TY - JOUR
AU - Bundy, Jacob G
AB - Abstract Heterologous protein production in the yeast Pichia pastoris can be limited by biological responses to high expression levels; the unfolded protein response (UPR) is a key determinant of the success of protein production in this organism. Here, we used untargeted NMR metabolic profiling (metabolomics) of a number of different recombinant strains, carried out in a miniaturized format suitable for screening-level experiments. We identified a number of metabolites (from both cell extracts and supernatants) which correlated well with UPR-relevant gene transcripts, and so could be potential biomarkers for future high-throughput screening of large numbers of P. pastoris clones. G. D. Tredwell and R. Aw contributed equally. Electronic supplementary material The online version of this article (doi:10.1007/s10295-017-1904-5) contains supplementary material, which is available to authorized users. Introduction The yeast Pichia pastoris is an important platform for process-scale production of recombinant secreted proteins. It can be grown to very high cell densities in bioreactors, under forced aeration, to improve yields. However, the specific productivity is often low. Simply increasing promoter strength for recombinant genes is not a panacea, as biological feedback loops may result in decreased levels of secreted protein. A key mechanism in this regard is a homeostatic response to the buildup of unfolded protein in the endoplasmic reticulum (ER), the unfolded protein response (UPR), a transcriptional programme that is regulated by activation at UPR elements (UPRE) by a transcription factor generated by splicing of HAC1 mRNA [21]. Although there is some argument about the detailed mechanism, in one model an increase of unfolded protein in the ER binds the chaperone Kar2p, causing it to dissociate from the transmembrane protein Ire1p. This then activates a cytoplasmic endoribonuclease activity in Ire1p, allowing it to splice HAC1 mRNA [11], generating a transcriptionally competent and active gene encoding the transcription factor, Hac1p. This binds to UPRE sites in UPR-responsive promoter regions, including HAC1, enhancing the expression. As many of the UPR-responsive genes encode chaperones and related factors which can enhance protein folding, this leads to a positive feedback loop. Low levels of UPR can improve protein production by increasing the expression of chaperones, but high levels of UPR can decrease it by shutting down translation altogether and activating the ER-associated degradation (ERAD) pathway leading to protein degradation [7, 25]. As the UPR is so critical for protein secretion, it is often used as an indicator to determine whether cells are stressed, and based on the premise that stressed cells will result in lower secretion, this is often used in strain design to find good secretors. However, traditional methods for screening for UPR require separate steps for extracting RNA and then determining transcript levels, or analysis of protein concentrations, which can also be time consuming and laborious. Therefore, any biological indicator that could be used to estimate potential levels of cellular stress could be very valuable. A key step in scaling up secreted protein production is in selection of strains for testing at larger volumes. There is a wide variation in secretion levels in recombinant clones, and companies often screen thousands of colonies to identify the best secretor before scaling up to the bioreactor culture [6, 27]. Metabolic profiling is widely used as a strategy to report on cellular physiology. Metabolite changes within a cell are extremely dynamic, so metabolic profiles give a real-time picture of cellular responses and metabolites form arguably the most direct representation of cellular phenotype [23]. Furthermore, metabolomic results are readily converted into target assays well suited to rapid screening and/or online monitoring [5]. There is also considerable interest in using metabolic profiling of bioprocess supernatants for rational optimization of medium composition [2, 16, 24]. Metabolomic analysis has been used to determine the impact of recombinant protein expression in P. pastoris under varying growth conditions, including oxygen limitation and glucose–methanol co-utilization [4, 14]. Furthermore, Jorda et al. used the combination of metabolic flux analysis and metabolomic analysis to determine the impact on metabolism of expressing Rhizopus oryzae lipase: trehalose levels were significantly upregulated, indicating an increased flux through the ATP futile cycle, which may be related to the induction of the UPR [15]. These investigations into the use of metabolomics analysis provide a basis for potential metabolic engineering that may assist with identifying bottlenecks in the production of recombinant proteins. Here, we generated metabolic profiles for a series of P. pastoris constructs that varied in their levels of UPR induction and carried out a proof-of-principle study to determine if there were any potential metabolite biomarkers of UPR. Materials and methods Strains and growth conditions Pichia pastoris GS115 (recently reclassified as Komagataella sp. [17]) was obtained from Invitrogen, Paisley, UK. The three-copy trypsinogen strain was generated as previously described [10]. The human lysozyme strains were a gift from Prof. David Archer (Nottingham) [28]. The Mfe-23 and BC1 strains were a gift from Dr. Kate Royle. P. pastoris strains were grown in 24-well plates (Corning, NY, USA) in BMG [100 mM potassium phosphate, pH 6.0, 1.34% (w/v) yeast nitrogen base (YNB), 4 × 10–5% (w/v) D-Biotin, 1% (v/v) glycerol)] for 24 h. Cells were centrifuged for 5 min at 4000 rpm at room temperature and the supernatant removed. The cultures were then re-suspended in the methanol-containing medium BMM for expression (as BMG but with 0.5% v/v methanol replacing glycerol) and incubated for 4 h. Reverse transcription-qPCR For reverse transcription (RT)-qPCR, RNA was isolated using RiboPure Yeast Kit, according to the manufacturer’s instructions (Applied Biosystems, Warrington, UK). cDNA was prepared using the High-Capacity cDNA Archive Kit (Applied Biosystems, Warrington, UK). 1 μg RNA was used in a total reaction volume of 20 μL. RT-qPCR reactions were set up using the 2X SYBR® Green JumpStart Taq Ready Mix (Sigma-Aldrich, Dorset, UK). A Chromo4™ Real-Time Detector using the thermal cycler software Opticon 3 (Bio-Rad, Hemel Hempstead, UK) was used. Data were analysed using the Pfaffl method, based on ΔΔ-Ct [19, 22] and normalized to ACT1 as the housekeeping gene. Primers for ACT1 were GCT TTG TTC CAC CCA TCT GT and TGC ATA CGC TCA GCA ATA CC. Primers for HAC1 were CGA CTA CAT TAC TAC AGC TCC ATC A and TGC TGT AAT GTG TAA AGA TGA ATC C, for PDI, GCC GTT AAA TTC GGT AAG CA and TCA GCT CGG TCA CAT CTT TG and for KAR2, TCA AAG ACG CTG GTG TCA AG and TAT GCG ACA GCT TCA TCT GG. Metabolite analysis Extracellular and intracellular metabolite concentrations were measured by 1H NMR. For extracellular metabolite measurements, samples from the incubation plate (1 ml) were quickly centrifuged and decanted before being frozen on dry ice. For intracellular metabolite measurements, a total quenching procedure of whole broth (cells + media) with 60% aqueous methanol and 0.11 M ammonium bicarbonate was used to halt metabolism, as previously described [26]. The samples were dried under reduced pressure and reconstituted in 600 μl of NMR buffer (1 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP), 0.1 M phosphate buffer, pH 7.4, in 95% D2O). Samples (550 μl) were transferred to 5 mm NMR tubes and spectra were acquired on a Bruker Avance DRX600 NMR spectrometer (Bruker BioSpin, Rheinstetten, Germany), equipped with a 5 mm inverse probe, operating at a 1H frequency of 600 MHz. Samples were introduced with an automatic sampler and spectra were acquired using standard approaches (Dona, 2014). Briefly, 2D JRES 1H NMR spectra were acquired with the pulse-sequence d1-90y-τ-180-τ-acquire FID, with suppression of the water resonance during d1. Data were acquired into 32 k data points in F2 with a d1 of 2 s and 16 transients using 32 increments of τ; the spectral widths in F2 and F1 were 12,019 and 50 Hz, respectively. Spectra were processed in iNMR 3.4 (Nucleomatica, Molfetta, Italy). Fourier transform of the free-induction decay was applied with a line broadening of 0.5 Hz. Metabolite peak integrals were obtained with rNMR [18] and expressed relative to TSP (Supplementary Material). Results and discussion We used eight different strains expressing different recombinant proteins (Table 1) and grew them in miniaturized cultures in 24 deep-well microtitre plates. Strains with either one or three copies of the trypsinogen gene and human lysozyme (HuL) mutational variants that have been shown to have different stabilities were used, as these have been shown to induce the UPR [13, 28]. Hesketh et al. [12] selected two of the mutant variants, T70N and I56T, and using continuous culture observed variations using RNAseq. Although they determined “reporter metabolites” for these two strains, this was done solely through annotations of the transcriptomic data and did not include any direct metabolite analysis. We also included BC1 and Mfe-23, two single chain antibody fragments (ScFvs), which were chosen as these are industrially relevant products, showing the applicability of our work in an industrial context. All genes were expressed using the AOX1 promoter and α-mating factor (α-MF) secretion signal from Saccharomyces cerevisiae that targets the protein to the secretory pathway. Cultures were grown for 24 h in a buffered glycerol minimal medium and then switched to a buffered methanol minimal medium to induce protein expression. Samples were taken after 4 h of induction. We monitored UPR levels by measuring HAC1, KAR2 and PDI1 transcripts by qRT-PCR using ACT1 as a reference gene. All strains were normalized to wild type to allow for the background of potential cellular stress effects caused by the switch to methanol medium [10]. The two trypsinogen strains had the highest levels of HAC1 induction. Summary table of strains used in this study indicating the recombinant protein expressed, dry cell weight (g/L) growth after 24 h in glycerol, followed by 4 h expression in methanol-containing medium and fold difference of transcript levels of HAC1, KAR2 and PDI1 compared to wild-type GS115 Strain . Recombinant protein . Source . Dry cell weight (g/L) . Fold difference compared to GS115 at 0 h . HAC1 . KAR2 . PDI1 . T1 Trypsinogen (1 copy) [15] 0.492 ± 0.175 2.132 ± 0.656 −0.353 ± 0.603 0.511 ± 0.326 T3 Trypsinogen (3 copy) [15] 0.436 ± 0.072 2.912 ± 0.314 0.392 ± 0.753 1.378 ± 0.826 L Lysozyme (wild-type) [20] 0.990 ± 0.592 0.307 ± 0.412 0.145 ± 0.225 0.170 ± 0.527 I8 Lysozyme (I89V variant) [20] 1.128 ± 0.439 −0.268 ± 1.117 0.073 ± 0.446 0.491 ± 0.591 T7 Lysozyme (T70A variant) [20] 0.895 ± 0.489 0.608 ± 0.600 0.200 ± 0.563 0.532 ± 0.475 I5 Lysozyme (I56T variant) [20] 0.866 ± 0.173 −0.327 ± 0.675 0.567 ± 0.333 0.504 ± 0.340 B BC1 Unpublished work in our laboratory 0.563 ± 0.058 1.173 ± 0.090 −1.130 ± 0.912 0.246 ± 0.606 M Mfe23 Unpublished work in our laboratory 0.651 ± 0.172 0.843 ± 0.440 −1.295 ± 1.206 0.487 ± 0.540 Strain . Recombinant protein . Source . Dry cell weight (g/L) . Fold difference compared to GS115 at 0 h . HAC1 . KAR2 . PDI1 . T1 Trypsinogen (1 copy) [15] 0.492 ± 0.175 2.132 ± 0.656 −0.353 ± 0.603 0.511 ± 0.326 T3 Trypsinogen (3 copy) [15] 0.436 ± 0.072 2.912 ± 0.314 0.392 ± 0.753 1.378 ± 0.826 L Lysozyme (wild-type) [20] 0.990 ± 0.592 0.307 ± 0.412 0.145 ± 0.225 0.170 ± 0.527 I8 Lysozyme (I89V variant) [20] 1.128 ± 0.439 −0.268 ± 1.117 0.073 ± 0.446 0.491 ± 0.591 T7 Lysozyme (T70A variant) [20] 0.895 ± 0.489 0.608 ± 0.600 0.200 ± 0.563 0.532 ± 0.475 I5 Lysozyme (I56T variant) [20] 0.866 ± 0.173 −0.327 ± 0.675 0.567 ± 0.333 0.504 ± 0.340 B BC1 Unpublished work in our laboratory 0.563 ± 0.058 1.173 ± 0.090 −1.130 ± 0.912 0.246 ± 0.606 M Mfe23 Unpublished work in our laboratory 0.651 ± 0.172 0.843 ± 0.440 −1.295 ± 1.206 0.487 ± 0.540 All data represented as the average of three biological repeats with standard deviation shown Open in new tab Summary table of strains used in this study indicating the recombinant protein expressed, dry cell weight (g/L) growth after 24 h in glycerol, followed by 4 h expression in methanol-containing medium and fold difference of transcript levels of HAC1, KAR2 and PDI1 compared to wild-type GS115 Strain . Recombinant protein . Source . Dry cell weight (g/L) . Fold difference compared to GS115 at 0 h . HAC1 . KAR2 . PDI1 . T1 Trypsinogen (1 copy) [15] 0.492 ± 0.175 2.132 ± 0.656 −0.353 ± 0.603 0.511 ± 0.326 T3 Trypsinogen (3 copy) [15] 0.436 ± 0.072 2.912 ± 0.314 0.392 ± 0.753 1.378 ± 0.826 L Lysozyme (wild-type) [20] 0.990 ± 0.592 0.307 ± 0.412 0.145 ± 0.225 0.170 ± 0.527 I8 Lysozyme (I89V variant) [20] 1.128 ± 0.439 −0.268 ± 1.117 0.073 ± 0.446 0.491 ± 0.591 T7 Lysozyme (T70A variant) [20] 0.895 ± 0.489 0.608 ± 0.600 0.200 ± 0.563 0.532 ± 0.475 I5 Lysozyme (I56T variant) [20] 0.866 ± 0.173 −0.327 ± 0.675 0.567 ± 0.333 0.504 ± 0.340 B BC1 Unpublished work in our laboratory 0.563 ± 0.058 1.173 ± 0.090 −1.130 ± 0.912 0.246 ± 0.606 M Mfe23 Unpublished work in our laboratory 0.651 ± 0.172 0.843 ± 0.440 −1.295 ± 1.206 0.487 ± 0.540 Strain . Recombinant protein . Source . Dry cell weight (g/L) . Fold difference compared to GS115 at 0 h . HAC1 . KAR2 . PDI1 . T1 Trypsinogen (1 copy) [15] 0.492 ± 0.175 2.132 ± 0.656 −0.353 ± 0.603 0.511 ± 0.326 T3 Trypsinogen (3 copy) [15] 0.436 ± 0.072 2.912 ± 0.314 0.392 ± 0.753 1.378 ± 0.826 L Lysozyme (wild-type) [20] 0.990 ± 0.592 0.307 ± 0.412 0.145 ± 0.225 0.170 ± 0.527 I8 Lysozyme (I89V variant) [20] 1.128 ± 0.439 −0.268 ± 1.117 0.073 ± 0.446 0.491 ± 0.591 T7 Lysozyme (T70A variant) [20] 0.895 ± 0.489 0.608 ± 0.600 0.200 ± 0.563 0.532 ± 0.475 I5 Lysozyme (I56T variant) [20] 0.866 ± 0.173 −0.327 ± 0.675 0.567 ± 0.333 0.504 ± 0.340 B BC1 Unpublished work in our laboratory 0.563 ± 0.058 1.173 ± 0.090 −1.130 ± 0.912 0.246 ± 0.606 M Mfe23 Unpublished work in our laboratory 0.651 ± 0.172 0.843 ± 0.440 −1.295 ± 1.206 0.487 ± 0.540 All data represented as the average of three biological repeats with standard deviation shown Open in new tab We carried out untargeted proton NMR metabolic profiling of both the exometabolome (i.e. cell supernatants) and “total quenching” of both exo- and endometabolome (i.e. cells + supernatants together). Total quenching of P. pastoris broths combined with NMR profiling gives useful data on intracellular metabolites that contain effectively as much information as does quenching in cold solvent and removing the supernatant [26]. Most metabolites showed quite different patterns between the exometabolome and the total quench results, with the exception of some organic acids (formate, 2-oxoisocaproate, 3-methyl-2-oxovalerate), which we already knew to be supernatant contaminants, and glycerophosphocholine; the implication is that the culture supernatant and the total quench (whole broth) data represent two independent datasets, both of which could contain potential biomarkers. There were some robust correlations between the individual gene expression levels (HAC1, KAR2, PDI1) and individual metabolites, although there were no metabolites which were significantly correlated for all three genes (Fig. 1). Induction of HAC1 under these conditions can be used as an indicator of UPR, as this is assumed to reflect activation at the UPRE in the promoter region by Hac1p. The lack of complete correlation is only to be expected, as there were no strong correlations between the different transcript levels: HAC1 vs. PDI1 was the only pair that was significantly related at the 0.05 level (P = 0.024), but the relationship was still weak (r2 = 0.21). However, HAC1 expression was also strongly associated with the extent of growth for each strain (as represented by dry cell weight, DCW). Because of this, we also calculated partial correlations for each gene in turn against both DCW and metabolite concentration, i.e. representing the gene/metabolite association once the effect of DCW has been allowed for. Fig. 1 Open in new tabDownload slide Metabolites associated with genes relevant to the unfolded protein response; axes represent P values derived from partial correlations. Metabolites were measured by 2D J-resolved NMR spectroscopy using standard approaches [8], which offers benefits of reduced overlap between resonances in the crowded 1H spectrum, and individual metabolite peaks were selected and quantitated using rNMR [15]. Each dot represents a single metabolite; blue dot isoleucine; red square aspartate; yellow diamond arabitol (color figure online) In general, HAC1 levels were much more strongly associated with DCW than individual metabolite concentrations based on total quenching, whereas the relative effect of DCW was much less for KAR2 and PDI1 (Fig. S1). There were no metabolites which were associated with all three genes at the P < 0.05 level, but on comparing across all three, it was possible to identify some potential biomarker candidates: arabitol, aspartate and isoleucine were among the most highly associated metabolites for all genes (Fig. 1). (The associations were much weaker for the supernatant metabolites (Fig. S2), so only the total quenching results are discussed from here on.) Why were those specific metabolites correlated with gene expression? Arabitol is produced as an osmolyte by many fungi, including P. pastoris. It probably acts here as part of a general mechanism to reduce cell stress: different stresses in P. pastoris, including misfolded protein, can increase arabitol levels [1, 9]. For aspartate and isoleucine, though, it is also worth testing whether the changes in intracellular amino acid pool sizes were associated with the amino acid requirement of the different recombinant proteins—i.e. were Asp and Ile particular bottlenecks for the recombinant proteins? There are some severe limitations to this: for instance, the data we have on the amino acid composition of P. pastoris do not distinguish between Gln and Glu, or Asp and Asn, and so we had to combine data for Glx and Asx [3]; and the recombinant protein composition data are obviously not different for the one-copy and three-copy trypsinogen strain and differ only slightly for the lysozyme strains. Nonetheless, as the mismatch for Asp and Ile fall within the range of all the amino acids for all the strains (Fig. S3), we conclude that there is no evidence of an association with amino acid requirements. Similarly, Carnicer et al. also found no association between amino acid pools and recombinant protein sequence for P. pastoris, although this was for the different case of a single protein grown under multiple conditions [4]. Additionally, Jorda et al. found the same result when comparing an expressing and non-expressing strain of Rol. However, it was determined that an adaptation of the amino acid concentration distribution may have been as a consequence of additional energy demands during recombinant protein production (with particular reference to an increase in trehalose) [14, 15]. Conclusions Our current data show that monitoring UPR levels by gene transcription is certainly feasible at a potentially high-throughput miniaturized scale and can therefore address a key bottleneck in the pathway to produced secreted proteins. However, this is relatively time consuming and laborious to do by traditional amplification-based methods, once all the different steps required have been allowed for [20]. We have found robust correlations between metabolites and KAR2/PDI1 transcript levels, in particular (once DCW has been allowed for), which indicate that metabolites could potentially be used as proxy indicators of UPR stress. Metabolite biomarkers have high precision and are well suited to high-throughput sampling and analysis, and therefore offer an alternative approach to screen for clones with low levels of UPR. Furthermore, this approach could be easily extended to process monitoring in bioreactors, especially as some of the potential biomarkers included supernatant metabolites. Acknowledgements This study was wholly funded by the Bioprocessing Research Industry Club (BRIC), a partnership between BBSRC, EPSRC and a consortium of leading companies (http://www.bbsrc.ac.uk/business/collaborative-research/industry-clubs/bric/background.aspx, Grant reference BBF0049071). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank David Archer (University of Nottingham) and Kate Royle (Imperial College London) for kindly supplying strains used in our current study. References 1. Baumann K , Carnicer M, Dragosits Met al. A multi-level study of recombinant Pichia pastoris in different oxygen conditions BMC Syst Biol 2010 2987880 Google Scholar OpenURL Placeholder Text WorldCat 2. Bradley SA , Ouyang A, Purdie Jet al. Fermentanomics: monitoring mammalian cell cultures with NMR spectroscopy J Am Chem Soc 2010 132 9531 9533 10.1021/ja101962c Google Scholar Crossref Search ADS PubMed WorldCat 3. Carnicer M , Baumann K, Töplitz Iet al. Macromolecular and elemental composition analysis and extracellular metabolite balances of Pichia pastoris growing at different oxygen levels Microb Cell Fact 2009 8 65 10.1186/1475-2859-8-65 2799386 Google Scholar Crossref Search ADS PubMed WorldCat 4. Carnicer M , Ten Pierick A, van Dam Jet al. Quantitative metabolomics analysis of amino acid metabolism in recombinant Pichia pastoris under different oxygen availability conditions Microb Cell Fact 2012 11 83 10.1186/1475-2859-11-83 3538582 Google Scholar Crossref Search ADS PubMed WorldCat 5. Chong WPK , Thng SH, Hiu APet al. LC–MS-based metabolic characterization of high monoclonal antibody-producing Chinese hamster ovary cells Biotechnol Bioeng 2012 109 3103 3111 10.1002/bit.24580 Google Scholar Crossref Search ADS PubMed WorldCat 6. Cregg JM , Vedvick TS, Raschke WC Recent advances in the expression of foreign genes in Pichia pastoris Biotechnology (NY) 1993 11 905 910 10.1038/nbt0893-905 Google Scholar OpenURL Placeholder Text WorldCat Crossref 7. Delic M , Rebnegger C, Wanka Fet al. Oxidative protein folding and unfolded protein response elicit differing redox regulation in endoplasmic reticulum and cytosol of yeast Free Radic Biol Med 2012 52 2000 2012 10.1016/j.freeradbiomed.2012.02.048 Google Scholar Crossref Search ADS PubMed WorldCat 8. Dona AC , Jiménez B, Schäfer Het al. Precision high-throughput proton NMR spectroscopy of human urine, serum, and plasma for large-scale metabolic phenotyping Anal Chem 2014 86 9887 9894 10.1021/ac5025039 Google Scholar Crossref Search ADS PubMed WorldCat 9. Dragosits M , Stadlmann J, Graf ABet al. The response to unfolded protein is involved in osmotolerance of Pichia pastoris BMC Genomics 2015 11 207 10.1186/1471-2164-11-207 Google Scholar Crossref Search ADS WorldCat 10. Edwards-Jones B , Aw R, Barton GRet al. Translational arrest due to cytoplasmic redox stress delays adaptation to growth on methanol and heterologous protein expression in a typical fed-batch culture of Pichia pastoris PLoS One 2015 10 e0119637 10.1371/journal.pone.0119637 4364781 Google Scholar Crossref Search ADS PubMed WorldCat 11. Guerfal M , Ryckaert S, Jacobs PPet al. The HAC1 gene from Pichia pastoris: characterization and effect of its overexpression on the production of secreted, surface displayed and membrane proteins Microb Cell Fact 2010 9 49 10.1186/1475-2859-9-49 2905327 Google Scholar Crossref Search ADS PubMed WorldCat 12. Hesketh AR , Castrillo JI, Sawyer Tet al. Investigating the physiological response of Pichia (Komagataella) pastoris GS115 to the heterologous expression of misfolded proteins using chemostat cultures Appl Microbiol Biotechnol 2013 97 9747 9762 10.1007/s00253-013-5186-1 3825213 Google Scholar Crossref Search ADS PubMed WorldCat 13. Hohenblum H , Gasser B, Maurer Met al. Effects of gene dosage, promoters, and substrates on unfolded protein stress of recombinant Pichia pastoris Biotechnol Bioeng 2004 85 367 375 10.1002/bit.10904 Google Scholar Crossref Search ADS PubMed WorldCat 14. Jorda J , Suarez C, Carnicer Met al. Glucose-methanol co-utilization in Pichia pastoris studied by metabolomics and in stationary (1)(3)C flux analysis BMC Syst Biol 2013 7 17 10.1186/1752-0509-7-17 3626722 Google Scholar Crossref Search ADS PubMed WorldCat 15. Jorda J et al. Quantitative metabolomics and instationary 13C-metabolic flux analysis reveals impact of recombinant protein production on trehalose and energy metabolism in Pichia pastoris Metabolites 2014 4 281 299 10.3390/metabo4020281 4101507 Google Scholar Crossref Search ADS PubMed WorldCat 16. Korneli C , Bolten CJ, Godard Tet al. Debottlenecking recombinant protein production in Bacillus megaterium under large-scale conditions—targeted precursor feeding designed from metabolomics Biotechnol Bioeng 2012 109 1538 1550 10.1002/bit.24434 Google Scholar Crossref Search ADS PubMed WorldCat 17. Kurtzman CP Description of Komagataella phaffii sp. nov. and the transfer of Pichia pseudopastoris to the methylotrophic yeast genus Komagataella Int J Syst Evol Microbiol 2005 55 973 976 10.1099/ijs.0.63491-0 Google Scholar Crossref Search ADS PubMed WorldCat 18. Lewis IA , Schommer SC, Markley JL rNMR: open source software for identifying and quantifying metabolites in NMR spectra Magn Reson Chem 2009 47 Suppl 1 S123 S126 10.1002/mrc.2526 2798074 Google Scholar PubMed OpenURL Placeholder Text WorldCat 19. Livak KJ , Schmittgen TD Analysis of relative gene expression data using real-time quantitative PCR and the 2−∆∆CT method Methods 2001 25 402 408 10.1006/meth.2001.1262 Google Scholar Crossref Search ADS PubMed WorldCat 20. Love KR , Politano TJ, Panagiotou Vet al. Systematic single-cell analysis of Pichia pastoris reveals secretory capacity limits productivity PLoS One 2012 7 e37915 10.1371/journal.pone.0037915 3369916 Google Scholar Crossref Search ADS PubMed WorldCat 21. Mattanovich D , Gasser B, Hohenblum H, Sauer M Stress in recombinant protein producing yeasts J Biotechnol 2004 113 121 135 10.1016/j.jbiotec.2004.04.035 Google Scholar Crossref Search ADS PubMed WorldCat 22. Pfaffl MW A new mathematical model for relative quantification in real-time RT-PCR Nucleic Acids Res 2001 29 2002 2007 10.1093/nar/29.9.e45 Google Scholar Crossref Search ADS WorldCat 23. Raamsdonk LM , Teusink B, Broadhurst Det al. A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations Nat Biotechnol 2001 19 45 50 10.1038/83496 Google Scholar Crossref Search ADS PubMed WorldCat 24. Sellick CA , Croxford AS, Maqsood ARet al. Metabolite profiling of recombinant CHO cells: designing tailored feeding regimes that enhance recombinant antibody production Biotechnol Bioeng 2011 108 3025 3031 10.1002/bit.23269 Google Scholar Crossref Search ADS PubMed WorldCat 25. Smith MH , Ploegh HL, Weissman JS Road to ruin: targeting proteins for degradation in the endoplasmic reticulum Science 2011 334 1086 1090 10.1126/science.1209235 Google Scholar Crossref Search ADS PubMed WorldCat 26. Tredwell GD , Edwards-Jones B, Leak DJ, Bundy JG The development of metabolomic sampling procedures for Pichia pastoris, and baseline metabolome data PLoS One 2011 6 e16286 10.1371/journal.pone.0016286 3025026 Google Scholar Crossref Search ADS PubMed WorldCat 27. Viader-Salvadó JM , Cab-Barrera EL, Galán-Wong LJ, Guerrero-Olazarán M Genotyping of recombinant Pichia pastoris strains Cell Mol Biol Lett 2006 11 348 359 10.2478/s11658-006-0029-z Google Scholar Crossref Search ADS PubMed WorldCat 28. Whyteside G , Alcocer MJC, Kumita JRet al. Native-state stability determines the extent of degradation relative to secretion of protein variants from Pichia pastoris PLoS One 2011 6 e22692 10.1371/journal.pone.0022692 3144928 Google Scholar Crossref Search ADS PubMed WorldCat © Society for Industrial Microbiology 2017 This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © Society for Industrial Microbiology 2017
TI - Rapid screening of cellular stress responses in recombinant Pichia pastoris strains using metabolite profiling
JF - Journal of Industrial Microbiology and Biotechnology
DO - 10.1007/s10295-017-1904-5
DA - 2017-03-01
UR - https://www.deepdyve.com/lp/oxford-university-press/rapid-screening-of-cellular-stress-responses-in-recombinant-pichia-UC5ZE0kM1E
SP - 413
EP - 417
VL - 44
IS - 3
DP - DeepDyve
ER -