Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) is known to contain an active-site cysteine residue undergoing oxidation in response to hydrogen peroxide, leading to rapid inactivation of the enzyme. Here we show that human and mouse cells expressing a GAPDH mutant lacking this redox switch retain catalytic activity but are unable to stimulate the oxidative pentose phosphate pathway and enhance their reductive capacity. Specifically, we find that anchorage-independent growth of cells and spheroids is limited by an elevation of endogenous peroxide levels and is largely dependent on a functional GAPDH redox switch. Likewise, tumour growth in vivo is limited by peroxide stress and suppressed when the GAPDH redox switch is disabled in tumour cells. The induction of additional intratumoural oxidative stress by chemo- or radiotherapy synergized with the deactivation of the GAPDH redox switch. Mice lacking the GAPDH redox switch exhibit altered fatty acid metabolism in kidney and heart, apparently in compensation for the lack of the redox switch. Together, our findings demonstrate the physiological and pathophysiological relevance of oxidative GAPDH inactivation in mammals.

Two Cu^II complexes with branched tetrapeptides undergo PCET and catalyse water oxidation, processes promoted by the presence of C-terminal histidine.

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care.

Quantitative mass spectrometry (MS)-based proteomics has become a streamlined technology with a wide range of usage. Many emerging applications, such as single-cell proteomics, spatial proteomics of tissue sections and the profiling of low-abundant posttranslational modifications, require the analysis of minimal sample amounts and are thus constrained by the sensitivity of the workflow. Here, we present Slice-PASEF, a mass spectrometry technology that leverages trapped ion mobility separation of ions to attain the theoretical maximum of tandem MS sensitivity. We implement Slice-PASEF using a new module in our DIA-NN software and show that Slice-PASEF uniquely enables precise quantitative proteomics of low sample amounts. We further demonstrate its utility towards a range of applications, including single cell proteomics and degrader drug screens via ubiquitinomics.