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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.

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs and facilitate new approaches in systems biology and biomedical research. Here we propose Speedy-PASEF, a combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition and data analysis with the DIA-NN software suite, for conducting fast, high-quality proteomic experiments that require only moderate sample amounts. For instance, using a 500-μl/min flow rate and a 3-minute chromatographic gradient, Speedy-PASEF quantified 5,211 proteins from 2 μg of a mammalian cell-line standard at high quantitative accuracy and precision. We further used Speedy-PASEF to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-minute chromatographic gradient and alternating column regeneration on a dual pump system, for processing 398 samples per day. Speedy-PASEF delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates. Speedy-PASEF thus facilitates acquisition of high-quality proteomes in large numbers.

Genetically identical cells are known to differ in many physiological parameters such as growth rate and drug tolerance. Metabolic specialization is believed to be a cause of such phenotypic heterogeneity, but detection of metabolically divergent subpopulations remains technically challenging. We developed a proteomics-based technology, termed differential isotope labelling by amino acids (DILAC), that can detect producer and consumer subpopulations of a particular amino acid within an isogenic cell population by monitoring peptides with multiple occurrences of the amino acid. We reveal that young, morphologically undifferentiated yeast colonies contain subpopulations of lysine producers and consumers that emerge due to nutrient gradients. Deconvoluting their proteomes using DILAC, we find evidence for in situ cross-feeding where rapidly growing cells ferment and provide the more slowly growing, respiring cells with ethanol. Finally, by combining DILAC with fluorescence-activated cell sorting, we show that the metabolic subpopulations diverge phenotypically, as exemplified by a different tolerance to the antifungal drug amphotericin B. Overall, DILAC captures previously unnoticed metabolic heterogeneity and provides experimental evidence for the role of metabolic specialization and cross-feeding interactions as a source of phenotypic heterogeneity in isogenic cell populations.

We present Slice-PASEF, a novel mass spectrometry technology based on trapped ion mobility separation of ions. Slice-PASEF allows to achieve the theoretical maximum of MS/MS sensitivity and boosts proteomics of low sample amounts. Leveraging Slice-PASEF, we show, for the first time, that comprehensive profiling of single cell-level peptide amounts is possible using ultra-fast microflow chromatography and a general-purpose mass spectrometer, allowing quantification of 1417 proteins from 200 picograms of a HeLa cell peptide standard on an Evosep One LC system coupled to a timsTOF Pro 2, at a 200 samples per day throughput. We implemented a Slice-PASEF module in our DIA-NN data processing software, to make it readily available for the proteomics community.

Metabolism is fundamentally intertwined with the ageing process. We here report that a key determinant of cellular lifespan is not only nutrient supply and intracellular metabolism, but also metabolite exchange interactions that occur between cells. Studying chronological ageing in yeast, we observed that metabolites exported by young, exponentially growing, cells are re- imported during the stationary phase when cells age chronologically, indicating the existence of cross-generational metabolic interactions. We then used self-establishing metabolically cooperating communities (SeMeCos) to boost cell-cell metabolic interactions and observed a significant lifespan extension. A search for the underlying mechanisms, coupling SeMeCos, metabolic profiling, proteomics and genome-scale metabolic modelling, attributed a specific role to methionine consumer cells. These cells were enriched over time, adopted glycolytic metabolism and increased export of protective metabolites. Glycerol, in particular, accumulated in the communal metabolic environment and extended the lifespan of all cells in the community in a paracrine fashion. Our results hence establish metabolite exchange interactions as a determinant of the ageing process and show that metabolically cooperating cells shape their metabolic environment to achieve lifespan extension.

Metabolism is deeply intertwined with aging. Effects of metabolic interventions on aging have been explained with intracellular metabolism, growth control, and signaling. Studying chronological aging in yeast, we reveal a so far overlooked metabolic property that influences aging via the exchange of metabolites. We observed that metabolites exported by young cells are re-imported by chronologically aging cells, resulting in cross-generational metabolic interactions. Then, we used self-establishing metabolically cooperating communities (SeMeCo) as a tool to increase metabolite exchange and observed significant lifespan extensions. The longevity of the SeMeCo was attributable to metabolic reconfigurations in methionine consumer cells. These obtained a more glycolytic metabolism and increased the export of protective metabolites that in turn extended the lifespan of cells that supplied them with methionine. Our results establish metabolite exchange interactions as a determinant of cellular aging and show that metabolically cooperating cells can shape the metabolic environment to extend their lifespan.

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease., Graphical abstract, Demichev, Tober-Lau et al., present a time-resolved molecular map of the COVID-19, measuring plasma proteomes of patients with COVID-19 along with an extensive panel of clinical diagnostic parameters at 687-time points. They describe the specificity and dynamics, as well as the predictive and prognostic power of the molecular signatures in COVID-19.

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. Comprehensively capturing the host 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 were poor predictors of survival. Plasma proteomics instead identified 14 proteins that showed concentration trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors in an exploratory (AUROC 0.81) as well as in the independent validation cohort (AUROC of 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 predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care.Trial registrationGerman Clinical Trials Register DRKS00021688

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. There is an urgent need for predictive markers that can guide clinical decision-making, inform about the effect of experimental therapies, and point to novel therapeutic targets. Here, we characterize the time-dependent progression of COVID-19 through different stages of the disease, by measuring 86 accredited diagnostic parameters and plasma proteomes at 687 sampling points, in a cohort of 139 patients during hospitalization. We report that the time-resolved patient molecular phenotypes reflect an initial spike in the systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution and immunomodulation. Further, we show that the early host response is predictive for the disease trajectory and gives rise to proteomic and diagnostic marker signatures that classify the need for supplemental oxygen therapy and mechanical ventilation, and that predict the time to recovery of mildly ill patients. In severely ill patients, the molecular phenotype of the early host response predicts survival, in two independent cohorts and weeks before outcome. We also identify age-specific molecular response to COVID-19, which involves increased inflammation and lipoprotein dysregulation in older patients. Our study provides a deep and time resolved molecular characterization of COVID-19 disease progression, and reports biomarkers for risk-adapted treatment strategies and molecular disease monitoring. Our study demonstrates accurate prognosis of COVID-19 outcome from proteomic signatures recorded weeks earlier.

How metabolic pathways emerged in early evolution remains largely unknown. Recently discovered chemical networks driven by iron and sulfur resemble reaction sequences found within glycolysis, gluconeogenesis, the oxidative and reductive Krebs cycle, the Wood Ljungdahl as well as the S-adenosylmethionine pathways, components of the core cellular metabolic network. These findings suggest that the evolution of central metabolism was primed by environmental chemical reactions, implying that non-enzymatic reaction networks served as a “template” in the evolution of enzymatic activities. We speculated that the turning point for this transition would depend on the catalytic properties of the simplest structural components of proteins, single amino acids. Here, we systematically combine constituents of Fe(II)-driven non-enzymatic reactions resembling glycolysis and pentose phosphate pathway (PPP), with single proteinogenic amino acids. Multiple reaction rates are enhanced by amino acids. In particular, cysteine is able to replace (and/or complement) the metal ion Fe(II) in driving the non-enzymatic formation of the RNA-backbone metabolite ribose 5-phosphate from 6-phosphogluconate, a rate-limiting reaction of the oxidative PPP. In the presence of both Fe(II) and cysteine, a complex is formed, enabling the non-enzymatic reaction to proceed at a wide range of temperatures. At mundane temperatures, this ‘minimal enzyme-like complex’ achieves a much higher specificity in the formation of ribose 5-phosphate than the Fe(II)-driven reaction at high temperatures. Hence, simple amino acids can accelerate key steps within metal-promoted metabolism-like chemical networks. Our results imply a stepwise scenario, in which environmental chemical networks served as primers in the early evolution of the metabolic network structure.Significance StatementThe evolutionary roots of metabolic pathways are barely understood. Here we show results consistent with a stepwise scenario during the evolution of (enzymatic) metabolism, starting from non-enzymatic chemical networks. By systematic screening of metabolic-like reactivities in vitro, and using high-throughput analytical techniques, we identify an iron/cysteine complex to act as a ‘minimal enzymelike complex’, which consists of a metal ion, an amino acid, and a sugar phosphate ligand. Integrated in a metal-driven, non-enzymatic pentose phosphate pathway, it promotes the formation of the RNA-backbone precursor ribose 5-phosphate at ambient temperature.

The reaction of Salen-like ZnII and NiII precursors with carbacylamidophosphate lanthanide moieties yields six new types of 3d–4f compounds. The complexes were characterized by means of 1H, 31P NMR and IR spectroscopy, elemental analysis, ESI mass spectrometry, and X-ray diffraction analysis. Depending on the Schiff base ligands, the NiII ion adopts either a square-planar or an octahedral geometry, whereas the ZnII ion has a tetragonal-pyramidal geometry. The coordination number of lanthanides is nine or ten. Hydrolytic activities of some heterobimetallic Zn–Ln and Ni–Ln coordination compounds in the reaction of intramolecular hydrolytic degradation of the 2-(hydroxypropyl)-p-nitrophenyl phosphate were investigated.

One of the treatments of Parkinson disease is based on increasing the brain dopamine level by L-DOPA (LD) applications. To prevent the peripheral degradation of levodopa, another drug, benserazide is applied. On the other hand, during this neurodegenerative disease changes in the homeostasis of metals are observed and the increasing brain zinc levels are postulated to have therapeutic effects. Here we present studies on interactions of Zn(II), Cu(II), Fe(II) ions with benserazide and with benserazide/levodopa in ternary system. By applying mass spectrometry and UV-vis methods we describe the interactions between selected metal ions and the drug additives in the investigated systems. The results show forming of equimolar complexes in the binary and ternary systems.

Recent technological developments have enabled the imaging of chemical elements in cells, although quantitative analyses, such as by inductively coupled plasma mass spectrometry, were developed previously. Applications allowing high-resolution imaging at the single-cell level are anticipated in cell biology and medicine, where the roles of elements, especially in relation to intracellular molecules such as proteins, nucleic acids, lipids, and sugars, are essential for understanding cellular functions. The expression of proteins and genes varies depending on cellular function, and multiple elements are likely to be associated with biological molecules in the functioning of cell proliferation, differentiation, aging, and stress responses. In this review, we describe a scanning X-ray fluorescence microscopy system, which can reliably determine the cellular distribution of multiple elements by a sub-100-nm focusing approach, together with its applications. Visualizing intracellular elements and understanding their dynamics at the single-cell level may provide great insight into their behaviors.

Fatty acids are taken up by cells and incorporated into complex lipids such as neutral lipids and glycerophospholipids. Glycerophospholipids are major constituents of cellular membranes. More than 1000 molecular species of glycerophospholipids differ in their polar head groups and fatty acid compositions. They are related to cellular functions and diseases and have been well analyzed by mass spectrometry. However, intracellular imaging of fatty acids and glycerophospholipids has not been successful due to insufficient resolution using conventional methods. Here, we developed a method for labeling fatty acids with bromine (Br) and applied scanning X-ray fluorescence microscopy (SXFM) to obtain intracellular Br mapping data with submicrometer resolution. Mass spectrometry showed that cells took up Br-labeled fatty acids and metabolized them mainly into glycerophospholipids in CHO cells. Most Br signals observed by SXFM were in the perinuclear region. Higher resolution revealed a spot-like distribution of Br in the cytoplasm. The current method enabled successful visualization of intracellular Br-labeled fatty acids. Single-element labeling combined with SXFM technology facilitates the intracellular imaging of fatty acids, which provides a new tool to determine dynamic changes in fatty acids and their derivatives at the single-cell level.-Shimura, M., Shindou, H., Szyrwiel, L., Tokuoka, S. M., Hamano, F., Matsuyama, S., Okamoto, M., Matsunaga, A., Kita, Y., Ishizaka, Y., Yamauchi, K., Kohmura, Y., Lobinski, R., Shimizu, I., Shimizu, T. Imaging of intracellular fatty acids by scanning X-ray fluorescence microscopy.

The medicinal application of bismuth compounds is focused in two fields: antimicrobial and anticancer. Bi(III) complexes have been used in medicine as an effective treatment of microbial infections, such as peptic ulcers, diarrhoea, gastritis and syphilis. (212)Bi and (213)Bi are strong alpha-particle emitters, which, bound to specific ligands, could be promising targeted radio-therapeutic agents for the treatment of cancer. In this work, the coordination of bismuth to three peptides with the Cys-Xaa-Cys motif was studied by potentiometric, spectroscopic, mass spectrometric and NMR methods. We have shown, that sulfur atoms from cysteines are critical donors for the coordination of Bi(III). Our investigation provides insight towards an understanding of the chemistry of bismuth-containing complexes and may lead to the further application of this metal in medicine.

Multi-histidine peptide fragments of zebra-fish prion protein are effective ligands for Zn(II) ions. Moreover the formation of a dinuclear complex species with a longer peptide can suggest the existence of the cooperative effect in the metal ion binding.