Oxonium ion scanning mass spectrometry for large-scale plasma glycoproteomics

Protein glycosylation, a complex and heterogeneous post-translational modification that is frequently dysregulated in disease, has been difficult to analyse at scale. Here we report a data-independent acquisition technique for the large-scale mass-spectrometric quantification of glycopeptides in plasma samples. The technique, which we named 'OxoScan-MS', identifies oxonium ions as glycopeptide fragments and exploits a sliding-quadrupole dimension to generate comprehensive and untargeted oxonium ion maps of precursor masses assigned to fragment ions from non-enriched plasma samples. By applying OxoScan-MS to quantify 1,002 glycopeptide features in the plasma glycoproteomes from patients with COVID-19 and healthy controls, we found that severe COVID-19 induces differential glycosylation in IgA, haptoglobin, transferrin and other disease-relevant plasma glycoproteins. OxoScan-MS may allow for the quantitative mapping of glycoproteomes at the scale of hundreds to thousands of samples.

Persistent complement dysregulation with signs of thromboinflammation in active Long Covid

Long Covid is a debilitating condition of unknown etiology. We performed multimodal proteomics analyses of blood serum from COVID-19 patients followed up to 12 months after confirmed severe acute respiratory syndrome coronavirus 2 infection. Analysis of >6500 proteins in 268 longitudinal samples revealed dysregulated activation of the complement system, an innate immune protection and homeostasis mechanism, in individuals experiencing Long Covid. Thus, active Long Covid was characterized by terminal complement system dysregulation and ongoing activation of the alternative and classical complement pathways, the latter associated with increased antibody titers against several herpesviruses possibly stimulating this pathway. Moreover, markers of hemolysis, tissue injury, platelet activation, and monocyte-platelet aggregates were increased in Long Covid. Machine learning confirmed complement and thromboinflammatory proteins as top biomarkers, warranting diagnostic and therapeutic interrogation of these systems.

Cellular control of protein levels: A systems biology perspective

How cells regulate protein levels is a central question of biology. Over the past decades, molecular biology research has provided profound insights into the mechanisms and the molecular machinery governing each step of the gene expression process, from transcription to protein degradation. Recent advances in transcriptomics and proteomics have complemented our understanding of these fundamental cellular processes with a quantitative, systems-level perspective. Multi-omic studies revealed significant quantitative, kinetic and functional differences between the genome, transcriptome and proteome. While protein levels often correlate with mRNA levels, quantitative investigations have demonstrated a substantial impact of translation and protein degradation on protein expression control. In addition, protein-level regulation appears to play a crucial role in buffering protein abundances against undesirable mRNA expression variation. These findings have practical implications for many fields, including gene function prediction and precision medicine.

The proteomic landscape of genome-wide genetic perturbations

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.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

Cell-cell metabolite exchange creates a pro-survival metabolic environment that extends lifespan

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.

Inorganic sulfur fixation via a new homocysteine synthase allows yeast cells to cooperatively compensate for methionine auxotrophy

The assimilation, incorporation, and metabolism of sulfur is a fundamental process across all domains of life, yet how cells deal with varying sulfur availability is not well understood. We studied an unresolved conundrum of sulfur fixation in yeast, in which organosulfur auxotrophy caused by deletion of the homocysteine synthase Met17p is overcome when cells are inoculated at high cell density. In combining the use of self-establishing metabolically cooperating (SeMeCo) communities with proteomic, genetic, and biochemical approaches, we discovered an uncharacterized gene product YLL058Wp, herein named Hydrogen Sulfide Utilizing-1 (HSU1). Hsu1p acts as a homocysteine synthase and allows the cells to substitute for Met17p by reassimilating hydrosulfide ions leaked from met17Δ cells into O-acetyl-homoserine and forming homocysteine. Our results show that cells can cooperate to achieve sulfur fixation, indicating that the collective properties of microbial communities facilitate their basic metabolic capacity to overcome sulfur limitation.

Global analysis of cytosine and adenine DNA modifications across the tree of life

Interpreting the function and metabolism of enzymatic DNA modifications requires both position-specific and global quantities. Sequencing-based techniques that deliver the former have become broadly accessible, but analytical methods for the global quantification of DNA modifications have thus far been applied mostly to individual problems. We established a mass spectrometric method for the sensitive and accurate quantification of multiple enzymatic DNA modifications. Then, we isolated DNA from 124 archean, bacterial, fungal, plant, and mammalian species, and several tissues and created a resource of global DNA modification quantities. Our dataset provides insights into the general nature of enzymatic DNA modifications, reveals unique biological cases, and provides complementary quantitative information to normalize and assess the accuracy of sequencing-based detection of DNA modifications. We report that only three of the studied DNA modifications, methylcytosine (5mdC), methyladenine (N6mdA) and hydroxymethylcytosine (5hmdC), were detected above a picomolar detection limit across species, and dominated in higher eukaryotes (5mdC), in bacteria (N6mdA), or the vertebrate central nervous systems (5hmdC). All three modifications were detected simultaneously in only one of the tested species, Raphanus sativus. In contrast, these modifications were either absent or detected only at trace quantities, across all yeasts and insect genomes studied. Further, we reveal interesting biological cases. For instance, in Allium cepa, Helianthus annuus, or Andropogon gerardi, more than 35% of cytosines were methylated. Additionally, next to the mammlian CNS, 5hmdC was also detected in plants like Lepidium sativum and was found on 8% of cytosines in the Garra barreimiae brain samples. Thus, identifying unexpected levels of DNA modifications in several wild species, our resource underscores the need to address biological diversity for studying DNA modifications.

Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance

Microbial communities are composed of cells of varying metabolic capacity, and regularly include auxotrophs that lack essential metabolic pathways. Through analysis of auxotrophs for amino acid biosynthesis pathways in microbiome data derived from >12,000 natural microbial communities obtained as part of the Earth Microbiome Project (EMP), and study of auxotrophic–prototrophic interactions in self-establishing metabolically cooperating yeast communities (SeMeCos), we reveal a metabolically imprinted mechanism that links the presence of auxotrophs to an increase in metabolic interactions and gains in antimicrobial drug tolerance. As a consequence of the metabolic adaptations necessary to uptake specific metabolites, auxotrophs obtain altered metabolic flux distributions, export more metabolites and, in this way, enrich community environments in metabolites. Moreover, increased efflux activities reduce intracellular drug concentrations, allowing cells to grow in the presence of drug levels above minimal inhibitory concentrations. For example, we show that the antifungal action of azoles is greatly diminished in yeast cells that uptake metabolites from a metabolically enriched environment. Our results hence provide a mechanism that explains why cells are more robust to drug exposure when they interact metabolically.

Using microbiome data analysis and a self-establishing metabolically cooperating yeast community model, the authors show that the presence of auxotrophs in a microbial community increases metabolic interactions between cells and fosters antimicrobial drug tolerance.

A proteomic survival predictor for COVID-19 patients in intensive care

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.

Cysteine and iron accelerate the formation of ribose-5-phosphate, providing insights into the evolutionary origins of the metabolic network structure

The structure of the metabolic network is highly conserved, but we know little about its evolutionary origins. Key for explaining the early evolution of metabolism is solving a chicken–egg dilemma, which describes that enzymes are made from the very same molecules they produce. The recent discovery of several nonenzymatic reaction sequences that topologically resemble central metabolism has provided experimental support for a “metabolism first” theory, in which at least part of the extant metabolic network emerged on the basis of nonenzymatic reactions. But how could evolution kick-start on the basis of a metal catalyzed reaction sequence, and how could the structure of nonenzymatic reaction sequences be imprinted on the metabolic network to remain conserved for billions of years? We performed an in vitro screening where we add the simplest components of metabolic enzymes, proteinogenic amino acids, to a nonenzymatic, iron-driven reaction network that resembles glycolysis and the pentose phosphate pathway (PPP). We observe that the presence of the amino acids enhanced several of the nonenzymatic reactions. Particular attention was triggered by a reaction that resembles a rate-limiting step in the oxidative PPP. A prebiotically available, proteinogenic amino acid cysteine accelerated the formation of RNA nucleoside precursor ribose-5-phosphate from 6-phosphogluconate. We report that iron and cysteine interact and have additive effects on the reaction rate so that ribose-5-phosphate forms at high specificity under mild, metabolism typical temperature and environmental conditions. We speculate that accelerating effects of amino acids on rate-limiting nonenzymatic reactions could have facilitated a stepwise enzymatization of nonenzymatic reaction sequences, imprinting their structure on the evolving metabolic network.

The evolutionary origins of metabolism are largely unknown. This study shows that the prebiotically available proteinogenic amino acid cysteine can promote the metabolism-like rate-limiting formation of ribose-5-phosphate, suggesting that early metabolic pathways could have emerged thought the stepwise enzymatization of non-enzymatic reaction sequences.

A time-resolved proteomic and prognostic map of COVID-19

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.

Ultra-fast proteomics with Scanning SWATH

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.

Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection

The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.

DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput

We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatographic methods.

Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts

A challenge in solving the genotype-to-phenotype relationship is to predict a cell’s metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.

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The proteome of kinase knockouts is dominated by enzyme abundance changes

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The enzyme expression profiles of kinase knockouts are non-redundant

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Metabolism is regulated by many expression changes acting in concert

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Machine learning accurately predicts the metabolome from enzyme abundance

1H-NMR as implemented in several origin of life studies artificially implies the absence of metabolism-like non-enzymatic reactions by being signal-suppressed

Background. Life depends on small subsets of chemically possible reactions. A chemical process can hence be prebiotically plausible, yet be unrelated to the origins of life. An example is the synthesis of nucleotides from hydrogen cyanide, considered prebiotically plausible, but incompatible with metabolic evolution. In contrast, only few metabolism-compatible prebiotic reactions were known until recently. Here, we question whether technical limitations may have contributed to the situation. Methods: Enzymes evolved to accelerate and control biochemical reactions. This situation dictates that compared to modern metabolic pathways, precursors to enzymatic reactions have been slower and less efficient, yielding lower metabolite quantities. This situation demands for the application of highly sensitive analytical techniques for studying ‘proto-metabolism’. We noticed that a set of proto-metabolism studies derive conclusions from the absence of metabolism-like signals, yet do not report detection limits. We here benchmark the respective 1H-NMR implementation for the ability to detect Krebs cycle intermediates, considered examples of plausible metabolic precursors. Results: Compared to metabolomics ‘gold-standard’ methods, 1H-NMR as implemented is i) at least one hundred- to thousand-fold less sensitive, ii) prone to selective metabolite loss, and iii) subject to signal suppression by Fe(II) concentrations as extrapolated from Archean sediment. In sum these restrictions mount to huge sensitivity deficits, so that even highly concentrated Krebs cycle intermediates are rendered undetectable unless the method is altered to boost sensitivity. Conclusions 1H-NMR as implemented in several origin of life studies does not achieve the sensitivity to detect cellular metabolite concentrations, let alone evolutionary precursors at even lower concentration. These studies can hence not serve as proof-of-absence for metabolism-like reactions. Origin of life theories that essentially depend on this assumption, i.e. those that consider proto-metabolism to consist of non-metabolism-like reactions derived from non-metabolic precursors like hydrogen cyanide, may have been derived from a misinterpretation of negative analytical results.

Primordial Krebs-cycle-like non-enzymatic reactions detected by mass spectrometry and nuclear magnetic resonance

Background: Metabolism is the process of nutrient uptake and conversion, and executed by the metabolic network. Its evolutionary precursors most likely originated in non-enzymatic chemistry. To be exploitable in a Darwinian process that forms a metabolic pathway, non-enzymatic reactions need to form a chemical network that produces advantage-providing metabolites in a single, life compatible condition. In a hypothesis-generating, large-scale experiment, we recently screened iron and sulfur-rich solutions, and report that upon the formation of sulfate radicals, Krebs cycle intermediates establish metabolism-like non-enzymatic reactivity. A challenge to our results claims that the results obtained by liquid chromatography-selective reaction monitoring (LC-SRM) would not be reproducible by nuclear magnetic resonance spectroscopy (1H-NMR). Methods: This study compared the application of the two techniques to the relevant samples. Results: We detect hundred- to thousand-fold differences in the specific limits of detection between LC-SRM and 1H-NMR to detect Krebs cycle intermediates. Further, the use of 1H-NMR was found generally problematic to characterize early metabolic reactions, as Archean-sediment typical iron concentrations cause paramagnetic signal suppression. Consequently, we selected non-enzymatic Krebs cycle reactions that fall within the determined technical limits. We confirm that these proceed unequivocally as evidenced by both LC-SRM and 1H-NMR. Conclusions: These results strengthen our previous conclusions about the existence of unifying reaction conditions that enables a series of co-occurring metabolism-like non-enzymatic Krebs cycle reactions. We further discuss why constraints applying to metabolism disentangle concentration from importance of any reaction intermediates, and why evolutionary precursors to metabolic pathways must have had much lower metabolite concentrations compared to modern metabolic networks. Research into the chemical origins of life will hence miss out on the chemistry relevant for metabolism if its focus is restricted solely to highly abundant and unreactive metabolites, including when it ignores life-compatibility of the reaction conditions as an essential constraint in enzyme evolution.

QM/MM MD simulations of La(iii)–phosphopeptide complexes

Hybrid quantum mechanical/molecular mechanical simulations have been used to study the structural and dynamical properties of a La(iii)–phosphopeptide complex.

Structure and dynamics of chromatographically relevant Fe(III)-chelates

Immobilized metal ion affinity chromatography (IMAC) is an important chromatographic technique for biomolecules. In order to get a detailed understanding of the hydration of immobilized Fe(III), complexes of Fe(III) with methyl substituted iminodiacetate ([Fe(MSIDA)(H2O)3](+)) as well as with methyl substituted nitrilotriacetate ([Fe(MSNTA)(H2O)2]) were simulated in aqueous solutions with the quantum mechanical charge field molecular dynamics (QMCF MD) approach. The simulations were carried out at the Hartree-Fock (HF) level of theory, since cluster calculations at the HF, MP2, and B3LYP levels of theory showed that this method results in a good compromise between computational effort and accuracy. None of the coordinating water molecules were exchanged during the simulation period of 15 ps. The Fe-OH2O bond distances as well as the Fe-OH2O stretching motions differed among the coordinating water molecules, indicating different bond strengths. For the water molecules in the second hydration layer, mean residence times of 2.7 and 1.9 ps were obtained for [Fe(MSIDA)(H2O)3](+) and [Fe(MSNTA)(H2O)2], respectively. Furthermore, infrared measurements were carried out to characterize the most prominent bond features of aqueous Fe(III)-NTA and to discuss these results in conjunction with the computationally derived frequencies.

Erbium(III) in aqueous solution: an ab initio molecular dynamics study

Structural and dynamical properties of the erbium(III) ion in water have been obtained by means of ab initio quantum mechanical charge field molecular dynamics (QMCF-MD) simulations for the ground state and an excited state. The quality of the simulations has been monitored by recording UV/vis and Raman spectra of dilute solutions of ErCl3 and Er(NO3)3 in water and by comparison with EXAFS data from literature. Slight deviations between these data can be mainly attributed to relativistic effects, which are not sufficiently considered by the methodological framework. In both simulations, a mixture of coordination numbers eight and nine and a ligand exchange on the picosecond range are observed. The strength of the Er-ligand bond is considerably lower than that of trivalent transition metal ions but higher than that for La(III) and Ce(III) in aqueous solution. The main difference between ground state and excited state is the ligand exchange rate of the first shell. The second hydration shell is stable in both cases but with significantly different properties.

Combined Ab Initio Computational and Infrared Spectroscopic Study of the cis- and trans-Bis(glycinato)copper(II) Complexes in Aqueous Environment

The cis- and trans-bis(glycinato)copper(II) complexes in aqueous solution have been investigated by means of a combined theoretical and experimental approach. The conducted quantum mechanical charge field molecular dynamics (QMCF-MD) studies, being the first quantum mechanical simulations of organometallic complexes by this method, yielded accurate structural details of the investigated isomers as well as novel dynamic data, which has successfully been confirmed and extended by subsequent mid-infrared measurements. The spectroscopic results, critically assessed by adjacent multivariate data analysis, indicate an isomeric stability at ambient conditions, vanishing at elevated temperatures.