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.

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.

Natural proteome diversity links aneuploidy tolerance to protein turnover.

Accessing the natural genetic diversity of species unveils hidden genetic traits, clarifies gene functions and allows the generalizability of laboratory findings to be assessed. One notable discovery made in natural isolates of Saccharomyces cerevisiae is that aneuploidy-an imbalance in chromosome copy numbers-is frequent1,2 (around 20%), which seems to contradict the substantial fitness costs and transient nature of aneuploidy when it is engineered in the laboratory3-5. Here we generate a proteomic resource and merge it with genomic1 and transcriptomic6 data for 796 euploid and aneuploid natural isolates. We find that natural and lab-generated aneuploids differ specifically at the proteome. In lab-generated aneuploids, some proteins-especially subunits of protein complexes-show reduced expression, but the overall protein levels correspond to the aneuploid gene dosage. By contrast, in natural isolates, more than 70% of proteins encoded on aneuploid chromosomes are dosage compensated, and average protein levels are shifted towards the euploid state chromosome-wide. At the molecular level, we detect an induction of structural components of the proteasome, increased levels of ubiquitination, and reveal an interdependency of protein turnover rates and attenuation. Our study thus highlights the role of protein turnover in mediating aneuploidy tolerance, and shows the utility of exploiting the natural diversity of species to attain generalizable molecular insights into complex biological processes.

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.

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.

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.

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.

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.

Nonenzymatic gluconeogenesis-like formation of fructose 1,6-bisphosphate in ice.

The evolutionary origins of metabolism, in particular the emergence of the sugar phosphates that constitute glycolysis, the pentose phosphate pathway, and the RNA and DNA backbone, are largely unknown. In cells, a major source of glucose and the large sugar phosphates is gluconeogenesis. This ancient anabolic pathway (re-)builds carbon bonds as cleaved in glycolysis in an aldol condensation of the unstable catabolites glyceraldehyde 3-phosphate and dihydroxyacetone phosphate, forming the much more stable fructose 1,6-bisphosphate. We here report the discovery of a nonenzymatic counterpart to this reaction. The in-ice nonenzymatic aldol addition leads to the continuous accumulation of fructose 1,6-bisphosphate in a permanently frozen solution as followed over months. Moreover, the in-ice reaction is accelerated by simple amino acids, in particular glycine and lysine. Revealing that gluconeogenesis may be of nonenzymatic origin, our results shed light on how glucose anabolism could have emerged in early life forms. Furthermore, the amino acid acceleration of a key cellular anabolic reaction may indicate a link between prebiotic chemistry and the nature of the first metabolic enzymes.

Development of erbium phosphate doped poly(glycidyl methacrylate/ethylene dimethacrylate) spin columns for selective enrichment of phosphopeptides.

In this study, a novel method for the highly selective enrichment of phosphopeptides using erbium phosphate doped poly(glycidyl methacrylate/ethylene dimethacrylate) spin columns is presented. Erbium phosphate was synthesized by precipitation from boiling phosphoric acid and incubated overnight in erbium chloride solutions. The resulting powder was embedded in a monolithic poly(glycidyl methacrylate/ethylene dimethacrylate) polymer. The monolith was synthesized in a spin column by radical polymerization. Erbium phosphate demonstrated a high affinity and selectivity for phosphopeptides due to the strong interaction of trivalent erbium ions with the phosphate groups of phosphopeptides. The high selectivity and performance of the designed spin columns were demonstrated by successfully enriching phosphopeptides from tryptically digested protein mixtures containing the model phosphoproteins α- and ß-casein, bovine milk, and human saliva. By the implementation of several washing steps, unspecific components were removed and the enriched phosphopeptides were effectively eluted from the spin columns under alkaline conditions. The selective performance of the presented method was further demonstrated by the enrichment of two synthetic phosphopeptides, which were spiked in tryptically digested and dephosphorylated HeLa cell lysates at low ratios. Finally, the presented approach was compared to conventional phosphopeptide enrichment by titanium oxide and revealed higher recoveries for the erbium phosphate doped monoliths.

Lanthanide-IMAC enrichment of carbohydrates and polyols.

In this study a new type of immobilized metal ion affinity chromatography resin for the enrichment of carbohydrates and polyols was synthesized by radical polymerization reaction of vinyl phosphonic acid and 1,4-butandiole dimethacrylate using azo-bis-isobutyronitrile as radical initiator. Interaction between the chelated trivalent lanthanide ions and negatively charged hydroxyl groups of carbohydrates and polyols was observed by applying high pH values. The new method was evaluated by single standard solutions, mixtures of standards, honey and a more complex extract of Cynara scolymus. The washing step was accomplished by acetonitrile in excess volumes. Elution of enriched carbohydrates was successfully performed with deionized water. The subsequent analysis was carried out with matrix-free laser desorption/ionization-time of flight mass spectrometry involving a TiO2 -coated steel target, especially suitable for the measurement of low-molecular-weight substances. Quantitative analysis of the sugar alcohol xylitol as well as the determination of the maximal loading capacity was performed by gas chromatography in conjunction with mass spectrometric detection after chemical derivatization. In a parallel approach quantum mechanical geometry optimizations were performed in order to compare the coordination behavior of various trivalent lanthanide ions.

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.

Highly selective recovery of phosphopeptides using trypsin-assisted digestion of precipitated lanthanide-phosphoprotein complexes.

The basic idea of this study was to recover phosphopeptides after trypsin-assisted digestion of precipitated phosphoproteins using trivalent lanthanide ions. In the first step, phosphoproteins were extracted from the protein solution by precipitation with La(3+) and Ce(3+) ions, forming stable pellets. Additionally, the precipitated lanthanide-phosphoprotein complexes were suspended and directly digested on-pellet using trypsin. Non-phosphorylated peptides were released into the supernatants by enzymatic cleavage and phosphopeptides remained bound on the precipitated pellet. Further washing steps improved the removal of non-phosphorylated peptides. For the recovery of phosphopeptides the precipitated pellets were dissolved in 3.7% hydrochloric acid. The performance of this method was evaluated by several experiments using MALDI-TOF MS measurements and delivered the highest selectivity for phosphopeptides. This can be explained by the overwhelming preference of lanthanides for binding to oxygen-containing anions such as phosphates. The developed enrichment method was evaluated with several types of biological samples, including fresh milk and egg white. The uniqueness and the main advantages of the presented approach are the enrichment on the protein-level and the recovery of phosphopeptides on the peptide-level. This allows much easier handling, as the number of molecules on the peptide level is unavoidably higher, by complicating every enrichment strategy.

A new type of metal chelate affinity chromatography using trivalent lanthanide ions for phosphopeptide enrichment.

In this study, a new type of immobilized metal-ion affinity chromatography (IMAC) resin for the isolation of phosphopeptides was synthesized which is based on the specific interaction between phosphate groups and chelated lanthanide metal ions. In this regard trivalent lanthanum, holmium and erbium ions were chelated to a highly porous phosphonate polymer which was prepared by radical polymerization of vinylphosphonic acid (VPA) and divinylbenzene (DVB). The developed method was evaluated with peptide mixtures from digested standard proteins (α-casein, ß-casein and ovalbumin) as well as with bovine milk, egg white and a spiked HeLa cell lysate. Compared to the commonly used TiO2 approach, the presented method showed higher selectivity for phosphorylated peptides. This can be explained by the strong preference of trivalent lanthanide ions for phosphates with which they form very tight ionic bonds. Mono- and multiply phosphorylated peptides could be enriched and released in a single basic elution step, while non-phosphorylated peptides remained on the resin. Ab initio quantum mechanical energy minimizations of model complexes for polymer-ion-ligand interactions provided geometries, binding energies and charges which are discussed in conjunction with the observed experimental properties, leading to the most satisfying agreement. The presented lanthanide-IMAC resins represent promising affinity materials for the selective isolation of phosphopeptides from biological samples.

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.

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.

Structure and dynamics of the Zr(4+) ion in water.

The four-times positively charged zirconium ion in aqueous solution was simulated, using an ab initio quantum mechanical charge field molecular dynamics approach. As no hydrolysis reaction occurred during the simulation time of 10 ps, the target of this study was the evaluation of the structure and dynamics of the monomeric hydrated zirconium(iv) ion. The ion forms three hydration shells. In the first hydration shell the ion is 8-fold coordinated with a maximum probability of the Zr-O distance at 2.25 Å. While no exchanges occurred between the first and second shell, the mean residence time of the water molecules in the second shell is 5.5 ps. A geometry of the first hydration shell in-between a bi-capped trigonal prism and a square antiprism was found and a Zr-O force constant of 188 N m(-1) was evaluated.

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.