Translational arrest due to cytoplasmic redox stress delays adaptation to growth on methanol and heterologous protein expression in a typical fed-batch culture of <i>Pichia pastoris</i>

<div><p>Results</p><p>We have followed a typical fed-batch induction regime for heterologous protein production under the control of the <i>AOX1</i> promoter using both microarray and metabolomic analysis. The genetic constructs involved 1 and 3 copies of the <i>TRY1</i> gene, encoding human trypsinogen. In small-scale laboratory cultures, expression of the 3 copy-number construct induced the unfolded protein response (UPR) sufficiently that titres of extracellular trypsinogen were lower in the 3-copy construct than with the 1-copy construct. In the fed-batch-culture, a similar pattern was observed, with higher expression from the 1-copy construct, but in this case there was no significant induction of UPR with the 3-copy strain. Analysis of the microarray and metabolomic information indicates that the 3-copy strain was undergoing cytoplasmic redox stress at the point of induction with methanol. In this Crabtree-negative yeast, this redox stress appeared to delay the adaptation to growth on methanol and supressed heterologous protein production, probably due to a block in translation.</p><p>Conclusion</p><p>Although redox imbalance as a result of artificially imposed hypoxia has previously been described, this is the first time that it has been characterised as a result of a transient metabolic imbalance and shown to involve a stress response which can lead to translational arrest. Without detailed analysis of the underlying processes it could easily have been mis-interpreted as secretion stress, transmitted through the UPR.</p></div

Persistence of Epigenomic Effects After Recovery From Repeated Treatment With Two Nephrocarcinogens

The discovery of the epigenetic regulation of transcription has provided a new source of mechanistic understanding to long lasting effects of chemicals. However, this information is still seldom exploited in a toxicological context and studies of chemical effect after washout remain rare. Here we studied the effects of two nephrocarcinogens on the human proximal tubule cell line RPTEC/TERT1 using high-content mRNA microarrays coupled with miRNA, histone acetylation (HA) and DNA methylation (DM) arrays and metabolomics during a 5-day repeat-dose exposure and 3 days after washout. The mycotoxin ochratoxin A (OTA) was chosen as a model compound for its known impact on HA and DM. The foremost effect observed was the modulation of thousands of mRNAs and histones by OTA during and after exposure. In comparison, the oxidant potassium bromate (KBrO3) had a milder impact on gene expression and epigenetics. However, there was no strong correlation between epigenetic modifications and mRNA changes with OTA while with KBrO3 the gene expression data correlated better with HA for both up- and down-regulated genes. Even when focusing on the genes with persistent epigenetic modifications after washout, only half were coupled to matching changes in gene expression induced by OTA, suggesting that while OTA causes a major effect on the two epigenetic mechanisms studied, these alone cannot explain its impact on gene expression. Mechanistic analysis confirmed the known activation of Nrf2 and p53 by KBrO3, while OTA inhibited most of the same genes, and genes involved in the unfolded protein response. A few miRNAs could be linked to these effects of OTA, albeit without clear contribution of epigenetics to the modulation of the pathways at large. Metabolomics revealed disturbances in amino acid balance, energy catabolism, nucleotide metabolism and polyamine metabolism with both chemicals. In conclusion, the large impact of OTA on transcription was confirmed at the mRNA level but also with two high-content epigenomic methodologies. Transcriptomic data confirmed the previously reported activation (by KBrO3) and inhibition (by OTA) of protective pathways. However, the integration of omic datasets suggested that HA and DM were not driving forces in the gene expression changes induced by either chemical

The Development of Metabolomic Sampling Procedures for Pichia pastoris, and Baseline Metabolome Data

Metabolic profiling is increasingly being used to investigate a diverse range of biological questions. Due to the rapid turnover of intracellular metabolites it is important to have reliable, reproducible techniques for sampling and sample treatment. Through the use of non-targeted analytical techniques such as NMR and GC-MS we have performed a comprehensive quantitative investigation of sampling techniques for Pichia pastoris. It was clear that quenching metabolism using solutions based on the standard cold methanol protocol caused some metabolite losses from P. pastoris cells. However, these were at a low level, with the NMR results indicating metabolite increases in the quenching solution below 5% of their intracellular level for 75% of metabolites identified; while the GC-MS results suggest a slightly higher level with increases below 15% of their intracellular values. There were subtle differences between the four quenching solutions investigated but broadly, they all gave similar results. Total culture extraction of cells + broth using high cell density cultures typical of P. pastoris fermentations, was an efficient sampling technique for NMR analysis and provided a gold standard of intracellular metabolite levels; however, salts in the media affected the GC-MS analysis. Furthermore, there was no benefit in including an additional washing step in the quenching process, as the results were essentially identical to those obtained just by a single centrifugation step. We have identified the major high-concentration metabolites found in both the extra- and intracellular locations of P. pastoris cultures by NMR spectroscopy and GC-MS. This has provided us with a baseline metabolome for P. pastoris for future studies. The P. pastoris metabolome is significantly different from that of Saccharomyces cerevisiae, with the most notable difference being the production of high concentrations of arabitol by P. pastoris

A software complement to AMDIS for processing GC-MS metabolomic data

The software package AMDIS performs GC-MS peak deconvolution, but tends to produce false positives and leaves missing values where peaks are found in only a proportion of a set of chromatograms. We have developed a software complement to AMDIS that (a) allows rapid manual inspection of chromatographic peaks across all samples, to confirm data quality; and (b) for a given sample set, integrates peak areas across all samples, even where AMDIS deconvolution would leave missing values. The software is a freely available package that runs within the commercial Matlab environment, and is useful for metabolomics and other situations where GC-MS is used to profile many peaks from complex mixtures

Between-Person Comparison of Metabolite Fitting for NMR-Based Quantitative Metabolomics

Nuclear magnetic resonance (NMR) spectroscopy is widely used as an analytical platform for metabolomics. Many studies make use of 1D spectra, which have the advantages of relative simplicity and rapid acquisition times. The spectral data can then be analysed either with a chemometric workflow, or by an initial deconvolution, or fitting, step to generate a list of identified metabolites and associated sample concentrations. Various software tools exist to simplify the fitting process but at least for 1D spectra, this still requires a degree of skilled operator input. It is of critical importance that we know how much person-to-person variability affects the results, in order to be able to judge between different studies. Here we tested a commercially-available software package (Chenomx’ NMR Suite) for fitting metabolites to a set of NMR spectra of yeast extracts, and compared the output of five different people for both metabolite identification and quantitation. An initial comparison showed good agreement for a restricted set of common metabolites with characteristic well-resolved resonances, but wide divergence in the overall identities and number of compounds fitted; re-fitting according to an agreed set of metabolites and spectral processing approach increased the total number of metabolites fitted, but did not dramatically increase the quality of the metabolites that could be fitted without prior knowledge about peak identity. Hence, robust peak assignments are required in advance of manual deconvolution, when the widest range of metabolites is desired. However, very low concentration metabolites still had high coefficients of variation even with shared information on peak assignment. Overall, the effect of person was less than experimental group (in this case, sampling method) for almost all metabolites

Between-Person Comparison of Metabolite Fitting for NMR-Based Quantitative Metabolomics

Nuclear magnetic resonance (NMR) spectroscopy is widely used as an analytical platform for metabolomics. Many studies make use of 1D spectra, which have the advantages of relative simplicity and rapid acquisition times. The spectral data can then be analyzed either with a chemometric workflow or by an initial deconvolution or fitting step to generate a list of identified metabolites and associated sample concentrations. Various software tools exist to simplify the fitting process, but at least for 1D spectra, this still requires a degree of skilled operator input. It is of critical importance that we know how much person-to-person variability affects the results, in order to be able to judge between different studies. Here we tested a commercially available software package (Chenomx’ NMR Suite) for fitting metabolites to a set of NMR spectra of yeast extracts and compared the output of five different people for both metabolite identification and quantitation. An initial comparison showed good agreement for a restricted set of common metabolites with characteristic well-resolved resonances but wide divergence in the overall identities and number of compounds fitted; refitting according to an agreed set of metabolites and spectral processing approach increased the total number of metabolites fitted but did not dramatically increase the quality of the metabolites that could be fitted without prior knowledge about peak identity. Hence, robust peak assignments are required in advance of manual deconvolution, when the widest range of metabolites is desired. However, very low concentration metabolites still had high coefficients of variation even with shared information on peak assignment. Overall, the effect of the person was less than the experimental group (in this case, sampling method) for almost all of the metabolites

Metabolite accumulation profiles during fed-batch culture from 0 (before) to 24 hours after the start of methanol addition to wild-type GS115 (black), <i>TRY1-1</i>(blue) and <i>TRY1-3</i> (red), strains containing 1 and 3 gene copies of the human typsinogen gene, respectively, under the control of the <i>AOX1</i> promoter.

<p>From top to bottom, panels represent a) formate (external), b) arabitol (external), c) trehalose (external), d) α / β-D-glucose (external), e) lactate (internal). Error bars show SEM.</p

The number of unique probes on the micro-array per gene.

<p>A total of 3371 genes were targeted by 9 unique probes, with the average length of these genes being 1844 bases.</p><p>The number of unique probes on the micro-array per gene.</p

Expression of <i>HAC1</i> during fed-batch culture, 0 (before), 2 and 4 hours after the start of methanol addition to wild-type GS115 (black), <i>TRY1-1</i>(blue) and <i>TRY1-3</i> (red), strains containing 1 and 3 gene copies of the human typsinogen gene, respectively, under the control of the <i>AOX1</i> promoter.

<p>Error bars show SEM.</p

Expression of <i>PCK1</i>, <i>PYC</i> and <i>PYK</i> during fed-batch culture, 0 (before), 2 and 4 hours after the start of methanol addition to wild-type GS115 (black), <i>TRY1</i>-1 (blue) and <i>TRY1</i>-3 (red).

<p>Error bars show SEM.</p