Impact of cellular redox state on protein production and secretion in yeast
SUPERVISORS: Brigitte GASSER and Jürgen ZANGHELLINI
The yeast Pichia pastoris (syn. Komagataella spp.) has emerged as an efficient host for recombinant protein production, both for biotherapeutics and industrial enzymes. However, folding and secretion of complex proteins is often limited, leading to reduced yields.
We have previously seen that overexpression of recombinant secretory proteins in the yeast P. pastoris leads to a reduction of the redox state in the cytosol (Delic et al. 2012). Overexpression of the transcription factor Yap1 rescued this phenotype by rebalancing the cytosolic redox state to wild type levels (Delic et al. 2014). In yeasts, Yap1 is responsible for inducing the oxidative stress response, including up-regulation of the glutathione, thioredoxin and glutaredoxin pathways and superoxide dismutase-based detoxification of reactive oxygen species (reviewed by Herrero et al. 2008, López-Mirabal & Winther, 2008 and Toledano et al. 2012). Specifically, we found 37 genes involved in oxidative stress response or encoding proteins with oxidoreductase activity to be regulated by Yap1 overexpression in P. pastoris (Delic et al. 2014). In P. pastoris Yap1 has also been implicated with oxidative stress caused by utilization of the substrate methanol, and glutathione peroxidase Gpx1/Hyr1 and glutathione reductase Glr1 were the mainly involved redox active enzymes (Yano et al. 2009). In contrast, overexpression of Glr1 and Gpx1/Hyr1 did not counteract the cytosolic reduction in recombinant protein secreting P. pastoris grown in glucose (Delic et al. 2012), even though the latter had a positive influence on productivity (Delic et al. 2012; Ben Azoun et al. 2016).
As rational selection of single targets of the Yap1-regulon did not lead to the anticipated response, a model-based approach is needed that allows to account for the apparent systems effect in order to identify which genes or group of genes are responsible for the phenotype. Furthermore, there is an interdependence between glutathione and other redox equivalents. For example, in yeast and mammalian cells oxidative stress immediately induces a rerouting of metabolic flux from glycolysis into the pentose phosphate pathway to compensate the acute demand of NADPH upon oxidative stress (Kuehne et al., 2015). Indeed, enhancing NADPH levels increased protein production in P. pastoris (Nocon et al. 2012; 2014), however, no information on the glutathione redox status is available for these strains.
Which members of the Yap1-regulon create the positive effect on protein secretion?
Which cellular reactions need to be modulated to rebalance the cytosolic redox state during protein secretion?
Model the impact of increased levels of proteins belonging to the Yap1 regulon to identify key components that regulate glutathione oxidation and NADPH production.
- Analyse available P. pastoris mutants for ratios of oxidized and reduced glutathione and NAD(P)H in ER, cytosol and mitochondria by fluorescent biosensors (GASSER) and LC-MS/MS (group of HANN)
- Employ “cycle of knowledge” strategy starting from available metabolic model (ZANGHELLINI)
- Measure enzyme activity to get kinetic parameters where necessary (group of HALTRICH)
Predict engineering targets that create redox conditions beneficial for protein folding and secretion (ZANGHELLINI)
Experimentally validate engineering targets in P. pastoris and their impact on protein secretion (GASSER)
In depth knowledge on key components that regulate glutathione oxidation and NADPH production in P. pastoris
P. pastoris strains with modified redox conditions and improved secretion
Project plan and Methods.
Initially, available P. pastoris mutants with distinct perturbations in the redox metabolism (such as Gpx1 or Glr1-overproducing strains, strains with enhanced NADPH levels or impaired NADPH regeneration) will be analyzed with the recently established fluorescent redox biosensors (as in Tao et al. 2017). As there is an interdependence between glutathione and other redox equivalents (NADP(H), NAD(H), FAD(H)), these systems cannot be considered independently, but the influence of mutating one pathway on the others needs to be taken into account.
Starting from an approximate mathematical model derived and adapted from Adimora et al. (2010) and Komalapriya et al. (2015), a “cycle of knowledge” strategy will then be employed to identify key components of the Yap1-regulon that are regulating glutathione oxidation and NADPH production in recombinant P. pastoris. If necessary, enzyme activity will be analysed to get kinetic parameter under a standardized set of conditions. Based on this bottom up reconstruction, the main points of control will be identified and advanced intervention strategies will be predicted. The respective overexpression or knock out strains (single or multiple target genes) will be generated using state-of-the-art methods such as the established Golden Gate platform GoldenPiCS (Prielhofer et al. 2017) and CRISPR/Cas9 (Gassler et al. 2018). Subsequently, total cellular pools of the redox equivalents will be measured (Haberhauer et al. 2013, Ortmayr et al. 2014, Nocon et al. 2014), and compartment-specific redox ratios will be determined with the established fluorescence based sensors for glutathione (roGFP_iX; Delic et al. 2010), NAD(H) and NADPH (Sonar and iNAP; Zhao & Yang, 2016; Tao et al. 2017; adapted for P. pastoris during the thesis of David Pena) targeted to the cytosol, ER and mitochondria, respectively. Furthermore, the strains will be tested for their ability to secrete recombinant proteins. Finally, the results of these analyses should be re-fed into the computational modelling pipeline for further analysis.
Collaborations and time plan
Analysis of existing P. pastoris mutants (6-9 months in the 1st year), and generation of novel strains based on the predictions and their characterization (15 months in years 2 and 3) will be performed in the group of Brigitte GASSER. Computational modelling (modelling of relevant parts of the Yap1 regulon; prediction of cell engineering targets) will take place in the group of Jürgen ZANGHELLINI (9-12 months in the 1st/2nd year plus 3 months in the last year). Further collaborations within this thesis will include the group of Dietmar HALTRICH for help with enzyme activity assays. Analysis of redox active metabolites such as glutathione will be measured in the group of Stephan HANN (measurements to be performed by BioToP faculty; for methods see: Haberhauer-Troyer et al., 2013; Ortmayr et al., 2014; Nocon et al. 2016).
A stay abroad of 6 months is anticipated, depending on the PhD student´s background this might be e.g. at the Autonomous University of Barcelona (Spain) in Pau FERRER's lab or at Stefan KLAMT´s lab at the Max Plank Institute Magdeburg (Germany).
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