerevisiae, and could be closely associated with oxidative metabolism [47]. GABA shunt mediates thermotolerance by restricting reactive oxygen intermediates to protect Saccharomyces cerevisiae from heat damage [48]. In addition, GAD is important for oxidative stress tolerance in Saccharomyces cerevisiae [49]. Moreover, succinate was found to stimulate glucose utilization in some thermophilic fungi [50] and excretion of succinate is related to anaerobic conditions [51]. Therefore, the Δmig1Δmig2Δnrg1-Mit1 strain could reduce heat production and oxygen demand compared to wild stains.
highest levels of energy metabolites and amino acids, suggesting that these conditions are optimal for facilitating growth and protein synthesis. In contrast, F6P and FBP were elevated in mutant strains and may contribute to enhanced glycolysis in the presence of glycerol. Indeed, higher levels of sorbitol and fructose in mutants suggests enhanced glucose uptake [40], although an accumulation of G6P, GC3P, and PEP and the end PYR in mutants grown on glycerol may fuel oxidative metabolism, as evidenced by the accumulation of the TCA intermediates CIT, SUCC, and MAL. The metabolite accumulation levels of GN6P, ICIT, and AKG were observed in both W_M and W_G. The high levels of GN6P and S7P in mutants cultured in glycerol may reflect glucose shuttling to PPP to support and maintain NADPH regeneration and anabolic growth capacity, which indicated that GN6P might be associated with induced PAOX1. The result also suggested that AKG and ICIT may be associated with the regression of the AOXI promoter. Moreover, metabolic alterations in methanol metabolism pathway, which were not captured in the present study, may also contribute to the discrepancy. Taken together, the mutant exhibited an indication of a stronger metabolic flux in primary metabolism simultaneously owning the specific methanol-induced metabolic characteristics when grown on glycerol. Although methanol is the inducer of PAOX1 and is frequently used for heterologous protein production in P. pastoris, the yeast typically grows very slowly in this carbon source [41]. According to the first criterion of high yield, glycerol may serve as the most promising candidate for the efficient production of recombinant protein [39]. However, PAOX1 is naturally repressed by glycerol in P. pastoris [42]. The productive ability to grow and utilize PAOX1 for the recombinant protein expression with glycerol indicates that a novel P. pastoris expression system, developed by our laboratory, will be a potential expression system, particularly for pharmaceutical application. To investigate the mechanism underlying the methanol-free strain for production of recombinant protein, the integrated analysis was performed. The two isoforms of glutamate decarboxylase, GAD1 and GAD2, are encoded by two different genes located on different chromosomes [43]. GAD67 (GAD1) is a soluble cytosolic protein, whereas GAD65 (GAD2) is membrane-associated. In our results, GAD1 was down-regulated and GAD2 is upregulated. Both are involved in the rate-limiting step in GABA synthesis and are essential for sustaining GABA levels [44,45]. Previous study indicated that GABA not only a metabolite, but also probably plays a
5. Conclusion As a result, the Δmig1Δmig2Δnrg1-Mit1 strain is a potential recombinant protein expression system. The genes involved in methanol metabolism pathway and peroxisome biosynthesis could be closely related to AOX1 activation. Some metabolites, including GN6P, may serve as the inducer of PAOX1, and ICIT and AKG may be associated with the suppression of the promoter. GAD1 and GAD2 and succinate may play an important role in reducing heat production and oxygen demand in P. pastoris. Furthermore, glycosylation could be optimized to be a promising candidate for methanol-independent protein expression in P. pastoris. Conflict of interest statement The authors have declared that no conflict of interests exists. Author contributions LS, ZS and XZ contributed conception and design of the research; LS, JW and XW acquired data; LS draft the manuscript; MC and YZ revise the manuscript for important intellectual content. All authors contributed to manuscript revision, read and approved the submitted version. 7
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Fig. 7. Metabolomic alteration of the two expression systems (methanol induced and glycerol induced). (A) Intracellular levels of central metabolites in wild-type/glycerol, mutant/glycerol, and wild-type/methanol conditions. The bubble areas are proportional to the pool sizes of each intracellular metabolite. The numbers above each the bubbles represent the normalized mean value. The metabolites without value could not be detected. (B) Primary metabolism pathways in P. pastoris. Blue words indicated the genes involved in these pathways, and black words indicated the metabolites during these pathways (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
Acknowledgments
Biotechnol. 72 (2006) 837–844. [6] H.R. Waterham, M.E. Digan, P.J. Koutz, S.V. Lair, J.M. Cregg, Isolation of the Pichia pastoris glyceraldehyde-3-phosphate dehydrogenase gene and regulation and use of its promoter, Gene 186 (1997) 37–44. [7] J. Menendez, I. Valdes, N. Cabrera, The ICL1 gene of Pichia pastoris, transcriptional regulation and use of its promoter, Yeast 20 (2003) 1097–1108. [8] J. Ahn, J. Hong, H. Lee, M. Park, E. Lee, C. Kim, E. Choi, J. Jung, H. Lee, Translation elongation factor 1-α gene from Pichia pastoris: molecular cloning, sequence, and use of its promoter, Appl. Microbiol. Biotechnol. 74 (2007) 601–608. [9] J.R.M. De Almeida, L.M.P. de Moraes, F.A.G. Torres, Molecular characterization of the 3‐phosphoglycerate kinase gene (PGK1) from the methylotrophic yeast Pichia pastoris, Yeast 22 (2005) 725–737. [10] N. Segev, J. Mulholland, D. Botstein, The yeast GTP-binding YPT1 protein and a mammalian counterpart are associated with the secretion machinery, Cell 52 (1988) 915–924. [11] J. Ahn, J. Hong, M. Park, H. Lee, E. Lee, C. Kim, J. Lee, E. Choi, J. Jung, H. Lee, Phosphate-responsive promoter of a Pichia pastoris sodium phosphate symporter, Appl. Environ. Microbiol. 75 (2009) 3528–3534. [12] S. Liang, C. Zou, Y. Lin, X. Zhang, Y. Ye, Identification and characterization of P GCW14 : a novel, strong constitutive promoter of Pichia pastoris, Biotechnol. Lett. 35 (2013) 1865–1871. [13] J. Jordà, C. Suarez, M. Carnicer, A.T. Pierick, J.J. Heijnen, W.V. Gulik, P. Ferrer, J. Albiol, A. Wahl, Glucose-methanol co-utilization in Pichia pastoris studied by metabolomics and instationary 13 C flux analysis, BMC Syst. Biol. 7 (2013) 1–16. [14] D. Zalai, C. Dietzsch, C. Herwig, O. Spadiut, A dynamic fed batch strategy for a Pichia pastoris mixed feed system to increase process understanding, Biotechnol. Prog. 28 (2012) 878–886.
None. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.procbio.2018.10.014. References [1] B. Gasser, R. Prielhofer, H. Marx, M. Maurer, J. Nocon, M. Steiger, V. Puxbaum, M. Sauer, D. Mattanovich, Pichia pastoris: protein production host and model organism for biomedical research, Future Microbiol. 8 (2013) 191–208. [2] T. Vogl, A. Glieder, Regulation of Pichia pastoris promoters and its consequences for protein production, N. Biotechnol. 30 (2013) 385–404. [3] W. Shen, Y. Xue, Y. Liu, C. Kong, X. Wang, M. Huang, M. Cai, X. Zhou, Y. Zhang, M. Zhou, A novel methanol-free Pichia pastoris system for recombinant protein expression, Microb. Cell Fact. 15 (2016) 178–188. [4] J. Villadsen, J. Nielsen, G. Lidén, Design of fermentation processes, Bioreaction Engineering Principles, Springer, Boston, US, 2011, pp. 383–458. [5] A. Xiao, X. Zhou, L. Zhou, Y. Zhang, Improvement of cell viability and hirudin production by ascorbic acid in Pichia pastoris fermentation, Appl. Microbiol.
8
Process Biochemistry xxx (xxxx) xxx–xxx
L. Shi et al.
sarcosine in prostate cancer progression, Nature 457 (2009) 910–914. [33] Y. Qi, Random Forest for Bioinformatics, Ensemble Machine Learning, Springer, Boston, MA, 2012, pp. 307–323. [34] R.C. Gentleman, V.J. Carey, D.M. Bates, B. Bolstad, M. Dettling, S. Dudoit, B. Ellis, L. Gautier, Y. Ge, J. Gentry, K. Hornik, T. Hothorn, W. Huber, S. Iacus, R. Irizarry, F. Leisch, C. Li, M. Maechler, A.J. Rossini, G. Sawitzki, C. Smith, G. Smyth, L. Tierney, J.Y. Yang, J. Zhang, Bioconductor: open software development for computational biology and bioinformatics, Genome Biol. 5 (2004) 2004–2005. [35] A. Kamburov, R. Cavill, T.M.D. Ebbels, R. Herwig, H.C. Keun, Integrated pathwaylevel analysis of transcriptomics and metabolomics data with IMPaLA, Bioinformatics 27 (2011) 2917–2918. [36] M. Kanehisa, S. Goto, M. Furumichi, M. Tanabe, M. Hirakawa, KEGG for representation and analysis of molecular networks involving diseases and drugs, Nucleic Acids Res. 38 (2010) 30. [37] J.M. Cregg, T.S. Vedvick, W.C. Raschke, Recent advances in the expression of foreign genes in Pichia pastoris, Biotechnology 11 (1993) 905–910. [38] V.D.K. Ij, H. Yurimoto, Y. Sakai, M. Veenhuis, The significance of peroxisomes in methanol metabolism in methylotrophic yeast, Biochim. Biophys. Acta Mol. Cell Res. 1763 (2006) 1453–1462. [39] B.K. Chung, S. Selvarasu, C. Andrea, J. Ryu, H. Lee, J. Ahn, D.Y. Lee, Genome-scale metabolic reconstruction and in silico analysis of methylotrophic yeast Pichia pastoris for strain improvement, Microb. Cell Fact. 9 (2010) 50–64. [40] R. Yair, M. Allen, The effects of fructose and phosphate infusions on dry matter intake of lactating cows, J. Dairy Sci. 100 (2017) 2651–2659. [41] J.M. Cregg, Introduction: distinctions between Pichia pastoris and other expression systems, Methods Mol. Biol. 389 (2007) 1–10. [42] M. Inan, M.M. Meagher, Non-repressing carbon sources for alcohol oxidase (AOX1) promoter of Pichia pastoris, J. Biosci. Bioeng. 92 (2001) 585–589. [43] J. Soghomonian, D. Martin, Two isoforms of glutamate decarboxylase: why? Trends Pharmacol. Sci. 19 (1998) 500–505. [44] G. Battaglioli, H. Liu, D.L. Martin, Kinetic differences between the isoforms of glutamate decarboxylase: implications for the regulation of GABA synthesis, J. Neurochem. 86 (2010) 879–887. [45] N. Bouché, A. Fait, M. Zik, H. Fromm, The root-specific glutamate decarboxylase (GAD1) is essential for sustaining GABA levels in Arabidopsis, Plant Mol. Biol. 55 (2004) 315–325. [46] G. Panagiotou, P. Christakopoulos, L. Olsson, The influence of different cultivation conditions on the metabolome of Fusarium oxysporum, J. Biotechnol. 118 (2005) 304–315. [47] M. Kaliňák, T. Liptaj, N. Prónayová, Ľ. Varečka, Intracellular Changes of GABA metabolism in Trichoderma viride and other fungi with age and carbon source, Acta Chim. Slov. 1 (2008) 134–142. [48] J. Cao, J.M. Barbosa, N.K. Singh, R.D. Locy, GABA shunt mediates thermotolerance in Saccharomyces cerevisiae by reducing reactive oxygen production, Yeast 30 (2013) 129–144. [49] S.T. Coleman, T.K. Fang, S.A. Rovinsky, F.J. Turano, W.S. Moyerowley, Expression of a glutamate decarboxylase homologue is required for normal oxidative stress tolerance in Saccharomyces cerevisiae, J. Biol. Chem. 276 (2001) 244–250. [50] A.S. Wali, A.K. Mattoo, V.V. Modi, Stimulation of growth and glucose catabolite enzymes by succinate in some thermophilic fungi, Arch. Microbiol. 118 (1978) 49–53. [51] M. Gallmetzer, J. Meraner, W. Burgstaller, Succinate synthesis and excretion by Penicillium simplicissimum under aerobic and anaerobic conditions, FEMS Microbiol. Lett. 210 (2002) 221–225.
[15] H. Niu, L. Jost, N. Pirlot, H. Sassi, M. Daukandt, C. Rodriguez, P. Fickers, A quantitative study of methanol/sorbitol co-feeding process of a Pichia pastoris Mut + /pAOX1-lacZ strain, Microb. Cell Fact. 12 (2013) 33–40. [16] F.S. Hartner, C. Ruth, D. Langenegger, S.N. Johnson, P. Hyka, G.P. Lincereghino, J. Lincereghino, K. Kovar, J.M. Cregg, A. Glieder, Promoter library designed for fine-tuned gene expression in Pichia pastoris, Nucleic Acids Res. 36 (2008) e76–e90. [17] C. Ruth, T. Zuellig, A. Mellitzer, R. Weis, V. Looser, K. Kovar, A. Glieder, Variable production windows for porcine trypsinogen employing synthetic inducible promoter variants in Pichia pastoris, Syst. Synth. Biol. 4 (2010) 181–191. [18] U. Sahu, R.K. Krishna, P.N. Rangarajan, Trm1p, a Zn(II)₂Cys₆-type transcription factor, is essential for the transcriptional activation of genes of methanol utilization pathway, in Pichia pastoris, Biochem. Biophys. Res. Commun. 451 (2014) 158–164. [19] X. Wang, M. Cai, L. Shi, Q. Wang, J. Zhu, J. Wang, M. Zhou, X. Zhou, Y. Zhang, PpNrg1 is a transcriptional repressor for glucose and glycerol repression of AOX1 promoter in methylotrophic yeast Pichia pastoris, Biotechnol. Lett. 38 (2016) 291–298. [20] P. Zhang, W. Zhang, X. Zhou, P. Bai, J.M. Cregg, Y. Zhang, Catabolite repression of Aox in Pichia pastoris is dependent on hexose transporter PpHxt1 and pexophagy, Appl. Environ. Microbiol. 76 (2010) 6108–6118. [21] A.S. Polupanov, V.Y. Nazarko, A.A. Sibirny, Gss1 protein of the methylotrophic yeast Pichia pastoris is involved in glucose sensing, pexophagy and catabolite repression, Int. J. Biochem. Cell Biol. 44 (2012) 1906–1918. [22] J.O. Westholm, N. Nordberg, E. Murén, A. Ameur, J. Komorowski, H. Ronne, Combinatorial control of gene expression by the three yeast repressors Mig1, Mig2 and Mig3, BMC Genomics 9 (2008) 601–615. [23] J. Wang, X. Wang, L. Shi, F. Qi, P. Zhang, Y. Zhang, X. Zhou, Z. Song, M. Cai, Methanol-independent protein expression by AOX1 promoter with trans-acting elements engineering and glucose-glycerol-shift induction in Pichia pastoris, Sci. Rep. 7 (2017) 41850–41861. [24] Y. Murakami, S. Kubo, A. Tamori, S. Itami, E. Kawamura, K. Iwaisako, K. Ikeda, N. Kawada, T. Ochiya, Y.H. Taguchi, Comprehensive analysis of transcriptome and metabolome analysis in Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma, Sci. Rep. 5 (2015) 16294–16305. [25] F.A. Trikka, A. Nikolaidis, C. Ignea, A. Tsaballa, L.A. Tziveleka, E. Ioannou, V. Roussis, E.A. Stea, D. Božić, A. Argiriou, Combined metabolome and transcriptome profiling provides new insights into diterpene biosynthesis in S. Pomifera glandular trichomes, BMC Genomics 16 (2015) 935–953. [26] D. Kim, G. Pertea, C. Trapnell, H. Pimentel, R. Kelley, S.L. Salzberg, TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions, Genome Biol. 14 (2013) R36–R48. [27] S. Anders, P.T. Pyl, W. Huber, HTSeq–a Python framework to work with highthroughput sequencing data, Bioinformatics 31 (2015) 166–169. [28] S. Anders, W. Huber, Differential expression analysis for sequence count data, Genome Biol. 11 (2010) R106–R117. [29] Y. Benjamini, Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. R. Stat. Soc. Ser. B 57 (1995) 289–300. [30] X. Mao, C. Tao, J.G. Olyarchuk, L. Wei, Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary, Bioinformatics 21 (2005) 3787–3793. [31] Y. Kodama, M. Shumway, R. Leinonen, The Sequence read archive: explosive growth of sequencing data, Nucleic Acids Res. 40 (2012) 18. [32] A. Sreekumar, L.M. Poisson, T.M. Rajendiran, A.P. Khan, Q. Cao, J. Yu, B. Laxman, R. Mehra, R.J. Lonigro, Y. Li, Metabolomic profiles delineate potential role for
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Process Biochemistry journal homepage: www.elsevier.com/locate/procbio
Transcriptome and metabolome analyses reveal global behaviour of a genetically engineered methanol-independent Pichia pastoris strain Lei Shia, Jinjia Wanga, Xiaolong Wanga, Yuanxing Zhanga,b, Zhiwei Songc,d, Menghao Caia, ⁎ Xiangshan Zhoua, a
State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China Shanghai Collaborative Innovation Center for Biomanufacturing, Shanghai 200237, China c Bioprocessing Technology Institute, Singapore d Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore b
A R T I C LE I N FO
A B S T R A C T
Keywords: Pichia pastoris Δmig1Δmig2Δnrg1-Mit1 AOX1 promoter Glycerol Methanol Glutamic acid decarboxylase
Pichia pastoris is greatly used as a protein expression system based on tightly regulated aldehyde oxidase 1 (AOX1) promoter (PAOX1) that is induced by methanol; however, the use of methanol of highly expensive and dangerous. By deleting transcription repressors and overexpressing transcription activator of the PAOX1, a methanol-independent strain MF1 (Δmig1Δmig2Δnrg1-Mit1) has been previously constructed. This study investigated the transcriptomic and metabolomic profiles of the MF1 strain in response to different carbon sources, including methanol, glucose, and glycerol, in comparison with the wild-type strain. AOX expression was observed in all groups except the wild-type strains cultured in glucose and glycerol. Genes involved in methanol utilization and peroxisome biosynthesis were mainly upregulated in the mutant strain grown on glucose or glycerol. Metabolomics data showed significant increase of the products related to metabolic pathways in amino acid and energy metabolism in the mutant strain grown in glycerol. Genes such as glutamic acid decarboxylase (GAD) and some metabolites were both involved in the pathways such as GABAergic synapse. GAD1, GAD2, and succinate may be associated with the recombinant protein expression system of the mutant MF1 strain of P. pastoris grown on glucose or glycerol.
1. Introduction The methylotrophic yeast Pichia pastoris is most extensively used beyond Escherichia coli in the production of a wide variety of recombinant proteins [1]. The most important factor contributing to the popularity of this expression system is the ability to produce heterologous proteins at high levels using alcohol oxidase 1 (AOX1) promoter PAOX1 [2]. The PAOX1 promoter is strongly induced only in response to methanol but repressed in the presence of glucose, glycerol or ethanol and other alternative carbon sources [3]. However, the use of methanol in large-scale industrial fermentation processes can be highly expensive and dangerous for its toxicity and flammability [4]. Additionally, its special and high requirements on transportation, storage, and operator's occupational skills, the need for high oxygen supply and heat removal during fermentation, and the byproduct of methanol metabolism hydrogen peroxide (H2O2) leading to the degradation of specific recombinant proteins [5] also pose challenges to the industrial
application of the P. pastoris methanol-induced expression system. Several alternatives to methanol for the aforementioned processing have been investigated. Different promoters such as PGAP [6], PICL1 [7], PTEF1 [8], PPGK1 [9], PYPT1 [10], PPHO89 [11] and PGCW14 [12] have been investigated; however, none of these have been widely adopted because of their weak or non-controllable features. Although the mixed carbon substrate feeding strategy by combining methanol with other carbon source such as glucose [13], glycerol [14], or sorbitol [15] reduces oxygen consumption and H2O2 production without compromising protein yield, it does not completely eliminate the need of methanol. Furthermore, cis-acting regulatory element rewiring as a frequently used genetic engineering approach for potentially controlling PAOX1 has previously been investigated. By deletion or duplication of the putative transcription factor-binding sites within the promoter, some of the variants of PAOX1 were able to produce moderate levels of recombinant proteins without methanol [16,17]. Recently, trans-acting regulatory elements of PAOX1 have also been identified and comprehensively
⁎ Corresponding author at: State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China. E-mail address: [email protected] (X. Zhou).
https://doi.org/10.1016/j.procbio.2018.10.014 Received 9 August 2018; Received in revised form 23 October 2018; Accepted 25 October 2018 1359-5113/ © 2018 Elsevier Ltd. All rights reserved.
Please cite this article as: Shi, L., Process Biochemistry, https://doi.org/10.1016/j.procbio.2018.10.014
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Life Technologies) according to the manufacturer’s protocol, RNA concentration was measured using Qubit® RNA Assay Kit with Qubit® 2.0 Flurometer (Life Technologies, CA, USA), and RNA integrity was assessed with the RNA Nano 6000 Assay Kit by the Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Sequencing libraries were generated using NEBNext® Ultra™ Directional RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s instructions, and library quality was assessed with the Agilent Bioanalyzer 2100 system and sequenced on an Illumina Hiseq 2000 platform. Quality control of raw data (raw reads) in fastq format was processed by removing reads containing adapter, ploy-N, and low-quality data. All the downstream analyses were based on clean, high-quality data. Paired-end clean reads were aligned to the reference genome using TopHat v2.0.9 [26]. HTSeq v0.5.4p3 software [27] was used to count the number of reads mapped to each gene. The number of reads per kilobase of exon region per million mapped reads (RPKM) was calculated to quantify gene expression levels. Differential expression analysis of two conditions/ groups was performed using the R package DESeq (1.10.1) [28]. The resulting P-values were adjusted for multiple testing using Benjamini and Hochberg’s method [29]. Genes with an adjusted P-value < 0.05 and |log2(fold change, FC)| ≥ 1 were considered significantly differentially expressed genes (DEGs). The clustering algorithms of hierarchical clustering and k-means clustering of genes were used. KOBAS software was used to test the statistical enrichment of DEGs in KEGG pathways [30], with the threshold of corrected P-value < 0.05. The transcriptome sequence of methanol-independent Pichia pastoris strain has been deposited at National Center of Biotechnology Information (NCBI) Sequence Read Archive (SRA) [31] under the accession numbers SRP132617.
investigated. Particularly, Prm1 and Mit1, belonging to Zn(II)2Cys6 family transcription factors, which positively activate PAOX1, in response to methanol were shown to bind to PAOX1 at a different site [18,19], while Nrg1 was observed to act as a transcriptional repressor of PAOX1 [19]. Besides, many genes, including the hexose transporter Hxt1 [20] or the glucose sensor Gss1 [21], are known to regulate derepression of glucose-regulated genes. Thus, genetic engineering of the trans-acting elements of PAOX1 for methanol-free protein expression is possible. Mig1 and Mig2 are zinc finger proteins and both are involved in yeast glucose repression [22]. By overexpression of Mit1 under a constitutive GAP promoter in a strain with triple-deletion of Nrg1, Mig1, and Mig2, we successfully constructed a robust methanol-free PAOX1 strain MF1 (Δmig1Δmig2Δnrg1-Mit1), which expresses 77% GFP in glycerol compared to the wild-type in methanol [23]. However, how these genetic variations affect global cell metabolism and protein synthesis remain unexplored. Moreover, gene-to-metabolite correlation and distribution in different pathways can be well interpreted through the comprehensive analysis of gene expression (transcriptome) and metabolite accumulation (metabolome) [24,25]. Thus, transcriptome and metabolome analyses were performed in this study to plot variations in the metabolic and gene expression profile of the MF1 strain using different carbon sources in comparison with the wild-type strain; this will facilitate further assessment of the novel genetically engineered strain for extensive pharmaceutical application. 2. Materials and methods 2.1. Strain cultivation The strain MF1 (Δmig1Δmig2Δnrg1-Mit1) stored in −80 °C was inoculated directly into 250 mL flasks containing 50 mL yeast nitrogenbase/dextrose (YND) medium (0.67% yeast nitrogen base medium (YNB) and 1% dextrose) as the first seed culture. After cultivation at 30 °C for 24 h with agitation (200 rpm), the seed cultures (12.5 mL) were used to inoculate into 500 mL flasks containing 100 mL YND medium as the second seed culture. The wild-type strain GS115 (WT) was cultured in MGY medium (0.67% YNB and 1% glycerol) under the same conditions. After 16 h of cultivation at 30 °C, batch cultivation was started according to the inoculum concentration of 1:10. Continuous cultivations were performed at a working volume of 3.5 L in a 5 L stirred tank bioreactor at 30 °C, pH 5.0 (controlled by 25% ammonium hydroxide), and a minimum of 30% dissolved oxygen tension. When carbon source was depleted, and the concentration of dissolved oxygen increased sharply, the chemostat medium using glucose (20.0 g/ L), glycerol (20.0 g/L), or methanol (20.0 g/L) as the sole carbon source was supplemented. While feeding in the chemostatic medium, a peristaltic pump was used to drain the fermentation broth to maintain a constant volume of the fermentation broth. Samples were taken when the specific growth rate reached μ = 0.03 h−1. AOX enzyme activity was measured by a colorimetric assay, as described previously [20]. Six groups, including wild-type strain in dextrose/glucose (W_D), wild-type strain in glycerol (W_G), wild-type strain in methanol (W_M), mutant strain in dextrose/ glucose (M_D), mutant strain in glycerol (M_G), and mutant strain in methanol (M_M) were analyzed in the present study. The detailed information of medium was described elsewhere [23]. For RNA-seq analysis, two biological replicates were available for each strain. For LC–MS\GC–MS analysis (metabolome), the chemostat sample was divided into five 2 mL aliquots (five identical samples) for each strain. After centrifugation at 4 °C and 5000 r/min, the supernatant was removed and the cells were rapidly cooled with liquid nitrogen and placed at −80 °C.
2.3. Metabolome analysis Metabolomic profile analysis was performed by Metabolon Inc. (Durham, NC), as previously described [32]. In brief, each sample was accessioned into the Metabolon laboratory information management system (LIMS) system and was assigned by LIMS as a unique identifier. Samples were prepared using the automated MicroLab STAR® system from Hamilton Company. The resulting extract was divided into two fractions: one for analysis by LC and another for analysis by GC. The representative (QA/QC) samples were used to evaluate the process control for the study. The liquid chromatography/mass spectrometry (LC/MS, LC/MS2) portion of the platform was based on a Waters ACQUITY UPLC and a Thermo-Finnigan LTQ mass spectrometer. The MS analysis alternated between MS and data-dependent MS2 scans using dynamic exclusion. For gas chromatography/mass spectrometry (GC/MS), samples were analyzed using a Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole mass spectrometer. The samples for GC/MS analysis were vacuum dried for 24 h prior to being derivatized under dried nitrogen using bistrimethyl-silyl-triflouroacetamide (BSTFA). The GC column comprised 5% benzene and the temperature of the ramp was set from 40 °C to 300 °C for a 16-min period. Also, accurate LC/MS determination and MS/MS fragmentation (LC/MS/MS) was conducted for ions with counts higher than 2 million and the parent ion as well as fragments. The bioinformatics system comprised four components, the LIMS, the data extraction and peak-identification software, data processing tools for QC and compound identification, and a collection of information interpretation and visualization tools for data analysis. It was based on a database server running Oracle 10.2.0.1 Enterprise Edition (Redwood Stores, CA). Metabolites were identified by comparison to library entries of purified standards or recurrent unknown entities using the Metabolon LIMS system. For pairwise comparisons, Welch’s t-tests and/or Wilcoxon’s rank-sum tests were used to identify differential metabolites. For other statistical analyses, ANOVA procedures (e.g., repeated measures ANOVA) were used. A random forest approach was applied for clustering [33]. Statistical analyses were performed using
2.2. Transcriptome analysis Total RNA was extracted using the RiboPure™-Yeast Kit (Ambion, 2
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the program “R” [34].
involved in Subcluster_1; 90 genes, including MIG1 and MIG2, in Subcluster_3; and 168 genes, including MIT1, in Subcluster_7 (Fig. 2). Moreover, a strong negative correlation of gene expression trend between those in Subcluster_1 and Subcluster_3 was found. AOX gene expression was detected in four groups of samples except in W_D and W_G. There were 73 up-regulated DEGs overlapped in comparisons of W_M vs. W_G, W_M vs. W_D, M_G vs. W_G, and M_D vs. W_D. Pathway enrichment analysis of those 73 up-regulated genes showed that these genes were mainly associated with methanol utilization and peroxisome biosynthesis (Supplementary Table 1). To investigate the difference in the transcriptome of the two expression systems (methanol induced and glycerol induced) in primary metabolism pathways (Supplementary Fig. 2), comparative analysis between M_G and W_G was performed. Fig. 3 depicted DEGs in the metabolic pathway of methanol utilization and the peroxisome biosynthetic pathway. The genes involved in two pathways were significantly upregulated for M_G compared with W_G, among which, AOX1 and AOX2 were significantly upregulated by 15.8-fold and 8.3fold, respectively. Some other genes, such as S-formylglutathione hydrolase (FGH), formate dehydrogenase (FDH), and dihydroxyacetone synthase (DAS1 and DAS2), were also highly expressed (Fig. 3A) in M_G compared with W_G. Fig. 3C showed that the expression levels of Peroxisomal Biogenesis Factor 1 (PEX1), PEX5, PEX6, PEX13 and PEX14 in M_G were much higher than those in W_G. For glycerol metabolism (Fig. 3E), the mitochondrial glycerol-3-phosphate dehydrogenase gene (GUT2), the active glycerol importer gene (STL1), and the homolog of S. cerevisiae putative passive glycerol channel gene (YFL054C) exhibited no noticeable changes at the transcriptional levels in M_G and W_G, while were higher than those in W_M. However, the cytosolic glycerol kinase gene (GUT1) was downregulated in M_G compared with the expression level of that in W_G, and close to the level of W_M. For primary metabolism, no significant changes were observed in the glycolytic pathway (Fig. 3D), TCA cycle (Fig. 3F), and pentose phosphate pathway (PPP, Fig. 3B) between mutant and wild type grown on glycerol at the transcriptional level, except that ribose-5-phosphate ketolisomerase (RPI) involved in PPP was upregulated in M_G as compared with W_G. The newly developed P. pastoris expression system produces exogenous proteins in glycerol. Expression of the genes related to protein secretion pathway was investigated through the comparative analysis of M_G and W_M. Based on the threshold of adjusted P-value < 0.05 and |log2(fold change, FC)| ≥ 1), we found upregulation of UGP1 and GFA1 involved in the process of nucleotide sugar synthesis, UGGT involved in the process of quality, and YPS2 and PAS_chr3_1157 involved in the process of proteolysis, along with downregulation of PAS_chr2-1_0706 and PNO1 involved in Golgi N-glycan processing (Supplementary Table 2).
2.4. Integrated analysis of transcriptome and metabolome To integrate transcriptome and metabolomics data, the IMPaLA tool [35] was used for over-representation analysis (ORA) and joint analysis of both sets of data based on the KEGG database [36]. The pathways significantly enriched by differential metabolites and DEGs were identified. When the number of significant genes or metabolites is relatively small, the joint analysis is preferable. The thresholds were num_overlapping_genes ≥ 1, num_overlapping_metabolites ≥ 1, and P_joint < 0.05. 3. Results 3.1. Alternation of gene expression levels of P. pastoris under different carbon sources The concentration (> 50 ng/μl), purity (OD260/280 > 1.8) and integrity [value of RNA Integrity Number (RIN) > 7.0] of extracted total RNA were sufficient for sequencing. In total, approximately 402,803,524 reads were obtained from the samples of six groups grown under six different conditions. As a result, 2,263, 2,349, and 2,403 DEGs were obtained from M_D, M_G, and M_M, respectively, in comparison with the respective wild-type genes. In total, 975 overlapping genes were identified from three comparisons. The hierarchical clustering, as illustrated in Fig. 1, demonstrated that the wild-type and mutant strains grown on methanol clustered together, except that the wild-type strain was devoid of AOX expression in glucose and glycerol conditions (supplementary Fig. 1). The k-means clustering of all DEGs in four comparisons indicated that 52 genes, including AOX1, were
3.2. Alteration of metabolites revealed by metabolomic profile A total of 233 known compounds were identified from the metabolic profiles by LC–MS/MS and GC–MS analyses. The total metabolomics data were summarized in supplementary table 3. The principal component analysis (PCA) revealed that the wild-type strain was significantly separated from the mutant strains in glucose and glycerol unlike the partial overlap in methanol (Fig. 4). Similarly, hierarchical clustering demonstrated that the wild-type and mutant strains grown on methanol clustered together; however, they were separated based on the genotype when grown on glucose or glycerol (Fig. 5). The LC/MS-GC/MS platform used methanol as an intermediate reagent so that the methanol content was not detected during the methanol metabolic pathway analysis. Only the levels of fructose-6-phosphate (F6P) and fructose-1, 6-bisphosphate (FBP) were determined and they were found to be elevated in M_G and M_D compared with W_D and W_G (Supplementary Fig. 3). Enhanced levels of glycolysis-, PPP-, and TCA cycle-related metabolites and amino acids were also observed
Fig. 1. Hierarchical clustering analysis of differentially expressed genes. D, G, M represent glucose, glycerol, and methanol chemostat medium, respectively; M represents mutant strain, and W represents wild-type strain. 3
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Fig. 2. k-means clustering of differentially expressed genes. The gray lines in each subgraph represent the relative expression of a gene in a cluster under different experimental conditions, and the blue line represents the average of the relative expression of all genes in the cluster under different experimental conditions. The x-axis represents experimental conditions, the y-axis represents relative expression, and the number at the top of the subgraph represents the number of genes contained in each cluster. Each of these groups was compared with the expression of the gene in M_D (mutant strain cultured in glucose medium) as a reference value (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
in strains grown on glycerol compared with those in the strains grown on methanol and glucose (Figs. 6A and B). Moreover, the levels of branched-chain amino acids, including valine and isoleucine, were
increased in the mutant strain grown in glucose and particularly in glycerol (Supplementary Fig. 4). However, the NAD level was enhanced in three mutant strains regardless of the nutrient source 4
Process Biochemistry xxx (xxxx) xxx–xxx 4.14E-05 6.43E-06 0.00276 0.000281 0.0227 0.0421 0.000514 0.00329 0.0396 0.0887 0.0573 0.251 0.346 citrate; succinate; cis-aconitate; thiamin diphosphate methionine; arginine; aspartate; tyrosine; lysine; putrescine citrate; succinate; aspartate pantothenate; aspartate; 3-hydroxypropanoate; spermidine aspartate; arginine putrescine; arginine; spermidine succinate; flavin mononucleotide (FMN); phosphate citrate; succinate; cis-aconitate; glycerate succinate; thiamin diphosphate succinate putrescine; spermidine lysine succinate C00158;C00042;C00417;C00068 C00073;C00062;C00049;C00082;C00047;C00134 C00158;C00042;C00049 C00864;C00049;C01013;C00315 C00049;C00062 C00134;C00062;C00315 C00042;C00061;C00009 C00158;C00042;C00417;C00258 C00042;C00068 C00042 C00134;C00315 C00047 C00042
(Supplementary Fig. 5). A significant accumulation of the phospholipid catabolites ethanolamine, glycerophosphorylcholine (GPC), glycerophosphoethanolamine, and glycerol-3-phosphate (G3P) was also observed in the mutant strain (Supplementary Fig. 6). The metabolomic alteration of the two expression systems (methanol induced and glycerol induced) was analyzed by comparing M_G and W_M with W_G as a reference. As shown in Fig. 7, most of the metabolites levels in PPP, glycolytic pathway, and TCA of M_G were higher than those of W_M and W_G, except that the 2-oxoglutarate (AKG) and isocitrate (ICIT) of W_G were higher than M_G, and were lowest in W_M. In addition, the metabolite accumulation levels of GN6P and F6P were comparable between W_M and W_G. 3.3. Pathways enriched by differential metabolites and DEGs There were 2263 DEGs and 199 differential metabolites, 2349 DEGs and 160 differential metabolites, and 2403 DEGs and 75 differential metabolites from M_D vs. W_D, M_G vs. W_G, and M_M vs. W_M, respectively. There were 975 genes and 60 metabolites were overlapped among these 3 comparison groups. Pathway enrichment analysis on the overlapped genes and metabolites revealed that thirteen pathways, such as TCA cycle, were significantly enriched by overlapping DEGs and differential metabolites (Table 1). Especially, glutamic acid decarboxylase/glutamate decarboxylase (GAD1) and GAD2, as well as metabolites of succinate (C00042) and aspartate (C00049) were enriched in four pathways, including alanine, aspartate, and glutamate metabolism, beta-alanine metabolism, GABAergic synapse and butanoate metabolism. Moreover, succinate (C00042) also related to some other pathways, including oxidative phosphorylation and TCA cycle, which indicated the crucial role of succinate. 4. Discussion Changes in the transcriptional and metabolite profiles of P. pastoris grown in different carbon sources were compared using the mass profile data of transcriptome and metabolome. Specific genes and processes which were potentially associated with gene expression pattern of AOX1 as well as metabolic signature alterations demonstrated the potential of Δmig1Δmig2Δnrg1-Mit1 to serve as a host line for recombinant protein production. In addition, the integrated analysis potentially revealed the underlying mechanism of the application potential of the mutant strain. AOX was expressed in four groups of samples except in W_D and W_G, which demonstrated the significance of Δmig1Δmig2Δnrg1-Mit1 as an effective host line for recombinant protein production. Similarly, clustering patterns between transcriptome and metabolome indicated that the global transcript and metabolite profiles were strongly complementing each other. PCA of metabolome revealed a clear separation between wild-type and mutant strains in the presence of glucose and glycerol. This indicated that both genotype and nutrient-dependent alterations influenced metabolic signature. In contrast, only a partial overlap was observed between wild-type and mutant strains grown on methanol. Similarly, hierarchical clustering demonstrated that wildtype and mutant strains grown on methanol clustered together; however, except that wild-type strain has no AOX1 gene expression in glucose and glycerol conditions, other samples had AOX1 gene expression. These findings confirm methanol-induced AOX1 expression in the wild-type strain, as previously reported [37]. Moreover, the study also demonstrated that up-regulated genes in strain with a positive AOX expression were mainly enriched in methanol metabolic pathway and peroxisome biosynthesis. Genes, including FGH, FDH, DAS1, and DAS2, involved in the methanol metabolism pathway were significantly up-regulated in M_G compared with W_G; however, their expression was lower than that observed in W_M. A similar trend in the expression of genes involved in peroxisome biosynthesis was observed. This suggested that genes involved in the
ACO1;PCK1;MDH1 CPA1;CPA2 NIT2;GAD1;GAD2 GAD1;GAD2 ARG2;OTC;ARG1 ARG2;ODC1;ARG1;LAP3 CYC1;COX15;COX17 ACO1;MDH1 PCK1;GLO1;MDH1 HAP1;GAD1;GAD2 LAP3;ODC1 OXSM GAD1;GAD2 Citrate cycle (TCA cycle) Protein digestion and absorption Alanine_ aspartate and glutamate metabolism beta-Alanine metabolism Arginine biosynthesis Arginine and proline metabolism Oxidative phosphorylation Glyoxylate and dicarboxylate metabolism Pyruvate metabolism GABAergic synapse Glutathione metabolism Biotin metabolism Butanoate metabolism
0.00119 0.132 0.00187 0.0199 0.000408 0.00038 0.0672 0.0164 0.00256 0.024 0.0553 0.021 0.0164
P values Description of metabolites overlapping_genes Pathways
Table 1 Pathways enriched by the overlapping DEGs and differential metabolites.
P values
overlapping_metabolites
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Fig. 3. Expression levels of the genes involved in the main metabolism pathway. Asterisk indicates the significant difference between mutant/glycerol condition and wild-type/glycerol condition.
Fig. 4. Principal component analysis (PCA) of the metabolome data of different samples. PCA revealed a distinct separation between WT and mutants in the presence of glucose and glycerol that may reflect both genotype and nutrient dependent alterations in the metabolic signature. In contrast, partial overlap was observed between WT and mutant samples grown on methanol.WT, wildtype. Mut, mutant.
pathways of methanol metabolism and peroxisome biosynthesis in M_G exhibited an expression level similar to those in W_M. Importantly, in P. pastoris, number of peroxisomes was strongly reduced in cells in which methanol utilization is repressed while are abundantly present in methanol-grown cells [38]. For primary metabolism, no significant changes in gene expression profile of the glycolytic pathway, TCA cycle, and PPP were observed between mutant and wild-type strains grown on glycerol at the transcriptional level; however, RPI involved in PPP was upregulated in M_G compared with W_G. From the k-means clustering results, when the relative expression level of genes similar to the AOX1 gene expression pattern increased, the relative expression levels of genes similar to the MIG1 and MIG2 gene expression patterns decreased, suggesting that there are potential repressors in genes similar to the MIG1 and MIG2 gene expression patterns.
Fig. 5. Hierarchical clustering of metabolites of WT and mutant strains in different medium.Wild-type and mutant strains grown on methanol clustered together; however, they were separated by the genotype when grown on glucose or glycerol.
Evidence demonstrates that the methanol-free PAOX1 start-up mutant strain induced by glycerol could produce recombinant proteins nearly as efficiently as the wide-type in methanol [23]. In addition, metabolomics revealed enhanced levels of amino acids and glycolysis-, PPP-, and TCA cycle-related metabolites in wild-type strains grown on glycerol compared with those in the strains grown on methanol, and indicated that glycerol could serve as a favorable carbon source in this study [39]. Notably, mutant strains grown on glycerol exhibited the 6
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Fig. 6. Enhanced levels of amino acids (A) and glycolysis-, pentose phosphate pathway (PPP)-, and TCA cycle-related metabolites (B) were observed in wild-type yeast grown on glycerol as compared with those in the strains grown on glucose or methanol, this suggests that the former is the best carbon source observed in this study.
dual role as a signaling molecule. This dual role of it could be similar to some other metabolites, such as glutamate [45]. GABA is degraded to succinate (C00042) via the GABA shunt. The concentration of GABA in Fusarium oxysporum was increased in anaerobiosis [46]. Kaliňák et al. speculated that the GABA shunt is active during glucose catabolism compared to that of gluconeogenesis in Saccharomyces cerevisiae, and could be closely associated with oxidative metabolism [47]. GABA shunt mediates thermotolerance by restricting reactive oxygen intermediates to protect Saccharomyces cerevisiae from heat damage [48]. In addition, GAD is important for oxidative stress tolerance in Saccharomyces cerevisiae [49]. Moreover, succinate was found to stimulate glucose utilization in some thermophilic fungi [50] and excretion of succinate is related to anaerobic conditions [51]. Therefore, the Δmig1Δmig2Δnrg1-Mit1 strain could reduce heat production and oxygen demand compared to wild stains.
highest levels of energy metabolites and amino acids, suggesting that these conditions are optimal for facilitating growth and protein synthesis. In contrast, F6P and FBP were elevated in mutant strains and may contribute to enhanced glycolysis in the presence of glycerol. Indeed, higher levels of sorbitol and fructose in mutants suggests enhanced glucose uptake [40], although an accumulation of G6P, GC3P, and PEP and the end PYR in mutants grown on glycerol may fuel oxidative metabolism, as evidenced by the accumulation of the TCA intermediates CIT, SUCC, and MAL. The metabolite accumulation levels of GN6P, ICIT, and AKG were observed in both W_M and W_G. The high levels of GN6P and S7P in mutants cultured in glycerol may reflect glucose shuttling to PPP to support and maintain NADPH regeneration and anabolic growth capacity, which indicated that GN6P might be associated with induced PAOX1. The result also suggested that AKG and ICIT may be associated with the regression of the AOXI promoter. Moreover, metabolic alterations in methanol metabolism pathway, which were not captured in the present study, may also contribute to the discrepancy. Taken together, the mutant exhibited an indication of a stronger metabolic flux in primary metabolism simultaneously owning the specific methanol-induced metabolic characteristics when grown on glycerol. Although methanol is the inducer of PAOX1 and is frequently used for heterologous protein production in P. pastoris, the yeast typically grows very slowly in this carbon source [41]. According to the first criterion of high yield, glycerol may serve as the most promising candidate for the efficient production of recombinant protein [39]. However, PAOX1 is naturally repressed by glycerol in P. pastoris [42]. The productive ability to grow and utilize PAOX1 for the recombinant protein expression with glycerol indicates that a novel P. pastoris expression system, developed by our laboratory, will be a potential expression system, particularly for pharmaceutical application. To investigate the mechanism underlying the methanol-free strain for production of recombinant protein, the integrated analysis was performed. The two isoforms of glutamate decarboxylase, GAD1 and GAD2, are encoded by two different genes located on different chromosomes [43]. GAD67 (GAD1) is a soluble cytosolic protein, whereas GAD65 (GAD2) is membrane-associated. In our results, GAD1 was down-regulated and GAD2 is upregulated. Both are involved in the rate-limiting step in GABA synthesis and are essential for sustaining GABA levels [44,45]. Previous study indicated that GABA not only a metabolite, but also probably plays a
5. Conclusion As a result, the Δmig1Δmig2Δnrg1-Mit1 strain is a potential recombinant protein expression system. The genes involved in methanol metabolism pathway and peroxisome biosynthesis could be closely related to AOX1 activation. Some metabolites, including GN6P, may serve as the inducer of PAOX1, and ICIT and AKG may be associated with the suppression of the promoter. GAD1 and GAD2 and succinate may play an important role in reducing heat production and oxygen demand in P. pastoris. Furthermore, glycosylation could be optimized to be a promising candidate for methanol-independent protein expression in P. pastoris. Conflict of interest statement The authors have declared that no conflict of interests exists. Author contributions LS, ZS and XZ contributed conception and design of the research; LS, JW and XW acquired data; LS draft the manuscript; MC and YZ revise the manuscript for important intellectual content. All authors contributed to manuscript revision, read and approved the submitted version. 7
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Fig. 7. Metabolomic alteration of the two expression systems (methanol induced and glycerol induced). (A) Intracellular levels of central metabolites in wild-type/glycerol, mutant/glycerol, and wild-type/methanol conditions. The bubble areas are proportional to the pool sizes of each intracellular metabolite. The numbers above each the bubbles represent the normalized mean value. The metabolites without value could not be detected. (B) Primary metabolism pathways in P. pastoris. Blue words indicated the genes involved in these pathways, and black words indicated the metabolites during these pathways (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
Acknowledgments
Biotechnol. 72 (2006) 837–844. [6] H.R. Waterham, M.E. Digan, P.J. Koutz, S.V. Lair, J.M. Cregg, Isolation of the Pichia pastoris glyceraldehyde-3-phosphate dehydrogenase gene and regulation and use of its promoter, Gene 186 (1997) 37–44. [7] J. Menendez, I. Valdes, N. Cabrera, The ICL1 gene of Pichia pastoris, transcriptional regulation and use of its promoter, Yeast 20 (2003) 1097–1108. [8] J. Ahn, J. Hong, H. Lee, M. Park, E. Lee, C. Kim, E. Choi, J. Jung, H. Lee, Translation elongation factor 1-α gene from Pichia pastoris: molecular cloning, sequence, and use of its promoter, Appl. Microbiol. Biotechnol. 74 (2007) 601–608. [9] J.R.M. De Almeida, L.M.P. de Moraes, F.A.G. Torres, Molecular characterization of the 3‐phosphoglycerate kinase gene (PGK1) from the methylotrophic yeast Pichia pastoris, Yeast 22 (2005) 725–737. [10] N. Segev, J. Mulholland, D. Botstein, The yeast GTP-binding YPT1 protein and a mammalian counterpart are associated with the secretion machinery, Cell 52 (1988) 915–924. [11] J. Ahn, J. Hong, M. Park, H. Lee, E. Lee, C. Kim, J. Lee, E. Choi, J. Jung, H. Lee, Phosphate-responsive promoter of a Pichia pastoris sodium phosphate symporter, Appl. Environ. Microbiol. 75 (2009) 3528–3534. [12] S. Liang, C. Zou, Y. Lin, X. Zhang, Y. Ye, Identification and characterization of P GCW14 : a novel, strong constitutive promoter of Pichia pastoris, Biotechnol. Lett. 35 (2013) 1865–1871. [13] J. Jordà, C. Suarez, M. Carnicer, A.T. Pierick, J.J. Heijnen, W.V. Gulik, P. Ferrer, J. Albiol, A. Wahl, Glucose-methanol co-utilization in Pichia pastoris studied by metabolomics and instationary 13 C flux analysis, BMC Syst. Biol. 7 (2013) 1–16. [14] D. Zalai, C. Dietzsch, C. Herwig, O. Spadiut, A dynamic fed batch strategy for a Pichia pastoris mixed feed system to increase process understanding, Biotechnol. Prog. 28 (2012) 878–886.
None. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.procbio.2018.10.014. References [1] B. Gasser, R. Prielhofer, H. Marx, M. Maurer, J. Nocon, M. Steiger, V. Puxbaum, M. Sauer, D. Mattanovich, Pichia pastoris: protein production host and model organism for biomedical research, Future Microbiol. 8 (2013) 191–208. [2] T. Vogl, A. Glieder, Regulation of Pichia pastoris promoters and its consequences for protein production, N. Biotechnol. 30 (2013) 385–404. [3] W. Shen, Y. Xue, Y. Liu, C. Kong, X. Wang, M. Huang, M. Cai, X. Zhou, Y. Zhang, M. Zhou, A novel methanol-free Pichia pastoris system for recombinant protein expression, Microb. Cell Fact. 15 (2016) 178–188. [4] J. Villadsen, J. Nielsen, G. Lidén, Design of fermentation processes, Bioreaction Engineering Principles, Springer, Boston, US, 2011, pp. 383–458. [5] A. Xiao, X. Zhou, L. Zhou, Y. Zhang, Improvement of cell viability and hirudin production by ascorbic acid in Pichia pastoris fermentation, Appl. Microbiol.
8
Process Biochemistry xxx (xxxx) xxx–xxx
L. Shi et al.
sarcosine in prostate cancer progression, Nature 457 (2009) 910–914. [33] Y. Qi, Random Forest for Bioinformatics, Ensemble Machine Learning, Springer, Boston, MA, 2012, pp. 307–323. [34] R.C. Gentleman, V.J. Carey, D.M. Bates, B. Bolstad, M. Dettling, S. Dudoit, B. Ellis, L. Gautier, Y. Ge, J. Gentry, K. Hornik, T. Hothorn, W. Huber, S. Iacus, R. Irizarry, F. Leisch, C. Li, M. Maechler, A.J. Rossini, G. Sawitzki, C. Smith, G. Smyth, L. Tierney, J.Y. Yang, J. Zhang, Bioconductor: open software development for computational biology and bioinformatics, Genome Biol. 5 (2004) 2004–2005. [35] A. Kamburov, R. Cavill, T.M.D. Ebbels, R. Herwig, H.C. Keun, Integrated pathwaylevel analysis of transcriptomics and metabolomics data with IMPaLA, Bioinformatics 27 (2011) 2917–2918. [36] M. Kanehisa, S. Goto, M. Furumichi, M. Tanabe, M. Hirakawa, KEGG for representation and analysis of molecular networks involving diseases and drugs, Nucleic Acids Res. 38 (2010) 30. [37] J.M. Cregg, T.S. Vedvick, W.C. Raschke, Recent advances in the expression of foreign genes in Pichia pastoris, Biotechnology 11 (1993) 905–910. [38] V.D.K. Ij, H. Yurimoto, Y. Sakai, M. Veenhuis, The significance of peroxisomes in methanol metabolism in methylotrophic yeast, Biochim. Biophys. Acta Mol. Cell Res. 1763 (2006) 1453–1462. [39] B.K. Chung, S. Selvarasu, C. Andrea, J. Ryu, H. Lee, J. Ahn, D.Y. Lee, Genome-scale metabolic reconstruction and in silico analysis of methylotrophic yeast Pichia pastoris for strain improvement, Microb. Cell Fact. 9 (2010) 50–64. [40] R. Yair, M. Allen, The effects of fructose and phosphate infusions on dry matter intake of lactating cows, J. Dairy Sci. 100 (2017) 2651–2659. [41] J.M. Cregg, Introduction: distinctions between Pichia pastoris and other expression systems, Methods Mol. Biol. 389 (2007) 1–10. [42] M. Inan, M.M. Meagher, Non-repressing carbon sources for alcohol oxidase (AOX1) promoter of Pichia pastoris, J. Biosci. Bioeng. 92 (2001) 585–589. [43] J. Soghomonian, D. Martin, Two isoforms of glutamate decarboxylase: why? Trends Pharmacol. Sci. 19 (1998) 500–505. [44] G. Battaglioli, H. Liu, D.L. Martin, Kinetic differences between the isoforms of glutamate decarboxylase: implications for the regulation of GABA synthesis, J. Neurochem. 86 (2010) 879–887. [45] N. Bouché, A. Fait, M. Zik, H. Fromm, The root-specific glutamate decarboxylase (GAD1) is essential for sustaining GABA levels in Arabidopsis, Plant Mol. Biol. 55 (2004) 315–325. [46] G. Panagiotou, P. Christakopoulos, L. Olsson, The influence of different cultivation conditions on the metabolome of Fusarium oxysporum, J. Biotechnol. 118 (2005) 304–315. [47] M. Kaliňák, T. Liptaj, N. Prónayová, Ľ. Varečka, Intracellular Changes of GABA metabolism in Trichoderma viride and other fungi with age and carbon source, Acta Chim. Slov. 1 (2008) 134–142. [48] J. Cao, J.M. Barbosa, N.K. Singh, R.D. Locy, GABA shunt mediates thermotolerance in Saccharomyces cerevisiae by reducing reactive oxygen production, Yeast 30 (2013) 129–144. [49] S.T. Coleman, T.K. Fang, S.A. Rovinsky, F.J. Turano, W.S. Moyerowley, Expression of a glutamate decarboxylase homologue is required for normal oxidative stress tolerance in Saccharomyces cerevisiae, J. Biol. Chem. 276 (2001) 244–250. [50] A.S. Wali, A.K. Mattoo, V.V. Modi, Stimulation of growth and glucose catabolite enzymes by succinate in some thermophilic fungi, Arch. Microbiol. 118 (1978) 49–53. [51] M. Gallmetzer, J. Meraner, W. Burgstaller, Succinate synthesis and excretion by Penicillium simplicissimum under aerobic and anaerobic conditions, FEMS Microbiol. Lett. 210 (2002) 221–225.
[15] H. Niu, L. Jost, N. Pirlot, H. Sassi, M. Daukandt, C. Rodriguez, P. Fickers, A quantitative study of methanol/sorbitol co-feeding process of a Pichia pastoris Mut + /pAOX1-lacZ strain, Microb. Cell Fact. 12 (2013) 33–40. [16] F.S. Hartner, C. Ruth, D. Langenegger, S.N. Johnson, P. Hyka, G.P. Lincereghino, J. Lincereghino, K. Kovar, J.M. Cregg, A. Glieder, Promoter library designed for fine-tuned gene expression in Pichia pastoris, Nucleic Acids Res. 36 (2008) e76–e90. [17] C. Ruth, T. Zuellig, A. Mellitzer, R. Weis, V. Looser, K. Kovar, A. Glieder, Variable production windows for porcine trypsinogen employing synthetic inducible promoter variants in Pichia pastoris, Syst. Synth. Biol. 4 (2010) 181–191. [18] U. Sahu, R.K. Krishna, P.N. Rangarajan, Trm1p, a Zn(II)₂Cys₆-type transcription factor, is essential for the transcriptional activation of genes of methanol utilization pathway, in Pichia pastoris, Biochem. Biophys. Res. Commun. 451 (2014) 158–164. [19] X. Wang, M. Cai, L. Shi, Q. Wang, J. Zhu, J. Wang, M. Zhou, X. Zhou, Y. Zhang, PpNrg1 is a transcriptional repressor for glucose and glycerol repression of AOX1 promoter in methylotrophic yeast Pichia pastoris, Biotechnol. Lett. 38 (2016) 291–298. [20] P. Zhang, W. Zhang, X. Zhou, P. Bai, J.M. Cregg, Y. Zhang, Catabolite repression of Aox in Pichia pastoris is dependent on hexose transporter PpHxt1 and pexophagy, Appl. Environ. Microbiol. 76 (2010) 6108–6118. [21] A.S. Polupanov, V.Y. Nazarko, A.A. Sibirny, Gss1 protein of the methylotrophic yeast Pichia pastoris is involved in glucose sensing, pexophagy and catabolite repression, Int. J. Biochem. Cell Biol. 44 (2012) 1906–1918. [22] J.O. Westholm, N. Nordberg, E. Murén, A. Ameur, J. Komorowski, H. Ronne, Combinatorial control of gene expression by the three yeast repressors Mig1, Mig2 and Mig3, BMC Genomics 9 (2008) 601–615. [23] J. Wang, X. Wang, L. Shi, F. Qi, P. Zhang, Y. Zhang, X. Zhou, Z. Song, M. Cai, Methanol-independent protein expression by AOX1 promoter with trans-acting elements engineering and glucose-glycerol-shift induction in Pichia pastoris, Sci. Rep. 7 (2017) 41850–41861. [24] Y. Murakami, S. Kubo, A. Tamori, S. Itami, E. Kawamura, K. Iwaisako, K. Ikeda, N. Kawada, T. Ochiya, Y.H. Taguchi, Comprehensive analysis of transcriptome and metabolome analysis in Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma, Sci. Rep. 5 (2015) 16294–16305. [25] F.A. Trikka, A. Nikolaidis, C. Ignea, A. Tsaballa, L.A. Tziveleka, E. Ioannou, V. Roussis, E.A. Stea, D. Božić, A. Argiriou, Combined metabolome and transcriptome profiling provides new insights into diterpene biosynthesis in S. Pomifera glandular trichomes, BMC Genomics 16 (2015) 935–953. [26] D. Kim, G. Pertea, C. Trapnell, H. Pimentel, R. Kelley, S.L. Salzberg, TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions, Genome Biol. 14 (2013) R36–R48. [27] S. Anders, P.T. Pyl, W. Huber, HTSeq–a Python framework to work with highthroughput sequencing data, Bioinformatics 31 (2015) 166–169. [28] S. Anders, W. Huber, Differential expression analysis for sequence count data, Genome Biol. 11 (2010) R106–R117. [29] Y. Benjamini, Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. R. Stat. Soc. Ser. B 57 (1995) 289–300. [30] X. Mao, C. Tao, J.G. Olyarchuk, L. Wei, Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary, Bioinformatics 21 (2005) 3787–3793. [31] Y. Kodama, M. Shumway, R. Leinonen, The Sequence read archive: explosive growth of sequencing data, Nucleic Acids Res. 40 (2012) 18. [32] A. Sreekumar, L.M. Poisson, T.M. Rajendiran, A.P. Khan, Q. Cao, J. Yu, B. Laxman, R. Mehra, R.J. Lonigro, Y. Li, Metabolomic profiles delineate potential role for
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